Distributed power supply and energy storage joint operation optimization method and device

Abstract

The invention relates to the technical field of electric power markets, in particular to a distributed power supply and energy storage joint operation optimization method and device, which can construct a target function of distributed power supply and energy storage joint operation. And taking the charging duration and power of each energy storage time period as decision variables. And deploying a CMA-ES algorithm, generating an initial solution by applying distributed power supply output sample data, and performing mutation iteration to obtain a global optimal solution. According to the technical scheme disclosed by the invention, the uncertainty of the output of the distributed power supply is considered, the energy storage charging and discharging duration and the charging and discharging power are taken as decision variables, the real joint distribution of the output of the distributed power supply does not need to be known, a global optimal solution can be given by applying the CMA-ES algorithm, the benefit of system operation is improved, the new energy consumption level is improved, and the method is suitable for popularization and application. And the light and wind abandoning problem is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a method and device for optimizing the joint operation of distributed power sources and energy storage. Background Art

[0002] As the world actively responds to climate change and my country vigorously promotes its dual-carbon strategy, the proportion of renewable energy in the energy system continues to rise. With their significant advantages as clean and renewable, renewable energy sources such as solar and wind power have become a key force in the energy transition, playing a significant role in alleviating environmental pressures and reducing dependence on traditional fossil fuels.

[0003] However, the inherent intermittency and instability of renewable energy sources pose significant challenges to the safe and stable operation of the power system. For example, wind power is significantly affected by meteorological conditions, with variable wind direction and erratic wind speeds, making accurate prediction and effective control of wind power output difficult. Solar power, on the other hand, is highly dependent on sunlight conditions. The alternation of day and night and weather changes can cause significant fluctuations in photovoltaic power generation. This output uncertainty makes it easy for renewable energy sources to cause instability in overall grid power and frequency when integrated into the grid, leading to frequent wind and solar power curtailment. Data shows that in some areas with high installed capacity of renewable energy, wind and solar power curtailment rates have exceeded 10%. This has undoubtedly resulted in significant energy waste, severely impacting the overall efficiency of the energy system, and hindering the healthy and sustainable development of the renewable energy industry.

[0004] At the same time, with the continuous deepening of my country's electricity market reform, new market entities and operating models, represented by virtual power plants and energy storage power stations, have emerged. In actual market operations, distributed energy and energy storage often participate in market activities as an organic asset combination. Energy storage, especially electrochemical energy storage batteries, plays a key role in the power system due to its excellent response characteristics and strong ramping performance. On the one hand, it can effectively smooth the impact of fluctuations in distributed energy output on the power grid, ensuring stable operation of the power grid. On the other hand, during peak and off-peak periods, energy storage can flexibly absorb excess renewable energy power, storing it and releasing it when energy supply is insufficient, filling the power supply gap and optimizing the spatial and temporal allocation of power resources.

[0005] From the perspective of the actual operating logic of the power market, energy storage and distributed energy resources are price takers when making efficiency decisions and lack control over market prices. Furthermore, the uncertainty of distributed energy output makes energy storage charging and discharging planning extremely complex. How to scientifically and rationally plan energy storage charging and discharging within the decision-making period, fully leverage the regulatory role of energy storage, and improve the overall system operating efficiency and the level of new energy absorption has become a core issue that urgently needs to be addressed in current power market operations. Existing operational optimization methods have many shortcomings in dealing with the uncertainty of distributed energy output. Some methods rely on a priori assumptions about the probability distribution of new energy output. However, in actual applications, these assumptions often do not match reality, resulting in deviations between optimization results and actual needs, making it difficult to achieve efficient and stable operation of the power system and maximize economic benefits. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a method and device for optimizing the joint operation of distributed power sources and energy storage, so as to solve the above-mentioned problems in the prior art to a certain extent.

[0007] According to a first aspect of an embodiment of the present invention, a method for optimizing the joint operation of a distributed power source and energy storage is provided, comprising:

[0008] Construct an objective function for the joint operation of distributed power and energy storage based on the revenue of distributed power and energy storage, the cost of charging in each period, the operating cost of energy storage in each period, the maintenance cost of energy storage on the day, and the maintenance cost of distributed energy on the day.

[0009] Extract photovoltaic and wind power output samples of length L, substitute the output samples into the objective function, and generate L initial solutions using the CMA-ES algorithm; generate an initial covariance matrix with all elements equal to 1;

[0010] The initial solution is mutated and iterated using an iterative formula to obtain a set of solutions for each iteration; the iterative formula includes a covariance matrix, which is generated by generating the initial covariance matrix according to the evolutionary parameters; a reward function is set according to the overall expectation and the objective function; each element in the set of solutions obtained after the iteration is substituted into the reward function to obtain λ optimal solutions, where the value of λ is obtained according to the order of the covariance matrix of the iteration; after each iteration, the iterative formula is iteratively updated;

[0011] When the mutation iteration reaches the preset exit condition, the current λ optimal solutions are output as the global optimal solution; the global optimal solution includes the arrangement of the charging and discharging time and the charging and discharging power of each energy storage period.

[0012] Preferably, the generated initial solution constitutes a set of solutions: X0 = {x 0,1 ,x 0,2 ,…x0,L}, where element x 0,i represents the i-th initial solution;

[0013] The generated initial covariance matrix is: C0 = I, where I is an L × L matrix with all elements equal to 1;

[0014] The iteration formula is: t+1,i =μ t +δ t y i , where μ t is the set X of solutions for the tth iteration t The sample value obtained by averaging; δ t The overall step size of the tth iteration; y i Indicates that the sampling of the i-th solution is generated by the following distribution:

[0015] y i ~N(0,C t )

[0016] Among them, N(0,C t ) means the mean is 0 and the standard deviation is C t Normal distribution; C t is C0 according to the evolution parameter p C The covariance matrix generated at iteration t.

[0017] Preferably, the reward function is set as: Γ(ψ i )=E(F|Ψ i ), where E(F|ψ i ) represents the overall expectation based on the strategy; F is the objective function.

[0018] Preferably, for μ in the iterative formula t , updated by the following formula:

[0019]

[0020] Among them, α μ For μ t The learning rate is a network parameter;

[0021] For the δ in the iterative formula t and its evolution parameter p δ,t , updated by the following formula:

[0022]

[0023] Among them, p δ,t Indicates the step length δ at step t t The evolution parameter, α δRepresents the learning rate about the step size, which is a network parameter; d δ represents the damping coefficient of the step iteration; ‖·‖ represents the internal two-dimensional modulus.

[0024] Preferably, the covariance matrix C t The evolution parameter p C,t , updated according to the following formula:

[0025]

[0026] in, Indicates about p C,t The learning rate is a network parameter;

[0027] Covariance matrix C t Update according to the following formula:

[0028]

[0029] Among them, α Cλ and α C1 are two learning rates, both of which are network parameters; p C,t+1 T and p C,t+1 and y i,t+1 The vector transpose of .

[0030] Preferably, the method further comprises:

[0031] Based on the discharge power and discharge duration of energy storage in each time period, the discharge conversion efficiency of energy storage, the time-of-use electricity price in each time period, and the declared output of distributed energy by the operator in each time period, a profit function for distributed power generation and energy storage is constructed;

[0032] Based on the charging power and charging time of the energy storage in each period, the charging conversion efficiency of the energy storage, the time-of-use electricity price in each period, and the deviation between the operator's declared output and actual output of distributed energy in each period, a cost function for charging in each period is constructed;

[0033] Based on the energy storage operation cost coefficient, the calendar aging loss coefficient of energy storage in each period, and the charging power and discharging power of energy storage in each period, the operation cost function of energy storage in each period is constructed;

[0034] Construct the maintenance cost function of energy storage on that day based on the daily amortization coefficient of energy storage and the residual book value on that day;

[0035] According to the weighted average amortization coefficient of all distributed power units and the residual book value of all distributed power units on that day, the maintenance cost function of distributed energy on that day is constructed.

[0036] Preferably, the profit function of the distributed power supply and energy storage is constructed as follows:

[0037]

[0038] Among them, R t represents the income generated by energy storage discharge and new energy output in period t; ρ t represents the time-of-use electricity price during period t; represents the discharge power of the i-th energy storage in period t; represents the discharge conversion efficiency of energy storage i; represents the discharge duration of the i-th energy storage in period t; It represents the declared output of distributed energy by the operating entity during period t;

[0039] The cost function generated by charging in each period is constructed as follows:

[0040]

[0041] in, represents the cost of energy storage due to charging during period t; represents the charging power of the i-th energy storage in period t; represents the charging conversion efficiency of energy storage i; represents the charging time of the i-th energy storage in period t; It represents the deviation between the declared output and the actual output, which is considered as a random variable;

[0042] The operation cost function and maintenance cost function of energy storage in each period are constructed as follows:

[0043]

[0044] in, represents the operating cost of energy storage during period t; ρ es represents the energy storage operation cost coefficient; γ t represents the calendar aging loss coefficient of energy storage during period t; Indicates the maintenance cost of energy storage on that day; represents the daily amortization coefficient of energy storage i; represents the remaining book value of energy storage i on that day;

[0045] The maintenance cost function of distributed energy on the day is constructed as follows:

[0046]

[0047] in, Indicates the maintenance cost of distributed energy on that day; Represents the weighted average amortization coefficient of all distributed generation units; Represents the remaining book value of all distributed generation units on that day.

[0048] Preferably, the objective function for the joint operation of distributed power generation and energy storage is constructed as follows:

[0049]

[0050] Where T represents the total operating time of the day.

[0051] Preferably, the constraints of the objective function of the joint operation of the distributed power source and energy storage include: state of charge constraints, power balance constraints and power output constraints of the energy storage.

[0052] According to a second aspect of an embodiment of the present invention, there is provided a distributed power supply and energy storage joint operation optimization device, comprising:

[0053] A main controller, and a memory connected to the main controller;

[0054] The memory stores program instructions;

[0055] The main controller is used to execute program instructions stored in the memory and perform any of the above methods.

[0056] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0057] It is understandable that the technical solution shown in the present invention can construct an objective function for the joint operation of distributed power sources and energy storage; the charging time and power of energy storage in each period are used as decision variables. The CMA-ES algorithm is deployed, and the distributed power output sample data is used to generate an initial solution. After mutation iteration, the global optimal solution is obtained. The technical solution shown in the present invention takes into account the uncertainty of the distributed power output, and uses the energy storage charging and discharging time and charging and discharging power as decision variables. It does not need to know the true joint distribution of the distributed power output. The use of the CMA-ES algorithm can provide a global optimal solution, thereby improving the efficiency of system operation, improving the level of new energy consumption, and reducing the problem of abandoned solar and wind power.

[0058] 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 invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0060] Figure 1The figure is a schematic diagram of the steps of a method for optimizing the joint operation of distributed power sources and energy storage according to an exemplary embodiment. DETAILED DESCRIPTION

[0061] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0062] In one embodiment, Figure 1 This is a schematic diagram of the steps of a method for optimizing the joint operation of distributed power supply and energy storage according to an exemplary embodiment. Figure 1 , provides a distributed power supply and energy storage joint operation optimization method, including:

[0063] Step S11: Construct an objective function for the joint operation of distributed power and energy storage based on the revenue of distributed power and energy storage, the cost of charging in each time period, the operating cost of energy storage in each time period, the maintenance cost of energy storage on the day, and the maintenance cost of distributed energy on the day.

[0064] Step S12: intercept photovoltaic and wind power output samples of length L, bring the output samples into the objective function, generate L initial solutions using the CMA-ES algorithm; and generate an initial covariance matrix in which all elements are 1.

[0065] Step S13, using an iterative formula to mutate and iterate the initial solution to obtain a set of solutions for each iteration; the iterative formula includes a covariance matrix, which is generated by generating the initial covariance matrix according to the evolution parameters; setting a reward function according to the overall expectation and the objective function; substituting each element in the set of solutions obtained after the iteration into the reward function to obtain λ optimal solutions, and the value of λ is obtained according to the order of the covariance matrix of the iteration; after each iteration, the iterative formula will be iteratively updated.

[0066] Step S14: When the mutation iteration reaches a preset exit condition, the current λ optimal solutions are output as the global optimal solution; the global optimal solution includes the arrangement of the charging and discharging duration and the charging and discharging power of each energy storage period.

[0067] It can be understood that the technical solution shown in this embodiment can construct an objective function for the joint operation of distributed power sources and energy storage; the charging time and power of energy storage in each period are used as decision variables. The CMA-ES algorithm is deployed, and the distributed power output sample data is used to generate an initial solution. After mutation iteration, the global optimal solution is obtained. The technical solution shown in the present invention takes into account the uncertainty of the output of distributed power sources, and uses the charging and discharging time and charging and discharging power of energy storage as decision variables. It is not necessary to know the true joint distribution of the distributed power output. The use of the CMA-ES algorithm can provide a global optimal solution, thereby improving the efficiency of system operation, improving the level of new energy consumption, and reducing the problem of abandoned solar and wind power.

[0068] In actual practice, the main uncertainty of the system of joint operation of distributed energy and energy storage comes from the uncertainty of the output of distributed energy. Without considering the influence of other external factors, since energy storage needs to give priority to absorbing new energy, the charging and discharging arrangements of energy storage mainly depend on the output of distributed energy.

[0069] At the same time, since scheduling has certain response characteristic limitations, for the convenience of analysis, it is assumed that the time required for the energy storage charge and discharge cycle in each scheduling period or trading period is within a constant threshold.

[0070] Taking the state of charge as the core object of investigation, the state of charge of a single energy storage in each scheduling period is expressed according to the charge and discharge status as follows:

[0071]

[0072] In the above formula, SOC i,t represents the state of charge of the i-th energy storage at the end of period t; represents the charging power of the i-th energy storage in period t; represents the discharge power of the i-th energy storage in period t; and They represent the charging and discharging time of the i-th energy storage in period t respectively; represents the charging conversion efficiency of energy storage i; It represents the discharge conversion efficiency of energy storage i.

[0073] The charging and discharging times must comply with the following constraints:

[0074]

[0075] Among them, κ t Indicates the maximum response time interval set within the t period according to the scheduling response requirements.

[0076] Distributed energy includes photovoltaic and wind power. It is generally believed that the output probability of photovoltaic follows the Beta distribution, and the output of wind power follows the Weibull distribution. However, this prior distribution assumption often fails. At the same time, in decision-making, staff prefer to give their joint distribution.

[0077] The output of distributed energy can be expressed as:

[0078]

[0079] in, It represents the output of photovoltaic power in the system during period t; It represents the wind power output in the system during period t.

[0080] Part of the output of distributed energy is absorbed by the market, while the excess needs to be absorbed by energy storage. In the day-ahead phase, the operator submits a report based on the output forecast of renewable energy in each time period. The predicted output and actual output of distributed energy can be expressed as follows:

[0081]

[0082] in, It represents the declared output of distributed energy by the operating entity during period t; It represents the deviation between the declared output and the actual output, which is regarded as a random variable.

[0083] As mentioned above, distributed energy and energy storage are both price takers. Assuming that new energy is still consumed according to time-of-use electricity prices, energy storage charges incur costs by paying fees to the grid or other entities. Taking the charging duration as the independent variable, the cost incurred by charging in each period in step S11 can be expressed by the following cost function:

[0084]

[0085] in, represents the cost of energy storage due to charging during period t; represents the charging power of the i-th energy storage in period t; represents the charging conversion efficiency of energy storage i; represents the charging time of the i-th energy storage in period t; It represents the deviation between the declared output and the actual output, and is considered as a random variable.

[0086] At the same time, the external discharge of energy storage and the output of distributed energy will generate electricity sales revenue. In step S11, the revenue of distributed power supply and energy storage can be expressed by the following revenue function:

[0087]

[0088] Among them, R t represents the income generated by energy storage discharge and new energy output in period t; ρ t represents the time-of-use electricity price during period t; represents the discharge power of the i-th energy storage in period t; represents the discharge conversion efficiency of energy storage i; represents the discharge duration of the i-th energy storage in period t; It represents the declared output of distributed energy by the operating entity during period t.

[0089] In addition, energy storage will incur operation and maintenance costs during daily operation. These two costs are represented by the following cost functions:

[0090]

[0091] in, represents the operating cost of energy storage during period t; ρ es represents the energy storage operation cost coefficient; γ t represents the calendar aging loss coefficient of energy storage during period t; Indicates the maintenance cost of energy storage on that day; represents the daily amortization coefficient of energy storage i; It represents the remaining book value of energy storage i on that day.

[0092] Distributed energy generally does not incur operating costs when it is generating power, so only its intraday maintenance costs are considered, which can be expressed by the following cost function:

[0093]

[0094] in, Indicates the maintenance cost of distributed energy on that day; Represents the weighted average amortization coefficient of all distributed generation units; Represents the remaining book value of all distributed generation units on that day.

[0095] According to the above formulas, the objective function of the joint operation of distributed generation and energy storage is:

[0096]

[0097] Where T represents the total operating time of the day.

[0098] It should be noted that the constraints of the objective function of the joint operation of distributed power sources and energy storage include: state of charge constraints, power balance constraints and energy storage power output constraints.

[0099] The state of charge constraints are as follows:

[0100]

[0101] In the above formula, and They represent the minimum and maximum state of charge that the energy storage i should maintain for normal operation. At the same time, the state of charge of the energy storage should remain unchanged at the beginning and end of each working day, so:

[0102]

[0103] To maintain normal working conditions, the charging and discharging power of energy storage should meet the following constraints:

[0104]

[0105] From the system's perspective, power balance conditions must be met in each time period, specifically:

[0106]

[0107] After constructing the objective function for the joint operation of distributed power generation and energy storage, it is necessary to use the CMA-ES algorithm to solve the optimal operation plan.

[0108] Note that in the above objective function, the output of renewable energy includes the output of photovoltaic and wind power, and their respective outputs contain random variables; time-of-use electricity prices and energy storage performance parameters are all constant coefficients; the decision variables to be determined can be listed as the following multivariate sequence:

[0109] Ψ i =[K i ,P i ]

[0110] where K i Indicates the arrangement of charging and discharging time for each period of energy storage i; P i It represents the arrangement of charging and discharging power of energy storage i in each period; the two decision variable sequences can be refined as:

[0111]

[0112] As mentioned above, when using the CMA-ES algorithm (Covariance Matrix Adaptive Evolutionary Algorithm), it is not necessary to know the true joint distribution of the random variables.

[0113] First, step S12 is performed: photovoltaic and wind power output samples of length L are intercepted, the output samples are brought into the objective function, and L initial solutions are generated using the CMA-ES algorithm.

[0114] It should be noted that the set of solutions formed by the generated initial solution is:

[0115] X0={x 0,1 ,x 0,2 ,…x 0,L}

[0116] Among them, the element x 0,i represents the i-th initial solution.

[0117] The generated initial covariance matrix is:

[0118] C0=I

[0119] Where I is an L×L matrix with all elements set to 1.

[0120] Then the subsequent solutions are all mutated and iterated according to the following iterative formula:

[0121] x t+1,i =μ t +δ t y i

[0122] Among them, μ t is the set X of solutions for the tth iteration t The sample value obtained by averaging; δ t The overall step size of the tth iteration; y i Indicates that the sampling of the i-th solution is generated by the following distribution:

[0123] y i ~N(0,C t )

[0124] Among them, N(0,C t ) means the mean is 0 and the standard deviation is C t Normal distribution; C t is C0 according to the evolution parameter p C The covariance matrix generated at iteration t.

[0125] It should be noted that, considering the specific objective function form of the model, a reward function can be set for the algorithm network, specifically:

[0126] Γ(ψ i )=E(F|ψ i )

[0127] where E(F|ψ i ) represents the overall expectation based on the strategy; F is the objective function.

[0128] The set of solutions obtained after iteration X t+1 Substitute each element in the reward function, sort the results in order of high and low, and select the λ best solutions. The value of λ depends on C t The order of λ, for example, the value of λ is Ct The order of .

[0129] It should be noted that for μ in the iterative formula t , updated by the following formula:

[0130]

[0131] Among them, α μ For μ t The learning rate is a network parameter;

[0132] For the δ in the iterative formula t and its evolution parameter p δ,t , updated by the following formula:

[0133]

[0134] Among them, p δ,t Indicates the step length δ at step t t The evolution parameter, α δ Represents the learning rate about the step size, which is a network parameter; d δ represents the damping coefficient of the step iteration; ‖·‖ represents the internal two-dimensional modulus.

[0135] The covariance matrix C t The evolution parameter p C,t , updated according to the following formula:

[0136]

[0137] in, Indicates about p C,t The learning rate is a network parameter;

[0138] Covariance matrix C t Update according to the following formula:

[0139]

[0140] Among them, α Cλ and α c1 are two learning rates, both of which are network parameters; p C,t+1 T and p C,t+1 and y i,t+1 The vector transpose of .

[0141] Finally, when the mutation iteration reaches the preset exit condition, the current λ optimal solutions are output as the global optimal solution; the global optimal solution includes the arrangement of the charging and discharging time and the charging and discharging power of each energy storage period.

[0142] Since this algorithm selects λ optimal solutions in the offspring, it can avoid the local optimum that is easily caused by the elite strategy of other evolutionary algorithms.

[0143] The exit condition for a mutation iteration can be reaching the maximum number of iterations or comparing the difference between the currently generated optimal solution and the optimal solutions from several previous iterations. If the difference between these solutions in key decision variables (such as energy storage charge and discharge duration and charge and discharge power) is less than a set threshold, it means that the algorithm has difficulty finding a better solution, which can be used as an exit condition. Assuming the solution is a vector containing energy storage charge and discharge parameters, if the absolute value of the difference between the corresponding elements of the optimal solution vectors obtained from multiple consecutive iterations is less than 0.01, the exit condition is met.

[0144] In another embodiment, a distributed power supply and energy storage joint operation optimization device is provided, comprising:

[0145] A main controller, and a memory connected to the main controller;

[0146] The memory stores program instructions;

[0147] The main controller is used to execute program instructions stored in the memory and perform any of the above methods.

[0148] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0149] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0150] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0151] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0152] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0153] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0154] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0155] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0156] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing the joint operation of distributed power sources and energy storage, characterized in that: include: Construct an objective function for the joint operation of distributed power and energy storage based on the revenue of distributed power and energy storage, the cost of charging in each period, the operating cost of energy storage in each period, the maintenance cost of energy storage on the day, and the maintenance cost of distributed energy on the day. Extracting photovoltaic and wind power output samples of length L, substituting the output samples into the objective function, and generating L initial solutions using the CMA-ES algorithm; Generate an initial covariance matrix with all elements equal to 1; Performing mutation iteration on the initial solution using an iterative formula to obtain a set of solutions for each iteration; The iterative formula includes a covariance matrix, which is generated by generating the initial covariance matrix according to the evolution parameters; setting a reward function based on the overall expectation and the objective function; substituting each element in the set of solutions obtained after the iteration into the reward function to obtain λ optimal solutions, where the value of λ is obtained according to the order of the covariance matrix of the iteration; After each iteration, the iteration formula is updated iteratively; When the mutation iteration reaches the preset exit condition, the current λ optimal solutions are output as the global optimal solution; the global optimal solution includes the arrangement of the charging and discharging time and the charging and discharging power of each energy storage period.

2. The method according to claim 1, characterized in that The solution set of the generated initial solution is: X0={x 0,1 ,x 0,2 ,…x 0,L }, where element x 0,i represents the i-th initial solution; The generated initial covariance matrix is: C0 = I, where I is an L × L matrix with all elements equal to 1; The iteration formula is: t+1,i =μ t +δ t y i , where μ t is the set X of solutions for the tth iteration t The sample value obtained by averaging; δ t The overall step size of the tth iteration; y i Indicates that the sampling of the i-th solution is generated by the following distribution: y i ~N(0,C t ) Among them, N(0,C t ) means the mean is 0 and the standard deviation is C t Normal distribution; C t is C0 according to the evolution parameter p C The covariance matrix generated at iteration t.

3. The method according to claim 2, characterized in that The reward function is set as: Γ(ψ i )=E(F|ψ i ), where E(F|Ψ i ) represents the overall expectation based on the strategy; F is the objective function.

4. The method according to claim 3, characterized in that For μ in the iterative formula t , updated by the following formula: Among them, α μ For μ t The learning rate is a network parameter; For the δ in the iterative formula t and its evolution parameter p δ,t , updated by the following formula: Among them, p δ,t Indicates the step length δ at step t t The evolution parameter, α δ Represents the learning rate about the step size, which is a network parameter; d δ represents the damping coefficient of the step iteration; ‖·‖ represents the internal two-dimensional modulus.

5. The method according to claim 4, characterized in that The covariance matrix C t The evolution parameter p C,t , updated according to the following formula: in, Indicates about p C,t The learning rate is a network parameter; Covariance matrix C t Update according to the following formula: Among them, α Cλ and α C1 are two learning rates, both of which are network parameters; p C,t+1 T and p C,t+1 and y i,t+1 The vector transpose of .

6. The method according to claim 1, wherein Also includes: Based on the discharge power and discharge duration of energy storage in each time period, the discharge conversion efficiency of energy storage, the time-of-use electricity price in each time period, and the declared output of distributed energy by the operator in each time period, a profit function for distributed power generation and energy storage is constructed; Based on the charging power and charging time of the energy storage in each period, the charging conversion efficiency of the energy storage, the time-of-use electricity price in each period, and the deviation between the operator's declared output and actual output of distributed energy in each period, a cost function for charging in each period is constructed; Based on the energy storage operation cost coefficient, the calendar aging loss coefficient of energy storage in each period, and the charging power and discharging power of energy storage in each period, the operation cost function of energy storage in each period is constructed; Construct the maintenance cost function of energy storage on that day based on the daily amortization coefficient of energy storage and the residual book value on that day; According to the weighted average amortization coefficient of all distributed power units and the residual book value of all distributed power units on that day, the maintenance cost function of distributed energy on that day is constructed.

7. The method according to claim 6, characterized in that The constructed profit function of distributed power supply and energy storage is as follows: Among them, R t represents the income generated by energy storage discharge and new energy output in period t; ρ t represents the time-of-use electricity price during period t; represents the discharge power of the i-th energy storage in period t; represents the discharge conversion efficiency of energy storage i; represents the discharge duration of the i-th energy storage in period t; It represents the declared output of distributed energy by the operating entity during period t; The cost function generated by charging in each period is constructed as follows: in, represents the cost of energy storage due to charging during period t; represents the charging power of the i-th energy storage in period t; represents the charging conversion efficiency of energy storage i; represents the charging time of the i-th energy storage in period t; It represents the deviation between the declared output and the actual output, which is considered as a random variable; The operation cost function and maintenance cost function of energy storage in each period are constructed as follows: in, represents the operating cost of energy storage during period t; ρ es represents the energy storage operation cost coefficient; γ t represents the calendar aging loss coefficient of energy storage during period t; Indicates the maintenance cost of energy storage on that day; represents the daily amortization coefficient of energy storage i; represents the remaining book value of energy storage i on that day; The maintenance cost function of distributed energy on the day is constructed as follows: in, Indicates the maintenance cost of distributed energy on that day; Represents the weighted average amortization coefficient of all distributed generation units; Represents the remaining book value of all distributed generation units on that day.

8. The method according to claim 7, characterized in that The objective function for the joint operation of distributed power generation and energy storage is constructed as follows: Where T represents the total operating time of the day.

9. The method according to claim 8, characterized in that The constraints of the objective function of the joint operation of distributed power sources and energy storage include: state of charge constraints, power balance constraints and energy storage power output constraints.

10. A distributed power supply and energy storage joint operation optimization device, characterized in that: include: A main controller, and a memory connected to the main controller; The memory stores program instructions; The main controller is used to execute program instructions stored in the memory and perform the method according to any one of claims 1 to 9.

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