Photovoltaic absorption and electrochemical energy storage capacity optimization method and system

Abstract

The invention discloses a photovoltaic absorption and electrochemical energy storage capacity optimization method and system, and relates to the technical field of power system optimization, and the method comprises the steps: defining a target function, and determining an optimization direction; setting constraint conditions, and establishing a multi-objective optimization model; and carrying out model solving to find an optimal scheme meeting the objective function and the constraint condition. The method improves the energy utilization rate of the photovoltaic power station, reduces the light abandoning phenomenon, reduces the overall operation cost, improves the flexibility and reliability of the system, optimizes the configuration of the energy storage system, ensures the safe and stable operation of the power system, improves the photovoltaic consumption rate, optimizes the configuration of the energy storage capacity, and improves the energy utilization rate of the photovoltaic power station. The optimal capacity and power configuration is provided for the energy storage system, the total cost of the system is reduced, the operation efficiency of the system is improved, the effective balance of multi-objective optimization is realized, and the optimal compromise between the consumption rate and the economical efficiency of the power system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a method and system for optimizing photovoltaic consumption and electrochemical energy storage capacity. Background Art

[0002] With the transformation of the global energy structure and the popularization of clean energy, photovoltaic power generation, as one of the important renewable energy sources, has seen a rapid growth in installed capacity. However, the intermittent and volatile nature of photovoltaic power generation has brought huge challenges to the stable operation of the power grid and the absorption of electric energy. To solve this problem, electrochemical energy storage technology has been widely used in photovoltaic power generation systems. By smoothing out photovoltaic output fluctuations and adjusting load demand, the photovoltaic absorption rate can be improved to ensure the safe and stable operation of the power grid. In recent years, multi-objective optimization algorithms, such as the non-dominated sorting genetic algorithm (NSGA-II), have been widely used in the optimal configuration of energy storage capacity and have achieved remarkable results.

[0003] Although the existing technology has made certain progress, there are still some shortcomings. The existing optimization methods often only focus on a single goal, such as photovoltaic absorption rate or economy, and ignore other important factors, resulting in incomplete optimization schemes. Only considering the photovoltaic absorption rate may lead to excessive configuration of energy storage capacity and increase system costs, while only considering economy may lead to insufficient photovoltaic absorption rate and cause energy waste. Some existing methods fail to fully consider all relevant constraints when establishing optimization models, such as power balance constraints, unit output constraints, and spare capacity constraints, resulting in safety hazards in the actual application of the optimization scheme. For example, ignoring the power balance constraint may cause the system to have a power gap in some periods of time. Affects power supply reliability. Ignoring the unit output constraint may cause the unit to operate beyond the safe range and cause equipment damage. The optimization algorithms used in some existing methods are inefficient and take a long time to calculate, making it difficult to meet the needs of actual applications. When the system is large or there are many optimization targets, the long calculation time may cause the optimization solution to be unable to be applied to actual operation in a timely manner. Existing optimization methods usually only provide one optimal solution, but cannot provide multiple options, resulting in a lack of flexibility for decision makers and difficulty in making adjustments based on actual conditions. For example, in actual operation, due to factors such as load forecasting errors or fluctuations in new energy output, a single optimal solution may no longer be applicable, requiring decision makers to make adjustments based on actual conditions. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing electrochemical energy storage capacity optimization technology has a single goal, insufficient consideration of constraints, insufficient solution flexibility, and how to achieve a dual increase in photovoltaic absorption rate and economy to promote safe and stable operation of the system.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing photovoltaic absorption and electrochemical energy storage capacity, comprising: determining the optimization direction by defining an objective function; setting constraints and establishing a multi-objective optimization model; solving the model and finding the best solution that meets the objective function and constraints.

[0007] As a preferred solution of the photovoltaic absorption and electrochemical energy storage capacity optimization method described in the present invention, wherein: the definition of the objective function includes defining the photovoltaic absorption effect in a typical day, when the photovoltaic output exceeds the power load and the remaining energy storage capacity in the current period, the optimal objective function maxF1 of the photovoltaic absorption effect is calculated, which is expressed as:

[0008]

[0009] in, is the active power output of the photovoltaic power station at time t, T is the number of typical daily time periods, is the load that needs to be met by photovoltaic output at moment t.

[0010] As a preferred solution of the photovoltaic consumption and electrochemical energy storage capacity optimization method described in the present invention, wherein: the definition of the objective function also includes defining the economic optimum, and the operating cost of the thermal power unit is calculated based on the operating cost of the thermal power unit and the investment and maintenance cost of the energy storage system, which is expressed as:

[0011]

[0012] Where N is the number of thermal power units, C on is the coal consumption cost of thermal power units, C en is the environmental cost of thermal power units, P G (t, n) The output of the nth thermal power unit at time t.

[0013]

[0014] Among them, A n B n is the coal consumption coefficient of the nth thermal power unit, D n E n is the environmental coefficient of the nth thermal power unit.

[0015] The cost of energy storage system includes initial investment cost and operation and maintenance cost. The initial investment cost C in，stored It is expressed as:

[0016] C in，stlred =C p，cost C stored +C e，cost E stored

[0017] Among them, C p，cost is the power cost of energy storage, C e，cost is the unit investment of capacity cost, P stored is the rated power of energy storage, E stored is the rated capacity of the energy storage.

[0018] Calculate the operation and maintenance cost C om，cost， It is expressed as:

[0019] C lm，cost =C m，cost ×E stlred ×N stored

[0020] Among them, C m，cost is the unit operation and maintenance cost of the energy storage system, N stored The energy storage cost is calculated by the number of times the energy storage system is charged and discharged, which is expressed as:

[0021] C3=λ BE ×S be ×δ s ×n be

[0022] Among them, λ BE is the electricity cost, S he ×δ S is the charge / discharge amount of the energy storage system each time, n be is the number of cycles of the energy storage system.

[0023] Calculate the economic optimal objective function minF2, expressed as:

[0024] minF2=min(C2+C3)

[0025] Among them, C2 is the operating cost of the thermal power unit and C3 is the energy storage cost.

[0026] As a preferred solution of the photovoltaic consumption and electrochemical energy storage capacity optimization method described in the present invention, the setting of constraint conditions includes setting power balance constraints, upper and lower limit constraints on conventional unit output, rotating reserve constraints, and photovoltaic output constraints.

[0027] The power balance constraint is expressed as:

[0028]

[0029] in, is the charging and discharging power of the energy storage system at any moment, is the output of surrounding photovoltaic power stations and wind power stations at time t, is the total load of the energy storage system at that moment.

[0030] The upper and lower limits of conventional unit output are expressed as:

[0031]

[0032] in, is the output of conventional unit i at time t-1, is the rising output limit of unit i, It is the output reduction limit of unit i.

[0033] The spinning reserve constraint is expressed as:

[0034]

[0035] in, is the maximum available output of conventional unit i at time t, is the minimum output of conventional unit i at time t, is the positive spinning reserve required to cope with the load forecast error at time t, is the negative spinning reserve required to cope with the load forecast error at time t, and is the positive spinning reserve required to cope with the PV power fluctuation at time t, is the negative spinning reserve required to cope with PV power fluctuations at time t.

[0036] The photovoltaic output constraint is expressed as:

[0037]

[0038] in, is the maximum output of the photovoltaic power station at time t.

[0039] As a preferred solution of the photovoltaic absorption and electrochemical energy storage capacity optimization method described in the present invention, wherein: the setting of constraint conditions also includes setting energy storage system power constraints, energy storage capacity constraints, and energy storage system charge state constraints.

[0040] The power constraint of the energy storage system is expressed as:

[0041]

[0042] Among them, P ess (t) is the charging and discharging power of the energy storage system at time t, P ess (t)>0 is energy storage charging state, equivalent to load, P ess (t)<0 indicates the discharge state, E stored is the rated capacity of the energy storage system, E stored (t-1) is the remaining capacity of the energy storage system at time t-1, S s，up is the upper limit of the state of charge of the energy storage system, Ss,down is the lower limit of the energy storage system charge state, P Rcdp is the rated charging and discharging power of the energy storage system.

[0043] Energy storage capacity constraint, expressed as:

[0044]

[0045] Among them, η cp is the charging efficiency of the energy storage system, η dp is the discharge efficiency of the energy storage system.

[0046] The state of charge constraint of the energy storage system is expressed as:

[0047] S s,down ≤S s (t)≤S s，up

[0048] Among them, S s (t) is the charge state of the energy storage system at time t.

[0049] As a preferred solution of the photovoltaic consumption and electrochemical energy storage capacity optimization method of the present invention, the model solving includes selecting an equilibrium solution from the Pareto solution set and establishing a membership function for each objective function, which is expressed as:

[0050]

[0051] η i =F i，max -θ i (F i，max -F i，min ) 0≤θ i ≤1

[0052] i=1,2x=1,2,3....N

[0053] Among them, F 1,max is the maximum value of target F1, F 1，min is the minimum value of target F1, F 2,max is the maximum value of target F2, F 2,min is the target F2 minimum value, is the membership value of the solution corresponding to the objective function, η i is the cutoff value of the objective function, N is the number of candidate solutions in the Pareto solution set, when θ1=1 and θ2=0, the solution obtained is the optimal solution for the absorption rate, when θ1=0 and θ2=1, the solution obtained is the optimal solution for the economy, and when θ1=1 and θ2=1, the solution obtained represents a balanced solution that compromises the two.

[0054] As a preferred solution of the photovoltaic consumption and electrochemical energy storage capacity optimization method described in the present invention, the model solving also includes optimizing and solving the energy storage capacity based on the NSGA-II algorithm, setting the algorithm operation parameters, setting the population size, the number of generations, and the parameters of the crossover mutation probability, initializing the population, taking the energy storage system capacity and power as the decision variables in the model, and generating the initial population, determining the output power of the photovoltaic power generation system within a cycle according to the 24-hour meteorological data of a typical day in the current area, inputting the load demand of a typical day for 24 hours, and loading the conventional calculation parameters, calling the model to calculate the objective functions FI and F2, using the fitness function to calculate the fitness value of the individual, and performing the parent population P t Perform selection operations, sort the individuals in the solution set in layers, and the solutions with smaller individual values ​​are better. Perform crowding distance calculation on the solutions in the same layer. The larger the distance value, the better the performance of the solution. Perform crossover and mutation operations to generate the offspring population Q. t , merge P t , Q t Perform non-dominated sorting and select, P t +1 to get the next parent population and determine whether the number of iterations has been reached. If so, the Pareto solution set of the optimized energy storage capacity is output, and the membership function is called to select the equilibrium solution with the largest cancellation rate and the best economy. If not, the update iteration is continued.

[0055] Another object of the present invention is to provide a photovoltaic absorption and electrochemical energy storage capacity optimization system, which can establish a multi-objective optimization model by setting constraints, thereby solving the problems of incomplete consideration of constraints, insufficient system operation safety, and unreasonable configuration of energy storage systems in current power system optimization technologies.

[0056] As a preferred solution of the photovoltaic consumption and electrochemical energy storage capacity optimization system described in the present invention, it includes: an objective function construction module, a constraint condition setting module, and a model solving module.

[0057] The objective function construction module is used to determine the optimization direction by defining the objective function; the constraint condition setting module is used to set the constraint conditions and establish a multi-objective optimization model; the model solving module is used to solve the model and find the best solution that meets the objective function and the constraint conditions.

[0058] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a method for optimizing photovoltaic energy consumption and electrochemical energy storage capacity.

[0059] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for optimizing photovoltaic absorption and electrochemical energy storage capacity.

[0060] Beneficial effects of the present invention: The photovoltaic absorption and electrochemical energy storage capacity optimization method provided by the present invention improves the energy utilization rate of the photovoltaic power station by defining the objective function and determining the optimization direction. By optimizing the objective function, it is ensured that the photovoltaic power can be absorbed to the maximum extent during the peak period of photovoltaic output, the phenomenon of abandoned light is reduced, and the overall operating cost is reduced. Constraints are set and a multi-objective optimization model is established to improve the flexibility and reliability of the system, optimize the configuration of the energy storage system, ensure the safe and stable operation of the power system, solve the model and find the best solution that meets the objective function and constraints, improve the photovoltaic absorption rate, optimize the energy storage capacity configuration, provide the energy storage system with the best capacity and power configuration, reduce the overall cost of the system, and improve the operating efficiency of the system. Through the application of the membership function, an effective balance of multi-objective optimization is achieved, ensuring the optimal compromise between the absorption rate and the economy of the power system. The present invention achieves better results in terms of improving the photovoltaic absorption rate, the economy of power system operation and the stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 An overall flow chart of a method for optimizing photovoltaic absorption and electrochemical energy storage capacity provided for the first embodiment of the present invention.

[0063] Figure 2 A flow chart of the NSGA-II algorithm optimization model for a photovoltaic consumption and electrochemical energy storage capacity optimization method provided in the second embodiment of the present invention.

[0064] Figure 3 A workflow diagram of a method for optimizing photovoltaic absorption and electrochemical energy storage capacity provided for the second embodiment of the present invention.

[0065] Figure 4 An overall flow chart of a photovoltaic absorption and electrochemical energy storage capacity optimization system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0067] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for optimizing photovoltaic energy consumption and electrochemical energy storage capacity, comprising:

[0068] S1: Determine the optimization direction by defining the objective function.

[0069] Furthermore, the objective function is defined including defining the photovoltaic absorption effect in a typical day. When the photovoltaic output exceeds the power load and the remaining energy storage capacity in the current period, the optimal objective function maxF1 of the photovoltaic absorption effect is calculated, which is expressed as:

[0070]

[0071] in, is the active power output of the photovoltaic power station at time t, T is the number of typical daily time periods, is the load that needs to be met by photovoltaic output at moment t.

[0072] It should be noted that defining the objective function also includes defining the economic optimum. Based on the operating cost of the thermal power unit and the investment and maintenance cost of the energy storage system, the operating cost of the thermal power unit is calculated and expressed as:

[0073]

[0074] Where N is the number of thermal power units, C on is the coal consumption cost of thermal power units, C en is the environmental cost of thermal power units, P G (t, n) The output of the nth thermal power unit at time t.

[0075]

[0076] Among them, A n B n is the coal consumption coefficient of the nth thermal power unit, D n E n is the environmental coefficient of the nth thermal power unit.

[0077] The cost of energy storage system includes initial investment cost and operation and maintenance cost. The initial investment cost C in，stored It is expressed as:

[0078] C in，stlred =C p，cost C stored +C e，cost E stored

[0079] Among them, C p，cost is the power cost of energy storage, C e，cost is the unit investment of capacity cost, P stored is the rated power of energy storage, E stored is the rated capacity of the energy storage.

[0080] Calculate the operation and maintenance cost C om，cost， It is expressed as:

[0081] C lm，cost =C m，cost ×E stlred ×N stored

[0082] Among them, C m，cost is the unit operation and maintenance cost of the energy storage system, N stored The energy storage cost is calculated by the number of times the energy storage system is charged and discharged, which is expressed as:

[0083] C3=λ BE ×S be ×δ s ×n be

[0084] Among them, λ BE is the electricity cost, S he ×δ S is the charge / discharge amount of the energy storage system each time, n be is the number of cycles of the energy storage system.

[0085] Calculate the economic optimal objective function minF2, expressed as:

[0086] minF2=min(C2+C3)

[0087] Among them, C2 is the operating cost of the thermal power unit and C3 is the energy storage cost.

[0088] It should also be noted that by calculating the optimal objective function of the photovoltaic absorption effect on a typical day, it is ensured that photovoltaic electricity can be maximized during the peak period of photovoltaic output, and the economic optimal objective function is defined to reduce the phenomenon of abandoned light. The operating costs of thermal power units and the investment and maintenance costs of the energy storage system are comprehensively considered, thereby providing a basis for cost-benefit analysis for system operation and improving the energy utilization rate of photovoltaic power stations; by optimizing the objective function, photovoltaic electricity is more effectively absorbed, energy waste is reduced, energy utilization efficiency is improved, and the economy of the power system is guaranteed. By accurately calculating the operating costs of thermal power units and the costs of energy storage systems, it is helpful to reduce the overall operating costs and improve economic benefits while ensuring the safety of system operation, and provide data support for the long-term planning of the power system. Through the establishment of the objective function, a scientific basis can be provided for the expansion and transformation of the power system, and the sustainable development of the power system can be promoted.

[0089] S2: Set constraints and establish a multi-objective optimization model.

[0090] Furthermore, setting constraints includes setting power balance constraints, upper and lower limit constraints on conventional unit output, spinning reserve constraints, and photovoltaic output constraints.

[0091] The power balance constraint is expressed as:

[0092]

[0093] in, is the charging and discharging power of the energy storage system at any moment, is the output of surrounding photovoltaic power stations and wind power stations at time t, is the total load of the energy storage system at that moment.

[0094] The upper and lower limits of conventional unit output are expressed as:

[0095]

[0096] in, is the output of conventional unit i at time t-1, is the rising output limit of unit i, It is the output reduction limit of unit i.

[0097] The spinning reserve constraint is expressed as:

[0098]

[0099]

[0100] in, is the maximum available output of conventional unit i at time t, is the minimum output of conventional unit i at time t, is the positive spinning reserve required to cope with the load forecast error at time t, is the negative spinning reserve required to cope with the load forecast error at time t, and is the positive spinning reserve required to cope with the PV power fluctuation at time t, is the negative spinning reserve required to cope with PV power fluctuations at time t.

[0101] The photovoltaic output constraint is expressed as:

[0102]

[0103] in, is the maximum output of the photovoltaic power station at time t.

[0104] It should be noted that setting constraints also includes setting energy storage system power constraints, energy storage capacity constraints, and energy storage system charge state constraints.

[0105] The power constraint of the energy storage system is expressed as:

[0106]

[0107] Among them, P ess (t) is the charging and discharging power of the energy storage system at time t, P ess (t)>0 is energy storage charging state, equivalent to load, P ess (t)<0 indicates the discharge state, E stored is the rated capacity of the energy storage system, E stored (t-1) is the remaining capacity of the energy storage system at time t-1, S s,up is the upper limit of the state of charge of the energy storage system, S s,down is the lower limit of the energy storage system charge state, P Rcdp is the rated charging and discharging power of the energy storage system.

[0108] Energy storage capacity constraint, expressed as:

[0109]

[0110] Among them, η cp is the charging efficiency of the energy storage system, η dp is the discharge efficiency of the energy storage system.

[0111] The state of charge constraint of the energy storage system is expressed as:

[0112] S s,down ≤S s (t)≤S s,up

[0113] Among them, S s (t) is the charge state of the energy storage system at time t.

[0114] It should also be noted that through power balance constraints, upper and lower limit constraints on unit output, etc., the risks of imbalance in power supply and demand and unit overload are avoided, ensuring the safe and stable operation of the power system. Through rotating reserve constraints and photovoltaic output constraints, the system's ability to respond to load fluctuations and photovoltaic power fluctuations is improved, and the system's flexibility and reliability are improved. Through energy storage system power constraints and capacity constraints, the configuration of the energy storage system is optimized, ensuring the effective operation of the energy storage system and improving the economy of energy storage investment.

[0115] S3: Solve the model and find the best solution that satisfies the objective function and constraints.

[0116] Furthermore, solving the model involves selecting an equilibrium solution from the Pareto solution set and establishing a membership function for each objective function, expressed as:

[0117]

[0118] η i =F i，max -θ i (F i，max -F i,min ) 0≤θ i ≤1

[0119] i=1,2x=1,2,3....N

[0120] Among them, F 1，max is the maximum value of target F1, F 1，min is the minimum value of target F1, F 2,max is the maximum value of target F2, F 2,min is the target F2 minimum value, is the membership value of the solution corresponding to the objective function, η i is the cutoff value of the objective function, N is the number of candidate solutions in the Pareto solution set, when θ1=1 and θ2=0, the solution obtained is the optimal solution for the absorption rate, when θ1=0 and θ2=1, the solution obtained is the optimal solution for the economy, and when θ1=1 and θ2=1, the solution obtained represents a balanced solution that compromises the two.

[0121] It should be noted that the model solution also includes optimizing the energy storage capacity based on the NSGA-II algorithm, setting the algorithm operation parameters, setting the population size, number of generations, and crossover probability parameters, initializing the population, using the energy storage system capacity and power as decision variables in the model, and generating the initial population. According to the 24-hour meteorological data of a typical day in the current area, the output power of the photovoltaic power generation system within a cycle is determined, the load demand of a typical day for 24 hours is input, and the conventional calculation parameters are loaded. The model is called to calculate the objective functions FI and F2, and the fitness function is used to calculate the fitness value of the individual. The parent population P t Perform selection operations, sort the individuals in the solution set in layers, and the solutions with smaller individual values ​​are better. Perform crowding distance calculation on the solutions in the same layer. The larger the distance value, the better the performance of the solution. Perform crossover and mutation operations to generate the offspring population Q. t , merge P t , Q t Perform non-dominated sorting and select, P t +1 to get the next parent population and determine whether the number of iterations has been reached. If so, the Pareto solution set of the optimized energy storage capacity is output, and the membership function is called to select the equilibrium solution with the largest cancellation rate and the best economy. If not, the update iteration is continued.

[0122] It should also be noted that by selecting the equilibrium solution from the Pareto solution set and establishing the membership function for each objective function, a balance is achieved between maximizing the consumption rate and optimizing the economy. By applying the NSGA-Ⅱ algorithm, the optimal solution for the energy storage capacity is quickly found, which reduces the calculation time, improves the accuracy of the solution, and improves the efficiency and accuracy of the solution process. By applying the membership function, it is ensured that the optimal compromise between the consumption rate and the economy is found, which meets the various needs of the power system operation and improves the adaptability of the power system. Through the iterative update of the algorithm, the optimization model can adapt to the meteorological data and load requirements of different regions, improving the overall performance of the system and its ability to adapt to environmental changes.

[0123] Example 2, reference Figure 2-Figure 3 , which is an embodiment of the present invention, provides a method for optimizing photovoltaic absorption and electrochemical energy storage capacity. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0124] First, the experiment was divided into three experimental groups and one control group. Each group of experiments was carried out under different energy storage configurations and operation strategies. The design of the experimental group was based on the following process: objective function construction: for the photovoltaic absorption effect, an objective function was constructed to minimize the mismatch between photovoltaic output and load demand. At the same time, an economic objective function was constructed, considering the investment cost and operation and maintenance costs of the energy storage system; constraint setting: the experiment set constraints including power balance, unit output limit, spinning standby and photovoltaic output limit to ensure the feasibility of the optimization model under actual operating conditions.

[0125] Experimental implementation: The historical output data and load demand data of the photovoltaic power stations in the experimental area are collected as model inputs. According to the technical parameters of the energy storage system, the initial population is set, including the energy storage capacity and power configuration. The multi-objective optimization algorithm is used to iteratively solve the objective function while satisfying the set constraints. After reaching the predetermined number of iterations, the best solution that meets the photovoltaic consumption and economic goals is selected from the solution set; Figure 2 Represents the NSGA-Ⅱ algorithm optimization model process, Figure 3 The work flow is represented. From the experimental results, it can be obtained that the present invention improves the photovoltaic absorption rate, reduces the economic cost, improves the operating efficiency of the energy storage system, and provides effective technical support for the optimized operation of the power system.

[0126] Example 3, reference Figure 4 , which is an embodiment of the present invention, provides a photovoltaic consumption and electrochemical energy storage capacity optimization system, including an objective function construction module, a constraint condition setting module, and a model solving module.

[0127] The objective function construction module is used to determine the optimization direction by defining the objective function; the constraint setting module is used to set constraints and establish a multi-objective optimization model; the model solving module is used to solve the model and find the best solution that meets the objective function and constraints.

[0128] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0129] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0130] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0131] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing photovoltaic energy consumption and electrochemical energy storage capacity, characterized in that: include: Determine the optimization direction by defining the objective function; Set constraints and establish a multi-objective optimization model; Solve the model and find the best solution that meets the objective function and constraints.

2. The photovoltaic absorption and electrochemical energy storage capacity optimization method according to claim 1, characterized in that: The objective function definition includes defining the photovoltaic absorption effect in a typical day. When the photovoltaic output exceeds the power load and the remaining energy storage capacity in the current period, the optimal objective function maxF1 of the photovoltaic absorption effect is calculated, which is expressed as: in, is the active power output of the photovoltaic power station at time t, T is the number of typical daily time periods, is the load that needs to be met by photovoltaic output at moment t.

3. The photovoltaic absorption and electrochemical energy storage capacity optimization method according to claim 2, characterized in that: The objective function definition also includes defining the economic optimum, and calculating the operating cost of the thermal power unit based on the operating cost of the thermal power unit and the investment and maintenance cost of the energy storage system, which is expressed as: Where N is the number of thermal power units, C on is the coal consumption cost of thermal power units, C en is the environmental cost of thermal power units, P G (t,n) the output of the nth thermal power unit at time t; Among them, A n B n is the coal consumption coefficient of the nth thermal power unit, D n E n is the environmental coefficient of the nth thermal power unit; The cost of energy storage system includes initial investment cost and operation and maintenance cost. The initial investment cost C in,stored It is expressed as: C in,stored =C p,cost C stored +C e,cost E stored Among them, C p,cost is the power cost of energy storage, C e,cost is the unit investment of capacity cost, P stored is the rated power of energy storage, E stored is the rated capacity of the energy storage; Calculate the operation and maintenance cost C om,cost , expressed as: C om,cost =C m,cost ×E stored ×N stored Among them, C m,cost is the unit operation and maintenance cost of the energy storage system, N stored The energy storage cost is calculated by the number of times the energy storage system is charged and discharged, which is expressed as: C3=λ BE ×S be ×δ s ×n be Among them, λ BE is the electricity cost, S he ×δ S is the charge / discharge amount of the energy storage system each time, n be is the number of cycles of the energy storage system; Calculate the economic optimal objective function minF2, expressed as: minF2=min(C2+C3) Among them, C2 is the operating cost of the thermal power unit and C3 is the energy storage cost.

4. The photovoltaic absorption and electrochemical energy storage capacity optimization method according to claim 3, characterized in that: The setting of constraint conditions includes setting power balance constraints, upper and lower limit constraints of conventional unit output, spinning reserve constraints, and photovoltaic output constraints; The power balance constraint is expressed as: in, is the charging and discharging power of the energy storage system at any moment, is the output of surrounding photovoltaic power stations and wind power stations at time t, is the total load of the energy storage system at that moment; The upper and lower limits of conventional unit output are expressed as: in, is the output of conventional unit i at time t-1, is the rising output limit of unit i, The descent output limit of unit i; The spinning reserve constraint is expressed as: in, is the maximum available output of conventional unit i at time t, is the minimum output of conventional unit i at time t, is the positive spinning reserve required to cope with the load forecast error at time t, is the negative spinning reserve required to cope with the load forecast error at time t, and is the positive spinning reserve required to cope with the PV power fluctuation at time t, is the negative spinning reserve required to cope with the PV power fluctuation at time t; The photovoltaic output constraint is expressed as: in, is the maximum output of the photovoltaic power station at time t.

5. The method for optimizing photovoltaic energy consumption and electrochemical energy storage capacity according to claim 4, characterized in that: The setting of constraint conditions also includes setting energy storage system power constraints, energy storage capacity constraints, and energy storage system charge state constraints; The power constraint of the energy storage system is expressed as: Among them, P ess (t) is the charging and discharging power of the energy storage system at time t, P ess (t)>0 is energy storage charging state, equivalent to load, P ess (t)<0 indicates the discharge state, E stored is the rated capacity of the energy storage system, E stored (t-1) is the remaining capacity of the energy storage system at time t-1, S s,up is the upper limit of the state of charge of the energy storage system, S s,down is the lower limit of the energy storage system charge state, P Rcdp is the rated charging and discharging power of the energy storage system; Energy storage capacity constraint, expressed as: Among them, η cp is the charging efficiency of the energy storage system, η dp is the discharge efficiency of the energy storage system; The state of charge constraint of the energy storage system is expressed as: S s,down ≤S s (t)≤S s,up Among them, S s (t) is the charge state of the energy storage system at time t.

6. The method for optimizing photovoltaic energy consumption and electrochemical energy storage capacity according to claim 5, characterized in that: The model solving includes selecting an equilibrium solution from the Pareto solution set and establishing a membership function for each objective function, which is expressed as: or i =F i,max -θ i (F i,max -F i,min )0≤θ i ≤1 i=1,2 x=1,2,3....N Among them, F 1,max is the maximum value of target F1, F 1,min is the minimum value of target F1, F 2,max is the maximum value of target F2, F 2,min is the target F2 minimum value, is the membership value of the solution corresponding to the objective function, η i is the cutoff value of the objective function, N is the number of candidate solutions in the Pareto solution set, when θ1=1 and θ2=0, the solution obtained is the optimal solution for the absorption rate, when θ1=0 and θ2=1, the solution obtained is the optimal solution for the economy, and when θ1=1 and θ2=1, the solution obtained represents a balanced solution that compromises the two.

7. The method for optimizing photovoltaic energy consumption and electrochemical energy storage capacity according to claim 6, characterized in that: The model solving also includes optimizing and solving the energy storage capacity based on the NSGA-Ⅱ algorithm, setting the algorithm operation parameters, setting the parameters of the population size, the number of generations, and the crossover mutation probability, initializing the population, taking the capacity and power of the energy storage system as the decision variables in the model, and generating the initial population, determining the output power of the photovoltaic power generation system within a cycle according to the 24-hour meteorological data of a typical day in the current area, inputting the load demand of a typical day for 24 hours, and loading the conventional calculation parameters, calling the model to calculate the objective functions FI and F2, and using the fitness function to calculate the fitness value of the individual, and performing the parent population P t Perform selection operations, sort the individuals in the solution set in layers, and the solutions with smaller individual values ​​are better. Perform crowding distance calculation on the solutions in the same layer. The larger the distance value, the better the performance of the solution. Perform crossover and mutation operations to generate the offspring population Q. t , merge P t , Q t Perform non-dominated sorting and select, P t +1 to get the next parent population and determine whether the number of iterations has been reached. If so, the Pareto solution set of the optimized energy storage capacity is output, and the membership function is called to select the equilibrium solution with the largest cancellation rate and the best economy. If not, the update iteration is continued.

8. A system using the photovoltaic energy consumption and electrochemical energy storage capacity optimization method according to any one of claims 1 to 7, characterized in that: It includes objective function building module, constraint condition setting module and model solving module; The objective function building module is used to determine the optimization direction by defining the objective function; The constraint condition setting module is used to set constraint conditions and establish a multi-objective optimization model; The model solving module is used to solve the model and find the best solution that meets the objective function and constraint conditions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the photovoltaic absorption and electrochemical energy storage capacity optimization method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the photovoltaic absorption and electrochemical energy storage capacity optimization method described in any one of claims 1 to 7 are implemented.

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