Shared energy storage configuration method considering different electricity consumption scenarios

Abstract

The invention discloses a shared energy storage configuration method considering different power consumption situations, and relates to the technical field of power system planning. Comprising the steps of determining important parameters of operation of shared energy storage in different power consumption scenes according to basic data and actual conditions; constructing a shared energy storage upper-layer configuration model to optimize capacity and power configuration under different power consumption situations; constructing a shared energy storage lower-layer optimization model to optimize a daily operation output strategy; solving the constructed shared energy storage upper-layer configuration model, and generating different configuration capacities and configuration powers; calculating the optimal solutions of the real-time electricity purchase quantity and the real-time power of the lower-layer target function; and returning the optimal solution to the shared energy storage upper-layer configuration model, if a convergence condition is met, outputting the optimal solution, and if the convergence condition is not met, continuing iteration until the shared energy storage optimal configuration and a system daily operation scheme are output, and finally obtaining a global optimal solution. According to the invention, shared energy storage is promoted to play an important role in development of a novel power system and realization of a dual-carbon target.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and more specifically to a shared energy storage configuration method considering different power usage scenarios. Background Art

[0002] Vigorously developing a new power system based on new energy can effectively reduce carbon emissions, but due to its own characteristics, it requires the assistance of auxiliary equipment such as energy storage. With the rise of the sharing economy, shared energy storage has brought new ideas to solve problems such as the poor economic efficiency of single energy storage, but different electricity usage scenarios and different shared energy storage operation modes and operation strategies have different impacts on its economic benefits.

[0003] At present, many scholars have studied the configuration methods of shared energy storage, such as the multi-agent shared energy storage capacity configuration method of microgrid group based on negotiation game invented by Liu Chao et al. and the configuration method and system of user-side shared energy storage system invented by Wu Jianbin et al. However, the existing research on shared energy storage configuration usually uses a single-layer optimization model. By using Nash bargaining, target programming method, master-slave game and other methods to optimize strategy configuration, it is proved that shared energy storage can improve the new energy consumption capacity, energy utilization rate, reduce costs, and achieve overall optimization of the power generation side, grid side and user side. However, there are not many articles that use a double-layer optimization model to simultaneously consider multiple optimization objectives such as the economic benefits, cost, coal reduction and carbon reduction of shared energy storage operation to study the configuration problem of shared energy storage in different electricity consumption scenarios; and some literatures do not consider the capacity configuration optimization research of energy storage facilities, and the results obtained lack the key variable of configuration capacity. In addition, few literatures and methods set up optimization models based on electricity prices in different seasons, and the results obtained do not fully consider the operation of my country's power market.

[0004] Therefore, it is an urgent problem for those skilled in the art to propose a shared energy storage configuration method that takes into account different electricity usage scenarios to solve the difficulties existing in the prior art. Summary of the invention

[0005] In view of this, the present invention provides a shared energy storage configuration method taking into account different electricity usage scenarios, so as to solve the technical problems existing in the prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A shared energy storage configuration method considering different electricity consumption scenarios includes the following steps:

[0008] Determine the 24-hour electricity price of shared energy storage under different electricity usage scenarios, collect basic data of shared energy storage under various electricity usage scenarios, and determine the important parameters for the operation of shared energy storage under different electricity usage scenarios based on the basic data and actual conditions;

[0009] Taking the configuration capacity and configuration power of the shared energy storage system as decision variables, and taking the minimum cost of the shared energy storage system, the maximum coal reduction per unit capacity, and the maximum carbon dioxide emission reduction per unit capacity as optimization goals, a shared energy storage upper-level configuration model is constructed to optimize the capacity and power configuration under different electricity consumption scenarios.

[0010] Taking the real-time electricity purchase amount and real-time power of the shared energy storage system as decision variables and maximizing the daily economic benefits of the shared energy storage system as the goal, a shared energy storage lower-level optimization model is constructed to optimize the daily operation output strategy.

[0011] The shared energy storage upper configuration model is solved and constructed using a multi-objective genetic algorithm and the evolutionary algorithm library of the Geatpy2 framework to generate different configuration capacities and configuration powers;

[0012] The generated configuration capacity and configuration power parameters are imported into the constructed shared energy storage lower-level optimization model, and Gurobi+Python is used to calculate the optimal solution of the lower-level objective function of real-time purchased electricity and real-time power;

[0013] The calculated optimal solution is returned to the shared energy storage upper configuration model to complete the objective function calculation of the shared energy storage upper configuration model, and the upper population is selected and cross-operated. If the convergence conditions are met, the optimal solution is output. If not, the iteration continues. The shared energy storage upper configuration model and the shared energy storage lower optimization model are iterated until the shared energy storage optimal configuration and the system daily operation plan are output, and finally the global optimal solution is obtained.

[0014] The above method optionally includes basic data such as revenue, cost, operation constraints, coal consumption coefficient and carbon dioxide coefficient.

[0015] The above method is optional, and the specific content of the optimization objectives of minimizing the cost of the shared energy storage system, maximizing the coal reduction per unit capacity, and maximizing the carbon dioxide emission reduction per unit capacity is as follows:

[0016] The cost calculation formula of the shared energy storage system is:

[0017] LCC=IC+OC+RC+DC

[0018] Among them, LCC is the cumulative sum of all costs in the cost-benefit analysis of the shared energy storage system; IC is the initial investment cost; OC is the operation and maintenance cost; RC is the equipment replacement cost; DC is the recovery cost;

[0019] The calculation formula for coal reduction per unit capacity is:

[0020]

[0021] ΔW煤 =λ 煤 *S year

[0022] S year =P rate *Charge and discharge times

[0023] Among them, δ 煤 The amount of coal reduced per unit capacity of the shared energy storage system; ΔW 煤 The amount of coal consumption reduced during the life cycle of the shared energy storage system; E rate is the configuration capacity of the shared energy storage system; 煤 is the coal consumption coefficient; S year Promote the consumption of electricity for shared energy storage systems; rate Configure power for shared energy storage systems;

[0024] The calculation formula for carbon dioxide emission reduction per unit capacity is:

[0025]

[0026] in, The carbon dioxide emission reduction per unit capacity of the shared energy storage system; The amount of carbon dioxide emissions reduced during the life cycle of the shared energy storage system; E rate Allocate capacity for shared energy storage systems; is the carbon dioxide emission coefficient; S year Promote electricity consumption for shared energy storage systems.

[0027] In the above method, optionally, the constraints of the upper configuration model of shared energy storage are:

[0028] The rated power and capacity constraints of the shared energy storage system are:

[0029] P rate ≤P M

[0030] E rate ≤E M

[0031] Among them, P M is the maximum power of shared energy storage; EM is the maximum capacity of shared energy storage;

[0032] The charging and discharging power limits of the shared energy storage system are:

[0033] P rate ≥|P ES (t)|

[0034] Among them, P ES (t) is the charging and discharging power of the shared energy storage during period t;

[0035] The state of charge constraint of the shared energy storage system is:

[0036]

[0037] Where j is the time period, j = 1, 2, ..., 24; SOC max The highest state of charge; SOC min is the lowest charge state; Δt is the interval between the t period and the t+1 period; η c is the charging efficiency; η d is the discharge efficiency; δ(t) is the charging and discharging efficiency coefficient of the shared energy storage in period t.

[0038] The above method is optional, and the specific content of maximizing the daily economic benefits of the shared energy storage system is as follows:

[0039]

[0040] Among them, F is the economic benefit of the shared energy storage system operating for one day; γ t is the electricity price of the system in period t; β is the charging and discharging cost coefficient of the shared energy storage system; α t P is the price of electricity purchased from the power grid during period t; load (t) is the power load during period t; P ES (t) is the charging and discharging power of the shared energy storage during period t, negative means charging and positive means discharging; P G (t) is the amount of electricity purchased from the power grid during period t; OC is the daily operation and maintenance cost; E rate is the configuration capacity; P rate is the configured power; D is the annual operating days; R is the monthly rental fee.

[0041] In the above method, optionally, the constraints of the shared energy storage lower layer optimization model are:

[0042] The system power balance constraint is:

[0043] P G (t)+P ES (t) = P load (t)

[0044] Among them, P G (t) is the amount of electricity purchased from the power grid during period t; P ES (t) is the charging and discharging power of the shared energy storage during period t; P load (t) is the electricity load during period t;

[0045] The output constraint of the shared energy storage system is:

[0046] P min ≤P ES(t)≤P max

[0047] Among them, P min is the minimum output power of the shared energy storage system; P max is the maximum output power of the shared energy storage system;

[0048] The daily operation constraints of the shared energy storage system are:

[0049]

[0050] The state of charge constraint of the shared energy storage system is:

[0051]

[0052] SOC min ≤SOC(t)≤SOC max

[0053] Where SOC(t) is the state of charge of the battery in period t; SOC(t-1) is the state of charge of the battery in period t-1; ω is the battery self-discharge coefficient; E rate is the rated capacity of the shared energy storage system; η c is the charging efficiency of the energy storage system; η d is the discharge efficiency of the energy storage system; Δt is the interval between period t and period t+1; SOC min SOC is the lower limit of the battery state of charge; max The upper limit of the battery state of charge.

[0054] The above method, optionally, the specific content of the multi-objective genetic algorithm is:

[0055] Under given constraints, solve multiple objective function maximum problems:

[0056] max{z1=f(x),z2=f(x),…,z r =f(x)}

[0057] stg i (x),i=1,2…,m

[0058] where z1 = f(x), z2 = f(x),…,z r = f(x) represents r linear or nonlinear objective functions of the variable x; stg i (x) are m constraint functions on the variable x;

[0059] There is not necessarily only one optimal solution to a multi-objective optimization problem. In order to find a reasonable solution, a set of reasonable solutions to the multi-objective optimization problem, namely the Pareto optimal solution, is introduced.

[0060] The above method optionally determines the optimal solution of the multi-objective problem as follows:

[0061] Given a multi-objective optimization problem minf(x), let X * ∈Ω, if , so that the following conditions are met:

[0062] For any sub-objective function f of f(x) i (x) has f i (X)≤f i (X * ), and there is at least one sub-objective function f i (x) makes f i (X) <f i (X * ), then call X * It is a strong Pareto optimal solution;

[0063] Given a multi-objective optimization problem minf(x), let X * ∈Ω, if , so that the following conditions are met:

[0064] For any sub-objective function f of f(x) i (x) has f i (X) <f i (X * ), then call X * It is a weak Pareto optimal solution, and the strong Pareto optimal solution is referred to as the Pareto optimal solution.

[0065] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a shared energy storage configuration method that takes into account different power usage scenarios, and its beneficial effects are:

[0066] For different electricity consumption scenarios such as summer, winter and other seasons, the configuration capacity, configuration power, real-time electricity purchase amount and real-time power of the shared energy storage system are comprehensively considered, so that it can adjust the charging and discharging strategies in real time according to electricity price fluctuations, and obtain the optimal rated capacity configuration and rated power configuration of shared energy storage in various scenarios that take into account electricity price changes, load fluctuations, coal reduction, carbon dioxide emissions reduction and cost-benefit, effectively minimizing system costs and maximizing economic benefits, reducing system coal consumption and carbon dioxide emissions, and promoting shared energy storage to play an important role in the development of new power systems and achieving the "dual carbon" goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0068] Figure 1 A flow chart of a shared energy storage configuration method considering different electricity usage scenarios provided by the present invention;

[0069] Figure 2 An example diagram of strong and weak Pareto optimal solutions provided by the present invention;

[0070] Figure 3 A flow chart for solving the double-layer optimization model provided by the present invention;

[0071] Figure 4 A summer electricity purchase price chart provided by an embodiment of the present invention;

[0072] Figure 5 A schematic diagram of summer electricity prices and energy storage charging and discharging power provided by an embodiment of the present invention;

[0073] Figure 6 A schematic diagram of the optimal solution for the summer electricity consumption scenario provided by the present invention;

[0074] Figure 7 Schematic diagram of the optimal solution of the upper objective function under the summer electricity consumption scenario provided by the present invention; wherein 7a is a schematic diagram of equal annual value cost, 7b is a schematic diagram of coal reduction per unit capacity, and 7c is a schematic diagram of carbon dioxide emission reduction per unit capacity. DETAILED DESCRIPTION

[0075] 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.

[0076] See also Figure 1 As shown, the present invention discloses a shared energy storage configuration method considering different electricity usage scenarios, comprising the following steps:

[0077] Determine the 24-hour electricity price of shared energy storage under different electricity usage scenarios, collect basic data of shared energy storage under various electricity usage scenarios, and determine the important parameters for the operation of shared energy storage under different electricity usage scenarios based on the basic data and actual conditions;

[0078] Taking the configuration capacity and configuration power of the shared energy storage system as decision variables, and taking the minimum cost of the shared energy storage system, the maximum coal reduction per unit capacity, and the maximum carbon dioxide emission reduction per unit capacity as optimization goals, a shared energy storage upper-level configuration model is constructed to optimize the capacity and power configuration under different electricity consumption scenarios.

[0079] Taking the real-time electricity purchase amount and real-time power of the shared energy storage system as decision variables and maximizing the daily economic benefits of the shared energy storage system as the goal, a shared energy storage lower-level optimization model is constructed to optimize the daily operation output strategy.

[0080] The shared energy storage upper configuration model is solved and constructed using a multi-objective genetic algorithm and the evolutionary algorithm library of the Geatpy2 framework to generate different configuration capacities and configuration powers;

[0081] The generated configuration capacity and configuration power parameters are imported into the constructed shared energy storage lower-level optimization model, and Gurobi+Python is used to calculate the optimal solution of the lower-level objective function of real-time purchased electricity and real-time power;

[0082] See also Figure 3 As shown, the calculated optimal solution is returned to the shared energy storage upper configuration model, the objective function calculation of the shared energy storage upper configuration model is completed, and the upper population is selected and crossover operations are performed. If the convergence conditions are met, the optimal solution is output. If not, the iteration continues. The shared energy storage upper configuration model and the shared energy storage lower optimization model are iterated mutually until the shared energy storage optimal configuration and the system daily operation plan are output, and finally the global optimal solution is obtained.

[0083] Furthermore, the basic data include revenue, cost, operating constraints, coal consumption coefficient, and carbon dioxide coefficient.

[0084] Furthermore, the specific contents of the optimization goals of minimizing the cost of the shared energy storage system, maximizing the coal reduction per unit capacity, and maximizing the carbon dioxide emission reduction per unit capacity are as follows:

[0085] The cost calculation formula of the shared energy storage system is:

[0086] LCC=IC+OC+RC+DC

[0087] Among them, LCC is the cumulative sum of all costs in the cost-benefit analysis of the shared energy storage system; IC is the initial investment cost; OC is the operation and maintenance cost; RC is the equipment replacement cost; DC is the recovery cost;

[0088] Specifically, the initial investment cost is:

[0089] IC=c1E rate +c2P rate +c3E rate

[0090] Among them, c1 is the unit capacity cost of the shared energy storage system; c2 is the unit power cost of the shared energy storage system; c3 is the unit capacity cost of the shared energy storage auxiliary facilities; E rate is the configuration capacity; P rate To configure the power.

[0091] Considering the time value, the annual cost needs to be calculated by introducing the equal annual value coefficient. The initial investment cost is expressed as follows:

[0092] IC=C(r,n)(c1E rate +c2P rate +c3E rate )

[0093] Among them, C(r,n) is the equal-year value coefficient and its calculation formula is:

[0094]

[0095] Among them, r is the discount rate; n is the project operation cycle.

[0096] The operation and maintenance costs are:

[0097] OC=c4P rate +βQ

[0098]

[0099] Among them, c4 is the annual operation and maintenance cost per unit power of the shared energy storage system; β is the charging and discharging cost coefficient of the shared energy storage system; D is the annual operation days of the system; Q is the annual charging and discharging total amount; P ES (t) is the charging and discharging power of the shared energy storage during period t.

[0100] Equipment replacement costs:

[0101] RC=(1-α) km c1E rate

[0102] Among them, α is the average annual reduction rate of energy storage battery cost; m is the life of the energy storage battery; k is the number of times the energy storage battery is replaced.

[0103] Therefore, the equivalent annual replacement cost of the shared energy storage system is expressed as:

[0104]

[0105] Wherein, L is the Lth replacement of the battery body.

[0106] Recovery cost:

[0107] DC=(c P Prate +c e E rate )C(r,n)(1+r) -n -c res (IC+RC)

[0108] Among them, c P is the unit power scrapping cost of the shared energy storage system; c e is the cost of scrapping per unit capacity of the shared energy storage system; c res Recover the residual value rate for the shared energy storage system.

[0109] The calculation formula for coal reduction per unit capacity is:

[0110]

[0111] ΔW 煤 =λ 煤 *S year

[0112] S year =P rate *Charge and discharge times

[0113] Among them, δ 煤 The amount of coal reduced per unit capacity of the shared energy storage system; ΔW 煤 The amount of coal consumption reduced during the life cycle of the shared energy storage system; E rate is the configuration capacity of the shared energy storage system; 煤 is the coal consumption coefficient; S year Promote the consumption of electricity for shared energy storage systems; rate Configure power for shared energy storage systems;

[0114] The calculation formula for carbon dioxide emission reduction per unit capacity is:

[0115]

[0116] in, The carbon dioxide emission reduction per unit capacity of the shared energy storage system; The amount of carbon dioxide emissions reduced during the life cycle of the shared energy storage system; E rate Allocate capacity for shared energy storage systems; is the carbon dioxide emission coefficient; S year Promote electricity consumption for shared energy storage systems.

[0117] Furthermore, the constraints of the shared energy storage upper configuration model are:

[0118] The rated power and capacity constraints of the shared energy storage system are:

[0119] P rate≤P M

[0120] E rate ≤E M

[0121] Among them, P M is the maximum power of shared energy storage; E M is the maximum capacity of shared energy storage;

[0122] The charging and discharging power limits of the shared energy storage system are:

[0123] P rate ≥|P ES (t)|

[0124] Among them, P ES (t) is the charging and discharging power of the shared energy storage during period t;

[0125] The state of charge constraint of the shared energy storage system is:

[0126]

[0127]

[0128] Where j is the time period, j = 1, 2, ..., 24; SOC max The highest state of charge; SOC min is the lowest charge state; Δt is the interval between the t period and the t+1 period; η c is the charging efficiency; η d is the discharge efficiency; δ(t) is the charging and discharging efficiency coefficient of the shared energy storage in period t.

[0129] Furthermore, the specific contents of the goal of maximizing the economic benefits of the daily operation of the shared energy storage system are as follows:

[0130]

[0131] Among them, F is the economic benefit of the shared energy storage system operating for one day; γ t is the electricity price of the system in period t; β is the charging and discharging cost coefficient of the shared energy storage system; α t P is the price of electricity purchased from the power grid during period t; load (t) is the power load during period t; P ES (t) is the charging and discharging power of the shared energy storage during period t, negative means charging and positive means discharging; P G (t) is the amount of electricity purchased from the power grid during period t; OC is the daily operation and maintenance cost; E rate is the configuration capacity; P rate is the configured power; D is the annual operating days; R is the monthly rental fee.

[0132] Furthermore, the constraints of the shared energy storage lower-level optimization model are:

[0133] The system power balance constraint is:

[0134] P G (t)+P ES (t) = P load (t)

[0135] Among them, P G (t) is the amount of electricity purchased from the power grid during period t; P ES (t) is the charging and discharging power of the shared energy storage during period t; P load (t) is the electricity load during period t;

[0136] The output constraint of the shared energy storage system is:

[0137] P min ≤P ES (t)≤P max

[0138] Among them, P min is the minimum output power of the shared energy storage system; P max is the maximum output power of the shared energy storage system;

[0139] The daily operation constraints of the shared energy storage system are:

[0140]

[0141] The state of charge constraint of the shared energy storage system is:

[0142]

[0143] SOC min ≤SOC(t)≤SOC max

[0144] Where SOC(t) is the state of charge of the battery in period t; SOC(t-1) is the state of charge of the battery in period t-1; ω is the battery self-discharge coefficient; E rate is the rated capacity of the shared energy storage system; η c is the charging efficiency of the energy storage system; η d is the discharge efficiency of the energy storage system; Δt is the interval between period t and period t+1; SOC min SOC is the lower limit of the battery state of charge; max The upper limit of the battery state of charge.

[0145] Furthermore, the specific content of the multi-objective genetic algorithm is:

[0146] Under given constraints, solve multiple objective function maximum problems:

[0147] max{z1=f(x),z2=f(x),…,z r =f(x)}

[0148] stg i (x),i=1,2…,m

[0149] where z1 = f(x), z2 = f(x),…,z r =f(x) represents r linear or nonlinear objective functions of the variable x; st g i(x) is m constraint functions about variable x;

[0150] There is not necessarily only one optimal solution to a multi-objective optimization problem. In order to find a reasonable solution, a set of reasonable solutions to the multi-objective optimization problem, namely the Pareto optimal solution, is introduced.

[0151] Furthermore, the optimal solution to the multi-objective problem is determined as follows:

[0152] Given a multi-objective optimization problem minf(x), let X * ∈Ω, if , so that the following conditions are met:

[0153] For any sub-objective function f of f(x) i (x) has f i (X)≤f i (X * ), and there is at least one sub-objective function f i (x) makes f i (X) <f i (X * ), then call X * It is a strong Pareto optimal solution;

[0154] Given a multi-objective optimization problem minf(x), let X * ∈Ω, if , so that the following conditions are met:

[0155] For any sub-objective function f of f(x) i (x) has f i (X) <f i (X * ), then call X * It is a weak Pareto optimal solution, and the strong Pareto optimal solution is referred to as the Pareto optimal solution.

[0156] Specifically, from the definition, we know that Figure 2In the domain S, the strong Pareto optimal solution will fall on the thick curve; the weak Pareto optimal solution will fall on the thin straight line. In solving practical problems, what we want is the optimal solution that falls on the thick curve.

[0157] In a specific embodiment, the 24-hour electricity price of shared energy storage in the summer electricity consumption scenario is first determined. The electricity price data in this example comes from the monthly electricity price notice for industrial and commercial users in Province H. The electricity price can be divided into valley period, normal period, peak period and peak period according to the time period. The electricity price in different time periods corresponds to different prices. For details, see Figure 4 The summer electricity purchase price.

[0158] Then, basic data including revenue, cost, operation constraints, coal consumption coefficient, and carbon dioxide coefficient of shared energy storage in the summer electricity consumption scenario are collected, and the important parameters of shared energy storage in different electricity consumption scenarios are determined based on the basic data and actual conditions. The main parameters are shown in Table 1.

[0159] Table 1 Main parameters in two-level planning

[0160]

[0161]

[0162] In the summer electricity consumption scenario, shared energy storage equipment can be charged when electricity prices are low and discharged when electricity prices are high or peak. The charging and discharging power of shared energy storage equipment under different electricity prices is as follows: Figure 5 The schematic diagram of summer electricity price and energy storage charging and discharging power is shown.

[0163] Using the evolutionary algorithm library of the Geatpy2 framework and the Gurobi+Python framework, based on the established core assumptions and the constructed double-layer optimization model, the optimal solutions for rated power, rated capacity and economic benefits in the double-layer optimization model composed of multi-objective genetic algorithm and integer programming under the summer electricity consumption scenario are as follows Figure 6 The schematic diagram of the optimal solution under the summer electricity consumption scenario is shown.

[0164] In the summer electricity consumption scenario, the three objective functions in the upper multi-objective genetic algorithm have Pareto optimal solutions in each iteration, such as Figure 7The schematic diagram of the optimal solution of the upper objective function under the summer electricity consumption scenario is shown in Figure 7a, which is a schematic diagram of the equal annual value cost, 7b is a schematic diagram of the unit capacity coal reduction, and 7c is a schematic diagram of the unit capacity carbon dioxide emission reduction. Due to the characteristics of the multi-objective genetic algorithm, there may be multiple optimal solutions to the objective function under the constraints. The blue point is the maximum economic benefit under the capacity power configuration, and all blue points constitute the Pareto optimal solution under this scenario. The rated power range of this scenario is between 5000kW-9000kW, and the rated capacity range is between 38571kWh-39771kWh, which are in line with the target parameter settings. The economic benefit range corresponding to each configuration is 51386 yuan-52586 yuan, with a small fluctuation range, which can well achieve the economic benefit target of the shared energy storage project. The example proves that the rated capacity and rated power output by this method can take into account the changes in electricity prices and load fluctuations in various scenarios, and obtain the optimal rated capacity configuration and rated power configuration of shared energy storage in various scenarios that take into account changes in electricity prices, load fluctuations, coal reduction, carbon dioxide emission reduction and cost benefits.

[0165] It should be noted that the above embodiment is only one possible situation. There may be more electricity usage scenarios and more peak, flat and valley electricity prices, but the shared energy storage configuration allocation method is the same.

[0166] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0167] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A shared energy storage configuration method considering different electricity usage scenarios, characterized in that: The following steps are involved: Determine the 24-hour electricity price of shared energy storage under different electricity usage scenarios, collect basic data of shared energy storage under various electricity usage scenarios, and determine the important parameters for the operation of shared energy storage under different electricity usage scenarios based on the basic data and actual conditions; Taking the configuration capacity and configuration power of the shared energy storage system as decision variables, and taking the minimum cost of the shared energy storage system, the maximum coal reduction per unit capacity, and the maximum carbon dioxide emission reduction per unit capacity as optimization goals, a shared energy storage upper-level configuration model is constructed to optimize the capacity and power configuration under different electricity consumption scenarios. Taking the real-time electricity purchase amount and real-time power of the shared energy storage system as decision variables and maximizing the daily economic benefits of the shared energy storage system as the goal, a shared energy storage lower-level optimization model is constructed to optimize the daily operation output strategy. The shared energy storage upper configuration model is solved and constructed using a multi-objective genetic algorithm and the evolutionary algorithm library of the Geatpy2 framework to generate different configuration capacities and configuration powers; The generated configuration capacity and configuration power parameters are imported into the constructed shared energy storage lower-level optimization model, and Gurobi+Python is used to calculate the optimal solution of the lower-level objective function of real-time purchased electricity and real-time power; The calculated optimal solution is returned to the shared energy storage upper configuration model to complete the objective function calculation of the shared energy storage upper configuration model, and the upper population is selected and cross-operated. If the convergence conditions are met, the optimal solution is output. If not, the iteration continues. The shared energy storage upper configuration model and the shared energy storage lower optimization model are iterated until the shared energy storage optimal configuration and the system daily operation plan are output, and finally the global optimal solution is obtained.

2. A method for configuring shared energy storage considering different electricity usage scenarios according to claim 1, characterized in that: Basic data include revenue, cost, operating constraints, coal consumption coefficient, and carbon dioxide coefficient.

3. A method for configuring shared energy storage considering different electricity usage scenarios according to claim 1, characterized in that: The specific contents of the optimization goals of minimizing the cost of the shared energy storage system, maximizing the coal reduction per unit capacity, and maximizing the carbon dioxide emission reduction per unit capacity are as follows: The cost calculation formula of the shared energy storage system is: LCC=IC+OC+RC+DC Among them, LCC is the cumulative sum of all costs in the cost-benefit analysis of the shared energy storage system; IC is the initial investment cost; OC is the operation and maintenance cost; RC is the equipment replacement cost; DC is the recovery cost; The calculation formula for coal reduction per unit capacity is: ΔW 煤 =λ 煤 *S year S year =P rate *Charge and discharge times Among them, δ 煤 The amount of coal reduced per unit capacity of the shared energy storage system; ΔW 煤 The amount of coal consumption reduced during the life cycle of the shared energy storage system; E rate is the configuration capacity of the shared energy storage system; 煤 is the coal consumption coefficient; S year Promote the consumption of electricity for shared energy storage systems; rate Configure power for shared energy storage systems; The calculation formula for carbon dioxide emission reduction per unit capacity is: in, The carbon dioxide emission reduction per unit capacity of the shared energy storage system; The amount of carbon dioxide emissions reduced during the life cycle of the shared energy storage system; E rate Allocate capacity for shared energy storage systems; is the carbon dioxide emission coefficient; S year Promote electricity consumption for shared energy storage systems.

4. A method for configuring shared energy storage considering different electricity usage scenarios according to claim 3, characterized in that: The constraints of the shared energy storage upper configuration model are: The rated power and capacity constraints of the shared energy storage system are: P rate ≤P M AND rate ≤E M Among them, P M is the maximum power of shared energy storage; E M is the maximum capacity of shared energy storage; The charging and discharging power limits of the shared energy storage system are: P rate ≥|P ES (t)| Among them, P ES (t) is the charging and discharging power of the shared energy storage during period t; The state of charge constraint of the shared energy storage system is: Where j is the time period, j = 1, 2, ..., 24; SOC max The highest state of charge; SOC min is the lowest charge state; Δt is the interval between the t period and the t+1 period; η c is the charging efficiency; η d is the discharge efficiency; δ(t) is the charging and discharging efficiency coefficient of the shared energy storage in period t.

5. A method for configuring shared energy storage considering different electricity usage scenarios according to claim 1, characterized in that: The specific contents of the shared energy storage system with the goal of maximizing the daily economic benefits are as follows: Among them, F is the economic benefit of the shared energy storage system operating for one day; γ t is the electricity price of the system in period t; β is the charging and discharging cost coefficient of the shared energy storage system; α t P is the price of electricity purchased from the power grid during period t; load (t) is the power load during period t; P ES (t) is the charging and discharging power of the shared energy storage during period t, negative means charging and positive means discharging; P G (t) is the amount of electricity purchased from the power grid during period t; OC is the daily operation and maintenance cost; E rate is the configuration capacity; P rate is the configured power; D is the annual operating days; R is the monthly rental fee.

6. A method for configuring shared energy storage considering different electricity usage scenarios according to claim 5, characterized in that: The constraints of the shared energy storage lower-level optimization model are: The system power balance constraint is: P G (t)+P ES (t)=P load (t) Among them, P G (t) is the amount of electricity purchased from the power grid during period t; P ES (t) is the charging and discharging power of the shared energy storage during period t; P load (t) is the electricity load during period t; The output constraint of the shared energy storage system is: P min ≤P ES (t)≤P max Among them, P min is the minimum output power of the shared energy storage system; P max is the maximum output power of the shared energy storage system; The daily operation constraints of the shared energy storage system are: The state of charge constraint of the shared energy storage system is: SOC min ≤SOC(t)≤SOC max Where SOC(t) is the state of charge of the battery in period t; SOC(t-1) is the state of charge of the battery in period t-1; ω is the battery self-discharge coefficient; E rate is the rated capacity of the shared energy storage system; η c is the charging efficiency of the energy storage system; η d is the discharge efficiency of the energy storage system; Δt is the interval between period t and period t+1; SOC min SOC is the lower limit of the battery state of charge; max The upper limit of the battery state of charge.

7. A method for configuring shared energy storage considering different electricity usage scenarios according to claim 1, characterized in that: The specific contents of the multi-objective genetic algorithm are: Under given constraints, solve multiple objective function maximum problems: max{z1=f(x),z2=f(x),…,z r =f(x)} s.t.g i (x),i=1,2…,m where z1 = f(x), z2 = f(x),…,z r = f(x) represents r linear or nonlinear objective functions of the variable x; stg i (x) are m constraint functions on the variable x; There is not necessarily only one optimal solution to a multi-objective optimization problem. In order to find a reasonable solution, a set of reasonable solutions to the multi-objective optimization problem, namely the Pareto optimal solution, is introduced.

8. A method for configuring shared energy storage considering different electricity usage scenarios according to claim 7, characterized in that: The content of determining the optimal solution to the multi-objective problem is: Given a multi-objective optimization problem minf(x), let X * ∈Ω, if So that the following conditions are met: For any sub-objective function f of f(x) i (x) has f i (X)≤f i (X * ), and there is at least one sub-objective function f i (x) makes f i (X) <f i (X * ), then call X * It is a strong Pareto optimal solution; Given a multi-objective optimization problem minf(x), let X * ∈Ω, if So that the following conditions are met: For any sub-objective function f of f(x) i (x) has f i (X) <f i (X * ), then call X * It is a weak Pareto optimal solution, and the strong Pareto optimal solution is referred to as the Pareto optimal solution.

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