A shared energy storage configuration method considering different electricity usage scenarios

Abstract

The present invention discloses a shared energy storage configuration method that takes into account different electricity consumption scenarios, and relates to the technical field of power system planning. It includes: determining the important parameters for the operation of shared energy storage in different electricity consumption scenarios based on basic data and actual conditions; constructing a shared energy storage upper-layer configuration model to optimize the capacity and power configuration under different electricity consumption scenarios; constructing a shared energy storage lower-layer optimization model to optimize the daily operation output strategy; solving the constructed shared energy storage upper-layer configuration model to generate different configuration capacities and configuration powers; calculating the optimal solution of the lower-layer objective function of real-time electricity purchase volume and real-time power; returning the optimal solution to the shared energy storage upper-layer configuration model, and outputting the optimal solution if the convergence condition is met, and continuing to iterate if it is not met, until the shared energy storage optimal configuration and the system daily operation plan are output, and finally the global optimal solution is obtained. The present invention promotes shared energy storage to play an important role in the development of new power systems and the realization of the "dual carbon" goals.

Description

Technical Field

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

[0002] Vigorously developing a new power system based primarily on renewable energy can effectively reduce carbon emissions, but due to its inherent characteristics, it requires the assistance of auxiliary equipment such as energy storage. With the rise of the sharing economy, shared energy storage has provided new solutions to the poor economic efficiency of single-use energy storage. However, different electricity usage scenarios and different shared energy storage operating modes and strategies have different impacts on its economic benefits.

[0003] Numerous studies have investigated shared energy storage configuration methods, including a negotiation-based multi-agent shared energy storage capacity configuration method for microgrid clusters developed by Liu Chao et al. and a user-side shared energy storage system configuration method and system developed by Wu Jianbin et al. However, these studies typically employ single-layer optimization models. Using methods such as Nash bargaining, goal programming, and master-slave game theory to optimize strategic configuration, these studies have demonstrated that shared energy storage can improve renewable energy absorption capacity, energy utilization, and reduce costs, achieving overall optimization across the generation, grid, and user sides. However, relatively few studies have used two-layer optimization models to simultaneously consider multiple optimization objectives, such as the economic benefits of shared energy storage operations, costs, coal consumption reduction, and carbon emissions reduction, to address shared energy storage configuration issues across different electricity consumption scenarios. Furthermore, some studies fail to consider the capacity configuration optimization of energy storage facilities, resulting in results that lack the key variable of configuration capacity. Furthermore, few studies and methods employ seasonal electricity prices to set optimization models, resulting in results that fail to fully consider the operational state of my country's electricity 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 existing technology. Summary of the Invention

[0005] In view of this, the present invention provides a shared energy storage configuration method that takes 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 usage scenarios includes the following steps:

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

[0009] Taking the configured capacity and configured power of the shared energy storage system as decision variables, and minimizing the cost of the shared energy storage system, maximizing coal reduction per unit capacity, and maximizing 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 usage scenarios.

[0010] Taking the real-time purchased electricity 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-layer configuration model is 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-level configuration model to complete the objective function calculation of the shared energy storage upper-level configuration model. The upper-level population is selected and cross-operated. If the convergence conditions are met, the optimal solution is output. If not, the iteration is continued. The shared energy storage upper-level configuration model and the shared energy storage lower-level 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 uses basic data including 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 for 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, CO2 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 in shared energy storage systems.

[0027] In the above method, optionally, the constraints of the shared energy storage upper layer configuration model 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 period t and period t+1; η c is the charging efficiency; η d is the discharge efficiency; δ(t) is the charge and discharge efficiency coefficient of the shared energy storage in period t.

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

[0039]

[0040] Where F is the economic benefit of the shared energy storage system operating for one day; γ t is the electricity price of the system during 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 electricity load during period t; P ES (t) is the charging and discharging power of the shared energy storage during period t, where negative indicates charging and positive indicates 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-level 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 operating 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 during period t; SOC(t-1) is the state of charge of the battery during 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 is optional. The specific content of the multi-objective genetic algorithm is:

[0055] Under given constraints, solve multiple objective function maximum value 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 about the variable x; stg i (x) is m constraint functions about variable x;

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

[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(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(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 electricity 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 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, taking into account electricity price changes, load fluctuations, coal reduction, carbon dioxide emission 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

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

[0069] Figure 2 Example diagrams of strong and weak Pareto optimal solutions provided by the present invention;

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

[0071] Figure 4 A summer electricity 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 This is 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 the equal annual value cost, 7b is a schematic diagram of the coal reduction per unit capacity, and 7c is a schematic diagram of the carbon dioxide emission reduction per unit capacity. DETAILED DESCRIPTION

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts 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 on shared energy storage under each electricity usage scenario, and determine the important operating parameters of shared energy storage under different electricity usage scenarios based on basic data and actual conditions;

[0078] Taking the configured capacity and configured power of the shared energy storage system as decision variables, and minimizing the cost of the shared energy storage system, maximizing coal reduction per unit capacity, and maximizing 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 usage scenarios.

[0079] Taking the real-time purchased electricity 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-layer configuration model is 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 cross-operated. If the convergence condition is met, the optimal solution is output. If not, the iteration is continued. 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.

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

[0084] Furthermore, the specific 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 for 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 power.

[0091] Taking into account 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] Where r is the discount rate and n is the project operation cycle.

[0096] The operation and maintenance costs are:

[0097] OC=c4P rate +βQ

[0098]

[0099] Where 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 operating days of the system; Q is the total annual charging and discharging volume; P ES (t) is the charging and discharging power of the shared energy storage during period t.

[0100] Equipment replacement cost:

[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, CO2 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 in shared energy storage systems.

[0117] Furthermore, the constraints of the shared energy storage upper-level 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 period t and period t+1; η c is the charging efficiency; η d is the discharge efficiency; δ(t) is the charge and discharge efficiency coefficient of the shared energy storage in period t.

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

[0130]

[0131] Where F is the economic benefit of the shared energy storage system operating for one day; γ t is the electricity price of the system during 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 electricity load during period t; P ES (t) is the charging and discharging power of the shared energy storage during period t, where negative indicates charging and positive indicates 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 operating 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 during period t; SOC(t-1) is the state of charge of the battery during 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 value 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 about 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, the Pareto optimal solution is introduced as a set of reasonable solutions to the multi-objective optimization problem.

[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(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(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, according to the definition, 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 off-peak period, normal period, peak period and peak period according to the time period. The electricity price in different periods corresponds to different prices. For details, see Figure 4 The summer electricity purchase price in China.

[0158] Next, we collected basic data on shared energy storage's revenue, costs, operational constraints, coal consumption coefficient, and CO2 coefficient under summer electricity usage scenarios. Based on this data and actual conditions, we determined key parameters for shared energy storage's operation under different electricity usage scenarios. The key parameters for the two-tier planning are shown in Table 1.

[0159] Table 1 Main parameters in two-tier 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 on 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 prices 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 two-level optimization model, the optimal solutions for rated power, rated capacity, and economic benefits in the two-level 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 optimal solution for the upper-level objective function for the summer electricity consumption scenario is shown in Figure 7a. Figure 7b shows the equivalent annualized cost, Figure 7b shows the coal reduction per unit capacity, and Figure 7c shows the CO2 emissions reduction per unit capacity. Due to the characteristics of the multi-objective genetic algorithm, multiple optimal solutions to the objective function may exist under the constraints. The blue dots represent the maximum economic benefit for each capacity and power configuration, and all blue dots constitute the Pareto optimal solution for this scenario. The rated power for this scenario ranges from 5000 kW to 9000 kW, and the rated capacity ranges from 38571 kWh to 39771 kWh, all meeting the target parameter settings. The corresponding economic benefit range for each configuration is 51,386 yuan to 52,586 yuan, with a relatively small fluctuation range, effectively achieving the economic benefit goals of the shared energy storage project. A numerical example demonstrates that the rated capacity and rated power output using this method can account for electricity price fluctuations and load fluctuations under various scenarios. The optimal rated capacity and rated power configurations for shared energy storage are obtained for each scenario, balancing electricity price fluctuations, load fluctuations, coal reduction, CO2 emissions reduction, and cost-benefit.

[0165] It should be noted that the above embodiment is only one possible scenario. There may be more electricity usage scenarios and more peak, flat and valley electricity prices, but the shared energy storage configuration and 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 is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily 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 is not limited to the embodiments shown herein but is intended to conform 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 on shared energy storage under each electricity usage scenario, and determine the important operating parameters of shared energy storage under different electricity usage scenarios based on basic data and actual conditions; Taking the configured capacity and configured power of the shared energy storage system as decision variables, and minimizing the cost of the shared energy storage system, maximizing coal reduction per unit capacity, and maximizing 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 usage scenarios. Taking the real-time purchased electricity 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-layer configuration model is 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. The upper population is selected and cross-operated. If the convergence conditions are met, the optimal solution is output. If not, the iteration is continued. 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. The specific optimization goals of minimizing the cost of the shared energy storage system, maximizing coal reduction per unit capacity, and maximizing carbon dioxide emission reduction per unit capacity are as follows: The cost calculation formula for 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, CO2 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 in shared energy storage systems.

2. A shared energy storage configuration method 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 shared energy storage configuration method considering different electricity usage scenarios according to claim 1, characterized in that: The constraints of the shared energy storage upper-level 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 period t and period t+1; η c is the charging efficiency; η d is the discharge efficiency; δ(t) is the charge and discharge efficiency coefficient of the shared energy storage in period t.

4. A shared energy storage configuration method considering different electricity usage scenarios according to claim 1, characterized in that: The specific contents of the project, which aims to maximize the daily economic benefits of the shared energy storage system, are as follows: Where F is the economic benefit of the shared energy storage system operating for one day; γ t is the electricity price of the system during 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 electricity load during period t; P ES (t) is the charging and discharging power of the shared energy storage during period t, where negative indicates charging and positive indicates 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.

5. A shared energy storage configuration method considering different electricity usage scenarios according to claim 4, 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 operating 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 during period t; SOC(t-1) is the state of charge of the battery during 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.

6. A shared energy storage configuration method 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 value 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 about the variable x; stg i (x) is m constraint functions about variable x; There is not necessarily only one optimal solution to a multi-objective optimization problem. In order to find a reasonable solution, the Pareto optimal solution is introduced as a set of reasonable solutions to the multi-objective optimization problem.

7. A shared energy storage configuration method considering different electricity usage scenarios according to claim 6, 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(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(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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