A novel power system full-factor collaborative multi-time-and-space scale energy storage planning method and device

Abstract

A novel collaborative multi-temporal and spatial scale energy storage planning method and device for power systems, belonging to the technical field of new power system construction and planning, includes the following steps: determining the total capacity demand and boundary conditions of the power system's regulation capacity; generating a set of candidate schemes including different power source structures, energy storage types, and configuration capacities; establishing upper, middle, and lower-level optimization models with the objectives of maximizing system net benefits, minimizing system investment and construction costs, and minimizing system operating costs, including the objective function and constraints of each level; using a multi-objective optimization algorithm to comprehensively optimize from the candidate scheme set according to the optimization model; and determining whether the obtained optimal scheme's indicators meet the boundary condition requirements. If not, the model parameters are adjusted and iterated again. This method, through the collaborative planning of all elements of power source, grid, load, and energy storage, can achieve coordinated development of power sources, transmission networks, user loads, and energy storage facilities, improving the economy and efficiency of the entire system.

Description

Technical Field

[0001] This invention relates to the field of new power system construction planning technology, and in particular to a new power system full-element collaborative multi-temporal and spatial scale energy storage planning method and device. Background Technology

[0002] During the process of large-scale, high-proportion renewable energy grid connection, both the source and load ends exhibit high uncertainty, and the fluctuation of the power system's "net load" increases. In order to maintain power and energy balance at different time scales, the new power system form has gradually shifted from the traditional three elements of "source, grid, and load" to the four elements of "source, grid, load, and storage". Energy storage, as an efficient and flexible resource, has become a new core component of the power system.

[0003] In energy storage planning, we should not blindly pursue "the more the better" and avoid inefficient phenomena such as resource allocation and redundant construction. At present, most of the research in this area focuses on a single aspect, such as the coordinated optimization and scheduling of power generation, grid and load, while there is less research on the coordinated planning of all elements of power generation, grid, load and storage. At the same time, traditional planning methods often lack consideration for power and energy balance at different time scales, making it difficult to meet the multi-dimensional flexibility requirements of new power systems. Summary of the Invention

[0004] To address the aforementioned issues, this disclosure provides a comprehensive, multi-temporal-scale collaborative energy storage planning method and device for new power systems. This method aims to solve key problems in energy storage planning under new power systems and improve the overall system's economic efficiency. Here, multi-spatial scale refers to the power source side, grid side, and load side of the new power system; multi-temporal scale refers to long-term, medium-term, and short-term scales.

[0005] The novel power system all-element collaborative multi-temporal scale energy storage planning method disclosed herein mainly constructs an upper-level novel power system all-element collaborative planning model, a middle-level multi-spatial scale energy storage optimization configuration model, and a lower-level multi-temporal scale source-grid-load-storage optimization operation model, and solves the optimal solution of the energy storage planning scheme through a multi-objective comprehensive optimization algorithm.

[0006] Specifically, the novel power system all-element collaborative multi-temporal-scale energy storage planning method provided in this disclosure mainly includes the following steps: S1: Obtain economic and social development policies, current status and planned development data of the power system for the corresponding planning year, and determine the total capacity demand and boundary conditions of the power system's regulation capacity through policy analysis and demand calculation. S2 generates a set of candidate solutions containing different power structures, energy storage types and configuration capacities; S3 establishes three-level optimization models—upper, middle, and lower—with the objectives of maximizing system net benefits, minimizing system investment and construction costs, and minimizing system operating costs, respectively. These models are used for collaborative planning of all elements of the new power system, optimal configuration of energy storage at multiple spatial scales, and optimal operation of energy storage at multiple time scales. S4. According to the three-layer optimization model of upper, middle and lower layers, a multi-objective optimization algorithm is used to perform comprehensive optimization from the obtained candidate solution set. S5. Determine whether the optimal solution index obtained in step S4 meets the preset power system boundary condition requirements. If it does, proceed to step S6; otherwise, return to step S3 to adjust the model parameters and iterate again. S6 outputs the overall energy storage planning scale for the power system and its various zones, including the power supply side, grid side, and load side, as well as the total system cost and various individual costs.

[0007] Furthermore, step S1 specifically includes: To obtain economic and social development policies of the power system at the planning level, as well as current status and planning data of the power system, including the characteristics of various power sources and energy storage resources, grid structure, and load demand characteristics; Policy Analysis: Analyze the dual carbon targets, non-fossil energy consumption ratio, and renewable energy utilization rate data of the region where the power system is located to formulate boundary conditions for regulation capacity planning; Demand Calculation: Based on the historical output curves and load data of various power sources, combined with the characteristics of power grid zoning, the reserve demand of coal-fired power generation and the peak-shaving demand of the system, net load calculation and power, electricity and green energy gap analysis are performed to quantify the total capacity demand of the system for flexible regulation resources.

[0008] Furthermore, step S2 specifically includes: Based on the total capacity demand and boundary conditions obtained in step S1, and in combination with technical feasibility, the capacity configuration range and combination method of key elements such as thermal power, wind power, photovoltaic power, hydropower and energy storage in the power configuration scheme are set. Through permutation and combination or scenario generation technology, multiple differentiated candidate planning schemes are formed as a candidate scheme set. The candidate scheme set is used as the input basis for the subsequent upper, middle and lower three-level optimization model.

[0009] Furthermore, in step S3, the upper-level optimization model for the collaborative planning of all elements of the new power system takes maximizing the net benefit of the system as the optimization objective, and is used to determine the total technical and economic capacity demand of the system's energy storage. Its objective function expression is:

[0010] In the formula, , , , , , , , , , These represent the optimal net benefit, operating revenue, annualized investment cost, unit start-up and shutdown cost, energy storage electricity cost, fuel cost, grid loss cost, environmental cost, renewable energy curtailment penalty, and demand-side response dispatch cost, respectively. The constraints of the upper-level optimization model include: power supply and demand balance constraints, system reserve constraints, inter-regional power interaction constraints, regional boundary constraints, resource condition constraints, and policy-level constraints.

[0011] Furthermore, in step S3, the mid-level optimization model for multi-spatial-scale energy storage optimization configuration uses the minimum system investment and construction cost as the optimization objective to determine the optimal energy storage scale for the power supply side, grid side, and load side; its objective function expression is:

[0012] in, , , , These represent the annualized investment costs for various new power sources, power grids, flexible loads, and energy storage, respectively. The various power sources include one or more of the following: wind power, photovoltaic power, thermal power, hydropower, and gas power. The constraints of the mid-level optimization model include: various power source output constraints, grid constraints, flexible load constraints, and energy storage constraints.

[0013] Furthermore, in step S3, the lower-level optimization model uses the minimum system operating cost as the optimization objective, and is used to perform coupled production simulations at different time scales (long, medium, and short). Its objective function expression is:

[0014] In the formula, Indicates the minimum operating cost. , , , , , , These represent the unit start-up and shutdown costs, energy storage electricity costs, fuel costs, grid loss costs, environmental costs, renewable energy curtailment penalties, and demand-side response dispatch costs, respectively. The constraints of the lower-level optimization model include: various power source operation constraints, flexible load scheduling constraints, and energy storage operation constraints.

[0015] Furthermore, the specific method of step S4 includes: Based on the candidate solution set obtained in step S2, the NSGA-III multi-objective optimization algorithm is used to solve the Pareto optimal solution set according to the upper, middle and lower three-layer optimization model constructed in step S3. The NSGA-III algorithm is used to handle multiple optimization objectives, namely, the upper-level model with the best net benefit, the middle-level model with the lowest investment cost, and the lower-level model with the lowest operating cost. Through its reference point mechanism, it ensures that a Pareto non-dominated solution set with satisfactory convergence and uniform distribution is obtained in the high-dimensional objective space.

[0016] Furthermore, step S5 specifically includes: Select the comprehensive optimal solution from the Pareto optimal solution set, and determine whether its various indicators meet the preset power system boundary conditions and performance requirements. If they meet the requirements, proceed to step S6; otherwise, generate a feedback instruction and return to step S3, adjust the model parameters, and recalculate iteratively. The indicators used for evaluation include: power supply level, primary energy consumption level, greening level, and economic efficiency level.

[0017] Furthermore, the output of step S6 includes: 1) The planned configuration capacity and power of various types of energy storage on the power source side, grid side, and load side within the overall power system and its various zones; 2) The total cost of achieving the above-mentioned scale configuration during the system planning period, including various costs such as power supply investment, grid investment, energy storage investment and system operation costs.

[0018] A novel power system energy storage planning device employing the above method, comprising all elements coordinated across multiple temporal and spatial scales, mainly includes: The data acquisition module is used to acquire data on the economic and social development policies, current status, and planned development of the power system in the corresponding planning year. The policy analysis module is used to analyze the dual carbon targets, non-fossil energy consumption ratio, and new energy utilization rate requirements of the region where the power system is located, based on the data acquired by the data acquisition module, in order to determine the policy boundary conditions for planning. The demand calculation module is used to calculate the system's power, electricity, peak shaving and green electricity gaps based on the power and load data provided by the data acquisition module and the policy boundary conditions determined by the policy analysis module, through net load calculation and power balance analysis, so as to quantify the system's total capacity demand for flexible adjustment resources. The scheme formulation module is used to generate a set of candidate schemes containing different power structure, energy storage type and configuration capacity based on the total capacity demand boundary conditions determined by the demand calculation module. The model building module is used to establish three-layer optimization models: upper, middle and lower layers, which are used to realize the collaborative planning of all elements of the new power system, the optimal configuration of energy storage at multiple spatial scales, and the optimal operation of energy storage at multiple time scales, respectively. The model solving module, based on the candidate solution set, uses a multi-objective optimization algorithm to collaboratively solve the constructed three-layer optimization model and obtain the Pareto optimal solution set. The result judgment module is used to select the optimal solution from the Pareto optimal solution set that satisfies the preset system boundary conditions and performance indicators.

[0019] With the goal of maximizing the net system benefit, a novel power system all-element collaborative planning model is constructed to obtain the total technical and economic capacity demand for system energy storage. With the goal of minimizing system investment and construction costs, a multi-spatial-scale energy storage optimization configuration model is constructed to obtain the optimal energy storage scale for each type on the source side, grid side, and load side. A multi-time-scale source-grid-load-storage optimization operation model is constructed, and with the goal of minimizing system operating costs, coupled production simulations at different time scales are conducted to obtain the overall power system and the energy storage planning for each region's source, grid, and load sides. Compared with existing technologies, the beneficial effects of this disclosure are: ① By constructing a new type of power system with new energy as the main body, the coordinated planning of all elements of power source, grid, load and storage has been realized, and the coordinated development of power source, transmission network, user load and energy storage facilities has been achieved, thus improving the economy and efficiency of the entire system. ② By constructing a three-layer optimization model and multi-objective optimization, a comprehensive balance of multiple factors was achieved; ③ Through the coordinated planning of all elements, the capacity and layout of various facilities are rationally determined, avoiding inefficient phenomena such as resource allocation and duplication of construction, and realizing efficient allocation and utilization of resources; ④ The multi-temporal and spatial scale planning method fully considers the power and energy balance issues at different time scales, effectively copes with the system net load fluctuations caused by the high proportion of new energy grid connection, and improves the system's flexibility and stability; ⑤ It strengthens the synergistic effect of energy storage and other flexible resources, realizes the multi-dimensional regulation function of the power system, and improves the overall regulation capability and operational flexibility of the system. Attached Figure Description

[0020] The above and other objects, features and advantages of this disclosure will become more apparent from the more detailed description of exemplary embodiments of this disclosure taken in conjunction with the accompanying drawings, in which the same reference numerals generally represent the same components.

[0021] Figure 1 The flowchart is a process for a full-element collaborative multi-temporal-scale energy storage planning method applied to a new type of power system according to the present disclosure; Figure 2 This is a schematic diagram illustrating the logical structure of the upper, middle, and lower levels of the planning model in an exemplary embodiment. Figure 3 This is a schematic diagram of the multi-timescale optimization process in an exemplary embodiment; Figure 4 This is a flowchart illustrating the NSGA-III multi-objective optimization algorithm in an exemplary embodiment. Figure 5 Output diagram of the 2030 scheme as an exemplary embodiment; Figure 6 Output diagram of the 2035 scheme as an exemplary embodiment; Figure 7 A schematic diagram of a full-element collaborative multi-temporal-scale energy storage planning device as an exemplary embodiment; Figure 8 A schematic diagram of an electronic device computer system architecture for an exemplary embodiment. Detailed Implementation

[0022] Preferred embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0023] This disclosure provides a multi-temporal and spatial scale energy storage planning method and device for the coordinated development of all elements in a new power system, which is used to realize multi-temporal and spatial scale energy storage planning that coordinates all elements of power source, grid, load and energy storage, and achieves coordinated development of power source, transmission network, user load and energy storage facilities.

[0024] This method obtains a solution by constructing a three-layer optimization model (upper, middle, and lower layers) and performing comprehensive optimization. The model includes: an upper-layer collaborative planning model for all elements of a new power system; a middle-layer multi-spatial-scale energy storage optimization configuration model; and a lower-layer multi-temporal-scale energy storage optimization operation model.

[0025] The specific steps are as follows: Step 1: Obtain economic and social development policies, system status, and planned development data for the target power system in the planning year. Then, through policy analysis and demand calculation, determine the total capacity demand and boundary conditions of the power system's regulation capacity. Step 2: Based on the total system regulation capacity requirement obtained in Step 1, and according to the total capacity requirement boundary and in combination with technical feasibility, set the capacity configuration range and combination method of key elements such as thermal power, wind power, photovoltaic power, hydropower and energy storage in the power configuration scheme. Through permutation and combination or scenario generation technology, form multiple differentiated candidate planning schemes that include different power structures, energy storage types and configuration capacities. Step 3: Establish three-level optimization models (upper, middle, and lower) with the objectives of maximizing system net benefits, minimizing system investment and construction costs, and minimizing system operating costs, respectively. These models include objective functions and constraints and are used for the upper-level collaborative planning of all elements of the new power system, the middle-level multi-spatial-scale energy storage optimization configuration, and the lower-level multi-time-scale source-grid-load-storage optimization operation. Step 4: Based on the candidate solution set obtained in Step 2, use a multi-objective optimization algorithm to solve the upper, middle and lower three-layer model constructed in Step 3 to obtain a comprehensive index that includes the system's various investment and construction costs and operating costs. Step 5: Determine whether the optimal solution index obtained in Step 4 meets the preset power system boundary condition requirements: If it does, proceed to Step 6; if it does not, return to Step 3 to adjust the model parameters and iterate again. Step 6: Output the overall power system and the energy storage planning scale for the power source side, grid side, and load side of each zone, as well as the total system cost and various costs.

[0026] The three-layer optimization model is described in detail below: 1. A new type of power system full-element collaborative planning model at the upper level In this embodiment, the upper-level optimization model is used for the collaborative planning of all elements of the new power system. With the goal of optimizing the net benefit of the system, it combines the development plans of power sources, power grids, and loads to obtain the total technical and economic capacity demand of the system's energy storage.

[0027] Specifically, in the upper-level new power system all-element collaborative planning model, the objective function is to maximize the system's net benefit. The system's net benefit includes system planning costs and system operating costs. The system benefit mainly consists of system operating revenue. System planning costs include investment costs for various resources such as power generation, grid, load, and storage. System operating costs include system generation costs, grid loss costs, and demand-side response costs. The objective function expression is: Equation (1) In the formula, , , , , , , , , , These represent the optimal net benefit, operating revenue, annualized investment cost, unit start-up and shutdown cost, energy storage electricity cost, fuel cost, grid loss cost, environmental cost, renewable energy curtailment penalty, and demand-side response dispatch cost, respectively.

[0028] The specific explanation is as follows: (1) Operating revenue Equation (2) In the formula, and They represent t Real-time generator power and grid connection price.

[0029] (2) Annualized investment cost Specifically, the annualized investment cost includes the investment in all elements of the power generation, grid, load, and storage system, and the formula is as follows: Equation (3) In the formula, , , , These represent the annualized investment costs for new power sources, the power grid, flexible loads, and energy storage, respectively.

[0030] Equation (4) in, , , , , , These represent the investment costs for wind power, photovoltaic power, solar thermal power, hydropower, coal power, and gas power, respectively. , , , These represent the costs of building new substations, upgrading and renovating existing substations, constructing new power lines, and investing in upgrading and renovating power lines, respectively. , , These represent the investment costs for loads that can be shifted, loads that can be transferred, and loads that can be reduced, respectively. , This indicates the investment cost of pumped storage and new energy storage technologies; Indicates the total investment cost; i represents the discount rate, typically taken as 8%. The investment payback period is indicated; in this disclosure, it is taken as 40 years.

[0031] (3) Unit start-up and shutdown costs Equation (5) In the formula, , , , They represent t The start-up and shutdown capacity and unit start-up and shutdown cost of coal-fired power units and gas-fired power units at all times.

[0032] (4) Energy storage electricity cost Equation (6) In the formula, , express t Pumped storage power station and new energy storage power station pumping power and charging power at all times; , express t The pumping electricity price and charging electricity price of pumped storage power stations and new energy storage power stations.

[0033] (5) Fuel costs Equation (7) In the formula, , express t The power generation capacity of coal-fired and gas-fired power units at any given time; , express t Real-time coal and natural gas prices; a, b, c This represents the fuel cost coefficient for coal-fired power units.

[0034] (6) Network loss cost Equation (8) In the formula, , , They represent t Expected power supply shortage at any given time, electricity price sold by the grid, and grid loss.

[0035] (7) Environmental costs Equation (9) In the formula, , They represent the corresponding first j The emissions of various pollutants and their treatment costs.

[0036] (8) Penalties for curtailment of renewable energy Equation (10) In the formula, , , They represent t Constantly discarding air volume, solar energy volume, and water volume; , , They represent t Punishment for abandoning wind, punishment for abandoning light, punishment for abandoning water.

[0037] (9) Demand-side response scheduling cost Equation (11) In the formula, , , This refers to the scheduling cost of loads that can be shifted, transferred, or reduced. , , Don't mean t Active power that can be shifted, transferred, or reduced at any time.

[0038] The constraints of the model include: power supply and demand balance constraints, system reserve constraints, inter-regional power interaction constraints, regional boundary constraints, resource condition constraints, and policy constraints.

[0039] The specific explanation is as follows: (1) Constraints on power supply and demand balance Equation (12) In the formula, , , , They represent t Time generator set g Power values, system load forecasts excluding flexible loads, load values ​​of flexible loads, tie lines k Power loss.

[0040] (2) System standby constraints Equation (13) In the formula, , , express t The upper limit of output for coal-fired power, hydropower, and gas-fired power at all times. , , , , Don't mean t The system's standby requirements, system discharge and charging power, and the error between the predicted and actual wind and solar power outputs are all considered.

[0041] (3) Inter-regional power interaction constraints 1) Zoning under the agreed-upon planned electricity volume i Power supply (received) power constraints: Equation (14) In the formula, , Indicates partition i A collection of DC and AC tie lines connecting to other zones; Indicates partition i Agreements with other regions regarding planned electricity exchange values; This indicates the range of power fluctuations.

[0042] 2) Upper and lower limits of tie line transmission constraints: Equation (15) In the formula, , This represents the minimum and maximum transmission power allowed by the k-th tie line in the power interaction.

[0043] 3) Power constraints for connecting lines climbing slopes: Equation (16) In the formula, , Do not represent the first in the interconnected power grid k The upper and lower limits of the ramp power of a DC tie line; , Do not indicate that the DC tie line is in t Time and t- Average transmission power at time 1.

[0044] (4) Partition boundary constraints 1) Zone branch flow constraints Equation (17) In the formula, , Representing partition nodes respectively n Lower branch road Maximum and minimum transmission power.

[0045] 2) Internal cross-sectional power supply boundary constraints Equation (18) In the formula, Representation and partitioning i Total outbound capacity of all associated cross-sections; Representation and partitioning i Total power capacity of all associated cross sections; Indicates partition i The total power exchanged with other zones (exporting power is negative, receiving power is positive). The expression for the power limit value of the critical section is: Equation (19) In the formula, For partitioning i Network topology and system parameters.

[0046] (5) Resource constraints 1) Developable / usable size constraints Equation (20) 2) Constraints on the proportion of renewable energy installed capacity Equation (21) In the formula, This indicates the lower limit of installed capacity for renewable energy.

[0047] (6) Policy constraints 1) Constraints on renewable energy absorption rate Equation (22) In the formula, , , They represent t The power output from wind power to the power grid, pumped storage power station, and energy storage power station at all times; Indicates wind power capacity, Indicates the unit capacity of wind power at t Maximum output at any moment This indicates the upper limit of the curtailment rate for renewable energy.

[0048] Equation (23) In the formula, , , They represent t The power output from photovoltaic power to the grid, pumped storage power station, and energy storage power station at all times; Indicates photovoltaic capacity; Indicates the unit capacity of photovoltaic power. t Maximum output at any given moment.

[0049] 2) Constraints on the proportion of renewable energy power generation Equation (24) In the formula, the proportion of new energy power should not be less than 50%.

[0050] 2. Multi-spatial-scale energy storage optimization configuration model in the middle layer In this embodiment, the mid-level optimization model is used for multi-spatial-scale energy storage optimization configuration. With the goal of minimizing system investment and construction costs, it combines power source, grid, and load development plans, and establishes source-storage optimization configuration models, grid-storage optimization configuration models, and load-storage optimization configuration models according to various power source models, grid zoning models, flexible load models, and energy storage models, respectively, to obtain the optimal energy storage scale for each type on the power source side, grid side, and load side.

[0051] Specifically, in the mid-level optimization model, the objective function is to minimize the system investment and construction cost, which includes the investment costs of various resources such as power sources, grids, loads, and storage. The expression is: Equation (25) Specifically, the meanings of each object in equation (25) are shown in equation (3).

[0052] In this embodiment, the constraints of the mid-level model include: various power source constraints, grid constraints, flexible load constraints, energy storage constraints, etc.

[0053] 2.1 Various power supply constraints Various power sources mainly refer to wind power, photovoltaic power, thermal power, hydropower, gas power, etc., and their models and constraints include: (1) Wind power model and its constraints Specifically, the wind power output calculation model is as follows: Equation (26) in, , , , , , These represent air density, rotor radius, power coefficient, tip speed ratio, rotor pitch angle, and rated power of the wind turbine, respectively. , , , They represent t Wind speed at the wind turbine, wind turbine cut-in wind speed, cut-out wind speed, rated wind speed, and the first i The power generation output of each wind farm and the total wind power output.

[0054] 1) Wind farm power generation constraints Equation (27) In the formula, They represent the first i A wind farm in t The upper and lower limits of power generation output at any given time.

[0055] 2) Wind farm power balance constraints Equation (28) In the formula, , They represent the first i The installed capacity and utilization hours of each wind farm.

[0056] (2) Photovoltaic model and its constraints Specifically, the photovoltaic power output calculation model is as follows: Equation (29) In the formula, , They represent t Time of the first j Photovoltaic output and solar radiation intensity of a photovoltaic power station The rated power of the photovoltaic power station These represent solar radiation intensity and temperature under standard test conditions, typically taken as 1000 W / m². 2 and 25℃; and These represent the temperature-to-power conversion factor and the solar panel temperature, respectively.

[0057] 1) Power generation constraints of photovoltaic power plants Equation (30) In the formula, They represent the first j The upper and lower limits of power generation output of a photovoltaic power station.

[0058] 2) Power balance constraints of photovoltaic power plants Equation (31) In the formula, , They represent the first j The installed capacity and utilization hours of each photovoltaic power station.

[0059] (3) Hydropower output model and its constraints Specifically, the hydropower output calculation model is as follows: Equation (32) In the formula, , They represent hydroelectric power stations. h unit g The efficiency of the water turbine and the efficiency of the generator. , They represent t Shike Hydropower Station h The power generation flow rate and operating head.

[0060] 1) Hydraulic connection between upstream and downstream reservoirs Equation (33) In the formula, Indicates hydroelectric power station h exist t Inbound traffic at any given time Indicates upstream and downstream hydropower stations h- 1 and hydroelectric power station h When the water flow is stagnant, Indicates hydroelectric power station h- 1 in Power generation flow rate at any given moment Indicates hydroelectric power station h- 1 and hydroelectric power station h Interval flow between.

[0061] 2) Reservoir water balance constraints Equation (34) In the formula, Reservoir h exist t Storage capacity at the end of the time period. The time step is indicated; in this public disclosure, it is taken as 1 hour.

[0062] 3) Reservoir water level constraints Equation (35) In the formula, Reservoir h exist t The water level in front of the dam at the end of the time. Reservoir h Lower and upper limits of water level in front of the dam Reservoir h The initial and final target control water levels.

[0063] 4) Reservoir outflow constraints Equation (36) In the formula, They represent hydroelectric power stations. h The lower and upper limits of power generation flow.

[0064] 5) Output constraints of hydropower station units Equation (37) In the formula, They represent hydroelectric power stations. h unit g Lower and upper limits of output; Indicates hydroelectric power station h unit g exist t The operating status variable at any given time; if the unit is in the powered-on state, then... ,otherwise .

[0065] 6) Generating head constraints of the unit Equation (38) In the formula, , , They represent the generating units. g exist t Real-time head of power generation, tailwater level, and head loss. Reservoir h The relationship function between tailwater level and discharge.

[0066] (4) Coal-fired power output model and its constraints Specifically, the coal-fired power output calculation model is as follows: Equation (39) In the formula, express t Contribute effort at all times Coal consumption below This represents the coal consumption coefficient of a coal-fired power unit. The output of the coal-fired power unit... The carbon dioxide emissions are as follows: Equation (40) In the formula, The carbon emission factor for standard coal is given in this disclosure as 0.67 tc / tce; The coefficient representing the carbon dioxide emissions per unit of carbon combustion is taken as 3.67tCO2 / tc in this disclosure.

[0067] 1) Output range constraints of coal-fired power units Equation (41) In the formula, c Indicates the type serial number of the coal-fired power unit. Indicates the first c Coal-fired power units t Power generation at any given moment Integer variables represent the first... c Coal-fired power units t Number of running units at any given time Indicates the first c Maximum and minimum technical output of coal-fired power units.

[0068] (5) Gas-electric output model and its constraints Specifically, the calculation model for gas-fired power output is as follows: Equation (42) In the formula, , Indicates gas turbine generator set g exist t The output power and the volume of natural gas consumed at any given time. Indicates gas turbine generator set g Energy conversion efficiency, It represents the low calorific value of natural gas during its complete combustion process.

[0069] 1) Technical output constraints of gas turbine generator sets Equation (43) In the formula, , Indicates gas turbine generator set g exist t Minimum and maximum technical output at any given moment.

[0070] 2.2 Power Grid Constraints The grid zoning model and its constraints in the mid-level multi-spatial-scale energy storage optimization configuration model include: (1) Partitioned Adjustable Capacity Model Equation (44) In the formula, , , , , , , , , , These respectively represent the adjustable capacity of wind power, photovoltaic power, output reliability, adjustable capacity of pure condensing units, adjustable capacity of heating units, adjustable capacity of hydropower, adjustable capacity of pumped storage, and adjustable capacity of AC / DC interconnection lines.

[0071] In addition to the constraints in equations (15) to (19), the power grid constraints also include the following constraints: (2) Power generation and consumption balance constraints of the entire grid Equation (45) In the formula, , , They represent t Time zone i Total power generation, total load demand, and the first k The transmission capacity of the inter-regional interconnection line Indicates the region i The total number of connecting lines between other regions.

[0072] (3) Zonal load balancing constraints Equation (46) In the formula, and Indicates AC / DC tie line k The equivalent generating unit and equivalent load in t The power value at any given time.

[0073] (4) Zone branch flow constraints Equation (47) In the formula, , This indicates a partition node. n Lower branch road Maximum and minimum transmission power.

[0074] (5) Power characteristics of zoned branches Equation (48) In the formula, Indicates peak-to-valley difference, Indicates a branch Transmission efficiency during the dispatching cycle, i.e., the total electrical energy transmitted by the lines. With the maximum electrical energy that can be transmitted The ratio, Indicates the line l exist t The power at any given moment is positive if it is in the same direction as the reference direction, and negative if it is not.

[0075] 2.3 Flexible load constraints The flexible load model and its constraints in the mid-level multi-spatial-scale energy storage optimization configuration model include: Equation (49) In the formula, , , express t The shiftable load, transferable load, and load that can be reduced at any given time. The expression for the adjustable power of flexible load after zone aggregation is: Equation (50) In the formula, , , Indicates partition i After aggregation t The transfer power, upward adjustment power, and downward adjustment power of the adjustable flexible load at any time. Indicates partition i of t The 0-1 variables representing the state of renewable energy curtailment at any given moment.

[0076] (1) Transferable load constraints 1) Power balance constraint Equation (51) In the formula, , These represent the shiftable loads before and after participating in demand response, respectively. t The workload of the moment.

[0077] 2) Power transfer constraint Equation (52) In the formula, , These respectively represent the participation of movable loads in demand response. t The upper limit for increasing or decreasing load at any time.

[0078] (2) Transferable load constraints 1) Minimum continuous time constraint Equation (53) In the formula, 0-1 variables represent t Transition state at any moment This indicates the shortest time for continuous operation.

[0079] 2) Power transfer constraint Equation (54) In the formula, express t Power transferred at any time , Indicates the upper and lower limits of the transferred power.

[0080] 3) Power balance constraints.

[0081] Equation (55) In the formula, , This indicates the transferable load before and after participating in demand response.

[0082] (3) Load constraints can be reduced 1) Reduce power constraints Equation (56) In the formula, , , This indicates that the load can be reduced in t Power, maximum and minimum load reductions that participate in demand response at all times.

[0083] 2) Continuous reduction time constraint Equation (57) In the formula, , , These represent the load that can be reduced at... t -1 represents the cumulative reduction time, minimum reduction time, and maximum continuous reduction time.

[0084] 3) Reduction frequency constraint Equation (58) In the formula, Indicates the maximum number of cuts.

[0085] 2.4 Energy Storage Constraints The various energy storage models in the mid-level multi-scale energy storage optimization configuration model mainly include pumped hydro storage, electrochemical energy storage, solar thermal, and hydrogen energy storage models. Their models and constraints include: (1) Pumped storage model and its constraints Specifically, the pumped storage calculation model is as follows: Equation (59) In the formula, Indicates the density of water; Expresses the acceleration due to gravity, in m / s² 2 ; , , , , , , , These represent pumped storage power stations. z No. n Taiwan pumped storage unit t Power, flow rate, effective head, and efficiency under both power generation and pumping conditions at all times; , These represent pumped storage power stations. z exist t The energy storage capacity at any given time, and the amount of water available in the upper reservoir.

[0086] 1) Storage capacity constraints Equation (60) In the formula, , , , These represent pumped storage power stations. z exist t The water storage capacity of the upper and lower reservoirs, minimum reservoir capacity, maximum reservoir capacity, and water level control targets for the last period are specified for each time period.

[0087] 2) Power upper and lower limit constraints Equation (61) In the formula, , These represent pumped storage power stations. z No. n The minimum and maximum output of the pumped storage unit under power generation and pumping conditions.

[0088] 3) Constraints on effluent / volume conversion efficiency Equation (62) In the formula, These represent pumped storage power stations. z No. n The efficiency of power output to flow conversion of a pumped storage unit under power generation and pumping conditions; Indicates pumped storage power station z The average head height.

[0089] (2) Electrochemical energy storage model and its constraints Specifically, the principle for energy storage power configuration is to meet the maximum power demand during the operating cycle. The principle for energy storage capacity configuration is to avoid operation during overcharging and over-discharging. The specific electrochemical energy storage calculation model is as follows: The total annual energy storage power command sequence T Divided into K One charge-discharge cycle, the energy storage charging and discharging process: Equation (63) In the formula, , , , They represent energy storage power stations b exist t At any given time, the stored energy, charging power, discharging power, and state of charge (SOC) are all measured. , , , , , These represent energy storage power stations. b Self-discharge rate, charging efficiency, discharging efficiency, energy storage capacity per cycle, rated capacity, and rated power; This refers to the duration of each charge / discharge cycle.

[0090] 1) Charge and discharge power constraints Equation (64) In the formula, Indicates energy storage power station b The maximum and minimum charging and discharging power.

[0091] 2) Charge state constraints Equation (65) In the formula, Indicates energy storage power station b The upper and lower limits of the state of charge.

[0092] 3) Energy storage capacity constraints Equation (66) In the formula, Indicates energy storage power station b exist t Battery level at any given moment; For energy storage power stations b Inverter rated capacity.

[0093] (3) Photothermal model and its constraints Specifically, the photothermal calculation model is as follows: Equation (67) In the formula, , They represent solar thermal power plants. k The heat energy from the light field and thermal storage system is transmitted to the power conversion module for power generation. This represents the overall cycle thermal efficiency of the organic Rankine cycle.

[0094] 1) Heat balance constraint Equation (68) In the formula, , , , , They represent t Momentary solar thermal power plant k The theoretical input thermal power, waste thermal power, stored thermal power, released thermal power, and stored thermal power of the solar collector; , Indicates a solar thermal power plant k The heat storage and heat release efficiency.

[0095] 2) Constraints on the range of heat storage / release power of thermal storage tanks Equation (69) In the formula, express t The number of times the solar thermal power plant operates for heat storage and heat release is an integer variable; , This represents the maximum heat storage and release power of the solar thermal power plant k.

[0096] 3) Limitations on the heat storage capacity of the thermal storage tank Equation (70) In the formula, , , They represent solar thermal power plants. k The maximum and minimum heat storage capacity of the thermal storage tanks and the number of solar thermal power plants.

[0097] 4) Power plant output range constraints Equation (71) In the formula, , They represent solar thermal power plants. k The maximum and minimum power generation capacity.

[0098] 5) Constraints on the number of power plants in operation Equation (72) 6) Thermoelectric power conversion constraints Equation (73) In the formula, Indicates a solar thermal power plant k Thermoelectric conversion coefficient; express t Momentary solar thermal power plant k The power generation and thermal output.

[0099] 7) Constraints on the balance of power generation and heat Equation (74) In the formula, express t Momentary solar thermal power plant k Thermal power required for startup.

[0100] (7) Hydrogen energy storage model and its constraints Specifically, the hydrogen energy storage calculation model is as follows: Equation (75) In the formula, , , , Let represent the power of hydrogen production by water electrolysis, the charging power, the discharging power, and the total amount of hydrogen stored at time t, respectively. , This indicates the electrolysis efficiency and fuel cell efficiency of hydrogen energy storage.

[0101] 1) Electrolytic cell power constraint Equation (76) In the formula, This indicates the maximum power of the electrolytic cell.

[0102] 2) Fuel cell power constraints Equation (77) In the formula, This indicates the maximum power of the fuel cell.

[0103] 3) Dynamic equilibrium constraints of hydrogen storage Equation (78) 3. Multi-timescale source-grid-load-storage optimization operation model at the lower level The lower-level optimization model is used for multi-timescale source-grid-load-storage optimization operation. With the goal of minimizing system operating costs, it combines the planning results passed from the upper and middle layers to carry out coupled production simulations of the system at three time scales: long, medium, and short, and obtains the optimal operation scheduling strategy of the system.

[0104] Specifically, the objective function of the lower-level optimization model is to minimize the system operating cost, which includes: unit start-up and shutdown costs, energy storage electricity costs, system fuel costs, system network loss costs, system environmental costs, system renewable energy curtailment penalties, and system demand-side response scheduling costs. The expression is: Equation (79) In the formula, This represents the minimum operating cost. The specific expressions for other variables are shown in equations (5) to (11).

[0105] The constraints of this optimization model include: various power source operation constraints, flexible load dispatching constraints, and energy storage operation constraints. Specifically: (1) Operating constraints of various power sources 1) Coal-fired power units Specifically, the operating constraints of coal-fired power units include constraints on the number of units started and stopped, start / stop status, number of units in operation, number of starts, number of stops, power constraints for upward and downward ramps. Equation (80) In the formula, , Indicates the first c Coal-fired power units t The startup and shutdown states at any given time are both 0-1 variables. , Indicates the first c Coal-fired power units t Maximum and minimum number of running units at any given time; N This indicates the maximum number of times the unit can be started / stopped. Indicates the first c The maximum uphill and downhill power of coal-fired power units.

[0106] 2) Gas turbine generator set Specifically, the operating constraints of gas turbine generator sets include ramping constraints. Equation (81) In the formula, , They represent gas turbine generator sets. g exist t The lower and upper limits of the ramp at any given moment.

[0107] 3) Hydropower units Specifically, the operating constraints of hydropower units include climbing ability limits, power generation flow limits, unit vibration zone limits, and start-up and shutdown duration limits.

[0108] Equation (82) In the formula, Indicates the unit g Its climbing ability; , They represent hydroelectric power stations. h unit g The lower and upper limits of output are related to the first k The upper and lower limits of the output force in each vibration zone.

[0109] Equation (83) In the formula, , , , They represent the generating units. g Minimum start-up and stop-down durations and start-up and stop-down operation variables; Indicates the unit g exist t Always perform a power-on operation; Indicates the unit g Perform a shutdown operation.

[0110] (2) Power grid operation constraints Specifically, grid operation constraints include DC tie-line constraints: 1) DC power regulation number constraint Equation (84) In the formula, This represents the number of power adjustments allowed for DC within one cycle.

[0111] 2) DC power regulation direction constraint Equation (85) 3) DC power regulation rate constraint Equation (86) 4) DC power operating time constraints Equation (87) In the formula, This represents the minimum number of time periods during which DC adjustment is allowed.

[0112] 5) DC daily power peak regulation range constraints Equation (88) In the formula, Indicates DC power in the first... d The largest peak-to-valley difference within the day.

[0113] (3) Flexible load dispatch constraints 1) Loads that can be moved Specifically, the constraints for shiftable load operation include operating time constraints and transfer time constraints: Equation (89) In the formula, , This indicates the continuous working time and fixed working time of the transferable load; , Indicates the start and end times of load shifting.

[0114] 2) Load can be reduced Specifically, load reduction constraints include continuous reduction time constraints and reduction frequency constraints: Equation (90) In the formula, To reduce the cumulative reduction time of the load at time t-1, , The minimum and maximum reducible time for load reduction; This represents the maximum number of reductions.

[0115] (4) Constraints on Energy Storage Operation 1) Pumped storage unit Specifically, the operating constraints of pumped-storage units include unit start-up and shutdown constraints: Equation (91) In the formula, , , , , , , , They represent t Pumped storage power station z No. nThe 0-1 variables of the operating status of the pumped storage unit under power generation and pumping conditions, the 0-1 variables of start-up and shutdown actions, and the maximum number of start-ups and shutdowns per day.

[0116] 2) Photothermal Specifically, the operating constraints of solar thermal power units include constraints on the number of units started and constraints on the rate of increase and decrease in ramp rate: Equation (92) In the formula, This indicates the number of units started up in the concentrated solar power plant; Indicates a solar thermal power plant k Maximum power for climbing uphill and downhill.

[0117] Furthermore, taking a provincial power system in a certain region as an example, the specific implementation steps are explained as follows: Step 1: Data Acquisition and Analysis Obtain economic and social development policies, current status, and planned development data of the power system for the corresponding planning year, and determine the total capacity demand and boundary conditions of the power system's regulation capacity through policy analysis and demand calculation.

[0118] In this embodiment, the acquired data mainly includes: Economic and social development policies, including key policy orientations such as the dual carbon targets of the region where the power system is located, the proportion of non-fossil energy consumption, and the requirements for the utilization rate of new energy sources; Data on the current status and planned development of the power system, including resource characteristic parameters of various power sources (such as coal power, wind power, photovoltaic power, etc.) and energy storage, grid structure information, and load demand characteristic data.

[0119] In this step, the method for determining the total capacity requirement of the system regulation capacity is as follows: based on the annual historical output curves of various power sources and the annual load curves of the load, combined with the spatial distribution characteristics of resources and loads within the power grid zone, and considering the reserve of coal-fired power generation and the system peak-shaving demand, the system's power, electricity, peak-shaving and green electricity gaps are identified through net load calculation and power balance analysis, thereby quantifying the total capacity requirement for flexible regulation resources.

[0120] Specifically as follows: Step 101: Obtain system data, including planning data for load, power grid, power sources, and energy storage. Power source output data includes real-time and historical output data for new energy sources such as wind and solar power, as well as output data for hydropower in different dry years and data information for thermal power units; load data includes load curves at different time scales.

[0121] Table 1 shows the current status and load data of a provincial power system in a certain region in this embodiment, as well as the load data for the planned year. Table 2 shows the power supply and energy storage data of a provincial power system in a certain region in this embodiment, as well as the load data for the planned year.

[0122] Table 1 Load Development Data

[0123] Table 2 Power Supply Development Data

[0124] Step 102, Policy Analysis: The basic strategy for power development in this region is to accelerate the construction of coal-fired power projects that meet national standards, rationally plan the scale and layout of new energy development, orderly introduce electricity from outside the region, and strengthen the construction of peak-shaving power sources such as electrochemical energy storage and pumped storage power stations. By 2030 and 2035, the proportion of non-fossil energy consumption will reach approximately 25% and 30% respectively, with a new energy utilization rate of no less than 95%.

[0125] Step 103, Demand Calculation: Calculate the electricity, power, peak-shaving, and green energy gaps for the corresponding planning year. Table 3 shows the results of electricity, peak-shaving, power, and green energy gaps for the planning year in this exemplary embodiment.

[0126] Table 3. Electricity, Peak Shaving, Electricity, and Green Energy Shortage for the Planning Horizon

[0127] Step 2: Plan Formulation Step 201: Based on relevant development plans and various resource conditions, formulate a planning scheme direction. In 2030, the focus will be on coal-fired power, new energy storage, and external power generation; in 2035, the focus will be on hydropower expansion, pumped storage, external power generation, and new energy storage. Table 4 shows the power planning organization scheme for the planning year 2030 in this embodiment; Table 5 shows the power planning organization scheme for the planning year 2035 in this embodiment, in 10,000 kilowatts.

[0128] Table 4. Power Planning Organization Scheme for the 2030 Planning Horizon (Unit: 10,000 kW)

[0129] Table 5. Power Planning Organization Scheme for 2035 (Planning Horizontal Year) Unit: 10,000 kilowatts

[0130] Step 3: Optimization Model Construction With the objectives of maximizing system net benefits, minimizing system investment and construction costs, and minimizing system operating expenses, corresponding upper, middle, and lower-level optimization models are established. These models include objective functions and constraints for each of the upper, middle, and lower levels, and are used for collaborative planning of all elements of new power systems, optimal configuration of energy storage at multiple spatial scales, and optimal operation of energy storage at multiple time scales, respectively.

[0131] Specifically, with the goal of maximizing the net benefit of the system, a new power system all-element collaborative planning model is constructed to obtain the total technical and economic capacity demand of the system's energy storage; with the goal of minimizing the system's investment and construction costs, a multi-spatial-scale energy storage optimization configuration model is constructed to obtain the optimal energy storage scale for each type on the source side, grid side, and load side; with the goal of minimizing the system's operating costs, a multi-time-scale source-grid-load-storage optimization operation model is constructed to conduct production simulations coupled at different time scales, and obtain the overall power system and the energy storage planning for each region's source, grid, and load sides.

[0132] It includes: Step 301: Develop an upper-level optimization model to determine the total technical and economic capacity requirement of the system energy storage based on the total system regulation capacity requirement obtained in step S103.

[0133] In this model, the goal is to maximize the net benefit of the system. The net benefit of the system = operating revenue - total cost. The total cost includes: annualized investment cost, unit start-up and shutdown cost, energy storage electricity cost, fuel cost, grid cost, environmental cost, renewable energy curtailment penalty, and demand-side response dispatch cost.

[0134] Step 302: Develop a mid-level optimization model to determine the optimal energy storage capacity for each type on the source side, grid side, and load side based on the total technical and economic capacity demand of the system energy storage determined in step S301.

[0135] In this model, the goal is to minimize the system investment and construction cost, which includes the investment costs of various resources such as power sources, grids, loads, and storage.

[0136] In this embodiment, the calculation model for system investment and construction costs is constructed by integrating various power output models, grid zoning models, flexible load models, and energy storage models, specifically including: (1) Establish power source models to describe the output characteristics of thermal power, hydropower, and new energy sources. Based on the types and operating characteristics of thermal power, hydropower, and new energy sources, establish corresponding output models.

[0137] (2) Construct a power grid model, including the transmission network structure and the current load model. Based on the existing transmission network structure, establish the impedance model of the transmission lines and construct a current load model considering the load characteristics of different regions.

[0138] (3) Establish a load model and consider the load characteristics under different seasons.

[0139] (4) Establish an energy storage model to describe the performance and operating characteristics of the energy storage system. Based on the characteristics of energy storage technology, establish a charge-discharge efficiency model and a cycle life model for the energy storage system.

[0140] Step 303: Develop a lower-level optimization model to perform production simulations coupled with different time scales, such as long, medium, and short, based on the energy storage configuration scale obtained in steps S301 and S302.

[0141] The model aims to minimize system operating costs. System operating costs include: energy storage electricity costs, system fuel costs, system network loss costs, system environmental costs, system renewable energy curtailment penalties, and system demand-side response scheduling costs.

[0142] In this embodiment, the time resolutions for long time scales, medium time scales, and short time scales are set to months, weeks, and hours, respectively.

[0143] Step 4: Solving the model The NSGA-III multi-objective optimization algorithm is used to comprehensively optimize the above model from the proposed schemes obtained in step 2, thereby obtaining the comprehensive index of the optimal scheme. The specific process is as follows: Step 401: Population Initialization and Weight Presetting. Based on the candidate scheme set generated in Step 2, a parent population containing multiple individuals is initialized, with each individual representing a specific power supply and energy storage configuration scheme. Using the entropy weight method, a set of initial weights is preset for the objectives of the upper-level model (system net benefit), the middle-level model (system investment and construction cost), and the lower-level model (system operating cost) to quantify the relative importance of each objective in the optimization search. In this embodiment, the preset initial weights are 0.5, 0.25, and 0.25.

[0144] Step 402: Genetic operations. Through genetic operations such as selection, crossover, and mutation, offspring populations are generated based on the parent population to explore new solution spaces and enhance population diversity.

[0145] Step 403: Population merging. The parent population and the offspring population are merged to form a new merged population, which provides a basis for subsequent non-dominant ranking and elite selection.

[0146] Step 404: Non-dominated sorting and dynamic weight adjustment. Based on the objective functions of the upper-level model, the middle-level model, and the lower-level model, individuals in the merged population are non-dominatedly sorted and divided into different non-dominated levels (Pareto levels). On this basis, combined with the distribution characteristics of the current population and the convergence status of each objective function, the weights are adaptively fine-tuned to promote the convergence of the Pareto solution set in the key objective direction and maintain the distribution breadth of the solution set among different trade-off schemes.

[0147] Step 405: Normalization and Reference Point Association. The objective function values ​​of individuals in the population are normalized to eliminate dimensional differences. Preset or dynamically adjusted weights are transformed into a set of reference points in the objective space, each representing a specific trade-off preference direction. Normalized individuals are then associated with the nearest reference point to guide the search process to focus on the preference regions of interest to decision-makers.

[0148] Step 406: Microhabitat preservation. Based on the association relationship of reference points, select reference points with fewer currently associated individuals, and select individuals that are closer to the reference point to preserve to the next generation, thereby maintaining the even distribution of the population on the Pareto front.

[0149] Step 407: Iterative convergence judgment. Repeat steps 402 to 406 until the maximum number of iterations is reached or the convergence condition is met. Finally, output the Pareto optimal solution set, which is a set of planning schemes that achieve the best trade-off among multiple objectives such as system net benefits, investment costs, and operating expenses.

[0150] Appendix Figure 4 This is a schematic diagram of the NSGA-III algorithm solution process in this embodiment.

[0151] Step 5: Optimization result judgment and iterative adjustment Analyze the optimization results and evaluate whether the system's economic and flexibility indicators meet the preset power system boundary conditions. If they do, proceed to step S6; otherwise, return to step S3 to adjust the model parameters and iterate again.

[0152] In this embodiment, various indicator values ​​are calculated, including power supply level, primary energy consumption level, greening level, and economic efficiency level. Taking into account the above indicators, the recommended scheme is determined based on the optimal net system benefit.

[0153] The output results are as follows. Figure 5 and attached Figure 6 As shown. (Attached) Figure 5 This is a diagram showing the output results of the 2030 scheme in this embodiment; attached. Figure 6 This is the output diagram of the 2035 scheme in this embodiment.

[0154] Step 6: Output Results Step 601: Optimal power supply planning scheme for the entire power system. Table 6 shows the optimal power supply planning scheme for the entire system in the planning year of this embodiment.

[0155] Table 6 Optimal Power Planning Scheme for the Entire System in the Planning Horizon Year

[0156] Step 602: Rational Layout Analysis. Based on the optimal recommended scheme for the entire system obtained in Step 601, and considering the situation of each zone, a rational layout analysis is performed. Table 7 shows the power planning scheme for each zone in the planning year 2035 of this embodiment.

[0157] Table 7 Power Planning Schemes for Each Zone of the System in the Planning Horizontal Year (Unit: 10,000 kW)

[0158] Step 603, Cost Output: Table 8 shows the total cost and system operating cost of the system in 2030 and 2035.

[0159] Table 8. Total System Cost and Operating Costs for the Planning Horizon

[0160] Figure 7 This embodiment shows a schematic diagram of a full-element collaborative multi-temporal-scale energy storage planning device that applies the above method. Figure 8 This is a schematic diagram of the structure of an electronic computer system.

[0161] The above technical solutions are merely exemplary embodiments of the present invention. For those skilled in the art, based on the application methods and principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the methods described in the specific embodiments of the present invention. Therefore, the methods described above are merely preferred and not restrictive.

Claims

1. A novel full-factor collaborative multi-time-and-space-scale energy storage planning method for power systems, characterized in that, Includes the following steps: S1: Obtain economic and social development policies, current status and planned development data of the power system for the corresponding planning level year, and determine the total capacity demand and boundary conditions of the power system's regulation capacity through policy analysis and demand calculation. S2 generates a set of candidate solutions containing different power structures, energy storage types and configuration capacities; S3 establishes three-level optimization models—upper, middle, and lower—with the objectives of maximizing system net benefits, minimizing system investment and construction costs, and minimizing system operating costs, respectively. These models are used for collaborative planning of all elements of the new power system, optimal configuration of energy storage at multiple spatial scales, and optimal operation of energy storage at multiple time scales. S4. According to the three-layer optimization model of upper, middle and lower layers, a multi-objective optimization algorithm is used to perform comprehensive optimization from the obtained candidate solution set. S5. Determine whether the optimal solution index obtained in step S4 meets the preset power system boundary condition requirements. If it does, proceed to step S6; otherwise, return to step S3 to adjust the model parameters and iterate again. S6 outputs the overall energy storage planning scale for the power system and its various zones, including the power supply side, grid side, and load side, as well as the total system cost and various individual costs.

2. The method of claim 1, wherein, Step S1 specifically includes: To obtain economic and social development policies of the power system at the planning level, as well as current status and planning data of the power system, including the characteristics of various power sources and energy storage resources, grid structure, and load demand characteristics; Policy Analysis: Analyze the dual carbon targets, non-fossil energy consumption ratio, and renewable energy utilization rate data of the region where the power system is located to formulate boundary conditions for regulation capacity planning; Demand Calculation: Based on the historical output curves and load data of various power sources, combined with the characteristics of power grid zoning, the reserve demand of coal-fired power generation and the peak-shaving demand of the system, net load calculation and power, electricity and green energy gap analysis are performed to quantify the total capacity demand of the system for flexible regulation resources.

3. The method of claim 1, wherein, Step S2 specifically includes: Based on the total capacity demand and boundary conditions obtained in step S1, and in combination with technical feasibility, the capacity configuration range and combination method of key elements such as thermal power, wind power, photovoltaic power, hydropower and energy storage in the power configuration scheme are set. Through permutation and combination or scenario generation technology, multiple differentiated candidate planning schemes are formed as a candidate scheme set. The candidate scheme set is used as the input basis for the subsequent upper, middle and lower three-level optimization model.

4. The method of claim 1, wherein, In step S3, the upper-level optimization model for the collaborative planning of all elements of the new power system takes maximizing the net benefit of the system as the optimization objective, and is used to determine the total technical and economic capacity demand of the system's energy storage. Its objective function expression is: In the formula, , , , , , , , , , These represent the optimal net benefit, operating revenue, annualized investment cost, unit start-up and shutdown cost, energy storage electricity cost, fuel cost, grid loss cost, environmental cost, renewable energy curtailment penalty, and demand-side response dispatch cost, respectively. The constraints of the upper-level optimization model include: power supply and demand balance constraints, system reserve constraints, inter-regional power interaction constraints, regional boundary constraints, resource condition constraints, and policy-level constraints.

5. The method of claim 1, wherein, In step S3, the mid-level optimization model for multi-spatial-scale energy storage optimization configuration takes the minimum system investment and construction cost as the optimization objective and is used to determine the optimal energy storage scale for the power supply side, grid side, and load side. Its objective function expression is: wherein, , , , respectively represent the annualized investment cost of adding each type of power source, power grid, flexible load, and energy storage. The various power sources include one or more of the following: wind power, photovoltaic power, thermal power, hydropower, and gas power. The constraints of the mid-level optimization model include: various power source output constraints, grid constraints, flexible load constraints, and energy storage constraints.

6. The method of claim 1, wherein, In step S3, the lower-level optimization model uses the minimum system operating cost as the optimization objective, and is used to perform coupled production simulations at different time scales (long, medium, and short). Its objective function expression is: wherein, represents the minimum operation cost, , , , , , , respectively represent the unit commitment cost, the energy storage electricity cost, the fuel cost, the network loss cost, the environmental cost, the renewable energy curtailment penalty, and the demand side response scheduling cost. The constraints of the lower-level optimization model include: various power source operation constraints, flexible load scheduling constraints, and energy storage operation constraints.

7. The method of claim 1, wherein, The specific method of step S4 includes: Based on the candidate solution set obtained in step S2, the NSGA-III multi-objective optimization algorithm is used to solve the Pareto optimal solution set according to the upper, middle and lower three-layer optimization model constructed in step S3. The NSGA-III multi-objective optimization algorithm is used to handle multiple optimization objectives, namely, the upper-level model with the best net benefit, the middle-level model with the lowest investment cost, and the lower-level model with the lowest operating cost. Through its reference point mechanism, it ensures that a Pareto non-dominated solution set with satisfactory convergence and uniform distribution is obtained in the high-dimensional objective space.

8. The method of claim 7, wherein, Step S5 specifically includes: Select the comprehensive optimal solution from the Pareto optimal solution set, and determine whether its various indicators meet the preset power system boundary conditions and performance requirements. If they meet the requirements, proceed to step S6; otherwise, generate a feedback instruction and return to step S3, adjust the model parameters, and recalculate iteratively. The indicators used for evaluation include: power supply level, primary energy consumption level, greening level, and economic efficiency level.

9. The method according to any one of claims 1-8, characterized in that, The output of step S6 includes: 1) The planned configuration capacity and power of various types of energy storage on the power source side, grid side, and load side within the overall power system and its various zones; 2) The total cost of achieving the above-mentioned scale configuration during the system planning period, including various costs such as power supply investment, grid investment, energy storage investment and system operation costs.

10. A novel all-element collaborative multi-time-and-space scale energy storage planning device for a power system, characterized in that, include: The data acquisition module is used to acquire data on the economic and social development policies, current status, and planned development of the power system in the corresponding planning year. The policy analysis module is used to analyze and determine the policy boundary conditions for the plan based on the acquired data. The demand calculation module is used to quantify the system's total capacity demand for flexible adjustment resources based on the acquired power and load data and in combination with policy boundary conditions. The scheme formulation module is used to generate a set of candidate schemes containing different power structure, energy storage type and configuration capacity based on the total capacity demand boundary conditions determined by the demand calculation module. The model building module is used to establish three-layer optimization models: upper, middle and lower layers, which are used to realize the collaborative planning of all elements of the new power system, the optimal configuration of energy storage at multiple spatial scales, and the optimal operation of energy storage at multiple time scales, respectively. The model solving module, based on the candidate solution set, uses a multi-objective optimization algorithm to collaboratively solve the constructed three-layer optimization model and obtain the Pareto optimal solution set. The result judgment module is used to select the optimal solution from the Pareto optimal solution set that satisfies the preset system boundary conditions and performance indicators.

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