Method and apparatus for optimizing energy storage system, and device and storage medium

Abstract

Provided are a method and apparatus for optimizing an energy storage system, and a device and a storage medium. The method comprises: on the basis of an energy storage configuration to be solved, unit energy storage configuration cost, demand response cost to be acquired, net load fluctuation value to be acquired, cost discount rate and energy storage service life of an energy storage system to be optimized, constructing an energy storage configuration objective function; on the basis of a total demand response duration, demand response load, system output value, electricity price to be solved and unit clean energy curtailment cost of the energy storage system to be optimized, constructing a demand response objective function; on the basis of the energy storage configuration objective function, energy storage configuration constraints, the demand response objective function and demand response constraints, jointly solving the energy storage configuration to be solved and the electricity price to be solved to obtain a target energy storage configuration and a target electricity price, so as to realize the optimization of the energy storage system to be optimized. Thus, the operating costs are reduced and clean energy integration is also maximized.

Description

Energy storage system optimization methods, devices, equipment and storage media

[0001] This application claims priority to Chinese Patent Application No. 202410685747.9, filed with the Chinese Patent Office on May 30, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of power system technology, and more particularly to the field of power distribution system control technology, specifically to an energy storage system optimization method, device, equipment, and storage medium. Background Technology

[0003] The integration of clean energy provides more available resources for the distribution network. However, distributed power sources such as photovoltaic and clean energy generators are severely affected by weather factors, exhibiting volatility and randomness, which greatly restricts the scale of distributed power generation connected to the grid and energy utilization rate. At the same time, the uncertainty of distributed power sources further exacerbates the volatility of the original load. This two-sided randomness brings great difficulties to planning and scheduling.

[0004] On the grid side, energy storage systems can be applied to distributed systems, new energy microgrids, and ordinary power systems to improve the effectiveness and economy of users' electricity cost management, thereby realizing the comprehensive utilization value of electricity, such as reducing losses, improving power quality, reducing electricity costs, providing emergency backup, reactive power compensation, and demand-side response.

[0005] Grid-side energy storage systems (centralized or distributed) can partially or completely replace thermal power peak-shaving units, frequency-regulating units, and standby units. Due to their unparalleled load response speed and control precision, energy storage systems achieve far superior frequency and peak-shaving performance compared to traditional rotating generators. Furthermore, the modular design, flexible configuration, and distributed use of energy storage systems enable large-scale grid-connected applications. With its dual attributes as both a load and a power source, energy storage systems will interact more effectively with various power sources, the grid, and demand-side resources in a smart grid, flexibly and efficiently promoting optimized power system operation, significantly facilitating the consumption of clean energy, optimizing the value chain of clean energy consumption on the grid side, and ultimately minimizing grid overhead through the utilization of clean energy. Summary of the Invention

[0006] This application provides a method, apparatus, equipment, and storage medium for optimizing an energy storage system, so as to maximize the utilization of clean energy while reducing operating costs.

[0007] According to one aspect of this application, an energy storage system optimization method is provided, the method comprising:

[0008] Based on the energy storage configuration to be solved, the unit energy storage configuration cost, the demand response cost to be obtained, the net load fluctuation value to be obtained, the cost discount rate, and the energy storage life of the energy storage system to be optimized, an objective function for energy storage configuration is constructed; the energy storage configuration to be solved includes the energy storage power to be solved and the energy storage capacity to be solved; the unit energy storage configuration cost includes the unit energy storage power configuration cost and the unit energy storage capacity configuration cost.

[0009] Based on the historical energy storage, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, unit energy storage capacity power, and energy storage charging and discharging state of the energy storage system to be optimized, energy storage configuration constraints are constructed.

[0010] Based on the total demand response duration, demand response load, system output, electricity price to be solved, and unit cost of abandoned clean energy of the energy storage system to be optimized, a demand response objective function is constructed.

[0011] Based on the system output power of the energy storage system to be optimized, the electricity price to be solved, the number of system units, the status of system units, the demand response load, the energy storage charging and discharging power, and the energy storage charging and discharging status, demand response constraints are constructed.

[0012] Based on the energy storage configuration objective function, the energy storage configuration constraints, the demand response objective function, and the demand response constraints, the energy storage configuration to be solved and the electricity price to be solved are jointly solved to obtain the target energy storage configuration and the target electricity price, so as to achieve the optimization of the energy storage system to be optimized.

[0013] According to another aspect of this application, an energy storage system optimization device is provided, the device comprising:

[0014] The first function construction module is used to construct an energy storage configuration objective function based on the energy storage configuration to be solved, the unit energy storage configuration cost, the demand response cost to be obtained, the net load fluctuation value to be obtained, the cost discount rate, and the energy storage life of the energy storage system to be optimized; the energy storage configuration to be solved includes the energy storage power to be solved and the energy storage capacity to be solved; the unit energy storage configuration cost includes the unit energy storage power configuration cost and the unit energy storage capacity configuration cost;

[0015] The first constraint construction module is used to construct energy storage configuration constraints based on the historical energy storage power, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, power per unit energy storage capacity, and energy storage charging and discharging state of the energy storage system to be optimized.

[0016] The second function construction module is used to construct the demand response objective function based on the total demand response duration, demand response load, system output value, electricity price to be solved and unit cost of abandoned clean energy of the energy storage system to be optimized.

[0017] The second constraint construction module is used to construct demand response constraints based on the system output power of the energy storage system to be optimized, the electricity price to be solved, the number of system units, the status of system units, the demand response load, the energy storage charging and discharging power, and the energy storage charging and discharging status.

[0018] The function solving module is used to jointly solve the energy storage configuration to be solved and the electricity price to be solved based on the energy storage configuration objective function, the energy storage configuration constraints, the demand response objective function, and the demand response constraints, so as to obtain the target energy storage configuration and the target electricity price, thereby realizing the optimization of the energy storage system to be optimized.

[0019] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0020] One or more processors;

[0021] Memory, used to store one or more programs;

[0022] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the energy storage system optimization methods provided in the embodiments of this application.

[0023] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements any of the energy storage system optimization methods provided in the embodiments of this application.

[0024] This application constructs an objective function for energy storage configuration based on the energy storage configuration to be solved, unit energy storage configuration cost, demand response cost to be acquired, net load fluctuation value to be acquired, cost discount rate, and energy storage life of the energy storage system to be optimized. The energy storage configuration to be solved includes the energy storage power and the energy storage capacity to be solved. The unit energy storage configuration cost includes the configuration cost per unit energy storage power and the configuration cost per unit energy storage capacity. Energy storage configuration constraints are constructed based on the historical energy storage, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, power per unit energy storage capacity, and energy storage charging and discharging state of the energy storage system to be optimized. The demand response objective function is constructed based on the total demand response duration, demand response load, system output, unsolved electricity price, and unit clean energy curtailment cost. Demand response constraints are then constructed based on the system output, unsolved electricity price, number of system units, system unit status, demand response load, energy storage charging / discharging power, and energy storage charging / discharging status of the energy storage system to be optimized. Finally, the energy storage configuration and unsolved electricity price are jointly solved based on the energy storage configuration objective function, energy storage configuration constraints, demand response objective function, and demand response constraints to obtain the target energy storage configuration and target electricity price, thereby optimizing the energy storage system to be optimized. This technical solution, through the optimization of energy storage and demand response, maximizes the consumption of clean energy while reducing the load peak-valley difference, thus reducing distribution network operating costs. Attached Figure Description

[0025] Figure 1 is a flowchart of an energy storage system optimization method according to Embodiment 1 of this application;

[0026] Figure 2 is a flowchart of an energy storage system optimization method according to Embodiment 2 of this application;

[0027] Figure 3 is a schematic diagram of an energy storage system optimization device according to Embodiment 3 of this application;

[0028] Figure 4 is a schematic diagram of the structure of an electronic device that implements the energy storage system optimization method of the present application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Furthermore, it should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of relevant data such as unit energy storage configuration cost and cost discount rate involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0032] Example 1

[0033] Figure 1 is a flowchart of an energy storage system optimization method according to Embodiment 1 of this application. This embodiment is applicable to the optimization of energy storage systems in clean energy distribution networks. The optimization can be performed by an energy storage system optimization device, which can be implemented in hardware and / or software. This device can be configured in a computer device, such as an energy storage system in a clean energy distribution network. As shown in Figure 1, the method includes:

[0034] S110. Based on the energy storage configuration to be solved, the unit energy storage configuration cost, the demand response cost to be obtained, the net load fluctuation value to be obtained, the cost discount rate, and the energy storage life of the energy storage system to be optimized, construct the energy storage configuration objective function; the energy storage configuration to be solved includes the energy storage power to be solved and the energy storage capacity to be solved; the unit energy storage configuration cost includes the unit energy storage power configuration cost and the unit energy storage capacity configuration cost.

[0035] In this context, "energy storage system to be optimized" refers to the energy storage system that currently requires configuration and demand response optimization. "Energy storage configuration to be optimized" refers to the configuration of the energy storage system that needs optimization, which may include at least one of energy storage power and energy storage capacity. "Unit energy storage configuration cost" refers to the unit configuration cost of the energy storage system. "Demand response cost to be acquired" refers to the cost incurred through demand response strategies during load fluctuations. "Net load fluctuation value to be acquired" refers to the load fluctuation value within a certain time range. "Cost discount rate" is a ratio used to discount future costs to present value, used to calculate the current net present value. "Energy storage lifespan" refers to the service life of the energy storage system, i.e., its lifecycle. "Energy storage objective function" is a function constructed based on minimizing total cost, used to solve for the optimal energy storage configuration. "Energy storage power to be optimized" refers to the maximum power that the energy storage system can provide or absorb. "Energy storage capacity to be optimized" refers to the maximum energy that the energy storage system can store. "Unit energy storage power configuration cost" refers to the cost required to configure each unit of power. "Unit energy storage capacity configuration cost" refers to the cost required to configure each unit of capacity.

[0036] Optionally, energy storage cost processing is performed on the energy storage configuration to be solved, the unit energy storage configuration cost, the cost discount rate, and the energy storage life of the energy storage system to be optimized to determine the target energy storage cost of the energy storage system to be optimized; the target energy storage cost and the demand response cost to be acquired are summed to obtain the total system cost of the energy storage system to be optimized; and an energy storage configuration objective function is constructed based on the total system cost and the net load fluctuation value to be acquired.

[0037] The target energy storage cost refers to the cost of the energy storage configuration scheme that minimizes the system cost while meeting system requirements, calculated through optimization algorithms. The total system cost refers to the total economic cost that the energy storage system must pay to meet electricity demand.

[0038] For example, to determine the target energy storage cost of the energy storage system to be optimized, the energy storage configuration to be solved, the unit energy storage configuration cost, the cost discount rate, and the energy storage life are processed by energy storage cost processing. This can be done by processing the energy storage configuration to be solved and the unit energy storage configuration cost of the energy storage system to be optimized to determine the ideal energy storage cost of the energy storage system to be optimized; and by performing cost conversion on the ideal energy storage cost, the cost discount rate, and the energy storage life to determine the target energy storage cost.

[0039] Ideal energy storage cost refers to the lowest energy storage cost that can be theoretically achieved without considering actual constraints and limitations.

[0040] Furthermore, the energy storage configuration and unit energy storage configuration cost of the energy storage system to be optimized are processed to determine the ideal energy storage cost of the energy storage system to be optimized. This can be achieved by multiplying the energy storage power to be solved and the unit energy storage power configuration cost to obtain the energy storage power cost; multiplying the energy storage capacity to be solved and the unit energy storage capacity configuration cost to obtain the energy storage capacity cost; and summing the energy storage power cost and the energy storage capacity cost to obtain the ideal energy storage cost of the energy storage system to be optimized.

[0041] In one alternative implementation, the energy storage configuration objective function can be constructed using the following formula: F = F1 + f1;

[0042] Where F represents the total system cost. f2 represents the net load fluctuation value to be obtained. X1 = [C cp C cc [] represents the investment cost. stg() represents the energy storage configuration constraints. F1 represents the target energy storage cost. f1 represents the demand response cost to be acquired. d represents the discount rate of energy storage. L represents the lifespan of energy storage. C cp P represents the energy storage power to be solved. ESS This represents the configuration cost per unit of energy storage capacity. (C) cc E represents the energy storage capacity to be solved. ESS This represents the cost per unit of energy storage capacity. (C) cp P ESS This represents the cost of energy storage power. (C) cc E ESS This represents the cost of energy storage capacity. (C) cp P ESS +C cc E ESS This represents the ideal energy storage cost.

[0043] S120. Based on the historical energy storage, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, power per unit energy storage capacity, and energy storage charging and discharging state of the energy storage system to be optimized, construct energy storage configuration constraints.

[0044] Historical energy storage refers to the total amount of electrical energy stored in the energy storage system at historical nodes. Energy storage charge / discharge power refers to the power output capability of the energy storage system during charging and discharging, and can include both charging and discharging power. Energy storage charge / discharge efficiency refers to the ratio of energy lost when storing and releasing electrical energy into the energy storage system to the original energy, and can include both charging and discharging efficiency; charging efficiency refers to the efficiency of storing energy into the energy storage system, and discharging efficiency refers to the efficiency of releasing energy from the energy storage system. Time difference refers to the time distance between the current time node and a historical time node. Energy storage charge / discharge status refers to the ratio of electrical energy currently received and stored from external energy sources to the storable electrical energy, or the ratio of electrical energy being released by the energy storage system to the grid or connected loads to the storable electrical energy, and can include both charging and discharging status; charging status refers to the ratio of electrical energy received and stored from external energy sources to the storable electrical energy, and discharging status refers to the ratio of electrical energy being released by the system to the grid or connected loads to the storable electrical energy. Energy storage configuration constraints refer to the boundary conditions used to constrain the configuration of energy storage systems, ensuring the safe, efficient, and reliable operation of the systems.

[0045] Optionally, based on the historical energy storage, charging and discharging power, charging and discharging efficiency, and time difference of the energy storage system to be optimized, the current energy storage of the energy storage system to be optimized is determined; the current energy storage state of the energy storage system to be optimized is obtained by dividing the current energy storage and the power per unit energy storage capacity; and energy storage configuration constraints are constructed based on the current energy storage state and the energy storage charging and discharging state.

[0046] In one alternative implementation, the energy storage configuration constraints can be constructed using the following formula: E t =E t-1 +P ch η ch Δt-P dc η dc Δt; SOC min ≤SOC t ≤SOC max ; 0≤V d,t ≤1; 0≤V c,t ≤1; 0≤V c,t +V d,t ≤1;

[0047] Among them, E t E represents the current stored electrical energy, that is, the electrical energy stored at time t. t-1 This represents historical energy storage, specifically the energy stored at time t-1. P ch Indicates the energy storage charging power. η ch This indicates the energy storage and discharge efficiency. P dcIndicates the energy storage discharge power. η dc This represents the energy storage discharge efficiency. Δt represents the time difference, specifically the time difference between time t and time t-1. SOC t This represents the current energy storage state, i.e., the energy storage state at time t. SOC max This refers to the maximum value of the energy storage state (SOC). min This refers to the minimum value of the energy storage state. V d,t This indicates the energy storage discharge state. V c,t This indicates the energy storage charging status.

[0048] S130. Based on the total demand response duration, demand response load, system output, electricity price to be solved, and unit cost of abandoned clean energy of the energy storage system to be optimized, construct the demand response objective function.

[0049] The total demand response duration refers to the total time the energy storage system participates in demand response, which typically determines the number and duration of charge and discharge operations the system can perform. Demand response load refers to the load change the energy storage system needs to respond to during demand response. System output value refers to the output value of various energy sources from the energy storage system at a specific point in time. The electricity price to be solved refers to the price at which the energy storage system charges and discharges at different points in time. The unit cost of clean energy wasted refers to the cost of clean energy wasted due to the mismatch between electricity demand and clean energy generation.

[0050] Optionally, the demand response load includes the pre-response load, the clean energy load, and the post-response load; the system output value includes the clean energy output value, the energy storage output value, the motor output value, the actual total system output value, and the ideal total system output value.

[0051] In this context, "pre-response load" refers to the load borne by the power grid or electrical facilities before the energy storage system participates in demand response. "Post-response load" refers to the actual load borne by the power grid or electrical facilities after the energy storage system participates in demand response. "Clean energy load" refers to the electrical load generated by clean energy sources (such as solar and wind power). "Clean energy output" refers to the electrical power generated by clean energy equipment (such as solar panels and wind turbines) within a specific time period. "Energy storage output" refers to the electrical output that an energy storage system (such as a battery energy storage system) can provide within a specific time period. "Motor output" refers to the electrical power generated or consumed by motor equipment (such as generators and motors) within a specific time period. "Actual total system output" refers to the total amount of electricity actually generated by the entire system (including clean energy, energy storage systems, and motors) within a specific time period. "Ideal total system output" refers to the total amount of electricity the system should generate under ideal conditions.

[0052] Furthermore, based on the clean energy load, post-response load, motor output, energy storage output, clean energy output, and total demand response duration, the net load fluctuation value to be acquired is determined; based on the pre-response load, post-response load, the electricity price to be solved, the actual total output of the system, the ideal total output of the system, and the unit cost of abandoning clean energy, the demand response cost to be acquired is determined; based on the demand response cost to be acquired and the net load fluctuation value to be acquired, the demand response objective function is constructed.

[0053] For example, determining the net load fluctuation value to be obtained based on the clean energy load, post-response load, motor output value, energy storage output value, clean energy output value, and total demand response duration can be done by: determining the energy access output based on the clean energy load, motor output value, energy storage output value, clean energy output value, and total demand response duration; and determining the net load fluctuation value to be obtained based on the energy access output, clean energy output value, motor output value, energy storage output value, post-response load, and total demand response duration.

[0054] Energy access output refers to the total power supplied to the power grid or electrical equipment through some means (such as power grid, microgrid or energy storage system) within a specific time period.

[0055] For example, determining the demand response cost to be acquired based on the pre-response load, post-response load, the electricity price to be solved, the actual total output of the system, the ideal total output of the system, and the unit cost of abandoning clean energy can be done by: determining candidate response costs based on the pre-response load, post-response load, and the electricity price to be solved; determining the clean energy abandonment penalty cost based on the actual total output of the system, the ideal total output of the system, and the unit cost of abandoning clean energy; and summing the candidate response costs and the clean energy abandonment costs to obtain the demand response cost to be acquired.

[0056] Candidate response cost refers to the economic cost incurred due to adjustments in electricity usage habits (i.e., demand response).

[0057] In one alternative implementation, the demand response objective function can be constructed using the following formula: f1 = c pdr +c cw C pdr =L t C0-L t* C t C cw =C wg (P wd -P wu );

[0058] Where, min() represents the minimum value function. f1 represents the demand response cost to be obtained; the demand response cost mainly includes the candidate response cost and the penalty cost for abandoning clean energy. f2 represents the net load fluctuation value to be obtained. sth() represents the energy storage configuration constraints. X2=[a pv ,b fv ] represents the difference between the average and average valley levels, a pv Indicates the peak-valley electricity price difference, b fv This indicates the price difference in electricity prices in Pinggu. (C) pdr This represents the cost of the candidate response, i.e., the cost of implementing the demand response. cw This refers to the penalty cost of abandoning clean energy. t This represents the load before the demand response. C0 represents the electricity price to be solved, specifically the unit electricity price before the demand response. L t* Indicates the load after the response. C t This represents the electricity price to be solved, specifically the unit electricity price after the demand response. (C) wg This represents the cost per unit of clean energy abandoned. (P) wd P represents the ideal total output value of the system. wu This represents the actual total output of the system. N represents the total demand response time. t represents the current moment in the calculation. P ES (t) represents the energy storage output value, which refers to the energy storage output at time t. G (t) represents the generator output value, which refers to the generator output at time t. wd,t P represents the output value of clean energy, specifically the output of clean energy at time t. av The term "energy access output" refers to the output of clean energy at time t used for grid connection or energy storage. t′ The term "clean energy load" refers to the load when clean energy is connected to the power grid or stored at time t.

[0059] S140. Based on the system output power of the energy storage system to be optimized, the electricity price to be solved, the number of system units, the status of system units, the demand response load, the energy storage charging and discharging power, and the energy storage charging and discharging status, construct the demand response constraints.

[0060] Here, system output power refers to the total amount of electricity that the energy storage system and its associated generator units can provide within a specific time period. The number of system units refers to the number of generator units included in the energy storage system; these units may include various types of energy conversion equipment, such as wind turbines, solar panels, and gas turbines. System unit status describes the current operating status of each unit in the system, such as operating status, shutdown status, and maintenance status; these statuses determine whether the unit can participate in power output and its output capacity. Energy storage charging and discharging power refers to the maximum power that the energy storage system can provide during charging and discharging.

[0061] Optionally, based on the system output power, number of system units, system unit status, energy storage charging and discharging status, demand response load, and energy storage charging and discharging power of the energy storage system to be optimized, system power balance constraints are constructed; based on the actual output power and maximum output power of the system output power, clean energy output constraints are constructed; based on the peak electricity price, flat electricity price, and valley electricity price of the electricity price to be solved, electricity price difference constraints are constructed; and the system power balance constraints, clean energy output constraints, and electricity price difference constraints are integrated to obtain the demand response constraints.

[0062] The actual output power refers to the maximum power that the system can achieve during actual charging and discharging. The maximum output power refers to the maximum power that the system can achieve under ideal conditions. System balance constraints ensure that at any given time, the system's output (including energy storage systems and generator sets) can meet load demand; this is typically achieved by comparing the system's output power with the demand response load. Clean energy output constraints limit the range of clean energy output, ensuring it does not exceed its maximum output power. Electricity price difference constraints are based on electricity prices at different times (such as peak, off-peak, and valley prices), constructing constraints to optimize the charging and discharging strategy of the energy storage system. Peak electricity prices refer to the electricity price applied during periods of high electricity demand and load; these periods are typically concentrated during the daytime, especially the mornings and afternoons of weekdays; due to high electricity demand and correspondingly higher generation and supply costs during these periods, the electricity price is set higher. Off-peak electricity pricing refers to the electricity price applied during periods of day when electricity demand is low and electricity load is low. This period typically occurs in the evening and early morning, when most electricity users are resting or using electricity at low loads, resulting in relatively abundant electricity supply. To encourage electricity consumption during off-peak hours, off-peak prices are usually set lower. Neutral electricity pricing refers to the electricity price applied during periods of relatively stable electricity demand and moderate electricity load. This period typically falls between peak and off-peak prices, and the price level also falls between the two.

[0063] In one alternative implementation, the demand response constraints can be constructed using the following formula: 0≤P wu,t ≤P wd,t ; 12×P f ≤P p ≤2×P f ; 0.3×P f ≤P v ≤0.8×P f ;

[0064] Where g represents the number of system units, specifically the number of conventional units. i represents the current unit. u i,tThis indicates the system unit status, specifically the unit's start-up or shutdown status. P i,t This indicates the current unit output power. d,t This indicates the energy storage discharge state. P dc This represents the energy storage discharge power. P wd,t Indicates the maximum output power. L t* Indicates the load after the response. c,t Indicates the energy storage charging status. P ch This indicates the energy storage charging power. P wu,t P represents the actual output power. p This indicates the electricity price at peak times. (P) f P represents the electricity price at the moment of wave level. v This indicates the electricity price at off-peak times.

[0065] In another alternative implementation, the demand response constraint may also include an energy constraint; the energy constraint means that the total energy consumption remains unchanged before and after the demand response.

[0066] S150. Based on the energy storage configuration objective function, energy storage configuration constraints, demand response objective function, and demand response constraints, the energy storage configuration to be solved and the electricity price to be solved are jointly solved to obtain the target energy storage configuration and target electricity price, so as to achieve the optimization of the energy storage system to be optimized.

[0067] Optionally, the energy storage configuration objective function is used as a long-term plan, and the demand response objective function is used for short-term response. Based on the energy storage configuration constraints and the demand response constraints, a two-level joint objective function of energy storage and demand response is constructed. Then, based on a multi-objective genetic algorithm, the two-level joint objective function of energy storage and demand response is solved to obtain the target energy storage configuration and the target electricity price, so as to achieve the optimization of the energy storage system to be optimized.

[0068] The target energy storage configuration refers to the energy storage system configuration scheme obtained after optimization algorithms, including parameters such as the capacity, type, and location of energy storage devices. The target electricity price refers to the electricity pricing strategy obtained after optimization algorithms, used to guide the electricity consumption behavior of power users.

[0069] It should be noted that the two-layer joint objective function of energy storage and demand response is constructed with the energy storage configuration objective function as the upper layer and the demand response objective function as the lower layer.

[0070] This application embodiment constructs an energy storage configuration objective function based on the energy storage configuration to be solved, unit energy storage configuration cost, demand response cost to be obtained, net load fluctuation value to be obtained, cost discount rate, and energy storage life of the energy storage system to be optimized. The energy storage configuration to be solved includes the energy storage power to be solved and the energy storage capacity to be solved; the unit energy storage configuration cost includes the energy storage power configuration cost and the energy storage capacity configuration cost; energy storage configuration constraints are constructed based on the historical energy storage, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, unit energy storage capacity power, and energy storage charging and discharging state of the energy storage system to be optimized; based on the energy storage system to be optimized... The system constructs a demand response objective function based on the total demand response duration, demand response load, system output, unsolved electricity price, and unit clean energy curtailment cost. Demand response constraints are then established based on the system output, unsolved electricity price, number of units, unit status, demand response load, energy storage charging / discharging power, and energy storage charging / discharging status of the energy storage system to be optimized. Finally, the energy storage configuration and unsolved electricity price are jointly solved using the energy storage configuration objective function, energy storage configuration constraints, demand response objective function, and demand response constraints to obtain the target energy storage configuration and target electricity price, thereby optimizing the energy storage system. This technical solution, through optimization of energy storage and demand response, maximizes the absorption of clean energy while reducing peak-valley load differences, thus lowering distribution network operating costs.

[0071] Example 2

[0072] Figure 2 is a flowchart of an energy storage system optimization method according to Embodiment 2 of this application. Based on the technical solutions of the above embodiments, this embodiment refines the process of "jointly solving the energy storage configuration and the electricity price to be solved according to the energy storage configuration objective function, energy storage configuration constraints, demand response objective function, and demand response constraints to obtain the target energy storage configuration and the target electricity price" into "solving the first target value of the demand response cost to be obtained by the energy storage system to be optimized at the current moment and the second target value of the net load fluctuation value to be obtained at the current moment according to the demand response objective function and the demand response constraints; wherein, the system output value in the demand response objective function and the system output power in the demand response constraints are affected by the previous energy storage configuration of the energy storage system to be optimized at the previous moment; the target energy storage configuration at the current moment is solved according to the energy storage configuration objective function, energy storage configuration constraints, the first target value, and the second target value; the target electricity price of the energy storage system to be optimized at the current moment is solved according to the demand response objective function, the demand response constraints, and the target energy storage configuration at the current moment." It should be noted that for parts not described in detail in this embodiment, please refer to the relevant descriptions in other embodiments. As shown in Figure 2, the method includes:

[0073] S210. Based on the energy storage configuration to be solved, the unit energy storage configuration cost, the demand response cost to be obtained, the net load fluctuation value to be obtained, the cost discount rate, and the energy storage life of the energy storage system to be optimized, construct the energy storage configuration objective function; the energy storage configuration to be solved includes the energy storage power to be solved and the energy storage capacity to be solved; the unit energy storage configuration cost includes the unit energy storage power configuration cost and the unit energy storage capacity configuration cost.

[0074] S220. Based on the historical energy storage, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, power per unit energy storage capacity, and energy storage charging and discharging state of the energy storage system to be optimized, construct energy storage configuration constraints.

[0075] S230. Based on the total demand response duration, demand response load, system output, electricity price to be solved, and unit cost of abandoned clean energy of the energy storage system to be optimized, construct the demand response objective function.

[0076] S240. Based on the system output power of the energy storage system to be optimized, the electricity price to be solved, the number of system units, the status of system units, the demand response load, the energy storage charging and discharging power, and the energy storage charging and discharging status, construct the demand response constraints.

[0077] S250. Based on the demand response objective function and demand response constraints, solve for the first objective value of the demand response cost to be acquired by the energy storage system to be optimized at the current moment and the second objective value of the net load fluctuation value to be acquired at the current moment; wherein, the system output value in the demand response objective function and the system output power in the demand response constraints are affected by the previous energy storage configuration of the energy storage system to be optimized at the previous moment.

[0078] The first objective value refers to the cost value related to the demand response strategy obtained by solving the demand response objective function. The second objective value is an indicator that measures the degree of fluctuation in the system's net load.

[0079] S260. Based on the energy storage configuration objective function, energy storage configuration constraints, first objective value, and second objective value, the target energy storage configuration at the current moment is obtained.

[0080] S270. Based on the demand response objective function, demand response constraints, and the target energy storage configuration at the current moment, the target electricity price of the energy storage system to be optimized at the current moment is obtained.

[0081] In one alternative implementation, a joint model can be established with the energy storage configuration objective function as the upper layer and the demand response objective function as the lower layer. Based on the NSGA-II algorithm, the joint model is solved according to the energy storage configuration objective function, energy storage configuration constraints, demand response objective function, and demand response constraints to obtain the target energy storage configuration and target electricity price of the energy storage system to be optimized.

[0082] Among them, the NSGA-II (Nondominated Sorting Genetic Algorithm II) algorithm is an improvement on the NSGA algorithm. It introduces nondominated sorting and crowding distance calculation into the GA (Genetic Algorithm), which has excellent generalization performance and robustness. It can perform fast nondominated sorting on the solution set to obtain the corresponding Patero (Pareto Improvement) optimal solution set.

[0083] Specifically, the application of the NSGA-II algorithm in the joint model can be achieved through the following steps:

[0084] 1) After generating the variable population of energy storage configurations, calculate the root mean square of the energy storage cost, demand response cost, and net load fluctuation of the upper layer, and then bring the individual energy storage configurations into the lower layer for the calculation in step 2).

[0085] 2) The energy storage configuration passed from the upper layer is brought into the lower layer for calculation. The corresponding control variables are the electricity price difference in each time period. The demand response cost and the root mean square of net load fluctuation are obtained through iterative solution.

[0086] 3) If there is only a single load clean energy scenario, the demand response cost and the root mean square of net load fluctuation obtained are used as the lower-level result and substituted into the upper-level result in each upper-lower-level cycle. If there are multiple scenarios, the calculation in step 2) is performed in each scenario, and then the average value is obtained and substituted into the upper-level result as the lower-level result.

[0087] 4) The upper layer substitutes the solution of the lower layer for iterative optimization, and finally obtains the Patero solution of the energy storage configuration scheme and outputs it. Then, it is substituted into the lower layer to obtain the demand response optimization scheme under each time series characteristic.

[0088] Furthermore, demand response adjusts user electricity consumption habits by regulating electricity prices, altering the timing of energy consumption, reducing peak loads, increasing nighttime loads, and shifting flexible resources to improve load profiles. This is a flexible control method with lower costs but inherent uncontrollability. Energy storage, on the other hand, possesses dual charging and discharging characteristics, functioning as both a power source and a load. Discharging reduces peak loads, while charging increases the absorption of clean energy, providing energy transfer capabilities. It is easy to control but more expensive. Therefore, when optimizing energy storage configurations, the energy storage scheme should meet the following charging and discharging strategies:

[0089] 1) Charging rules: During off-peak periods, the energy storage is charged at the rated power. If the excess output of clean energy is insufficient to meet the energy storage demand, the energy storage capacity is increased by increasing the output of the generator unit. The "low charge and high discharge" approach is implemented to increase the energy storage revenue. If the load is at a flat period, but there is still a peak period during the scheduling period of the day, the energy storage is charged at the rated power until it is fully charged or the peak period begins.

[0090] 2) Discharge rules: When the system load is at its peak, the energy storage will discharge at its rated power until the minimum energy storage state is reached or the discharge ends at the peak.

[0091] In another alternative implementation, before constructing the energy storage configuration objective function, the method further includes determining the load change data before and after demand response based on the collected user electricity consumption data and the historical load data of the energy storage system to be optimized, so as to determine the load required for the model construction process based on the load change data.

[0092] Specifically, load change data can be determined using the following formula: μ(t)=a×t 2 +b×t+c;

[0093] Where μ(t) represents the load rebound rate curve. a represents the rebound rate calculation coefficient. b represents the rebound rate calculation coefficient. c represents the rebound rate calculation coefficient. t represents the time of load calculation. DR t This indicates the required load. This indicates the amount of load transferred at the peak of the wave. This indicates the amount of load transfer during the plateau. This indicates the amount of load transfer during a trough. This indicates the rebound load that originates from the trough period after the peak-to-flat response. This indicates the rebound load that originates from the trough period after the peak-to-trough response. This represents the rebound load originating from the trough period after a flat-to-trough response. λ pv L t This represents the load response capacity during the trough period. λ pf L t This represents the load reduction rate during peak-to-normal periods. λ fv L t This indicates the load reduction rate during the transition from flat to valley load. This indicates the load response capacity during the calm period. T p T f T v These represent the peak, flat, and trough periods, respectively. This indicates the load response capacity during peak periods. This indicates the load response capacity during the trough period. pv represents the peak-to-trough period. pf represents the peak-to-normal period. fv represents the normal-to-trough period.

[0094] This application embodiment constructs an energy storage configuration objective function based on the energy storage configuration to be solved, unit energy storage configuration cost, demand response cost to be obtained, net load fluctuation value to be obtained, cost discount rate, and energy storage life of the energy storage system to be optimized. The energy storage configuration to be solved includes the energy storage power and energy storage capacity to be solved; the unit energy storage configuration cost includes the energy storage power configuration cost and the energy storage capacity configuration cost; energy storage configuration constraints are constructed based on the historical energy storage, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, unit energy storage capacity power, and energy storage charging and discharging state of the energy storage system to be optimized; a demand response objective function is constructed based on the total demand response duration, demand response load, system output value, electricity price to be solved, and unit clean energy abandonment cost of the energy storage system to be optimized; and the system output power and electricity price to be solved are also considered. The system's energy storage configuration is determined by considering factors such as the number of generating units, their status, demand response load, energy storage charging / discharging power, and energy storage charging / discharging status. Demand response constraints are then constructed. Based on the demand response objective function and these constraints, a first objective value for the current demand response cost and a second objective value for the current net load fluctuation of the energy storage system to be optimized are obtained. The system output value in the demand response objective function and the system output power in the demand response constraints are influenced by the previous energy storage configuration of the energy storage system to be optimized. Based on the energy storage configuration objective function, energy storage configuration constraints, the first objective value, and the second objective value, the target energy storage configuration for the current time is obtained. Finally, based on the demand response objective function, demand response constraints, and the target energy storage configuration for the current time, the target electricity price for the energy storage system to be optimized is obtained. This technical solution, by jointly solving the energy storage configuration objective function and the demand response objective function, maximizes the absorption of clean energy while reducing the load peak-valley difference, thereby reducing the operating costs of the distribution network.

[0095] Example 3

[0096] Figure 3 is a schematic diagram of an energy storage system optimization device according to Embodiment 3 of this application. It is applicable to optimizing energy storage systems in clean energy distribution networks. This energy storage system optimization device can be implemented in hardware and / or software and can be configured in computer equipment, such as in the energy storage system of a clean energy distribution network. As shown in Figure 3, the device includes:

[0097] The first function construction module 310 is used to construct an energy storage configuration objective function based on the energy storage configuration to be solved, the unit energy storage configuration cost, the demand response cost to be obtained, the net load fluctuation value to be obtained, the cost discount rate, and the energy storage life of the energy storage system to be optimized. The energy storage configuration to be solved includes the energy storage power to be solved and the energy storage capacity to be solved. The unit energy storage configuration cost includes the unit energy storage power configuration cost and the unit energy storage capacity configuration cost.

[0098] The first constraint construction module 320 is used to construct energy storage configuration constraints based on the historical energy storage power, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, power per unit energy storage capacity and energy storage charging and discharging state of the energy storage system to be optimized.

[0099] The second function construction module 330 is used to construct the demand response objective function based on the total demand response duration, demand response load, system output value, electricity price to be solved and unit cost of abandoned clean energy of the energy storage system to be optimized.

[0100] The second constraint construction module 340 is used to construct demand response constraints based on the system output power of the energy storage system to be optimized, the electricity price to be solved, the number of system units, the status of system units, the demand response load, the energy storage charging and discharging power, and the energy storage charging and discharging status.

[0101] The function solving module 350 is used to jointly solve the energy storage configuration to be solved and the electricity price to be solved based on the energy storage configuration objective function, energy storage configuration constraints, demand response objective function and demand response constraints, so as to obtain the target energy storage configuration and target electricity price, thereby realizing the optimization of the energy storage system to be optimized.

[0102] This application embodiment constructs an energy storage configuration objective function based on the energy storage configuration to be solved, unit energy storage configuration cost, demand response cost to be obtained, net load fluctuation value to be obtained, cost discount rate, and energy storage life of the energy storage system to be optimized. The energy storage configuration to be solved includes the energy storage power to be solved and the energy storage capacity to be solved; the unit energy storage configuration cost includes the energy storage power configuration cost and the energy storage capacity configuration cost; energy storage configuration constraints are constructed based on the historical energy storage, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, unit energy storage capacity power, and energy storage charging and discharging state of the energy storage system to be optimized; based on the energy storage system to be optimized... The system constructs a demand response objective function based on the total demand response duration, demand response load, system output, unsolved electricity price, and unit clean energy curtailment cost. Demand response constraints are then established based on the system output, unsolved electricity price, number of units, unit status, demand response load, energy storage charging / discharging power, and energy storage charging / discharging status of the energy storage system to be optimized. Finally, the energy storage configuration and unsolved electricity price are jointly solved using the energy storage configuration objective function, energy storage configuration constraints, demand response objective function, and demand response constraints to obtain the target energy storage configuration and target electricity price, thereby optimizing the energy storage system. This technical solution, through optimization of energy storage and demand response, maximizes the absorption of clean energy while reducing peak-valley load differences, thus lowering distribution network operating costs.

[0103] Optionally, the first function building module 310 includes:

[0104] The first cost determination unit is used to process the energy storage cost of the energy storage configuration to be solved, the unit energy storage configuration cost, the cost discount rate and the energy storage life of the energy storage system to be optimized, and to determine the target energy storage cost of the energy storage system to be optimized.

[0105] The second cost determination unit is used to sum the target energy storage cost and the demand response cost to be acquired to obtain the total system cost of the energy storage system to be optimized.

[0106] The first function construction unit is used to construct the energy storage configuration objective function based on the total system cost and the net load fluctuation value to be obtained.

[0107] Optionally, the first cost determination unit is specifically used for:

[0108] Energy storage cost processing is performed on the energy storage configuration to be solved and the unit energy storage configuration cost of the energy storage system to be optimized, and the ideal energy storage cost of the energy storage system to be optimized is determined.

[0109] The target energy storage cost is determined by discounting the ideal energy storage cost, the cost discount rate, and the energy storage life.

[0110] Optional, the function solver module 350 is specifically used for:

[0111] Based on the demand response objective function and demand response constraints, the first objective value of the demand response cost to be acquired by the energy storage system to be optimized at the current moment and the second objective value of the net load fluctuation value to be acquired at the current moment are obtained. Among them, the system output value in the demand response objective function and the system output power in the demand response constraints are affected by the previous energy storage configuration of the energy storage system to be optimized at the previous moment.

[0112] Based on the energy storage configuration objective function, energy storage configuration constraints, first objective value, and second objective value, the target energy storage configuration at the current moment is obtained.

[0113] Based on the demand response objective function, demand response constraints, and the target energy storage configuration at the current moment, the target electricity price of the energy storage system to be optimized at the current moment is obtained.

[0114] Optionally, the first constraint construction module 320 is specifically used for:

[0115] Based on the historical energy storage capacity, energy storage charging and discharging power, energy storage charging and discharging efficiency, and time difference of the energy storage system to be optimized, the current energy storage capacity of the energy storage system to be optimized is determined.

[0116] The current energy storage state of the energy storage system to be optimized is obtained by dividing the current stored electrical energy and the power per unit energy storage capacity.

[0117] Based on the current energy storage status and energy storage charge / discharge status, construct energy storage configuration constraints.

[0118] Optionally, the demand response load includes pre-response load, clean energy load, and post-response load; the system output value includes clean energy output value, energy storage output value, motor output value, actual total system output value, and ideal total system output value; correspondingly, the second function construction module 330 is specifically used for:

[0119] The net load fluctuation value to be obtained is determined based on the clean energy load, post-response load, motor output, energy storage output, clean energy output, and total demand response duration.

[0120] The demand response cost to be obtained is determined based on the load before response, the load after response, the electricity price to be solved, the actual total output of the system, the ideal total output of the system, and the cost per unit of clean energy abandoned.

[0121] Based on the demand response cost to be acquired and the net load fluctuation value to be acquired, construct the demand response objective function.

[0122] Optionally, the second constraint construction module 340 is specifically used for:

[0123] Based on the system output power, number of system units, status of system units, energy storage charging and discharging status, demand response load, and energy storage charging and discharging power of the energy storage system to be optimized, system power balance constraints are constructed.

[0124] Based on the actual output power and maximum output power of the system, construct clean energy output constraints.

[0125] Based on the peak electricity price, flat electricity price, and valley electricity price in the electricity price to be solved, construct the electricity price difference constraint conditions;

[0126] By integrating the system power balance constraints, clean energy output constraints, and electricity price difference constraints, the demand response constraints are obtained.

[0127] The energy storage system optimization device provided in this application embodiment can execute the energy storage system optimization method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing each energy storage system optimization method.

[0128] Example 4

[0129] Figure 4 is a schematic diagram of the structure of an electronic device 410 implementing the energy storage system optimization method of this application embodiment. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0130] As shown in Figure 4, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 can also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0131] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0132] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as energy storage system optimization methods.

[0133] In some embodiments, the energy storage system optimization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the energy storage system optimization method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured as the energy storage system optimization method by any other suitable means (e.g., by means of firmware).

[0134] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0135] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable energy storage system optimization device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0136] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0137] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0138] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0139] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0140] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0141] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An energy storage system optimization method, comprising: Based on the energy storage configuration to be solved, the unit energy storage configuration cost, the demand response cost to be obtained, the net load fluctuation value to be obtained, the cost discount rate, and the energy storage life of the energy storage system to be optimized, an objective function for energy storage configuration is constructed; the energy storage configuration to be solved includes the energy storage power to be solved and the energy storage capacity to be solved; the unit energy storage configuration cost includes the unit energy storage power configuration cost and the unit energy storage capacity configuration cost. Based on the historical energy storage, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, unit energy storage capacity power, and energy storage charging and discharging state of the energy storage system to be optimized, energy storage configuration constraints are constructed. Based on the total demand response duration, demand response load, system output, electricity price to be solved, and unit cost of abandoned clean energy of the energy storage system to be optimized, a demand response objective function is constructed. Based on the system output power of the energy storage system to be optimized, the electricity price to be solved, the number of system units, the status of system units, the demand response load, the energy storage charging and discharging power, and the energy storage charging and discharging status, demand response constraints are constructed. Based on the energy storage configuration objective function, the energy storage configuration constraints, the demand response objective function, and the demand response constraints, the energy storage configuration to be solved and the electricity price to be solved are jointly solved to obtain the target energy storage configuration and the target electricity price, so as to achieve the optimization of the energy storage system to be optimized.

2. The method according to claim 1, wherein, The objective function for energy storage configuration is constructed based on the energy storage configuration to be solved, the unit energy storage configuration cost, the demand response cost to be obtained, the net load fluctuation value to be obtained, the cost discount rate, and the energy storage life of the energy storage system to be optimized. This includes: Energy storage cost processing is performed on the energy storage configuration to be solved for the energy storage system to be optimized, the unit energy storage configuration cost, the cost discount rate and the energy storage life, to determine the target energy storage cost of the energy storage system to be optimized. The total system cost of the energy storage system to be optimized is obtained by summing the target energy storage cost and the demand response cost to be acquired. Based on the total system cost and the net load fluctuation value to be obtained, an objective function for energy storage configuration is constructed.

3. The method according to claim 2, wherein, The step of determining the target energy storage cost of the energy storage system to be optimized, based on the energy storage configuration to be solved, the unit energy storage configuration cost, the cost discount rate, and the energy storage life, includes: Energy storage cost processing is performed on the energy storage configuration to be solved and the unit energy storage configuration cost of the energy storage system to be optimized, and the ideal energy storage cost of the energy storage system to be optimized is determined. The target energy storage cost is determined by performing cost discounting on the ideal energy storage cost, the cost discount rate, and the energy storage period.

4. The method according to claim 2, wherein, Based on the energy storage configuration objective function, the energy storage configuration constraints, the demand response objective function, and the demand response constraints, the unsolved energy storage configuration and the unsolved electricity price are jointly solved to obtain the target energy storage configuration and the target electricity price, including: Based on the demand response objective function and the demand response constraints, the first objective value of the demand response cost to be acquired by the energy storage system to be optimized at the current moment and the second objective value of the net load fluctuation value to be acquired at the current moment are obtained; wherein, the system output value in the demand response objective function and the system output power in the demand response constraints are affected by the previous energy storage configuration of the energy storage system to be optimized at the previous moment. Based on the energy storage configuration objective function, the energy storage configuration constraints, the first objective value, and the second objective value, the target energy storage configuration at the current moment is obtained. Based on the demand response objective function, the demand response constraints, and the target energy storage configuration at the current moment, the target electricity price of the energy storage system to be optimized at the current moment is obtained.

5. The method according to claim 1, wherein, The energy storage configuration constraints are constructed based on the historical stored electrical energy, energy storage charging and discharging power, energy storage charging and discharging efficiency, time difference, power per unit energy storage capacity, and energy storage charging and discharging state of the energy storage system to be optimized, including: Based on the historical stored energy, energy storage charging and discharging power, energy storage charging and discharging efficiency, and time difference of the energy storage system to be optimized, the current stored energy of the energy storage system to be optimized is determined. The current energy storage state of the energy storage system to be optimized is obtained by performing a division operation on the current energy storage power and the power per unit energy storage capacity. Based on the current energy storage state and the energy storage charge / discharge state, construct energy storage configuration constraints.

6. The method according to claim 1, wherein, The demand response load includes the pre-response load, the clean energy load, and the post-response load; the system output includes the clean energy output, the energy storage output, the motor output, the actual total system output, and the ideal total system output; correspondingly, the demand response objective function is constructed based on the total demand response duration, demand response load, system output, the electricity price to be solved, and the unit cost of abandoned clean energy of the energy storage system to be optimized, including: The net load fluctuation value to be obtained is determined based on the clean energy load, the post-response load, the motor output value, the energy storage output value, the clean energy output value, and the total demand response duration. The demand response cost to be obtained is determined based on the pre-response load, the post-response load, the electricity price to be solved, the actual total output of the system, the ideal total output of the system, and the unit cost of discarded clean energy. Based on the demand response cost to be acquired and the net load fluctuation value to be acquired, a demand response objective function is constructed.

7. The method according to claim 1, wherein, The step of constructing demand response constraints based on the system output power of the energy storage system to be optimized, the electricity price to be solved, the number of system units, the status of system units, the demand response load, the energy storage charging and discharging power, and the energy storage charging and discharging status includes: Based on the system output power, number of system units, status of system units, energy storage charging and discharging status, demand response load, and energy storage charging and discharging power of the energy storage system to be optimized, system power balance constraints are constructed. Based on the actual output power and maximum output power of the system, construct clean energy output constraints. Based on the peak electricity price, flat electricity price, and valley electricity price in the electricity price to be solved, construct the electricity price difference constraint condition; The system power balance constraint, the clean energy output constraint, and the electricity price difference constraint are integrated to obtain the demand response constraint.

8. An electronic device, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the energy storage system optimization method as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the energy storage system optimization method as described in any one of claims 1-7.

10. A computer program product comprising a computer program that, when executed by a processor, implements the energy storage system optimization method as described in any one of claims 1-7.

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