A method and system for optimizing and adjusting a photovoltaic storage system based on particle swarm algorithm

Abstract

The present invention relates to a method and system for optimizing and regulating a photovoltaic storage system based on a particle swarm algorithm, the method comprising the following steps: collecting relevant data of the photovoltaic storage system and the spot market; based on the relevant data, comprehensively considering the economics of photovoltaic green electricity participating in the spot market transaction link and the real-time supply and demand balance of electricity, establishing a dual-objective photovoltaic storage joint optimization scheduling model; using an improved particle swarm algorithm to solve the dual-objective photovoltaic storage joint optimization scheduling model; optimizing and scheduling the photovoltaic storage system according to the solution result. Compared with the prior art, the present invention has the advantages of achieving maximum benefits while ensuring the real-time supply and demand balance of the transaction electricity.

Description

Technical Field

[0001] The present invention relates to the technical field of energy planning, and in particular to a method and system for optimizing and regulating a photovoltaic storage system based on a particle swarm algorithm. Background Art

[0002] Many researchers have studied the optimal dispatching strategy of photovoltaic and energy storage combined power generation systems. Among them, Ma Lin proposed a photovoltaic and battery power interaction strategy based on genetic algorithm in the document "Optimal Dispatching of Operation of Household Grid-connected Photovoltaic Power Generation System under Time-of-Use Electricity Price / Tiered Electricity Price" with the goal of maximizing the benefits of electricity sales to users. This strategy uses batteries to balance the power fluctuations when photovoltaic power is connected to the grid, but the frequent high-power charging and discharging of batteries seriously affects the service life of batteries. In the document "Research on Operation Control of DC Microgrid Based on Photovoltaic-Hybrid Energy Storage", Li Ying conducted energy optimization control research on microgrids containing photovoltaic and energy storage systems, proposed a photovoltaic and energy storage microgrid control strategy based on grid connection without reverse flow, and simulated and analyzed various charging and discharging operation conditions. This study focuses on the static analysis and simulation of power generation systems, ignoring that the grid connection of photovoltaic and energy storage systems is a dynamic, multi-angle joint dispatching process. Li Peiqiang et al. also studied the energy management strategy of photovoltaic and energy storage microgrids in the document "Energy Management Strategy of Photovoltaic DC Microgrid with Distributed Hybrid Energy Storage System". The researchers divided the microgrid into multiple areas according to the different capacity ratios of supercapacitors and batteries, and implemented different management strategies in different areas. This energy management strategy optimizes the output of each component of the system, but due to the large number of system partitions, its construction and operation control costs are high and it is not suitable for large-scale construction. Su Su et al. studied the optimal dispatching of photovoltaic storage systems including electric vehicles in the document "Energy Optimization Management of Microgrids Considering Electric Vehicles and Photovoltaic-Energy Storage", and proposed a dispatching model that takes into account photovoltaic output, energy storage output, electric vehicle charging and discharging power, and electricity prices with the goal of reducing system operating costs. This strategy uses the charging and discharging of electric vehicles to maximize the utilization of renewable energy generation. Gu Fei et al. studied the power distribution of each energy storage battery in the photovoltaic storage system in the document "Economic Optimization Dispatching Strategy of Energy Storage Power Station in Photovoltaic Storage System", and built a dispatching model with the goal of minimizing the total daily dispatching cost. This model reduces the dispatching cost by reducing the number of overcharge and over-discharge of energy storage batteries and extending the battery life. In the document "Research on Comprehensive Control Strategy of Photovoltaic Storage Power Station Participating in Grid Dispatching", Shi Jianxiong proposed a dispatching method for energy storage batteries to be zoned and regulated according to grid frequency and power with the goal of maximizing the economic benefits of photovoltaic storage power stations. This method optimizes the output characteristics of energy storage batteries and extends their life. However, since the energy of the energy storage battery in this scheduling mode can only come from photovoltaics, the regulation ability of the power grid system is reduced when photovoltaics are not working, resulting in poor system flexibility. In summary, due to the high construction cost of energy storage, most researchers have considered the economic efficiency of the photovoltaic storage system when optimizing the scheduling of the photovoltaic storage system.

[0003] In the paper "Multi-objective Capacity Optimization Configuration of Photovoltaic Microgrid Energy Storage System Based on Demand Response", Zhou Nan et al. used the improved non-inferior genetic algorithm II to solve the photovoltaic storage microgrid capacity configuration when the photovoltaic utilization rate and annual net profit are maximized, and realized the optimal scheduling of energy storage charging and discharging under the time-of-use electricity price. This method takes into account the demand response of the photovoltaic storage system and extends the offline operation time of the photovoltaic storage microgrid. However, under this scheduling, the microgrid system needs to be equipped with a large capacity of energy storage equipment, and the economic efficiency of the system operation is poor. With the decline of energy storage costs in the future, the potential of this model will be further reflected. In the paper "Optimal Configuration Method of Multi-Photovoltaic Storage Microgrid System in Distribution Network", Liu Jiaoyang et al. established a two-layer optimization scheduling model with the goal of reducing the operating cost and total economic cost of solar photovoltaic storage microgrids, and solved the optimal solution through a multi-attribute decision method. This model not only improves the peak-shaving and valley-filling capacity of the power grid, but also improves the photovoltaic absorption capacity, while reducing the operating cost of the distribution network under the time-of-use electricity price mechanism. However, the solution process of the two-layer model is relatively complicated, and the solution results between the two-layer models affect each other, making the calculation too complicated. In the document "Optimal Operation Method of Photovoltaic Storage Charging Station Energy Storage System Based on NSGA-Ⅲ and Fuzzy Clustering", Fu Zhangjie et al. combined genetic algorithm and fuzzy clustering method to optimize the energy storage operation strategy in the photovoltaic storage charging station. Under the strategy, the load fluctuation level on the grid side is guaranteed to be small, the energy supply quality is guaranteed, and the supply and demand balance level is improved. However, this strategy only considers the minimum load variance on the grid side, and ignores the load fluctuation on both sides of the generation and use. In the document "Configuration Optimization Strategy of Distribution Network and Photovoltaic Storage Microgrid Energy Storage System", Li Jianlin et al. proposed an energy storage capacity configuration optimization method based on the improved particle swarm algorithm with the goal of reducing node voltage fluctuations, load fluctuations and reducing energy storage costs. This method improves the limitations of the traditional particle swarm algorithm in this scenario, and reduces the number of energy storage batteries configured by enhancing the regulation capability of a single group of energy storage batteries. However, the connection between photovoltaic output and energy storage regulation in this strategy is not close enough, and the effect of smoothing system voltage fluctuations is not good, resulting in an imbalance between supply and demand. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for optimizing and regulating a photovoltaic storage system based on a particle swarm algorithm that satisfies the real-time supply and demand balance of trading electricity under spot trading.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for optimizing and adjusting a photovoltaic storage system based on a particle swarm algorithm comprises the following steps:

[0007] Collect relevant data on solar-storage systems and spot markets;

[0008] Based on the relevant data, establish a photovoltaic power generation output model and a photovoltaic storage system operation model;

[0009] Based on the output model and operation model, the economic benefits of photovoltaic green electricity participating in spot market transactions and the real-time supply and demand balance of electricity are comprehensively considered to establish a dual-objective photovoltaic and storage joint optimization scheduling model;

[0010] An improved particle swarm algorithm is used to solve the dual-objective photovoltaic-storage joint optimization scheduling model.

[0011] According to the solution result, the photovoltaic storage system is optimized and scheduled.

[0012] Furthermore, the expression of the output model is:

[0013]

[0014] The expression of the running model is:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020] In the formula, is the photovoltaic power generation, , and They represent the energy conversion efficiency of the photovoltaic inverter, the size of the power generation panel, and the rated output power per unit size under the standard experimental environment. and are the actual solar irradiance and the solar irradiance intensity in the standard experimental scene, is the actual ambient temperature in Celsius. and Indicates that the energy storage system t Moment and t -1 moment of energy storage status, , , , Respectively represent the charging and discharging power and efficiency of the energy storage system, and is the maximum charge and discharge power, and represents the upper and lower bounds of the energy storage state of the energy storage system, and are the initial energy storage state and the final energy storage state, TFor the entire optimization cycle, is the time interval.

[0021] Furthermore, the dual-objective photovoltaic-storage joint optimization scheduling model includes an objective function and constraints. The objective function includes the maximum profit of photovoltaic green electricity participating in the spot trading link as the first-level objective function, and the second-level objective function taking into account the real-time electricity balance and the minimum net load variance value.

[0022] Furthermore, the first layer objective function is:

[0023]

[0024] In the formula, F np,MAX To maximize the benefits of photovoltaic green electricity participating in spot trading; F grid For the revenue from electricity sales; M pv Cost of deviation assessment; F pun Punishment for abandoning the light; LCOS is the levelized cost of energy storage during the payback period;

[0025] Revenue from electricity sales F grid :

[0026]

[0027]

[0028] In the formula, F g ( t )for t The electricity sales revenue at the time; γ pv The photovoltaic grid-connected electricity price; P pv ( t )for t Photovoltaic power generation at the moment; γ bess The on-grid electricity price for energy storage; P bess ( t )for t The energy storage discharge power at the moment, Δt is the time interval;

[0029] Deviation Assessment Cost M pv :

[0030]

[0031]

[0032] In the formula, ΔQ m is the deviation power; α To avoid the assessment factor; Q load,pre Plan the power generation for the PV and storage power station; P* The price for unit deviation electricity quantity assessment; Q load is the actual power generation of the photovoltaic power station;

[0033] Abandonment Penalty F pun :

[0034]

[0035] In the formula, γ pun is the penalty price per unit of abandoned photovoltaic power; P pv,pun for t The abandoned optical power at the moment;

[0036] Levelized storage cost during payback period LCOS :

[0037]

[0038] In the formula, C E is the installation cost that varies with capacity C P is the installed cost that varies with power; d is the discharge time at rated power; T The life of the energy storage system; O&M ( t ) is the t Annual operation and maintenance costs; P C The price of electricity purchased during charging; r is the discount rate; η The cycle efficiency of the energy storage system; n ( t ) is the annual cycle number.

[0039] Furthermore, the second layer objective function is:

[0040]

[0041]

[0042]

[0043] In the formula,f min is the minimum daily net load variance value after energy storage; P NL ( t )for t Net load value at the time; P NL,ave for t The average value of the net load at the time; P load ( t ) is in t The grid-connected power of solar energy storage at the moment; P PV ( t ) is in t Photovoltaic power generation at the moment; P ES ( t ) is in t The energy storage output power at each moment.

[0044] Furthermore, the constraints include power market transaction deviation assessment mechanism constraints, power balance constraints, energy storage power constraints, energy storage SOC constraints, energy storage capacity constraints, deviation constraints and charge and discharge energy conservation constraints within a daily cycle, wherein the power market transaction deviation assessment mechanism constraints refer to the consideration of the deviation between the planned output and the actual output of photovoltaic power generation in the optimization scheduling process, and the power balance constraints are determined by the output model, and the power balance constraints are:

[0045]

[0046] The energy storage power constraint is determined by the operation model, and the energy storage power constraint is:

[0047]

[0048] The energy storage SOC constraint is:

[0049]

[0050] The energy storage capacity constraint is:

[0051]

[0052]

[0053]

[0054] The deviation constraint is:

[0055]

[0056] The charge and discharge energy conservation constraint within the daily cycle is:

[0057] In the formula, P load ( t ) is in t The grid-connected power of solar energy storage at the moment; P PV ( t ) is in t Photovoltaic power generation at the moment; P ES ( t ) is in t Energy storage output power at all times; P max is the maximum discharge power of energy storage; SOC min is the minimum charge rate of the energy storage system; SOC ( t ) is the initial charge rate of the energy storage system; SOC max is the maximum charge rate of the energy storage system; E min , E max is the minimum and maximum power of the energy storage system; ε is the hourly loss rate of remaining power; P e,c , P e,dis is the charging power and discharging power; α、β are the charge and discharge rates of the energy storage device, respectively; ΔE t for t The amount of charge and discharge during the time period; E ( t ) is the energy storage system in t Battery level at all times; P load,pre ( t )for t The planned grid-connected power of the photovoltaic power station at the moment; Δt For the time interval.

[0058] Furthermore, the assessment process of the power market transaction deviation assessment mechanism includes:

[0059] Determine whether the actual electricity consumption is equal to the market transaction electricity.

[0060] If yes, there is no deviation in electricity settlement.

[0061] If not, it is further determined whether the deviation rate is within the allowable value for exemption from assessment. The range of the allowable value for exemption from assessment is m <Deviation rate< NIf yes, then there is no deviation in electricity consumption settlement. If no, then judge m , N The relationship between the size of the deviation rate is m > Deviation rate, the assessment fee is the assessment electricity price a × power; when the deviation rate > N When b × Electricity, where the deviation rate is the core indicator of the deviation assessment mechanism for electricity market transactions, which is used to measure the degree of deviation of market participants. The expression of the deviation rate is:

[0062]

[0063] In the formula, is the deviation rate, Q α is the actual power consumption, Q γ Trade electricity for the electricity market.

[0064] Furthermore, the step of solving the problem by improving the particle swarm algorithm includes:

[0065] 1) Randomly initialize the position and velocity of each particle in the population, where the particle represents the optimal scheduling solution;

[0066] 2) Based on the dual-objective solar-storage joint optimization scheduling model, the objective function value of each particle in the population under each objective is calculated as the fitness value of the particle;

[0067] 3) Save the current set of non-inferior solutions in the population;

[0068] 4) Each particle saves the best position it has experienced as the historical optimal solution pbest ;

[0069] 5) Compare the fitness value of each particle, sort them, determine the quality of the particles, and decide the update direction of the particles according to the fitness value;

[0070] 6) Setting an inertia factor to control the update speed of the particles, wherein the inertia factor is gradually reduced during the iterative calculation process;

[0071] 7) Determine the location of the global optimal solution based on the global extreme value selection method qbest ;

[0072] 8) Use improved learning factors to update the velocity and position of particles;

[0073] 9) Determine whether the particle position crosses the boundary of the decision variable. If so, set the particle position to the boundary and update the particle velocity in reverse. If not, execute the next step.

[0074] 10) Perform Gaussian mutation operation on the particle position;

[0075] 11) Combine the newly generated population with the particle information of the original population to update the non-inferior solution set;

[0076] 12) Update and maintain the non-inferior solution set to ensure that each solution in the solution set is the relatively optimal solution in the current population;

[0077] 13) Determine whether the termination condition is met. If so, stop searching. If not, return to step 3), update the historical best position of the particle, and continue iterative searching until the termination condition is met.

[0078] Furthermore, the updating equations of the velocity and position of the particle are:

[0079]

[0080]

[0081] In the formula, , For particles i In the t +1, t The speed at the iteration, c 1 and c 2 They are all learning factors. c 1 Represents the individual cognition of the particle, c 2 represents the social cognition of particles, w is the inertia weight; rand 1 and rand 2 are random numbers uniformly distributed in the range [0, 1]. For particles i The individual optimal solution, is the optimal solution for the particle swarm, , Particles i In the t , t +1 position at iteration.

[0082] The present invention also provides a photovoltaic storage system optimization and regulation system based on a particle swarm algorithm, comprising:

[0083] Data collection module: used to collect relevant data of the solar storage system and the spot market;

[0084] Output and operation model building module: used to establish the output model of photovoltaic power generation and the operation model of the photovoltaic storage system based on the relevant data;

[0085] The scheduling model building module is used to establish a dual-objective photovoltaic storage joint optimization scheduling model based on the output model and operation model, taking into account the economic efficiency of photovoltaic green electricity participating in the spot market transaction link and the real-time supply and demand balance of electricity;

[0086] Solving module: used for solving the dual-objective photovoltaic energy storage joint optimization scheduling model by using an improved particle swarm algorithm;

[0087] System scheduling module: used to optimize the scheduling of the photovoltaic storage system according to the solution results.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] (1) The present invention comprehensively considers the economic benefits of photovoltaic green electricity participating in the spot market transaction link and the real-time supply and demand balance of electricity, and constructs a dual-objective photovoltaic-storage joint optimization scheduling model based on the photovoltaic power generation output model and the operation model of the photovoltaic-storage system. The purpose is to optimize the green electricity output curve through the photovoltaic-storage joint scheduling strategy to meet the real-time supply and demand balance of trading electricity.

[0090] (2) The present invention studies the deviation assessment mechanism for electricity market transactions, and constructs the maximum profit of photovoltaic green electricity participating in spot transactions as the first-level objective function with electricity sales revenue, deviation assessment cost, penalty for abandoned photovoltaic power and levelized energy storage cost as the elements, and the minimum variance value of net load as the second-level objective function. The power balance constraint, energy storage power constraint, energy storage SOC constraint, energy storage capacity constraint, deviation constraint and energy conservation constraint of charging and discharging within the daily cycle are used as constraint conditions. To a certain extent, the utilization rate of renewable energy is improved, the amount of abandoned photovoltaic power is reduced, and the profit of photovoltaic participating in spot electricity market transactions is increased.

[0091] (3) The improved particle swarm algorithm of the present invention reduces the inertia weight to escape the local optimal trap, promotes the global search for the optimal solution, and realizes a more accurate local search of the current area of ​​the particle, which helps to accelerate the convergence speed of the calculation. By using the improved particle swarm algorithm to solve the target model, the optimal scheduling method of the photovoltaic storage system is obtained. Under the premise of ensuring the real-time power supply and demand balance of photovoltaic green electricity participating in spot transactions, by controlling the charging and discharging of the energy storage system and the purchase and sale of electricity decisions in the spot market, the maximum profit of photovoltaic power generators when participating in spot transactions is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0093] Figure 2The improved particle swarm algorithm solution flow chart of the present invention;

[0094] Figure 3 A flow chart of the optimization scheduling strategy of the photovoltaic storage system of the present invention;

[0095] Figure 4 A curve diagram showing the deviation of photovoltaic power generation output from the planned output according to an embodiment of the present invention;

[0096] Figure 5 This is a curve diagram of the deviation of the combined output of photovoltaic and energy storage from the planned output according to an embodiment of the present invention;

[0097] Figure 6 This is a system net load curve diagram before and after configuring the energy storage device in an embodiment of the present invention. DETAILED DESCRIPTION

[0098] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0099] Example 1

[0100] This embodiment provides a method for optimizing and adjusting a photovoltaic storage system based on a particle swarm algorithm. Figure 1 As shown, the method comprises the following steps:

[0101] Study the deviation assessment mechanism of electricity market transaction; build an operation model of photovoltaic storage system; use electricity sales income, deviation assessment cost, penalty for abandoned light and levelized energy storage cost as factors to construct the maximum profit of photovoltaic green electricity participating in spot trading as the first-level objective function, and the minimum variance value of net load as the second-level objective function; use power balance constraint, energy storage power constraint, energy storage SOC constraint, energy storage capacity constraint, deviation constraint and energy conservation constraint of charging and discharging within a daily cycle as constraints; use improved particle swarm algorithm to solve the problem, and the improvement of improved particle swarm algorithm is: use lower inertia weight; optimize the scheduling of photovoltaic storage system according to the optimal solution.

[0102] S1. Determine the assessment mechanism for deviations in electricity market transactions.

[0103] The specific assessment mechanism for deviations in electricity market transactions includes:

[0104] The deviation rate is defined as:

[0105]

[0106] In the formula, Q α is the actual power consumption, Q γFor electricity market trading volume, if β >0, indicating a positive deviation; if β<0, indicating a negative deviation. When there is a difference between the actual electricity consumption and the market transaction electricity, the deviation electricity exceeding the allowable value for exemption from assessment shall be subject to additional deviation penalty.

[0107] The specific process of assessing deviations in electricity market transactions includes:

[0108] Determine whether the actual electricity consumption is equal to the market transaction electricity. If so, there is no deviation in electricity settlement. Otherwise, determine whether the deviation rate is within the allowable value for exemption from assessment ( m <Deviation rate< N ), if yes, then there is no deviation in electricity consumption settlement, otherwise, m、N The relationship between the size of the deviation rate is m > deviation rate, the assessment fee is the assessment electricity price a × electricity volume; when the deviation rate > N When b ×Battery capacity.

[0109] In the optimization scheduling strategy of the photovoltaic storage system, the deviation assessment mechanism runs through the whole process. Through the study of the deviation assessment mechanism, important constraints are provided for the optimization scheduling model of the photovoltaic storage system. In the process of optimization scheduling, it is necessary to consider how to control the deviation between the planned output and the actual output of photovoltaic power generation to avoid high deviation assessment costs. The deviation assessment cost directly affects the objective function of the maximum profit of photovoltaic green electricity participating in the spot trading link.

[0110] S2. Construct the output model of photovoltaic power generation and the operation model of the photovoltaic storage system.

[0111] (1) Constructing a photovoltaic power generation output model

[0112] (2)

[0113] in, Represents the power generation of the photovoltaic system, , and They represent the energy conversion efficiency of the photovoltaic inverter, the size of the power generation panel, and the rated output power per unit size under the standard experimental environment. and represent the actual solar irradiance and the solar irradiance intensity in the standard experimental scene, respectively. A value representing the actual ambient temperature in Celsius;

[0114] (2) Constructing an operation model of the energy storage system

[0115]

[0116] in, and Indicates that the energy storage system t Moment and t -1 moment of energy storage status, , 、 、 Respectively represent the charging and discharging power and efficiency of the energy storage system, and is the maximum charge and discharge power, and represents the upper and lower bounds of the energy storage state of the energy storage system, and are the initial energy storage state and the final energy storage state, T For the entire optimization run cycle.

[0117] S3. The maximum profit of photovoltaic green electricity participating in spot trading is constructed with electricity sales revenue, deviation assessment cost, penalty for abandoned light and levelized energy storage cost as the first-level objective function, and the minimum variance value of net load is constructed as the second-level objective function.

[0118] The maximum benefit of photovoltaic green electricity participating in spot trading is constructed by taking electricity sales revenue, deviation assessment cost, abandoned light penalty and levelized energy storage cost as the elements as the first-level objective function, which specifically includes:

[0119] Taking the maximum benefit of photovoltaic power generation when participating in spot trading as the goal, the objective function can be obtained from the power generation income, deviation assessment cost, abandoned light penalty and levelized energy storage cost:

[0120]

[0121] In the formula, F np,MAX To maximize the benefits of photovoltaic green electricity participating in spot trading; F grid For the revenue from electricity sales; M pv Cost of deviation assessment; F pun Punishment for abandoning the light; LCOS is the levelized cost of energy storage (within the payback period).

[0122] Defining Generation Revenue F grid :

[0123]

[0124] In the formula, F g ( t )for tThe electricity sales revenue at the time; γ pv The photovoltaic grid-connected electricity price; P pv ( t )for t Photovoltaic power generation at the moment; γ bess The on-grid electricity price for energy storage; P bess ( t )for t The energy storage discharge power at the moment; Expression (3) describes the accumulation of electricity sales revenue in 96 time periods within the scheduling day.

[0125] Defining Deviation Assessment Costs M pv :

[0126]

[0127] In the formula, ΔQ m is the deviation power; α To avoid the assessment factor; Q load,pre Plan the power generation for the PV and storage power station; P* The price for unit deviation electricity quantity assessment; Q load It is the actual power generation of the photovoltaic power station.

[0128] Defining the light abandonment penalty F pun :

[0129]

[0130] In the formula, γ pun is the penalty price per unit of abandoned photovoltaic power; P pv,pun for t The abandoned optical power at the moment.

[0131] Defining the Levelized Cost of Energy Storage LCOS :

[0132]

[0133] In the formula, C E is the installation cost that varies with capacity C P is the installed cost that varies with power; d is the discharge time at rated power; T The life of the energy storage system; O&M ( t ) is thet Annual operation and maintenance costs; P C The price of electricity purchased during charging; r is the discount rate; η The cycle efficiency of the energy storage system; n ( t ) is the annual cycle number.

[0134] The second-layer objective function of taking the net load minimum variance value as the second-layer objective function specifically includes:

[0135] Daily net load variance after energy storage f Taking the minimum as the goal and considering the real-time power balance, the net load value objective function can be obtained:

[0136]

[0137] In the formula, f min is the minimum daily net load variance value after energy storage; P NL ( t )for t Net load value at the time; P NL,ave for t The average value of the net load at a given moment.

[0138] S4. The constraints of the electricity market transaction deviation assessment mechanism, power balance constraints, energy storage power constraints, energy storage SOC constraints, energy storage capacity constraints, deviation constraints and energy conservation constraints for charging and discharging within the daily cycle are used as constraints.

[0139] This step takes the constraints of the power market transaction deviation assessment mechanism, power balance constraints, energy storage power constraints, energy storage constraints, energy storage capacity constraints, deviation constraints and energy conservation constraints for charging and discharging within the daily cycle as constraints, specifically including:

[0140] The constraint of the deviation assessment mechanism for electricity market transactions means that in the process of optimizing scheduling, it is necessary to consider how to control the deviation between the planned output and the actual output of photovoltaic power generation to avoid high deviation assessment costs.

[0141] In the photovoltaic storage system, the power balance constraint is constructed according to the photovoltaic power generation output model, which is:

[0142]

[0143] In the formula, P load ( t ) is in t The grid-connected power of solar energy storage at the moment; P PV (t ) is in t Photovoltaic power generation at the moment; P ES ( t ) is in t The energy storage output power at each moment. PES ( t ) is greater than 0, it is discharging; otherwise, it is charging.

[0144] The energy storage power constraint is constructed according to the operation model of the energy storage system:

[0145]

[0146] In the formula, P max is the maximum discharge power of energy storage; P ES ( t ) is greater than 0, it means that the energy storage is in a discharging state; otherwise, it is in a charging state.

[0147] Energy storage SOC constraint, the battery value should be within a certain range:

[0148]

[0149] In the formula, SOC min is the minimum charge rate of the energy storage system; SOC ( t ) is the initial charge rate of the energy storage system; SOC max is the maximum charge rate of the energy storage system.

[0150] The energy storage capacity constraint is:

[0151]

[0152] In the formula, E min , E max is the minimum and maximum capacity of the energy storage device; ε is the hourly loss rate of remaining power; P e,c , P e,dis is the charging power and discharging power; α , β are the charge and discharge rates of the energy storage device, respectively.

[0153] The deviation constraint is:

[0154]

[0155] In the formula,P load ( t )for t The grid-connected power of the photovoltaic power station at the moment; P load,pre ( t )for t Planned grid-connected power of the photovoltaic power station at this moment.

[0156] The energy conservation constraint for charging and discharging during the daily cycle is:

[0157]

[0158] In the formula, P e,c ( t ) is the t Energy storage charging power at all times; P e,dis ( t ) is the t The energy storage discharge power at the moment.

[0159] S5. Use improved particle swarm algorithm to solve.

[0160] The improvement of the improved particle swarm algorithm is: using a lower inertia weight. The particle swarm (PSO) algorithm specifically includes:

[0161] In particle swarm optimization, the solution to the optimization problem is considered to be a bird in the search space, called a "particle". When the PSO algorithm is running, each particle in the particle swarm will continuously update its own speed and position during iteration, thereby iterating the optimal solution in the particle swarm. The optimal solution mainly includes the group optimal solution q best and the individual optimal solution p best It consists of two categories, and the relationship between them is the subordinate relationship between individuals and groups.

[0162] Among them, the improved particle swarm algorithm is specifically improved as follows:

[0163] In the actual application of particle swarm algorithm, it is found that when the inertia weight w When the inertia weight is less than 0.9, it is easier to escape the local optimal trap and promote the global search for the optimal solution. w When it is greater than 0.4, a more accurate local search will be performed on the current area of ​​the particle, which helps to speed up the convergence of the calculation. In order to achieve better calculation results and calculation accuracy, the inertia weight w It is reduced from 0.9 to 0.4 to optimize and improve the particle swarm algorithm.

[0164] The improved particle swarm algorithm is used to solve the optimization problems of two objective functions of the photovoltaic storage system, such as Figure 2 As shown, specifically including:

[0165] (1) Randomly initialize the position and velocity of each particle in the population. The position of the particle is usually initialized according to the range of the decision variable.

[0166] (2) Calculate the objective function value (fitness) of each particle in the population to evaluate the quality of the particle.

[0167] (3) In the population P The current non-inferior solution set (Pareto solution set) is saved in , which is the set of optimal solutions in the multi-objective optimization problem.

[0168] (4) Each particle saves the best position it has experienced as the historical optimal solution p best .

[0169] (5) Compare the fitness value of each particle, sort them, determine the quality of the particles, and decide the update direction of the particles based on the fitness value.

[0170] (6) Set the inertia factor to 0.4 to affect the particle speed update. The inertia factor controls the degree to which the particle speed is updated based on the previous speed. If the weight is small, the particle will be more inclined to update based on its own historical experience and the global optimal solution.

[0171] (7) Determine the location of the global optimal solution based on the global extreme value selection method q best .

[0172] (8) Update the particle's velocity and position according to the update formula, using the improved learning factor:

[0173]

[0174] In the formula, t is the number of iterations. c 1 and c 2 They are all learning factors. c 1 Represents the individual cognition of the particle, c 2 represents the social cognition of particles; w =0.4 is the inertia weight; rand 1 and rand 2 are random numbers uniformly distributed in the range [0, 1].

[0175] (9) If the position of a particle crosses the boundary of a decision variable, the position of the particle is set to the boundary and its velocity is updated in the opposite direction to prevent the particle from flying out of the search space.

[0176] (10) Perform Gaussian mutation on the particle positions to perturb the particle positions to increase the diversity of the search and prevent the algorithm from falling into the local optimum.

[0177] (11) Combine the particle information of the newly generated population with that of the original population to update the non-inferior solution set (Pareto solution set).

[0178] (12) Update and maintain the set of non-inferior solutions to ensure that each solution in the solution set is the relatively optimal solution in the current population.

[0179] (13) If the termination condition is met (such as reaching the maximum number of iterations or the fitness meets the target accuracy), the search is stopped; if the termination condition is not met, the non-inferior solution set is returned, the historical best position of the particle is updated, and the iterative search continues.

[0180] S6. Optimize the scheduling of the photovoltaic storage system according to the obtained optimal solution.

[0181] The improved particle swarm algorithm is used to optimize the scheduling strategy of the photovoltaic storage system. Figure 3 As shown, specifically including:

[0182] The photovoltaic power station collects data on the dispatch day's photovoltaic output curve, commercial load curve, spot market transaction electricity price, and energy storage charging and discharging status. First, determine whether a certain moment is at a peak electricity consumption period;

[0183] (1) If it is a peak period of electricity consumption, determine whether the photovoltaic output is greater than the commercial load. If so, use the excess photovoltaic power generation for energy storage charging, and sell the remaining redundant power in the spot market. Otherwise, control the energy storage discharge (full discharge), and buy the remaining missing power in the spot market;

[0184] (2) If it is not a peak period of electricity consumption, determine whether the photovoltaic output is greater than the commercial load. If it is greater, the missing electricity is purchased in the spot market and the energy storage charging is controlled (full charge); otherwise, electricity is purchased in the spot market.

[0185] On the premise of ensuring the real-time supply and demand balance of photovoltaic green electricity participating in spot transactions, by controlling the charging and discharging of the energy storage system and the purchase and sale of electricity decisions in the spot market, the profits of photovoltaic power generators when participating in spot transactions can be maximized to the greatest extent.

[0186] Figure 4 : is a graph showing the photovoltaic power generation output deviation from the planned output according to an embodiment of the present invention. Figure 3As shown, the photovoltaic power generation system in the control group was affected by weather factors, resulting in uncertainty in its output power. The output curve of the photovoltaic power generation system deviated significantly from the planned output curve, especially in the 56th period, when the power generation deviation between the two was 7.9MW, the largest deviation.

[0187] Figure 5 The combined output of photovoltaic and energy storage deviates from the planned output curve according to an embodiment of the present invention. Figure 4 As shown in the figure, after adding the energy storage equipment, the energy storage system played an auxiliary role in balancing the difference between the real-time power of the photovoltaic power generation system and the declared output power of the photovoltaic storage system, and significantly improved the consistency between the real-time power of the photovoltaic storage system and the planned output curve. The maximum power deviation occurred in time period 72, with a power deviation of 2.8MW.

[0188] Among them, after the photovoltaic power generation system is equipped with energy storage equipment, by controlling the charging and discharging of energy storage, the system's planned output can be well tracked, the matching degree between the planned output and the actual output of the power generation system can be improved, and the deviation assessment cost can be reduced. In addition, since the power consumption of commercial loads is relatively stable, the gap between the predicted load and the actual load is small. This enables the photovoltaic storage system to better track the user load when declaring the planned output in spot transactions, thereby further improving the degree of supply and demand balance.

[0189] Figure 6 It is the net load curve of the system before and after the energy storage device is configured in the embodiment provided by the present invention. By comparing the net load curves before and after the energy storage device is configured, after adding the energy storage device, the combined output of the photovoltaic storage system smoothes the net load curve, reduces the fluctuation of the net load, and improves the working efficiency of the photovoltaic storage system, especially in the time periods of 0:00-08:00 and 23:00-24:00, under the action of the energy storage device, the net load is close to 0.

[0190] The present invention improves the particle swarm algorithm, establishes a dual-objective photovoltaic-storage joint optimization scheduling model applicable to spot market trading rules, and uses photovoltaic green electricity to participate in spot trading. It is of great significance to optimize the green electricity output curve, meet the real-time supply and demand balance of trading electricity, and break through the bottleneck of photovoltaic green electricity participating in spot market trading on a large scale.

[0191] Example 2

[0192] This embodiment provides a photovoltaic storage system optimization and regulation system based on a particle swarm algorithm, including:

[0193] Data collection module: used to collect relevant data of the solar storage system and the spot market;

[0194] Output and operation model building module: used to establish the output model of photovoltaic power generation and the operation model of the photovoltaic storage system based on the relevant data;

[0195] The scheduling model building module is used to establish a dual-objective photovoltaic storage joint optimization scheduling model based on the output model and operation model, taking into account the economic efficiency of photovoltaic green electricity participating in the spot market transaction link and the real-time supply and demand balance of electricity;

[0196] Solving module: used for solving the dual-objective photovoltaic energy storage joint optimization scheduling model by using an improved particle swarm algorithm;

[0197] System scheduling module: used to optimize the scheduling of the photovoltaic storage system according to the solution results.

[0198] The rest is the same as in Example 1.

[0199] If the above functions are implemented in the form of software functional units and sold or used as independent products, they 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 to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in 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., which can store program codes.

[0200] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0201] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0202] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0203] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0204] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0205] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing and adjusting a photovoltaic storage system based on a particle swarm algorithm, characterized in that: The following steps are involved: Collect relevant data on solar-storage systems and spot markets; Based on the relevant data, establish a photovoltaic power generation output model and a photovoltaic storage system operation model; Based on the output model and operation model, the economic benefits of photovoltaic green electricity participating in spot market transactions and the real-time supply and demand balance of electricity are comprehensively considered to establish a dual-objective photovoltaic and storage joint optimization scheduling model; An improved particle swarm algorithm is used to solve the dual-objective photovoltaic-storage joint optimization scheduling model. According to the solution result, the photovoltaic storage system is optimally scheduled; The expression of the output model is: The expression of the running model is: Ess min ≤Ess t ≤Ess max Ess0=Ess T In the formula, is the photovoltaic power generation, η pv , pv and pv They represent the energy conversion efficiency of the photovoltaic inverter, the size of the power generation panel and the rated output power per unit size under the standard experimental environment, Sr pv and Sr are the actual solar irradiance and the solar irradiance in the standard experimental scene, respectively. ct is the actual ambient temperature in Celsius, Ess t and Ess t-1 represents the energy storage state of the energy storage system at time t and time t-1, Respectively represent the charging and discharging power and efficiency of the energy storage system, P ch,max and P dch,max is the maximum charge and discharge power, Ess min and Ess max Indicates the upper and lower bounds of the energy storage state of the energy storage system, Ess0 and Ess T are the initial energy storage state and the final energy storage state, T is the entire optimization operation cycle, and Δt is the time interval; The dual-objective photovoltaic-storage joint optimization scheduling model includes an objective function and constraints. The objective function includes a first-level objective function that takes the maximum profit of photovoltaic green electricity participating in the spot trading link, and a second-level objective function that takes real-time electricity balance and takes the minimum net load variance value as the objective function.

2. The method for optimizing and adjusting a photovoltaic storage system based on a particle swarm algorithm according to claim 1, characterized in that: The first-layer objective function is: F np,MAX =(F grid -M pv -F pun -LCOS) In the formula, F np,MAX To maximize the benefits of photovoltaic green electricity participating in spot trading; grid is the revenue from electricity sales; M pv is the deviation assessment cost; F pun is the penalty for abandoned solar power; LCOS is the levelized cost of energy storage during the payback period; Electricity sales revenue F grid : In the formula, F g (t) is the electricity sales revenue at time t; γ pv is the photovoltaic grid-connected electricity price; P pv (t) is the photovoltaic power generation at time t; γ bess P is the energy storage grid-connected electricity price; bess (t) is the energy storage discharge power at time t, Δt is the time interval; Deviation assessment cost M pv : ΔQ m =Q load -Q load,pre Where ΔQ m is the deviation power; α is the exemption coefficient; Q load,pre is the planned power generation of the photovoltaic power station; P* is the unit deviation power assessment price; Q load is the actual power generation of the photovoltaic power station; Abandon Light Penalty F pun : In the formula, γ pun P is the penalty price per unit of abandoned photovoltaic power; pv,pun is the abandoned optical power at time t; Levelized storage cost LCOS during the payback period: In the formula, C E is the installation cost C that varies with capacity P is the installed cost that varies with power; d is the discharge time at rated power; T is the life of the energy storage system; O&M(t) is the operation and maintenance cost in the tth year; P C is the electricity purchase price during charging; r is the discount rate; η is the cycle efficiency of the energy storage system; n(t) is the number of annual cycles.

3. The method for optimizing and adjusting a photovoltaic storage system based on a particle swarm algorithm according to claim 1, characterized in that: The second layer objective function is: In the formula, f min is the minimum daily net load variance after energy storage; P NL (t) is the net load value at time t; P NL,ave is the average value of the net load at time t; P load (t) is the grid-connected power of the photovoltaic energy storage system at time t; P PV (t) is the photovoltaic power generation at time t; P ES (t) is the energy storage output power at time t.

4. The method for optimizing and adjusting a photovoltaic storage system based on a particle swarm algorithm according to claim 1, characterized in that: The constraints include power market transaction deviation assessment mechanism constraints, power balance constraints, energy storage power constraints, energy storage SOC constraints, energy storage capacity constraints, deviation constraints and daily cycle charge and discharge energy conservation constraints, wherein the power market transaction deviation assessment mechanism constraints refer to the consideration of the deviation between the planned output and the actual output of photovoltaic power generation in the optimization scheduling process, and the power balance constraints are determined by the output model, and the power balance constraints are: P load (t)=P PV (t)+P Es (t) The energy storage power constraint is determined by the operation model, and the energy storage power constraint is: |P ES (t)|≤P max The energy storage SOC constraint is: SOC min ≤SOC(t)≤SOC max The energy storage capacity constraint is: E min ≤E(t)≤E max E(t)=(1-ε)E t-1 +ΔE t The deviation constraint is: |P load (t)-P load,pre (t)|≤10% The charge and discharge energy conservation constraint within the daily cycle is: Where P load (t) is the grid-connected power of photovoltaic energy storage at time t; P PV (t) is the photovoltaic power generation at time t; P ES (t) is the energy storage output power at time t; P max is the maximum discharge power of energy storage; SOC min is the minimum charge rate of the energy storage system; SOC(t) is the initial charge rate of the energy storage system; SOC max is the maximum charge rate of the energy storage system; E min 、E max is the minimum and maximum power of the energy storage system; ε is the hourly loss rate of the remaining power; P e,c , P e,dis is the charging power and discharging power; α and β are the charging and discharging rates of the energy storage device respectively; ΔE t is the charge and discharge amount in time period t; E(t) is the amount of energy storage system at time t; P load,pre (t) is the planned grid-connected power of the photovoltaic power station at time t; Δt is the time interval.

5. The method for optimizing and adjusting a photovoltaic storage system based on a particle swarm algorithm according to claim 4, characterized in that: The assessment process of the power market transaction deviation assessment mechanism includes: Determine whether the actual electricity consumption is equal to the market transaction electricity. If yes, there is no deviation in electricity settlement. If not, it is further determined whether the deviation rate is within the allowable value for exemption from assessment. The range of the allowable value for exemption from assessment is m<deviation rate<N. If so, there is no deviation in electricity volume settlement. If not, it is determined the relationship between m, N and the deviation rate. When m>deviation rate, the assessment fee is the assessment electricity price a×electricity volume; when deviation rate>N, the assessment fee is the assessment electricity price b×electricity volume. The deviation rate is the core indicator of the deviation assessment mechanism for electricity market transactions, which is used to measure the degree of deviation of market participants. The expression of the deviation rate is: Where β is the deviation rate, Q α is the actual power consumption, Q γ Trade electricity for the electricity market.

6. The method for optimizing and adjusting a photovoltaic storage system based on a particle swarm algorithm according to claim 1, characterized in that: The steps of solving the problem by the improved particle swarm algorithm include: 1) Randomly initialize the position and velocity of each particle in the population, where the particle represents the optimal scheduling solution; 2) Based on the dual-objective solar-storage joint optimization scheduling model, the objective function value of each particle in the population under each objective is calculated as the fitness value of the particle; 3) Save the current set of non-inferior solutions in the population; 4) Each particle saves the best position it has experienced as the historical optimal solution pbest; 5) Compare the fitness values ​​of each particle, sort them, determine the quality of the particles, and determine the update direction of the particles according to the fitness values; 6) Setting an inertia factor to control the update speed of the particles, wherein the inertia factor is gradually reduced during the iterative calculation process; 7) Determine the position qbest of the global optimal solution according to the global extreme value selection method; 8) Use improved learning factors to update the velocity and position of particles; 9) Determine whether the particle position crosses the boundary of the decision variable. If so, set the particle position to the boundary and update the particle velocity in reverse. If not, execute the next step. 10) Performing Gaussian mutation operation on the particle position; 11) Combine the newly generated population with the particle information of the original population to update the non-inferior solution set; 12) Update and maintain the non-inferior solution set to ensure that each solution in the solution set is the relatively optimal solution in the current population; 13) Determine whether the termination condition is met. If so, stop searching. If not, return to step 3), update the historical best position of the particle, and continue iterative searching until the termination condition is met.

7. The method for optimizing and adjusting a photovoltaic storage system based on a particle swarm algorithm according to claim 6, characterized in that: The update equations for the velocity and position of the particle are: v i (t+1)=w·v i (t)+c1·rand1·(pbest i -x i (t))+c2·rand2·(qbest-x i (t)) x i (t+1)=x i (t)+v i (t+1) In the formula, v i (t+1), v i (t) is the velocity of particle i at the t+1th and tth iterations, c1 and c2 are learning factors, c1 represents the individual cognition of the particle, c2 represents the social cognition of the particle, w is the inertia weight; rand1 and rand2 are random numbers, which are uniformly distributed in the range of [0, 1], pbest i is the optimal solution for particle i, qbest is the optimal solution for the particle group, x i (t), x i (t+1) are the positions of particle i at the tth and t+1th iterations respectively.

8. A photovoltaic storage system optimization and regulation system based on particle swarm algorithm, characterized in that: include: Data collection module: used to collect relevant data of the solar storage system and the spot market; Output and operation model building module: used to establish the output model of photovoltaic power generation and the operation model of the photovoltaic storage system based on the relevant data; The scheduling model building module is used to establish a dual-objective photovoltaic storage joint optimization scheduling model based on the output model and operation model, taking into account the economic efficiency of photovoltaic green electricity participating in the spot market transaction link and the real-time supply and demand balance of electricity; Solving module: used for solving the dual-objective photovoltaic energy storage joint optimization scheduling model by using an improved particle swarm algorithm; System scheduling module: used to optimize the scheduling of the photovoltaic storage system according to the solution results; The expression of the output model is: The expression of the running model is: Ess min ≤Ess i ≤Ess max Ess0=Ess T In the formula, is the photovoltaic power generation, η pv , pv and pv They represent the energy conversion efficiency of the photovoltaic inverter, the size of the power generation panel and the rated output power per unit size under the standard experimental environment, Sr pv and Sr are the actual solar irradiance and the solar irradiance in the standard experimental scene, respectively. ct is the actual ambient temperature in Celsius, Ess t and Ess t-1 represents the energy storage state of the energy storage system at time t and time t-1, Respectively represent the charging and discharging power and efficiency of the energy storage system, P ch,max and P dch,max is the maximum charge and discharge power, Ess min and Ess max Indicates the upper and lower bounds of the energy storage state of the energy storage system, Ess0 and Ess T are the initial energy storage state and the final energy storage state, T is the entire optimization operation cycle, and Δt is the time interval; The dual-objective photovoltaic-storage joint optimization scheduling model includes an objective function and constraints. The objective function includes a first-level objective function that takes the maximum profit of photovoltaic green electricity participating in the spot trading link, and a second-level objective function that takes real-time electricity balance and takes the minimum net load variance value as the objective function.

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