Independent energy storage configuration optimization method considering full life cycle cost

Abstract

The invention discloses an independent energy storage configuration optimization method considering full life cycle cost, and relates to the technical field of independent energy storage system optimization configuration. Comprising the following steps: constructing an independent energy storage upper-layer configuration model by taking configuration capacity and power of the independent energy storage system as decision variables and taking minimization of cost of the independent energy storage system and maximization of coal reduction amount per unit capacity and carbon dioxide emission reduction amount as optimization objectives; taking the real-time electricity purchase quantity and the real-time power of the independent energy storage system as decision variables, and taking the daily operation economic benefit maximization of the independent energy storage system as an optimization target to construct an independent energy storage lower-layer optimization model; and constructing a double-layer optimization model based on the upper-layer configuration model and the lower-layer optimization model, solving the double-layer optimization model through a multi-objective genetic algorithm and an integer programming method, and outputting the optimal configuration of independent energy storage and a system daily operation scheme. According to the invention, through accurate technology configuration and resource allocation, the economical efficiency and safety of the independent energy storage system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of independent energy storage system optimization configuration, and particularly to an independent energy storage configuration optimization method considering the full life cycle cost. Background Art

[0002] With the continuous increase in the proportion of new energy power generation, the volatility and uncertainty of the power system have increased, and the importance of energy storage technology as a regulation means has become increasingly prominent. To improve the economic efficiency and operation efficiency of the energy storage system, researchers have proposed a variety of energy storage configuration optimization strategies to meet the needs of different scenarios. Current research not only covers the configuration optimization of single energy storage and multi-energy storage systems, but also particularly focuses on the cost sharing and collaborative optimization issues in the independent energy storage mode. However, due to the huge investment in the energy storage system, how to minimize costs, maximize benefits, and ensure safe operation throughout the life cycle remains the core difficulty in the research. Based on this, by reviewing the existing technologies, different models and methods of energy storage configuration optimization are sorted out, and their advantages and disadvantages in terms of economy, cost sharing, fairness, etc. are discussed, providing a reference for constructing an independent energy storage configuration optimization strategy considering the full life cycle cost.

[0003] In recent years, with the development of energy storage technology and the diversification of application scenarios, the research on energy storage configuration optimization has gradually become the focus. Men Xiangyang et al. found that the multi-component hybrid energy storage is the most economically advantageous by comparing the single energy storage, double energy storage, and multi-component hybrid energy storage configuration models, but the rationality of the configured capacity has a significant impact on the economy. Chen et al. constructed a day-ahead scheduling model for energy storage through the cooperative game method, and the results showed that the introduction of energy storage can effectively reduce the system operation cost. Jo and Park further used the game theory model to demonstrate the role of energy storage in reducing the grid operation cost and peak value. Wang Zimou proposed an uncertainty set description of the frequency modulation signal, real-time electric energy, and frequency modulation market price based on the polyhedron and Wasserstein distance, constructed a distributed robust optimization model for the independent energy storage power station, optimized the market declaration capacity, and verified the effectiveness of the model through the Gurobi solver. Xu Yanchun et al. combined the user demand response with the shared energy storage through the two-layer Stackelberg model and verified its effectiveness in different scenarios. Ma et al. proposed a two-layer model with the goals of maximizing the interests of relevant parties and minimizing the system operation cost respectively, and the results showed that the energy storage system can significantly improve the peak shaving and valley filling effect of the power system. In addition, Liu Shu et al. proposed a hybrid energy storage configuration method with the goal of minimizing the energy storage capacity based on the upper and lower bound constraint method, and the effectiveness of this method was also verified by experiments. Shuai Xuanyue et al. and Wang Qiya respectively explored the application potential of the energy storage system in multi-energy complementarity and different operation modes based on the Nash bargaining theory and the two-layer optimization model.

[0004] In summary, the existing technologies have proven that energy storage systems can improve the accommodation capacity of new energy, energy utilization efficiency, and reduce costs, and can achieve overall optimization on the power generation side, grid side, and user side. Research on the optimization of energy storage configuration has covered different optimization models and scenarios. In particular, the economic and cost allocation issues of energy storage systems have been gradually studied in depth. However, there is still a lack of systematic consideration of the full-life cycle cost in the independent energy storage mode, especially in the collaborative optimization of configuration capacity, operation mode, and revenue sharing.

[0005] Therefore, proposing an independent energy storage configuration optimization method considering the full-life cycle cost to solve the difficulties existing in the existing technologies is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides an independent energy storage configuration optimization method considering the full-life cycle cost, which not only improves the safety and economy of the energy storage system, but also significantly enhances the adaptability and sustainability of the system, and helps to realize the comprehensive promotion and application of the independent energy storage system in the new power system.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] An independent energy storage configuration optimization method considering the full-life cycle cost includes the following steps:

[0009] Determine the 24-hour electricity price of the independent energy storage power station under different electricity consumption scenarios, collect the basic data under each electricity consumption scenario, and determine the key operation parameters of the independent energy storage system under each electricity consumption scenario;

[0010] Taking the configuration capacity and power of the independent energy storage system as decision variables, taking the minimization of the independent energy storage system cost, the maximum reduction of coal consumption per unit capacity, and the maximum reduction of carbon dioxide emissions as optimization objectives, and taking the rated power, capacity, and state of charge as constraint conditions, construct an upper-layer configuration model of the independent energy storage;

[0011] Taking the real-time electricity purchase volume and real-time power of the independent energy storage system as decision variables, taking the maximization of the daily operation economic benefit of the independent energy storage system as the optimization objective, and taking power balance, output, operation, and state of charge as constraint conditions, construct a lower-layer optimization model of the independent energy storage;

[0012] Construct a two-layer optimization model based on the upper-layer configuration model and the lower-layer optimization model, solve the two-layer optimization model through the multi-objective genetic algorithm and integer programming method, and output the optimal configuration of the independent energy storage and the daily operation plan of the system.

[0013] Optionally, the specific content of determining the 24-hour electricity price of the independent energy storage power station under different electricity consumption scenarios, collecting the basic data under each electricity consumption scenario, and determining the key operation parameters of the independent energy storage system under each electricity consumption scenario is as follows:

[0014] The basic data includes grid electricity price data, power load data, operating cost data, and emission reduction data;

[0015] The key operating parameters include power parameters, capacity parameters, state of charge parameters, economic parameters, and optimization target parameters.

[0016] Optionally, taking the configured capacity and power of the independent energy storage system as decision variables, with the goal of minimizing the cost of the independent energy storage system, maximizing the coal reduction per unit capacity, and maximizing the carbon dioxide emission reduction, and using the rated power, capacity, and state of charge as constraint conditions, the specific content of constructing the upper-layer configuration model of the independent energy storage is as follows:

[0017] The cost of the independent energy storage system is the cumulative sum of all relevant cost expenses generated during the entire cycle from design and commissioning to scrapping and recycling, that is, the life cycle cost LCC is shown in the following formula:

[0018] minLCC = IC + OC + RC + DC

[0019] In the formula, IC is the initial investment cost, OC is the operation and maintenance cost, RC is the equipment replacement cost, and DC is the recycling cost;

[0020] The coal reduction per unit capacity of the independent energy storage system δ coal is shown in the following formula:

[0021]

[0022] ΔW coal = λ coal * S year

[0023] S year = P rate * Number of charge and discharge cycles

[0024] In the formula, ΔW coal is the reduced coal consumption during the life cycle of the independent energy storage system, E rate is the configured capacity of the independent energy storage system, λ coal is the coal consumption coefficient, S year is the promoted consumption volume of the independent energy storage system, P rate is the configured power of the independent energy storage system;

[0025] The carbon dioxide emission reduction per unit capacity of the independent energy storage system δ CO2 is shown in the following formula:

[0026]

[0027] In the formula, The reduced carbon dioxide emissions during the life cycle of the independent energy storage system is the carbon dioxide emission factor, S year is the electricity consumption promoted by the independent energy storage system.

[0028] Optionally, the specific content with the rated power, capacity, and state of charge as constraints is:

[0029] The rated power and capacity of the independent energy storage system are shown as follows:

[0030] P rate ≤P M

[0031] E rate ≤E M

[0032] In the formula, P M is the maximum power of the independent energy storage, and E M is the maximum capacity of the independent energy storage;

[0033] The charge and discharge power limit of the independent energy storage system is shown as follows:

[0034] P rate ≥|P ES (t)|

[0035] In the formula, P ES (t) is the charge and discharge power of the independent energy storage at time t;

[0036] The state of charge constraint of the independent energy storage system is shown as follows:

[0037]

[0038] In the formula, j is the time period, j = 1, 2,..., 24, SOC max is the highest state of charge, SOC min is the lowest state of charge, Δt is the interval between time t and time t + 1, η c is the charging efficiency, η d is the discharging efficiency.

[0039] Optionally, with the real-time electricity purchase volume and real-time power of the independent energy storage system as decision variables, with the maximization of the daily operating economic benefit of the independent energy storage system as the optimization goal, and with power balance, output, operation, and state of charge as constraints, the specific content of constructing the lower-layer optimization model of the independent energy storage is:

[0040] The daily operating economic benefit F of the independent energy storage system is shown as follows:

[0041]

[0042] In the formula, γt Let \(p_{s}(t)\) be the selling price of electricity of the system during period \(t\), \(\beta\) be the charge-discharge cost coefficient of the independent energy storage system, and \(\alpha\) t be the electricity price for purchasing electricity from the power grid during period \(t\), \(P_{L}\) load (t) be the electricity load during period \(t\), \(P_{G}\) G (t) be the electricity purchased from the power grid during period \(t\), \(OC\) be the daily operation and maintenance cost, \(D\) be the annual operation days, and \(R\) be the monthly rental cost.

[0043] Optionally, the specific contents of the constraints based on power balance, output, operation, and state of charge are as follows:

[0044] The power balance constraint of the independent energy storage system is shown in the following formula:

[0045] \(P_{G}\) G (t) + \(P_{D}\) ES (t) = \(P_{L}\) load (t)

[0046] The output constraint of the independent energy storage system is shown in the following formula:

[0047] \(P_{G}\) min ≤ \(P_{G}\) ES (t) ≤ \(P_{G}\) max

[0048] In the formula, \(P_{G}\) min is the minimum output power of the independent energy storage system, and \(P_{G}\) max is the maximum output power of the independent energy storage system;

[0049] The daily operation constraint of the independent energy storage system is shown in the following formula:

[0050]

[0051] The state of charge constraint of the independent energy storage system is shown in the following formula:

[0052]

[0053] SOC min ≤ SOC(t) ≤ SOC max

[0054] In the formula, 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 \(t - 1\), and \(\omega\) is the self-discharge coefficient of the battery.

[0055] Optionally, a two-layer optimization model based on the upper-layer configuration model and the lower-layer optimization model is constructed, and the two-layer optimization model is solved by the multi-objective genetic algorithm and the integer programming method. The specific content of the optimal configuration of the independent energy storage and the daily operation plan of the system is as follows:

[0056] In the upper-layer configuration model, the multi-objective genetic algorithm in the Geatpy2 framework is used to generate a population and make configuration decisions, and the installed capacity and power decision variables are passed to the lower-layer optimization model;

[0057] The lower-layer optimization model is solved based on the Gurobi+Python framework and integer programming, and the decision variables of the upper-layer configuration model are substituted as parameters. Decisions are made according to the optimization objectives, and at the same time, the optimal solution is returned to the upper-layer configuration model to calculate the objective function value of the upper-layer configuration model;

[0058] Selection and crossover operations are performed on the upper-layer population, and thus mutual iteration is carried out between the upper-layer configuration model and the lower-layer optimization model. If the convergence condition is met, the optimal solution is output; otherwise, the iteration continues, so as to output the optimal configuration of the independent energy storage and the daily operation plan of the system.

[0059] As can be seen from the above technical solutions, compared with the prior art, the present invention provides an optimization method for the configuration of an independent energy storage considering the full life cycle cost, and has the following beneficial effects:

[0060] (1) Through the double-layer optimization model, the present invention comprehensively considers the initial investment, operation and maintenance, equipment replacement, and the final recovery cost, and realizes the minimization of the cost and the maximization of the benefit of the independent energy storage system within the full life cycle;

[0061] (2) The double-layer optimization model of the present invention combines the multi-objective genetic algorithm and integer programming, so that the configuration decision of the upper-layer configuration model can accurately affect the daily operation benefit of the lower-layer optimization model, and at the same time, the optimal solution of the lower-layer optimization model is returned to the upper layer to calculate the objective function value of the upper-layer configuration model; the upper-layer configuration model mainly solves the optimization of the energy storage configuration capacity and power, maximizes the cost minimization and the emission reduction and coal reduction effects per unit capacity, and at the same time is constrained within the power and capacity range, so that the independent energy storage system has economic and environmental benefits throughout the life cycle. The lower-layer optimization model focuses on maximizing the benefit in daily operation, including selling electricity minus operation and power purchase costs, etc. Under the coordination of the upper-layer configuration model and the lower-layer optimization model, the independent energy storage system realizes the dynamic optimal configuration of the energy storage device;

[0062] (3) To ensure the solution efficiency of the double-layer optimization model, the present invention uses the multi-objective genetic algorithm in the Geatpy2 framework and the Gurobi optimizer for solution, and through loop iteration and elitist retention mechanism, realizes the global search for the optimal solution of the double-layer optimization model. Description of the Drawings

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained according to the provided drawings.

[0064] Figure 1 Flowchart of an independent energy storage configuration optimization method considering the full life cycle cost provided by the present invention;

[0065] Figure 2 Logic diagram of the double-layer optimization model provided by the present invention;

[0066] Figure 3 Flowchart for solving the double-layer optimization model provided by the present invention;

[0067] Figure 4 24-hour electricity price in summer in Province H provided by the present invention;

[0068] Figure 5 24-hour electricity price in winter in Province H provided by the present invention;

[0069] Figure 6 24-hour electricity price in other seasons in Province H provided by the present invention;

[0070] Figure 7 Schematic diagram of summer electricity price and energy storage charge and discharge power provided by the present invention;

[0071] Figure 8 Schematic diagram of the optimal solution in the summer electricity consumption scenario provided by the present invention;

[0072] Figure 9 Schematic diagram of winter electricity price and energy storage charge and discharge power provided by the present invention;

[0073] Figure 10 Schematic diagram of the optimal solution in the winter electricity consumption scenario provided by the present invention;

[0074] Figure 11 Schematic diagram of electricity price in other seasons and energy storage charge and discharge power provided by the present invention;

[0075] Figure 12 Schematic diagram of the optimal solution in the electricity consumption scenario in other seasons provided by the present invention. Detailed implementation manners

[0076] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0077] Referring to Figure 1 As shown, the present invention discloses an independent energy storage configuration optimization method considering the full life cycle cost, including the following steps:

[0078] Determine the 24-hour electricity price of the independent energy storage power station under different electricity consumption scenarios, collect the basic data under each electricity consumption scenario, and determine the key operation parameters of the independent energy storage system under each electricity consumption scenario;

[0079] Taking the configuration capacity and power of the independent energy storage system as decision variables, with the minimization of the cost of the independent energy storage system, the maximum reduction in coal consumption per unit capacity, and the maximum reduction in carbon dioxide emissions as the optimization objectives, and the rated power, capacity, and state of charge as constraints, construct an upper-layer configuration model for independent energy storage;

[0080] Taking the real-time electricity purchase volume and real-time power of the independent energy storage system as decision variables, with the maximization of the daily operating economic benefits of the independent energy storage system as the optimization objective, and power balance, output, operation, and state of charge as constraints, construct a lower-layer optimization model for independent energy storage;

[0081] Construct a two-layer optimization model based on the upper-layer configuration model and the lower-layer optimization model, solve the two-layer optimization model through a multi-objective genetic algorithm and an integer programming method, and output the optimal configuration of the independent energy storage and the daily operation plan of the system.

[0082] Further, the specific content of determining the 24-hour electricity price of the independent energy storage power station under different electricity consumption scenarios, collecting the basic data under each electricity consumption scenario, and determining the key operation parameters of the independent energy storage system under each electricity consumption scenario is as follows:

[0083] The basic data includes grid electricity price data, power load data, operation cost data, and emission reduction data;

[0084] The key operation parameters include power parameters, capacity parameters, state of charge parameters, economic parameters, and optimization objective parameters.

[0085] Specifically, three different electricity consumption scenarios are set, namely the summer electricity consumption scenario, the winter electricity consumption scenario, and the other-season electricity consumption scenario.

[0086] The basic data includes grid electricity price data, power load data, operation cost data, and emission reduction data;

[0087] The grid electricity price data is the 24-hour electricity price curve of the independent energy storage power station under different electricity consumption scenarios;

[0088] The power load data is the power load data under each electricity consumption scenario;

[0089] The operation cost data includes initial investment cost, operation and maintenance cost, equipment replacement cost, recovery cost, etc.;

[0090] The emission reduction data are coal consumption coefficient and carbon dioxide emission coefficient;

[0091] The key operation parameters include power parameters, capacity parameters, state of charge parameters, economic parameters, and optimization target parameters;

[0092] The power parameters are the real-time electricity purchase volume and real-time power of the independent energy storage system;

[0093] The capacity parameters are the configured capacity and rated capacity of the independent energy storage system;

[0094] The state of charge parameters are the highest state of charge, the lowest state of charge, charge efficiency, and discharge efficiency;

[0095] The economic parameters are daily operation and maintenance cost and monthly lease cost;

[0096] The optimization target parameters are the reduced coal consumption during the life cycle of the independent energy storage system, the promoted consumption volume, the configured power, as well as the reduced carbon dioxide emissions and the promoted consumption electricity volume during the life cycle, etc.

[0097] Furthermore, taking the configured capacity and power of the independent energy storage system as decision variables, with the minimization of the independent energy storage system cost, the maximization of coal reduction per unit capacity and carbon dioxide emission reduction as optimization objectives, and the rated power, capacity, and state of charge as constraint conditions, the specific content of constructing the upper-layer configuration model of the independent energy storage is as follows:

[0098] The independent energy storage system cost is the cumulative sum of all relevant cost expenses generated during the entire cycle from design and commissioning to scrapping and recovery, that is, the life cycle cost LCC is shown as the following formula:

[0099] minLCC = IC + OC + RC + DC

[0100] In the formula, IC is the initial investment cost, OC is the operation and maintenance cost, RC is the equipment replacement cost, and DC is the recovery cost;

[0101] The coal reduction per unit capacity δ of the independent energy storage system coal is shown as the following formula:

[0102]

[0103] ΔW coal= λ coal * S year

[0104] S year = P rate * Number of charge and discharge cycles

[0105] Wherein, ΔW coal is the reduction in coal consumption during the life cycle of the independent energy storage system, E rate is the configured capacity of the independent energy storage system, λ coal is the coal consumption coefficient, S year is the promoted consumption volume of the independent energy storage system, P rate is the configured power of the independent energy storage system;

[0106] The carbon dioxide emission reduction per unit capacity δ of the independent energy storage system CO2 is shown in the following formula:

[0107]

[0108] Wherein, is the reduction in carbon dioxide emissions during the life cycle of the independent energy storage system, is the carbon dioxide emission coefficient, S year is the promoted consumption electricity of the independent energy storage system.

[0109] Furthermore, the specific contents with the rated power, capacity, and state of charge as constraints are:

[0110] The rated power and capacity of the independent energy storage system are shown in the following formula:

[0111] P rate ≤ P M

[0112] E rate ≤ E M

[0113] Wherein, P M is the maximum power of the independent energy storage, E M is the maximum capacity of the independent energy storage;

[0114] The charge and discharge power limit of the independent energy storage system is shown in the following formula:

[0115] P rate ≥ |P ES (t)|

[0116] Wherein, P ES (t) is the charge and discharge power of the independent energy storage at time t;

[0117] The state of charge constraint of the independent energy storage system is shown in the following formula:

[0118]

[0119] In the formula, j is the time period, j = 1, 2, …, 24, SOC max is the highest charge state, SOC min is the lowest charge state, Δt is the interval between the t-th time period and the (t + 1)-th time period, η c is the charging efficiency, η d is the discharging efficiency.

[0120] Furthermore, taking the real-time electricity purchase quantity and real-time power of the independent energy storage system as decision variables, taking the maximization of the daily operation economic benefit of the independent energy storage system as the optimization goal, and taking power balance, output, operation, and state of charge as constraint conditions, the specific content of constructing the lower-layer optimization model of the independent energy storage is as follows:

[0121] The daily operation economic benefit F of the independent energy storage system is shown in the following formula:

[0122]

[0123] In the formula, γ t is the selling electricity price of the system within the t-th time period, β is the charge and discharge cost coefficient of the independent energy storage system, α t is the electricity price for purchasing electricity from the power grid within the t-th time period, P load (t) is the electricity load within the t-th time period, P G (t) is the electricity purchase quantity from the power grid within the t-th time period, OC is the daily operation and maintenance cost, D is the annual operation days, and R is the monthly lease cost.

[0124] Specifically, the daily operation economic benefit includes income and cost. The income includes selling electricity income, and the cost includes charge and discharge cost, operation and maintenance cost, and electricity purchase cost.

[0125] Furthermore, the specific content of taking power balance, output, operation, and state of charge as constraint conditions is as follows:

[0126] The power balance constraint of the independent energy storage system is shown in the following formula:

[0127] P G (t) + P ES (t) = P load (t)

[0128] The output constraint of the independent energy storage system is shown in the following formula:

[0129] P min ≤ P ES (t) ≤ P max

[0130] In the formula, P min is the minimum output power of the independent energy storage system, P maxis the maximum output power of the independent energy storage system;

[0131] The daily operation constraints of the independent energy storage system are shown in the following formula:

[0132]

[0133] The state-of-charge constraint of the independent energy storage system is shown in the following formula:

[0134]

[0135] SOC min ≤SOC(t)≤SOC max

[0136] In the formula, SOC(t) is the state of charge of the battery at time t, SOC(t - 1) is the state of charge of the battery at time t - 1, and ω is the self-discharge coefficient of the battery.

[0137] Furthermore, as Figure 2 and Figure 3 shown, a two-layer optimization model based on the upper-layer configuration model and the lower-layer optimization model is constructed. The two-layer optimization model is solved by the multi-objective genetic algorithm and the integer programming method. The specific content of the optimal configuration of the independent energy storage and the daily operation plan of the system is as follows:

[0138] In the upper-layer configuration model, the multi-objective genetic algorithm in the Geatpy2 framework is used to generate a population and make configuration decisions, and the installed capacity and power decision variables are passed to the lower-layer optimization model;

[0139] The lower-layer optimization model is solved based on the Gurobi + Python framework and integer programming, and the decision variables of the upper-layer configuration model are substituted as parameters. Decisions are made according to the optimization objectives, and at the same time, the optimal solution is returned to the upper-layer configuration model to calculate the objective function value of the upper-layer configuration model;

[0140] Selection and crossover operations are performed on the upper-layer population, and thus mutual iteration is carried out between the upper-layer configuration model and the lower-layer optimization model. If the convergence condition is met, the optimal solution is output; otherwise, the iteration continues, so as to output the optimal configuration of the independent energy storage and the daily operation plan of the system.

[0141] Specifically, the core process of the multi-objective genetic algorithm includes population initialization, fitness evaluation, selection, crossover operation, and elite retention mechanism. During the algorithm iteration process, the generation of new individuals is based on the information of the previous generation individuals and is continuously optimized through natural evolution processes such as mutation, gradually evolving into better solutions; integer programming is a class of linear programming problems that require some or all variables to take integer values. Common solution methods include the branch and bound method and the cutting plane method. Both methods make the integer optimal solution fall on a vertex (extreme point) of the feasible region of the linear programming by adding constraints, and then the optimal solution can be obtained using methods such as the simplex method. The difference lies in the selection and addition methods of the constraint conditions.

[0142] Embodiment 1

[0143] Solve the two-layer optimization model using the genetic algorithm based on the Geatpy2 framework, the Gurobi+Python framework, and the integer programming method. Use the multi-objective genetic optimization algorithm to perform simulation calculations on the upper-layer configuration model and the lower-layer optimization model. The relevant algorithm parameters are set as follows: the encoding method uses real-integer hybrid encoding, the population size is 50, and the maximum number of genetic generations is 100 generations. The electricity price data of a specific case comes from the electricity purchase price notice for industrial and commercial users agented by Province H every month. Among them, the electricity price can be divided into off-peak period, normal period, peak period, and super-peak period according to time periods. In order to simulate the actual operation scenario of the independent energy storage to the greatest extent, three different operation scenarios are set, namely the summer electricity consumption scenario, the winter electricity consumption scenario, and the electricity consumption scenario in other seasons. Among them, there is a super-peak period only in winter and summer, and the electricity prices in different time periods correspond to different prices. See Figure 4 、 Figure 5 and Figure 6 as shown. According to the actual situation of Province H, the main parameters in the two-layer optimization model are shown in Table 1.

[0144] Table 1 Main parameters in the two-layer optimization model

[0145]

[0146] In the summer electricity consumption scenario, the independent energy storage system realizes efficient charging and discharging operations and maximizes the benefits by taking advantage of the peak-valley electricity price difference. The charging and discharging powers of the independent energy storage device under different time period electricity prices are as Figure 7 shown.

[0147] Specifically, the independent energy storage device charges for 4 hours until full capacity at the electricity price valley from 2 to 5 o'clock; then, it discharges for 2 hours at the electricity price peak from 10 to 11 o'clock; then it charges for 2 hours again at the normal period from 13 to 14 o'clock to reach full capacity again; finally, it discharges for 4 hours at the peak period from 17 to 20 o'clock to completely release the stored energy, thus achieving the maximum benefit for the day. In this scenario, the two-layer optimization model based on the multi-objective genetic algorithm and integer programming obtains the optimal solutions for the rated power, rated capacity, and economic benefits (seeFigure 8 ) Due to the characteristics of the multi-objective genetic algorithm, there may be multiple optimal solutions in the double-layer optimization model under the constraint conditions. Figure 8 The blue dots in it represent the maximum economic benefits under different capacity-power configurations, and all the blue dots constitute the Pareto optimal solution set in this scenario. The rated power range in this scenario is 5000kW - 9000kW, and the rated capacity range is 38571kWh - 39771kWh, both of which meet the target parameter requirements. The economic benefits are between 51386 yuan and 52586 yuan, with small fluctuations, fully realizing the economic benefit goal of the independent energy storage system. In addition, during the iterative process of the multi-objective genetic algorithm, the randomly generated population each time will cause differences in the optimal solutions, but each optimal solution falls within a certain interval range. Specifically, the optimal solution interval with the minimum equivalent annual cost is approximately 1.20 - 1.25, the optimal solution interval with the maximum coal reduction per unit capacity is approximately 0.12 - 0.22, and the optimal solution interval with the maximum carbon dioxide emission reduction per unit capacity is approximately 0.3 - 0.6.

[0148] Example 2

[0149] In the winter electricity consumption scenario, the independent energy storage system can also achieve charging during the low electricity price period and discharging during the high and peak electricity price periods. The charging and discharging powers of the independent energy storage device at different electricity prices are as Figure 9 shown. The independent energy storage power station charges for 4 hours during the low valley period from 1 to 4 o'clock to reach full capacity, then discharges for 2 hours during the peak period from 8 to 9 o'clock, then continues to charge for 2 hours during the low valley period from 12 to 13 o'clock to reach full capacity, and finally discharges for 4 hours during the peak period from 16 to 19 o'clock to clear all stored energy, achieving the maximum daily profit. In this electricity consumption scenario, the optimal solutions of the rated power, rated capacity, and economic benefits in the double-layer optimization model are as Figure 10 shown. The blue dots are the maximum economic benefits under this capacity-power configuration, and all the blue dots form the Pareto optimal solutions in this scenario. The rated power range in this scenario is between 5000kW and 9000kW, and the rated capacity range is between 38571kWh and 39771kWh, both of which meet the target parameter settings. The economic benefit interval is 51226 yuan - 52426 yuan, with a small fluctuation range. At the same time, there are Pareto optimal solutions for each iteration of the three objective functions in the upper-layer multi-objective genetic algorithm. The optimal solution interval with the minimum equivalent annual cost of the objective function is approximately 1.20 - 1.25, the optimal solution interval with the maximum coal reduction per unit capacity of the objective function is approximately 0.12 - 0.22, and the optimal solution interval with the maximum carbon dioxide emission reduction per unit capacity of the objective function is approximately 0.3 - 0.6.

[0150] Example 3

[0151] In the electricity consumption scenarios of other seasons, the independent energy storage power station can still maximize its benefits by taking advantage of the peak-valley electricity price difference. The charging and discharging powers of the independent energy storage device at different electricity prices are as Figure 11 shown. Specifically, the energy storage device charges for 4 hours until full capacity at the low electricity price period from 1 to 4 o'clock; then discharges for 2 hours at the high electricity price period from 8 to 9 o'clock; charges for 2 hours during the low electricity price period from 12 to 13 o'clock to restore full capacity; and finally discharges for 4 hours during the high electricity price period from 16 to 19 o'clock to empty all energy storage, so as to achieve the maximum daily benefit. In this scenario, the double-layer optimization model obtains the optimal solutions for the rated power, rated capacity and economic benefits (see Figure 12 ). The optimal solution interval of the rated power is 5000kW - 9000kW, and the rated capacity interval is 38571kWh - 39771kWh, both of which meet the target parameter settings. The economic benefit range is 47416 yuan - 48616 yuan. Compared with summer and winter, due to the lack of peak electricity price periods and the smaller peak-valley electricity price difference in other seasons, the benefits are slightly lower, but the economic benefit gap is not significant, still meeting the economic benefit target of the independent energy storage project. In addition, for the electricity consumption scenarios in other seasons, the multi-objective genetic algorithm can find the Pareto optimal solution in each iteration. In the objective function, the optimal solution interval with the minimum average annual cost is 1.20 - 1.25, the optimal solution interval with the maximum coal reduction per unit capacity is 0.12 - 0.22, and the optimal solution interval with the maximum carbon dioxide emission reduction per unit capacity is 0.3 - 0.6. The optimal solution intervals of the three scenarios are basically the same, indicating that the independent energy storage power station can achieve the minimum cost, maximum coal reduction and maximum emission reduction in all scenarios, promoting the development of the new power system and helping to achieve the "dual carbon" goal.

[0152] In summary, through a detailed study of the electricity consumption scenarios in summer, winter and other seasons, the results show that the double-layer optimization model has optimal solutions in all scenarios. The independent energy storage system can adjust its charging and discharging strategies in real time according to the electricity price fluctuations, effectively achieving cost minimization and economic benefit maximization. Especially in summer and winter, due to the existence of peak periods, the independent energy storage system can obtain higher economic benefits by taking advantage of the peak-valley electricity price difference and can get the best rated capacity configuration and rated power configuration under the optimal solution. In addition, through the iterative optimization of the double-layer optimization model, it is confirmed that the independent energy storage system can not only reduce the operating cost, but also play an important role in reducing coal consumption and carbon dioxide emissions. The independent energy storage system can play an important value in promoting the development of the new power system and achieving the "dual carbon" goal.

[0153] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For related parts, reference can be made to the description in the method section.

[0154] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can 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 broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing independent energy storage configuration taking into account the full life cycle cost, characterized in that: The following steps are involved: Determine the 24-hour electricity price of independent energy storage power stations under different electricity usage scenarios, collect basic data under each electricity usage scenario, and determine the key operating parameters of the independent energy storage system under each electricity usage scenario; Taking the configuration capacity and power of the independent energy storage system as decision variables, minimizing the cost of the independent energy storage system, maximizing the coal reduction per unit capacity and the carbon dioxide emission reduction as optimization goals, and taking the rated power, capacity and state of charge as constraints, an independent energy storage upper-level configuration model is constructed. Taking the real-time power purchase and real-time power of the independent energy storage system as decision variables, maximizing the daily economic benefits of the independent energy storage system as the optimization goal, and power balance, output, operation and charge state as constraints, the lower-level optimization model of independent energy storage is constructed; A two-layer optimization model based on the upper-layer configuration model and the lower-layer optimization model is constructed. The two-layer optimization model is solved by multi-objective genetic algorithm and integer programming method to output the optimal configuration of independent energy storage and the daily operation plan of the system.

2. The independent energy storage configuration optimization method taking into account the full life cycle cost according to claim 1 is characterized in that: Determine the 24-hour electricity price of the independent energy storage power station under different electricity usage scenarios, collect basic data under each electricity usage scenario, and determine the specific contents of the key operating parameters of the independent energy storage system under each electricity usage scenario: Basic data include grid electricity price data, power load data, operating cost data and emission reduction data; Key operating parameters include power parameters, capacity parameters, state of charge parameters, economic parameters and optimization target parameters.

3. The independent energy storage configuration optimization method taking into account the full life cycle cost according to claim 1 is characterized in that: Taking the configuration capacity and power of the independent energy storage system as decision variables, minimizing the cost of the independent energy storage system, maximizing the coal reduction per unit capacity and the carbon dioxide emission reduction as optimization goals, and taking the rated power, capacity and state of charge as constraints to construct the upper-level configuration model of independent energy storage, the specific contents are as follows: The cost of an independent energy storage system is the cumulative sum of all relevant costs incurred during the entire cycle from design and commissioning to scrapping and recycling, that is, the life cycle cost LCC is shown in the following formula: minLCC=IC+OC+RC+DC In the formula, IC is the initial investment cost, OC is the operation and maintenance cost, RC is the equipment replacement cost, and DC is the recovery cost; Coal reduction per unit capacity of independent energy storage systemδ coal As shown below: ΔW coal =λ coal *S year S year =P rate *Charge and discharge times In the formula, ΔW coal E is the amount of coal consumption reduced during the life cycle of the independent energy storage system. rate is the configuration capacity of the independent energy storage system, λ coal is the coal consumption coefficient, S year To promote the consumption of independent energy storage system, P rate The configuration power of the independent energy storage system; Carbon dioxide emission reduction per unit capacity of independent energy storage system As shown below: In the formula, The amount of carbon dioxide emissions reduced during the life cycle of an independent energy storage system. is the carbon dioxide emission coefficient, S year Promote electricity consumption for independent energy storage systems.

4. The independent energy storage configuration optimization method taking into account the full life cycle cost according to claim 3 is characterized in that: The specific contents with rated power, capacity and state of charge as constraints are as follows: The rated power and capacity of the independent energy storage system are shown as follows: P rate ≤P M AND rate ≤E M Where P M is the maximum power of independent energy storage, E M is the maximum capacity of independent energy storage; The charging and discharging power limit of the independent energy storage system is as follows: P rate ≥|P ES (t)| Where P ES (t) is the charging and discharging power of the independent energy storage during period t; The charge state constraint of the independent energy storage system is as follows: 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.

5. The independent energy storage configuration optimization method taking into account the full life cycle cost according to claim 1 is characterized in that: Taking the real-time power purchase and real-time power of the independent energy storage system as decision variables, maximizing the daily economic benefits of the independent energy storage system as the optimization goal, and power balance, output, operation and charge state as constraints, the specific contents of constructing the lower-level optimization model of independent energy storage are as follows: The daily operating economic benefit F of the independent energy storage system is shown as follows: In the formula, γ t is the electricity price of the system in period t, β is the charging and discharging cost coefficient of the independent energy storage system, α t is the price of electricity purchased from the power grid during period t, P load (t) is the power load during period t, P G (t) is the amount of electricity purchased from the grid during period t, OC is the daily operation and maintenance cost, D is the annual operating days, and R is the monthly rental fee.

6. The independent energy storage configuration optimization method taking into account the full life cycle cost according to claim 5 is characterized in that: The specific contents with power balance, output, operation and state of charge as constraints are as follows: The power balance constraint of the independent energy storage system is as follows: P G (t)+P ES (t)=P load (t) The output constraint of the independent energy storage system is as follows: P min ≤P ES (t)≤P max Where P min is the minimum output power of the independent energy storage system, P max is the maximum output power of the independent energy storage system; The daily operation constraints of the independent energy storage system are as follows: The charge state constraint of the independent energy storage system is as follows: 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, and ω is the battery self-discharge coefficient.

7. The independent energy storage configuration optimization method taking into account the full life cycle cost according to claim 1 is characterized in that: A two-layer optimization model based on the upper configuration model and the lower optimization model is constructed. The two-layer optimization model is solved by multi-objective genetic algorithm and integer programming method, and the optimal configuration of independent energy storage and the specific content of the system daily operation plan are output as follows: In the upper configuration model, the multi-objective genetic algorithm in the Geatpy2 framework is used to generate populations and make configuration decisions, and the installed capacity and power decision variables are passed to the lower optimization model; The lower-level optimization model is based on the Gurobi+Python framework and integer programming solution, and substitutes the decision variables of the upper-level configuration model as parameters, makes decisions according to the optimization objectives, and returns the optimal solution to the upper-level configuration model to calculate the objective function value of the upper-level configuration model; The upper population is selected and cross-operated to iterate between the upper configuration model and the lower optimization model. If the convergence conditions are met, the optimal solution is output; otherwise, the iteration continues to output the optimal configuration of independent energy storage and the daily operation plan of the system.

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