Energy system economic low-carbon optimization operation method and system based on ai and mechanism fusion

Abstract

The present application relates to the technical field of carbon optimization of source network load storage integrated energy system, and provides an AI and mechanism integrated energy system economic low-carbon optimization operation method and system, which comprises the following steps: constructing a detailed mathematical model of AI and mechanism integration of source side, network side, load side and storage side containing multiple types of objects; then, establishing a source network load storage integrated energy system model of AC and DC coupled interconnection; secondly, establishing a day-ahead economic low-carbon optimization operation model; finally, setting the day-ahead coupling nesting boundary condition, establishing a day-ahead economic low-carbon optimization operation model, solving the multi-objective optimization, and outputting the integrated energy system information. The present application constructs a detailed model of AI and mechanism integrated source network load storage integrated energy system of AC and DC coupled interconnection, which is helpful for the economic low-carbon operation of the integrated energy system, and is conducive to the coordinated and stable operation of source network load storage, the promotion and application of AC and DC coupled interconnection, and the deep integration of AI model and mechanism model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of AI and mechanism model integrated source-grid-load-storage integrated energy system economic and low-carbon optimization, in particular to an AI and mechanism integrated energy system economic and low-carbon optimization operation method and system. BACKGROUND

[0002] With the promotion of global energy structure transformation, the source-grid-load-storage integrated energy system, as an important part of the new power system, is facing the major challenge of economic and low-carbon collaborative optimization. The traditional energy system operation method mainly relies on mechanism model, although it can provide certain theoretical guidance, but it is difficult to cope with the strong uncertainty problem brought by renewable energy access, often there are problems such as insufficient model accuracy and poor adaptability. AI (artificial intelligence) technology has strong data processing and prediction ability, which can learn and analyze historical data to mine potential operation rules and provide a new path for the optimization operation of energy system. However, the current research on the deep integration of AI technology and mechanism model applied to source-grid-load-storage integrated energy system is still relatively less, and the AI method driven by data alone lacks physical interpretability and is difficult to meet the basic requirements of safe operation of new energy system.

[0003] At present, the source-grid-load-storage integrated energy system involves multiple types of equipment and systems, such as various types of power generation equipment, AC / DC power grid, multiple types of load, and multiple energy storage devices. The coordinated regulation and control between each part and the optimization operation of multi-time scale nesting are crucial. However, the current operation method is not perfect in the coordinated regulation and control between equipment and AC / DC power grid, and the optimization operation, which cannot fully exert the advantages of each part and realize the overall optimization operation of the system. The existing technology mainly has the following deficiencies: (1) the coupling mechanism of AC / DC power grid is complex, the existing AC / DC power grid coupling model is too simplified, the traditional energy balance modeling method is difficult to accurately describe the interaction characteristics of AC / DC power grid, and the existing model does not fully consider the key factors such as power flow mutual aid and source-grid-load-storage at different nodes of AC / DC power grid, which affects the feasibility of the optimization result; (2) the precision and adaptability of traditional mechanism model in complex uncertain operation environment are insufficient, the existing method cannot effectively integrate the advantages of AI and the physical interpretability of mechanism model, resulting in difficulty in balancing prediction accuracy and operation safety; (3) the day-ahead and intra-day multi-time scale optimization lacks effective coupling mechanism, and the ability to cope with renewable energy generation uncertainty is limited, which is difficult to adapt to the real-time fluctuations of renewable energy output and load demand, and the existing operation method is difficult to realize the global optimization operation of the system, and the single economic optimization method cannot meet the demand of energy system economic and low-carbon operation. SUMMARY

[0004] The present application aims to solve at least one technical problem in the background art, and provide an AI and mechanism integrated energy system economic low-carbon optimal operation method and system.

[0005] To achieve the above-mentioned purpose, the present application provides an AI and mechanism integrated energy system economic low-carbon optimal operation method, comprising:

[0006] Establishing a source-side model, including a large power grid power supply model, a photovoltaic power generation AI prediction model;

[0007] Establishing a network-side model, including an alternating current power grid power flow model and a direct current distribution network power balance model;

[0008] Establishing a load-side model, including a water pump motor model and an uncontrollable power load prediction model;

[0009] Establishing a storage-side model, including a power storage facility model and a water storage facility model;

[0010] Coupling and interconnecting the source-side model, the network-side model, the load-side model and the storage-side model to form an integrated energy system model;

[0011] Setting a day-ahead economic low-carbon optimal operation strategy for the integrated energy system model;

[0012] Setting a day-ahead economic low-carbon optimal operation strategy for the integrated energy system model, and setting a coupling and connection condition of the day-ahead economic low-carbon optimal operation strategy and the day-ahead economic low-carbon optimal operation strategy, and forming an integrated energy system after the coupling and connection condition is set;

[0013] Inputting integrated energy system information into the integrated energy system, and outputting integrated energy system economic low-carbon optimization result information through the integrated energy system.

[0014] According to one aspect of the present application, the expression of the large power grid power supply model is as follows:

[0015] ;

[0016] In the formula: is the power supply power of the large power grid to the source network load storage integrated energy system through the tie line at time t; is the upper limit power of the power supply capacity of the power grid to the source network load storage integrated energy system through the tie line; is the power return power of the source network load storage integrated energy system to the large power grid through the tie line at time t; is the upper limit power of the power return capacity of the source network load storage integrated energy system to the power grid through the tie line;

[0017] The photovoltaic power generation AI prediction model is a sequence-to-sequence model architecture based on a long short-term memory network combined with an attention mechanism processing time-dependent relationship model, including an encoder part model and a decoder part model;

[0018] The expression of the encoder part model is as follows:

[0019] ;

[0020] In the formula: is the t time forgetting gate output; is the t time input gate output; is the t time output gate output; is the t time candidate cell state; is the t time current cell state; is the t time current cell state; is the t time hidden state; is the t time hidden state; is the t time hidden state; is the t time hidden state; is the t time input feature vector, including normalized photovoltaic power generation meteorological data and historical power; 、 、 、 are weight matrices corresponding to the weights of the forgetting gate, the input gate, the output gate and the cell state, respectively; 、 、 、 are bias vectors corresponding to the bias terms of the forgetting gate, the input gate, the output gate and the cell state, respectively; is a Sigmoid function about variable ; is a hyperbolic tangent function about variable ;

[0021] The expression of the decoder part model is as follows:

[0022] ;

[0023] In the formula: is the t time hidden state of the decoder, storing decoding process information; is the t time hidden state of the decoder, storing decoding process information; is the t time hidden state of the decoder, storing decoding process information; is the t time predicted output of the previous step, the normalized photovoltaic power generation power value; z is a context vector, initially being the final hidden state of the encoder; is an attention weight vector used to calculate an attention score; is a Sigmoid function about variable ; The hyperbolic tangent function of 、 Both are attention weight matrices, which handle the trainable parameters of the decoder and encoder states respectively; is the hidden state of the encoder at step i; is the unnormalized attention score, which is the correlation strength between the decoder state at time t and the encoder state at time i; is the unnormalized attention score, which is the strength of the association between the decoder state at time t and the encoder state at time j; is the attention weight, the importance of the encoder at moment i to the decoder at moment t; is the dynamic context vector at time t; To predict the output at time t, it is necessary to denormalize to obtain the actual power value; is the output layer weight matrix, mapping the concatenated state to the output; is the output layer bias, which predicts the output bias term; T is the encoder sequence length, that is, the total number of input time steps; It is a complete long short-term memory network unit calculation process function, which contains the input gate that controls the inflow of new information, the forget gate that controls the retention of historical information, the output gate that controls the state output, and the cell state of the long-term memory carrier;

[0024] The expression of the photovoltaic power generation mechanical model is as follows:

[0025] ;

[0026] Where: is the photovoltaic power generation power at time t; The comprehensive conversion efficiency of the photovoltaic power generation system; The total effective surface area of ​​the photovoltaic power generation system battery components; is the solar radiation intensity at time t; The conversion coefficient between photovoltaic power generation and temperature adjustment; is the working environment temperature of the photovoltaic solar panel at time t; The operating temperature of solar panels under standard test conditions for photovoltaic power generation; is the photovoltaic panel tilt angle correction function; is the tilt angle of the photovoltaic panel; 、 for Different parameters of the function; is the weather type influence coefficient, where Indicates the sunny day coefficient, which is usually set to 1. Indicates the cloudy coefficient, which is generally set to a value between 0.6 and 0.8. It represents the rain day coefficient, and is generally set to 0.2 to 0.5; is the weather type at time t; is the weather type conversion code, which converts the text amount at different times into a numerical amount for operation;

[0027] The expression of the input model of the long short-term memory network time series prediction model is as follows:

[0028] ;

[0029] In the formula: is the solar radiation intensity at time t; is the photovoltaic power generation working environment temperature at time t; is the relative humidity at time t; is the wind speed at time t; is the weather type at time t; is is the photovoltaic power generation power at time t; is is the photovoltaic power generation power at time t; , , , are minute, hour, day, and month time labels, respectively; is the input feature vector at time t, including normalized photovoltaic power generation meteorological data and historical power;

[0030] The expression of the output prediction target model of the long short-term memory network time series prediction model is as follows:

[0031] ;

[0032] In the formula: is a complete long short-term memory network neural network mapping function for time series, which realizes the input and output mapping function, including the whole process model content of the encoder time series and the decoder prediction generation; T is the length of the encoder sequence, that is, the total number of input time steps; n is the total number of historical feature vectors; , , are the photovoltaic power generation prediction powers at times , , ; , , are the input feature vectors at times , ;

[0033] According to one aspect of the present invention, the expression of the AC power grid flow model is as follows:

[0034] ;

[0035] Where: is the total active power injected into the AC grid node j at time t; is the total reactive power injected into the AC grid node j at time t; is the AC power grid line at time t Active power on top; is the AC power grid line at time t Reactive power; is the AC power grid line at time t Active power on top; is the AC power grid line at time t Reactive power; is the AC power grid line at time t Current amplitude; AC grid line resistance; AC grid line reactance; is the conductance of AC grid node j; is the susceptance of AC grid node j; is the voltage amplitude of node j in the AC grid at time t; is the voltage amplitude of the AC grid node i at time t; is the set of first nodes in the AC power grid with j as the last node, and Indicates that the first node i is connected to the last node j; is the set of tail nodes in the AC power grid with j as the first node, and Indicates that the tail node i is connected to the head node j;

[0036] The expression of the DC distribution network power balance model is as follows:

[0037] ;

[0038] Where: is the net active power injected into the DC distribution network node i at time t; is the active power flowing from node i to node j in the DC distribution network at time t; The power branch formed by the DC distribution network node i and node j at time t Power loss; is the voltage of DC distribution network node i at time t; is the voltage of DC distribution network node j at time t; is the conductance between nodes i and j in the DC distribution network; Node j is directly connected to node i in the DC distribution network.

[0039] According to one aspect of the present invention, the water pump motor model includes: a fully autonomous optimization water pump motor operation mode model, a semi-autonomous optimization water pump motor operation mode model, a semi-autonomous optimization water pump motor operation mode model, and a set optimization water pump motor operation mode model, and the operation mode of each model is respectively recorded as The operating mode, The operating mode, In actual operation, an operation mode is selected for optimization. The setting of the operation mode meets the following conditions:

[0040] ;

[0041] Where: Select a variable for the fully autonomous optimization water pump motor operation mode model, with a value of 0 or 1. When the value is 1, it means that the water pump motor model operates using the fully autonomous optimization water pump motor operation mode model. When the value is 0, it means that the water pump motor model does not operate using the fully autonomous optimization water pump motor operation mode model. A variable is selected for the semi-autonomous optimization water pump motor operation mode model, with a value of 0 or 1. When the value is 1, it indicates that the water pump motor model operates using the semi-autonomous optimization water pump motor operation mode model; when the value is 0, it indicates that the water pump motor model does not operate using the semi-autonomous optimization water pump motor operation mode model; The variable is selected for the set-type optimized water pump motor operation mode model, and the value is 0 or 1. When the value is 1, it means that the water pump motor model is operated using the set-type optimized water pump motor operation mode model. When the value is 0, it means that the water pump motor model is not operated using the set-type optimized water pump motor operation mode model.

[0042] The specific expression of the fully autonomous optimization water pump motor operation mode model is as follows:

[0043] ;

[0044] Where: To optimize the operating cycle; For the mth water pump motor The running state variable at the moment, the value is 0 or 1; is the operating state variable of the m-th water pump motor at time t, and its value is 0 or 1; is the maximum number of starts in the mth water pump motor operation cycle; is the operating power of the m-th water pump motor at time t; is the functional relationship of the m-th water pump motor with respect to the water inlet volume flow rate; The sum of the running electric power of the water system at time t is improved; The total number of water pump motors in the water system is improved; The maximum working time of the mth water pump motor in the running cycle is improved; The minimum working time of the mth water pump motor in the running cycle is improved;

[0045] The specific expression of the semi-autonomous optimization water pump motor running mode model is as follows:

[0046] ;

[0047] In the formula: The optimization running cycle is improved; The mth water pump motor The running state variable at time t is 0 or 1; The running state variable at time t of the mth water pump motor is 0 or 1; The maximum start-up times of the mth water pump motor in the running cycle are improved; The running electric power of the mth water pump motor at time t is improved; The functional relationship of the mth water pump motor with the water inflow volume flow rate is improved; The sum of the running electric power of the water system at time t is improved; The total number of water pump motors in the water system is improved; The maximum working time of the mth water pump motor in the running cycle is improved; The minimum working time of the mth water pump motor in the running cycle is improved; The running permission period set for the mth water pump motor in the running cycle, in which the water pump motor is permitted to start running, and in the non-permission period, the water pump motor is not permitted to start running, at this time The value is 0;

[0048] The specific expression of the set formula optimization water pump motor running mode model is as follows:

[0049] ;

[0050] In the formula: The running state variable at time t of the mth water pump motor is 0 or 1; The running permission period set for the mth water pump motor in the running cycle, in which the water pump motor is permitted to start running, and in the non-permission period, the water pump motor is not permitted to start running, at this time The value is 0; The running electric power of the mth water pump motor at time t is improved; The functional relationship of the mth water pump motor with the water inflow volume flow rate is improved; To increase the total operating power of the water system at time t; To increase the total number of pump motors in the water system;

[0051] The uncontrollable power load prediction model predicts the uncontrollable power load prediction data point results, including:

[0052] Get 6 uncontrollable power load data points in the recent history, which are recorded as 、 、 、 、 、 ;

[0053] The next predicted uncontrollable power load data point is recorded as ;

[0054] Set the moving average calculation step size is 3;

[0055] Calculate the first moving average. The expression of the first moving average calculation model is as follows:

[0056] ;

[0057] Where: is the result of the first moving average calculation at time t; 、 、 They are time t, time, The historical uncontrollable power load data points at the time; The step size for moving average calculation is set;

[0058] After calculating the first moving average, the data sequence is formed: 、 、 、 ;

[0059] Calculate the second moving average, and the expression of the calculation model is as follows:

[0060] ;

[0061] Where: for The second moving average calculation result of the time; 、 、 They are time t, time, The first moving average calculation result of the moment; The step size for moving average calculation is set;

[0062] To predict the next latest data point of uncontrollable power load, the prediction model expression is as follows:

[0063]

[0064] Where: To predict the next latest data point of uncontrollable power load, that is, the predicted The data point at that moment, in particular, when hour, Indicates the first latest data point for predicting future uncontrollable power load; is the result of the first moving average calculation at time t; is the result of the second moving average calculation at time t;

[0065] A new sequence of uncontrollable power load data points is formed, which is recorded as 、 、 、 、 、 ;

[0066] Based on the new sequence of uncontrollable power load data points, predict the future ;

[0067] According to the need of predicting the future time period, predict the 、 and other data points within the time period, thereby obtaining the results of all data points of uncontrollable power load prediction.

[0068] According to one aspect of the present invention, the expression of the electricity storage facility model is as follows:

[0069] ;

[0070] Where: 、 are the stored energy of the storage facility at time t and at time t, respectively; is the loss rate of the charging and discharging process of the electricity storage facility; 、 are the discharge power and charging power of the power storage facility at time t respectively; 、 are the charging efficiency and discharging efficiency of the power storage facility respectively; To optimize the running step resolution; 、 are the discharge power and charging power of the power storage facility at time t respectively; 、 They are the upper limit of discharge power and charging power of the power storage facility respectively; 、 They are the lower limit of discharge power regulation and protection of the power storage facility and the lower limit of charging power regulation and protection; 、 They are the upper limit coefficient and lower limit coefficient of the real-time storage energy of the power storage facility respectively; The installed rated capacity of the electricity storage facility; 、 Optimize the storage energy at the beginning and end of the operation cycle of the storage facility respectively; In order to optimize the start and end of the operation cycle, the balance coefficient of the energy storage facility at the start and end states is set. The value range is between 0 and 1. When the value is 0, it means that there is no requirement for the balance between the start and end states; when the value is 1, it means that a complete balance is required, and the start and end states are completely consistent. To optimize the operating cycle;

[0071] The expression of the water storage facility model is as follows:

[0072] ;

[0073] Where: The operating power of the water pump motor at time t; is the functional relationship of the pump motor with respect to the water inlet volume flow rate; is the density of water; is the acceleration due to gravity; is the pump motor head at time t, including static head and pipeline loss; is the water inlet volume flow rate of the pump motor at time t; is the comprehensive energy conversion efficiency of the pump motor at time t, including motor and hydraulic efficiency; 、 、 Different parameters for the functional relationship between the pump motor head and the water inlet volume flow rate; 、 、 Different parameters for the functional relationship between the comprehensive efficiency of energy conversion of the water pump motor and the volume flow rate of water inlet; for The water storage capacity of water storage facilities at any given time; is the water storage capacity of the water storage facility at time t; 、 They are the upper and lower limits of water storage capacity of water storage facilities respectively; is the outlet volume flow rate of the water storage facility at time t; The lower limit of the water discharge volume flow rate during the permitted water discharge period of the water storage facility; The maximum upper limit of the water outlet volume flow rate of the water storage facility; The upper limit of the water inflow volume for the safe operation of the water pump motor at time t; , The permitted water outlet time period and the non-water outlet time period set for the water storage facility during operation and maintenance.

[0074] According to one aspect of the present application, the integrated energy system model comprises a converter model, a DC bus power balance model, an AC power grid power node model, an AC power grid safe operation constraint model, and a DC power grid safe operation constraint model.

[0075] The expression of the converter model is as follows:

[0076] ;

[0077] In the formula: is the working efficiency of the converter from DC to AC; is the working efficiency of the converter from AC to DC; is the DC side input active power when the converter is in the DC-to-AC working mode at time t; is the AC side input active power when the converter is in the AC-to-DC working mode at time t; is the AC side output active power when the converter is in the DC-to-AC working mode at time t; is the DC side output active power when the converter is in the AC-to-DC working mode at time t; is the average power factor angle when the converter is working; is the AC side reactive power when the converter is in the DC-to-AC working mode at time t; is the AC side reactive power when the converter is in the AC-to-DC working mode at time t; is the tangent function;

[0078] The expression of the DC bus power balance model is as follows:

[0079] ;

[0080] In the formula: is the net active power injected by the DC distribution network node i at time t; is the predicted power of photovoltaic power generation at time t; is the actual power consumption of photovoltaic power generation in the optimal operation at time t; , are the discharge power and the charging power of the power storage facility at time t, respectively; is the DC side input active power when the converter is in the DC-to-AC working mode at time t; is the DC side output active power when the converter switches from AC to DC working mode at time t;

[0081] The expression of the AC power grid node model is as follows:

[0082] ;

[0083] Where: is the total active power injected into the AC grid node j at time t; The power supplied by the large power grid to the source-grid-load-storage integrated energy system at time t for the grid connection line; The grid connection line at time t is used to return power to the large power grid through the integrated energy system of source, grid, load and storage; is the AC side input active power when the converter switches from AC to DC working mode at time t; is the AC side output active power when the converter converts DC to AC working mode at time t; is the data point at time t for predicting uncontrollable power load; To increase the total operating power of the water system at time t; is the total reactive power injected into the AC grid node j at time t; is the reactive power of the node at the tie line of the grid connection point at time t; is the reactive power on the AC side when the converter converts DC to AC working mode at time t; is the reactive power on the AC side when the converter switches from AC to DC mode at time t; It is the average power factor angle when the uncontrollable power load is working; is the average power factor angle when the water pump motor is working; is the tangent function;

[0084] The expression of the AC power grid safe operation constraint model is as follows:

[0085] ;

[0086] Where: is the voltage amplitude of the AC grid node i at time t; is the maximum upper limit of the voltage amplitude of the AC grid node i; is the minimum lower limit of the voltage amplitude of the AC grid node i; is the voltage amplitude of AC grid node j at time t; is the maximum upper limit of the voltage amplitude at node j in the AC grid; is the minimum lower limit of the voltage amplitude of the AC grid node j; is the AC power grid line at time t Current amplitude; AC grid line maximum upper limit of current amplitude; for AC grid line minimum lower limit of current amplitude; total active power injected by AC grid node j at time t; maximum upper limit of total active power injected by AC grid node j, and the active power of each type of power generation and consumption equipment of the node meets its operating physical constraint boundary; minimum lower limit of total active power injected by AC grid node j, and the reactive power of each type of power generation and consumption equipment of the node meets its operating physical constraint boundary; total reactive power injected by AC grid node j at time t; maximum upper limit of total reactive power injected by AC grid node j; minimum lower limit of total reactive power injected by AC grid node j;

[0087] The expression of the DC grid safe operation constraint model is as follows:

[0088] ;

[0089] In the formula: voltage of DC grid node i at time t; maximum upper limit of voltage of DC grid node i; minimum lower limit of voltage of DC grid node i; voltage of DC grid node j at time t; maximum upper limit of voltage of DC grid node j; minimum lower limit of voltage of DC grid node j; net active power injected by DC grid node i at time t; maximum upper limit of net active power injected by DC grid node i, and the active power of each type of power generation and consumption equipment of the node meets its operating physical constraint boundary; minimum lower limit of net active power injected by DC grid node i, and the reactive power of each type of power generation and consumption equipment of the node meets its operating physical constraint boundary; active power flowing from DC grid node i to node j at time t; maximum upper limit of active power flowing from DC grid node i to node j; minimum lower limit of active power flowing from DC grid node i to node j.

[0090] According to one aspect of the present application, the day-ahead economic low-carbon optimal operation strategy comprises: an economic optimal operation model, a low-carbon optimal operation model, and an economic low-carbon optimal operation model;

[0091] The expression of the economic optimization operation model is as follows:

[0092] ;

[0093] In the formula, is an economic optimization operation target of the source-grid-load-storage integrated energy system in the optimization operation period; is the optimization operation period; is the power supplied by the large power grid to the source-grid-load-storage integrated energy system through the tie line at the grid-connected point at t; is the power returned by the source-grid-load-storage integrated energy system to the large power grid through the tie line at the grid-connected point at t; is the power purchase price of the source-grid-load-storage integrated energy system from the large power grid at t; is the power sale price of the source-grid-load-storage integrated energy system to the large power grid at t;

[0094] The expression of the low-carbon optimization operation model is as follows:

[0095] ;

[0096] In the formula, is a low-carbon optimization operation target of the source-grid-load-storage integrated energy system in the optimization operation period; is the optimization operation period; is the power supplied by the large power grid to the source-grid-load-storage integrated energy system through the tie line at the grid-connected point at t; is the actual consumption power of photovoltaic power in the optimization operation at t; is the equivalent carbon emission coefficient of the power purchased by the source-grid-load-storage integrated energy system from the large power grid;

[0097] The expression of the economic low-carbon optimization operation model is as follows:

[0098] ;

[0099] In the formula, is an economic optimization operation target of the source-grid-load-storage integrated energy system in the optimization operation period; is a low-carbon optimization operation target of the source-grid-load-storage integrated energy system in the optimization operation period; is an economic low-carbon optimization operation target of the source-grid-load-storage integrated energy system in the optimization operation period; , , , are respectively a positive deviation weight of the economic optimization target, a negative deviation weight of the economic optimization target, a positive deviation weight of the low-carbon optimization target, and a negative deviation weight of the low-carbon optimization target; , , 、 They are respectively the positive deviation of economic optimization target, the negative deviation of economic optimization target, the positive deviation of low-carbon optimization target, and the negative deviation of low-carbon optimization target; Acceptable tolerance coefficient for economic optimization objectives; Acceptable tolerance coefficient for low carbon optimization goals; It is the optimal result for single-objective economic optimization; This is the optimal result for single-objective low-carbon optimization.

[0100] According to one aspect of the present invention, the integrated energy system model is configured with an intraday economic low-carbon optimization operation strategy, and coupling connection conditions between the intraday economic low-carbon optimization operation strategy and the day-ahead economic low-carbon optimization operation strategy are configured. After the coupling connection conditions are configured, an integrated energy system is formed, including:

[0101] Solve the day-ahead economic low-carbon optimization operation model and obtain the optimal solution. The optimal solution includes: the power value of the grid connection line at time t supplied to the source-grid-load-storage integrated energy system is recorded as The power returned to the large power grid by the source-grid-load-storage integrated energy system at time t is recorded as , the discharge power of the storage facility at time t is recorded as , the charging power of the storage facility at time t is recorded as The total operating power of the water lifting system at time t is recorded as The outflow volume flow rate of the water storage facility at time t is recorded as , when the converter switches from AC to DC working mode at time t, the AC side input active power is recorded as When the converter converts DC to AC working mode at time t, the AC side output active power is recorded as The economic and low-carbon optimization operation target of the source-grid-load-storage integrated energy system under the optimized operation cycle is recorded as ;

[0102] Set the daily economic low-carbon optimization operation cycle to ;

[0103] According to the photovoltaic power generation AI prediction model, the daily economic low-carbon optimization operation cycle is re-predicted as The photovoltaic power generation power at time t is recorded as ,use Replace the photovoltaic power generation forecast power at time t in the economic low-carbon optimization operation of the day before ;

[0104] According to the uncontrollable power load forecasting model, the daily economic low-carbon optimization operation cycle is re-forecasted as The uncontrollable power load power at time t is recorded as ,use Replace the t-time data point of the uncontrollable power load predicted in the day-ahead economic low-carbon optimization operation ;

[0105] The daily economic low-carbon optimization operation cycle is On this basis, the economic low-carbon optimization operation model is re-solved, and the intraday day-ahead coupled nested boundary conditions are additionally set. The expression of the intraday day-ahead coupled nested boundary condition model is as follows:

[0106] ;

[0107] Where: To obtain the power value of the grid connection line supplied to the source-grid-load-storage integrated energy system at time t after solving the economic low-carbon optimization operation model of the day before; The power supplied by the large power grid to the source-grid-load-storage integrated energy system at time t for the grid connection line; The fluctuation tolerance coefficient of power supply power is nested and optimized for intraday and day-ahead coupling; To obtain the total operating power of the water lifting system at time t after solving the day-ahead economic low-carbon optimization operation model; To increase the total operating power of the water system at time t; In order to improve the total power of the water system, the day-ahead coupled nested optimization operation fluctuation tolerance coefficient is adopted; To solve the day-ahead economic low-carbon optimization operation model, the grid connection line is obtained at time t and the power returned to the large power grid by the source-grid-load-storage integrated energy system; The grid connection line at time t is used to return power to the large power grid through the integrated energy system of source, grid, load and storage; The day-ahead coupled nested optimization operation fluctuation tolerance coefficient of the returned power is used; To obtain the AC side input active power when the converter switches from AC to DC working mode at time t after solving the day-ahead economic low-carbon optimization operation model; is the AC side input active power when the converter switches from AC to DC working mode at time t; The permissible fluctuation coefficient of the intraday and day-ahead coupled nested optimization operation of the AC side input active power; To solve the economic low-carbon optimization operation model obtained after the day before, the AC side output active power of the converter when the DC is converted to AC working mode at time t; is the AC side output active power when the converter converts DC to AC working mode at time t; The permissible fluctuation coefficient of the AC side output active power is determined by nested optimization of intraday and day-ahead coupling operation; To obtain the economic low-carbon optimization operation target value of the source-grid-load-storage integrated energy system under the optimization operation cycle after solving the day-ahead economic low-carbon optimization operation model; To optimize the low-carbon operation goals of the source-grid-load-storage integrated energy system during the operation cycle; The day-ahead coupled nested optimization operation fluctuation tolerance coefficient within the low-carbon optimization operation target day; To obtain the discharge power of the power storage facility at time t after solving the day-ahead economic low-carbon optimization operation model; is the discharge power of the storage facility at time t; The discharge power intraday day-ahead coupled nested optimization operation fluctuation tolerance coefficient; To obtain the charging power of the power storage facility at time t after solving the day-ahead economic low-carbon optimization operation model; is the charging power of the power storage facility at time t; The charging power intraday and day-ahead coupled nested optimization operation fluctuation tolerance coefficient; To obtain the outflow volume flow of the water storage facility at time t after solving the day-ahead economic low-carbon optimization operation model; is the outlet volume flow rate of the water storage facility at time t; It is the permissible coefficient of fluctuation of the outlet volume flow rate during the day and the day before the day coupled nested optimization operation.

[0108] To achieve the above objectives, the present invention further provides an energy system economic low-carbon optimization operation system integrating AI and mechanism, comprising:

[0109] Source-side model building module, which builds the source-side model, including the large-scale power grid power supply model and the photovoltaic power generation AI prediction model;

[0110] Grid-side model building module, which builds the grid-side model, including the AC grid power flow model and the DC distribution network power balance model;

[0111] Load-side model building module, which builds the load-side model, including the water pump motor model and the uncontrollable power load prediction model;

[0112] The reservoir side model establishment module establishes the reservoir side model, including the power storage facility model and the water storage facility model;

[0113] An integrated energy system model building module couples and interconnects the source side model, grid side model, load side model, and storage side model to form an integrated energy system model;

[0114] The day-ahead economic low-carbon optimization operation strategy setting module sets the day-ahead economic low-carbon optimization operation strategy for the integrated energy system model;

[0115] The day-ahead economic low-carbon optimization operation strategy setting module sets a day-ahead economic low-carbon optimization operation strategy for the integrated energy system model, and sets a coupling connection condition between the day-ahead economic low-carbon optimization operation strategy and the day-ahead economic low-carbon optimization operation strategy, and after the coupling connection condition is set, the integrated energy system is formed;

[0116] The result output module inputs integrated energy system information to the integrated energy system, and outputs integrated energy system economic low-carbon optimization result information through the integrated energy system.

[0117] To achieve the above-mentioned purpose, the application further provides an electronic device, which comprises a processor, a memory, and a computer program stored on the memory and executable on the processor, and the computer program is executed by the processor to realize the AI and mechanism integrated energy system economic low-carbon optimization operation method.

[0118] To achieve the above-mentioned purpose, the application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the AI and mechanism integrated energy system economic low-carbon optimization operation method.

[0119] According to the scheme of the application, the application balances economic benefits and emission reduction benefits through purchase and sale of electricity strategy optimization and carbon emission sensitive device optimization, enhances the ability of the source network load storage integrated energy system to cope with new energy power generation uncertainty, and fully develops the potential of renewable energy; the application improves the operation stability and safety of source network load storage devices and AC / DC power grids through all-round modeling of source network load storage and AC / DC power grid safety constraints, optimizes the coordination and control and operation strategy between devices and between AC / DC power grids, improves the overall performance of the system, realizes global optimization operation of the energy system, and provides strong support for economic low-carbon operation of the system; the application can accurately reflect the actual operation of the energy system through the AI and mechanism deep integration model, provides a more reliable basis for optimization operation, not only realizes theoretical method innovation, but also provides a feasible technical scheme for coordinated operation of the source network load storage integrated energy system under the background of the new power system through the engineering model architecture and solving algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0120] Figure 1 A flow chart schematically showing an AI and mechanism integrated energy system economic low-carbon optimization operation method according to an embodiment of the application. DETAILED DESCRIPTION

[0121] The present application will now be discussed with reference to exemplary embodiments. It should be understood that the discussed embodiments are merely to better enable those of ordinary skill in the art to better understand and thus implement the present application, and are not intended to impose any limitations on the scope of the present application.

[0122] As used herein, the term "comprises" and variations thereof are to be construed as meaning "comprising, but not limited to." The term "based on" is to be construed as "based at least in part on." The terms "one embodiment" and "an embodiment" are to be construed as "at least one embodiment."

[0123] Figure 1 The flow chart schematically represents an AI and mechanism integrated energy system economic low-carbon optimization operation method according to an embodiment of the present application. As shown in Figure 1 The AI and mechanism integrated energy system economic low-carbon optimization operation method is characterized in that it comprises:

[0124] A source-side model is established, including a large power grid power supply model, a photovoltaic power generation AI (artificial intelligence) prediction model;

[0125] A network-side model is established, including an alternating current power grid power flow model and a direct current distribution network power balance model;

[0126] A load-side model is established, including a water pump motor model and an uncontrollable power load prediction model;

[0127] A storage-side model is established, including a power storage facility model and a water storage facility model;

[0128] The source-side model, the network-side model, the load-side model and the storage-side model are coupled and interconnected to form an integrated energy system model;

[0129] A day-ahead economic low-carbon optimization operation strategy is set for the integrated energy system model;

[0130] A day-ahead economic low-carbon optimization operation strategy is set for the integrated energy system model, and a coupling and connection condition of the day-ahead economic low-carbon optimization operation strategy and the day-ahead economic low-carbon optimization operation strategy is set, and after the coupling and connection condition is set, an integrated energy system is formed;

[0131] Integrated energy system information is input to the integrated energy system, and integrated energy system economic low-carbon optimization result information is output through the integrated energy system.

[0132] Further, according to an embodiment of the present application, the expression of the large power grid power supply model is as follows:

[0133] ;

[0134] In the formula: The power supplied by the large power grid to the source-grid-load-storage integrated energy system at time t for the grid connection line; The upper limit power of the power supply capacity of the grid-connected point tie line power grid to the source-grid-load-storage integrated energy system; The grid connection line at time t is used to return power to the large power grid through the integrated energy system of source, grid, load and storage; The grid connection line is the upper limit power of the power capacity returned to the grid by the source-grid-load-storage integrated energy system; in the large grid power supply model, when the source-grid-load-storage integrated energy system is interconnected with the large grid, there are two grid connection modes, one is to purchase electricity from the large grid and return power to the large grid; the other is to only purchase electricity from the large grid, with anti-backflow equipment installed, and cannot return power to the large grid. At this time, the large grid power supply model Set the value to 0;

[0135] The photovoltaic power generation AI prediction model is a sequence-to-sequence model architecture based on a long short-term memory network combined with an attention mechanism to process temporal dependency models. It includes an encoder model and a decoder model.

[0136] The expression of the encoder part model is as follows:

[0137] ;

[0138] Where: is the output of the forget gate at time t; is the input gate output at time t; is the output gate output at time t; is the candidate cell state at time t; is the current cell state at time t; for Current cell state at any moment; is the hidden state at time t; for Always hide the status; The input feature vector at time t contains the standardized PV power generation meteorological data and historical power; 、 、 、 are all weight matrices, corresponding to the weights of the forget gate, input gate, output gate and cell state respectively; 、 、 、 are bias vectors, corresponding to the bias terms of the forget gate, input gate, output gate and cell state respectively; Sigmoid function; For variables The hyperbolic tangent function of

[0139] The expression of the decoder part model is as follows:

[0140] ;

[0141] Where: Hidden state of the decoder at time t, storing decoding process information; For decoder Always hide the state and store the decoding process information; For the previous step The predicted output at the moment is the normalized photovoltaic power value; z is the context vector, which is initially the final hidden state of the encoder; is the attention weight vector, used to calculate the attention score; For variables The hyperbolic tangent function of 、 Both are attention weight matrices, which handle the trainable parameters of the decoder and encoder states respectively; is the hidden state of the encoder at step i; is the unnormalized attention score, which is the correlation strength between the decoder state at time t and the encoder state at time i; is the unnormalized attention score, which is the strength of the association between the decoder state at time t and the encoder state at time j; is the attention weight, the importance of the encoder at moment i to the decoder at moment t; is the dynamic context vector at time t; To predict the output at time t, it is necessary to denormalize to obtain the actual power value; is the output layer weight matrix, mapping the concatenated state to the output; is the output layer bias, which predicts the output bias term; T is the encoder sequence length, that is, the total number of input time steps; It is a complete long short-term memory network unit calculation process function, which contains the input gate that controls the inflow of new information, the forget gate that controls the retention of historical information, the output gate that controls the state output, and the cell state of the long-term memory carrier;

[0142] The expression of the photovoltaic power generation mechanism model is as follows:

[0143] ;

[0144] Where: is the photovoltaic power generation power at time t; The comprehensive conversion efficiency of the photovoltaic power generation system; The total effective surface area of ​​the photovoltaic power generation system battery components; is the solar radiation intensity at time t; The conversion coefficient between photovoltaic power generation and temperature adjustment; is the working environment temperature of the photovoltaic solar panel at time t; The operating temperature of solar panels under standard test conditions for photovoltaic power generation; is the photovoltaic panel tilt angle correction function; is the tilt angle of the photovoltaic panel; 、 for Different parameters of the function; is the weather type influence coefficient, where Indicates the sunny day coefficient, which is usually set to 1. Indicates the cloudy coefficient, which is generally set to a value between 0.6 and 0.8. Indicates the rainy day coefficient, which is generally set to a value between 0.2 and 0.5; is the weather type at time t; Convert weather type codes and convert text values ​​at different times into numerical values;

[0145] The expression of the input model of the long short-term memory network time series prediction model is as follows:

[0146] ;

[0147] Where: is the solar radiation intensity at time t; is the working environment temperature of photovoltaic power generation at time t; is the relative humidity at time t; is the wind speed at time t; is the weather type at time t; for Photovoltaic power generation at any moment; for Photovoltaic power generation at any moment; 、 、 、 They are minute, hour, day, and month time labels respectively; The input feature vector at time t contains the standardized PV power generation meteorological data and historical power;

[0148] The expression of the output prediction target model of the long short-term memory network time series prediction model is as follows:

[0149] ;

[0150] Where: is a mapping function of a complete long short-term memory network neural network for a time sequence, and realizes an input and output mapping function, including an encoder time sequence, a decoder prediction, and generates a whole process model content; T is an encoder sequence length, that is, a total number of input time steps; n is a total number of historical feature vectors; 、 、 are photovoltaic power prediction powers at t time, time, time, time; 、 、 are input feature vectors at t time, time, time, and contain normalized photovoltaic power meteorological data and historical power;

[0151] Further, according to an embodiment of the present application, an expression of an alternating current power grid power flow model is as follows:

[0152] ;

[0153] In the formula, is a total active power injected by an alternating current power grid node j at t time; is a total reactive power injected by the alternating current power grid node j at t time; is an active power on an alternating current power grid line at t time; is a reactive power on the alternating current power grid line at t time; is an active power on an alternating current power grid line at t time; is a reactive power on the alternating current power grid line at t time; is an alternating current power grid line current amplitude at t time; is a resistance of the alternating current power grid line ; is a reactance of the alternating current power grid line ; is a conductance of the alternating current power grid node j; is a susceptance of the alternating current power grid node j; is a voltage amplitude of the alternating current power grid node j at t time; is a voltage amplitude of the alternating current power grid node i at t time; is a first node set of the alternating current power grid with j as a tail node, and indicates that the first node i is connected with the tail node j; is a tail node set of the alternating current power grid with j as a first node, and indicates that the tail node i is connected with the head node j;

[0154] The expression of the DC distribution network power balance model is as follows:

[0155]

[0156] In the formula, Pnet,i(t) is the net active power injected by the DC distribution network node i at time t; Pij(t) is the active power flowing from the DC distribution network node i to the node j at time t; Pij(t) is the active power flowing from the DC distribution network node i to the node j at time t; Pij(t) is the power loss of the power branch formed by the DC distribution network node i and the node j at time t; V i(t) is the voltage of the DC distribution network node i at time t; V i(t) is the voltage of the DC distribution network node i at time t; V i(t) is the voltage of the DC distribution network node i at time t; Gij is the conductance between the DC distribution network node i and the node j; Gij is the conductance between the DC distribution network node i and the node j.

[0157] Further, according to an embodiment of the present application, the water pump motor model comprises: a fully autonomous optimization water pump motor operation mode model, a semi-autonomous optimization water pump motor operation mode model, a semi-autonomous optimization water pump motor operation mode model, and a set optimization water pump motor operation mode model, and the operation modes of the respective models are respectively denoted as the operation mode of the fully autonomous optimization water pump motor operation mode model, the operation mode of the semi-autonomous optimization water pump motor operation mode model, the operation mode of the set optimization water pump motor operation mode model.In actual operation, one operation mode is selected for optimization operation, and the setting of the operation mode satisfies the following conditions:

[0158]

[0159] In the formula, Pnet,i(t) is the net active power injected by the DC distribution network node i at time t; is a fully autonomous optimization water pump motor operation mode model selection variable, which takes a value of 0 or 1, when the value is 1, it indicates that the water pump motor model adopts the fully autonomous optimization water pump motor operation mode model for operation, and when the value is 0, it indicates that the water pump motor model does not adopt the fully autonomous optimization water pump motor operation mode model for operation; is a semi-autonomous optimization water pump motor operation mode model selection variable, which takes a value of 0 or 1, when the value is 1, it indicates that the water pump motor model adopts the semi-autonomous optimization water pump motor operation mode model for operation, and when the value is 0, it indicates that the water pump motor model does not adopt the semi-autonomous optimization water pump motor operation mode model for operation; is a set optimization water pump motor operation mode model selection variable, which takes a value of 0 or 1, when the value is 1, it indicates that the water pump motor model adopts the set optimization water pump motor operation mode model for operation, and when the value is 0, it indicates that the water pump motor model does not adopt the set optimization water pump motor operation mode model for operation;​

[0160] The specific expression of the fully autonomous optimization water pump motor operation mode model is as follows:

[0161] ;

[0162] Where: To optimize the operating cycle; For the mth water pump motor The running state variable at the moment, the value is 0 or 1; is the operating state variable of the m-th water pump motor at time t, and its value is 0 or 1; is the maximum number of starts in the mth water pump motor operation cycle; is the operating power of the m-th water pump motor at time t; is the functional relationship of the m-th water pump motor with respect to the water inlet volume flow rate; To increase the total operating power of the water system at time t; To increase the total number of pump motors in the water system; is the maximum working time in the mth water pump motor operation cycle; is the minimum working time in the mth water pump motor operation cycle;

[0163] The specific expression of the semi-autonomous optimization water pump motor operation mode model is as follows:

[0164] ;

[0165] Where: To optimize the operating cycle; For the mth water pump motor The running state variable at the moment, the value is 0 or 1; is the operating state variable of the m-th water pump motor at time t, and its value is 0 or 1; is the maximum number of starts in the mth water pump motor operation cycle; is the operating power of the m-th water pump motor at time t; is the functional relationship of the m-th water pump motor with respect to the water inlet volume flow rate; To increase the total operating power of the water system at time t; To increase the total number of pump motors in the water system; is the maximum working time in the mth water pump motor operation cycle; is the minimum working time in the mth water pump motor operation cycle; The operation permission period is set in the mth water pump motor operation cycle. During the operation permission period, the water pump motor is allowed to start and run. During the non-permitted period, the water pump motor cannot start and run. 0;

[0166] The specific expression of the set-point optimization water pump motor operation mode model is as follows:

[0167] ;

[0168] In the formula: is the running state variable of the mth water pump motor at time t, and takes the value of 0 or 1; is the running permission period set in the running period of the mth water pump motor, and the water pump motor is permitted to start running in the running permission period. In the non-permission period, the water pump motor cannot start running, and at this time takes the value of 0; is the running electric power of the mth water pump motor at time t; is the functional relationship of the mth water pump motor with respect to the water volume flow; is the total running electric power of the water system at time t; is the total number of water pump motors in the water system;

[0169] The uncontrollable power load prediction model predicts uncontrollable power load prediction data points, including:

[0170] The latest 6 historical uncontrollable power load data points are obtained, respectively denoted as ;

[0171] The next uncontrollable power load data point is predicted, denoted as

[0172] The moving average calculation step is set to 3;

[0173] The first moving average is calculated, and the expression of the first moving average calculation model is as follows:

[0174] ;

[0175] In the formula: is the first moving average calculation result at time t; are the historical uncontrollable power load data points at time t, time, time; is the set moving average calculation step;

[0176] After the first moving average is calculated, the data sequence is formed as: ;

[0177] The second moving average is calculated, and the expression of the calculation model is as follows:

[0178] ;

[0179] Where: for The second moving average calculation result of the time; They are time t, time, The first moving average calculation result of the moment; The step size for moving average calculation is set;

[0180] To predict the next latest data point of uncontrollable power load, the prediction model expression is as follows:

[0181]

[0182] Where: To predict the next latest data point of uncontrollable power load, that is, the predicted The data point at that moment, in particular, when hour, Indicates the first latest data point for predicting future uncontrollable power load; is the result of the first moving average calculation at time t; is the result of the second moving average calculation at time t;

[0183] A new sequence of uncontrollable power load data points is formed, which is recorded as ;

[0184] Based on the new sequence of uncontrollable power load data points, predict the future ;

[0185] According to the need of predicting the future time period, predict the 、 and other data points within the time period, thereby obtaining the results of all data points of uncontrollable power load prediction.

[0186] Furthermore, according to one embodiment of the present invention, the expression of the power storage facility model is as follows:

[0187] ;

[0188] Where: 、 are the stored energy of the storage facility at time t and at time t, respectively; is the loss rate of the charging and discharging process of the electricity storage facility; 、 are the discharge power and charging power of the power storage facility at time t respectively; 、 are the charging efficiency and discharging efficiency of the power storage facility respectively; To optimize the running step resolution; 、 are the discharge power and charging power of the power storage facility at time t respectively; 、 They are the upper limit of discharge power and charging power of the power storage facility respectively; 、 They are the lower limit of discharge power regulation and protection of the power storage facility and the lower limit of charging power regulation and protection; 、 They are the upper limit coefficient and lower limit coefficient of the real-time storage energy of the power storage facility respectively; The installed rated capacity of the electricity storage facility; 、 Optimize the storage energy at the beginning and end of the operation cycle of the storage facility respectively; In order to optimize the start and end of the operation cycle, the balance coefficient of the energy storage facility at the start and end states is set. The value range is between 0 and 1. When the value is 0, it means that there is no requirement for the balance between the start and end states; when the value is 1, it means that a complete balance is required, and the start and end states are completely consistent. To optimize the operating cycle;

[0189] The expression of the water storage facility model is as follows:

[0190] ;

[0191] Where: The operating power of the water pump motor at time t; is the functional relationship of the pump motor with respect to the water inlet volume flow rate; is the density of water; is the acceleration due to gravity; is the pump motor head at time t, including static head and pipeline loss; is the water inlet volume flow rate of the pump motor at time t; is the comprehensive energy conversion efficiency of the pump motor at time t, including motor and hydraulic efficiency; Different parameters for the functional relationship between the pump motor head and the water inlet volume flow rate; Different parameters for the functional relationship between the comprehensive efficiency of energy conversion of the water pump motor and the volume flow rate of water inlet; for The water storage capacity of water storage facilities at any given time; is the water storage capacity of the water storage facility at time t; 、 They are the upper and lower limits of water storage capacity of water storage facilities respectively; is the outlet volume flow rate of the water storage facility at time t; The lower limit of the water discharge volume flow rate during the permitted water discharge period of the water storage facility; The maximum upper limit of the water outlet volume flow rate of the water storage facility; The upper limit of the water inflow volume for safe operation of the water pump motor at time t; , The permitted water outlet time period and the non-water outlet time period set for the water storage facility during operation and maintenance, respectively.

[0192] Further, according to an embodiment of the present application, the integrated energy system model comprises: a converter model, a DC bus power balance model, an AC power grid power node model, an AC power grid safe operation constraint model, and a DC power grid safe operation constraint model.

[0193] The expression of the converter model is as follows:

[0194] ;

[0195] In the formula: is the working efficiency of the converter from DC to AC; is the working efficiency of the converter from AC to DC; is the input active power of the DC side when the converter is in the DC-to-AC working mode at time t; is the input active power of the AC side when the converter is in the AC-to-DC working mode at time t; is the output active power of the AC side when the converter is in the DC-to-AC working mode at time t; is the output active power of the DC side when the converter is in the AC-to-DC working mode at time t; is the average power factor angle when the converter is working; is the reactive power of the AC side when the converter is in the DC-to-AC working mode at time t; is the reactive power of the AC side when the converter is in the AC-to-DC working mode at time t; is the tangent function;

[0196] The expression of the DC bus power balance model is as follows:

[0197] ;

[0198] In the formula: is the net active power injected by the DC distribution network node i at time t; is the predicted power of photovoltaic power generation at time t; is the actual power consumption of photovoltaic power generation in the optimal operation at time t; , are the discharging power and charging power of the power storage facility at time t, respectively; is the input active power of the DC side when the converter is in the DC-to-AC working mode at time t; is the DC side output active power when the converter switches from AC to DC working mode at time t;

[0199] The expression of the AC power grid node model is as follows:

[0200] ;

[0201] Where: is the total active power injected into the AC grid node j at time t; The power supplied by the large power grid to the source-grid-load-storage integrated energy system at time t for the grid connection line; The grid connection line at time t is used to return power to the large power grid through the integrated energy system of source, grid, load and storage; is the AC side input active power when the converter switches from AC to DC working mode at time t; is the AC side output active power when the converter converts DC to AC working mode at time t; is the data point at time t for predicting uncontrollable power load; To increase the total operating power of the water system at time t; is the total reactive power injected into the AC grid node j at time t; is the reactive power of the node at the tie line of the grid connection point at time t; is the reactive power on the AC side when the converter converts DC to AC working mode at time t; is the reactive power on the AC side when the converter switches from AC to DC mode at time t; It is the average power factor angle when the uncontrollable power load is working; is the average power factor angle when the water pump motor is working; is the tangent function;

[0202] The expression of the AC power grid safe operation constraint model is as follows:

[0203] ;

[0204] Where: is the voltage amplitude of the AC grid node i at time t; is the maximum upper limit of the voltage amplitude of the AC grid node i; is the minimum lower limit of the voltage amplitude of the AC grid node i; is the voltage amplitude of AC grid node j at time t; is the maximum upper limit of the voltage amplitude at node j in the AC grid; is the minimum lower limit of the voltage amplitude of the AC grid node j; is the AC power grid line at time t Current amplitude; AC grid line upper limit of current amplitude; for AC grid line lower limit of current amplitude; total active power injected by AC grid node j at time t; upper limit of total active power injected by AC grid node j, and the active power of each type of power generation and consumption equipment of the point meets the operation physical constraint boundary; lower limit of total active power injected by AC grid node j, and the reactive power of each type of power generation and consumption equipment of the point meets the operation physical constraint boundary; total reactive power injected by AC grid node j at time t; upper limit of total reactive power injected by AC grid node j; lower limit of total reactive power injected by AC grid node j;

[0205] The expression of the DC grid safe operation constraint model is as follows:

[0206] ;

[0207] In the formula: voltage of DC grid node i at time t; upper limit of voltage of DC grid node i; lower limit of voltage of DC grid node i; voltage of DC grid node j at time t; upper limit of voltage of DC grid node j; lower limit of voltage of DC grid node j; net active power injected by DC grid node i at time t; upper limit of net active power injected by DC grid node i, and the active power of each type of power generation and consumption equipment of the point meets the operation physical constraint boundary; lower limit of net active power injected by DC grid node i, and the reactive power of each type of power generation and consumption equipment of the point meets the operation physical constraint boundary; active power of DC grid from node i flowing to node j at time t; upper limit of active power of DC grid from node i flowing to node j; lower limit of active power of DC grid from node i flowing to node j.

[0208] Further, according to an embodiment of the present application, the day-ahead economic low-carbon optimal operation strategy comprises: an economic optimal operation model, a low-carbon optimal operation model, and an economic low-carbon optimal operation model;

[0209] The expression of the economic optimal operation model is as follows:

[0210] ;

[0211] In the formula, is an economic optimal operation target of the source-grid-load-storage integrated energy system in the optimal operation period; is the optimal operation period; is a power supply of the large power grid to the source-grid-load-storage integrated energy system through the tie line at the time t; is a power return of the source-grid-load-storage integrated energy system to the large power grid through the tie line at the time t; is a power purchase price of the source-grid-load-storage integrated energy system from the large power grid at the time t; is a power sale price of the source-grid-load-storage integrated energy system to the large power grid at the time t;

[0212] The expression of the low-carbon optimal operation model is as follows:

[0213] ;

[0214] In the formula, is a low-carbon optimal operation target of the source-grid-load-storage integrated energy system in the optimal operation period; is the optimal operation period; is a power supply of the large power grid to the source-grid-load-storage integrated energy system through the tie line at the time t; is an actual consumption of the photovoltaic power in the optimal operation at the time t; is an equivalent carbon emission coefficient of the source-grid-load-storage integrated energy system for purchasing power from the large power grid;

[0215] The expression of the economic low-carbon optimal operation model is as follows:

[0216] ;

[0217] In the formula, is an economic optimal operation target of the source-grid-load-storage integrated energy system in the optimal operation period; is a low-carbon optimal operation target of the source-grid-load-storage integrated energy system in the optimal operation period; is an economic low-carbon optimal operation target of the source-grid-load-storage integrated energy system in the optimal operation period; are respectively a positive deviation weight of the economic optimal target, a negative deviation weight of the economic optimal target, a positive deviation weight of the low-carbon optimal target, and a negative deviation weight of the low-carbon optimal target; are respectively a positive deviation amount of the economic optimal target, a negative deviation amount of the economic optimal target, a positive deviation amount of the low-carbon optimal target, and a negative deviation amount of the low-carbon optimal target; Acceptable tolerance coefficient for economic optimization objectives; Acceptable tolerance coefficient for low carbon optimization goals; It is the optimal result for single-objective economic optimization; This is the optimal result for single-objective low-carbon optimization.

[0218] Furthermore, according to one embodiment of the present invention, a daily economic low-carbon optimization operation strategy is set for the integrated energy system model, and coupling connection conditions between the daily economic low-carbon optimization operation strategy and the day-ahead economic low-carbon optimization operation strategy are set. After the coupling connection conditions are set, an integrated energy system is formed, including:

[0219] Solve the day-ahead economic low-carbon optimization operation model and obtain the optimal solution. The optimal solution includes: the power value of the grid connection line at time t supplied to the source-grid-load-storage integrated energy system is recorded as The power returned to the large power grid by the source-grid-load-storage integrated energy system at time t is recorded as , the discharge power of the storage facility at time t is recorded as , the charging power of the storage facility at time t is recorded as The total operating power of the water lifting system at time t is recorded as The outflow volume flow rate of the water storage facility at time t is recorded as , when the converter switches from AC to DC working mode at time t, the AC side input active power is recorded as When the converter converts DC to AC working mode at time t, the AC side output active power is recorded as The economic and low-carbon optimization operation target of the source-grid-load-storage integrated energy system under the optimized operation cycle is recorded as ;

[0220] Set the daily economic low-carbon optimization operation cycle to ;

[0221] According to the photovoltaic power generation AI prediction model, the daily economic low-carbon optimization operation cycle is re-predicted as The photovoltaic power generation power at time t is recorded as ,use Replace the photovoltaic power generation forecast power at time t in the economic low-carbon optimization operation of the day before ;

[0222] According to the uncontrollable power load forecasting model, the daily economic low-carbon optimization operation cycle is re-forecasted as The uncontrollable power load power at time t (uncontrollable power load forecast data point) is recorded as ,use Replace the t-time data point of the uncontrollable power load predicted in the day-ahead economic low-carbon optimization operation ;

[0223] The economic low-carbon optimization operation cycle in the day is On this basis, the economic low-carbon optimization operation model is solved again, and a day-ahead coupling nesting boundary condition (i.e., coupling condition) is additionally set. The expression of the day-ahead coupling nesting boundary condition model is as follows:

[0224] ;

[0225] In the formula, is the power value of the power grid to the source grid load storage integrated energy system at the tie-in point at time t obtained after solving the day-ahead economic low-carbon optimization operation model; is the power of the power grid to the source grid load storage integrated energy system at the tie-in point at time t; is the fluctuation allowance coefficient of the power supply in the day-ahead coupling nesting optimization operation; is the total running electric power of the water lifting system at time t obtained after solving the day-ahead economic low-carbon optimization operation model; is the total running electric power of the water lifting system at time t; is the fluctuation allowance coefficient of the total power of the water lifting system in the day-ahead coupling nesting optimization operation; is the power of the source grid load storage integrated energy system to the power grid at the tie-in point at time t obtained after solving the day-ahead economic low-carbon optimization operation model; is the power of the source grid load storage integrated energy system to the power grid at the tie-in point at time t; is the fluctuation allowance coefficient of the power supply in the day-ahead coupling nesting optimization operation; is the active power input on the AC side when the AC-DC converter is in the AC mode at time t obtained after solving the day-ahead economic low-carbon optimization operation model; is the active power input on the AC side when the AC-DC converter is in the AC mode at time t; is the fluctuation allowance coefficient of the active power input on the AC side in the day-ahead coupling nesting optimization operation; is the active power output on the AC side when the DC-AC converter is in the AC mode at time t obtained after solving the day-ahead economic low-carbon optimization operation model; is the active power output on the AC side when the DC-AC converter is in the AC mode at time t; is the fluctuation allowance coefficient of the active power output on the AC side in the day-ahead coupling nesting optimization operation; is the economic low-carbon optimization operation target value of the source grid load storage integrated energy system in the optimization operation cycle obtained after solving the day-ahead economic low-carbon optimization operation model; is the low-carbon optimization operation target of the source grid load storage integrated energy system in the optimization operation cycle; is a fluctuation allowance coefficient of the low-carbon optimal operation model for day-ahead and day-of-week coupling nested optimization operation; is the discharge power of the power storage facility at time t obtained after solving the day-ahead economic low-carbon optimal operation model; is the discharge power of the power storage facility at time t; is a fluctuation allowance coefficient of the discharge power for day-ahead and day-of-week coupling nested optimization operation; is the charge power of the power storage facility at time t obtained after solving the day-ahead economic low-carbon optimal operation model; is the charge power of the power storage facility at time t; is a fluctuation allowance coefficient of the charge power for day-ahead and day-of-week coupling nested optimization operation; is the water outflow volume flow rate of the water storage facility at time t obtained after solving the day-ahead economic low-carbon optimal operation model; is the water outflow volume flow rate of the water storage facility at time t; is a fluctuation allowance coefficient of the water outflow volume flow rate for day-ahead and day-of-week coupling nested optimization operation.

[0226] Further, according to an embodiment of the present application, the input integrated energy system information comprises:

[0227] Input the integrated energy system information, including the upper limit power of the power supply capacity of the grid-connected point interconnection line to the source-grid-load-storage integrated energy system, the upper limit power of the power return capacity of the grid-connected point interconnection line source-grid-load-storage integrated energy system to the grid, grid connection mode, encoder model parameters, decoder model parameters, comprehensive conversion efficiency of photovoltaic power generation system, effective total surface area of ​​battery components of photovoltaic power generation system, photovoltaic power generation power and temperature regulation conversion coefficient, working environment temperature of solar panels under standard test environment for photovoltaic power generation, weather type influence coefficient, solar radiation intensity, working environment temperature of photovoltaic power generation, relative humidity, wind speed, time label, AC power grid network topology, AC power grid network parameters, AC power grid power Node power generation parameters, pump motor operation mode, day-ahead optimized operation cycle, intraday optimized operation cycle, maximum number of starts within the pump motor operation cycle, pump motor function parameters with respect to water inlet volume flow rate, total number of pump motors in the water lifting system, maximum working time within the pump motor operation cycle, minimum working time within the pump motor operation cycle, operating permission period set within the pump motor operation cycle, historical uncontrollable power load data points, loss rate of the charge and discharge process of the power storage facility, day-ahead optimized operation step resolution, intraday optimized operation step resolution, upper limit of discharge power of the power storage facility, upper limit of charging power of the power storage facility, lower limit of discharge power regulation and protection of the power storage facility, charging power of the power storage facility The lower limit of rate regulation protection, the installed rated capacity of the power storage facility, the functional relationship parameters between the comprehensive energy conversion efficiency of the water pump motor and the water inlet volume flow rate, the upper limit of the water storage capacity of the water storage facility, the lower limit of the water storage capacity of the water storage facility, the lower limit of the water outlet volume flow rate during the permitted water outlet period of the water storage facility, the permitted water outlet time period set for the water storage facility during operation and maintenance, the permitted water outlet time period set for the water storage facility during operation and maintenance, the working efficiency of the converter DC to AC, the working efficiency of the converter AC to DC, the average power factor angle when the converter is working, the average power factor angle when the water pump motor is working, the average power factor angle when the uncontrollable power load is working, the maximum upper limit of the AC grid node voltage amplitude, the maximum AC grid line current amplitude The maximum upper limit, the maximum upper limit of the total reactive power injected into the AC grid nodes, the minimum lower limit of the total reactive power injected into the AC grid nodes, the DC grid network topology, the DC grid network parameters, the maximum upper limit of the DC grid node voltage, the minimum lower limit of the DC grid node voltage, the electricity purchase price, the electricity sales price, the unit power equivalent carbon emission coefficient, the acceptable tolerance coefficient of the economic optimization target, the acceptable tolerance coefficient of the low-carbon optimization target, the positive deviation weight of the economic optimization target, the negative deviation weight of the economic optimization target, the positive deviation weight of the low-carbon optimization target, the negative deviation weight of the low-carbon optimization target, the various intraday day-ahead coupled nested optimization operation fluctuation tolerance coefficients in the intraday day-ahead coupled nested boundary condition model, and other information.

[0228] Further, according to an embodiment of the present application, the output integrated energy system economic low-carbon optimization result information comprises:

[0229] The output integrated energy system information comprises the power supplied by the large power grid to the source-grid-load-storage integrated energy system at each moment of the tie line of the grid-connected point, the power returned by the source-grid-load-storage integrated energy system to the large power grid at each moment of the tie line of the grid-connected point, photovoltaic power at each moment, the sum of active power at each moment of each node of the alternating current power grid, the sum of reactive power at each moment of each node of the alternating current power grid, the voltage amplitude at each moment of each node of the alternating current power grid, the net active power injected at each moment of each node of the direct current distribution network, the voltage at each moment of each node of the direct current distribution network, the running state variable of the water pump motor at each moment, the running electric power of the water pump motor at each moment, the sum of the running electric power of the water lifting system at each moment, the predicted value of the uncontrollable power load at each moment, the discharge power of the electric storage facility at each moment, the charge power of the electric storage facility at each moment, the discharge power of the electric storage facility at each moment, the charge power of the electric storage facility at each moment, the water storage capacity of the water storage facility at each moment, the water volume flow of the water storage facility at each moment, the input active power at each moment of the alternating current side of the converter, the output active power at each moment of the alternating current side of the converter, the current amplitude of each line of the alternating current power grid at each moment, the voltage of each node of the direct current distribution network at each moment, the economic optimization running target of the source-grid-load-storage integrated energy system under the optimization running period, the low-carbon optimization running target of the source-grid-load-storage integrated energy system under the optimization running period, the economic low-carbon optimization running target of the source-grid-load-storage integrated energy system under the optimization running period, the positive deviation amount of the economic optimization target, the negative deviation amount of the economic optimization target, the positive deviation amount of the low-carbon optimization target, the negative deviation amount of the low-carbon optimization target, and the like.

[0230] According to the above scheme of the present application, the present application balances the economic benefit and emission reduction benefit through the purchase and sale of electricity strategy optimization and carbon emission sensitive equipment optimization, enhances the ability of the source-grid-load-storage integrated energy system to cope with the uncertainty of new energy power generation, and fully develops the potential of renewable energy; the present application improves the operation stability and safety of the source-grid-load-storage equipment and the alternating current and direct current power grid, optimizes the coordination and control and operation strategy between the equipment and between the alternating current and direct current power grids, improves the overall performance of the system, realizes the global optimization operation of the energy system, and provides strong support for the economic low-carbon operation of the system; the present application can accurately reflect the actual operation of the energy system through the AI and mechanism deep fusion model constructed, provides a more reliable basis for optimization operation, not only realizes theoretical method innovation, but also provides a feasible technical scheme for the coordinated operation of the source-grid-load-storage integrated energy system under the background of the new type of power system through the model architecture and solving algorithm which can be engineered.

[0231] Further, to achieve the above object, the present application also provides an AI and mechanism integrated energy system economic low-carbon optimal operation system, comprising:

[0232] A source side model establishing module, which establishes a source side model, including a large power grid power supply model, a photovoltaic power generation AI prediction model;

[0233] A network side model establishing module, which establishes a network side model, including an alternating current power grid power flow model and a direct current distribution network power balance model;

[0234] A load side model establishing module, which establishes a load side model, including a water pump motor model and an uncontrollable power load prediction model;

[0235] A storage side model establishing module, which establishes a storage side model, including a power storage facility model and a water storage facility model;

[0236] An integrated energy system model establishing module, which couples and connects the source side model, the network side model, the load side model and the storage side model to form an integrated energy system model;

[0237] A day-ahead economic low-carbon optimal operation strategy setting module, which sets a day-ahead economic low-carbon optimal operation strategy for the integrated energy system model;

[0238] An intra-day economic low-carbon optimal operation strategy setting module, which sets an intra-day economic low-carbon optimal operation strategy for the integrated energy system model, and sets a coupling and connection condition of the intra-day economic low-carbon optimal operation strategy and the day-ahead economic low-carbon optimal operation strategy, and forms an integrated energy system after the coupling and connection condition setting is completed;

[0239] A result output module, which inputs integrated energy system information to the integrated energy system, and outputs integrated energy system economic low-carbon optimal result information through the integrated energy system.

[0240] The AI and mechanism integrated energy system economic low-carbon optimal operation system according to the present application can realize the AI and mechanism integrated energy system economic low-carbon optimal operation method, and the specific process steps are as described above, which will not be repeated here.

[0241] Further, to achieve the above object, the present application also provides an electronic device, which comprises a processor, a memory, and a computer program stored on the memory and executable on the processor, and the computer program is executed by the processor to realize the AI and mechanism integrated energy system economic low-carbon optimal operation method as described above.

[0242] Further, to achieve the above object, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the AI and mechanism integrated energy system economic low-carbon optimal operation method as described above.

[0243] Those skilled in the art can clearly understand that the modules and algorithm steps described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software manner depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0244] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described apparatus and device can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0245] In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented by other ways. For example, the apparatus embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there can be another division in actual implementation, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed modules can be indirect coupling or communication connection through some interface, device or module, and can be electrical, mechanical or other forms.

[0246] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical modules, i.e. can be located in one place, or can be distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0247] In addition, each functional module in the embodiments of the present application can be integrated into a processing module, or each module can exist physically independently, or two or more modules can be integrated into one module.

[0248] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the energy-saving signal transmission / reception method of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, and various media that can store program codes.

[0249] The above description is merely preferred embodiments of the present application and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features can be replaced with the technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

[0250] It should be understood that the sequence of the steps in the summary and embodiments of the present application does not absolutely mean the order of execution, and the execution order of the processes should be determined according to their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Claims

1. The energy system economic low-carbon optimization operation method integrating AI and mechanism is characterized by: include: Establish source-side models, including large-scale power grid power supply models and photovoltaic power generation AI prediction models; Establish grid-side models, including AC grid power flow model and DC distribution network power balance model; Establish a load-side model, including a water pump motor model and an uncontrollable power load prediction model; Establish a storage-side model, including power storage facility model and water storage facility model; The source side model, grid side model, load side model and storage side model are coupled and interconnected to form an integrated energy system model; Set up a day-ahead economic low-carbon optimization operation strategy for the integrated energy system model; Setting a daily economic low-carbon optimization operation strategy for the integrated energy system model, and setting coupling connection conditions between the daily economic low-carbon optimization operation strategy and the day-ahead economic low-carbon optimization operation strategy. After the coupling connection conditions are set, an integrated energy system is formed. Inputting integrated energy system information into the integrated energy system, and outputting integrated energy system economic and low-carbon optimization result information through the integrated energy system; The integrated energy system model includes: a converter model, a DC bus power balance model, an AC power grid power node model, an AC power grid safe operation constraint model, and a DC power grid safe operation constraint model; The expression of the converter model is as follows: ； Where: The efficiency of the converter in converting DC to AC; The efficiency of the converter in converting AC to DC; is the DC side input active power when the converter converts DC to AC working mode at time t; is the AC side input active power when the converter switches from AC to DC working mode at time t; is the AC side output active power when the converter converts DC to AC working mode at time t; is the DC side output active power when the converter switches from AC to DC working mode at time t; is the average power factor angle when the converter is working; is the reactive power on the AC side when the converter converts DC to AC working mode at time t; is the reactive power on the AC side when the converter switches from AC to DC mode at time t; is the tangent function; The expression of the DC bus power balance model is as follows: ； Where: is the net active power injected into the DC distribution network node at time t; The predicted photovoltaic power generation power at time t; is the power actually consumed by photovoltaic power generation in optimized operation at time t; 、 are the discharge power and charging power of the power storage facility at time t respectively; is the DC side input active power when the converter converts DC to AC working mode at time t; is the DC side output active power when the converter switches from AC to DC working mode at time t; The expression of the AC power grid node model is as follows: ； Where: is the total active power injected into the AC grid node j at time t; The power supplied by the large power grid to the source-grid-load-storage integrated energy system at time t for the grid connection line; The grid connection line at time t is used to return power to the large power grid through the integrated energy system of source, grid, load and storage; is the AC side input active power when the converter switches from AC to DC working mode at time t; is the AC side output active power when the converter converts DC to AC working mode at time t; is the data point at time t for predicting uncontrollable power load; To increase the total operating power of the water system at time t; is the total reactive power injected into the AC grid node j at time t; is the reactive power of the node at the tie line of the grid connection point at time t; is the reactive power on the AC side when the converter converts DC to AC working mode at time t; is the reactive power on the AC side when the converter switches from AC to DC mode at time t; It is the average power factor angle when the uncontrollable power load is working; is the average power factor angle when the water pump motor is working; is the tangent function; The expression of the AC power grid safe operation constraint model is as follows: ； Where: is the voltage amplitude of the AC grid node i at time t; is the maximum upper limit of the voltage amplitude of the AC grid node i; is the minimum lower limit of the voltage amplitude of the AC grid node i; is the voltage amplitude of node j in the AC grid at time t; is the maximum upper limit of the voltage amplitude at node j in the AC grid; is the minimum lower limit of the voltage amplitude of the AC grid node j; is the current amplitude of the AC grid line ij at time t; is the maximum upper limit of the current amplitude of the AC grid line ij; is the minimum lower limit of the current amplitude of the AC grid line ij; is the total active power injected into the AC grid node j at time t; The maximum upper limit of the total active power injected into the AC grid node j, and the active power of each type of power generation and consumption equipment at this point meets its operating physical constraint boundary; The minimum lower limit of the total active power injected into the AC grid node j, and the reactive power of each type of power generation and consumption equipment at this point meets its operating physical constraint boundary; is the total reactive power injected into the AC grid node j at time t; is the maximum upper limit of the total reactive power injected into the AC grid node j; is the minimum lower limit of the total reactive power injected into the AC grid node j; The expression of the DC grid safe operation constraint model is as follows: ； Where: is the voltage of DC distribution network node i at time t; is the maximum upper limit of the voltage at the DC grid node i; is the minimum lower limit of the voltage at the DC grid node i; is the voltage of DC distribution network node j at time t; is the maximum upper limit of the voltage at node j in the DC grid; is the minimum lower limit of the voltage at node j in the DC grid; is the net active power injected into the DC distribution network node i at time t; The maximum upper limit of the net active power injected into the DC distribution network node i, and the active power of each type of power generation and consumption equipment at this point meets its operating physical constraint boundary; The minimum lower limit of the net active power injected into the DC distribution network node i, and the reactive power of each type of power generation and consumption equipment at this point meets its operating physical constraint boundary; is the active power flowing from node i to node j in the DC distribution network at time t; is the maximum upper limit of active power flowing from node i to node j in the DC distribution network; is the minimum lower limit of active power flowing from node i to node j in the DC distribution network; The day-ahead economic low-carbon optimization operation strategy includes: an economic optimization operation model, a low-carbon optimization operation model, and an economic low-carbon optimization operation model; The expression of the economic optimization operation model is as follows: ； Where: To optimize the economic operation objectives of the source-grid-load-storage integrated energy system during the operation cycle; To optimize the operating cycle; The power supplied by the large power grid to the source-grid-load-storage integrated energy system at time t for the grid connection line; The grid connection line at time t is used to return power to the large power grid through the integrated energy system of source, grid, load and storage; The electricity price purchased from the grid by the integrated energy system of source, grid, load and storage at time t; The electricity price sold by the integrated energy system of source, grid, load and storage to the large power grid at time t; The expression of the low-carbon optimization operation model is as follows: ； Where: To optimize the low-carbon operation goals of the source-grid-load-storage integrated energy system during the operation cycle; To optimize the operating cycle; The power supplied by the large power grid to the source-grid-load-storage integrated energy system at time t for the grid connection line; is the power actually consumed by photovoltaic power generation in optimized operation at time t; The unit power equivalent carbon emission coefficient of electricity purchased from the large power grid for the integrated energy system of source, grid, load and storage; The expression of the economic low-carbon optimization operation model is as follows: ； Where: To optimize the economic operation objectives of the source-grid-load-storage integrated energy system during the operation cycle; To optimize the low-carbon operation goals of the source-grid-load-storage integrated energy system during the operation cycle; To optimize the economic and low-carbon operation goals of the integrated energy system of source, grid, load and storage during the operation cycle; They are respectively the positive deviation weight of economic optimization target, the negative deviation weight of economic optimization target, the positive deviation weight of low-carbon optimization target, and the negative deviation weight of low-carbon optimization target; They are respectively the positive deviation of economic optimization target, the negative deviation of economic optimization target, the positive deviation of low-carbon optimization target, and the negative deviation of low-carbon optimization target; Acceptable tolerance coefficient for economic optimization objectives; Acceptable tolerance coefficient for low carbon optimization goals; It is the optimal result for single-objective economic optimization; The optimal result for single-objective low-carbon optimization; The integrated energy system model sets a daily economic low-carbon optimization operation strategy, and sets coupling connection conditions between the daily economic low-carbon optimization operation strategy and the day-ahead economic low-carbon optimization operation strategy. After the coupling connection conditions are set, an integrated energy system is formed, including: Solve the day-ahead economic low-carbon optimization operation model and obtain the optimal solution. The optimal solution includes: the power value of the grid connection line at time t supplied to the source-grid-load-storage integrated energy system is recorded as The power returned to the large power grid by the source-grid-load-storage integrated energy system at time t is recorded as , the discharge power of the storage facility at time t is recorded as , the charging power of the storage facility at time t is recorded as The total operating power of the water lifting system at time t is recorded as The outflow volume flow rate of the water storage facility at time t is recorded as , when the converter switches from AC to DC working mode at time t, the AC side input active power is recorded as When the converter converts DC to AC working mode at time t, the AC side output active power is recorded as The economic and low-carbon optimization operation target of the source-grid-load-storage integrated energy system under the optimized operation cycle is recorded as ; Set the daily economic low-carbon optimization operation cycle to ; According to the photovoltaic power generation AI prediction model, the daily economic low-carbon optimization operation cycle is re-predicted as The photovoltaic power generation power at time t is recorded as ,use Replace the photovoltaic power generation forecast power at the time of economic low-carbon optimization operation on the day before ; According to the uncontrollable power load forecasting model, the daily economic low-carbon optimization operation cycle is re-forecasted as The uncontrollable power load power at time t is recorded as ,use Replace the t-time data point of the uncontrollable power load predicted in the day-ahead economic low-carbon optimization operation ; The daily economic low-carbon optimization operation cycle is On this basis, the economic low-carbon optimization operation model is re-solved, and the intraday day-ahead coupled nested boundary conditions are additionally set. The expression of the intraday day-ahead coupled nested boundary condition model is as follows: ； Where: To obtain the power value of the grid connection line supplied to the source-grid-load-storage integrated energy system at time t after solving the economic low-carbon optimization operation model of the day before; The power supplied by the large power grid to the source-grid-load-storage integrated energy system at time t for the grid connection line; The fluctuation tolerance coefficient of power supply power is nested and optimized for intraday and day-ahead coupling; To obtain the total operating power of the water lifting system at time t after solving the day-ahead economic low-carbon optimization operation model; To increase the total operating power of the water system at time t; In order to improve the total power of the water system, the day-ahead coupled nested optimization operation fluctuation tolerance coefficient is adopted; To solve the day-ahead economic low-carbon optimization operation model, the grid connection line is obtained at time t and the power returned to the large power grid by the source-grid-load-storage integrated energy system; The grid connection line at time t is used to return power to the large power grid through the integrated energy system of source, grid, load and storage; The day-ahead coupled nested optimization operation fluctuation tolerance coefficient of the returned power is used; To obtain the AC side input active power when the converter switches from AC to DC working mode at time t after solving the day-ahead economic low-carbon optimization operation model; is the AC side input active power when the converter switches from AC to DC working mode at time t; The permissible fluctuation coefficient of the intraday and day-ahead coupled nested optimization operation of the AC side input active power; To obtain the economic low-carbon optimization operation target value of the source-grid-load-storage integrated energy system under the optimization operation cycle after solving the day-ahead economic low-carbon optimization operation model; To optimize the low-carbon operation goals of the source-grid-load-storage integrated energy system during the operation cycle; The day-ahead coupled nested optimization operation fluctuation tolerance coefficient within the low-carbon optimization operation target day; To obtain the discharge power of the power storage facility at time t after solving the day-ahead economic low-carbon optimization operation model; is the discharge power of the storage facility at time t; The discharge power intraday day-ahead coupled nested optimization operation fluctuation tolerance coefficient; To obtain the charging power of the power storage facility at time t after solving the day-ahead economic low-carbon optimization operation model; is the charging power of the power storage facility at time t; The charging power intraday and day-ahead coupled nested optimization operation fluctuation tolerance coefficient; To obtain the outflow volume flow of the water storage facility at time t after solving the day-ahead economic low-carbon optimization operation model; is the outlet volume flow rate of the water storage facility at time t; It is the permissible coefficient of fluctuation of the outlet volume flow rate during the day and the day before the day coupled nested optimization operation.

2. The method for economic low-carbon optimization operation of energy systems integrating AI and mechanism according to claim 1 is characterized in that: The expression of the large power grid power supply model is as follows: ； Where: The power supplied by the large power grid to the source-grid-load-storage integrated energy system at time t for the grid connection line; The upper limit power of the power supply capacity of the grid-connected point tie line power grid to the source-grid-load-storage integrated energy system; The grid connection line at time t is used to return power to the large power grid through the integrated energy system of source, grid, load and storage; The power upper limit of the capacity of the power returned to the grid by the source-grid-load-storage integrated energy system of the grid connection line; The photovoltaic power generation AI prediction model is a sequence-to-sequence model architecture based on the long short-term memory network combined with an attention mechanism to process temporal dependency models, including: an encoder part model, a decoder part model, a photovoltaic power generation mechanism model, an input model of the long short-term memory network temporal prediction model, and an output prediction target model of the long short-term memory network temporal prediction model; The expression of the encoder part model is as follows: ； Where: is the output of the forget gate at time t; is the input gate output at time t; is the output gate output at time t; is the candidate cell state at time t; is the current cell state at time t; for Current cell state at any moment; is the hidden state at time t; for Always hide the status; The input feature vector at time t contains the standardized PV power generation meteorological data and historical power; are all weight matrices, corresponding to the weights of the forget gate, input gate, output gate and cell state respectively; are all bias vectors, corresponding to the bias items of the forget gate, input gate, output gate and cell state respectively; For variables Sigmoid function; For variables The hyperbolic tangent function of The expression of the decoder part model is as follows: ； Where: Hidden state of the decoder at time t, storing decoding process information; For decoder Always hide the state and store the decoding process information; For the previous step The predicted output at the moment is the normalized photovoltaic power value; z is the context vector, which is initially the final hidden state of the encoder; is the attention weight vector, used to calculate the attention score; For variables The hyperbolic tangent function of Both are attention weight matrices, which handle the trainable parameters of the decoder and encoder states respectively; is the hidden state of the encoder at step i; is the unnormalized attention score, which is the correlation strength between the decoder state at time t and the encoder state at time i; is the unnormalized attention score, which is the strength of the association between the decoder state at time t and the encoder state at time j; is the attention weight, the importance of the encoder at moment i to the decoder at moment t; is the dynamic context vector at time t; To predict the output at time t, it is necessary to denormalize to obtain the actual power value; is the output layer weight matrix, mapping the concatenated state to the output; is the output layer bias, which predicts the output bias term; T is the encoder sequence length, that is, the total number of input time steps; It is a complete long short-term memory network unit calculation process function, which contains the input gate that controls the inflow of new information, the forget gate that controls the retention of historical information, the output gate that controls the state output, and the cell state of the long-term memory carrier; The expression of the photovoltaic power generation mechanical model is as follows: ； Where: is the photovoltaic power generation power at time t; The comprehensive conversion efficiency of the photovoltaic power generation system; The total effective surface area of ​​the photovoltaic power generation system battery components; is the solar radiation intensity at time t; The conversion coefficient between photovoltaic power generation and temperature adjustment; is the working environment temperature of the photovoltaic solar panel at time t; The operating temperature of solar panels under standard test conditions for photovoltaic power generation; is the photovoltaic panel tilt angle correction function; is the tilt angle of the photovoltaic panel; for Different parameters of the function; is the weather type influence coefficient, where Indicates the sunny day coefficient, set the value to 1. Indicates the cloudy factor, the setting value is 0.6 to 0.8, Indicates the rainy day coefficient, the setting value is 0.2 to 0.5; is the weather type at time t; Convert weather type codes and convert text values ​​at different times into numerical values; The expression of the input model of the long short-term memory network time series prediction model is as follows: ； Where: is the solar radiation intensity at time t; is the working environment temperature of photovoltaic power generation at time t; is the relative humidity at time t; is the wind speed at time t; is the weather type at time t; for Photovoltaic power generation at any moment; for Photovoltaic power generation at any moment; They are minute, hour, day, and month time labels respectively; The input feature vector at time t contains the standardized PV power generation meteorological data and historical power; The expression of the output prediction target model of the long short-term memory network time series prediction model is as follows: ； Where: It is a mapping function of a complete long short-term memory network neural network to the time series, realizing the input and output mapping function, including the encoder time series and decoder prediction generation process model content; T is the encoder sequence length, that is, the total number of input time steps; n is the total number of historical feature vectors; They are time, time, The predicted photovoltaic power generation power at the moment; They are time t, time, The input feature vector at the time contains the standardized photovoltaic power generation meteorological data and historical power.

3. The method for economic low-carbon optimization operation of energy systems integrating AI and mechanism according to claim 1 is characterized in that: The expression of the AC power grid flow model is as follows: ； Where: is the total active power injected into the AC grid node j at time t; is the total reactive power injected into the AC grid node j at time t; is the active power on the AC grid line ij at time t; is the reactive power on the AC grid line ij at time t; is the active power on AC grid line jk at time t; is the reactive power on AC grid line jk at time t; is the current amplitude of the AC grid line ij at time t; is the resistance of the AC grid line ij; is the reactance of the AC grid line ij; is the conductance of AC grid node j; is the susceptance of AC grid node j; is the voltage amplitude of node j in the AC grid at time t; is the voltage amplitude of the AC grid node i at time t; is the set of first nodes in the AC power grid with j as the last node, and Indicates that the first node i is connected to the last node j; is the set of tail nodes in the AC power grid with j as the first node, and Indicates that the tail node i is connected to the head node j; The expression of the DC distribution network power balance model is as follows: ； Where: is the net active power injected into the DC distribution network node i at time t; is the active power flowing from node i to node j in the DC distribution network at time t; is the power loss of the power branch ij formed by the DC distribution network node i and node j at time t; is the voltage of DC distribution network node i at time t; is the voltage of DC distribution network node j at time t; is the conductance between nodes i and j in the DC distribution network; Node j is directly connected to node i in the DC distribution network.

4. The method for economic low-carbon optimization operation of energy systems integrating AI and mechanism according to claim 1 is characterized in that: The water pump motor model includes: a fully autonomous optimization water pump motor operation mode model, a semi-autonomous optimization water pump motor operation mode model, a semi-autonomous optimization water pump motor operation mode model, and a set optimization water pump motor operation mode model, and the operation mode of each model is recorded as The operating mode, The operating mode, In actual operation, an operation mode is selected for optimization. The setting of the operation mode meets the following conditions: ； Where: Select a variable for the fully autonomous optimization water pump motor operation mode model, with a value of 0 or 1. When the value is 1, it means that the water pump motor model operates using the fully autonomous optimization water pump motor operation mode model. When the value is 0, it means that the water pump motor model does not operate using the fully autonomous optimization water pump motor operation mode model. A variable is selected for the semi-autonomous optimization water pump motor operation mode model, with a value of 0 or 1. When the value is 1, it indicates that the water pump motor model operates using the semi-autonomous optimization water pump motor operation mode model; when the value is 0, it indicates that the water pump motor model does not operate using the semi-autonomous optimization water pump motor operation mode model; The variable is selected for the set-type optimized water pump motor operation mode model, and the value is 0 or 1. When the value is 1, it means that the water pump motor model is operated using the set-type optimized water pump motor operation mode model. When the value is 0, it means that the water pump motor model is not operated using the set-type optimized water pump motor operation mode model. The specific expression of the fully autonomous optimization water pump motor operation mode model is as follows: ； Where: To optimize the operating cycle; For the mth water pump motor The running state variable at the moment, the value is 0 or 1; is the operating state variable of the m-th water pump motor at time t, and its value is 0 or 1; is the maximum number of starts in the mth water pump motor operation cycle; is the operating power of the m-th water pump motor at time t; is the functional relationship of the m-th water pump motor with respect to the water inlet volume flow rate; To increase the total operating power of the water system at time t; To increase the total number of pump motors in the water system; is the maximum working time in the mth water pump motor operation cycle; is the minimum working time in the mth water pump motor operation cycle; The specific expression of the semi-autonomous optimization water pump motor operation mode model is as follows: ； Where: To optimize the operating cycle; For the mth water pump motor The running state variable at the moment, the value is 0 or 1; is the operating state variable of the m-th water pump motor at time t, and its value is 0 or 1; is the maximum number of starts in the mth water pump motor operation cycle; is the operating power of the m-th water pump motor at time t; is the functional relationship of the m-th water pump motor with respect to the water inlet volume flow rate; To increase the total operating power of the water system at time t; To increase the total number of pump motors in the water system; is the maximum working time in the mth water pump motor operation cycle; is the minimum working time in the mth water pump motor operation cycle; The operation permission period is set in the mth water pump motor operation cycle. During the operation permission period, the water pump motor is allowed to start and run. During the non-permitted period, the water pump motor cannot start and run. The value is 0; The specific expression of the set-type optimization water pump motor operation mode model is as follows: ； Where: is the operating state variable of the m-th water pump motor at time t, and its value is 0 or 1; The operation permission period is set in the mth water pump motor operation cycle. During the operation permission period, the water pump motor is allowed to start and run. During the non-permitted period, the water pump motor cannot start and run. The value is 0; is the operating power of the m-th water pump motor at time t; is the functional relationship of the m-th water pump motor with respect to the water inlet volume flow rate; To increase the total operating power of the water system at time t; To increase the total number of pump motors in the water system; The uncontrollable power load prediction model predicts the uncontrollable power load prediction data point results, including: Get 6 uncontrollable power load data points in the recent history, which are recorded as ; The next predicted uncontrollable power load data point is recorded as ; Set the moving average calculation step size is 3; Calculate the first moving average. The expression of the first moving average calculation model is as follows: ； Where: is the result of the first moving average calculation at time t; They are time t, time, The historical uncontrollable power load data points at the time; The step size for moving average calculation is set; After calculating the first moving average, the data sequence is formed: ; Calculate the second moving average, and the expression of the calculation model is as follows: ； Where: for The second moving average calculation result of the time; They are time t, time, The first moving average calculation result of the moment; The step size for moving average calculation is set; To predict the next latest data point of uncontrollable power load, the prediction model expression is as follows: ， Where: To predict the next latest data point of uncontrollable power load, that is, the predicted The data point at that moment, in particular, when hour, Indicates the first latest data point for predicting future uncontrollable power load; is the result of the first moving average calculation at time t; is the result of the second moving average calculation at time t; A new sequence of uncontrollable power load data points is formed, which is recorded as ; Based on the new sequence of uncontrollable power load data points, predict the future , the prediction calculation process is the same as above; According to the need of predicting the future time period, predict the 、 and other data points within the time period, thereby obtaining the results of all data points of uncontrollable power load prediction.

5. The method for economic low-carbon optimization operation of energy systems integrating AI and mechanism according to claim 1 is characterized in that: The expression of the electricity storage facility model is as follows: ； Where: are the stored energy of the storage facility at time t and at time t, respectively; is the loss rate of the charging and discharging process of the electricity storage facility; are the discharge power and charging power of the power storage facility at time t respectively; They are the charging efficiency and discharging efficiency of the energy storage facility respectively; To optimize the running step resolution; are the discharge power and charging power of the power storage facility at time t respectively; They are the upper limit of discharge power and charging power of power storage facilities respectively; They are the lower limit of discharge power regulation and protection of the power storage facility and the lower limit of charging power regulation and protection; They are the upper limit coefficient and lower limit coefficient of the real-time storage energy of the power storage facility respectively; The installed rated capacity of the electricity storage facility; Optimize the storage energy at the beginning and end of the operation cycle of the storage facility respectively; In order to optimize the start and end of the operation cycle, the balance coefficient of the energy storage facility at the start and end states is set. The value range is between 0 and 1. When the value is 0, it means that there is no requirement for the balance between the start and end states; when the value is 1, it means that a complete balance is required, and the start and end states are completely consistent. To optimize the operating cycle; The expression of the water storage facility model is as follows: ； Where: The operating power of the water pump motor at time t; is the functional relationship of the pump motor with respect to the water inlet volume flow rate; is the density of water; is the acceleration due to gravity; is the pump motor head at time t, including static head and pipeline loss; is the water inlet volume flow rate of the pump motor at time t; is the comprehensive energy conversion efficiency of the pump motor at time t, including motor and hydraulic efficiency; Different parameters for the functional relationship between the pump motor head and the water inlet volume flow rate; Different parameters for the functional relationship between the comprehensive efficiency of energy conversion of the water pump motor and the volume flow rate of water inlet; for The water storage capacity of water storage facilities at any given time; is the water storage capacity of the water storage facility at time t; They are the upper and lower limits of water storage capacity of water storage facilities respectively; is the outlet volume flow rate of the water storage facility at time t; The lower limit of the water discharge volume flow rate during the permitted water discharge period of the water storage facility; The maximum upper limit of the water outlet volume flow rate of the water storage facility; is the upper limit of the water inlet volume flow rate for safe operation of the water pump motor at time t; They are respectively the permitted water-discharge periods and non-water-discharge periods set for water storage facilities during operation and maintenance.

6. An energy system economic low-carbon optimization operation system integrating AI and mechanism that implements the energy system economic low-carbon optimization operation method integrating AI and mechanism according to any one of claims 1 to 5, characterized in that: include: Source-side model building module, which builds the source-side model, including the large-scale power grid power supply model and the photovoltaic power generation AI prediction model; Grid-side model building module, which builds the grid-side model, including the AC grid power flow model and the DC distribution network power balance model; Load-side model building module, which builds the load-side model, including the water pump motor model and the uncontrollable power load prediction model; The reservoir side model establishment module establishes the reservoir side model, including the power storage facility model and the water storage facility model; An integrated energy system model building module couples and interconnects the source side model, grid side model, load side model, and storage side model to form an integrated energy system model; The day-ahead economic low-carbon optimization operation strategy setting module sets the day-ahead economic low-carbon optimization operation strategy for the integrated energy system model; The intraday economic low-carbon optimization operation strategy setting module sets the intraday economic low-carbon optimization operation strategy for the integrated energy system model, and sets the coupling connection conditions between the intraday economic low-carbon optimization operation strategy and the day-ahead economic low-carbon optimization operation strategy. After the coupling connection conditions are set, an integrated energy system is formed; The result output module inputs the integrated energy system information into the integrated energy system, and outputs the economic and low-carbon optimization result information of the integrated energy system through the integrated energy system.

7. An electronic device, characterized in that The invention comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for economic and low-carbon optimization operation of an energy system integrating AI and mechanism as described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the energy system economic low-carbon optimization operation method integrating AI and mechanism as described in any one of claims 1 to 5.

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