Multi-target optimization and next-day charging and discharging strategy optimization method and device for energy storage device

Abstract

The invention provides a multi-target optimization and energy storage device next-day charging and discharging strategy optimization method and device, relates to the technical field of charging and discharging strategies, can improve the flexibility and response capability of a power system, and achieves the smooth adjustment of a power load and the maximization of the peak-valley electricity price income. According to the specific technical scheme, a transformer area transformer load rate and peak-valley income mathematical model is constructed; determining constraint conditions, wherein the constraint conditions comprise a one-day charging and discharging energy balance constraint, a tie line power balance constraint, an energy storage charging and discharging power constraint, an energy storage system charge state dynamic constraint constraint, an energy storage charging and discharging frequency constraint within one day and an energy storage system capacity constraint; and solving the load rate and peak-valley income mathematical model of the transformer in the transformer area based on the constraint condition to obtain a target next-day charging and discharging strategy. According to the invention, optimization of the charging and discharging strategy of the transformer area energy storage system can be realized, the load rate of the transformer is effectively reduced, the energy utilization efficiency is improved, and the peak-valley electricity price income is increased.

Description

Technical Field

[0001] The present disclosure relates to the technical field of charge and discharge strategies, and in particular to a multi-objective optimization method and device for optimizing the next-day charge and discharge strategy of an energy storage device. Background Art

[0002] At present, the flexibility and efficiency of the energy system have become the focus of attention in the power industry, especially at the substation (low-voltage distribution) level. How to effectively use the substation energy storage system to achieve load balancing, increase local consumption of new energy, reduce the load rate of transformers, and maximize the benefits of peak and valley electricity prices has become an urgent problem to be solved in the current low-voltage substation.

[0003] In traditional power systems, the change of load factor in the substation is often affected by many factors, which lead to significant volatility and uncertainty in the load factor in the substation, bringing challenges to the stable operation of the power system. At the same time, the implementation of the peak-valley electricity price system also requires the power system to have stronger flexibility and responsiveness. Summary of the invention

[0004] The disclosed embodiment provides a multi-objective optimization method and device for optimizing the next-day charging and discharging strategy of an energy storage device, which can improve the flexibility and responsiveness of the power system, achieve smooth regulation of power loads and maximize the benefits of peak and valley electricity prices. The technical solution is as follows:

[0005] According to the first aspect of the embodiments of the present disclosure, a multi-objective optimization method and a next-day charging and discharging strategy optimization method for an energy storage device are provided, the method comprising: constructing a mathematical model of the load rate of a transformer in an area and peak-valley benefits; determining constraints, the constraints comprising a daily charging and discharging energy balance constraint, a tie line power balance constraint, an energy storage charging and discharging power constraint, an energy storage system state of charge (SOC) dynamic constraint, an energy storage charging and discharging times constraint within a day, and an energy storage system capacity constraint; solving the mathematical model of the load rate of the transformer in the area and the peak-valley benefits based on the constraints to obtain a target next-day charging and discharging strategy.

[0006] Based on the above scheme, the disclosed embodiment aims to solve the problems of large load fluctuations and low peak-valley electricity price benefits in the power system of the substation. By introducing the MOPSO multi-objective optimization algorithm, the optimization of the charging and discharging strategy of the substation energy storage system can be achieved, which can not only improve the flexibility and responsiveness of the power system, but also effectively reduce the transformer load rate, improve energy utilization efficiency, increase peak-valley electricity price benefits, and achieve smooth regulation of the next day's power load and maximize the peak-valley electricity price benefits, which has important practical application value and promotion prospects.

[0007] In some embodiments, the construction of the transformer load rate and peak-valley revenue mathematical model includes: constructing a transformer daily average load rate minimization objective function and a peak-valley arbitrage revenue maximization objective function, and the transformer daily average load rate minimization objective function and the peak-valley arbitrage revenue maximization objective function are dynamically optimized simultaneously, wherein:

[0008] The objective function for minimizing the daily average load rate of the transformer is defined as:

[0009]

[0010] in: is the daily load rate of the transformer. The smaller the value, the lower the load rate. T (t) is the actual average apparent power of the transformer during period t on the next day, P T (t) is the actual average active power of the transformer during period t on the next day, P L (t) is the actual load forecast power in the next day’s period t, P S (t) is the energy storage charging and discharging power during the next day t period, Cosθ(t) is the load power factor during the next day t period, C T is the rated capacity of the transformer, η S (t) is the energy storage charging and discharging efficiency in period t of the next day, LR(t) is the transformer load rate in period t, P L (t) is the actual load forecast power in the next day t period, T is the total number of time steps, where:

[0011]

[0012] The peak-to-valley arbitrage profit maximization objective function is defined as:

[0013]

[0014]

[0015] in: is the peak-to-valley arbitrage income of the next day, P EL (t) is the peak-valley-average electricity price at different times of the day, η S (t) is the energy storage charging and discharging efficiency during the next day t period, η ch is the charging efficiency of the energy storage system, η disch is the discharge efficiency of the energy storage system.

[0016] In some embodiments, the one-day charge and discharge energy balance constraint is:

[0017]

[0018] The tie line power balance constraint is:

[0019] S T (t)*Cosθ=P L (t)-P S (t)

[0020] The energy storage charging and discharging power constraints are:

[0021]

[0022] Among them: When P S When (t)>0, When P S When (t)<0, is the rated charge and discharge power of energy storage, P Lmax is the maximum load of the next day, C T is the transformer capacity, C TF is the remaining capacity of the transformer;

[0023] The SOC dynamic constraint of the energy storage system is:

[0024]

[0025] Among them: SOC min is the minimum SOC discharge threshold for energy storage, SOC max is the maximum SOC discharge threshold of energy storage, η s (t) is the charge and discharge efficiency, C s is the capacity of the energy storage system;

[0026] The number of energy storage charge and discharge times in one day is constrained as follows:

[0027]

[0028] in: is the number of daily cycles of the energy storage system, DOD is the depth of discharge of the battery, C s is the capacity of the energy storage system;

[0029] The capacity constraint of the energy storage system is:

[0030]

[0031] Where: T = 1 / V, V is the battery discharge rate.

[0032] In some embodiments, solving the mathematical model of the transformer load rate and peak-valley revenue in the substation based on the constraint condition includes:

[0033] Step 1: Based on the historical data of power load, meteorological data, real-time electricity prices and weekday / weekend information, the 24-hour load of the next day is predicted through the long-short term memory (LSTM) network model to obtain the P L (t), where t∈{1, 2, ..., 24}; Step 2: S (t), t∈{1, 2, ..., 24} is set as the decision variable of the objective function, 100 particle swarms with 24 decision variables and parameters are initialized, and the boundary conditions of the 24 decision variables are initialized according to the constraint condition, and the number of initialization iterations t is set to 0; Step 3: Let the local optimal particle P best = Initial fitness value P t , the fitness value of the initial particle group is calculated according to the objective function of minimizing the daily average load rate of the transformer and maximizing the peak-valley arbitrage profit, and the non-dominated solutions are sorted to obtain the non-dominated solutions, and an external archive Archive is established, which contains all candidate non-dominated solutions; the fourth step: select the global optimal particle g from the Archive through the roulette strategy best ; Step 5: Get the next generation particle position update and velocity update; Step 6: Merge historical archives and update according to the P of the t+1 generation t+1 Get a new Archive; Step 7: Combine the new Archive with the P t+1 Compare the dominance relationship and update P best ; Step 8: Determine whether t+1 reaches the maximum number of iterations. When t+1 reaches the maximum number of iterations, output the new Archive, which is the obtained Pareto optimal solution set and the target next-day charging and discharging strategy; when t+1 does not reach the maximum number of iterations, return to the fourth step.

[0034] Furthermore, after obtaining the target next-day charging and discharging strategy, the substation energy storage system can be controlled in real time to achieve smooth regulation of power load and maximization of peak and valley electricity price benefits.

[0035] According to a second aspect of an embodiment of the present disclosure, a multi-objective optimization and energy storage device next-day charging and discharging strategy optimization device is provided, comprising a memory and a processor. The memory has a stored program. When the program is executed in the processor, the processor is used to execute the method of the first aspect and any embodiment of the first aspect.

[0036] Based on the above equipment, the problems of large load fluctuations and low peak-valley electricity price benefits in the power system of the substation can be solved. By introducing the MOPSO multi-objective optimization algorithm, the optimization of the charging and discharging strategy of the substation energy storage system can be realized, which can not only improve the flexibility and responsiveness of the power system, but also effectively reduce the transformer load rate, improve energy utilization efficiency, increase peak-valley electricity price benefits, and realize the smooth regulation of the next day's power load and maximize the peak-valley electricity price benefits. It has important practical application value and promotion prospects.

[0037] According to the third aspect of the embodiment of the present disclosure, a multi-objective optimization and next-day charging and discharging strategy optimization device for an energy storage device is provided, including: a construction module for constructing a mathematical model of the load rate of the transformer in the substation and the peak-valley profit; a determination module for determining constraints, which include a daily charging and discharging energy balance constraint, a tie line power balance constraint, an energy storage charging and discharging power constraint, an energy storage system SOC dynamic constraint, an energy storage charging and discharging times constraint within a day, and an energy storage system capacity constraint; a solution module for solving the mathematical model of the load rate of the transformer in the substation and the peak-valley profit based on the constraints to obtain a target next-day charging and discharging strategy.

[0038] In some embodiments, the construction module is specifically used to construct an objective function for minimizing the daily average load rate of the transformer and an objective function for maximizing the peak-valley arbitrage profit, and the objective function for minimizing the daily average load rate of the transformer and the objective function for maximizing the peak-valley arbitrage profit are dynamically optimized simultaneously, wherein:

[0039] The objective function for minimizing the daily average load rate of the transformer is defined as:

[0040]

[0041] in: is the daily load rate of the transformer. The smaller the value, the lower the load rate. T (t) is the actual average apparent power of the transformer during period t on the next day, P T (t) is the actual average active power of the transformer during period t on the next day, P L (t) is the actual load forecast power in the next day’s period t, P S (t) is the energy storage charging and discharging power during the next day t period, Cosθ(t) is the load power factor during the next day t period, C T is the rated capacity of the transformer, η S (t) is the energy storage charging and discharging efficiency in period t of the next day, LR(t) is the transformer load rate in period t, P L (t) is the actual load forecast power in the next day t period, T is the total number of time steps, where:

[0042]

[0043] The peak-to-valley arbitrage profit maximization objective function is defined as:

[0044]

[0045]

[0046] in: is the peak-to-valley arbitrage income of the next day, P EL (t) is the peak-valley-average electricity price at different times of the day, η S (t) is the energy storage charging and discharging efficiency during the next day t period, η ch is the charging efficiency of the energy storage system, η disch is the discharge efficiency of the energy storage system.

[0047] In some embodiments, the one-day charge and discharge energy balance constraint is:

[0048]

[0049] The tie line power balance constraint is:

[0050] S T (t)*Cosθ=P L (t)-P S (t)

[0051] The energy storage charging and discharging power constraints are:

[0052]

[0053] Among them: When P S When (t)>0, When P S When (t)<0, is the rated charge and discharge power of energy storage, P Lmax is the maximum load of the next day, C T is the transformer capacity, C TF is the remaining capacity of the transformer;

[0054] The SOC dynamic constraint of the energy storage system is:

[0055]

[0056] Among them: SOC min is the minimum SOC discharge threshold for energy storage, SOC max is the maximum SOC discharge threshold of energy storage, η s (t) is the charge and discharge efficiency, C s is the capacity of the energy storage system;

[0057] The number of energy storage charge and discharge times in one day is constrained as follows:

[0058]

[0059] in: is the number of daily cycles of the energy storage system, DOD is the depth of discharge of the battery, C s is the capacity of the energy storage system;

[0060] The capacity constraint of the energy storage system is:

[0061]

[0062] Where: T = 1 / V, V is the battery discharge rate.

[0063] In some embodiments, the solution module specifically includes: a prediction submodule for predicting the load for the next 24 hours based on the historical data of power load, meteorological data, real-time electricity price and weekday / weekend information through a long short-term memory network LSTM network model to obtain the P L (t), where t∈{1, 2, ..., 24}; an initialization module is used to convert P S (t), t∈{1, 2, ..., 24} is set as the decision variable of the objective function, 100 particle swarms with 24 decision variables and parameters are initialized, and the boundary conditions of the 24 decision variables are initialized according to the constraint conditions, and the number of initialization iterations t is set to 0; the calculation submodule is used to make the local optimal particle P best = Initial fitness value P t , the fitness value of the initial particle swarm is calculated according to the objective function of minimizing the daily average load rate of the transformer and maximizing the peak-valley arbitrage profit, and the non-dominated solutions are sorted to obtain the non-dominated solutions, and an external archive Archive is established, which contains all candidate non-dominated solutions; the selection submodule is used to select the global optimal particle g from the Archive through the roulette strategy best ; The first acquisition submodule is used to obtain the next generation of particle position updates and velocity updates; the second acquisition submodule is used to merge historical archives and update the position and velocity of the next generation of particles according to the P of the t+1 generation t+1 Get a new Archive; update the submodule to associate the new Archive with the P t+1 Compare the dominance relationship and update P best ; A judgment submodule is used to judge whether t+1 reaches the maximum number of iterations. When t+1 reaches the maximum number of iterations, the new Archive is output, and the new Archive is the obtained Pareto optimal solution set and the target next-day charging and discharging strategy; when t+1 does not reach the maximum number of iterations, return to the fourth step.

[0064] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, characterized in that the computer program storage medium has program instructions, and when the program instructions are executed by a processor, the processor executes the method of the first aspect and any embodiment of the first aspect.

[0065] According to the fifth aspect of the embodiments of the present disclosure, a chip system is provided, characterized in that the chip system includes at least one processor, and when the program instructions are executed in the at least one processor, the at least one processor executes the method of the first aspect and any embodiment of the first aspect.

[0066] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0068] Figure 1 It is a schematic diagram of a multi-objective optimization and next-day charging and discharging strategy optimization method 100 for an energy storage device provided in an embodiment of the present disclosure;

[0069] Figure 2 It is a flow chart of a multi-objective optimization and next-day charging and discharging strategy dynamic optimization method 200 of an energy storage device provided in an embodiment of the present disclosure;

[0070] Figure 3 It is a structural diagram of a multi-objective optimization and energy storage device next-day charging and discharging strategy optimization device 300 provided in an embodiment of the present disclosure;

[0071] Figure 4 It is a structural diagram of a multi-objective optimization and energy storage device next-day charging and discharging strategy optimization device 400 provided in an embodiment of the present disclosure;

[0072] Figure 5 It is another structural diagram of a multi-objective optimization and energy storage device next-day charging and discharging strategy optimization device 400 provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0073] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0074] At present, with the rapid development of renewable energy technology and the in-depth promotion of smart grids, the flexibility and efficiency of energy systems have become the focus of the power industry. Especially at the substation (low-voltage distribution) level, how to effectively use the substation energy storage system to achieve load balancing, increase the local consumption of new energy, reduce the load rate of transformers, and maximize the benefits of peak and valley electricity prices has become an urgent problem to be solved in the current low-voltage substation.

[0075] In traditional power systems, changes in the load rate of power substations are often affected by a variety of factors, such as user electricity usage habits, weather conditions, equipment failures, etc. These factors lead to significant volatility and uncertainty in the load rate of power substations, which poses a challenge to the stable operation of the power system. At the same time, the implementation of the peak-valley electricity price system, although it helps to guide users to use electricity rationally and reduce the pressure on the power grid, also requires the power system to have greater flexibility and responsiveness.

[0076] In view of this, the present disclosure provides a multi-objective optimization method and a next-day charging and discharging strategy optimization method for an energy storage device, which can improve the above-mentioned problems and enhance the user experience.

[0077] The disclosed embodiment provides a multi-objective optimization method 100 for optimizing the next-day charging and discharging strategy of an energy storage device. Figure 1 As shown, the multi-objective optimization and energy storage device next day charging and discharging strategy optimization method 100 includes the following steps:

[0078] S110, construct a mathematical model of transformer load factor and peak-valley benefits in the substation area.

[0079] For example, a mathematical model of transformer load rate and peak-valley profit in a substation can be constructed by constructing an objective function for minimizing the daily average load rate of the transformer and an objective function for maximizing the peak-valley arbitrage profit, wherein the objective function for minimizing the daily average load rate of the transformer and the objective function for maximizing the peak-valley arbitrage profit are dynamically optimized at the same time.

[0080] For example, the objective function for minimizing the daily average load factor of the transformer can be defined as:

[0081]

[0082] in: is the daily load rate of the transformer. The smaller the value, the lower the load rate. T (t) is the actual average apparent power of the transformer during period t on the next day, P T (t) is the actual average active power of the transformer during period t on the next day, P L (t) is the actual load forecast power in the next day’s period t, P S (t) is the energy storage charging and discharging power during the next day t period, Cosθ(t) is the load power factor during the next day t period, C Tis the rated capacity of the transformer, η s (t) is the energy storage charging and discharging efficiency in period t of the next day, LR(t) is the transformer load rate in period t, P L (t) is the actual load forecast power in the next day t period, T is the total number of time steps (for example, one hour), in this embodiment T = 24, where:

[0083]

[0084] The peak-to-valley arbitrage profit maximization objective function can be defined as:

[0085]

[0086]

[0087] in: is the peak-to-valley arbitrage income of the next day, P EL (t) is the peak-valley-average electricity price at different times of the day, η S (t) is the energy storage charging and discharging efficiency during the next day t period, η ch is the charging efficiency of the energy storage system, η disch is the discharge efficiency of the energy storage system.

[0088] S120, determining constraint conditions.

[0089] Among them, the constraints include daily charging and discharging energy balance constraints, interconnection line power balance constraints, energy storage charging and discharging power constraints, energy storage system SOC dynamic constraints, energy storage charging and discharging times constraints within a day, and energy storage system capacity constraints.

[0090] For example:

[0091] The energy balance constraint for one day’s charge and discharge is:

[0092]

[0093] The tie line power balance constraint is:

[0094] S T (t)*Cosθ=P L (t)-P S (t)

[0095] That is, at any time t, the sum of the load power and the charging and discharging power should be equal to the actual output active power of the transformer.

[0096] The energy storage charging and discharging power constraints are:

[0097]

[0098] Among them: When P SWhen (t)>0, When P S When (t)<0, therefore, is the rated charge and discharge power of energy storage, P Lmax is the maximum load of the next day, C T is the transformer capacity, C TF is the remaining capacity of the transformer.

[0099] The SOC dynamic constraint of the energy storage system is:

[0100]

[0101] Among them: SOC min is the minimum SOC discharge threshold for energy storage, SOC max is the maximum SOC discharge threshold of energy storage, η s (t) is the charge and discharge efficiency, C s is the capacity of the energy storage system.

[0102] That is, the change in SOC is determined by the charge and discharge power and efficiency (simplified here without considering efficiency loss).

[0103] The number of energy storage charges and discharges in one day is constrained to:

[0104]

[0105] in: is the number of daily cycles of the energy storage system, DOD is the depth of discharge of the battery, Cs is the capacity of the energy storage system;

[0106] The capacity constraint of the energy storage system is:

[0107]

[0108] Where: T = 1 / V, V is the battery discharge rate.

[0109] S130, solving the mathematical model of transformer load rate and peak-valley benefit in the substation area based on the constraint conditions to obtain the target next-day charging and discharging strategy.

[0110] After defining the objective function and constraints, we can use the Multi-objective particle swarm optimization (MOPSO) algorithm to solve this problem. MOPSO is a variant of the particle swarm optimization (PSO) algorithm. It uses an external population archive to store all current non-dominated solutions, and regards individuals in the external archive as elite individuals. It controls the direction of population evolution through elite individuals and guides the population to approach the real Pareto frontier. After the algorithm runs, the particles in the external archive are used as the approximation of the obtained Pareto optimal solution.

[0111] In this problem, each particle will represent a possible solution, where the 24 decision variables of each particle represent the charge and discharge values ​​of each hourly segment in a day. The algorithm will optimize these solutions through multiple iterations to find the solution set that achieves the best balance between minimizing the average load factor of the transformer in 24 hours and maximizing the peak-valley arbitrage profit in the next day.

[0112] For example, this step can be implemented by the following method:

[0113] Step 1: Based on the historical data of power load, meteorological data, real-time electricity prices, and weekday / weekend information, the LSTM network model is used to predict the load for the next 24 hours and obtain P L (t), where t∈{1, 2, ..., 24};

[0114] Step 2: P S (t), t∈{1, 2, ..., 24} is set as the decision variable of the objective function, 100 particle swarms (position and speed) and parameters with a scale of 24 decision variables are initialized, and the boundary conditions of the 24 decision variables are initialized according to the constraints involved in this embodiment, and the number of initialization iterations t is set to 0;

[0115] Step 3: Let the local optimal particle P best = Initial fitness value P t , the fitness value of the initial particle swarm is calculated according to the objective function of minimizing the daily average load rate of the transformer and maximizing the peak-valley arbitrage profit, and the non-dominated solutions are sorted to obtain the non-dominated solutions, and an external archive is established, which contains all candidate non-dominated solutions;

[0116] Step 4: Select the global optimal particle g from Archive using the roulette strategy best ;

[0117] Step 5: Execute the operation of PSO particle swarm to obtain the position update and velocity update of the next generation of particles;

[0118] Step 6: Merge historical archives and calculate the P of the t+1th generation t+1 Get a new Archive;

[0119] Step 7: Link the new Archive to P t+1 Compare the dominance relationship and update P best ;

[0120] Step 8: Determine whether t+1 has reached the maximum number of iterations. When t+1 has reached the maximum number of iterations, output a new Archive, which is the Pareto optimal solution set obtained, that is, the target next-day charging and discharging strategy; when t+1 has not reached the maximum number of iterations, return to step 4.

[0121] Furthermore, after obtaining the target next-day charging and discharging strategy, the substation energy storage system can be controlled in real time to achieve smooth regulation of power load and maximization of peak and valley electricity price benefits.

[0122] For example, Figure 2 It is a flow chart of a multi-objective optimization and dynamic optimization method 200 of the next-day charging and discharging strategy of an energy storage device provided in an embodiment of the present disclosure.

[0123] Based on the above scheme, the disclosed embodiment first analyzes the changing rules and characteristics of the substation load according to the historical data of the substation load rate. Then, combined with technical parameters such as transformer capacity, energy storage capacity, maximum charge and discharge power of energy storage, energy storage SOC state, and peak-valley flat electricity price, with minimization of transformer load rate and maximization of peak-valley profit as optimization goals, a mathematical model of substation transformer load rate and peak-valley profit is constructed. Next, the MOPSO multi-objective optimization algorithm is used to solve the hourly charge and discharge plan of the energy storage system for the next day, and the charge and discharge strategy of the energy storage system is optimized, with the goal of minimizing the transformer load rate and maximizing the peak-valley profit, to generate the hourly charge and discharge plan for the next day. Finally, according to the optimization results, the substation energy storage system is controlled in real time.

[0124] Furthermore, the disclosed embodiments are intended to solve the problems of large load fluctuations and low peak-valley electricity price benefits in the power system of the substation. By introducing the MOPSO multi-objective optimization algorithm, the optimization of the charging and discharging strategy of the substation energy storage system can be achieved, which can not only improve the flexibility and responsiveness of the power system, but also effectively reduce the transformer load rate, improve energy utilization efficiency, increase peak-valley electricity price benefits, and achieve smooth regulation of the next day's power load and maximize the peak-valley electricity price benefits, which has important practical application value and promotion prospects.

[0125] Based on the above Figure 1The multi-objective optimization and next-day charging and discharging strategy optimization method for the energy storage device described in the corresponding embodiment is an embodiment of the device disclosed herein, which can be used to execute the method embodiment of the present disclosure.

[0126] The embodiment of the present disclosure provides a multi-objective optimization and energy storage device next day charging and discharging strategy optimization device 300, such as Figure 3 The multi-objective optimization and energy storage device next day charging and discharging strategy optimization device 300 includes: a memory 301 and a processor 302 .

[0127] The memory 301 is used to store programs.

[0128] When the program is executed in the processor 302, the processor 302 is used to execute the above-mentioned multi-objective optimization and energy storage device next-day charging and discharging strategy optimization method.

[0129] Processor 302 is used to: construct a mathematical model of the transformer load rate and peak-valley benefits in the substation area; determine constraints, which include daily charging and discharging energy balance constraints, interconnection line power balance constraints, energy storage charging and discharging power constraints, energy storage system state of charge SOC dynamic constraints, energy storage charging and discharging times in a day constraints, and energy storage system capacity constraints; solve the mathematical model of the transformer load rate and peak-valley benefits in the substation area based on the constraints to obtain a target next-day charging and discharging strategy.

[0130] Optionally, the processor 302 is specifically used to: construct an objective function for minimizing the daily average load rate of the transformer and an objective function for maximizing the peak-valley arbitrage profit, and the objective function for minimizing the daily average load rate of the transformer and the objective function for maximizing the peak-valley arbitrage profit are dynamically optimized simultaneously, wherein:

[0131] The transformer daily average load rate minimization objective function is defined as:

[0132]

[0133] in: is the daily load rate of the transformer. The smaller the value, the lower the load rate. T (t) is the actual average apparent power of the transformer during period t on the next day, P T (t) is the actual average active power of the transformer during period t on the next day, P L (t) is the actual load forecast power in the next day’s period t, P S (t) is the energy storage charging and discharging power during the next day t period, Cosθ(t) is the load power factor during the next day t period, C T is the rated capacity of the transformer, η s (t) is the energy storage charging and discharging efficiency in period t of the next day, LR(t) is the transformer load rate in period t, P L(t) is the actual load forecast power in the next day t period, T is the total number of time steps, where:

[0134]

[0135] The peak-to-valley arbitrage profit maximization objective function is defined as:

[0136]

[0137] in: is the peak-to-valley arbitrage income of the next day, P EL (t) is the peak-valley-average electricity price at different times of the day, η S (t) is the energy storage charging and discharging efficiency during the next day t period, η ch is the charging efficiency of the energy storage system, η disch is the discharge efficiency of the energy storage system.

[0138] Optionally, the one-day charge and discharge energy balance constraint is:

[0139]

[0140] The tie line power balance constraint is:

[0141] S T (t)*Cosθ=P L (t)-P S (t)

[0142] The energy storage charging and discharging power constraint is:

[0143]

[0144] Among them: When P S When (t)>0, When P S When (t)<0, is the rated charge and discharge power of energy storage, P Lmax is the maximum load of the next day, C T is the transformer capacity, C TF is the remaining capacity of the transformer;

[0145] The energy storage system SOC dynamic constraint is:

[0146]

[0147] Among them: SOC min is the minimum SOC discharge threshold for energy storage, SOC max is the maximum SOC discharge threshold of energy storage, η s (t) is the charge and discharge efficiency, Cs is the capacity of the energy storage system;

[0148] The energy storage charge and discharge times in one day are constrained as follows:

[0149]

[0150] in: is the number of daily cycles of the energy storage system, DOD is the depth of discharge of the battery, C s is the capacity of the energy storage system;

[0151] The energy storage system capacity constraint is:

[0152]

[0153] Where: T = 1 / V, V is the battery discharge rate.

[0154] Optionally, the processor 302 is specifically used for: Step 1: Based on the historical data of power load, meteorological data, real-time electricity price and weekday / weekend information, the 24-hour load of the next day is predicted by the long short-term memory network LSTM network model to obtain the P L (t), where t∈{1, 2, ..., 24};

[0155] Step 2: The P S (t), t∈{1, 2, ..., 24} is set as the decision variable of the objective function, 100 particle swarms and parameters with a scale of 24 decision variables are initialized, and the boundary conditions of the 24 decision variables are initialized according to the constraints, and the number of initialization iterations t is set to 0;

[0156] Step 3: Let the local optimal particle P best = Initial fitness value P t , calculating the fitness value of the initial particle swarm according to the objective function of minimizing the daily average load rate of the transformer and the objective function of maximizing the peak-valley arbitrage profit, sorting the non-dominated solutions to obtain the non-dominated solutions, and establishing an external archive, which contains all candidate non-dominated solutions;

[0157] Step 4: Select the global optimal particle gbes from the Archive using the roulette strategy t ;

[0158] Step 5: Get the next generation of particle position updates and velocity updates;

[0159] Step 6: Merge historical archives and calculate the P of the t+1th generation t+1 Get a new Archive;

[0160] Step 7: Associate the new Archive with the P t+1 Compare the dominance relationship and update P best ;

[0161] Step 8: Determine whether the t+1 reaches the maximum number of iterations. When the t+1 reaches the maximum number of iterations, output the new Archive, which is the obtained Pareto optimal solution set and the target next-day charging and discharging strategy; when the t+1 does not reach the maximum number of iterations, return to the fourth step.

[0162] The multi-objective optimization and energy storage device next-day charging and discharging strategy optimization equipment provided by the disclosed embodiment can solve the problems of large load fluctuations and low peak-valley electricity price benefits in the power system of the substation. By introducing the MOPSO multi-objective optimization algorithm, the charging and discharging strategy of the substation energy storage system can be optimized, which can not only improve the flexibility and responsiveness of the power system, but also effectively reduce the transformer load rate, improve energy utilization efficiency, increase peak-valley electricity price benefits, and achieve smooth regulation of the next-day power load and maximization of peak-valley electricity price benefits, which has important practical application value and promotion prospects.

[0163] Based on the above Figure 1 The multi-objective optimization and next-day charging and discharging strategy optimization method for the energy storage device described in the corresponding embodiment is an embodiment of the device disclosed herein, which can be used to execute the method embodiment of the present disclosure.

[0164] Based on the above Figure 1 In accordance with the multi-objective optimization method and the next-day charging and discharging strategy optimization method for energy storage devices described in the corresponding embodiments, the disclosed embodiments also provide a multi-objective optimization device and the next-day charging and discharging strategy optimization device 400, such as Figure 4 shown.

[0165] The multi-objective optimization and energy storage device next day charging and discharging strategy optimization device 400 includes:

[0166] A construction module 410 is used to construct a mathematical model of transformer load factor and peak-valley revenue in the substation area;

[0167] Determination module 420, used to determine constraints, which include daily charge and discharge energy balance constraints, tie line power balance constraints, energy storage charge and discharge power constraints, energy storage system SOC dynamic constraints, energy storage charge and discharge times constraints within a day, and energy storage system capacity constraints;

[0168] The solution module 430 is used to solve the mathematical model of the transformer load rate and peak-valley revenue in the substation based on the constraint condition to obtain the target next-day charging and discharging strategy.

[0169] Optionally, the construction module 410 is specifically used to construct an objective function for minimizing the daily average load rate of the transformer and an objective function for maximizing the peak-valley arbitrage profit. The objective function for minimizing the daily average load rate of the transformer and the objective function for maximizing the peak-valley arbitrage profit are dynamically optimized simultaneously, wherein:

[0170] The objective function for minimizing the daily average load rate of the transformer is defined as:

[0171]

[0172] in: is the daily load rate of the transformer. The smaller the value, the lower the load rate. T (t) is the actual average apparent power of the transformer during period t on the next day, P T (t) is the actual average active power of the transformer during period t on the next day, P L (t) is the actual load forecast power in the next day’s period t, P S (t) is the energy storage charging and discharging power during the next day t period, Cosθ(t) is the load power factor during the next day t period, C T is the rated capacity of the transformer, η S (t) is the energy storage charging and discharging efficiency in period t of the next day, LR(t) is the transformer load rate in period t, P L (t) is the actual load forecast power in the next day t period, T is the total number of time steps, where:

[0173]

[0174] The peak-to-valley arbitrage profit maximization objective function is defined as:

[0175]

[0176] in: is the peak-to-valley arbitrage income of the next day, P EL (t) is the peak-valley-average electricity price at different times of the day, η S (t) is the energy storage charging and discharging efficiency during the next day t period, η ch is the charging efficiency of the energy storage system, η disch is the discharge efficiency of the energy storage system.

[0177] Optionally, the one-day charge and discharge energy balance constraint is:

[0178]

[0179] The tie line power balance constraint is:

[0180] S T (t)*Cosθ=P L (t)-P S(t)

[0181] The energy storage charging and discharging power constraint is:

[0182]

[0183] Among them: When P S When (t)>0, When P S When (t)<0, is the rated charge and discharge power of energy storage, P Lmax is the maximum load of the next day, C T is the transformer capacity, C TF is the remaining capacity of the transformer;

[0184] The energy storage system SOC dynamic constraint is:

[0185]

[0186] Among them: SOC min is the minimum SOC discharge threshold for energy storage, SOC max is the maximum SOC discharge threshold of energy storage, η s (t) is the charge and discharge efficiency, C s is the capacity of the energy storage system;

[0187] The energy storage charge and discharge times in one day are constrained as follows:

[0188]

[0189] in: is the number of daily cycles of the energy storage system, DOD is the depth of discharge of the battery, C s is the capacity of the energy storage system;

[0190] The energy storage system capacity constraint is:

[0191]

[0192] Where: T = 1 / V, V is the battery discharge rate.

[0193] Alternatively, if Figure 5 As shown, the solution module 430 specifically includes: a prediction submodule 4301, which is used to predict the load for the next 24 hours based on the historical data of power load, meteorological data, real-time electricity price and weekday / weekend information through the long short-term memory network LSTM network model to obtain the P L (t), where t∈{1, 2, ..., 24}; initialization module 4302 is used to S(t), t∈{1, 2, ..., 24} is set as the decision variable of the objective function, 100 particle swarms with 24 decision variables and parameters are initialized, and the boundary conditions of the 24 decision variables are initialized according to the constraint condition, and the number of initialization iterations t is set to 0; the calculation submodule 4303 is used to make the local optimal particle P best = Initial fitness value P t , the fitness value of the initial particle group is calculated according to the objective function of minimizing the daily average load rate of the transformer and maximizing the peak-valley arbitrage profit, and the non-dominated solutions are sorted to obtain non-dominated solutions, and an external archive Archive is established, which contains all candidate non-dominated solutions; the selection submodule 4304 is used to select the global optimal particle g from the Archive through the roulette strategy best ; The first acquisition submodule 4305 is used to obtain the next generation particle position update and speed update; the second acquisition submodule 4306 is used to merge the historical archives and update the position and speed of the next generation particle according to the P of the t+1 generation t+1 Get a new Archive; Update submodule 4307, used to associate the new Archive with the P t+1 Compare the dominance relationship and update P best ; Judgment submodule 4308, used to judge whether t+1 reaches the maximum number of iterations. When t+1 reaches the maximum number of iterations, output the new Archive, which is the obtained Pareto optimal solution set and the target next-day charging and discharging strategy; when t+1 does not reach the maximum number of iterations, return to the fourth step.

[0194] Based on the above Figure 1 The multi-objective optimization and energy storage device next-day charging and discharging strategy optimization method described in the corresponding embodiment, the embodiment of the present disclosure also provides a computer-readable storage medium, for example, a non-temporary computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device. The storage medium stores computer instructions for executing the above Figure 1 The multi-objective optimization and next-day charging and discharging strategy optimization method of the energy storage device described in the corresponding embodiment will not be repeated here.

[0195] Based on the above Figure 1 The multi-objective optimization method and the next-day charging and discharging strategy optimization method of the energy storage device described in the corresponding embodiment, the embodiment of the present disclosure also provides a chip system, which includes at least one processor. When the program instruction is executed in the at least one processor, the at least one processor executes the above Figure 1The multi-objective optimization and next-day charging and discharging strategy optimization method of the energy storage device described in the corresponding embodiment will not be repeated here.

[0196] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0197] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A multi-objective optimization method and next-day charging and discharging strategy optimization method for energy storage devices, characterized in that: The method comprises: Construct a mathematical model of transformer load factor and peak-valley revenue in the substation area; Determine constraints, including daily charge and discharge energy balance constraints, tie line power balance constraints, energy storage charge and discharge power constraints, energy storage system state of charge SOC dynamic constraints, energy storage charge and discharge times constraints within a day, and energy storage system capacity constraints; Based on the constraint conditions, the mathematical model of the transformer load rate and peak-valley revenue in the substation area is solved to obtain the target next-day charging and discharging strategy.

2. The method according to claim 1, characterized in that The construction of the mathematical model of transformer load rate and peak-valley revenue in the substation area includes: The objective function of minimizing the daily average load rate of the transformer and the objective function of maximizing the peak-valley arbitrage profit are constructed, and the objective function of minimizing the daily average load rate of the transformer and the objective function of maximizing the peak-valley arbitrage profit are dynamically optimized simultaneously, wherein: The transformer daily average load rate minimization objective function is defined as: in: is the daily load rate of the transformer. The smaller the value, the lower the load rate. T (t) is the actual average apparent power of the transformer during period t on the next day, P T (t) is the actual average active power of the transformer during period t on the next day, P L (t) is the actual load forecast power in the next day’s period t, P S (t) is the energy storage charging and discharging power during the next day t period, Cosθ(t) is the load power factor during the next day t period, C T is the rated capacity of the transformer, η S (t) is the energy storage charging and discharging efficiency in period t of the next day, LR(t) is the transformer load rate in period t, PL(t) is the actual load forecast power in period t of the next day, and T is the total number of time steps, where: The peak-to-valley arbitrage profit maximization objective function is defined as: in: is the peak-to-valley arbitrage income of the next day, P EL (t) is the peak-valley-average electricity price at different times of the day, η S (t) is the energy storage charging and discharging efficiency during the next day t period, η ch is the charging efficiency of the energy storage system, η disch is the discharge efficiency of the energy storage system.

3. The method according to claim 1, characterized in that The one-day charge and discharge energy balance constraint is: The tie line power balance constraint is: S T (t)*Cosθ=P L (t)-P S (t) The energy storage charging and discharging power constraint is: Among them: When P S When (t)>0, When P S When (t)<0, is the rated charge and discharge power of energy storage, P Lmax is the maximum load of the next day, C T is the transformer capacity, C TF is the remaining capacity of the transformer; The energy storage system SOC dynamic constraint is: Among them: SOC min is the minimum SOC discharge threshold for energy storage, SOC max is the maximum SOC discharge threshold of energy storage, η s (t) is the charge and discharge efficiency, C s is the capacity of the energy storage system; The energy storage charge and discharge times in one day are constrained as follows: in: is the number of daily cycles of the energy storage system, DOD is the depth of discharge of the battery, C s is the capacity of the energy storage system; The energy storage system capacity constraint is: Where: T = 1 / V, V is the battery discharge rate.

4. The method according to claim 1, characterized in that: The method of solving the mathematical model of the transformer load rate and peak-valley benefit in the substation area based on the constraint conditions includes: Step 1: Based on the historical data of power load, meteorological data, real-time electricity price and weekday / weekend information, the 24-hour load of the next day is predicted by the long short-term memory network LSTM network model to obtain the PL(t), where t∈{1, 2, ..., 24}; Step 2: The P S (t), t∈{1, 2, ..., 24} is set as the decision variable of the objective function, 100 particle swarms and parameters with a scale of 24 decision variables are initialized, and the boundary conditions of the 24 decision variables are initialized according to the constraints, and the number of initialization iterations t is set to 0; Step 3: Let the local optimal particle P best = Initial fitness value P t , calculating the fitness value of the initial particle swarm according to the objective function of minimizing the daily average load rate of the transformer and the objective function of maximizing the peak-valley arbitrage profit, sorting the non-dominated solutions to obtain the non-dominated solutions, and establishing an external archive, which contains all candidate non-dominated solutions; Step 4: Select the global optimal particle g from the Archive using the roulette strategy best ; Step 5: Get the next generation of particle position updates and velocity updates; Step 6: Merge historical archives and calculate the P of the t+1th generation t+1 Get a new Archive; Step 7: Associate the new Archive with the P t+1 Compare the dominance relationship and update P best ; Step 8: Determine whether the t+1 reaches the maximum number of iterations. When the t+1 reaches the maximum number of iterations, the new Archive is output, where the new Archive is the obtained Pareto optimal solution set and the target next-day charging and discharging strategy. When the t+1 does not reach the maximum number of iterations, return to the fourth step.

5. A multi-objective optimization and energy storage device next day charging and discharging strategy optimization device, characterized in that: including memory and processor; The memory has a stored program; When the program is executed in the processor, the processor is configured to: Construct a mathematical model of transformer load factor and peak-valley revenue in the substation area; Determine constraints, including daily charge and discharge energy balance constraints, tie line power balance constraints, energy storage charge and discharge power constraints, energy storage system SOC dynamic constraints, energy storage charge and discharge times constraints within a day, and energy storage system capacity constraints; Based on the constraint conditions, the mathematical model of the transformer load rate and peak-valley revenue in the substation area is solved to obtain the target next-day charging and discharging strategy.

6. The device according to claim 5, characterized in that The processor is specifically used for: The objective function of minimizing the daily average load rate of the transformer and the objective function of maximizing the peak-valley arbitrage profit are constructed, and the objective function of minimizing the daily average load rate of the transformer and the objective function of maximizing the peak-valley arbitrage profit are dynamically optimized simultaneously, wherein: The transformer daily average load rate minimization objective function is defined as: in: is the daily load rate of the transformer. The smaller the value, the lower the load rate. T (t) is the actual average apparent power of the transformer during period t on the next day, P T (t) is the actual average active power of the transformer during period t on the next day, P L (t) is the actual load forecast power in the next day’s period t, P S (t) is the energy storage charging and discharging power during the next day t period, Cosθ(t) is the load power factor during the next day t period, C T is the rated capacity of the transformer, η S (t) is the energy storage charging and discharging efficiency in period t of the next day, LR(t) is the transformer load rate in period t, PL(t) is the actual load forecast power in period t of the next day, and T is the total number of time steps, where: The peak-to-valley arbitrage profit maximization objective function is defined as: in: is the peak-to-valley arbitrage income of the next day, P EL (t) is the peak-valley-average electricity price at different times of the day, η S (t) is the energy storage charging and discharging efficiency during the next day t period, η ch is the charging efficiency of the energy storage system, η disch is the discharge efficiency of the energy storage system.

7. The device according to claim 5, characterized in that The one-day charge and discharge energy balance constraint is: The tie line power balance constraint is: S T (t)*Cosθ=P L (t)-P S (t) The energy storage charging and discharging power constraint is: Among them: When P S When (t)>0, When P S When (t)<0, is the rated charge and discharge power of energy storage, P Lmax is the maximum load of the next day, C T is the transformer capacity, C TF is the remaining capacity of the transformer; The energy storage system SOC dynamic constraint is: Among them: SOC min is the minimum SOC discharge threshold for energy storage, SOC max is the maximum SOC discharge threshold of energy storage, η s (t) is the charge and discharge efficiency, C s is the capacity of the energy storage system; The energy storage charge and discharge times in one day are constrained as follows: in: is the number of daily cycles of the energy storage system, DOD is the depth of discharge of the battery, C s is the capacity of the energy storage system; The energy storage system capacity constraint is: Where: T = 1 / V, V is the battery discharge rate.

8. The device according to claim 5, characterized in that The processor is specifically used for: Step 1: Based on the historical data of power load, meteorological data, real-time electricity prices and weekday / weekend information, the LSTM network model is used to predict the load for the next 24 hours and obtain the P L (t), where t∈{1, 2, ..., 24}; Step 2: The P S (t), t∈{1, 2, ..., 24} is set as the decision variable of the objective function, 100 particle swarms and parameters with a scale of 24 decision variables are initialized, and the boundary conditions of the 24 decision variables are initialized according to the constraints, and the number of initialization iterations t is set to 0; Step 3: Let the local optimal particle P best = Initial fitness value P t , calculating the fitness value of the initial particle swarm according to the objective function of minimizing the daily average load rate of the transformer and the objective function of maximizing the peak-valley arbitrage profit, sorting the non-dominated solutions to obtain the non-dominated solutions, and establishing an external archive, which contains all candidate non-dominated solutions; Step 4: Select the global optimal particle gbes from the Archive using the roulette strategy t ; Step 5: Get the next generation of particle position updates and velocity updates; Step 6: Merge historical archives and calculate the P of the t+1th generation t+1 Get a new Archive; Step 7: Associate the new Archive with the P t+1 Compare the dominance relationship and update P best ; Step 8: Determine whether the t+1 reaches the maximum number of iterations. When the t+1 reaches the maximum number of iterations, the new Archive is output, where the new Archive is the obtained Pareto optimal solution set and the target next-day charging and discharging strategy. When the t+1 does not reach the maximum number of iterations, return to the fourth step.

9. A multi-objective optimization and energy storage device next day charging and discharging strategy optimization device, characterized in that: include: A construction module is used to construct a mathematical model of transformer load factor and peak-valley benefits in the substation area; A determination module is used to determine constraint conditions, wherein the constraint conditions include a daily charge and discharge energy balance constraint, a tie line power balance constraint, an energy storage charge and discharge power constraint, an energy storage system SOC dynamic constraint, an energy storage charge and discharge times constraint within a day, and an energy storage system capacity constraint; A solution module is used to solve the mathematical model of the transformer load rate and peak-valley revenue in the substation area based on the constraint conditions to obtain a target next-day charging and discharging strategy.

10. A computer-readable storage medium, characterized in that: The computer program storage medium has program instructions, and when the program instructions are executed by a processor, the processor is caused to execute the method according to any one of claims 1 to 4.

Images