New energy consumption optimization method and related device of multi-type main body power system

Abstract

The application belongs to a new energy consumption method, and aims at the technical problem that new energy resources are wasted and urgently need to be consumed, and provides a new energy consumption optimization method of a multi-type subject power system and related devices, an optimization dispatching model based on multi-type subject participation is constructed, a 96-point prediction curve of new energy and a 96-point baseline load curve are obtained, the optimization dispatching model is solved in combination with the 96-point prediction curve of new energy and the 96-point baseline load curve, an optimization strategy is obtained, and the power system is optimized by using the optimization strategy. Through market means, multi-type subjects participate in the market to improve the consumption capacity of new energy and reduce the power abandonment rate of the new energy blocked area.

Description

TECHNICAL FIELD

[0001] The application belongs to a new energy consumption method, and particularly relates to a new energy consumption optimization method of a multi-type main body power system and a related device. BACKGROUND

[0002] A new type of power system mainly based on new energy is rapidly advancing, and the installed capacity of power generation such as wind power and photovoltaic power has increased significantly. Renewable resources and load centers are inversely distributed. With the increase of new energy construction, local consumption is difficult, and there is a large amount of abandoned wind and light, which causes resource waste. Therefore, promoting new energy consumption has become a top priority, and it is necessary to efficiently optimize the allocation of power resources through market means, further expand the types of market subjects, encourage various types of resources such as sources, networks, loads and storages to actively participate in the market, and provide a new way for market-oriented large-scale consumption of new energy. SUMMARY

[0003] The application provides a new energy consumption optimization method of a multi-type main body power system and a related device to solve the technical problem of resource waste of new energy and the urgent need for consumption.

[0004] To achieve the above purpose, the application adopts the following technical solutions:

[0005] In a first aspect, the application provides a new energy consumption optimization method of a multi-type main body power system, comprising:

[0006] An optimization dispatching model based on participation of multi-type main bodies is constructed for the power system; and an optimization target of the optimization dispatching model is maximization of new energy consumption.

[0007] A 96-point prediction curve of new energy is obtained according to meteorological data and historical new energy generation curves corresponding to the power system;

[0008] A 96-point baseline load curve is obtained according to historical demand-side flexible resource load curves corresponding to the power system;

[0009] An optimization strategy is obtained by solving the optimization dispatching model in combination with the 96-point prediction curve of new energy and the 96-point baseline load curve; and the optimization strategy includes output of multi-type main bodies.

[0010] The power system is optimized by using the optimization strategy.

[0011] In a second aspect, the application provides a new energy consumption optimization system of a multi-type main body power system, comprising:

[0012] A model module is configured to construct an optimization dispatching model based on participation of multi-type main bodies for the power system; and an optimization target of the optimization dispatching model is maximization of new energy consumption.

[0013] a first curve module configured to obtain a 96-point new energy prediction curve according to meteorological data corresponding to the power system and a historical new energy generation curve;

[0014] a second curve module configured to obtain a 96-point baseline load curve according to a historical demand-side flexible resource load curve corresponding to the power system;

[0015] a solving module configured to solve the optimization dispatching model in combination with the 96-point new energy prediction curve and the 96-point baseline load curve to obtain an optimization strategy; the optimization strategy includes output of multiple types of subjects;

[0016] an optimization module configured to optimize the power system by using the optimization strategy.

[0017] In a third aspect, an electronic device is provided, which includes a memory and one or more processors; the memory is coupled to the processors; and the memory stores computer program codes including computer instructions, which, when executed by the processors, cause the electronic device to perform the steps of the method for new energy consumption optimization of a multi-type subject power system.

[0018] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program; when the computer program is executed by a processor, the steps of the method for new energy consumption optimization of a multi-type subject power system are implemented.

[0019] In a fifth aspect, a computer program product is provided, which includes instructions; when the instructions are executed by a processor, the method for new energy consumption optimization of a power system is implemented.

[0020] Compared with the prior art, the present application has the following beneficial effects:

[0021] The present application provides a method for new energy consumption optimization of a multi-type subject power system, constructs an optimization dispatching model based on participation of multiple types of subjects, obtains a 96-point new energy prediction curve and a 96-point baseline load curve, solves the optimization dispatching model in combination with the 96-point new energy prediction curve and the 96-point baseline load curve to obtain an optimization strategy, and then optimizes the power system by using the optimization strategy. With the vigorous development of new energy, the present application promotes new energy consumption through market means, takes the minimum new energy curtailment rate as an objective function, constructs a market dispatching model based on multiple types of market subject models such as wind power, photovoltaic power, hydropower, thermal power, independent energy storage, and adjustable load, promotes participation of multiple types of subjects in the market through market means, improves the consumption capacity of new energy, and reduces the curtailment rate of new energy in blocked areas.

[0022] The application also provides a new energy consumption optimization system of a multi-type subject power system and a computer program product, which have all the advantages of the new energy consumption optimization method of the multi-type subject power system. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed to be used in the embodiments will be briefly introduced as follows. It should be understood that the following drawings only show some of the embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0024] Figure 1 The first flowchart of the new energy consumption optimization method of the multi-type subject power system of the application;

[0025] Figure 2 The second flowchart of the new energy consumption optimization method of the multi-type subject power system of the application;

[0026] Figure 3 The new energy consumption optimization system of the multi-type subject power system of the application. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the application more clear, the technical solutions in the embodiments of the application will be described clearly and completely in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only some of the embodiments of the application, but not all the embodiments. The components of the embodiments of the application described and shown in the drawings can be arranged and designed in various different configurations.

[0028] Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of the protection of the application.

[0029] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0030] In the description of the embodiments of the present application, it should be explained that, if the terms "upper", "lower", "horizontal", "inner" and the like indicating the position or location relationship are based on the position or location relationship shown in the drawings, or the position or location relationship of the product of the present application when it is usually placed, which is only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" and the like are only used for differentiation, and cannot be understood as indicating or implying relative importance.

[0031] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly inclined. For example, "horizontal" only means that its direction is relatively more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0032] In the description of the embodiments of the present application, it should be explained that, unless otherwise explicitly specified and limited, if the terms "set", "install", "connect", "connect" appear, they should be understood in a broad sense, for example, they can be fixedly connected, or can be detachably connected, or integrally connected; can be mechanically connected, or can be electrically connected; can be directly connected, or indirectly connected through an intermediate medium; can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0033] The construction of new power systems dominated by new energy is advancing at an unprecedented speed. In this transition process, the installed capacity of clean energy such as wind power and photovoltaic power has increased significantly. A significant problem is the reverse distribution of renewable resources and load centers: that is, areas rich in wind and solar energy are often far from urban centers with high population density and high demand for electricity. This reverse distribution has made it difficult to locally consume new energy. In areas rich in wind and solar energy, when the power generation exceeds the local consumption demand, the excess power is often difficult to effectively transmit to areas with greater demand, resulting in a large amount of wind and solar energy being wasted, i.e. the "curtailment of wind and light" phenomenon. This not only reduces the return on investment of new energy, but also hinders the healthy development of the new energy industry, which is contrary to the original intention of promoting energy transformation and achieving carbon neutralization. Therefore, promoting the efficient consumption of new energy has become an important task in the development of the current power system. The solution lies in optimizing the allocation of power resources through market mechanisms.

[0034] Expanding the types of market subjects, especially encouraging the active participation of various types of resources such as source (generation side), network (transmission and distribution side), load (power consumption side), and storage (energy storage side), is the key to large-scale marketization of new energy consumption.

[0035] Therefore, the application proposes an optimization modeling method for promoting new energy consumption by multiple types of subjects through sufficient field research and according to actual needs, takes the minimum new energy curtailment rate as the objective function, constructs a market clearing model with multiple types of subjects based on multiple types of market subject models such as wind power, photovoltaic power, hydropower, thermal power, independent energy storage, and adjustable load, promotes multiple types of subjects to participate in the market through market means to improve the consumption capacity of new energy and reduce the curtailment rate of new energy in blocked areas.

[0036] As shown in Figure 1 , it is the first flowchart of the new energy consumption optimization method of the multi-type subject power system of the application, which can include:

[0037] S101, an optimization clearing model based on multiple types of subjects is constructed for the corresponding power system; the optimization objective of the optimization clearing model is to maximize the new energy consumption.

[0038] S102, according to the meteorological data and historical new energy generation curve corresponding to the power system, a 96-point prediction curve of new energy is obtained.

[0039] In practical applications, the 96-point prediction curve of new energy (such as wind energy and solar energy) can be used to predict the generation of new energy in a future period of time (usually one day, i.e. 96 15-minute time points). By collecting meteorological data (such as wind speed and light intensity) and historical new energy generation curve, advanced prediction algorithms such as time series analysis and machine learning can be used to generate a 96-point prediction curve of new energy.

[0040] S103, according to the historical demand side flexible resource load curve corresponding to the power system, a 96-point baseline load curve is obtained.

[0041] In practical applications, the load curve of demand side flexible resources can be obtained by analyzing historical data, which reflects the change of electricity demand of these resources in a day. Similarly, this curve is also divided into 96 points to match the time resolution of the new energy prediction curve. The baseline load curve is an important basis for the optimization clearing model to consider demand side response and flexibility.

[0042] S104, the optimization clearing model is solved in combination with the 96-point prediction curve of new energy and the 96-point baseline load curve, and an optimization strategy is obtained; the optimization strategy includes the output of multiple types of subjects.

[0043] The new energy prediction curve and the baseline load curve are taken as inputs to solve the constructed optimization dispatching model. The solving process can generally adopt common model solving algorithms to find the optimal solution that maximizes the new energy consumption under the premise of meeting all constraint conditions. The optimal solution is the optimization strategy, including the specific output plans of multiple types of subjects (thermal power, hydropower, new energy, energy storage, and adjustable load) in a day.

[0044] S105, optimizing the power system by using the optimization strategy.

[0045] Applying the obtained optimization strategy to the actual power system can improve the consumption proportion of new energy, reduce the operation cost of the power system, and at the same time enhance the stability and flexibility of the power grid.

[0046] The present application improves the utilization efficiency and consumption capacity of new energy, can stimulate the enthusiasm of various types of market subjects to consume new energy, and promotes the healthy development of the new energy industry. Mainly embodied in the following aspects:

[0047] (1) Promote new energy consumption: through optimizing the trading mechanism, improve the competitiveness and consumption proportion of new energy in the electricity market, reduce the phenomenon of abandoned wind and light.

[0048] (2) Improve system supply capacity: through real-time whole network balance, enhance the flexibility and regulation capacity of the power system, ensure the stability and reliability of power supply.

[0049] (3) Support the high-quality development of new energy: through scientific new energy utilization rate target setting and market mechanism improvement, promote the sustainable development of new energy industry, and at the same time reduce the energy consumption cost of the whole society.

[0050] As Figure 2 shown, it is a second flowchart of a new energy consumption optimization method of a multi-type subject power system according to the present application. In this embodiment, the multi-type subject includes thermal power, hydropower, new energy, energy storage, and adjustable load. The optimization method can include:

[0051] S201, based on meteorological data and historical new energy generation curve, a 96-point prediction curve of new energy is calculated.

[0052] (1) The meteorological data and power data of wind power and photovoltaic power stations are fused through the latitude and longitude positioning function.

[0053] The historical meteorological data (such as wind speed, wind direction, irradiance, temperature, etc.) and corresponding power output data of wind power and photovoltaic power stations can be collected. The latitude and longitude information of the power station is used to ensure the accurate matching of meteorological data and power data in time and space. Then the meteorological data and power data are aligned according to the time stamp to form a one-to-one data pair.

[0054] (2) Extract the meteorological and power individual characteristics of wind and photovoltaic power plants.

[0055] Select features that significantly affect power generation from meteorological data, such as wind speed, irradiance, etc. Calculate derived features such as average wind speed, maximum irradiance period, etc. and consider time characteristics such as date, hour. Determine the correlation between each feature and power output through statistical analysis methods.

[0056] (3) Build a sample library to train neural network model parameters.

[0057] In practical applications, data can be divided into training and test sets, and standardized or normalized. Select a suitable neural network model, such as a long short-term memory network, and optimize the number of layers, number of neurons, learning rate, and other hyperparameters through grid search or random search methods. Use the training set data to train the neural network model, and evaluate the model performance through cross-validation.

[0058] (4) Predict the power generation of wind and photovoltaic power plants at a specific time period through the solidified model.

[0059] S202, based on the historical demand side flexible resource load curve, calculate the baseline load curve of 96 points.

[0060] Specifically, the following methods can be used:

[0061] (1) According to the two types of dates, namely, double holidays and statutory holidays, respectively determine the baseline average load of each time period on the execution day peak and valley. If there is a rest, determine according to the actual holiday arrangement of the country;

[0062] (2) Double holiday baseline average load = average load of user's previous 4 double holidays peak and valley periods * (1 + last month's provincial dispatching electricity consumption growth rate);

[0063] (3) Statutory holiday baseline average load = last year's average load of the same period * K1 + user's previous 1 comparable statutory holiday peak and valley period average load * K2;

[0064] (4) Working day baseline average load = average load of user's previous 5 working days peak and valley periods * (1 + last month's provincial dispatching electricity consumption growth rate).

[0065] S203, based on the operation characteristics of different market subjects, build models of each type of market subject.

[0066] 1. Thermal power model

[0067] (1) Real-time generation power upper and lower limit constraints:

[0068] The real-time power generation of a thermal power unit must be within a certain output range, i.e.

[0069]

[0070] wherein, represents the output of the unit n at the time period t , and represents the output upper limit of the unit n at the time period t , and represents the output of the unit n .

[0071] (2) Unit ramping (sliding) constraint

[0072] The actual operation parameters of a thermal power unit determine the constraints on the power generation output of the unit in adjacent time periods, and the ramping and sliding rates must satisfy:

[0073]

[0074] wherein, represents the output of the unit n at the time period t , and represents the output of the unit n at the time period t -1, and represents the ramping or sliding rate of the unit n , and represents the start-stop state of the unit n at the time period t , and represents the start-stop state of the unit n at the time period t -1.

[0075] (3) Minimum unit start-up time and minimum unit shutdown time constraint

[0076] The minimum unit start-up time and minimum unit shutdown time constraint of a thermal power unit is:

[0077]

[0078] wherein, represents the start-stop state of the unit n at the time period , and represents the minimum continuous start-up time of the unit n , and represents the minimum continuous shutdown time of the unit n .

[0079] (4) Fixed unit output

[0080] The unit runs according to the given generation plan in a certain period, and does not participate in bidding in this certain period.

[0081]

[0082] wherein, represents the unit n active power in the period t , represents the unit n output set value in the period t .

[0083] (5) System generation reserve constraint

[0084] In order to ensure the real-time balance of system power, thermal power units must meet certain adjustment margin, provide enough rotating reserve space, and ensure the balance of system by increasing or reducing power generation output in real time. The system generation reserve constraint is expressed as follows:

[0085] Up-regulation reserve constraint:

[0086]

[0087] Down-regulation reserve constraint:

[0088]

[0089] wherein, represents the period t up-regulation reserve requirement, represents the period t down-regulation reserve requirement.

[0090] 2, Hydropower model

[0091] By establishing reservoir water balance constraint, water topology constraint, reservoir capacity and water level constraint, and water level water consumption rate constraint, a single reservoir balance model is constructed.

[0092] (1) Reservoir water balance constraint

[0093] In order to ensure the balance of water storage in the reservoir, the water storage in each period of the reservoir is related to the water storage in the previous period of the reservoir, the interval inflow forecast in the current period of the reservoir, the power generation water consumption in the current period of the reservoir, and the discharge in the current period of the reservoir:

[0094]

[0095] wherein, represents the water storage in the period t of the reservoir, represents the water storage in the period tForecast water inflow for a given time period Reservoir t Water consumption for power generation during different time periods Reservoir t The discharge volume for each time period is expressed in m³. Reservoir t Water storage volume during the -1 period.

[0096] By establishing water balance constraints for reservoirs, it is possible to effectively ensure that the water volume in reservoirs remains balanced, thereby ensuring that water requirements for flood control, irrigation, power generation, and navigation are met.

[0097] (2) Water topological constraints

[0098] Determining the inflow and outflow of water into the reservoir during the current period. For a single reservoir, the inflow during the current period is only the predicted inflow for the specified interval:

[0099]

[0100] in, w_in ( t () indicates the current time period of the reservoir t The amount of water entering the reservoir, f ( t () indicates the current time period of the reservoir t The predicted water inflow for the interval is given in m³.

[0101] The outflow from the reservoir at the current time period is determined by the water consumption for power generation and the water discharge during the current time period, both in cubic meters (m³), and can be described by the formula:

[0102]

[0103] in, Reservoir t The amount of water discharged from the reservoir during a given period.

[0104] Meanwhile, considering the upper and lower limits of reservoir outflow imposed by the reservoir's inherent properties, these limits can be preset based on the reservoir's operational data. Only when the outflow from the reservoir during the current period falls within these preset limits can the reservoir operate normally. By establishing water topology constraints, the topological structure between reservoirs can be effectively constructed, representing the spatiotemporal coupling relationships and topological structure of the reservoirs, similar to network topology.

[0105] (3) Reservoir capacity and water level constraints

[0106] The constraints characterizing the reservoir water level are determined by the reservoir's current water storage capacity and area, and can be described by the following formula:

[0107]

[0108] wherein, represents the water level of the reservoir t in the time period, in m, represents the water level of the reservoir t in the time period, in m³, represents the approved reservoir area, in m 2 .

[0109] At the same time, considering that the reservoir should be able to operate safely, the water level must have corresponding upper and lower limit requirements, so it is necessary to set the upper and lower limits of the water level of the reservoir in advance according to the operation data of the reservoir, and then ensure that the water level of the reservoir in the current time period is within the preset upper and lower limits of the water level of the reservoir, so as to ensure that the reservoir can operate safely.

[0110] (4) Water level water consumption rate constraint

[0111] The key constraint of the combination of water and electricity represents the relationship between the water quantity for power generation and the power generation power, converts the water quantity data of the reservoir into the power quantity data of the power spot market, and realizes the participation of the cascade hydropower in the power spot market. Specifically, the water quantity for power generation of the reservoir in the current time period is determined by the water level water consumption rate of the reservoir in the current time period and the power generation power of the reservoir in the current time period, which is described by the formula:

[0112]

[0113] wherein, represents the power generation power of the reservoir t in the time period, in kW, represents the water level water consumption rate of the reservoir t in the time period, in m³ / kWh.

[0114] According to the water level water consumption rate constraint, the relationship between the water quantity for power generation and the power generation power is clear. The amount of water for power generation can be determined by the power generation power, that is, the operation parameters are determined according to the demand of the power spot market; or the power generation power can be determined by the amount of water for power generation, and the power generation power can be declared to the power spot market, which is convenient for the dispatching of the power spot market.

[0115] 3. New energy model

[0116] (1) New energy unit output constraint

[0117] The output level of the new energy unit should not exceed the predicted level, and the constraint condition is as follows:

[0118]

[0119] wherein, represents the first nk The new energy units of Taichung t period output, represents the first nk The new energy units of Taichung t period expected forecast output, units are MW.

[0120] 4, energy storage model

[0121] The energy storage system is different from the traditional thermal power unit, which can be used as a power source and a load in the power system. The chemical energy storage system also has the characteristics of fast charging and discharging rate, and is limited by the electric energy capacity. With the rapid development of energy storage systems in the power system, the power characteristics of the energy storage system should be considered in the dispatching model. In the dispatching model, the power-energy constraints, charging and discharging power constraints, climbing constraints, and upper and lower limits of energy in each period of the energy storage system are mainly considered.

[0122] (1) Charging and discharging power constraints

[0123] The charging and discharging of the energy storage system is affected by factors such as the type of energy storage system and the energy value of the energy storage system. The charging and discharging power characteristic curve of the energy storage system is given, which can be represented by a piecewise broken line.

[0124] (3-1)

[0125] wherein, represents the energy storage si charging power in t period, represents the maximum charging power of the energy storage si , the charging state of the energy storage in si period, t represents the energy storage discharging power in si period, t represents the maximum discharging power of the energy storage , the discharging state of the energy storage si in period, the value is a number between 0 and 1. si t

[0126] 2) Energy capacity constraints

[0127] The service life of the energy storage system is related to the number of charging and discharging times and the depth of charging and discharging. In order to prolong the service life of the energy storage system as much as possible, the energy capacity range of the energy storage system is generally given.

[0128] (3-2)

[0129] wherein,​​ express t Time-of-use energy storage power station si Capacity status, express t -1 time period energy storage station si Capacity status, Indicates energy storage power station si Charging efficiency, Indicates energy storage power station si The discharge efficiency, Indicates energy storage power station si Capacity status at the end of the operating day. This indicates the energy storage power station for the final period of the declared operation day. si The expected value of the capacity state, Indicates energy storage power station si Capacity status at the beginning of the operating day. Indicates the energy storage power station at the beginning of the previous operating day. si Capacity status, Indicates energy storage power station si The maximum capacity period.

[0130] (3) Power-to-energy conversion loss constraints

[0131] Energy storage systems inherently suffer from energy losses, which can be quantified by the charging energy efficiency coefficient and the discharging energy efficiency coefficient of the energy storage system. η d ( i ) is used to represent this.

[0132] (4) Climbing constraint

[0133] The power ramping constraint of energy storage systems is similar to that of thermal power units, except that its power value can be negative, meaning that the energy storage system can operate in a charging state as a system load.

[0134] (3-3)

[0135] (3-4)

[0136] in, Indicates energy storage power station si The minimum uphill speed, Indicates energy storage power station si Maximum uphill speed Indicates energy storage power station si The minimum downhill / uphill rate Indicates energy storage power station si Maximum downhill / uphill speed Indicates energy storage power station si existt Climbing rate over time period Indicates energy storage power station si exist t Climbing rate during the -1 time period.

[0137] 5) Alternate Constraints

[0138] The backup capacity that an energy storage system can provide is affected by both its maximum charging and discharging power at different times and the amount of electrical energy stored in the system at different times. The two factors together determine the backup capacity that the energy storage system can provide at different times.

[0139] 6) Energy constraints in the final period

[0140] In some cases, it is necessary to establish an energy constraint for the energy storage system at the end of the planned cycle, meaning that the energy storage system must have a certain amount of stored energy after the charge-discharge plan is completed. The remaining energy of the stored energy is defined as the state of charge (SOC), which is the ratio of the remaining storage capacity to the rated capacity. Considering the remaining energy during charge and discharge, the SOC constraint is as follows:

[0141] (3-5)

[0142] (3-6)

[0143] in, express t State of charge of energy storage during time period express t Remaining stored energy during the time period Indicates the rated capacity of energy storage. This represents the minimum state of charge. This represents the maximum state of charge.

[0144] 5. Adjustable load model

[0145] Demand-side loads are generally divided into critical loads and adjustable loads, and adjustable loads are further divided into transferable loads and interruptible loads.

[0146] For transferable loads: The total power consumption of transferable loads remains unchanged within a scheduling cycle, mainly considering upper and lower load limits and ramping constraints.

[0147] (1) Total power constraint

[0148]

[0149] in, For transferable load ld exist tthe load of the period, the transferable load ld in t the baseline load of the period, the transferable load ld in t the adjusted load amount of the period.

[0150] (2) upper and lower load constraints

[0151]

[0152] wherein, the transferable load ld in t the minimum load of the period, the transferable load ld in t the maximum load of the period.

[0153] (3) ramping constraints

[0154]

[0155] wherein, the transferable load ld in t the maximum reducible load active power of the period, the transferable load ld in t the maximum increaseable load active power of the period.

[0156] (4) total adjustment amount constraints

[0157]

[0158] wherein, T is a dispatching adjustment period of the transferable load.

[0159] For interruptible load: the interruptible load can reduce the electricity consumption according to the demand, and the upper limit constraint of standby capacity in the load is considered.

[0160] (1) interruptible amount constraints

[0161]

[0162] wherein, the interruptible load i declared t the interruptible capacity in the period, the interruptible load i the minimum interruptible capacity that can be declared in t the period, the interruptible load i in tMaximum interruptible capacity that can be claimed for a period.

[0163] (2) Total interruption quantity constraint

[0164]

[0165] wherein, is the interruptible load i is the interruptible load t is the 0-1 state variable of the period, 1 indicates the interruptible load i is the 0-1 state variable of the period, 0 indicates the interruptible load i is the 0-1 state variable of the period, 0 indicates the interruptible load

[0166] S204, build an optimization out-clearing model.

[0167] 1. Optimization objective

[0168] Maximize the amount of new energy consumption as the out-clearing optimization objective:

[0169]

[0170] wherein, denotes the amount of new energy consumption, denotes period, denotes the total market out-clearing time, denotes the actual grid-connected power of the wind turbine in the new energy in the period, denotes the predicted grid-connected power of the wind turbine in the new energy in the period without opening the auxiliary service market, denotes the actual grid-connected power of the photovoltaic unit in the new energy in the period, denotes the predicted grid-connected power of the photovoltaic unit in the new energy in the period without opening the auxiliary service market.

[0171] 2. Constraint condition

[0172] In this embodiment, the constraints of the optimal clearing model include supply-demand balance constraints, power purchase constraints, power sale constraints, line flow constraints, section flow constraints, and grid operation boundary condition constraints. It should be noted that the supply-demand balance constraints can ensure that the power supply and demand are balanced at any time period, avoiding power shortage or excess. The power purchase constraints are used to limit the power purchase of each power purchaser (such as grid companies, large users, etc.), ensuring the fairness and stability of the power market. The power sale constraints can constrain the power generation of power sellers (such as thermal power, hydropower, and new energy power stations), preventing excessive power generation from causing system instability. The line flow constraints consider the physical limitations of power transmission lines to ensure that the power transmission does not exceed the carrying capacity of the lines. The section flow constraints set flow limits on specific sections of the power network (such as the interfaces of cross-region transmission lines) to maintain the overall stability and safety of the grid. The grid operation boundary condition constraints usually include voltage levels, frequency stability, and other basic conditions of grid operation, ensuring that the grid remains within a safe and stable operating range during optimization.

[0173] 1) Supply-demand balance constraints

[0174] The system supply-demand balance constraint means that at any time period of system operation, without considering network loss, the sum of the outputs of all generator units in the system should be equal to the sum of the planned power of all tie lines:

[0175]

[0176] Wherein:

[0177]

[0178]

[0179] Wherein, represents the number of conventional loads in the power system, represents the th conventional load in the power system at the time period, represents the number of generator units in the power system, represents the output of the th generator unit in the power system at the time period, represents the total number of tie lines in the power system, represents the planned power of the tie line at the time period.

[0180] 2) Power purchase constraints

[0181] ​

[0182] in, Indicates buyer i exist Time period n The winning bid for power in each output range express Buyer of the time period i No. n Each power output zone should declare its electricity supply. Indicates buyer i Total number of price segments.

[0183] 3) Electricity sales constraints

[0184]

[0185] in, express Time Seller The m Duan reported electricity. Indicates the number of segments for which electricity is declared. Indicates the seller Total number of quote segments.

[0186] 4) Power flow constraints of the line

[0187] Power flow constraints refer to the requirement that the power flow at the line and cross-section should not exceed the allowable power flow limit of the line.

[0188]

[0189] in, Indicates lines in the power system l The limit of current transmission, Indicates the unit n The node is connected to the line l The generator output power transfer distribution factor, express Time-of-use units n of efforts, Indicates the contact line tj The node is connected to the line l The generator output power transfer distribution factor, express Time-of-use communication line tj of efforts, Represents a node k For the line l The generator output power transfer distribution factor, Represents a node k During the period The bus load value, , They represent the lines respectively.l Forward and reverse current slack variables, N Indicates the total number of generating units. K This represents the total number of nodes.

[0190] 5) Cross-sectional power flow constraints

[0191] Considering the power flow constraints at the critical section, these constraints can be described as follows:

[0192]

[0193] in, Represents the cross-section of the power system s The limit of current transmission, Indicates the unit n The node is located on the cross section s The generator output power transfer distribution factor, Indicates the contact line tj The node is located on the cross section s The generator output power transfer distribution factor, Represents a node k cross section s The generator output power transfer distribution factor, Indicates cross-section s The limit of current transmission, , Representing cross-sections s The positive and negative current slack variables.

[0194] 6) Boundary condition constraints for power grid operation

[0195]

[0196] in, Indicates the unit n During the period t The set of various boundary conditions, including the power transmission curves of the interconnection lines formed by inter-provincial medium- and long-term transactions, and the units that must be turned on or off due to safety constraints, heating needs, or government requirements. Indicates the unit n During the period t The output meets the various boundary conditions and constraints of the market at present.

[0197] It can obtain planned and forecasted values ​​from new energy market entities. By using the planned and forecasted values ​​from new energy power plants, it can calculate the current consumption status of new energy and determine whether the market needs to be opened.

[0198] S205 performs clearing calculations in real time.

[0199] In practical applications, the above optimized dispatching model expression can be imported to form a CPLEX input file, and solved by using CPLEX software.

[0200] S206, publishing the dispatching result.

[0201] The result solved by the optimized dispatching model can be published to the buyers and sellers in the market after being checked for safety.

[0202] As shown in FIG. 1, a schematic diagram of a new energy consumption optimization system of a multi-type subject power system according to the present application can include: Figure 3 A model module is configured to construct an optimized dispatching model based on the participation of multi-type subjects. The optimization objective of the optimized dispatching model is to maximize the new energy consumption. The constraints of the optimized dispatching model include supply-demand balance constraints, power purchase constraints, power sale constraints, line flow constraints, cross-section flow constraints, and grid operation boundary condition constraints. The multi-type subjects include thermal power, hydropower, new energy, energy storage, and adjustable load.

[0203] A first curve module is configured to obtain a 96-point prediction curve of new energy according to meteorological data and historical new energy generation curve.

[0204] A second curve module is configured to obtain a 96-point baseline load curve according to historical demand-side flexible resource load curve.

[0205] A solving module is configured to solve the optimized dispatching model in combination with the 96-point prediction curve of new energy and the 96-point baseline load curve to obtain an optimization strategy. The optimization strategy includes the output of multi-type subjects.

[0206] An optimization module is configured to optimize the power system by using the optimization strategy.

[0207] In some embodiments of the new energy consumption optimization system of the multi-type subject power system according to the present application, the multi-type subjects can include thermal power, hydropower, new energy, energy storage, and adjustable load.

[0208] In some embodiments of the new energy consumption optimization system of the multi-type subject power system according to the present application, the optimized dispatching model includes:

[0209]

[0210] wherein,

[0211] represents the new energy consumption, represents the time period, represents the total market dispatching time, represents the ​​The actual grid-connected power of wind turbines in the new energy sector during the period. express Predicted grid-connected power of wind turbines in the new energy sector when the ancillary services market is not activated. express The actual grid-connected power of photovoltaic units in the new energy sector during this period express Predicted grid-connected power of photovoltaic units in the new energy sector when the ancillary services market is not activated during the specified period.

[0212] In some embodiments of the new energy consumption optimization system for multi-type main power systems in this application, the constraints of the optimization clearing model include supply and demand balance constraints, electricity purchase constraints, electricity sales constraints, line power flow constraints, cross-sectional power flow constraints, and grid operation boundary condition constraints.

[0213] In some embodiments of the renewable energy consumption optimization system for multi-type main power systems in this application, the supply and demand balance constraints include:

[0214]

[0215] in, Indicates the number of conventional loads in the power system. express The first time period in the power system A normal load Indicates the number of generator sets in the power system. express The first time period in the power system The output of each generator set This indicates the total number of tie lines in the power system. Indicates the contact line exist Planned power for a given time period.

[0216] In some embodiments of the renewable energy consumption optimization system for multi-type main power systems in this application, the power purchase constraint includes:

[0217]

[0218] in, Indicates buyer i exist Time period n The winning bid for power in each output range express Buyer during the period i No. n Each power output zone should declare its electricity supply. Indicates buyer i Total number of price segments.

[0219] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the power selling constraint comprises:

[0220]

[0221] wherein, represents the segment selling power of the time period, represents the number of segments of the declared power, m represents the total number of segments of the selling offer; In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the line flow constraint comprises:

[0222]

[0223]

[0224] wherein, represents the flow transmission limit of the line l in the power system, represents the generator output power transfer distribution factor of the line n between the nodes where the unit is located, l represents the output of the unit in the time period, represents the generator output power transfer distribution factor of the line n between the nodes where the tie line is located, represents the output of the tie line tj in the time period, l represents the generator output power transfer distribution factor of the line between the nodes, represents the bus load value of the node line tj in the time period, represents the generator output power transfer distribution factor of the line k between the nodes, l represents the bus load value of the node in the time period, k , respectively represent the positive and negative flow relaxation variables of the line , represents the total number of units, l represents the total number of nodes. N K In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the cross-section flow constraint comprises:

[0225]

[0226] ​​​​​

[0227] wherein, denotes the power flow transfer lower limit of the section of the power system, s denotes the generator output power transfer distribution factor of the section of the power system by the unit, n s denotes the generator output power transfer distribution factor of the section of the power system by the tie line, tj denotes the generator output power transfer distribution factor of the section of the power system by the node, s denotes the generator output power transfer distribution factor of the section of the power system by the node, k s denotes the power flow transfer upper limit of the section of the power system, s denotes the positive and negative power flow slack variable of the section of the power system, respectively. s In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the power grid operation boundary condition constraint comprises:

[0228]

[0229] wherein,

[0230] denotes that the output of the unit in the time period meets various boundary condition constraints of the day-ahead market, n denotes the set of various boundary conditions of the unit in the time period. t n In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraints corresponding to the thermal power in the multi-type subject comprise unit real-time power generation upper and lower limit constraints, unit ramping or sliding constraints, unit minimum start-up time and minimum shutdown time constraints, unit fixed output constraints, system power generation upward reserve constraints and system power generation downward reserve constraints. t In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the unit real-time power generation upper and lower limit constraints comprise:

[0231]

[0232]

[0233]

[0234] wherein, denotes the lower limit of the output of the unit in the time period, n t n t ​​​​​​​​​​​​​​​​​an upper limit of the output of the unit, representing the unit n .

[0235] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the unit ramping or sliding constraint comprises:

[0236]

[0237] wherein, representing the unit n the output of the unit in the time period t , representing the unit n the output of the unit in the time period t -1, representing the ramping or sliding rate of the unit n , representing the start-stop state of the unit n in the time period t , representing the start-stop state of the unit n in the time period t -1.

[0238] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the unit minimum start-up time and minimum shutdown time constraint comprises:

[0239]

[0240] wherein, representing the unit n the start-stop state of the unit in the time period , representing the minimum continuous start-up time of the unit n , representing the minimum continuous shutdown time of the unit n .

[0241] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the unit fixed output constraint comprises:

[0242]

[0243] wherein, representing the active power of the unit n in the time period t , representing the output set value of the unit n in the time period t .

[0244] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the system generation up-regulation standby constraint includes:

[0245] .

[0246] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the system generation down-regulation standby constraint includes:

[0247]

[0248] wherein, denotes a time period t up-regulation standby requirement, denotes a time period t down-regulation standby requirement.

[0249] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraint corresponding to the hydropower in the multi-type subject includes a reservoir water balance constraint, a water topology constraint, a reservoir capacity and water level constraint, and a water level water consumption rate constraint.

[0250] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the reservoir water balance constraint includes:

[0251]

[0252] wherein, denotes a reservoir t storage capacity of a time period, denotes a reservoir t interval inflow prediction of a time period, denotes a reservoir t water consumption for power generation of a time period, denotes a reservoir t discharge of a time period, denotes a reservoir t storage capacity of a -1 time period.

[0253] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the water topology constraint includes:

[0254]

[0255] wherein, denotes a reservoir t outlet water of a time period.

[0256] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the reservoir capacity and water level constraint includes:

[0257]

[0258] wherein, represents the water level of the reservoir t at the time period, represents the water storage of the reservoir t at the time period, represents the approved reservoir area.

[0259] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the water level water consumption rate constraint comprises:

[0260]

[0261] wherein, represents the power generation of the reservoir t at the time period, represents the water level water consumption rate of the reservoir t at the time period.

[0262] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraint corresponding to the new energy in the multi-type subject comprises:

[0263] New energy unit output constraint:

[0264]

[0265] wherein, represents the output of the nk th new energy unit t at the time period, represents the expected predicted output of the nk th new energy unit t at the time period.

[0266] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraint corresponding to the energy storage in the multi-type subject comprises a charge and discharge power constraint, an electric energy capacity constraint, a power-electric energy conversion loss constraint, an energy storage climbing constraint, a standby constraint and a last time period electric energy constraint.

[0267] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the charge and discharge power constraint comprises:

[0268]

[0269] wherein, represents the charge power of the energy storage si at the time period, t represents the discharge power of the energy storage at the time period.si Maximum charging power, Indicates energy storage si exist t Charging status during a period of time, Indicates energy storage si exist t Discharge power during the period Indicates energy storage si Maximum discharge power, Indicates energy storage si exist t The discharge state during a given period is a value between 0 and 1.

[0270] In some embodiments of the renewable energy consumption optimization system for multi-type main power systems in this application, the energy capacity constraint includes:

[0271]

[0272] in, express t Time-of-use energy storage power station si Capacity status, express t -1 time period energy storage station si Capacity status, Indicates energy storage power station si Charging efficiency, Indicates energy storage power station si The discharge efficiency, Indicates energy storage power station si Capacity status at the end of the operating day. This indicates the energy storage power station for the final period of the declared operation day. si The expected value of the capacity state, Indicates energy storage power station si Capacity status at the beginning of the operating day. Indicates the energy storage power station at the beginning of the previous operating day. si Capacity status, Indicates energy storage power station si Maximum capacity.

[0273] In some embodiments of the renewable energy consumption optimization system for multi-type main power systems in this application, the energy storage ramping constraint includes:

[0274]

[0275]

[0276] in, Indicates energy storage power station si The minimum uphill speed, the maximum up ramp rate of the energy storage power station si , the minimum down ramp rate of the energy storage power station si , the maximum down ramp rate of the energy storage power station si , the ramp rate of the energy storage power station si in the t time period, the ramp rate of the energy storage power station si in the t -1 time period.

[0277] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the last time period electric energy constraint comprises:

[0278]

[0279]

[0280] wherein, represents the state of charge of the energy storage in the t time period, represents the remaining storage energy of the energy storage in the t time period, represents the rated capacity of the energy storage, represents the minimum value of the state of charge, represents the maximum value of the state of charge.

[0281] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraints corresponding to the adjustable load in the multi-type subject comprise total power constraint, load upper and lower limit constraint, adjustable load ramp constraint and total adjustment amount constraint.

[0282] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the total power constraint comprises:

[0283] ;

[0284] wherein, is the load of the transferable load ld in the t time period, is the baseline load of the transferable load ld in the t time period, is the adjustment load amount of the transferable load ld in the t time period.

[0285] In some embodiments of the new energy consumption optimization system of the multi-type subject power system provided in the application, the load upper and lower limit constraint comprises:

[0286]

[0287] wherein, is the transferable load ld is the minimum load in the t time period, is the transferable load ld is the maximum load in the t time period.

[0288] In some embodiments of the new energy consumption optimization system of the multi-type subject power system provided in the application, the adjustable load ramp constraint comprises:

[0289]

[0290] wherein, is the transferable load ld is the maximum reducible load active power in the t time period, is the transferable load ld is the maximum increasable load active power in the t time period, is the transferable load ld is the load in the t -1 time period.

[0291] In some embodiments of the new energy consumption optimization system of the multi-type subject power system provided in the application, the total regulation amount constraint comprises:

[0292] It should be understood that, in several embodiments provided in the application, the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, for example, the division of each module is only a logical function division, and actual implementation can have another division manner, for example, multiple modules can be combined or integrated into another device, or some features can be ignored or not executed. The modules described as separate components can be or can not be physically separated, and the components displayed as modules can be a physical unit or multiple physical units, that is, can be located in one place, or can be distributed to multiple different places. According to actual needs, part or all of the modules can be selected to achieve the purpose of the embodiment scheme.

[0293] ​​In addition, each module in various embodiments of the present application can be integrated in one processing unit, or each module can be physically present separately, or two or more modules can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0294] The application embodiment also provides an electronic device, which can include one or more processors, memories and communication interfaces.

[0295] The memory, the communication interface and the processor are coupled together, for example, through a bus.

[0296] The communication interface is used for data transmission with other devices. The memory stores computer program codes. The computer program codes include computer instructions, which, when executed by the processor, cause the electronic device to perform the steps of the new energy consumption optimization method of the multi-type main body power system.

[0297] The processor can be a processor or a controller, for example, a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can realize or execute various exemplary logical blocks, modules and circuits described in combination with the present disclosure. The processor can also be a combination realizing computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc. The processor can be used to support the electronic device to perform the method steps provided in the above embodiments.

[0298] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0299] The computer readable storage medium provided by the embodiment of the application stores a computer program, and the computer program is executed by a processor to implement the steps of the new energy consumption optimization method of the multi-type subject power system.

[0300] The computer readable storage medium involved in the application includes random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the technical field.

[0301] The application further provides a computer program product containing instructions, which are executed by a processor to implement the steps in the new energy consumption optimization method of the multi-type subject power system.

[0302] The computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the application.

[0303] The carrier implementing the computer program product can be a computer device. The computer program product can also be stored in a computer storage medium.

[0304] The computer device can be a desktop computer, notebook, palm computer, cloud server, and the like. The computer device can include, but is not limited to, a processor and a memory.

[0305] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like.

[0306] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the computer device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory.

[0307] The modules / units integrated in the computer device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0308] The above only is the preferred embodiment of the present application and is not used to limit the present application. The present application can have various changes and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing the absorption of new energy sources in a multi-type main power system, characterized in that, include: Construct an optimized clearing model for the power system based on the participation of multiple types of stakeholders; The optimization objective of the optimized clearing model is to maximize the amount of new energy consumed. The optimized clearing model includes: in, This indicates the amount of new energy consumed. express Time period Indicates the total market clearing time. express The actual grid-connected power of wind turbines in the new energy sector during the period. express Predicted grid-connected power of wind turbines in the new energy sector when the ancillary services market is not activated. express The actual grid-connected power of photovoltaic units in the new energy sector during this period express Predicted grid-connected power of photovoltaic units in the new energy sector when the ancillary services market is not activated during the specified period; Based on the meteorological data corresponding to the power system and the historical new energy power generation curves, a 96-point prediction curve for new energy was obtained. Based on the historical demand-side flexible resource load curves corresponding to the power system, a baseline load curve of 96 points is obtained; The optimized clearing model is solved by combining the 96-point forecast curve of new energy and the baseline load curve of the 96-point system to obtain the optimization strategy; the optimization strategy includes the output of multiple types of entities. The aforementioned optimization strategy is used to optimize the power system.

2. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 1, characterized in that, The various types of entities include thermal power, hydropower, new energy, energy storage, and adjustable loads.

3. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 1, characterized in that, The constraints of the optimized clearing model include supply and demand balance constraints, electricity purchase constraints, electricity sales constraints, line power flow constraints, cross-sectional power flow constraints, and grid operation boundary condition constraints.

4. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 3, characterized in that, The supply and demand balance constraints include: in, Indicates the number of conventional loads in the power system. express The first time period in the power system A normal load Indicates the number of generator sets in the power system. express The first time period in the power system The output of each generator set This indicates the total number of tie lines in the power system. Indicates the contact line exist Planned power for a given time period.

5. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 3, characterized in that, The electricity purchase constraints include: in, Indicates buyer exist Time period The winning bid for power in each output range express Buyer of the time period No. Each power output zone should declare its electricity supply. Indicates buyer Total number of price segments.

6. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 3, characterized in that, The electricity sales constraints include: in, express Time Seller The Duan reported electricity. Indicates the number of segments for which electricity is declared. Indicates the seller Total number of quote segments.

7. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 3, characterized in that, The power flow constraints of the line include: in, Indicates lines in the power system The limit of current transmission, Indicates the unit The node is connected to the line The generator output power transfer distribution factor, express Time-of-use units of efforts, Indicates the contact line The node is connected to the line The generator output power transfer distribution factor, express Time-of-day contact Wire of efforts, Represents a node For the line The generator output power transfer distribution factor, Represents a node During the period The bus load value, , They represent the lines respectively. Forward and reverse current slack variables, Indicates the total number of generating units. This represents the total number of nodes.

8. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 3, characterized in that, The cross-sectional power flow constraint includes: in, Represents the cross-section of the power system The limit of current transmission, Indicates the unit The node is located on the cross section The generator output power transfer distribution factor, Indicates the contact line The node is located on the cross section The generator output power transfer distribution factor, Represents a node cross section The generator output power transfer distribution factor, Indicates cross-section The limit of current transmission, , Representing cross-sections The positive and negative current slack variables.

9. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 3, characterized in that, The power grid operation boundary condition constraints include: in, Indicates the unit During the period The output meets the current market boundary constraints. Indicates the unit During the period A set of various boundary conditions.

10. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 2, characterized in that, The constraints for thermal power in various types of entities include upper and lower limits of real-time generating power of units, unit ramp-up or ramp-down constraints, minimum start-up time and minimum shutdown time constraints, fixed output constraints of units, system generation reserve adjustment constraints, and system generation reserve adjustment constraints.

11. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 10, characterized in that, The upper and lower limits of the unit's real-time power generation include: in, Indicates the unit n During the period t The lower limit of output, Indicates the unit n During the period t The upper limit of output, Indicates the unit n of effort.

12. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 10, characterized in that, The unit's ramp-up or landslide constraints include: in, Indicates the unit n During the period t of efforts, Indicates the unit n During the period t -1 output, Indicates the unit n The rate of ascent or landslide, Indicates the unit n During the period t Start-stop status, Indicates the unit n During the period t -1 indicates the start / stop status. Indicates the unit n During the period t The lower limit of output.

13. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 10, characterized in that, The minimum start-up time and minimum downtime constraints for the generating units include: in, Indicates the unit n During the period Start-stop status, Indicates the unit n Minimum continuous power-on time, Indicates the unit n The minimum continuous downtime.

14. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 10, characterized in that, The fixed output constraint of the unit includes: in, Indicates the unit n During the period t active power, Indicates the unit n During the period t The output setting value.

15. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 10, characterized in that, The system's power generation reserve adjustment constraint includes: in, Indicates the unit n During the period t The upper limit of output, Indicates the unit n During the period t of efforts, Indicates time period t Increase standby requirements N This indicates the total number of generating units.

16. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 10, characterized in that, The system's power generation reserve reduction constraint includes: in, Indicates time period t Lowering standby requirements N Indicates the total number of generating units. Indicates the unit n During the period t of efforts, Indicates the unit n During the period t The lower limit of output.

17. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 2, characterized in that, The constraints corresponding to hydropower in the various types of entities include reservoir water balance constraints, water topology constraints, reservoir capacity and water level constraints, and water level consumption rate constraints.

18. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 17, characterized in that, The reservoir water balance constraints include: in, Reservoir t Water storage volume during a certain period Reservoir t Forecast water inflow for a given time period Reservoir t Water consumption for power generation during different time periods Reservoir t The amount of water discharged during a given period. Reservoir t Water storage volume during the -1 period.

19. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 17, characterized in that, The water topology constraints include: in, Reservoir t Water outflow during the period Reservoir t Water consumption for power generation during different time periods Reservoir t The amount of water discharged during a given period.

20. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 17, characterized in that, The reservoir capacity water level constraints include: in, Reservoir t Water level height during the period Reservoir t Water storage volume during a certain period This indicates the verified area of ​​the reservoir.

21. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 17, characterized in that, The water level consumption rate constraint includes: in, Reservoir t Power generation during the period Reservoir t Water consumption rate during a given period Reservoir t Water consumption for power generation during a given time period.

22. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 2, characterized in that, The constraints corresponding to new energy sources among the various types of entities include: Output constraints of new energy units: in, Indicates the first nk Taiwan's new energy power units t Time of day effort Indicates the first nk Taiwan's new energy power units t Expected output for the time period.

23. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 2, characterized in that, The constraints corresponding to energy storage in the various types of entities include charging and discharging power constraints, energy capacity constraints, power-to-energy conversion loss constraints, energy storage ramp-up constraints, reserve constraints, and energy constraints in the final period.

24. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 23, characterized in that, The charging and discharging power constraint includes: in, Indicates energy storage si exist t Charging power during the period Indicates energy storage si Maximum charging power, Indicates energy storage si exist t Charging status during a period of time, Indicates energy storage si exist t Discharge power during the period Indicates energy storage si Maximum discharge power, Indicates energy storage si exist t The discharge state during a given period is a value between 0 and 1.

25. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 23, characterized in that, The power capacity constraint includes: in, express t Time-of-use energy storage power station si Capacity status, express t -1 time period energy storage station si Capacity status, Indicates energy storage power station si Charging efficiency, Indicates energy storage power station si The discharge efficiency, Indicates energy storage power station si Capacity status at the end of the operating day. This indicates the energy storage power station for the final period of the declared operation day. si The expected value of the capacity state, Indicates energy storage power station si Capacity status at the beginning of the operating day. Indicates the energy storage power station at the beginning of the previous operating day. si Capacity status, Indicates energy storage power station si Maximum capacity, Indicates energy storage si exist t Charging power during the period Indicates energy storage si exist t Discharge power during a given period.

26. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 23, characterized in that, The energy storage ramping constraints include: in, Indicates energy storage power station si The minimum uphill speed, Indicates energy storage power station si Maximum uphill speed Indicates energy storage power station si The minimum downhill / uphill rate Indicates energy storage power station si Maximum downhill / uphill speed Indicates energy storage power station si exist t Climbing rate over time period Indicates energy storage power station si exist t Climbing rate during the -1 time period.

27. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 23, characterized in that, The final period of power constraints includes: in, express t State of charge of energy storage during time period express t Remaining stored energy during the time period Indicates the rated capacity of energy storage. This represents the minimum state of charge. This represents the maximum state of charge.

28. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 2, characterized in that, The constraints corresponding to the adjustable load in the various types of entities include total power constraints, upper and lower load limits constraints, adjustable load ramping constraints, and total adjustment constraints.

29. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 28, characterized in that, The total power constraint includes: ； in, For transferable load ld exist t Load during a specific time period For transferable load ld exist t Baseline load for the time period For transferable load ld exist t Adjusting load during different time periods.

30. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 28, characterized in that, The load upper and lower limit constraints include: ； in, For transferable load ld exist t Minimum load for the time period For transferable load ld exist t Maximum load during the period For transferable load ld exist t Load during a specific time period.

31. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 28, characterized in that, The adjustable load ramping constraint includes: ； in, For transferable load ld exist t The maximum reducible active power load during the time period. For transferable load ld exist t The maximum possible increase in active power load during a given time period For transferable load ld exist t Load during the -1 period, For transferable load ld exist t Load during a specific time period.

32. The method for optimizing the absorption of new energy in multi-type main power systems according to claim 28, characterized in that, The total adjustment constraint includes: in, For transferable load ld exist t The load during a given time period, where T is a scheduling and adjustment cycle for transferable load. This is the offset of the start time of load power consumption.

33. A new energy consumption optimization system for a multi-type main power system, characterized in that, include: The model module is used to build an optimization clearing model for the power system based on the participation of multiple types of entities. The optimization objective of the optimized clearing model is to maximize the amount of new energy consumed. The optimized clearing model includes: in, This indicates the amount of new energy consumed. express Time period Indicates the total market clearing time. express The actual grid-connected power of wind turbines in the new energy sector during the period. express Predicted grid-connected power of wind turbines in the new energy sector when the ancillary services market is not activated. express The actual grid-connected power of photovoltaic units in the new energy sector during this period express Predicted grid-connected power of photovoltaic units in the new energy sector when the ancillary services market is not activated during the specified period; The first curve module is used to obtain the 96-point prediction curve for new energy based on the meteorological data corresponding to the power system and the historical new energy power generation curves. The second curve module is used to obtain a 96-point baseline load curve based on the historical demand-side flexible resource load curve corresponding to the power system. The solution module is used to solve the optimized clearing model by combining the 96-point prediction curve of new energy and the baseline load curve of the 96-point, and obtain the optimization strategy; the optimization strategy includes the output of multiple types of entities; An optimization module is used to optimize the power system using the optimization strategy.

34. The new energy consumption optimization system for multi-type main power systems according to claim 33, characterized in that, The various types of entities include thermal power, hydropower, new energy, energy storage, and adjustable loads.

35. The new energy consumption optimization system for multi-type main power systems according to claim 33, characterized in that, The constraints of the optimized clearing model include supply and demand balance constraints, electricity purchase constraints, electricity sales constraints, line power flow constraints, cross-sectional power flow constraints, and grid operation boundary condition constraints.

36. The new energy consumption optimization system for multi-type main power systems according to claim 33, characterized in that, The constraints for thermal power in various types of entities include upper and lower limits of real-time generating power of units, unit ramp-up or ramp-down constraints, minimum start-up time and minimum shutdown time constraints, fixed output constraints of units, system generation reserve adjustment constraints, and system generation reserve adjustment constraints.

37. The new energy consumption optimization system for multi-type main power systems according to claim 33, characterized in that, The constraints corresponding to hydropower in the various types of entities include reservoir water balance constraints, water topology constraints, reservoir capacity and water level constraints, and water level consumption rate constraints.

38. The new energy consumption optimization system for multi-type main power systems according to claim 33, characterized in that, The constraints corresponding to energy storage in the various types of entities include charging and discharging power constraints, energy capacity constraints, power-to-energy conversion loss constraints, energy storage ramp-up constraints, reserve constraints, and energy constraints in the final period.

39. The new energy consumption optimization system for multi-type main power systems according to claim 33, characterized in that, The constraints corresponding to the adjustable load in the various types of entities include total power constraints, upper and lower load limits constraints, adjustable load ramping constraints, and total adjustment constraints.

40. An electronic device, characterized in that, include: The electronic device includes a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the new energy consumption optimization method for a multi-type main power system as described in any one of claims 1 to 32.

41. 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 steps of the new energy consumption optimization method for multi-type main power systems as described in any one of claims 1 to 32.

42. A computer program product, characterized in that: The computer program product includes instructions that, when executed by a processor, implement the new energy consumption optimization method for a power system as described in any one of claims 1-32.

Images