Source network load storage collaborative interaction optimization system for high-proportion new energy

Abstract

The invention relates to the technical field of power systems and energy management, in particular to a source network load storage collaborative interaction optimization system for high-proportion new energy. Comprising a multi-source information sensing data acquisition module used for monitoring various objects and an external environment in real time; the data modeling prediction analysis module is used for constructing a system parameter model and carrying out prediction analysis; the source network load storage collaborative optimization decision-making module is used for realizing multi-time-scale and multi-main-body optimization scheduling; according to the invention, the multi-source information sensing data acquisition module, the data modeling prediction analysis module, the source network load storage collaborative optimization decision module, the execution control instruction issuing module and the like are organically combined, so that the multi-source information sensing data acquisition module, the data modeling prediction analysis module, the source network load storage collaborative optimization decision module and the execution control instruction issuing module are integrated; omnibearing perception and prediction of new energy output, load demand, energy storage operation and power grid state are realized.

Description

Technical Field

[0001] This invention relates to the field of power system and energy management technology, specifically to a source-grid-load-storage collaborative optimization system for high proportion of new energy sources. Background Technology

[0002] With the large-scale integration of renewable energy sources such as wind and solar power into the power system, the traditional grid dispatching mode, which is mainly based on thermal power, can no longer meet the demand for a high proportion of new energy integration.

[0003] The existing power system suffers from insufficient coordination in areas such as renewable energy consumption, load regulation, energy storage management, and grid security constraints. On the one hand, renewable energy output is characterized by significant randomness and volatility, making it difficult to predict accurately and easily leading to wind and solar power curtailment. On the other hand, the regulation capabilities of the load side and energy storage system are not fully utilized, making it impossible to achieve dynamic optimization in the short term and within a day, resulting in a decline in grid operating efficiency and economy. Summary of the Invention

[0004] The purpose of this invention is to address the problem of insufficient coordination in existing power systems regarding renewable energy consumption, load regulation, energy storage management, and grid security constraints. On the one hand, renewable energy output exhibits significant randomness and volatility, making it difficult to predict accurately and easily leading to wind and solar power curtailment. On the other hand, the regulation capabilities of the load side and energy storage systems are not fully utilized, making it impossible to achieve dynamic optimization in the short term and within a day, resulting in a decline in grid operating efficiency and economy.

[0005] To achieve the above objectives, the present invention provides a source-grid-load-storage coordinated interaction optimization system for high-proportion renewable energy sources, comprising:

[0006] The multi-source information sensing data acquisition module is used for real-time monitoring of various objects and the external environment;

[0007] The data modeling and predictive analysis module constructs system parameter models and conducts predictive analysis.

[0008] The source-grid-load-storage collaborative optimization decision-making module enables optimized scheduling across multiple time scales and multiple stakeholders;

[0009] The execution control command issuing module is used to convert optimization results into executable control operations.

[0010] Furthermore, the operation process of the multi-source information sensing data acquisition module includes:

[0011] The multi-source information sensing and data acquisition module includes a new energy output acquisition unit, a power grid status acquisition unit, a load-side information acquisition unit, an energy storage status monitoring unit, and an external information acquisition unit such as meteorological and electricity price information.

[0012] In the new energy power acquisition unit, real-time power signals, voltage and current signals, and status variables of wind turbines and photovoltaic inverters are collected and sent to the edge monitoring and control terminal. The edge terminal collects the DC-side voltage and current of the inverter and the instantaneous power of the AC side through a high-speed sampler.

[0013] The collected data includes:

[0014] Wind turbine output active power P wind Photovoltaic array output power P PV (t), environmental quantities such as wind speed and solar irradiance E(t);

[0015] The power grid status acquisition unit uses PMU (phasor measurement device), smart substation monitoring and control devices, and distribution terminal units (FTU / DTU) to collect data on key node voltages, line power flow, frequency, and circuit breaker status.

[0016] The collected data includes: node voltage amplitude V i (t), node phase angle θ i (t), Line active power flow P ij (t), system frequency f(t).

[0017] Furthermore, the operation process of the multi-source information sensing data acquisition module includes: using a synchronous clock protocol for time calibration to ensure consistency in timestamps for all measurements; the platform uses an outlier detection algorithm to remove abnormal data to avoid sensor malfunctions affecting subsequent optimization decisions; and outlier detection based on standard deviation, assuming the data approximately follows a normal distribution, identifying outliers as abnormal when they deviate significantly from the mean, for time series x(t). Where μ is the mean of the sequence and σ is the standard deviation of the sequence. If |z(t)|>k, then x(t) is considered an outlier.

[0018] The load-side information acquisition unit collects instantaneous power and adjustable load status information from the user side through smart meters, energy monitoring terminals, and plant energy consumption metering devices. The collected information includes: total load power P. load The operating status of individual loads, and the available capacity C of the user-adjustable load. flex (t), load type is identified through an adaptive decomposition algorithm based on power characteristics, forming a load feature vector:

[0019]

[0020] The energy storage state monitoring unit collects data on the voltage, current, temperature, and charge / discharge power of the battery energy storage system (BESS), and calculates the state of charge (SOC) in real time.

[0021] The formula for calculating the state of charge of energy storage is:

[0022] Among them, C nom For rated capacity,

[0023] I(γ) is the instantaneous energy storage current.

[0024] Furthermore, the operation process of the multi-source information sensing data acquisition module includes: the external information acquisition unit establishes data interfaces with the meteorological service platform and the power trading platform to periodically acquire the following external information:

[0025] Meteorological variables such as wind speed, temperature, humidity, and solar radiation.

[0026] Current and real-time electricity prices and ancillary service prices

[0027] Carbon emission factors and policy parameters

[0028] To improve the timeliness of forecast data, a fixed period (e.g., 5 minutes) is used for data retrieval, and the data is converted into a unified format for input into the data bus. Optionally, meteorological data can be used to further refine the new energy power forecast, through the following relational model:

[0029] P PV (t) = ηAG(t), where η is the overall efficiency of the module, A is the photovoltaic area, and G(t) is the real-time solar irradiance.

[0030] Furthermore, the operation process of the data modeling and predictive analysis module includes: new energy power prediction subunit, load prediction subunit, electricity price prediction subunit, power grid digital twin model construction subunit, and energy storage and flexible load modeling subunit;

[0031] The new energy power prediction subunit combines photovoltaic and wind power data with historical output and meteorological information to establish a power prediction model. This includes: short-term prediction of photovoltaic power by establishing statistical or machine learning models based on solar irradiance, temperature, and photovoltaic module characteristics; and prediction of wind power by combining wind speed, wind direction, and wind turbine power curve models to achieve short-term and ultra-short-term power prediction. The measurement results can be output in time series format for intraday rolling optimization and real-time scheduling.

[0032] Furthermore, the operational process of the data modeling and predictive analysis module includes:

[0033] The load forecasting subunit analyzes historical load data, meteorological data, and external event information to generate forecast results for different load types, including: extracting features from total load and sub-loads; constructing a time series forecasting model that combines calendar factors, temperature changes, and load elasticity parameters to predict load demand for future time periods; and performing flexible modeling of adjustable loads to output adjustable range and available capacity, providing constraints for subsequent demand response and optimized scheduling.

[0034] The electricity price forecasting subunit generates electricity trading price forecasts by modeling day-ahead electricity prices, real-time electricity prices, and market fluctuation data. This includes: analyzing historical electricity price trends and time-period characteristics to establish a forecasting model; combining load forecasting and renewable energy output forecasting to make short-term adjustments to electricity prices and improve the reference value for market decisions; and the output results can be directly used as parameter inputs for economic dispatch and optimization objective functions.

[0035] Furthermore, the operational process of the data modeling and predictive analysis module includes:

[0036] The energy storage and flexible load modeling subunit is used to describe the charging and discharging characteristics, capacity constraints, and load regulation capabilities of energy storage systems. This includes: energy storage modeling: establishing models for energy storage power constraints, dynamic changes in state of charge, and energy loss; flexible load modeling: defining the power upper limit, regulation speed, and response delay of adjustable loads; and combining the energy storage and adjustable load models with prediction results to provide constraints and adjustment space for collaborative optimization decisions.

[0037] Furthermore, the operational process of the source-grid-load-storage collaborative optimization decision-making module includes:

[0038] The source-grid-load-storage collaborative optimization decision-making module includes: day-ahead collaborative optimization subunit, intraday rolling optimization subunit, real-time optimization control subunit, uncertainty optimization handling unit, and distributed coordination decision-making unit;

[0039] The collaborative optimization subunit, using the next day as the scheduling cycle, utilizes predicted renewable energy power, load demand, electricity prices, and energy storage status information to optimize the wind and solar power prediction results P. RES (t), load forecast result P load (t), available energy storage capacity C ESS (t) is used as input to construct an economic scheduling optimization objective, which minimizes the system operating cost or the amount of wind and solar power curtailment:

[0040] Among them, C gen (t) represents the power generation cost, including the power generation cost of adjustable generating units, C load_shed (t) represents the load reduction cost;

[0041] The intraday rolling optimization subunit adjusts the scheduling scheme in short time steps (e.g., 15 minutes) to address new energy forecasting errors and load fluctuations, acquiring the latest measurement data P at each rolling time step. RES,reld (t), P load,real (t) is based on the day-ahead optimization results as the initial solution. A short-term optimization model is constructed to correct the energy storage charging and discharging strategy and the adjustable load regulation scheme. The rolling adjustment of the scheduling instructions is output to ensure the maximization of new energy consumption, grid security and load balance.

[0042] Furthermore, the operation process of the source-grid-load-storage collaborative optimization decision-making module includes: the real-time optimization control subunit achieving rapid control at the second or minute level, handling short-term disturbances and system anomalies, and utilizing the real-time measured node voltage V i (t), line power flow P ij (t) and energy storage power P ESS (t) adjusts the output of energy storage, adjustable load, and new energy inverters to achieve frequency / voltage regulation, and uses a pre-calculation strategy to generate control commands u. t And issue the order for implementation:

[0043] Among them, V i (t) represents the node voltage, P ij (t) represents the power flow of the line, u k (t) is an adjustable variable, wv, wp, wu are weights that reflect the control priority, N is the set of nodes, and L is the set of lines.

[0044] Furthermore, the operation process of the control command issuance module includes: the control command issuance module includes: energy storage control unit, new energy control unit, load flexible adjustment control unit, and grid-side control unit;

[0045] The energy storage control unit generates charging and discharging control commands for the energy storage device based on the optimized scheduling scheme;

[0046] The new energy control unit applies the optimization results to photovoltaic arrays and wind turbines;

[0047] The load flexibility control unit is responsible for real-time regulation of adjustable loads;

[0048] The grid-side control unit applies optimization strategies to the distribution network or grid facilities at key nodes.

[0049] Beneficial effects

[0050] Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects:

[0051] This invention achieves comprehensive perception and prediction of new energy output, load demand, energy storage operation, and grid status by organically combining modules such as multi-source information sensing and data acquisition, data modeling and predictive analysis, source-grid-load-storage collaborative optimization decision-making, and execution control command issuance. It can generate executable optimized scheduling schemes based on multi-time scales and multi-entity collaboration, and accurately implement them to various equipment and grid facilities. This effectively improves the new energy absorption capacity, ensures the safe and stable operation of the grid, enhances the flexibility and economy of load response, and realizes efficient collaborative and optimized management of the source-grid-load-storage system. Attached Figure Description

[0052] Figure 1 This is a system flowchart of a source-grid-load-storage collaborative optimization system for high-proportion new energy sources according to the present invention. Detailed Implementation

[0053] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0054] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0055] The present invention will now be described in further detail with reference to the accompanying drawings:

[0056] Example:

[0057] like Figure 1 As shown, this invention provides a source-grid-load-storage coordinated interaction optimization system for high-proportion renewable energy sources, comprising:

[0058] The multi-source information sensing data acquisition module is used for real-time monitoring of various objects and the external environment;

[0059] Furthermore, the operation process of the multi-source information sensing data acquisition module includes:

[0060] The multi-source information sensing and data acquisition module includes a new energy output acquisition unit, a power grid status acquisition unit, a load-side information acquisition unit, an energy storage status monitoring unit, and an external information acquisition unit such as meteorological and electricity price information.

[0061] In the new energy power acquisition unit, real-time power signals, voltage and current signals, and status variables of wind turbines and photovoltaic inverters are collected and sent to the edge monitoring and control terminal. The edge terminal collects the DC-side voltage and current of the inverter and the instantaneous power of the AC side through a high-speed sampler.

[0062] The collected data includes:

[0063] Wind turbine output active power P wind Photovoltaic array output power P PV (t), environmental quantities such as wind speed and solar irradiance E(t);

[0064] The power grid status acquisition unit uses PMU (phasor measurement device), smart substation monitoring and control devices, and distribution terminal units (FTU / DTU) to collect data on key node voltages, line power flow, frequency, and circuit breaker status.

[0065] The collected data includes: node voltage amplitude V i (t), node phase angle θ i (t), Line active power flow P ij (t), system frequency f(t);

[0066] A synchronous clock protocol is used for time calibration to ensure timestamp consistency across all measurements. The platform employs an outlier detection algorithm to remove abnormal data, preventing sensor malfunctions from impacting subsequent optimization decisions. Based on standard deviation, outlier detection assumes the data approximately follows a normal distribution; outliers are identified as anomalies when they deviate significantly from the mean. For the time series x(t), Where μ is the mean of the sequence and σ is the standard deviation of the sequence. If |z(t)|>k, then x(t) is considered an outlier.

[0067] The load-side information acquisition unit collects instantaneous power and adjustable load status information from the user side through smart meters, energy monitoring terminals, and plant energy consumption metering devices. The collected information includes: total load power P. load The operating status of individual loads, and the available capacity C of the user-adjustable load. flex (t), load type is identified through an adaptive decomposition algorithm based on power characteristics, forming a load feature vector:

[0068]

[0069] The energy storage state monitoring unit collects data on the voltage, current, temperature, and charge / discharge power of the battery energy storage system (BESS), and calculates the state of charge (SOC) in real time.

[0070] The formula for calculating the state of charge of energy storage is:

[0071] Among them, C nom For rated capacity,

[0072] I(γ) is the instantaneous energy storage current.

[0073] Assuming a SOC of 80% and a charging current of 2MW for 30 minutes, the calculated SOC is approximately 88%, which differs from the battery management system's measurement by less than 200.

[0074] The external information acquisition unit establishes data interfaces with the meteorological service platform and the power trading platform to periodically acquire the following external information:

[0075] Meteorological variables such as wind speed, temperature, humidity, and solar radiation.

[0076] Current and real-time electricity prices and ancillary service prices

[0077] Carbon emission factors and policy parameters

[0078] To improve the timeliness of forecast data, a fixed period (e.g., 5 minutes) is used for data retrieval, and the data is converted into a unified format for input into the data bus. Optionally, meteorological data can be used to further refine the new energy power forecast, through the following relational model:

[0079] P PV (t)=ηAG(t), where η is the overall efficiency of the module, A is the photovoltaic area, and G(t) is the real-time solar irradiance;

[0080] Suppose a data acquisition experiment is conducted at a photovoltaic power station (capacity 50MW) and a wind farm (capacity 100MW):

[0081] Photovoltaic power plants collect DC voltage V through inverters. dc DC current I dc and AC side power P ac Samples are taken once every 1 second.

[0082] Wind turbine data collection: wind speed v, turbine speed n, and output power P wind Using a 5-second average, the relationship between photovoltaic power and solar irradiance G(t) on a sunny day can be estimated using the following formula.

[0083] P PV (t)=ηAG(t), and the results were obtained in the experiment:

[0084] η = 0.18, A = 2.5 × 10 5 m 2 G(t) = 800w / m 2 The estimated value of P is... PV ≈36MW, with a relative error of <5% compared to the real-time power collected by the inverter;

[0085] Each acquisition unit aggregates data via an edge gateway to a communication management unit, which then uploads it to the main server using MQTT or IEC 104 protocols. The server sorts the data by timestamp to form a unified time-series dataset.

[0086] Specifically, the new energy output acquisition unit collects real-time power and status information of photovoltaic arrays and wind turbines, and performs preliminary processing and uploading through edge terminals; the power grid status acquisition unit acquires key operating parameters such as node voltage, line power flow, and system frequency through phasor measurement devices and smart substation monitoring and control equipment, and uses a time synchronization mechanism to ensure data consistency; the load-side information acquisition unit collects the total load and adjustable load status of the park and users through smart meters and energy consumption monitoring equipment, providing data support for subsequent demand response and load optimization; the energy storage status monitoring unit acquires the charging and discharging power, state of charge, and operating status of the energy storage system in real time, providing a basis for energy storage to participate in collaborative optimization; the external information acquisition unit periodically pulls meteorological data, electricity price information, and carbon emission parameters, providing a reference for new energy forecasting and economic dispatch. All collected data is uploaded to the central data platform through a communication gateway, and after time-series synchronization and anomaly removal processing, a unified and reliable time-series dataset is formed.

[0087] The data modeling and predictive analysis module is used to build system parameter models and conduct predictive analysis.

[0088] Furthermore, the operation process of the data modeling and predictive analysis module includes: new energy power prediction subunit, load prediction subunit, electricity price prediction subunit, power grid digital twin model construction subunit, and energy storage and flexible load modeling subunit;

[0089] The new energy power prediction subunit combines photovoltaic and wind power data with historical output and meteorological information to establish a power prediction model. This includes: short-term photovoltaic power prediction, which utilizes statistical or machine learning models based on solar irradiance, temperature, and photovoltaic module characteristics; and wind power prediction, which combines wind speed, wind direction, and turbine power curve models to achieve short-term and ultra-short-term power prediction. The measurement results can be output in time series format for intraday rolling optimization and real-time scheduling.

[0090] Wind and solar power prediction models

[0091] f t=σ(w f ·[h t-1 ,x t ]+b f )

[0092] i t =σ(w i ·[h t-1 ,x t ]+b i )

[0093]

[0094] o t =σ(W0·[h t-1 ,x t ]+b o )

[0095] h t =o t Θtanh(C t )

[0096] Where, x t h is the input vector. t-1 f is the hidden state vector from the previous time step; t Forget gate vector; i t The input gate vector; C represents the candidate memory unit vector; t The current state of the memory unit, combined with the updated memory information from the forget gate and input gate; t h is the output gate vector, used to control the influence of the memory unit on the hidden state output; t The current hidden state vector can be used as the prediction output or the input for the next time step; w f w i W C W0 is the weight matrix, used for the linear transformation of the input and hidden states; b f b i b c b o σ is the bias vector; σ(·) is the Sigmoid activation function, mapping the input to the [0,1] interval; tanh(·) is the hyperbolic tangent function, mapping the input to the [-1,1] interval; Θ elements are multiplied one by one (Hadamard product); the LSTM model can capture the long-term dependence of wind and solar power time series and is suitable for short-term / ultra-short-term prediction, with the input feature vector x t It can include historical power, meteorological data, and time characteristics (such as hour and date), and output the hidden state h. t It can be directly used as the power prediction value for the next time step or further processed by a fully connected layer to obtain the final predicted power;

[0097] The load forecasting subunit analyzes historical load data, meteorological data, and external event information to generate forecast results for different load types, including: extracting features from total load and sub-loads (such as HVAC, motor drives, and lighting); constructing a time series forecasting model, combining calendar factors (weekdays / holidays), temperature changes, and load elasticity parameters to predict load demand for future time periods; and performing flexible modeling of adjustable loads, outputting adjustable ranges and available capacity to provide constraints for subsequent demand response and optimized scheduling.

[0098] The electricity price forecasting subunit generates electricity trading price forecasts by modeling day-ahead electricity prices, real-time electricity prices, and market fluctuation data. This includes: analyzing historical electricity price trends and time-period characteristics to establish a forecasting model; combining load forecasting and renewable energy output forecasting to make short-term adjustments to electricity prices and improve the reference value for market decisions; and the output results can be directly used as parameter inputs for economic dispatch and optimization objective functions.

[0099] The energy storage and flexible load modeling subunit is used to describe the charging and discharging characteristics, capacity constraints, and load regulation capabilities of energy storage systems. It includes: energy storage modeling: establishing models of energy storage power constraints, dynamic changes in state of charge, and energy loss; flexible load modeling: defining the power upper limit, regulation speed, and response delay of adjustable loads; and combining the energy storage and adjustable load models with prediction results to provide constraints and adjustment space for collaborative optimization decisions.

[0100] Specifically, by processing multi-source information sensing data, a system parameter model is constructed and predictive analysis is conducted. In the implementation process, firstly, the new energy power prediction submodule uses real-time collected data from photovoltaic and wind power, combined with historical output and meteorological information, to generate short-term and ultra-short-term power prediction results. Then, the load prediction submodule performs time-series analysis on total load and sub-loads, combining historical load data, meteorological information, and calendar factors to predict future load demand, and performs flexible modeling of adjustable loads to provide regulation constraints. Simultaneously, the electricity price prediction submodule analyzes day-ahead and real-time electricity price data to generate future electricity price prediction results, providing a reference for economic dispatch. The power grid digital twin model construction submodule, based on the power grid structure, equipment parameters, and real-time measurement data, constructs a simulateable and predictable power grid model for power flow calculation, stability assessment, and dispatch verification. The energy storage and flexible load modeling submodule establishes power constraints for the energy storage system, dynamic changes in the state of charge, and the adjustment range model for adjustable loads.

[0101] The source-grid-load-storage collaborative optimization decision-making module enables optimized scheduling across multiple time scales and multiple stakeholders;

[0102] Furthermore, the operational process of the source-grid-load-storage collaborative optimization decision-making module includes:

[0103] The source-grid-load-storage collaborative optimization decision-making module includes: day-ahead collaborative optimization subunit, intraday rolling optimization subunit, real-time optimization control subunit, uncertainty optimization handling unit, and distributed coordination decision-making unit;

[0104] The collaborative optimization subunit, using the next day as the scheduling cycle, utilizes predicted renewable energy power, load demand, electricity prices, and energy storage status information to optimize the wind and solar power prediction results P. RES (t), load forecast result P load (t), available energy storage capacity C ESS (t) is used as input to construct an economic scheduling optimization objective, which minimizes the system operating cost or the amount of wind and solar power curtailment:

[0105] Among them, C gen (t) represents the power generation cost, including the power generation cost of adjustable generating units, C load_shed (t) represents the load reduction cost;

[0106] The intraday rolling optimization subunit adjusts the scheduling scheme in short time steps (e.g., 15 minutes) to address new energy forecasting errors and load fluctuations, acquiring the latest measurement data P at each rolling time step. RES,reld (t), P load,real (t), based on the day-ahead optimization results as the initial solution, construct a short-term optimization model, correct the energy storage charging and discharging strategy and the adjustable load regulation scheme, and output the rolling adjusted dispatch instructions to ensure the maximization of new energy consumption, grid security and load balance;

[0107] The real-time optimization control subunit enables rapid control at the second or minute level, handling short-term disturbances and system anomalies, utilizing real-time measured node voltage V. i (t), line power flow P ij (t) and energy storage power P ESS (t) adjusts the output of energy storage, adjustable load, and new energy inverters to achieve frequency / voltage regulation, and uses a pre-calculation strategy to generate control commands u. t And issue the order for implementation:

[0108]

[0109] In the middle, V i (t) represents the node voltage, P ij (t) represents the power flow of the line, u k (t) is an adjustable variable, wv, wp, wu are weights that reflect the control priority, N is the set of nodes, and L is the set of lines;

[0110] Experimental microgrid parameters:

[0111] Node N = {1, 2, 3, 4, 5}, line L = {(1,2),(2,3),(3,4),(4,5),(2,4)}, one energy storage unit with a capacity of 1MWh, and an adjustable load of 100kW.

[0112] Real-time situation:

[0113] Solar and wind power output fluctuations: Sudden cloud cover or wind speed changes caused a sharp drop in solar power output of 150kW → the voltage at node 3 dropped to 0.94pu, and the power flow on lines {2,4} approached its rated capacity.

[0114] Optimize adjustment strategy:

[0115] Energy storage discharge of 120kW → voltage increase, adjustable load reduction of 30kW → reduced load on line (2,4), inverter reactive power output increased → supporting voltage.

[0116] result:

[0117] All node voltages return to 0.99–1.01 pu, line power flow is below rated value, control quantities are minimized, and overuse of energy storage or load regulation is avoided;

[0118] The uncertainty optimization processing unit is used to address wind and solar power and load forecasting errors, and to optimize the forecast power P. RES (t) Establish probability distributions or generate multiple scenarios: Construct a stochastic optimization or robust optimization model, assuming that there is uncertainty in wind and solar power prediction, which can be represented by probability distribution or multi-scenario generation: in, Let S be the wind and solar power in the s-th scene, and S be the total number of scenes.

[0119] Distributed coordination and decision-making units are used to achieve collaborative optimization among multiple stakeholders including energy sources, grids, loads, and storage. The system is divided into subsystems on the source side, grid side, load side, and energy storage side, with each subsystem independently optimizing its own costs or benefits: minf i (x i For each subsystem (i∈{source, grid, load, storage}), a distributed optimization algorithm is used to coordinate them, enabling optimal scheduling of each subsystem while satisfying global constraints. The resulting integrated scheduling result after coordination of all subsystems is output, forming a complete source-grid-load-storage scheduling scheme.

[0120] in, Power output after coordination on the source side (solar / wind power), For adjustable loads after load-side coordination, The charging and discharging power after coordination with the energy storage side.

[0121] Specifically, the source-grid-load-storage collaborative optimization decision-making module achieves comprehensive coordination of renewable energy output, grid operation, load demand, and energy storage operation through multi-timescale and multi-entity optimization scheduling. In the implementation process, firstly, the day-ahead collaborative optimization submodule establishes a next-day optimization model and generates a preliminary scheduling plan based on predicted renewable energy power, load demand, electricity prices, and energy storage status. Subsequently, the intraday rolling optimization submodule dynamically adjusts the day-ahead plan based on the latest measurement data and actual operating status, optimizing energy storage charging and discharging strategies and adjustable load adjustment schemes to cope with load fluctuations and renewable energy prediction errors. The real-time optimization control submodule rapidly regulates the energy storage system, adjustable load, and renewable energy inverter output based on second-level or minute-level real-time measurement data to achieve voltage, frequency, and power balance. The uncertainty optimization processing unit constructs multiple scenarios or probability distributions of power and load predictions to perform stochastic or robust optimization processing on the scheduling plan, improving the system's adaptability to uncertainty. The distributed coordination decision-making unit divides the source side, grid side, load side, and energy storage side into multiple subsystems and coordinates them through distributed optimization algorithms, enabling each subsystem to achieve optimal scheduling while satisfying global constraints.

[0122] The execution control command issuing module is used to convert optimization results into executable control operations;

[0123] Furthermore, the operation process of the control command issuance module includes:

[0124] The module for issuing control commands includes: energy storage control unit, new energy control unit, load flexible adjustment control unit, and grid-side control unit;

[0125] The energy storage control unit generates charging and discharging control commands for the energy storage devices based on the optimized scheduling scheme, and obtains the energy storage charging and discharging power plan output by the scheduling module. Based on the status information, the power plan is converted into control commands for the energy storage inverter, including current, voltage, and power limits; control signals are sent to the energy storage system to achieve precise charging and discharging operations, while dynamically monitoring the execution status to ensure that the SOC and power constraints are not exceeded;

[0126] The new energy control unit applies the optimization results to the photovoltaic array and wind turbine to obtain the new energy output adjustment strategy output by the scheduling module. The output strategy is converted into power commands and active / reactive power adjustment commands for the inverter, and commands are sent through the field communication interface to realize the dynamic adjustment of the power generation of new energy sources.

[0127] The load flexibility control unit is responsible for real-time regulation of adjustable loads and obtaining the power regulation plan of adjustable loads in the scheduling scheme. Within a given time window, load adjustment commands are sent via smart meters or control terminals, including switch status, upper and lower power limits, or power tiered adjustment strategies. The load response is monitored, and the actual execution results are fed back to the scheduling module for subsequent rolling optimization or real-time adjustment.

[0128] The grid-side control unit applies optimization strategies to power grid facilities in the distribution network or key nodes, obtains node power, line power flow, and switch operation plans output by the dispatch module, transforms the strategies into control commands for devices such as circuit breakers, voltage regulators, and load distributors, and issues commands through the communication network to realize voltage regulation, power flow optimization, and execution of grid safety constraints.

[0129] Specifically, the execution control command issuing module transforms the scheduling scheme output by the source-grid-load-storage collaborative optimization decision-making module into executable equipment control operations through the energy storage control unit, new energy control unit, load flexible adjustment control unit, and grid-side control unit. These operations are then sent to field equipment via a real-time communication network. Simultaneously, execution feedback is collected for dynamic adjustment and rolling optimization, thereby achieving precise execution of energy storage charging and discharging, new energy power regulation, adjustable load response, and grid facility constraints, ensuring that optimization strategies are accurately implemented in the system.

[0130] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A source-network-load-storage collaborative interaction optimization system for a high proportion of new energy, characterized in that, The method comprises the following steps: A multi-source information perception data acquisition module is used to monitor various objects and external environment in real time; A data modeling and prediction analysis module is used to build a system parameter model and carry out prediction analysis; A source network load and storage collaborative optimization decision module is used to realize multi-time scale and multi-agent optimization scheduling; An execution control instruction issuing module is used to convert the optimization results into executable control operations. 2.The source-grid-load-storage collaborative interaction optimization system for high proportion of new energy according to claim 1, characterized in that, The operation process of the multi-source information perception data acquisition module comprises the following steps: The multi-source information perception data acquisition module comprises a new energy output acquisition unit, a power grid state acquisition unit, a load side information acquisition unit, a storage state monitoring unit, and a meteorological and electricity price external information acquisition unit; In the new energy output acquisition unit, the real-time power signals, voltage and current signals, and state quantities of wind turbine generators and photovoltaic inverters are collected to the edge measurement and control terminal, and the edge terminal collects the DC side voltage, current, and instantaneous power of the inverter through a high-speed sampler, Wind turbine output active power P wind Photovoltaic array output power P PV (t), environmental quantities E(t) such as wind speed, solar irradiance, etc. The collected data comprises: The collected data include: node voltage amplitude V i (t), node phase angle θ i (t), line active power P ij (t), system frequency f(t). 3.The source-grid-load-storage collaborative interaction optimization system for high proportion of new energy according to claim 2, characterized in that, The power grid state acquisition unit uses PMU, intelligent substation measurement and control devices, and distribution terminal (FTU / DTU) equipment to collect the voltage of key nodes, line power flow, frequency, and circuit breaker state, The load side information collection unit collects the instantaneous power and adjustable load state information of the user side through the smart meter, energy monitoring terminal and plant energy consumption metering device. The collection content includes: total load power P load , sub-item load operating state, available capacity C flex (t) of user adjustable load, load type is identified through adaptive decomposition algorithm based on power characteristics, and load feature vector is formed: The operation process of the multi-source information perception data acquisition module comprises the following steps: The time is calibrated by using a synchronous clock protocol, so that each measurement has timestamp consistency, and the platform uses an outlier detection algorithm to remove abnormal data based on standard deviation; where C nom is the rated capacity, The storage state monitoring unit collects the voltage, current, temperature, and charge and discharge power of the battery energy storage system (BESS) and calculates the state of charge (SOC) of the storage in real time, 4. The source-network-load-storage collaborative interaction optimization system for high-proportion new energy according to claim 3, characterized in that, The formula for calculating the state of charge of the storage is as follows: I(γ) is the instantaneous current of the storage. The operation process of the multi-source information perception data acquisition module comprises the following steps: The external information acquisition unit establishes a data interface with a meteorological service platform and a power trading platform, and regularly pulls the following external information: Wind speed, temperature, humidity, solar radiation, and other meteorological variables, P PV (t) = ηAG(t), where η is the overall efficiency of the assembly, A is the photovoltaic area, and G(t) is the real-time solar irradiance.

5. The source-network-load-storage collaborative interaction optimization system for high-proportion new energy according to claim 4, characterized in that, Day-ahead, real-time electricity prices, and auxiliary service prices, Carbon emission factors and policy parameters, 6.The source-grid-load-storage collaborative interaction optimization system for high-proportion new energy of claim 5, wherein, To improve the timeliness of the prediction data, a fixed cycle is used to pull the data, and the data is converted into a unified format and input into a data bus. In optional cases, meteorological data can be used to further correct the new energy power prediction through the following relationship model: The operation process of the data modeling and prediction analysis module comprises the following steps: The new energy power prediction subunit combines the collected data of photovoltaic and wind power with historical output and meteorological information to establish a power prediction model, which comprises the following steps: A statistical or machine learning model is established for short-term prediction of photovoltaic power based on solar irradiance, temperature, and photovoltaic module characteristics; Wind power prediction is achieved by combining wind speed, wind direction, and wind turbine power curve model to realize short-term and ultra-short-term power prediction; The measurement results can be output in time series form for intra-day rolling optimization and real-time scheduling. The operation process of the data modeling and prediction analysis module comprises the following steps: The load prediction subunit generates prediction results of different load types by analyzing historical load data, meteorological data and external event information, including: extracting features of total load and sub-item load; constructing a time series prediction model, combining calendar factors, temperature changes and load elasticity parameters to predict load demand in each time period in the future; flexible modeling of adjustable load, outputting adjustable range and available capacity, providing constraint conditions for subsequent demand response and optimal scheduling; The electricity price prediction subunit generates prediction results of electricity trading price by modeling day-ahead electricity price, real-time electricity price and market fluctuation data, including: analyzing historical electricity price trends and time period characteristics to establish a prediction model; short-term correction of electricity price can be combined with load prediction and new energy output prediction to improve market decision reference; the output results can be directly used for economic dispatch and parameter input of optimization objective function.

7. The source-network-load-storage collaborative interaction optimization system for high-proportion new energy according to claim 6, characterized in that, The operation process of the data modeling prediction analysis module includes: The energy storage and flexible load modeling subunit is used to describe the charging and discharging characteristics, capacity constraints and load regulation capacity of the energy storage system, including: energy storage modeling: establishing energy storage power constraints, state of charge dynamic changes and energy loss models; flexible load modeling: defining the upper limit of adjustable load power, regulation speed and response time delay; combining the energy storage and adjustable load model with the prediction results to provide constraint conditions and regulation space for collaborative optimization decision. 8.The source-grid-load-storage collaborative interaction optimization system for high proportion of new energy of claim 7, wherein, The operation process of the source-grid-load-storage collaborative optimization decision module includes: the source-grid-load-storage collaborative optimization decision module includes: a day-ahead collaborative optimization subunit, an intra-day rolling optimization subunit, a real-time optimization control subunit, an uncertainty optimization processing unit, and a distributed coordination decision unit; The day-ahead collaborative optimization subunit takes the next day as a scheduling period, uses predicted new energy power, load demand, electricity price, and energy storage state information, takes wind and light power prediction results P RES (t) as input, constructs an economic scheduling optimization target, and minimizes system operation cost or wind and light curtailment amount: load (t) as input, constructs an economic scheduling optimization target, and minimizes system operation cost or wind and light curtailment amount: ESS (t) as input, constructs an economic scheduling optimization target, and minimizes system operation cost or wind and light curtailment amount: where C gen (t) is the generation cost, including the adjustable generation cost, C load_shed (t) is the load curtailment cost; The day-ahead rolling optimization subunit adjusts the dispatching scheme in a short time step (e.g., 15 minutes) in view of new energy prediction error and load fluctuation, and obtains the latest measurement data P every rolling time step RES,reld (t), P load,real (t), based on the day-ahead optimization result as an initial solution, constructs a short-term optimization model, corrects the energy storage charging and discharging strategy and the adjustable load adjustment scheme, and outputs the rolling adjusted dispatching instruction, to ensure maximization of new energy consumption, power grid safety and load balance. 9.The source-grid-load-storage collaborative interaction optimization system for high-proportion new energy of claim 8, wherein, The operation flow of the source-grid-load-storage collaborative optimization decision module includes: a real-time optimization control subunit realizes minute-level fast control, processes short-time disturbance and system abnormalities, utilizes real-time measured node voltage V i (t), line power flow P ij (t) and energy storage power P ESS (t), adjusts the output of energy storage, adjustable load and new energy inverter, realizes frequency / voltage regulation, adopts a pre-computation strategy to generate control instructions u t and issues for execution: where V i (t) is the node voltage, P ij (t) is the line flow, u k (t) is the adjustable variable, wv,wp,wu are weights, embodying control priorities, N is the node set, and L is the line set.

10. The source-network-load-storage collaborative interaction optimization system for high-proportion new energy according to claim 9, characterized in that, The operation process of the execution control instruction issuing module includes: the execution control instruction issuing module includes: an energy storage control unit, a new energy control unit, a load flexible regulation control unit, and a grid side control unit; The energy storage control unit generates charging and discharging control instructions for energy storage devices according to the optimal scheduling scheme; The new energy control unit applies the optimization results to photovoltaic arrays and wind turbines; The load flexible regulation control unit is responsible for real-time regulation and control of adjustable load; The grid side control unit applies the optimization strategy to power grid facilities in the distribution network or key nodes.

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