A method for optimal configuration of composite energy storage based on an improved Rafflesia algorithm

Abstract

This invention discloses a composite energy storage optimization configuration method based on an improved Rafflesia algorithm. Addressing the shortcomings of existing technologies, this method provides a composite energy storage optimization configuration method based on an improved Rafflesia algorithm applicable to various energy storage optimization configurations. The optimization configuration method provided includes: constructing a model based on a two-dimensional chaotic mapping-improved Rafflesia algorithm to solve an energy storage capacity optimization model. The capacity optimization model considers the economics of the energy storage system, its self-absorption capacity, and the penetration of new energy sources, aiming to minimize the quotient of the system's equivalent annual cost and the total annual demand load. This solution is not only reliable to implement but also capable of handling various energy storage configuration problems, exhibiting strong adaptability and generalization ability.

Description

Technical Field

[0001] This invention relates to the fields of energy storage technology and energy storage planning and configuration technology, and in particular to a method for optimizing the configuration of composite energy storage based on an improved Rafflesia algorithm. Background Technology

[0002] With the rapid development of new energy power generation technologies, distributed energy has been widely used due to its advantages of being pollution-free and low-cost. However, new energy power generation is intermittent and uncertain, resulting in large energy fluctuations that cannot ensure the safe, stable, and economical operation of the power system. To address the power imbalance between output and load, energy storage technologies are typically used to smooth power fluctuations and ensure the stability of the bus voltage. However, due to the complexity of power systems, no single energy storage technology can perfectly meet all needs. To address this issue, researchers have explored the field of synergistic optimization of various energy storage systems in power systems, among which energy storage configuration optimization, as a prerequisite for system coordinated control, has significant research value.

[0003] The main literature on energy storage configuration optimization methods currently includes:

[0004] (1) A method for optimizing the capacity configuration of a composite energy storage system based on an improved gray wolf algorithm (CN202311633501.9): This invention discloses a method for optimizing the capacity configuration of a composite energy storage system based on an improved gray wolf algorithm, focusing on energy storage configuration on the new energy power generation side, considering economic efficiency and wind and solar energy absorption capacity, and has good economic efficiency. Specifically, this invention constructs a model based on an improved IMGWO algorithm using particle swarm optimization (PSO) niche technology, and uses a multi-objective gray wolf algorithm improved by PSO niche technology to solve for the optimal energy storage capacity configuration scheme. However, this algorithm still suffers from the problem of getting trapped in local optima, especially in some complex situations where it fails to converge.

[0005] (2) A New Method for Optimizing the Capacity of Hybrid Electricity-Hydrogen-Gas Energy Storage in a Power System under the Green Certificate-Carbon Trading Mechanism (Paper): This paper proposes a method for optimizing the capacity of hybrid electricity-hydrogen-gas energy storage in a power system under the green certificate-carbon trading mechanism. The optimization model incorporates a green certificate-carbon joint trading mechanism. The Big M method is first used to linearize the optimization model, and then CPLEX is called to solve it. The proposed capacity configuration method uses a two-stage electricity-to-gas conversion as an energy bridge, and proposes a new hybrid energy storage system considering electrochemical energy storage, hydrogen energy storage, and gas energy storage. To improve the absorption of renewable energy and reduce carbon emissions, a green certificate-carbon joint trading mechanism and an energy supply interruption / waste mechanism are introduced into the optimization model, and then the model is solved using linear programming. However, this method is computationally difficult, and the computational load is too large when facing complex design situations, resulting in low practicality.

[0006] (3) A novel hybrid energy storage configuration method and system for black-start power sources (CN202410080268.4): This invention discloses a novel hybrid energy storage configuration method and system for black-start power sources. A typical black-start scenario is used as the original input signal. Local Mode Decomposition (LMD) is employed to obtain the RLMD decomposition of the new energy output. Based on the decomposition data, a hybrid energy storage configuration scheme is selected with the objective of minimizing the hybrid energy storage value function. Specifically, this invention first constructs a typical black-start scenario using historical meteorological data, then uses the typical black-start scenario as the original input signal, and employs LMD to obtain the RLMD decomposition of the new energy output. Finally, the optimal configuration scheme is selected with the objective of minimizing the hybrid energy storage value function. However, this method requires a significant amount of time for simulation calculations, and the computational cost may be very high for large-scale power systems and complex problems.

[0007] The above-mentioned literature utilizes heuristic algorithms, mathematical programming algorithms, and simulation-based methods to solve for optimal energy storage configurations. However, all three suffer from unstable solution quality and high computational costs, making them unsuitable for a wide range of planning and design fields. This patent proposes a multi-method energy storage optimization configuration based on an improved Rafflesia algorithm. This method enhances the algorithm's population exploration capability and can handle various energy storage configuration problems, exhibiting strong adaptability and generalization ability. Summary of the Invention

[0008] In view of this, the present invention proposes a method for optimizing the configuration of composite energy storage based on an improved Rafflesia algorithm.

[0009] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows:

[0010] A method for optimizing the configuration of composite energy storage based on an improved Rafflesia algorithm, comprising:

[0011] S01. Based on the preset energy storage unit characteristics, construct a charging and discharging model, and then construct an energy storage management strategy based on this model to make reasonable settings for charging and discharging management.

[0012] S02. Construct a capacity configuration model for energy storage systems with the aim of minimizing the annual investment cost of energy storage equipment and the total annual demand load;

[0013] S03. Combine two-dimensional chaotic mapping calculation with the Rafflesia algorithm to construct an improved Rafflesia algorithm model;

[0014] S04. Collect annual wind and solar power output data of wind farms and solar power plants in the preset area, and combine the improved Rafflesia algorithm model to optimize the parameters of the energy storage system capacity configuration model in order to obtain the optimal energy storage capacity configuration scheme.

[0015] As one possible implementation, further, in this solution S01, the preset energy storage unit is powered by wind farms and photovoltaic plants in a preset area.

[0016] As one possible implementation, the preset energy storage unit is a hybrid energy storage unit, which includes one or more of a battery, a supercapacitor, and a hydrogen storage device, wherein the hydrogen storage device includes an electrolyzer and a fuel cell.

[0017] As a preferred implementation option, the preferred method in this scheme S01 includes constructing a charge-discharge model, which includes:

[0018] Without considering energy loss, a hybrid energy storage system is used to balance the wind power generation P. WT Photovoltaic power generation P PV and power load P e_load The power balance relationship between the units is as follows:

[0019] ΔP=P Hess =P WT +P PV -P e_load (19)

[0020] In equation (1): P Hess The total power of the hybrid energy storage system is represented by a positive or negative value, indicating whether the power supply is in surplus or deficit. The hybrid energy storage system includes batteries, supercapacitors, and hydrogen storage devices.

[0021] As a preferred implementation option, preferably, in this solution S01, the energy storage management strategy includes:

[0022] When the output of new energy sources exceeds the load, i.e., ΔP > 0, the hybrid energy storage system begins to charge; otherwise, it begins to discharge.

[0023] The charging strategy prioritizes charging the batteries, followed by the supercapacitors, and finally the hydrogen storage devices. If all energy storage units of the hybrid energy storage system are fully charged, the remaining electricity will be connected to the grid and sold to the grid.

[0024] The discharge strategy prioritizes discharging the batteries, followed by the supercapacitors, and finally the hydrogen storage equipment. If there is still a power shortage after all the energy storage units of the hybrid energy storage system have been fully discharged, the power will be purchased from the grid.

[0025] As a preferred implementation option, the preferred method in this scheme S02, which involves constructing an energy storage system capacity configuration model aimed at minimizing the annual investment cost of energy storage equipment and the total annual demand load, includes:

[0026] 1. Objective function

[0027] The formula for minimizing the quotient of the annual cost of the composite energy storage system and the total annual demand load is as follows:

[0028]

[0029] Where: LCE (yuan / kWh) refers to the average energy cost over the entire life cycle; AC sys (RMB) represents the total annual cost of the composite energy storage system; AC grid (RMB) represents the annual electricity purchase cost of the composite energy storage system; E Dyear (kW) represents the total load demand of the composite energy storage system;

[0030] 1) Total annual cost of the composite energy storage system

[0031] AC sys =(PC) equip +PC rep )×CRF+AC O&M (twenty one)

[0032] Where: PC equip For equipment investment costs; PC rep Total replacement cost of load storage system; AC O&M Operating and maintenance costs; CRF is the capital recovery factor, which can be calculated by the following formula:

[0033]

[0034] In the formula: i is the annual interest rate; N is the life cycle of the energy storage system.

[0035]

[0036] In the formula: r O&M k is the conversion factor for operation and maintenance costs. SC k BA k EL and k HFC These refer to the unit price of energy storage equipment such as supercapacitors, batteries, electrolyzers, and fuel cells; N SC N BA N EL and N HFC These refer to the configuration capacity of energy storage devices such as supercapacitors, batteries, electrolyzers, and fuel cells.

[0037]

[0038] In the formula: L BA L SC L HFC L EL These refer to the lifespan of the storage battery, supercapacitor, fuel cell, and electrolyzer, respectively; yBA y SC y HFC y EL These are the number of times the storage battery, supercapacitor, fuel cell, and electrolyzer need to be replaced during the system's lifespan, respectively.

[0039] 2) Annual electricity purchase cost of the composite energy storage system

[0040]

[0041] In the formula: P e_buy Purchase electricity for each time period; k grid Electricity price at each time point;

[0042] 3) Total system load demand

[0043]

[0044] In the formula: P e_load For load demand at various times;

[0045] 2. Constraints

[0046] Constraints are set on the optimization variables for configuration optimization to ensure that the optimization results reflect actual conditions. The constraints are set as follows:

[0047] 1) Power balance constraints

[0048] P grid (t)+P WT (t)+P PV (t)+P BA (t)+P SC (t)+P HFC (t)=P EL (t)+P e_load (t) (27)

[0049] In the formula: P WT (t), P PV (t), P BA (t) and P SC (t) represent the power output of the wind turbine, photovoltaic system, battery, and supercapacitor during time period t, respectively; P grid (t) represents the electricity purchased / sold by the system to the main network; P EL (t), P HFC (t) represent the electricity consumed by the electrolyzer during time period t and the electricity output by the fuel cell during time period t, respectively; P e_load (t) represents the load during time period t;

[0050] 2) Constraints of storage batteries and supercapacitors

[0051] Batteries and supercapacitors have similar operating models. To ensure the safe operation of energy storage systems, a unified model is adopted for both:

[0052]

[0053] Where: N represents the charging and discharging power of the nth type of energy storage device during time period t; ES.n Let P represent the configuration capacity of the nth type of energy storage device; ES.n (t) represents the final output power of the nth type of energy storage device during time period t; These are binary variables, representing the charging and discharging states of the nth type of energy storage device, respectively; SOC ES.n (t) refers to the capacity of the nth type of energy storage device during time period t; SOC ES.n.min SOC ES.n.max These refer to the minimum and maximum capacities of the nth type of energy storage device, respectively; n represents the type of energy storage device, where n=1 is a battery and n=2 is a supercapacitor.

[0054] 3) Electrolytic cell constraint

[0055]

[0056] Where: P EL (t) represent the electrical energy input to the electrolyzer and the hydrogen energy output from the electrolyzer during time period t, respectively; η EL The energy conversion efficiency of the electrolytic cell; This refers to the upper limit of the energy input to the electrolytic cell; These refer to the upper and lower limits of the ramp rate for the electrolytic cell, respectively.

[0057] 4) Constraints of fuel cells

[0058]

[0059] Where: P HFC (t) represents the hydrogen energy input to the fuel cell and the fuel cell output during time period t, respectively; η HFC For electrical energy conversion efficiency; Input the upper limit of hydrogen energy; These refer to the upper and lower limits of the ramp rate for fuel cells, respectively.

[0060] 5) Planning constraints

[0061]

[0062]

[0063] Where: N SC_max N BA_max NEL_max and N HFC_max These refer to the maximum capacity of supercapacitors, batteries, electrolyzers, and fuel cells, respectively; P e_buy Purchase electricity for the energy storage system at various times; E Dyear This represents the total annual system load demand; GD max This represents the maximum dependency.

[0064] As a preferred implementation option, the preferred approach in this scheme S03, which combines two-dimensional chaotic mapping calculation with the Rafflesia algorithm to construct an improved Rafflesia algorithm model, includes:

[0065] Phase 1: Initialize parameters and generate the population, calculate the population fitness function, and sort the individuals to select the optimal ones. This phase enhances randomness and exploration capabilities by introducing a two-dimensional chaotic mapping. The calculation formula is shown below:

[0066]

[0067]

[0068] Where: χ t and These are two chaotic mapping variables in a two-dimensional chaotic mapping, i.e., a chaotic sequence; η and X are control parameters; i，k Let X be the k-th dimension variable of the i-th individual; D is the dimension of the variable; best,k The optimal individual position; X R d represents the location of a random individual; d represents the distance between variables; X represents the distance between variables. worst The individual with the worst fitness;

[0069] Phase 2: Update the positions of individuals with better fitness, using the following formula:

[0070]

[0071] In the formula: v1 and v2 represent translational speed and rotational speed, respectively; w0 and w1 represent the frequency period of the flapping wing and the side flapping wing, respectively, both with a value of 0.025; C is the influencing factor; r A random variable ranging from 0 to 1;

[0072] The second-stage strategy involves iterating over a period of time, then identifying the individual with the worst fitness in the group, namely X. worst Eliminating the population reduces the number of individuals by one, thus ensuring the quality of the optimal entity.

[0073] Third stage: Based on the population obtained after the first two stages of processing, find the optimal fitness solution, using the following formula:

[0074]

[0075] In the formula: iter is the current loop number; Max_iter is the maximum loop number; rd is the individual distribution range; ub and lb represent the upper and lower limits of the individual, respectively; rand is a random number from 0 to 1; sign takes -1 or 1.

[0076] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: The present invention provides a composite energy storage optimization configuration method based on the improved Rafflesia algorithm that can be used for various energy storage optimization configurations, based on the shortcomings of the prior art; The optimization configuration method provided by the present invention includes: by constructing a model based on the improved Rafflesia algorithm of two-dimensional chaotic mapping, solving the energy storage capacity optimization model, the capacity optimization model considers the economy of the energy storage system, self-absorption capacity and new energy penetration, with the goal of minimizing the quotient of the system's equivalent annual cost and the total annual demand load. The present invention is not only reliable to implement, but also able to cope with various energy storage configuration problems, and has strong adaptability and generalization ability. Attached Figure Description

[0077] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0078] Figure 1 This is a simplified implementation flowchart of the proposed method.

[0079] Figure 2 This is a microgrid topology diagram when the proposed method is applied to an instance;

[0080] Figure 3 This is a graph showing the meteorological data recorded when the method of this scheme was applied to an example;

[0081] Figure 4 This is the system output diagram when the proposed method is applied to an example;

[0082] Figure 5 This is a comparison chart of algorithm iterations when the proposed method is applied to an instance;

[0083] Figure 6 This is a flowchart of the improved Rafflesia algorithm in this scheme. Detailed Implementation

[0084] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0085] Combination Figure 1 As shown, the scheme in this embodiment is a composite energy storage optimization configuration method based on the improved Rafflesia algorithm, which includes:

[0086] S01. Based on the preset energy storage unit characteristics, construct a charging and discharging model, and then construct an energy storage management strategy based on this model to make reasonable settings for charging and discharging management.

[0087] S02. Construct a capacity configuration model for energy storage systems with the aim of minimizing the annual investment cost of energy storage equipment and the total annual demand load;

[0088] S03. Combine two-dimensional chaotic mapping calculation with the Rafflesia algorithm to construct an improved Rafflesia algorithm model;

[0089] S04. Collect annual wind and solar power output data of wind farms and solar power plants in the preset area, and combine the improved Rafflesia algorithm model to optimize the parameters of the energy storage system capacity configuration model in order to obtain the optimal energy storage capacity configuration scheme.

[0090] In this scheme S01, the preset energy storage unit is powered by wind farms and photovoltaic plants in a preset area. Furthermore, the preset energy storage unit is a hybrid energy storage system, comprising one or more of the following: batteries, supercapacitors, and hydrogen storage devices. The hydrogen storage devices include electrolyzers and fuel cells.

[0091] Taking a microgrid in Southwest China as an example, the microgrid topology is as follows: Figure 2 As shown, it is divided into three parts: the generation side, the user side, and the energy storage system. The generation side consists of photovoltaic (PV) power, wind turbines, and the main grid, while the energy storage system is constructed from batteries, supercapacitors, and hydrogen energy equipment. Based on local data from recent years, the following information has been compiled: wind power output, PV power output, and load for each time period. Appropriate parameters have been selected as typical daily data, such as... Figure 3 As shown in the figure. Based on the above data, the model is solved using the energy storage optimization configuration method proposed in this scheme. The obtained optimal solution can meet the system output balance requirements and suppress the output fluctuation of new energy sources. The system output results are as follows. Figure 4 As shown, various energy storage systems possess excellent regulation and coordination capabilities.

[0092] The specific solutions in this embodiment include the following:

[0093] 1.1 Charge and Discharge Model

[0094] Due to external environmental influences, the randomness and intermittency of new energy power generation systems lead to significant fluctuations in power output, affecting bus voltage balance. Without considering energy losses, a hybrid energy storage system is used to balance the wind power generation P. WT Photovoltaic power generation P PV and power load P e_load The power balance relationship between the units is as follows:

[0095] ΔP=P Hess =P WT +P PV -P e_load (37)

[0096] In the formula: P Hess The total power of the hybrid energy storage system is indicated by its positive and negative values, which represent the state of power supply surplus or deficit, respectively. The hybrid energy storage system consists of batteries, supercapacitors, and hydrogen storage devices.

[0097] 1.2 Energy Management of Multiple Energy Storage Systems

[0098] The energy management strategy adopted in this method is as follows: when the output of new energy sources exceeds the load (ΔP > 0), the energy storage system begins charging; otherwise, it begins discharging. In the charging strategy, batteries are charged first, followed by supercapacitors, and then hydrogen storage devices. If all energy storage devices are fully charged, the remaining electricity is sold to the grid. Similar to the charging strategy, the discharging sequence of the energy storage devices is the same. If there is still a power shortage after all energy storage devices have fully discharged, it is purchased from the grid.

[0099] 1.3 Energy Storage Capacity Optimization Model

[0100] 1.3.1 Objective Function

[0101] The goal is to minimize the quotient of the annual cost of a composite energy storage system and the total annual demand load. The calculation formula is as follows:

[0102]

[0103] Where: LCE (yuan / kWh) refers to the average energy cost over the entire life cycle; AC sys (RMB) represents the total annual cost of the composite energy storage system; AC grid (RMB) represents the annual electricity purchase cost of the composite energy storage system; E Dyear (kW) represents the total load demand of the composite energy storage system.

[0104] 1) Total annual cost of the composite energy storage system

[0105] AC sys =(PC) equip+PC rep )×CRF+AC O&M (39)

[0106] Where: PC equip For equipment investment costs; PC rep Total replacement cost of load storage system; AC O&M The operating and maintenance cost is CRF, which is the capital recovery factor, and can be calculated by the following formula:

[0107]

[0108] In the formula: i is the annual interest rate; N is the life cycle of the energy storage system.

[0109]

[0110] In the formula: r O&M k is the conversion factor for operation and maintenance costs. SC k BA k EL and k HFC These refer to the unit price of energy storage equipment such as supercapacitors, batteries, electrolyzers, and fuel cells; N SC N BA N EL and N HFC These refer to the configuration capacity of energy storage devices such as supercapacitors, batteries, electrolyzers, and fuel cells.

[0111]

[0112] In the formula: L BA L SC L HFC L EL These refer to the lifespan of the storage battery, supercapacitor, fuel cell, and electrolyzer, respectively; y BA y SC y HFC y EL These represent the number of times the battery, supercapacitor, fuel cell, and electrolyzer need to be replaced during the system's lifespan.

[0113] 2) Annual electricity purchase cost of the composite energy storage system

[0114]

[0115] In the formula: P e_buy Purchase electricity for each time period; k grid Electricity price at each time point.

[0116] 3) Total system load demand

[0117]

[0118] In the formula: P e_load This represents the load demand at any given time.

[0119] 1.3.2 Constraints

[0120] Constraints are set on the optimization variables for configuration optimization to ensure that the optimization results reflect actual conditions. The constraints are set as follows:

[0121] 1) Power balance constraints

[0122] P grid (t)+P WT (t)+P PV (t)+P BA (t)+P SC (t)+P HFC (t)=P EL (t)+P e_load (t) (45)

[0123] In the formula: P WT (t), P PV (t), P BA (t) and P SC (t) represent the power output of the wind turbine, photovoltaic system, battery, and supercapacitor during time period t, respectively; P grid (t) represents the electricity purchased / sold by the system to the main network; P EL (t), P HFC (t) represent the electricity consumed by the electrolyzer during time period t and the electricity output by the fuel cell during time period t, respectively; P e_load (t) represents the load during time period t.

[0124] 2) Constraints of storage batteries and supercapacitors

[0125] Batteries and supercapacitors have similar operating models. To ensure the safe operation of energy storage systems, a unified model is adopted for both:

[0126]

[0127] Where: N represents the charging and discharging power of the nth type of energy storage device during time period t; ES.n Let P represent the configuration capacity of the nth type of energy storage device; ES.n (t) represents the final output power of the nth type of energy storage device during time period t; These are binary variables, representing the charging and discharging states of the nth type of energy storage device, respectively; SOC ES.n (t) refers to the capacity of the nth type of energy storage device during time period t; SOC ES.n.min SOC ES.n.maxThese refer to the minimum and maximum capacities of the nth type of energy storage device, respectively; n represents the type of energy storage device, where n=1 is a battery and n=2 is a supercapacitor.

[0128] 3) Electrolytic cell constraint

[0129]

[0130] In the formula: P EL (t) represent the electrical energy input to the electrolyzer and the hydrogen energy output from the electrolyzer during time period t, respectively; η EL The energy conversion efficiency of the electrolytic cell; This refers to the upper limit of the energy input to the electrolytic cell; These refer to the upper and lower limits of the slope for the electrolytic cell, respectively.

[0131] 4) Constraints of fuel cells

[0132]

[0133] In the formula: P HFC (t) represents the hydrogen energy input to the fuel cell and the fuel cell output during time period t, respectively; η HFC For electrical energy conversion efficiency; Input the upper limit of hydrogen energy; These refer to the upper and lower limits of the ramp-up capability of fuel cells, respectively.

[0134] 5) Planning constraints

[0135]

[0136]

[0137] Where: N SC_max N BA_max N EL_max and N HFC_max These refer to the maximum capacity of supercapacitors, batteries, electrolyzers, and fuel cells, respectively; P e_buy Purchase electricity for the energy storage system at various times; E Dyear This represents the total annual system load demand; GD max This represents the maximum dependency.

[0138] 1.4 Improved Rafflesia Algorithm Model

[0139] The improved Rafflesia algorithm consists of three stages, the process of which is as follows: Figure 6 As shown. In the first stage, individuals are updated using two strategies. This scheme introduces a two-dimensional chaotic mapping in the first strategy to enhance the randomness and exploration capability of this stage. The calculation formula is shown below:

[0140]

[0141]

[0142] Where: χ t and These are two chaotic mapping variables in a two-dimensional chaotic mapping, i.e., a chaotic sequence; η and X are control parameters; i，k Let X be the k-th dimension variable of the i-th individual; D is the dimension of the variable; best,k The optimal individual position; X R d represents the location of a random individual; d represents the distance between variables; X represents the distance between variables. worst The individual with the worst fitness.

[0143] The second strategy updates the position of individuals with better fitness, using the following formula:

[0144]

[0145] In the formula: v1 and v2 represent translational speed and rotational speed, respectively; w0 and w1 represent the frequency period of flapping wing and side flapping wing, respectively, both with a value of 0.025; C is the influencing factor; r is a random variable from 0 to 1.

[0146] The second-stage strategy involves iterating over a period of time, then identifying the individual with the worst fitness in the group, namely X. worst Eliminating the population reduces the number of individuals by one, thus ensuring the quality of the optimal entity.

[0147] The third stage uses the population individuals obtained from the first two stages to find the optimal fitness solution, as shown in the following formula:

[0148]

[0149] In the formula: iter is the current loop number; Max_iter is the maximum loop number; rd is the individual distribution range; ub and lb represent the upper and lower limits of the individual, respectively; rand is a random number from 0 to 1; sign takes -1 or 1.

[0150] also, Figure 5 The iterative processes of the improved Rafflesia algorithm, the Rafflesia algorithm, and the Harris Eagle algorithm are shown in this paper, and compared. Figure 5 The iterative processes of the three algorithms show that the improved Rafflesia algorithm has a faster convergence speed, stronger optimization ability, and lower annual costs such as energy storage configuration, thus achieving the optimal energy storage capacity configuration scheme.

[0151] The above descriptions are only some embodiments of the present invention and do not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the configuration of composite energy storage based on an improved Rafflesia algorithm, characterized in that, It includes: S01. Based on the preset energy storage unit characteristics, construct a charging and discharging model, and then construct an energy storage management strategy based on this model to make reasonable settings for charging and discharging management. S02. Construct a capacity configuration model for energy storage systems with the aim of minimizing the annual investment cost of energy storage equipment and the total annual demand load; S03. Combining two-dimensional chaotic mapping computation with the Rafflesia algorithm to construct an improved Rafflesia algorithm model, which includes: Phase 1: Initialize parameters and generate the population, calculate the population fitness function, and sort the individuals to select the optimal ones. This phase enhances randomness and exploration capabilities by introducing a two-dimensional chaotic mapping. The calculation formula is shown below: Where: χ t and These are two chaotic mapping variables in a two-dimensional chaotic mapping, i.e., a chaotic sequence; η and X are control parameters; i，k Let X be the k-th dimension variable of the i-th individual; D is the dimension of the variable; best,k X is the k-th dimension variable representing the optimal individual location; d is the variable distance; worst The individual with the worst fitness; Phase 2: Update the positions of individuals with better fitness, using the following formula: In the formula: v1 and v2 represent translational speed and rotational speed, respectively; w0 and w1 represent the frequency period of the flapping wing and the side flapping wing, respectively, both with a value of 0.025; C is the influencing factor; r is a random variable from 0 to 1; The second-stage strategy involves iterating over a period of time, then identifying the individual with the worst fitness in the group, namely X. worst Eliminating the population reduces the number of individuals by one, thus ensuring the quality of the optimal entity. Third stage: Based on the population obtained after the first two stages of processing, find the optimal fitness solution, using the following formula: In the formula: iter is the current loop number; Max_iter is the maximum loop number; rd is the individual distribution range; ub and lb represent the upper and lower limits of the individual, respectively; rand is a random number from 0 to 1; sign takes -1 or 1; S04. Collect annual wind and solar power output data of wind farms and solar power plants in the preset area, and combine the improved Rafflesia algorithm model to optimize the parameters of the energy storage system capacity configuration model in order to obtain the optimal energy storage capacity configuration scheme.

2. The method for optimizing the configuration of composite energy storage based on the improved Rafflesia algorithm as described in claim 1, characterized in that, In S01, the preset energy storage unit is powered by wind farms and photovoltaic plants in the preset area; The preset energy storage unit is a hybrid energy storage unit, which includes one or more of the following: a battery, a supercapacitor, and a hydrogen storage device. The hydrogen storage device includes an electrolyzer and a fuel cell.

3. The method for optimizing the configuration of composite energy storage based on the improved Rafflesia algorithm as described in claim 2, characterized in that, In S01, the charge-discharge model is constructed as follows: Without considering energy loss, a hybrid energy storage system is used to balance the wind power generation P. WT Photovoltaic power generation P PV and power load P e_load The power balance relationship between the units is as follows: ΔP=P Hess =P WT +P PV -P e_load (1) In equation (1): P Hess The total power of the hybrid energy storage system is represented by a positive or negative value, indicating whether the power supply is in surplus or deficit. The hybrid energy storage system includes batteries, supercapacitors, and hydrogen storage devices.

4. The method for optimizing the configuration of composite energy storage based on the improved Rafflesia algorithm as described in claim 3, characterized in that, In S01, the energy storage management strategy includes: When the output of new energy sources exceeds the load, i.e., ΔP>0, the hybrid energy storage system begins to charge; otherwise, it begins to discharge. The charging strategy prioritizes charging the batteries, followed by the supercapacitors, and finally the hydrogen storage devices. If all energy storage units of the hybrid energy storage system are fully charged, the remaining electricity will be connected to the grid and sold to the grid. The discharge strategy prioritizes discharging the batteries, followed by the supercapacitors, and finally the hydrogen storage equipment. If there is still a power shortage after all the energy storage units of the hybrid energy storage system have been fully discharged, the power will be purchased from the grid.

5. The method for optimizing the configuration of composite energy storage based on the improved Rafflesia algorithm as described in claim 4, characterized in that, S02. Constructing an energy storage system capacity configuration model aimed at minimizing the annual investment cost of energy storage equipment and the total annual demand load includes: (I) Objective Function The formula for minimizing the quotient of the annual cost of the composite energy storage system and the total annual demand load is as follows: In the formula: LCE is in yuan / kWh, which refers to the average energy cost over the entire life cycle; AC sys The unit is yuan, which represents the total annual cost of the composite energy storage system; AC grid The unit is yuan, which represents the annual electricity purchase cost of the composite energy storage system; E Dyear The unit is kW, which represents the total load demand of the composite energy storage system. 1) Total annual cost of the composite energy storage system AC sys =(PC equip +PC rep )×CRF+AC O&M (3) Where: PC equip For equipment investment costs; PC rep Total replacement cost of load storage system; AC O&M The operating and maintenance cost is CRF, which is the capital recovery factor, and can be calculated by the following formula: Where: i is the annual interest rate; N is the lifespan of the energy storage system; In the formula: r O&M k is the conversion factor for operation and maintenance costs. SC k BA k EL and k HFC These refer to the unit price of supercapacitors, batteries, electrolyzers, and fuel cell energy storage equipment, respectively; N SC N BA N EL and N HFC These refer to the configuration capacity of supercapacitors, batteries, electrolyzers, and fuel cell energy storage devices, respectively. In the formula: L BA L SC L HFC L EL These refer to the lifespan of the storage battery, supercapacitor, fuel cell, and electrolyzer, respectively; y BA y SC y HFC y EL These are the number of times the storage battery, supercapacitor, fuel cell, and electrolyzer need to be replaced during the system's lifespan, respectively. 2) Annual electricity purchase cost of the composite energy storage system In the formula: P e_buy Purchase electricity for each time period; k grid Electricity price at each time point; 3) Total system load demand In the formula: P e_load For load demand at various times; (II) Constraints Constraints are set on the optimization variables for configuration optimization to ensure that the optimization results reflect actual conditions. The constraints are set as follows: 1) Power balance constraints P grid (t)+P WT (t)+P PV (t)+P BA (t)+P SC (t)+P HFC (t)=P EL (t)+P e_load (t) (9) In the formula: P WT (t), P PV (t), P BA (t) and P SC (t) represent the power output of the wind turbine, photovoltaic system, battery, and supercapacitor during time period t, respectively; P grid (t) represents the electricity purchased / sold by the system to the main network; P EL (t), P HFC (t) represent the electricity consumed by the electrolyzer during time period t and the electricity output by the fuel cell during time period t, respectively; P e_load (t) represents the load during time period t; 2) Constraints of storage batteries and supercapacitors Batteries and supercapacitors have similar operating models. To ensure the safe operation of energy storage systems, a unified model is adopted for both: In the formula: N represents the charging and discharging power of the nth type of energy storage device during time period t; ES.n Let P represent the configuration capacity of the nth type of energy storage device; ES.n (t) represents the final output power of the nth type of energy storage device during time period t; These are binary variables, representing the charging and discharging states of the nth type of energy storage device, respectively; SOC ES.n (t) refers to the capacity of the nth type of energy storage device during time period t; SOC ES.n.min SOC ES.n.max These refer to the minimum and maximum capacities of the nth type of energy storage device, respectively; n represents the type of energy storage device, where n=1 is a battery and n=2 is a supercapacitor. 3) Electrolytic cell constraint Where: P EL (t) represent the electrical energy input to the electrolyzer and the hydrogen energy output from the electrolyzer during time period t, respectively; η EL The energy conversion efficiency of the electrolytic cell; This refers to the upper limit of the energy input to the electrolytic cell; These refer to the upper and lower limits of the ramp rate for the electrolytic cell, respectively. 4) Constraints of fuel cells Where: P HFC (t) represents the hydrogen energy input to the fuel cell and the fuel cell output during time period t, respectively; η HFC For electrical energy conversion efficiency; Input the upper limit of hydrogen energy; These refer to the upper and lower limits of the ramp rate for fuel cells, respectively. 5) Planning constraints Where: N SC_max N BA_max N EL_max and N HFC_max These refer to the maximum capacity of supercapacitors, batteries, electrolyzers, and fuel cells, respectively; P e_buy Purchase electricity for the energy storage system at various times; E Dyear This represents the total annual system load demand; GD max This represents the maximum dependency.

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