Optical storage and charging station operation optimization model solving method

Abstract

The invention discloses an optical storage and charging station operation optimization model solving method, relates to the technical field of optical storage and charging coordinated regulation and control, and mainly aims to develop an intelligent solving method for effectively managing and optimizing operation of an optical storage and charging station through a probability optimization model and an advanced artificial intelligence algorithm. Specifically, the method aims to establish an optimization model which comprehensively considers photovoltaic power generation volatility and charging demand uncertainty, and the model is efficiently solved by using an artificial fish swarm algorithm so as to ensure that the optimal operation state can be achieved under various operation situations. Besides, parameters of the artificial fish swarm algorithm are corrected and optimized in real time by introducing an image group migration algorithm, the adaptability and robustness of the model are further enhanced, rapid changes of the market and the technical environment can be effectively handled, and therefore a brand-new and efficient solution is provided for operation management of the optical storage and charging station.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic-storage-charging coordinated control, and specifically to a method for solving a photovoltaic-storage-charging station operation optimization model. Background Art

[0002] With the rapid development of renewable energy, photovoltaic storage and charging stations, as a new type of energy infrastructure integrating photovoltaic power generation, energy storage systems and electric vehicle charging facilities, are of great significance for improving energy utilization efficiency and promoting energy structure transformation. Photovoltaic storage and charging stations can effectively alleviate the intermittent and instability of renewable energy supply, and play a positive supporting role in the regulation capacity and power supply reliability of the power grid. Therefore, optimizing the operation of photovoltaic storage and charging stations can not only improve energy utilization efficiency, but also enhance the flexibility and responsiveness of the power grid, which is an important research direction in the current context of energy transformation.

[0003] Existing research on the operation optimization of photovoltaic storage and charging stations focuses on static or deterministic models, which makes it difficult to fully cope with the uncertainty and dynamic changes in actual operation. In particular, when faced with the instability of photovoltaic power generation and the high volatility of charging demand, traditional models often show limitations in response and cannot accurately predict and adjust operation strategies. In addition, most existing methods fail to fully consider the interactions and constraints between the various components within the photovoltaic storage and charging station, which leads to the fact that the optimization results often fail to achieve the expected economy and reliability in actual operation, and thus fail to achieve the optimal balance between efficiency and reliability. Summary of the invention

[0004] The purpose of the present invention is to provide a method for solving an optimization model of a photovoltaic storage and charging station operation to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for solving an operation optimization model of a photovoltaic storage and charging station, comprising the following steps:

[0006] S1. The photovoltaic storage and charging station establishes a probabilistic optimization model for energy structure balance by considering the volatility of photovoltaic power generation and the uncertainty of charging demand;

[0007] S2. Solve the probability optimization model, establish an update model of the solution result, and determine the parameters to be adjusted according to the update model;

[0008] S3. Take the parameters to be adjusted as the analysis object, judge the changes in the quality of the operation effect of the probability optimization model, and find the optimal balance point of the energy structure.

[0009] Furthermore, the objective function of the probabilistic optimization model established in S1 that takes into account the volatility of photovoltaic power generation and the uncertainty of charging demand includes multiple objectives such as economic benefits and system reliability.

[0010] Economic benefits can be obtained by maximizing the operating income of the photovoltaic storage and charging station or minimizing the total operating costs. The income can come from electricity sales revenue, demand response services, etc., and the costs include the maintenance cost of photovoltaic power generation, depreciation and maintenance of the energy storage system, and other variable costs in operation. It can be expressed as:

[0011]

[0012] Among them, p t represents the electricity price in time period t; g t Represents the total output power of the photovoltaic system and the energy storage system; C t represents operating costs;

[0013] System reliability is used to describe the regulation demand based on the power grid and can be expressed as

[0014]

[0015] Among them, d t represents the grid power demand in time period t, and T represents T consecutive time periods.

[0016] The objective function of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station is:

[0017] max() means solving for the maximum value; α and γ represent weight factors, which are used to balance the importance of economic benefits, energy efficiency and system reliability; Q represents the maximum profit of the photovoltaic storage and charging station; P represents the minimum value of the grid regulation demand of the photovoltaic storage and charging station.

[0018] Furthermore, the S2 solves the probability optimization model and establishes an update model of the solution result. The specific steps of determining the parameters to be adjusted according to the update model are:

[0019] S201. Define the main parameters of the artificial fish swarm algorithm. The main parameters include: the number of decision variables in the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; the number of fish swarms used to find the optimal solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; the maximum number of rounds for finding the optimal solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; and the initial solution position of the artificial fish swarm for finding the optimal solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station.

[0020] S202, evaluate the fitness of the current solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station. The fitness of the current solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station is to compare the operation effect of the current solution with the ideal operation effect of the photovoltaic storage and charging station. Specifically:

[0021]

[0022] Among them, F f represents the fitness of the current solution of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; y r represents the operating effect of the current solution of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; y a It indicates the ideal operating effect of the photovoltaic storage and charging station.

[0023] S203, design an update model for the solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station. Specifically:

[0024]

[0025] Among them, θ t+1 and θ t They are the solutions of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station at time t+1 and time t respectively; represents the update strategy π of the solution of the probabilistic optimization model θ The logarithm of the gradient; β and ε are the parameters to be adjusted.

[0026] Furthermore, the step S3 takes the parameters to be adjusted as the analysis object, judges the change in the degree of the running effect of the probability optimization model, and finds the specific steps of the optimal energy structure balance point as follows:

[0027] S301, using the parameters β and ε to be adjusted in S2 as the solution space dimension of the elephant migration algorithm;

[0028] S302, defining the number of elephant groups in the elephant migration algorithm; defining the maximum number of migrations of the elephant migration algorithm;

[0029] S303, evaluating the quality of the solution of the current elephant migration algorithm.

[0030] Further,

[0031] Among them, F e is the evaluation result of the quality of the solution of the current elephant migration algorithm; u r is the operating effect of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station corresponding to the parameters to be adjusted in the current round; u a This is the operating effect of the probabilistic optimization model considering the operational uncertainty of the photovoltaic storage and charging station corresponding to the parameters to be adjusted in the previous round.

[0032] If the operating effect of the probability optimization model considering the operating uncertainty of the photovoltaic storage and charging station corresponding to β and ε in the current round is better than the operating effect of the probability optimization model considering the operating uncertainty of the photovoltaic storage and charging station corresponding to β and ε in the previous round, the elephant herd will maintain the original direction of searching for the solution of β and ε; otherwise, it will randomly select other migration directions.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] Photovoltaic storage and charging stations involve multiple energy systems, such as photovoltaics, energy storage, and charging facilities, and their operation is affected by variable factors, such as weather, equipment performance, and user needs. Traditional models may not be accurate enough or respond in a timely manner when dealing with these complex and dynamically changing factors. By establishing a probabilistic optimization model, the present invention can more accurately describe and predict the impact of these complex factors, thereby improving the practical applicability and prediction accuracy of the model. Therefore, the present invention has the advantages of efficiently handling the uncertainty of distributed new energy and intelligent optimization of models. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A comparison chart of changes in operating costs in a method for solving an optimization model for a photovoltaic storage and charging station operation according to the present invention;

[0036] Figure 2 The invention discloses a convergence process of an artificial fish swarm algorithm in a method for solving a photovoltaic storage and charging station operation optimization model. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The present invention provides a technical solution to a method for solving an optimization model of a photovoltaic storage and charging station operation, comprising the following steps:

[0039] S1. The photovoltaic storage and charging station establishes a probabilistic optimization model for energy structure balance by considering the volatility of photovoltaic power generation and the uncertainty of charging demand;

[0040] Furthermore, the probabilistic optimization model established in S1 that takes into account the uncertainty of the operation of the photovoltaic storage and charging station adopts a random process to describe the volatility of photovoltaic power generation and electric vehicle charging demand.

[0041] Furthermore, the volatility of photovoltaic power generation is mainly affected by sunshine conditions, and its volatility can be described by a random process model. Markov chains are usually used to simulate sunshine changes. The state set is defined to represent different sunshine conditions, such as sunny, cloudy, overcast, etc. Each state corresponds to a photovoltaic output power level. The state transition probability of the Markov chain can be estimated based on historical meteorological data. Specifically:

[0042]

[0043] Among them, P is the volatility transfer probability matrix of photovoltaic power generation, and its elements represent the transfer probability under different conditions.

[0044] Furthermore, the arrival and charging demand volatility of the electric vehicles is simulated by a Poisson process. Assuming that the process of electric vehicles arriving at charging stations is a Poisson process, its arrival rate λ can be determined based on historical data. The charging demand can be modeled by exponential distribution, and the probability density function of the charging demand X of each vehicle is as follows:

[0045] f X (x) = λe -λx ,x≥0;

[0046] The Poisson distribution formula for electric vehicle arrival rate is:

[0047]

[0048] Here, k represents the number of electric vehicles arriving within time t, and λ is the average arrival rate per unit time.

[0049] Furthermore, the objective function of the probability optimization model established in S1 considering the volatility of photovoltaic power generation and the uncertainty of charging demand includes multiple objectives such as economic benefits and system reliability.

[0050] Economic benefits can be obtained by maximizing the operating income of the photovoltaic storage and charging station or minimizing the total operating costs. The income can come from electricity sales revenue, demand response services, etc., and the costs include the maintenance cost of photovoltaic power generation, depreciation and maintenance of the energy storage system, and other variable costs in operation. It can be expressed as:

[0051]

[0052] Among them, p t represents the electricity price in time period t; g t Represents the total output power of the photovoltaic system and the energy storage system; C t represents operating costs;

[0053] System reliability is used to describe the regulation demand based on the power grid and can be expressed as

[0054]

[0055] Among them, d t represents the grid power demand in time period t, and T represents T consecutive time periods.

[0056] The objective function of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station is:

[0057] max() means solving for the maximum value; α and γ represent weight factors, which are used to balance the importance of economic benefits, energy efficiency and system reliability; Q represents the maximum profit of the photovoltaic storage and charging station; P represents the minimum value of the grid regulation demand of the photovoltaic storage and charging station.

[0058] S2. Solve the probability optimization model, establish an update model of the solution result, and determine the parameters to be adjusted according to the update model;

[0059] Furthermore, the S2 solves the probability optimization model and establishes an update model of the solution result. The specific steps of determining the parameters to be adjusted according to the update model are:

[0060] S201. Define the main parameters of the artificial fish swarm algorithm. The main parameters include: the number of decision variables in the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; the number of fish swarms used to find the optimal solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; the maximum number of rounds for finding the optimal solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; and the initial solution position of the artificial fish swarm for finding the optimal solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station.

[0061] The probability optimization model established in step S1 is difficult to solve directly because the probability optimization model established in step S1 involves many decision variables and the model is nonlinear; at this time, the decision variables of the probability optimization model established in step S1 are used as the fish school of the artificial fish school algorithm in step S2, and the fish school foraging method can be used to explore a feasible optimal solution;

[0062] It can be seen that during the initialization process in S201, the decision variables of the probability optimization model established in step S1 have been input into the algorithm as initialization conditions of the artificial fish swarm algorithm, thereby ensuring the stability and reliability of the algorithm operation.

[0063] S202, evaluate the fitness of the current solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station. The fitness of the current solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station is to compare the operation effect of the current solution with the ideal operation effect of the photovoltaic storage and charging station. Specifically:

[0064]

[0065] Among them, F f represents the fitness of the current solution of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; y r represents the operating effect of the current solution of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; y a It indicates the ideal operating effect of the photovoltaic storage and charging station.

[0066] S203, design an update model for the solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station. Specifically:

[0067]

[0068] Among them, θ t+1 and θ t They are the solutions of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station at time t+1 and time t respectively; represents the update strategy π of the solution of the probabilistic optimization model θ The logarithm of the gradient; β and ε are the parameters to be adjusted.

[0069] S3. Take the parameters to be adjusted as the analysis object, judge the changes in the quality of the operation effect of the probability optimization model, and find the optimal balance point of the energy structure.

[0070] Furthermore, the step S3 takes the parameters to be adjusted as the analysis object, judges the change in the degree of the running effect of the probability optimization model, and finds the specific steps of the optimal energy structure balance point as follows:

[0071] S301, using the parameters β and ε to be adjusted in S2 as the solution space dimension of the elephant migration algorithm;

[0072] S302, defining the number of elephant groups in the elephant migration algorithm; defining the maximum number of migrations of the elephant migration algorithm;

[0073] S303, evaluating the quality of the solution of the current elephant migration algorithm.

[0074] Further,

[0075] Among them, F e is the evaluation result of the quality of the solution of the current elephant migration algorithm; u r is the operating effect of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station corresponding to the parameters to be adjusted in the current round; u a This is the operating effect of the probabilistic optimization model considering the operational uncertainty of the photovoltaic storage and charging station corresponding to the parameters to be adjusted in the previous round.

[0076] If the operating effect of the probability optimization model considering the operating uncertainty of the photovoltaic storage and charging station corresponding to β and ε in the current round is better than the operating effect of the probability optimization model considering the operating uncertainty of the photovoltaic storage and charging station corresponding to β and ε in the previous round, the elephant herd will maintain the original direction of searching for the solution of β and ε; otherwise, it will randomly select other migration directions.

[0077] like Figure 1 As shown, this embodiment considers a typical photovoltaic storage and charging station, which includes a photovoltaic power generation system, a battery energy storage system and an electric vehicle charging station. The goal is to optimize the total energy management of this site to maximize economic benefits while ensuring the reliability of energy supply and meeting the charging needs of electric vehicle users. System parameter settings: Photovoltaic system: maximum power generation capacity is 500kW. Energy storage system: total battery capacity is 1000kWh. Charging station: has 10 charging piles, and the maximum output power of each charging pile is 22kW.

[0078] The method proposed in step S1 is used to perform probability optimization modeling on the photovoltaic storage and charging station of the embodiment. The decision variables in the model are the power of the photovoltaic system, energy storage system and charging station in each time period. The decision variables in the model are input into the parameters of the artificial fish school algorithm, and the decision variables are optimized and solved using the artificial fish school algorithm and the elephant swarm algorithm. The final solution obtained is the optimal solution for the power of the photovoltaic system, energy storage system and charging station in each time period in the photovoltaic storage and charging station of the embodiment.

[0079] Figure 1 The figure shows a significant reduction in operating costs throughout the day after the optimization model was implemented. The red line represents the operating costs before optimization, and the other green line represents the operating costs after optimization. The data points of each line are distributed on a 24-hour timeline, and the costs for each hour are marked with a circle (before optimization) and a cross (after optimization). This improvement in cost-effectiveness can be attributed to more effective energy management and equipment operation strategies, including more accurate photovoltaic power generation forecasts, optimized utilization of battery storage, and efficient operation of charging stations. The optimized cost curve is lower than before optimization at almost all time points, indicating the all-weather effect of the optimization measures.

[0080] Figure 2 The figure shows the convergence process of the artificial fish swarm algorithm in 50 iterations. This process is represented by a blue line, and each iteration point is marked by a circle. As can be seen from the figure, as the number of iterations increases, the value of the objective function gradually decreases and tends to stabilize, indicating that the artificial fish swarm algorithm can effectively find the potential optimal solution to the problem. The algorithm converges quickly, with a significant decrease in the early stage and then gradually stabilizes, which reflects the efficiency and practicality of the algorithm in solving optimization problems.

[0081] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for solving an optimization model of a photovoltaic storage and charging station operation, characterized in that: The photovoltaic storage and charging station establishes a probabilistic optimization model for energy structure balance by considering the volatility of photovoltaic power generation and the uncertainty of charging demand; Solve the probability optimization model, establish an update model of the solution results, and determine the parameters to be adjusted according to the updated model; The parameters to be adjusted are taken as the analysis objects to judge the changes in the quality of the operation effect of the probability optimization model and find the optimal balance point of the energy structure.

2. A method for solving the operation optimization model of a photovoltaic storage and charging station according to claim 1, characterized in that: The probabilistic optimization model considering the volatility of photovoltaic power generation and the uncertainty of charging demand is expressed by the objective function E; Where, E = max(α*Q-γ*P); max() means solving for the maximum value; α and γ represent weight factors respectively; Q represents the maximum profit of the photovoltaic storage and charging station; P represents the minimum value of the grid regulation demand of the photovoltaic storage and charging station.

3. A method for solving the operation optimization model of a photovoltaic storage and charging station according to claim 2, characterized in that: The maximum profit Q of the PV-storage-charging station is obtained by maximizing the operating income of the PV-storage-charging station or minimizing the total operating cost; in, p t represents the electricity price in time period t; g t Represents the total output power of the photovoltaic system and the energy storage system; C t represents operating costs; Minimum grid regulation requirements for photovoltaic storage and charging stations Among them, d t represents the grid power demand in time period t, and T represents T consecutive time periods.

4. The method for solving the operation optimization model of a photovoltaic storage and charging station according to claim 1 is characterized in that: Solving the probabilistic optimization model involves the following steps: S201, defining parameters of an artificial fish swarm algorithm; S202, evaluating the fitness of the current solution of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; S203. Design an update model for the solution of the probabilistic optimization model that takes into account the uncertainty of the operation of the photovoltaic storage and charging station.

5. The method for solving the operation optimization model of a photovoltaic storage and charging station according to claim 4 is characterized in that: In S201, the parameters include: the number of decision variables in the probabilistic optimization model considering the uncertainty in the operation of photovoltaic storage and charging stations; the number of fish schools used to find the optimal solution of the probabilistic optimization model considering the uncertainty in the operation of photovoltaic storage and charging stations; the maximum rounds of finding the optimal solution of the probabilistic optimization model considering the uncertainty in the operation of photovoltaic storage and charging stations; and the initial solution position of the artificial fish school for finding the optimal solution of the probabilistic optimization model considering the uncertainty in the operation of photovoltaic storage and charging stations.

6. A method for solving an operation optimization model of a photovoltaic storage and charging station according to claim 4, characterized in that: In S202, the fitness of the current solution of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station is expressed by comparing the operation effect of the current solution with the ideal operation effect of the photovoltaic storage and charging station: Among them, F f represents the fitness of the current solution of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; y r represents the operating effect of the current solution of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station; y a It indicates the ideal operating effect of the photovoltaic storage and charging station.

7. The method for solving the operation optimization model of a photovoltaic storage and charging station according to claim 4 is characterized in that: The update model is specifically: Among them, θ t+1 and θ t They are the solutions of the probabilistic optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station at time t+1 and time t respectively; represents the update strategy π of the solution of the probabilistic optimization model θ The logarithm of the gradient; β and ε are the parameters to be adjusted.

8. A method for solving an operation optimization model of a photovoltaic storage and charging station according to any one of claims 1 to 7, characterized in that: The method of using the elephant swarm migration algorithm to correct and adjust the parameters to be adjusted of the artificial fish swarm algorithm includes the following steps: S301, using the parameter to be adjusted as the spatial dimension of the solution of the elephant migration algorithm; S302, defining the number of elephant groups for the elephant group migration algorithm, and defining the maximum number of migrations for the elephant group migration algorithm; S303, evaluating the quality of the solution of the current elephant migration algorithm F e .

9. A method for solving the operation optimization model of a photovoltaic storage and charging station according to claim 8, characterized in that: The quality of the solution of the current elephant migration algorithm is measured by F e express, Among them, u r is the operating effect of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station corresponding to the parameters to be adjusted in the current round; u a The operating effect of the probability optimization model considering the uncertainty of the operation of the photovoltaic storage and charging station corresponding to the parameters to be adjusted in the previous round; If the operating effect of the probability optimization model considering the operating uncertainty of the photovoltaic storage and charging station corresponding to the parameters to be adjusted in the current round is better than the operating effect of the probability optimization model considering the operating uncertainty of the photovoltaic storage and charging station corresponding to the parameters to be adjusted in the previous round, the elephant herd will maintain the original direction of searching for the solution of the parameters to be adjusted; otherwise, it will randomly select other migration directions and correct and adjust the parameters to be adjusted again until the correct direction is found.

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