A capacity optimization method and related apparatus for an electrically heated molten salt energy storage peak-shaving system

Abstract

This invention belongs to the field of molten salt energy storage and discloses a capacity optimization method and related apparatus for an electrically heated molten salt energy storage peak-shaving system. By constructing models of the electrically heated molten salt system, the molten salt heat exchange system, and the power generation system, and based on these models, with the objectives of minimum construction cost, shortest payback period, and maximum peak-shaving capacity, and using the power of the electrically heated molten salt equipment and the quality of the molten salt as variables, a multi-objective optimization model including objective functions and constraints is established. The Pareto dominance method is then used to solve this model to obtain the Pareto optimal solution set as the capacity optimization scheme. This method effectively overcomes the extreme trade-offs in traditional capacity configuration, significantly reducing construction costs and shortening the investment payback period while ensuring the system has robust peak-shaving capacity, and improving power generation and peak-shaving performance. Ultimately, it enhances the overall economic efficiency of the system and its adaptability to practical application scenarios, meeting the energy dispatch requirements of new power systems.

Description

Technical Field

[0001] This invention belongs to the field of molten salt energy storage technology, and particularly relates to a capacity optimization method and related apparatus for an electrically heated molten salt energy storage peak shaving system. Background Technology

[0002] In the field of long-term energy storage technology, molten salt energy storage occupies an important position due to its outstanding market promotion potential. Its working principle is to convert excess electrical energy into heat energy for storage, and then convert the heat energy back into electrical energy when needed for energy utilization. With the integration of electric heating technology, the application scope of molten salt thermal energy storage has expanded beyond concentrated solar power generation, and can now be applied to new power systems to address the volatility of renewable energy and assist grid dispatch, becoming a key direction for the future development of molten salt thermal energy storage technology. Specifically, the electric heating molten salt power supply system couples the power grid with the molten salt energy storage unit. During periods of low electricity prices, the system purchases electricity, using a portion to meet user demand and another portion to heat the molten salt for energy storage. During peak electricity prices, the system utilizes the previously stored heat energy to generate electricity. This operating mode aims to ensure the stability and sustainability of power supply while pursuing higher economic efficiency and stronger peak-shaving capabilities.

[0003] However, the current capacity configuration of electrically heated molten salt power supply systems presents a clear trade-off and faces numerous problems and shortcomings: increasing the electric heating power and extending the thermal storage time can improve the system's power generation and peak-shaving capabilities, but it will significantly increase the system's construction costs and extend the investment payback period, ultimately leading to a decrease in the system's economic efficiency; conversely, reducing the electric heating power and shortening the thermal storage time can reduce the initial investment amount, but it will sacrifice the system's power generation and peak-shaving performance, making it difficult to meet the energy dispatch requirements of new power systems and adapt well to actual application scenarios.

[0004] Therefore, the core technical problem that urgently needs to be solved in the process of configuring the capacity of the electrically heated molten salt power supply system is to maximize economic benefits and shorten the investment payback period while ensuring that the system has a relatively strong peak-shaving capacity. Summary of the Invention

[0005] The purpose of this invention is to provide a capacity optimization method and related device for an electrically heated molten salt energy storage peak-shaving system. This method can achieve multi-objective optimization with high peak-shaving capacity, low construction cost and short payback period.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A capacity optimization method for an electrically heated molten salt energy storage and peak-shaving system includes:

[0008] Construct electric heating molten salt system models, molten salt heat exchange system models, and power generation system models for an electrically heated molten salt energy storage and peak-shaving system.

[0009] Based on the constructed electric heating molten salt system model, molten salt storage and heat exchange system model, and power generation system model, a multi-objective optimization model including objective function and constraints is established with the objectives of minimum construction cost, shortest payback time, and maximum peak-shaving capacity, and with the power of electric heating molten salt equipment and the molten salt quality in the molten salt storage and heat exchange system as variables.

[0010] The Pareto dominance method is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set, which serves as the capacity optimization scheme for the electrically heated molten salt energy storage peak-shaving system.

[0011] Furthermore, the electric heating molten salt system model, molten salt heat exchange system model, and power generation system model for constructing the electric heating molten salt energy storage peak-shaving system include:

[0012] The electric heating molten salt system model is used to purchase electricity during periods of low grid load and heat molten salt using an electric heater, thereby converting and storing electrical energy into thermal energy. The specific implementation is as follows:

[0013]

[0014] In the formula, This represents the mass flow rate of molten salt that can be heated in each time step. For time step, This represents the total power of the molten salt heater. The specific heat capacity of molten salt, The temperature at which the molten salt flows out of the cryogenic salt storage tank;

[0015] The molten salt heat exchange system model and the power generation system model are used to transfer the thermal energy stored in the molten salt to the working fluid through the heat exchange system and drive the steam turbine power generation system to complete the conversion of thermal energy into electrical energy.

[0016] The molten salt heat exchange system model is constructed based on the law of energy conservation, and is specifically expressed as follows:

[0017]

[0018] In the formula, Let be the mass of molten salt contained in the storage tank at time t. The temperature of the molten salt in the storage tank is The specific enthalpy corresponding to time, These represent the molten salt flow rates into and out of the storage tanks, respectively.

[0019] Furthermore, the power generation system model specifically includes a steam generation system model and a steam turbine power generation system model, wherein:

[0020] The steam generation system model is used to calculate the heat exchange processes of the preheater, superheater, and reheater using the efficiency-heat transfer unit method, and is specifically expressed as follows:

[0021]

[0022] In the formula, NTU represents the evaporator efficiency, and NTU represents the number of heat transfer units in the evaporator.

[0023]

[0024] In the formula, U is the overall heat transfer coefficient of the evaporator, and A is the heat transfer area of ​​the evaporator (m²). o This represents the molten salt mass flow rate. This refers to the specific heat capacity of molten salt.

[0025] Total heat load The specific expression is as follows:

[0026]

[0027] In the formula, This refers to the molten salt inlet temperature. This refers to the inlet temperature of the wet steam.

[0028] The steam turbine power generation system model is specifically expressed as follows:

[0029] Calculation of extraction pressures at each stage based on the Freuger formula:

[0030]

[0031] In the formula, This refers to the steam flow rate after the stage group changes. The steam flow rate after the stage before the flow rate change; The steam pressure before the stage after the flow rate change; The steam pressure before the stage group before the flow rate change; The steam pressure after the stage group after the flow rate change; The steam pressure after the stage before the flow rate change; where:

[0032]

[0033] extraction pressure The specific expression is as follows:

[0034]

[0035] In the formula, This refers to the extraction steam pressure under design conditions. The steam consumption of the steam turbine under varying operating conditions; This represents the steam consumption of the steam turbine under design operating conditions.

[0036] Furthermore, based on the constructed electric heating molten salt system model, molten salt storage and heat exchange system model, and power generation system model, a multi-objective optimization model is established with the objectives of minimum construction cost, shortest payback period, and maximum peak-shaving capacity, and with the power of the electric heating molten salt equipment and the molten salt quality in the molten salt storage and heat exchange system as variables. This model includes objective functions and constraints.

[0037] Based on the models of the electrically heated molten salt system, the molten salt storage and heat exchange system, and the power generation system, a multi-objective optimization model is established with the objectives of minimum construction cost, shortest payback period, and maximum peak-shaving capacity, and with the power of the electrically heated molten salt equipment and the molten salt mass in the molten salt storage and heat exchange system as variables. This model includes objective functions and constraints.

[0038] With the goals of lowest construction cost, shortest payback period, and maximum peak-shaving capacity, the following objectives are established:

[0039]

[0040] In the formula, For construction costs, In return for time, This represents the annual power generation of the molten salt thermal power generation system. This refers to the number of molten salt electric heating devices. For the molten salt mass in the system, the constraints are as follows: This refers to the molten salt level in the storage tank. For the power generation of the steam turbine system, Rated power of the steam turbine system;

[0041] Construction costs Including the cost of purchasing molten salt electric heating equipment Power generation system cost And the cost of purchasing molten salt ;

[0042] in,

[0043] In the formula, This refers to the unit price of molten salt electric heating equipment;

[0044] Annual electricity sales revenue The specific calculations are as follows:

[0045]

[0046] In the formula, Let be the electricity price at time t. Let t be the power output of the molten salt thermal power generation system. For time step;

[0047] Total cost The specific calculations are as follows:

[0048]

[0049] In the formula, This indicates the annual employee salary;

[0050] Annual power generation of molten salt thermal power generation system The specific calculations are as follows:

[0051] .

[0052] Furthermore, the method of using Pareto dominance to solve the multi-objective optimization model to obtain the Pareto optimal solution set includes:

[0053] Initialize the particle population to obtain the initial information of the population;

[0054] Calculate the fitness of each particle in the particle swarm;

[0055] Perform non-dominated sorting on the initial population;

[0056] The crowding distance of all individual particles is calculated using the dense distance method;

[0057] The global optimum is randomly selected from a preset proportional solution with a large crowding distance.

[0058] Record the number of iterations and begin iterative calculation;

[0059] Calculations are performed for each particle to obtain the individual optimal value and the group optimal value;

[0060] The calculation continues iteratively until the required number of iterations is reached, and the Pareto optimal solution set is output.

[0061] Furthermore, the initialization of the particle population to obtain initial population information includes:

[0062] Set the initial population size, spatial dimension, objective function dimension, and Pareto solution set size; define position and velocity boundaries; initialize the initial positions and velocities of the particle swarm to obtain the initial information of the particle swarm.

[0063] Furthermore, the calculation for each particle to obtain the individual optimal value and the group optimal value includes:

[0064] Update the particle's velocity and position, and the particle's position x at step k+1. i k+1 The calculation is as follows:

[0065]

[0066] In the formula, x i k v represents the position of the i-th particle at step k; i k Let be the velocity of the i-th particle at the k-th step;

[0067] The formula for the particle velocity v of the i-th individual particle at iteration step k+1 is as follows:

[0068]

[0069] In the formula, p i,best The best position in the history of an individual particle. G best This is the optimal location for the current population; c per and c en These are the self-learning factor and the group learning coefficient; r per and r en These are random numbers between 0 and 1; w These are the inertial adaptive weighting coefficients;

[0070] Determine if the updated particle velocity and position are within the constraints; if they exceed the boundary range, regenerate the particle velocity and position within the boundary; if they are within the boundary range, proceed to the next step.

[0071] Calculate the fitness of each particle in the updated particle swarm and determine whether the molten salt pressure drop and wet steam pressure drop are within the constrained boundary conditions; if they are outside the range, remove the current particle from the Pareto solution set; if the constraints are met, proceed to the next step.

[0072] Perform non-dominated sorting on the updated population;

[0073] Crowding is calculated for the updated population;

[0074] Randomly select the global optimum from the preset ratios of crowded distances;

[0075] Record the individual optimal value and the group optimal value.

[0076] A capacity optimization system for an electrically heated molten salt energy storage and peak-shaving system includes:

[0077] The first model construction module is used to construct the electric heating molten salt system model, the molten salt heat exchange system model, and the power generation system model of the electric heating molten salt energy storage peak shaving system.

[0078] The second model construction module is used to establish a multi-objective optimization model based on the constructed electric heating molten salt system model, molten salt storage and heat exchange system model, and power generation system model, with the objectives of the lowest construction cost, the shortest payback period, and the maximum peak-shaving capacity, and with the power of the electric heating molten salt equipment and the molten salt mass in the molten salt storage and heat exchange system as variables, including objective functions and constraints.

[0079] The model solving module is used to solve the multi-objective optimization model using the Pareto dominance method to obtain the Pareto optimal solution set, which serves as the capacity optimization scheme for the electrically heated molten salt energy storage peak-shaving system.

[0080] A capacity optimization device for an electrically heated molten salt energy storage and peak-shaving system includes:

[0081] Memory, used to store computer programs;

[0082] A processor is used to execute the computer program to implement the steps of the capacity optimization method for the electrically heated molten salt energy storage peak shaving system described above.

[0083] A computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of the capacity optimization method for the above-described electrically heated molten salt energy storage peak-shaving system.

[0084] Compared with the prior art, the present invention has the following beneficial effects:

[0085] This invention provides a capacity optimization method for an electrically heated molten salt energy storage peak-shaving system. It involves constructing models of the electrically heated molten salt system, the molten salt heat exchange system, and the power generation system. Based on these models, with the objectives of minimum construction cost, shortest payback period, and maximum peak-shaving capacity, and using the power of the electrically heated molten salt equipment and the quality of the molten salt as variables, a multi-objective optimization model is established, including objective functions and constraints. The Pareto dominance method is then used to solve this model to obtain the Pareto optimal solution set as the capacity optimization scheme. By simultaneously balancing the conflicting relationships between cost, payback period, and peak-shaving capacity through the multi-objective optimization model, and using the Pareto dominance method to filter out non-dominated solutions, it ensures that each solution cannot simultaneously improve all objectives within the variable space. This systematically derives the optimal trade-off configuration between cost, payback period, and peak-shaving performance. This method effectively overcomes the extreme trade-off problems in traditional capacity configuration. While ensuring the system has robust peak-shaving capacity, it significantly reduces construction costs, shortens the investment payback period, and improves power generation and peak-shaving performance, ultimately enhancing the overall economic efficiency and adaptability to practical application scenarios, and meeting the energy dispatch requirements of new power systems.

[0086] Preferably, this invention proposes the core functions of the electric heating molten salt system model and the construction principle of the molten salt storage and heat exchange system model. This accurately quantifies the key process mass flow rate of the conversion of electrical energy into molten salt heat energy during grid off-peak periods, as well as the dynamics of heat energy storage and transfer based on energy conservation within the storage and heat exchange system. This provides a solid physical process foundation for the entire optimization model and ensures the physical feasibility and computational accuracy of the capacity configuration scheme in the energy conversion and storage stages.

[0087] Preferably, in this invention, the heat load of a complex heat exchange process is accurately simulated using the efficiency-heat transfer unit method, and the extraction steam pressure characteristics of the steam turbine under varying operating conditions are effectively described using the Flueger formula. This enables the power generation system model to highly reflect the actual operating state and provides a high-fidelity calculation basis for evaluating the actual power generation and system performance under different capacity configuration schemes.

[0088] Preferably, in this invention, a comprehensive and practical economic and technical evaluation system is constructed, which calculates in detail the components of construction costs, the factors affecting annual electricity sales revenue, and the annual power generation of the system, and incorporates system operation constraints into the optimization framework, thereby ensuring that the final optimized solution is not only theoretically optimal, but also has significant advantages in engineering practice such as controllable costs, clear benefits, and safe and reliable operation.

[0089] Preferably, in this invention, by initializing the population, evaluating fitness, non-dominated sorting, calculating crowding, and selecting the global optimum, the solution space is explored efficiently. This enables the effective identification and convergence to a well-distributed and widely covered Pareto optimal frontier solution set in complex multi-objective optimization problems, providing decision-makers with an efficient and reliable computational means to select the most valuable capacity allocation scheme.

[0090] Preferably, in this invention, by reasonably defining key parameters such as population size, search space dimension, position and velocity boundaries, a good foundation is laid for the smooth start of the optimization algorithm, ensuring the diversity and feasibility of the initial solution, effectively avoiding the algorithm from getting trapped in a local optimum too early, improving the global search capability and the probability of finally obtaining a high-quality Pareto solution set.

[0091] Preferably, this invention provides core steps for velocity and position updates, boundary handling, fitness re-evaluation, and optimal solution maintenance during particle swarm iteration. This ensures that the algorithm continuously and effectively explores better solutions during iterative evolution, balances global exploration and local development capabilities through an adaptive mechanism, maintains the distribution of solutions by combining a crowding mechanism, and processes solutions that violate constraints. This ensures that the final Pareto optimal solution set not only satisfies all constraints but also achieves excellent balanced performance across multiple optimization objectives. Attached Figure Description

[0092] Figure 1 A schematic diagram of a molten salt thermal power generation system provided in an embodiment of the present invention;

[0093] Figure 2 Flowchart of the multi-objective optimization model for optimizing molten salt thermal power generation system configuration provided in this embodiment of the invention;

[0094] Figure 3 A flowchart for calculating the optimization objective is provided in this embodiment of the invention;

[0095] Figure 4 The flowchart of solving a multi-objective optimization model using the Pareto dominance method provided in this embodiment of the invention;

[0096] Figure 5 Verification of the multi-objective optimization model for the three standard function Pareto curves provided in this embodiment of the invention;

[0097] Figure 6 The optimal Pareto front solution set for configuring the capacity of a molten salt thermal power generation system provided in this embodiment of the invention;

[0098] Figure 7 A flowchart of a capacity optimization method for an electrically heated molten salt energy storage and peak-shaving system provided by the present invention;

[0099] Figure 8 This is a schematic diagram of the capacity optimization system of an electrically heated molten salt energy storage peak shaving system provided by the present invention. Detailed Implementation

[0100] like Figure 7 As shown, this embodiment provides a capacity optimization method for an electrically heated molten salt energy storage peak-shaving system, including:

[0101] An electric heating molten salt system model, a molten salt storage heat exchange system model, and a power generation system model of an electric heating molten salt energy storage peak-shaving system are constructed. Based on the constructed electric heating molten salt system model, a multi-objective optimization model is established with the objectives of minimum construction cost, shortest payback period, and maximum peak-shaving capacity, and with the power of the electric heating molten salt equipment and the molten salt mass in the molten salt storage heat exchange system as variables. The Pareto dominance method is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set, which serves as the capacity optimization scheme for the electric heating molten salt energy storage peak-shaving system.

[0102] For example, this embodiment provides a capacity optimization method for an electrically heated molten salt energy storage peak-shaving system, the specific steps of which are as follows:

[0103] S1. Establish a mathematical model of the system, which specifically includes three parts: the electric heating molten salt subsystem coupled to the power grid, the molten salt storage / heat exchange subsystem, and the thermoelectric power generation system.

[0104] S2. Based on the model established in S1, a multi-objective optimization model is established with the lowest construction cost, shortest payback period, and maximum peak-shaving capacity as optimization objectives, and the power of the electrically heated molten salt equipment and the molten salt quality in the molten salt storage and heat exchange system as decision variables, including objective functions and constraints.

[0105] S3. The model is solved using a Pareto-dominated multi-objective optimization algorithm, and the Pareto optimal solution set is finally output, providing a series of optimization schemes for system capacity configuration.

[0106] This includes establishing models for the electrically heated molten salt subsystem, the molten salt storage and heat exchange subsystem, and the power generation system of the coupled power grid, including:

[0107] (1) Molten salt electric heating subsystem: responsible for purchasing electricity during the off-peak period of the power grid load, heating molten salt through electric heaters, and realizing the conversion and storage of electrical energy into thermal energy;

[0108] (2) Molten salt heat exchange system and power generation system: responsible for transferring the heat energy stored in the molten salt to the working fluid through the heat exchange system and driving the steam turbine power generation system to complete the conversion of heat energy into electrical energy;

[0109] The establishment of the multi-objective particle swarm optimization model includes:

[0110] (1) Objective Function: The model includes three optimization objectives: minimum construction cost, shortest payback period, and maximum peak-shaving capacity. Construction cost refers to the total one-time cost in the initial stage of system construction, including the cost of configuring electric heating equipment, purchasing molten salt, and constructing the power generation system. Payback period is defined as the time required for the cumulative electricity sales revenue to exactly offset the total investment in the system. Peak-shaving capacity is the annual power generation of the molten salt thermal power generation system.

[0111] (2) Constraints: The model mainly includes two constraints: the total molten salt content in the system and the minimum operating power of the power generation system. Due to the maximum and minimum liquid levels of the storage tank, the total molten salt content in the system shall not be less than 498.5064 tons and shall not exceed 34895.448 tons; the minimum operating power of the steam turbine generator set is 15% of the rated power.

[0112] (2) The calculation process of the optimization target is as follows: Based on the input variable factors, the power of the electric heating molten salt equipment and the molten salt mass in the molten salt storage and heat exchange system, calculate the heat storage and abandoned power during the off-peak period according to the electric heating molten salt system model and the molten salt storage and heat exchange system model described in S1, calculate the power generation of the molten salt thermal power generation system according to the power generation system model described in S1, obtain the annual power generation of the system, the supplementary power purchase when the system power generation capacity is insufficient, obtain the total construction cost and annual profit, and further calculate the payback period.

[0113] The process of solving the problem using the Pareto dominance method is as follows:

[0114] (1) Initialize the particle swarm and filter the initial particle swarm for individual optima and global optima. Set the initial swarm size, spatial dimension, objective function dimension, and Pareto solution set size. Define position and velocity boundaries. Initialize the initial position and initial velocity of the particle swarm to obtain the initial information of the particle swarm. Calculate the fitness of each particle. Perform non-dominated sorting on the initialized swarm. Place the fitness of the initialized swarm into the Pareto solution set. For each particle, compare it with other particles. If it is dominated by other individuals, remove it from the Pareto solution set to obtain the non-dominated solution swarm. Calculate the crowding distance of all individual particles using the dense distance method. Randomly select the global optimum from the top 20% of solutions with the largest crowding distance. Record the position and fitness of the global optimum in the Pareto solution set. The individual optimum is the initial value.

[0115] (2) Record the number of iterations, and start the calculation of step (3) within the number of iterations.

[0116] (3) For each particle, perform the following calculations: Update the particle's velocity and position using the particle swarm optimization method. Use inertial adaptive weight coefficients to control the degree of change in each iteration to avoid premature convergence or local optimization. Determine whether the updated particle velocity and position are within the constraints (defined boundary range). If they exceed the boundary range, regenerate the particle's velocity and position within the boundary; if they are within the boundary range, proceed to the next step. Calculate the fitness of each particle in the updated particle swarm. Determine whether the calculated molten salt pressure drop and wet steam pressure drop are within the constrained boundary conditions. If they exceed the range, remove the particle from the Pareto solution set; if the constraints are met, proceed to the next step. Perform non-dominated sorting and crowding calculation on the updated population. Randomly select the global optimum from the top 20% of solutions with the largest crowding distance. Record the individual optimal value and the population optimal value.

[0117] After iterative calculations, a three-dimensional Pareto optimal solution set is obtained, with efficiency, cost, and evaporation rate as the coordinate axes. All points on this solution set are non-dominated solutions. During engineering operation, the molten salt flow rate and water flow rate can be adjusted according to different evaporation rate demands based on the Pareto optimal solution set, in order to achieve the goal of minimum cost and maximum efficiency.

[0118] The capacity optimization method for the electrically heated molten salt energy storage and peak-shaving system provided in this embodiment will be further explained below with reference to the accompanying drawings:

[0119] like Figure 1 As shown in the diagram, this embodiment provides a schematic of an electrically heated molten salt energy storage peak-shaving system. Its operation is as follows: During off-peak electricity periods, electricity is purchased from the grid to support the operation of the electrically heated molten salt system and supply power to the user side. During the heating process, the low-temperature molten salt from the low-temperature tank is heated to 560°C and then stored in the high-temperature tank. During other periods, the molten salt thermal power generation system is prioritized to supply power to the user side. The high-temperature molten salt enters the steam generation system and exchanges heat with the feedwater, generating high-temperature, high-pressure steam to drive the turbine power generation system. When the high-temperature molten salt storage is insufficient to supply power to the user side, electricity is purchased from the grid to meet the user side load demand. When the turbine system operates at a minimum power of 15% of its rated power, and the high-temperature molten salt storage is insufficient, the turbine shuts down, relying solely on purchasing electricity from the grid to meet the user side demand.

[0120] In this embodiment, the flowchart of the multi-objective optimization model of the electrically heated molten salt energy storage peak-shaving system (forced circulation steam generation system) is as follows: Figure 2 As shown, the specific steps include:

[0121] S1: Establish models for the electrically heated molten salt system, the molten salt storage and heat exchange system, and the power generation system of the coupled power grid;

[0122] S2: With the objectives of lowest construction cost, shortest return time, and maximum peak-shaving capacity, and with the power of the electrically heated molten salt equipment and the molten salt quality in the molten salt storage and heat exchange system as variables, a multi-objective optimization model including objective function and constraints is established.

[0123] S3: The Pareto dominance method is used to solve the multi-objective optimization model, and the Pareto optimal solution set is output to obtain an optimization scheme for improving the high-efficiency operation of the forced circulation system under varying load and low load.

[0124] The electric heating molten salt system model, molten salt storage and heat exchange system model, and power generation system model established in S1 include:

[0125] Establish the following model of an electrically heated molten salt system and calculate the amount of molten salt heated during off-peak electricity hours:

[0126]

[0127] in, Mass flow rate of molten salt that can be heated in each time step For time step, This represents the total power of the molten salt heater. The specific heat capacity of molten salt, The temperature at which the molten salt flows out of the cryogenic salt storage tank.

[0128] A molten salt storage tank model is established based on the following energy conservation formula:

[0129]

[0130] in, Let be the mass of molten salt contained in the storage tank at time t. The temperature of the molten salt in the storage tank is The specific enthalpy corresponding to time, The flow rates of molten salt flowing into and out of the storage tank. For time step.

[0131] Establish the following steam generation system model:

[0132] The heat transfer processes of the preheater, superheater, and reheater are calculated using the efficiency-heat transfer unit method (ε-NTU).

[0133]

[0134] in, NTU represents the evaporator efficiency and the number of heat transfer units in the evaporator.

[0135]

[0136] Where U is the overall heat transfer coefficient of the evaporator, and A is the heat transfer area of ​​the evaporator, in meters. o c is the molten salt mass flow rate. p,o This is the specific heat capacity of molten salt.

[0137] Total heat load Calculated as follows:

[0138]

[0139] in, This refers to the molten salt inlet temperature. This refers to the inlet temperature of the wet steam.

[0140] Establish the following steam turbine power generation system model:

[0141] Calculate the extraction pressure at each stage using the Freuger formula:

[0142]

[0143] in D The steam flow rate through the steam turbine. P The value represents the steam pressure. Subscripts 1 and 2 indicate the flow rate before and after the stage group. The presence of a subscript 0 indicates the flow rate before the change, while the absence of a subscript 0 indicates the flow rate after the change.

[0144] For condensing turbine units, if we discuss the stages between extraction sections, their pressure ratio is... It's always very small and can be ignored:

[0145]

[0146] For variable operating conditions with varying loads, the extraction steam rate can be considered to be proportional to the steam flow rate through the turbine. D This leads to the extraction steam pressure. Proportion to the steam consumption of the steam turbine:

[0147]

[0148] In S2 above, based on the coupled power grid electric heating molten salt system model, molten salt storage and heat exchange system model, and power generation system model established in S1, a multi-objective optimization model is established with the objectives of minimum construction cost, shortest payback period, and maximum peak-shaving capacity, and with the power of the electric heating molten salt equipment and the molten salt quality in the molten salt storage and heat exchange system as variables. The specific process is as follows:

[0149] 1) With the goals of lowest construction cost, shortest payback period, and maximum peak-shaving capacity, the following objectives are established:

[0150]

[0151] in For construction costs, In return for time, This represents the annual power generation of the molten salt thermal power generation system. This refers to the number of molten salt electric heating devices. For the molten salt mass in the system, the constraints are as follows: This refers to the molten salt level in the storage tank. For the power generation of the steam turbine system, This refers to the rated power of the steam turbine system.

[0152] Construction costs Includes the cost of purchasing molten salt electric heating equipment Power generation system cost And the cost of purchasing molten salt .

[0153]

[0154] Among them, the unit price of molten salt electric heating equipment The price is 5 million yuan per unit, the power generation system cost is 6,300 yuan per kilowatt, and the molten salt price is 12,000 yuan per ton.

[0155] Return time The duration for which the electricity sales revenue of the molten salt electric heating system exceeds the combined construction cost and cumulative labor and operating costs.

[0156] Annual electricity sales revenue is calculated as follows:

[0157]

[0158] in, Let be the electricity price at time t. Let t be the power output of the molten salt thermal power generation system at time t.

[0159] The total cost is calculated as follows:

[0160]

[0161] in, Costs such as annual staff salaries.

[0162] The annual power generation of the molten salt thermal power generation system is calculated as follows:

[0163]

[0164] In this embodiment, the flowchart for calculating the optimization objective is as follows: Figure 3As shown, based on the input variables of the number of molten salt electric heating devices and the quality of molten salt, and according to the coupled power grid electric heating molten salt system model, molten salt storage and heat exchange system model, and power generation system model described in S1, the annual operating conditions and construction costs of the system are calculated, yielding the molten salt thermal power generation, system electricity purchase, and total system operating cost at each time point. The annual power generation of the molten salt thermal power generation system is calculated, the total electricity sales revenue is calculated, and the total cost is compared to obtain the reporting time. Thus, the calculation results of the three optimization objectives are obtained.

[0165] In S3 above, the flowchart for solving the multi-objective optimization model in S2 using the Pareto dominance method is as follows: Figure 4 As shown, particle position represents the optimization variable, fitness stores the optimization objective, and particle velocity represents the search direction. Specifically, the Pareto dominance method is used to solve the multi-objective optimization model in S2 to obtain the capacity optimization scheme of the electrically heated molten salt energy storage peak-shaving system. The specific solution process is as follows:

[0166] S31: Initialize the particle swarm to obtain its initial information. Specifically, set the initial swarm size to 50, the spatial dimension to 2 (number of optimization variables), the objective function dimension to 3 (number of optimization objectives), and the Pareto solution set size to 20 (number of retained optimal solutions). Define position and velocity boundaries. Initialize the initial positions and velocities of the particle swarm to obtain its initial information.

[0167] S32: Calculate the fitness of each particle in the particle swarm according to the calculation method described in S2.

[0168] S33: Perform non-dominated sorting on the initial population. Place the fitness of the initial population into the Pareto solution set. For each particle, compare it with other particles. If it is dominated by other individuals, remove it from the Pareto solution set. This will give you a population with non-dominated solutions.

[0169] S34: The crowding distance of all individual particles is calculated using the dense distance method.

[0170] S35: Randomly select the global optimum from the top 20% of solutions with the largest crowding distance. Record the position and fitness of the global optimum in the Pareto solution set. The individual optimum is the initial value.

[0171] S36: Record the number of iterations and start iterative calculation.

[0172] S37: Perform the following calculations for each particle:

[0173] S371: Update the particle's velocity and position. The particle position x at step k+1. i k+1 Calculated as follows:

[0174]

[0175] Where x i k Let v be the position of the i-th particle at step k. i k Let v be the velocity of the i-th particle at step k. The formula for the particle velocity v of the i-th particle at iteration step k+1 is as follows:

[0176]

[0177] in p i,best It is the best position in the history of an individual particle, and G best This is the optimal location for the current population. c per and c en These are the self-learning factor and the group learning coefficient. r per and r en It is a random number between 0 and 1. w The inertial adaptive weight coefficients control the degree of change in each iteration, avoiding premature convergence or local optimization, and are calculated as follows:

[0178]

[0179] Where d is the overall dimension (the number of variable factors), which is 2 in this example. w max and w min These are the maximum and minimum values ​​of the set adaptive inertial weighting coefficients, respectively. x max and x min These are the maximum and minimum values ​​for the set particle positions, respectively.

[0180] Determine if the updated particle velocity and position are within the constraints (defined boundary range). If they exceed the boundary range, regenerate the particle velocity and position within the boundary; otherwise, proceed to the next step.

[0181] S372: Calculate the fitness of each particle in the updated particle swarm according to the calculation method described in S2. Determine the calculated ∆... P o and ∆ P i Check if the particle is within the bounded boundary conditions. If it is outside the range, remove it from the Pareto solution set and ignore it. If the constraints are met, proceed to the next step.

[0182] S373: Perform non-dominated sorting on the updated population.

[0183] S374: Calculate the crowding of the updated population.

[0184] S375: Randomly select the global optimum from the top 20% of solutions with the largest crowding distance.

[0185] S376: Record the individual optimal value and the group optimal value.

[0186] S38: Determine if the number of iterations has been reached. If it has, end the calculation. If not, increment the number of iterations by 1 and return to S36 to recalculate.

[0187] The established multi-objective particle swarm optimization iterative computational model was validated. This paper calculates three standard functions applicable to the evaluation of multi-objective optimization schemes. The results are as follows: Figure 5 As shown, all functions achieved excellent computational accuracy and exhibited a more uniformly distributed Pareto front curve.

[0188] The Pareto optimal solution set of the multi-objective optimization result in this embodiment is specifically as follows: Figure 6 As shown. Figure 6 Evaporation Q steam Evaporator efficiency ε and costs C total The Pareto front solution set is shown. It can be seen that the optimized Pareto optimal solution set forms an upward plane, where all points are non-dominated solutions. This indicates that an increase in evaporation is accompanied by a decrease in efficiency and an increase in cost. During engineering operation, the molten salt flow rate and water flow rate can be adjusted according to different changes in evaporation demand based on the Pareto optimal solution set, in order to achieve the goal of minimum cost and maximum efficiency.

[0189] Therefore, this embodiment provides a capacity optimization method for an electrically heated molten salt energy storage peak-shaving system, which has the following advantages:

[0190] This method, based on a coupled power grid model of an electrically heated molten salt system, a molten salt storage and heat exchange system, and a power generation system, employs a multi-objective particle swarm optimization (PSO) approach. Using the power of the electrically heated molten salt equipment and the molten salt mass in the storage and heat exchange system as variables, and aiming at the lowest construction cost, shortest payback period, and maximum peak-shaving capacity, it optimizes the configuration of the molten salt thermal power generation system. The Pareto optimal solution set calculated in this embodiment provides the configuration scheme with the strongest power generation capacity and highest economic efficiency when constructing a molten salt thermal power generation system. Secondly, this optimization method eliminates the need for mechanical and manual experimentation based on experience, significantly reducing optimization time and labor costs. Furthermore, the multi-objective PSO algorithm established in this method combines inertial adaptive weight coefficients, non-dominated sorting, and congestion calculation to control the degree of change in each iteration, avoiding premature convergence or local optimization, and quickly searching for a uniform and dispersed optimal solution set, thus improving the system's optimization efficiency. Finally, this method calculates three standard functions suitable for evaluating multi-objective optimization schemes. All functions achieve excellent computational accuracy and exhibit a more uniformly distributed Pareto pre-curve, verifying the accuracy of this optimization method.

[0191] like Figure 8 As shown in the figure, this embodiment also provides a capacity optimization system for an electrically heated molten salt energy storage peak-shaving system, including: a first model construction module, used to construct an electrically heated molten salt system model, a molten salt storage heat exchange system model, and a power generation system model of the electrically heated molten salt energy storage peak-shaving system; a second model construction module, used to establish a multi-objective optimization model based on the constructed electrically heated molten salt system model, molten salt storage heat exchange system model, and power generation system model, with the objectives of minimum construction cost, shortest payback period, and maximum peak-shaving capacity, and with the power of the electrically heated molten salt equipment and the molten salt mass in the molten salt storage heat exchange system as variables, including objective functions and constraints; and a model solution module, used to solve the multi-objective optimization model using the Pareto dominance method to obtain the Pareto optimal solution set, which serves as the capacity optimization scheme for the electrically heated molten salt energy storage peak-shaving system.

[0192] The present invention also provides a capacity optimization device for an electrically heated molten salt energy storage peak shaving system, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the capacity optimization method for the electrically heated molten salt energy storage peak shaving system.

[0193] When the processor executes the computer program, it implements the following steps for capacity optimization of the electrically heated molten salt energy storage peak-shaving system: constructing an electrically heated molten salt system model, a molten salt storage heat exchange system model, and a power generation system model; based on the constructed electrically heated molten salt system model, molten salt storage heat exchange system model, and power generation system model, with the objectives of minimum construction cost, shortest payback period, and maximum peak-shaving capacity, and with the power of the electrically heated molten salt equipment and the molten salt mass in the molten salt storage heat exchange system as variables, establishing a multi-objective optimization model including objective functions and constraints; using the Pareto dominance method to solve the multi-objective optimization model to obtain the Pareto optimal solution set, which serves as the capacity optimization scheme for the electrically heated molten salt energy storage peak-shaving system.

[0194] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system. For example: a first model building module is used to build an electric heating molten salt system model, a molten salt storage heat exchange system model, and a power generation system model of the electric heating molten salt energy storage peak shaving system; a second model building module is used to establish a multi-objective optimization model, including objective functions and constraints, based on the constructed electric heating molten salt system model, molten salt storage heat exchange system model, and power generation system model, with the objectives of lowest construction cost, shortest payback time, and maximum peak shaving capacity, and with the power of the electric heating molten salt equipment and the molten salt mass in the molten salt storage heat exchange system as variables; and a model solving module is used to solve the multi-objective optimization model using the Pareto dominance method to obtain the Pareto optimal solution set, which serves as the capacity optimization scheme for the electric heating molten salt energy storage peak shaving system.

[0195] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing preset functions, wherein the instruction segments describe the execution process of the computer program in the capacity optimization device of the electrically heated molten salt energy storage peak-shaving system. For example, the computer program can be divided into a first model building module, a second model building module, and a model solving module; the specific functions of each module are as follows: the first model building module is used to build the electric heating molten salt system model, the molten salt storage heat exchange system model, and the power generation system model of the electric heating molten salt energy storage peak shaving system; the second model building module is used to establish a multi-objective optimization model, including objective functions and constraints, based on the constructed electric heating molten salt system model, molten salt storage heat exchange system model, and power generation system model, with the objectives of lowest construction cost, shortest payback period, and maximum peak shaving capacity, and with the power of the electric heating molten salt equipment and the molten salt mass in the molten salt storage heat exchange system as variables; the model solving module is used to solve the multi-objective optimization model using the Pareto dominance method to obtain the Pareto optimal solution set, which serves as the capacity optimization scheme for the electric heating molten salt energy storage peak shaving system.

[0196] The capacity optimization device for the electrically heated molten salt energy storage peak shaving system can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the above are examples of capacity optimization devices for electrically heated molten salt energy storage peak shaving systems and do not constitute a limitation on such devices. The device may include more components than described above, or combine certain components, or use different components. For example, the capacity optimization device for the electrically heated molten salt energy storage peak shaving system may also include input / output devices, network access devices, buses, etc.

[0197] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or any conventional processor. The processor is the control center for capacity optimization of the electrically heated molten salt energy storage peak-shaving system, connecting various parts of the capacity optimization equipment of the entire system via various interfaces and lines.

[0198] The memory can be used to store the computer program and / or modules. The processor realizes various functions of the capacity optimization device of the electrically heated molten salt energy storage peak shaving system by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory.

[0199] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0200] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the capacity optimization method for an electrically heated molten salt energy storage peak-shaving system.

[0201] If the modules / units integrated into the capacity optimization system of the electrically heated molten salt energy storage and peak shaving system are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0202] Based on this understanding, the present invention can implement all or part of the processes in the capacity optimization method of the above-mentioned electrically heated molten salt energy storage peak-shaving system, which can also be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the capacity optimization method of the above-mentioned electrically heated molten salt energy storage peak-shaving system. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or preset intermediate form, etc.

[0203] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0204] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0205] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

Claims

1. A capacity optimization method for an electrically heated molten salt energy storage and peak-shaving system, characterized in that, include: Construct electric heating molten salt system models, molten salt heat exchange system models, and power generation system models for an electrically heated molten salt energy storage and peak-shaving system. Based on the constructed electric heating molten salt system model, molten salt storage and heat exchange system model, and power generation system model, a multi-objective optimization model including objective function and constraints is established with the objectives of minimum construction cost, shortest payback time, and maximum peak-shaving capacity, and with the power of electric heating molten salt equipment and the molten salt quality in the molten salt storage and heat exchange system as variables. The Pareto dominance method is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set, which serves as the capacity optimization scheme for the electrically heated molten salt energy storage peak-shaving system. The multi-objective optimization model specifically includes: With the goals of lowest construction cost, shortest payback period, and maximum peak-shaving capacity, the following objectives are established: In the formula, For construction costs, In return for time, This represents the annual power generation of the molten salt thermal power generation system. This refers to the number of molten salt electric heating devices. For the molten salt mass in the system, the constraints are as follows: This refers to the molten salt level in the storage tank. For the power generation of the steam turbine system, Rated power of the steam turbine system; Construction costs Including the cost of purchasing molten salt electric heating equipment Power generation system cost And the cost of purchasing molten salt ; in, In the formula, This refers to the unit price of molten salt electric heating equipment; Annual electricity sales revenue The specific calculations are as follows: In the formula, Let be the electricity price at time t. Let t be the power output of the molten salt thermal power generation system. For time step; Total cost The specific calculations are as follows: In the formula, This indicates the annual employee salary; Annual power generation of molten salt thermal power generation system The specific calculations are as follows: 。 2. The capacity optimization method for an electrically heated molten salt energy storage and peak-shaving system according to claim 1, characterized in that, The electric heating molten salt system model, molten salt heat exchange system model, and power generation system model for constructing the electric heating molten salt energy storage peak-shaving system include: The electric heating molten salt system model is used to purchase electricity during periods of low grid load and heat molten salt using an electric heater, thereby converting and storing electrical energy into thermal energy. The specific implementation is as follows: In the formula, This represents the mass flow rate of molten salt that can be heated in each time step. For time step, This represents the total power of the molten salt heater. The specific heat capacity of molten salt, The temperature at which the molten salt flows out of the cryogenic salt storage tank; The molten salt heat exchange system model and the power generation system model are used to transfer the thermal energy stored in the molten salt to the working fluid through the heat exchange system and drive the steam turbine power generation system to complete the conversion of thermal energy into electrical energy. The molten salt heat exchange system model is constructed based on the law of energy conservation, and is specifically expressed as follows: In the formula, Let be the mass of molten salt contained in the storage tank at time t. The temperature of the molten salt in the storage tank is The specific enthalpy corresponding to time, These represent the molten salt flow rates into and out of the storage tanks, respectively.

3. The capacity optimization method for an electrically heated molten salt energy storage and peak-shaving system according to claim 1, characterized in that, The power generation system model specifically includes a steam generation system model and a steam turbine power generation system model, wherein: The steam generation system model is used to calculate the heat exchange processes of the preheater, superheater, and reheater using the efficiency-heat transfer unit method, and is specifically expressed as follows: In the formula, NTU represents the evaporator efficiency, and NTU represents the number of heat transfer units in the evaporator. In the formula, U is the overall heat transfer coefficient of the evaporator, and A is the heat transfer area of ​​the evaporator (m²). o This represents the molten salt mass flow rate. This refers to the specific heat capacity of molten salt. Total heat load The specific expression is as follows: In the formula, This refers to the molten salt inlet temperature. This refers to the inlet temperature of the wet steam. The steam turbine power generation system model is specifically expressed as follows: Calculation of extraction pressures at each stage based on the Freuger formula: In the formula, This refers to the steam flow rate after the stage change; The steam flow rate after the stage before the flow rate change; The steam pressure before the stage after the flow rate change; The steam pressure before the stage group before the flow rate change; The steam pressure after the stage group after the flow rate change; The steam pressure after the stage before the flow rate change; where: extraction pressure The specific expression is as follows: In the formula, This refers to the extraction steam pressure under design conditions. The steam consumption of the steam turbine under varying operating conditions; This represents the steam consumption of the steam turbine under design operating conditions.

4. The capacity optimization method for an electrically heated molten salt energy storage and peak-shaving system according to claim 1, characterized in that, The method of Pareto dominance is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set, including: Initialize the particle population to obtain the initial information of the population; Calculate the fitness of each particle in the particle swarm; Perform non-dominated sorting on the initial population; The crowding distance of all individual particles is calculated using the dense distance method; The global optimum is randomly selected from a preset proportional solution with a large crowding distance. Record the number of iterations and begin iterative calculation; Calculations are performed for each particle to obtain the individual optimal value and the group optimal value; The calculation continues iteratively until the required number of iterations is reached, and the Pareto optimal solution set is output.

5. The capacity optimization method for an electrically heated molten salt energy storage and peak-shaving system according to claim 4, characterized in that, The initialization of the particle population, obtaining the initial information of the population, includes: Set the initial population size, spatial dimension, objective function dimension, and Pareto solution set size; define position and velocity boundaries; initialize the initial positions and velocities of the particle swarm to obtain the initial information of the particle swarm.

6. The capacity optimization method for an electrically heated molten salt energy storage and peak-shaving system according to claim 4, characterized in that, The calculations performed for each particle to obtain the individual optimal value and the group optimal value include: Update the particle's velocity and position, and the particle's position x at step k+1. i k+1 The calculation is as follows: In the formula, x i k v represents the position of the i-th particle at step k; i k Let be the velocity of the i-th particle at the k-th step; The formula for the particle velocity v of the i-th individual particle at iteration step k+1 is as follows: In the formula, p i,best The best position in the history of an individual particle. G best This is the optimal location for the current population; c per and c en These are the self-learning factor and the group learning coefficient; r per and r en These are random numbers between 0 and 1; w These are the inertial adaptive weighting coefficients; Determine if the updated particle velocity and position are within the constraints; if they exceed the boundary range, regenerate the particle velocity and position within the boundary; if they are within the boundary range, proceed to the next step. Calculate the fitness of each particle in the updated particle swarm and determine whether the molten salt pressure drop and wet steam pressure drop are within the constrained boundary conditions; if they are outside the range, remove the current particle from the Pareto solution set; if the constraints are met, proceed to the next step. Perform non-dominated sorting on the updated population; Crowding is calculated for the updated population; Randomly select the global optimum from the preset ratios of crowded distances; Record the individual optimal value and the group optimal value.

7. A capacity optimization system for an electrically heated molten salt energy storage and peak-shaving system, used to implement the capacity optimization method for the electrically heated molten salt energy storage and peak-shaving system according to any one of claims 1-6, characterized in that, include: The first model construction module is used to construct the electric heating molten salt system model, the molten salt heat exchange system model, and the power generation system model of the electric heating molten salt energy storage peak shaving system. The second model construction module is used to establish a multi-objective optimization model based on the constructed electric heating molten salt system model, molten salt storage and heat exchange system model, and power generation system model, with the objectives of the lowest construction cost, the shortest payback period, and the maximum peak-shaving capacity, and with the power of the electric heating molten salt equipment and the molten salt mass in the molten salt storage and heat exchange system as variables, including objective functions and constraints. The model solving module is used to solve the multi-objective optimization model using the Pareto dominance method to obtain the Pareto optimal solution set, which serves as the capacity optimization scheme for the electrically heated molten salt energy storage peak-shaving system.

8. A capacity optimization device for an electrically heated molten salt energy storage and peak-shaving system, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the capacity optimization method for the electrically heated molten salt energy storage and peak-shaving system according to any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of the capacity optimization method for the electrically heated molten salt energy storage and peak shaving system according to any one of claims 1-6.

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