Multi-type energy storage capacity double-layer optimization configuration method and system considering temperature change

Abstract

The invention discloses a multi-type energy storage capacity double-layer optimization configuration method and system considering temperature change, and belongs to the technical field of power system planning and energy management. According to the method, a typical daily method is combined with K-means clustering to extract a representative operation scene, and the energy storage efficiency and the energy retention coefficient are corrected through temperature sensitive parameters so as to reflect the influence of climate difference on the energy storage performance. In the aspect of optimization solution, an outer-inner layer decomposition solution strategy combining a multi-objective evolutionary algorithm NSGA-II and linear programming is provided: an outer layer completes multi-objective global search of energy storage capacity through the NSGA-II, and an inner layer accurately solves a typical daily operation problem in a linear programming form, so that the global optimization ability and the numerical calculation precision are both considered.

Description

Technical Field

[0001] This invention belongs to the technical field of power system planning and energy management, specifically relating to a two-layer optimization configuration method and system for multiple types of energy storage capacity considering temperature changes. Background Technology

[0002] With the large-scale integration of renewable energy into the power system, the uncertainty and volatility of wind and solar power generation have increased significantly, leading to prominent issues of wind and solar curtailment. To improve system flexibility and energy efficiency, energy storage systems play a crucial role in power system operation. Currently, research largely focuses on the economic configuration of single-type energy storage systems (such as lithium batteries or pumped hydro storage), neglecting the practical issue of performance differences between different energy storage types under varying climatic conditions.

[0003] Existing energy storage planning models typically configure capacity based on fixed parameters, failing to reflect the impact of temperature changes on energy storage efficiency, energy retention coefficient, and lifetime degradation. For example, electrochemical energy storage experiences a significant decrease in charge and discharge efficiency at low temperatures, flywheel energy storage suffers increased self-discharge at high temperatures, while pumped hydro storage is relatively less affected by temperature differences. If these temperature effects are not incorporated into the model, the capacity configuration will deviate from the optimal range, reducing system economy and operational reliability.

[0004] Therefore, it is necessary to propose a method for optimizing the allocation of multiple types of energy storage capacity that can comprehensively consider the impact of climate change, so as to achieve the overall optimal system investment and operation. Summary of the Invention

[0005] The purpose of this invention is to achieve a balance between the economy and flexibility of various types of energy storage systems under different climatic conditions, and to propose a two-layer optimization configuration method for the capacity of various types of energy storage considering temperature changes. This method comprehensively considers the performance differences of various energy storage technologies such as pumped hydro storage (PHS), lithium-ion battery energy storage (BESS), and flywheel energy storage (FES), as well as the impact of typical climatic scenarios such as hot days, normal temperature days, and cold days on energy storage efficiency and degradation characteristics, achieving coordinated optimization of capacity configuration and operation strategy at the system level. The model consists of two parts: a planning layer and an operation layer. The planning layer aims to minimize the annualized total cost of the system, determining the optimal installed power and capacity configuration for various types of energy storage, mainly covering investment costs, operating costs, and energy storage degradation costs. The operation layer, under the capacity constraints of the planning layer, aims to minimize wasted electricity and operating losses, optimizing the charging and discharging strategies and energy allocation of energy storage to achieve flexible adjustment of the system under different climatic conditions.

[0006] To achieve the above objectives, the present invention provides the following solution: a two-layer optimization configuration method for multiple types of energy storage capacity considering temperature changes, comprising the following steps: S1. Collect historical meteorological data and load characteristics, and divide the meteorological days of the year into scenarios; the scenarios include: hot days, normal temperature days, and cold days; S2. Extract typical days under different scenarios and obtain the weight of typical days based on the climate type of the typical days; S3. Construct an energy storage configuration optimization model for the energy storage system based on the weights of typical days, solve the energy storage configuration optimization model, and obtain the configuration results.

[0007] More preferably, the weight of a typical day is the proportion of days in different scenarios to the total number of days in the year.

[0008] More preferably, the objective function of the energy storage configuration optimization model is to minimize the system's annualized total cost: ; In the formula, f This represents the system's annualized total cost; This indicates the annualized investment cost of the energy storage system. This indicates the operating cost of the energy storage system. The degradation cost of energy storage systems; The constraints of the energy storage configuration optimization model include: system energy balance constraints, energy storage power and energy constraints, state of charge boundary constraints, charge and discharge mutual exclusion constraints, and operational constraints.

[0009] More preferably, the annualized investment cost of the energy storage system includes: ; In the formula, k Indicates the energy storage type index. k ∈{B(BESS),P(PHS),F(FES)}; and Indicates the isochronous value coefficient; Indicates power investment cost; This indicates the cost of capacity investment; and For the first k Optimal installed power and capacity for energy storage systems; The operating costs of an energy storage system include: ; In the formula, s Represents a typical daily scene index; This indicates the weight of the number of days represented by a typical day; t Indicates an arbitrary time step; Indicates the unit price of electricity; Indicates the power purchased by the power grid; Indicates the unit price of electricity; Indicates the power output sold to the grid; This indicates a penalty for abandoning electricity; This indicates the amount of wind and solar power curtailed. Indicates the time step; The degradation costs of energy storage systems include: ; In the formula, This represents the degradation cost of a battery for every 1 kWh of electrical energy it releases. This indicates the battery discharge power.

[0010] More preferably, the system energy balance constraint includes: ; In the formula, Indicates a typical day t System load at all times; , These represent wind power and solar power output, respectively. , These represent the power purchased and the power sold, respectively. , The charging and discharging power for energy storage; The energy storage power and energy constraints include: ; The charge state boundary constraints include: ; In the formula, , They represent k Minimum and maximum states of charge allowed for energy storage systems; e k,t,s Indicates energy storage type k In a typical daytime scenario s middle t Energy state at any given moment; e k,1,s Indicates energy storage type k In a typical daytime scenario s Energy state at the initial moment; e k,|T|+1,s Indicates energy storage type k In a typical daytime scenario s The energy state at the end of a typical day; The charge / discharge mutual exclusion constraint includes:

[0011] In the formula, , These represent the two variables representing the charging and discharging states, respectively. The operational constraints include: Flywheel climbing constraints: ; In the formula, This indicates the flywheel's maximum climbing speed; Pumped storage reservoir capacity constraints: ; In the formula, Indicates that the upper reservoir is t Water storage at any given time This indicates the maximum energy that the pumped storage system can store.

[0012] More preferably, the electrochemical energy storage model includes: ; In the formula, For the battery t Energy stored at time +1; , These represent the battery charging and discharging power, respectively. , These represent the battery charging and discharging efficiency, respectively. Pumped hydro storage models include: ; In the formula, Pumped storage indicates t Energy stored at time +1; , These represent the charging and discharging power of pumped hydro storage, respectively. , These represent pumping efficiency and power generation efficiency, respectively. Flywheel energy storage models include: ; In the formula, express s Typical energy retention factor of a Nissan flywheel; Indicates flywheel energy storage t Energy storage at all times; , These represent the flywheel charging and discharging power, respectively. , These represent the flywheel's energy storage charging and discharging power, respectively.

[0013] More preferably, based on the different energy storage characteristics under different climatic conditions in different scenarios, a scenario correction model is constructed to correct the parameters: ; In the formula, , , These are the rated parameters at standard temperature; , express s Typical daytime charge / discharge efficiency; Typical daily temperature difference; This refers to the temperature sensitivity coefficient. , Indicates the temperature sensitivity coefficient for energy storage charging / discharging; The temperature sensitivity coefficient represents the flywheel retention coefficient.

[0014] More preferably, in S3, the process of solving the energy storage configuration optimization model includes: an outer optimization process for capacity optimization and an inner optimization process for intraday scheduling.

[0015] This invention also provides a two-layer optimized configuration system for multiple types of energy storage capacity that takes into account temperature changes, including: The scene segmentation module is used to collect historical meteorological data and load characteristics, and to segment meteorological days throughout the year into scenes; the scenes include: hot days, normal temperature days, and cold days. The typical day extraction module is used to extract typical days under different scenarios and obtain the weight of typical days based on the climate type of the typical days; The configuration optimization module is used to construct an energy storage configuration optimization model for the energy storage system based on the weights of a typical day, and to solve the energy storage configuration optimization model to obtain the configuration results.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Enhanced climate adaptability: For the first time, the temperature sensitivity of energy storage is introduced into the capacity configuration model, resulting in results that better reflect the actual operating environment.

[0017] 2. Synergistic optimization of multiple energy storage types: Taking into account the complementary characteristics of pumped hydro storage, electrochemical energy storage and flywheel energy storage, a balance between system economy and flexibility is achieved.

[0018] 3. Balancing accuracy and efficiency: The combination of NSGA-II and hierarchical linear programming improves the globality and computational efficiency of the optimization solution.

[0019] 4. Highly applicable: Suitable for energy storage planning of new energy power systems under different regional climate conditions. Attached Figure Description

[0020] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1This is a framework diagram of a two-layer optimization configuration method for multiple types of energy storage capacity considering temperature changes, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the annual load and average temperature variation curves according to an embodiment of the present invention; Figure 3 A typical daily load diagram is extracted for an embodiment of the present invention; Figure 4 The following is a schematic diagram of the extraction of typical daily renewable energy output in an embodiment of the present invention; wherein, (a) is the wind power output curve of a typical day, and (b) is the photovoltaic power output curve of a typical day. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] Example 1: like Figure 1 As shown, this embodiment provides a two-layer optimization configuration method for multiple types of energy storage capacity considering temperature changes, including the following steps: S1. Collect historical meteorological data and load characteristics, and divide the meteorological days of the year into scenarios; among which, the scenarios include: hot days (high temperature days in summer), normal temperature days (spring and autumn), and cold days (low temperature days in winter).

[0025] S2. Extract typical days under different scenarios and obtain the weight of typical days based on the climate type of the typical days.

[0026] Representative daily load curves, wind power and photovoltaic output curves for various scenarios were selected using K-means clustering or feature clustering based on temperature and irradiance.

[0027] Further implementation involves the weighting of each typical day. w s The proportion of days in different scenarios out of the total number of days in the year: (1) In the formula, s Index for typical daily scenarios (hot days, normal temperature days, cold days).

[0028] Among them, battery-electrochemical energy storage (BESS) is mainly affected by temperature, resulting in variations in efficiency and available capacity; pumped hydro storage (PHS) is less affected by ambient temperature differences, but its pump efficiency decreases slightly under low-temperature conditions; and flywheel energy storage (FES) experiences increased self-discharge and a decreased retention factor under high-temperature conditions. This method effectively captures the impact of climate conditions on energy storage performance, thereby improving the accuracy of configuration planning results.

[0029] S3. Construct an energy storage configuration optimization model for the energy storage system based on the weights of typical days, solve the energy storage configuration optimization model, and obtain the configuration results.

[0030] In this embodiment, to maximize the economic benefits of the energy storage system, an optimization model for energy storage capacity configuration is established. Therefore, the objective function of the energy storage configuration optimization model is to minimize the system's annualized total cost, including reducing energy storage investment costs, system operating costs, and minimizing power curtailment and energy storage degradation costs. This model can systematically evaluate the balance between energy storage investment and operating costs, thereby determining the optimal energy storage configuration scheme. (2) In the formula, f This represents the system's annualized total cost; This indicates the annualized investment cost of the energy storage system. This indicates the operating cost of the energy storage system. This refers to the degradation cost of energy storage systems.

[0031] The annualized investment cost of the energy storage system includes: (3) In the formula, k Indicates the energy storage type index. k ∈{B(BESS),P(PHS),F(FES)}; and The isochronous value coefficient is calculated as follows: / = ,in, r The discount rate; N The term indicates the lifespan in years. In this embodiment, the pumped storage lifespan is 40 years, the battery lifespan is 10 years, and the flywheel lifespan is 20 years. This indicates the power investment cost (RMB / kW). This represents the cost of capacity investment (RMB / kWh). and For the first k Optimal installed power and capacity for energy storage systems.

[0032] The operating costs of an energy storage system include: (4) In the formula, This indicates the weight of the number of days represented by a typical day; t Indicates an arbitrary time step; This indicates the unit price of electricity (RMB / kWh); Indicates the power purchased by the power grid; This indicates the unit price of electricity (yuan / kWh); Indicates the power output sold to the grid; Indicates the penalty for power curtailment (yuan / kWh); This indicates the amount of wind and solar power curtailed. Indicates the time step. The degradation costs of energy storage systems include: (5) In the formula, This represents the degradation cost (yuan / kWh) of the battery for every 1 kWh of energy it releases. This indicates the battery discharge power.

[0033] Further implementation involves the following constraints for the energy storage configuration optimization model: system energy balance constraints, energy storage power and energy constraints, state of charge boundary constraints, charge and discharge mutual exclusion constraints, and operational constraints.

[0034] The system energy balance constraints include: (6) In the formula, Indicates a typical day t System load at all times; , These represent wind power and solar power output, respectively. , These represent the power purchased and the power sold, respectively. , This refers to the charging and discharging power of energy storage.

[0035] Energy storage power and energy constraints include: (7) The charged state boundary constraints include: (8) In the formula, , They represent k Minimum and maximum states of charge allowed for energy storage systems; e k,t,s Indicates energy storage type k In a typical daytime scenario s middle t Energy state at any given moment; e k,1,sIndicates energy storage type k In a typical daytime scenario s Energy state at the initial moment; e k,|T|+1,s Indicates energy storage type k In a typical daytime scenario s The energy state at the end of a typical day; In this embodiment, the dynamic changes in the internal energy (or state of charge SoC) of the energy storage follow the energy balance equation. Modeling is performed for different types of energy storage, including electrochemical energy storage models: (9) In the formula, For the battery t Energy stored at time +1 (kWh); , These represent the battery charging and discharging power (kW), respectively. , These represent the battery charging and discharging efficiency, respectively.

[0036] Pumped hydro storage models include: (10) In the formula, Pumped storage indicates t Energy stored at time +1 (kWh); , These represent the charging and discharging power (kW) of the pumped hydro storage system, respectively. , These represent pumping efficiency and power generation efficiency, respectively.

[0037] Flywheel energy storage models include: (11) In the formula, express s Typical energy retention factor of a Nissan flywheel; Indicates flywheel energy storage t Energy storage at all times; , These represent the flywheel charging and discharging power, respectively. , These represent the flywheel's energy storage charging and discharging power, respectively.

[0038] Charge-discharge mutual exclusion constraints include: (12) In the formula, , These represent the two variables representing the charging and discharging states, respectively.

[0039] Operational constraints include: Flywheel climbing constraints: (13) In the formula, This indicates the flywheel's maximum climbing speed.

[0040] Pumped storage reservoir capacity constraints: (14) In the formula, Indicates the upper reservoir at a certain time t Water storage capacity This indicates the maximum energy that the pumped storage system can store.

[0041] A further step involves constructing a scenario correction model to adjust the parameters based on the different energy storage characteristics under different climatic conditions in various scenarios: (15) In the formula, , , These are the rated parameters at standard temperature; , express s The charge / discharge efficiency under typical daily conditions; therefore, the parameters under typical daily scenarios. , and All are based on standard temperature parameters , , Temperature difference The modified model was used to replace the standard condition parameters with scene modification parameters, thus realizing the mapping and substitution from standard condition parameters to scene modification parameters. Typical daily temperature difference; For temperature sensitivity coefficient, , Indicates the temperature sensitivity coefficient for energy storage charging / discharging; The temperature sensitivity coefficient represents the flywheel retention coefficient.

[0042] The modified model described above can quantitatively reflect the impact of different climatic conditions on energy storage efficiency and self-discharge, thereby improving the physical rationality of the configuration results.

[0043] In this embodiment, the process of solving the energy storage configuration optimization model includes dividing the problem into inner-layer optimization and outer-layer optimization. To balance economy and flexibility, this invention employs the multi-objective evolutionary algorithm NSGA-II for capacity-layer optimization, and divides the intraday scheduling problem under a given capacity into inner-layer linear / mixed-integer linear programming (LP / MILP), which is solved exactly by CPLEX. The outer-inner decoupling maintains the robustness of NSGA-II in multi-objective non-convex cases while leveraging the numerical advantages of CPLEX in large-scale linear / mixed-linear models.

[0044] For the multi-type energy storage configuration planning problem, a two-layer optimization architecture is adopted for solution. This architecture includes an outer optimization process for capacity optimization and an inner optimization process for intraday scheduling. The specific steps are as follows: Step 1: Data Initialization: This includes typical daily load, wind and solar power output, efficiency parameters of the three types of energy storage at different temperatures, electricity price data, and system energy balance parameters. Simultaneously, the population size, maximum number of iterations, crossover probability, and mutation probability are set for the outer-layer optimization algorithm. The population size is set to 80–150, the maximum number of iterations to 100–300, the crossover probability to 0.8–0.95, and the mutation probability to 0.01–0.10. After initialization, the basic dataset for capacity optimization and scheduling solutions is formed.

[0045] Step 2: Outer-layer optimization model construction: The rated power and capacity of electrochemical energy storage, pumped hydro storage, and flywheel energy storage are used as outer-layer optimization variables to generate an initial population. The outer-layer optimization uses the NSGA-II multi-objective evolutionary algorithm, with operating cost and abandoned power as optimization objectives, and obtains the initial search direction for capacity configuration through non-dominated sorting.

[0046] Step 3: Solving the Inner Layer Model: For each capacity scheme in the outer layer, construct a linear / mixed-integer linear programming model for intraday scheduling. The scheduling model includes energy balance, energy storage charging and discharging power limits, energy state constraints, charging and discharging mutual exclusion constraints, consistency constraints between the first and last states of a typical day, and temperature correction efficiency constraints. After substituting the outer layer capacity parameters into the model, the optimal scheduling result is obtained using the CPLEX solver, and the corresponding operating cost and abandoned power are calculated as the fitness input for outer layer optimization.

[0047] Step 4: Fitness Calculation and Correction: For feasible solutions in the inner layer, the fitness is calculated based on the operating cost and abandoned power obtained from the inner layer. If the model is infeasible or constraints are violated, the objective is penalized and corrected. The correction term is composed of a weighted average of the squared violations, with a penalty coefficient of 10. 4 ~10 6 This is to ensure that infeasible solutions are eliminated during the evolutionary process.

[0048] Step 5: Outer Evolution: Based on NSGA-II, non-dominated sorting and crowding calculation are performed. A new generation of capacity schemes is generated through tournament selection, simulated binary crossover, and polynomial mutation. An elite retention strategy is then used to construct the next generation of the population. This process iterates continuously to gradually approach the Pareto optimal set of energy storage capacity.

[0049] Step Six: Output Optimal Configuration: Optimization terminates when the maximum number of iterations is reached or the Pareto front converges, and the final Pareto optimal energy storage configuration result is output. Users can choose the cost-optimal, power-wasting-optimal, or compromise solution according to their needs.

[0050] The outer decision vector contains only the capacity dimension: (16) In the given x Under the given conditions, the inner layer problem is solved for intraday scheduling according to formulas (3)-(14) to obtain the operating variables, and the inner layer solution yields the operating decision. And the target value: (17) In the formula, C tota The system's annualized total cost.

[0051] Meanwhile, since the energy storage configuration optimization model includes nonlinear constraints such as energy balance, power ceiling, and SoC constraints, if an individual violates the constraints, a penalty term will be introduced to modify the fitness function. (18) In the formula, g j ( X i ) indicates the first j A constraint violation quantity, ρ The penalty coefficient is used. For multi-objective optimization, the algorithm uses ( f 1, f 2) It participates in non-dominated ranking as a two-dimensional objective.

[0052] Example 2: This embodiment uses the power system of a province in Northeast China as a case study to illustrate the purpose, technical solution, and advantages of the present invention.

[0053] Located in a high-latitude, cold region, this area exhibits typical continental monsoon climate characteristics, characterized by long, cold winters and short, hot summers. Renewable energy capacity in the region is primarily based on wind and solar power, and the problem of wind and solar curtailment has long existed. The system consists of wind farms, solar power plants, and three types of energy storage units (BESS, PHS, and FES).

[0054] Annual load and average temperature as follows Figure 2 As shown in the figure, this graph illustrates the changing trends of the system's average load and average temperature over the 365-day year. Load fluctuation ranges are represented by shaded areas. Figure 2 It is evident that the load is significantly higher in winter than in summer, and the temperature changes exhibit a clear seasonal periodicity, providing a basis for subsequent typical day classification and temperature correction.

[0055] The optimization objective is to determine the optimal configuration scheme for three types of energy storage under different typical climatic days, in order to minimize the system's annualized total cost and the amount of abandoned electricity.

[0056] The technical and economic parameters of the three types of energy storage are shown in Table 1.

[0057] Table 1

[0058] The electricity prices are shown in Table 2.

[0059] Table 2

[0060] The meteorological characteristics and weight settings for three typical days are shown in Table 3.

[0061] Table 3

[0062] Figure 3 To extract typical daily loads, this invention employs K-means clustering to divide operating days into three typical climate scenarios based on annual meteorological and load data: hot days with temperatures above 15 °C, normal-temperature days with temperatures between 0 and 15 °C, and cold days with temperatures below 0 °C. The load curves for these three typical days are shown below. Figure 3 As shown, the load characteristics differ significantly under different temperature conditions, reflecting the direct impact of climate change on the system's electricity demand.

[0063] Figure 4 To extract the power output of new energy sources on a typical day. Among them, (a) is the wind power output curve on a typical day, and (b) is the photovoltaic power output curve on a typical day. Figure 4 The paper presents the trends in wind and solar power output under three climate scenarios: hot days, normal days, and cold days. Figure 4As can be seen, the load curve for hot days is generally at a high level, with the peak occurring between 2 PM and 5 PM. This is mainly due to the influence of summer air conditioning load, exhibiting significant diurnal fluctuations and a large peak-to-valley difference, characteristic of typical summer high-temperature days. The load curve for cold days shifts upwards overall, showing a double-peak structure between 7 AM and 9 AM and between 5 PM and 8 PM, corresponding to the periods when residential heating and lighting loads rise intensively. Compared to hot days, the peak-to-valley difference is slightly smaller, and the load changes are more stable. The load curve for normal-temperature days is at a moderate level, with relatively gentle load fluctuations. The peak usually occurs between 3 PM and 6 PM, and the curve is relatively smooth, representing the typical operating conditions in spring and autumn.

[0064] A comprehensive comparison reveals that the peak load on hot and cold days is significantly higher than that on days with normal temperatures, reflecting the peak electricity demand caused by summer cooling and winter heating, respectively. Meanwhile, the load on days with normal temperatures is relatively stable and can be considered the baseline load level for system operation. These three typical days, selected in this way, effectively represent the electricity demand characteristics under different climatic conditions throughout the year, providing a representative input basis for subsequent capacity planning and scheduling optimization of various types of energy storage systems.

[0065] The energy storage configuration scheme is shown in Table 4.

[0066] Table 4

[0067] Based on the established two-level optimization model, the optimal energy storage configuration scheme of the system is shown in Table 4. The results show that the total energy storage power of the system is 4,941 MW, corresponding to a total energy storage capacity of 23,835 MWh. Among them, PHS, as the main long-duration energy storage type, has a configured power of 3,174 MW, a duration of 6 hours, and a capacity of 19,044 MWh, playing a core role in peak shaving and seasonal energy regulation within the system. BESS has a configured power of 1,585 MW, a duration of 3 hours, and a capacity of 4,755 MWh, mainly used for intraday peak shaving and smoothing of renewable energy output. FES has a configured power of 182 MW, a duration of 0.2 hours, and a capacity of 36 MWh. Although its energy scale is relatively small, its response speed is fast, and it is mainly used for short-term frequency regulation and rapid power compensation.

[0068] Overall, this configuration fully demonstrates the functional complementarity of different types of energy storage systems: pumped hydro storage provides long-term energy storage support, electrochemical energy storage enhances operational flexibility, and flywheel energy storage strengthens the system's dynamic stability. The synergistic effect of these three elements effectively improves the system's renewable energy absorption capacity and operational economy. Example 3: This embodiment provides a two-layer optimization configuration system for multi-type energy storage capacity considering temperature changes, including: a scenario segmentation module, used to collect historical meteorological data and load characteristics, and segment the meteorological days of the year into scenarios; scenarios include: hot days, normal temperature days, and cold days; a typical day extraction module, used to extract typical days under different scenarios, and obtain the weight of typical days based on the climate type of typical days; and a configuration optimization module, used to construct an energy storage configuration optimization model of the energy storage system based on the weight of typical days, solve the energy storage configuration optimization model, and obtain the configuration results.

[0069] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A two-layer optimization configuration method for multiple types of energy storage capacity considering temperature changes, characterized in that, Includes the following steps: S1. Collect historical meteorological data and load characteristics, and divide the meteorological days of the year into scenarios; the scenarios include: hot days, normal temperature days, and cold days; S2. Extract typical days under different scenarios and obtain the weight of typical days based on the climate type of the typical days; S3. Construct an energy storage configuration optimization model for the energy storage system based on the weights of typical days, solve the energy storage configuration optimization model, and obtain the configuration results.

2. The method for optimizing the dual-layer configuration of multiple types of energy storage capacity considering temperature changes according to claim 1, characterized in that, The weight of a typical day is the proportion of days in different scenarios to the total number of days in the year.

3. The method for optimizing the dual-layer configuration of multiple types of energy storage capacity considering temperature changes according to claim 1, characterized in that, The objective function of the energy storage configuration optimization model is to minimize the annualized total cost of the system. ； In the formula, f This represents the system's annualized total cost; This indicates the annualized investment cost of the energy storage system. This indicates the operating cost of the energy storage system. The degradation cost of energy storage systems; The constraints of the energy storage configuration optimization model include: system energy balance constraints, energy storage power and energy constraints, state of charge boundary constraints, charge and discharge mutual exclusion constraints, and operational constraints.

4. The method for optimizing the dual-layer configuration of multiple types of energy storage capacity considering temperature changes according to claim 3, characterized in that, The annualized investment cost of an energy storage system includes: ； In the formula, k Indicates the energy storage type index. k ∈{B(BESS),P(PHS),F(FES)}; and Indicates the isochronous value coefficient; Indicates power investment cost; This indicates the cost of capacity investment; and For the first k Optimal installed power and capacity for energy storage systems; The operating costs of an energy storage system include: ； In the formula, s Represents a typical daily scene index; This indicates the weight of the number of days represented by a typical day; t Indicates an arbitrary time step; Indicates the unit price of electricity; Indicates the power purchased by the power grid; Indicates the unit price of electricity; Indicates the power output sold to the grid; This indicates a penalty for abandoning electricity; This indicates the amount of wind and solar power curtailed. Indicates the time step; The degradation costs of energy storage systems include: ； In the formula, This represents the degradation cost of a battery for every 1 kWh of electrical energy it releases. This indicates the battery discharge power.

5. The method for optimizing the dual-layer configuration of multiple types of energy storage capacity considering temperature changes according to claim 4, characterized in that, The system energy balance constraints include: ； In the formula, Indicates a typical day t System load at all times; , These represent wind power and solar power output, respectively. , These represent the power purchased and the power sold, respectively. , The charging and discharging power for energy storage; The energy storage power and energy constraints include: ； The charge state boundary constraints include: ； In the formula, , They represent k Minimum and maximum states of charge allowed for energy storage systems; e k,t,s Indicates energy storage type k In a typical daytime scenario s middle t Energy state at any given moment; e k,1,s Indicates energy storage type k In a typical daytime scenario s Energy state at the initial moment; e k,|T|+1,s Indicates energy storage type k In a typical daytime scenario s The energy state at the end of a typical day; The charge / discharge mutual exclusion constraint includes:

6. In the formula, , These represent the two variables representing the charging and discharging states, respectively. The operational constraints include: Flywheel climbing constraints: ； In the formula, This indicates the flywheel's maximum climbing speed; Pumped storage reservoir capacity constraints: ； In the formula, Indicates that the upper reservoir is t Water storage at any given time This indicates the maximum energy that the pumped storage system can store.

7. The method for optimizing the dual-layer configuration of multiple types of energy storage capacity considering temperature changes according to claim 5, characterized in that, Electrochemical energy storage models include: ； In the formula, For the battery t Energy stored at time +1; , These represent the battery charging and discharging power, respectively. , These represent the battery charging and discharging efficiency, respectively. Pumped hydro storage models include: ； In the formula, Pumped storage indicates t Energy stored at time +1; , These represent the charging and discharging power of pumped hydro storage, respectively. , These represent pumping efficiency and power generation efficiency, respectively. Flywheel energy storage models include: ； In the formula, express s Typical energy retention factor of a Nissan flywheel; Indicates flywheel energy storage t Time Science's energy storage; , These represent the flywheel charging and discharging power, respectively. , These represent the flywheel's energy storage charging and discharging power, respectively.

8. The method for optimizing the dual-layer configuration of multiple types of energy storage capacity considering temperature changes according to claim 6, characterized in that, Based on the different energy storage characteristics under different climatic conditions in various scenarios, a scenario correction model is constructed to adjust the parameters: ； In the formula, , , These are the rated parameters at standard temperature; , express s Typical daytime charge / discharge efficiency; Typical daily temperature difference; This refers to the temperature sensitivity coefficient. , Indicates the temperature sensitivity coefficient for energy storage charging / discharging; The temperature sensitivity coefficient represents the flywheel retention coefficient.

9. The method for optimizing the dual-layer configuration of multiple types of energy storage capacity considering temperature changes according to claim 1, characterized in that, In S3, the process of solving the energy storage configuration optimization model includes: the outer optimization process for capacity optimization and the inner optimization process for intraday scheduling.

10. A two-layer optimized configuration system for multiple types of energy storage capacity considering temperature variations, the system being used to implement the method described in any one of claims 1-8, characterized in that, include: The scene segmentation module is used to collect historical meteorological data and load characteristics, and to segment meteorological days throughout the year into scenes. The scenarios include: hot days, normal temperature days, and cold days; The typical day extraction module is used to extract typical days under different scenarios and obtain the weight of typical days based on the climate type of the typical days; The configuration optimization module is used to construct an energy storage configuration optimization model for the energy storage system based on the weights of a typical day, and to solve the energy storage configuration optimization model to obtain the configuration results.

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