Energy storage system optimization method and system based on benefit analysis

Abstract

The invention belongs to the technical field of power energy storage system optimization, and discloses an energy storage system optimization method and system based on benefit analysis, and the method comprises the steps: setting adaptability factors of different types of energy storage systems based on power distribution network load data, operation conditions and basic performance data of the energy storage systems; building a benefit analysis objective function by taking full-life-cycle economic benefit optimization as an optimization objective, building a constraint condition set according to power balance, energy storage system charging and discharging power limitation, life cycle limitation and safe operation constraint, and building a full-life-cycle economic benefit model based on the benefit analysis objective function and the constraint condition set; and solving the full-life-cycle economic benefit model by using a multi-population genetic algorithm to obtain optimal configuration schemes of different types of energy storage systems. The method is used for optimal configuration of the energy storage system in the regional power distribution network, the economic cost can be reduced, and the energy use efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power energy storage system optimization, and in particular to an energy storage system optimization method and system based on benefit analysis. Background Art

[0002] As the global energy transition deepens, the proportion of renewable energy in the power system continues to increase. The large-scale access to intermittent energy such as wind and solar energy has brought huge challenges to the operation of the power grid. The power system is facing a series of complex problems such as power supply and demand fluctuations and unstable power quality. Advanced regulation and peak-shaving technologies are urgently needed to balance the operation of the power system. As a key link between power production and consumption, energy storage systems play an increasingly important role in smoothing out fluctuations in renewable energy, improving the regulation capacity of the power grid, and optimizing the allocation of power resources. They have become an indispensable infrastructure for modern power systems.

[0003] At present, energy storage technology has formed a variety of technical systems including electrochemical energy storage, mechanical energy storage, electromagnetic energy storage, etc. Different energy storage technologies have their own characteristics in terms of performance, cost, and service life. The traditional energy storage system configuration method is mainly based on a single-dimensional evaluation, usually only considering the initial investment cost of the energy storage system or simple capacity matching requirements, and lacks an in-depth analysis of the comprehensive economic efficiency of the energy storage system throughout its life cycle. This configuration method is difficult to fully reflect the economic value of energy storage technology in practical applications, and cannot provide a scientific decision-making basis for the selection and configuration of different types of energy storage technologies, which restricts the widespread application of energy storage technology in power systems.

[0004] There are many limitations in the optimization of existing energy storage systems, mainly manifested in the inaccurate characterization of the characteristics of energy storage technology, focusing only on a single goal, such as capacity demand or cost-effectiveness, while ignoring the comprehensive economic benefit analysis of the energy storage system over its entire life cycle. At the same time, the traditional energy storage system configuration fails to fully consider the adaptability of different energy storage technologies in specific distribution network scenarios; these shortcomings make it difficult for energy storage system configuration plans to achieve optimal configuration, thus affecting the overall economy and reliability of the energy storage system.

[0005] Therefore, how to develop a more scientific and systematic energy storage system optimization method has become an urgent problem to be solved. Summary of the invention

[0006] The embodiments of the present invention provide a method and system for optimizing an energy storage system based on benefit analysis, so as to solve the problem that the prior art only focuses on a single goal, ignores the comprehensive economic benefit analysis of the energy storage system over its entire life cycle, and fails to fully consider the adaptability of different energy storage technologies in specific distribution network scenarios.

[0007] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a method for optimizing an energy storage system based on benefit analysis is provided.

[0009] In one embodiment, the energy storage system optimization method based on benefit analysis includes:

[0010] Based on the distribution network load data, operating conditions and basic performance data of the energy storage system, set the adaptability factors of different types of energy storage systems;

[0011] According to the adaptability factors of different types of energy storage systems, the optimization goal is to optimize the economic benefits of the entire life cycle, and to build a benefit analysis objective function. In addition, a constraint condition set is established based on power balance, energy storage system charging and discharging power limits, life cycle limits, and safe operation constraints. Based on the benefit analysis objective function and constraint condition set, a full life cycle economic benefit model is built.

[0012] A multi-population genetic algorithm is used to solve the full life cycle economic benefit model and obtain the optimal configuration scheme for different types of energy storage systems.

[0013] In one embodiment, the types of energy storage systems include lead-acid batteries, lithium batteries, compressed air energy storage, flywheel energy storage, supercapacitors, and hydrogen energy storage;

[0014] The basic performance data of the energy storage system include energy density, power density, life cycle, efficiency, charging and discharging characteristics, response speed and operating temperature;

[0015] The adaptability factors of the energy storage system include the construction cost adaptability factor and the operation and maintenance cost adaptability factor.

[0016] In one embodiment, based on the distribution network load data, operating conditions and basic performance data of the energy storage system, the adaptability factors of different types of energy storage systems are set, including:

[0017] According to the requirements of the target scenario, set the adaptability factors of different types of energy storage systems, including response speed influence factor, power density influence factor, temperature adaptability influence factor, efficiency influence factor and cycle life influence factor;

[0018] Based on the distribution network load data and operating condition analysis results, the weights of various indicators in the response speed influencing factor, power density influencing factor and temperature adaptability influencing factor are dynamically adjusted to obtain the construction cost adaptability factor;

[0019] According to the performance and adaptability of the energy storage system during operation and maintenance, the weights of various indicators in the efficiency influencing factor and the cycle life influencing factor are dynamically adjusted to obtain the operation and maintenance adaptability factor.

[0020] In one embodiment, the construction cost adaptability factor is expressed as:

[0021] η build.i =ω1α resp.i +ω2β pow.i +ω3ε temp.i

[0022] In the formula, α resp.i , β pow.i , γ temp.i are the response speed influencing factor, power density influencing factor, and temperature adaptability influencing factor of energy storage system i respectively; w1, w2, and w3 are the weights of the response speed influencing factor, the power density influencing factor, and the temperature adaptability influencing factor respectively;

[0023] The expression of operation and maintenance adaptability factor is:

[0024] η op.i =ω4δ eff.i +ω5δ life.i

[0025] In the formula, δ eff.i , δ life.i are the efficiency influencing factor and cycle life influencing factor of energy storage system i respectively; w4 and w5 are the efficiency influencing factor weight and cycle life influencing factor weight respectively.

[0026] In one embodiment, according to the adaptability factors of different types of energy storage systems, the optimization goal is to optimize the economic benefits of the entire life cycle, construct a benefit analysis objective function, and establish a constraint condition set based on power balance, energy storage system charging and discharging power limit, life cycle limit and safe operation constraint. Based on the benefit analysis objective function and the constraint condition set, the economic benefit model of the entire life cycle is constructed, including:

[0027] Based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, the full life cycle cost is established, and the benefit analysis objective function is established with the goal of minimizing the full life cycle cost;

[0028] Establish a set of constraints based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage equipment, the life cycle of the energy storage equipment, and the safe operation of the distribution network and the energy storage equipment;

[0029] Based on the benefit analysis objective function and constraint condition set, a full life cycle economic benefit model is constructed.

[0030] In one embodiment, based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, the full life cycle cost is established, and with the goal of minimizing the full life cycle cost, the benefit analysis objective function is established, including:

[0031] Based on the purchase cost, installation cost and commissioning cost of the equipment, combined with the construction cost adaptability factor of the energy storage system, calculate the construction investment cost of the energy storage system;

[0032] Analyze the operation and maintenance costs of the energy storage system based on the power management, equipment maintenance and system operation and maintenance costs, combined with the operation and maintenance cost adaptability factor of the energy storage system;

[0033] Based on the recycling and removal costs of energy storage equipment, the decommissioning costs of energy storage systems are solved;

[0034] Use life cycle assessment methods to obtain environmental impact data, transform environmental impact data through environmental cost factors, and establish the environmental cost of the energy storage system;

[0035] Based on the environmental cost of the energy storage system, the full life cycle cost is constructed by combining the construction investment cost, operation and maintenance cost, and decommissioning cost of the energy storage system. Based on the full life cycle cost, the benefit analysis objective function is established.

[0036] The expression of the benefit analysis objective function is:

[0037] F=λ build C build +λ op C op +λ ret C ret +λ en C env

[0038] In the formula, F is the life cycle cost, C build is the construction investment cost, C op is the operation and maintenance cost, C ret is the decommissioning cost, C env is the environmental protection cost, λ build is the weight of construction investment cost, λ op is the weight of operation and maintenance cost, λ ret is the weight of decommissioning cost, λ en The weight of environmental protection cost.

[0039] In one embodiment, based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage device, the life cycle of the energy storage device, and the safe operation of the distribution network and the energy storage device, a constraint condition set is established, including:

[0040] Based on the total load and power generation of the distribution network, establish the grid load balance constraints;

[0041] Construct charging and discharging limit constraints based on the charging and discharging power of the energy storage system;

[0042] Establish life cycle constraints based on the number of charge and discharge cycles of energy storage devices;

[0043] According to the voltage of the distribution network hub node and the operating temperature range of the energy storage equipment, the safe operation constraints are constructed, and the constraint condition set is obtained by combining the grid load balance constraints, charge and discharge limit constraints and life cycle constraints.

[0044] In one embodiment, the load balance constraint of the power grid is a load balance constraint of the distribution network to ensure that the total load of the distribution network matches the power generation in any period of time to avoid system overload or power shortage; the expression of the load balance constraint of the power grid is:

[0045]

[0046] Where P g (t) represents the power generated by the power supply at time t, P n (t) represents the power generated by renewable energy, P load (t) represents the active power demand of the load at time t, P ess,i (t) represents the charging and discharging power of energy storage system i at time t, which is positive during charging and negative during discharging;

[0047] According to a second aspect of an embodiment of the present invention, there is provided an energy storage system optimization system based on benefit analysis, the energy storage system optimization system based on benefit analysis comprising:

[0048] An adaptability factor unit, used to set adaptability factors of different types of energy storage systems based on distribution network load data, operating conditions and basic performance data of the energy storage system;

[0049] The full life cycle economic benefit model unit is used to construct a benefit analysis objective function based on the adaptability factors of different types of energy storage systems and the optimal economic benefit of the full life cycle as the optimization goal. It also establishes a constraint condition set based on power balance, energy storage system charging and discharging power limits, life cycle limits and safe operation constraints. Based on the benefit analysis objective function and the constraint condition set, a full life cycle economic benefit model is constructed.

[0050] The genetic algorithm solving unit is used to solve the full life cycle economic benefit model using a multi-population genetic algorithm to obtain the optimal configuration solutions for different types of energy storage systems.

[0051] In one embodiment, the types of energy storage systems include lead-acid batteries, lithium batteries, compressed air energy storage, flywheel energy storage, supercapacitors, and hydrogen energy storage;

[0052] The basic performance data of the energy storage system include energy density, power density, life cycle, efficiency, charging and discharging characteristics, response speed and operating temperature;

[0053] The adaptability factors of the energy storage system include the construction cost adaptability factor and the operation and maintenance cost adaptability factor.

[0054] In one embodiment, when the adaptability factor unit sets the adaptability factors of different types of energy storage systems based on the distribution network load data, operating conditions and basic performance data of the energy storage system, the adaptability factor unit sets the adaptability factors of different types of energy storage systems according to the requirements of the target scenario, including the response speed influence factor, the power density influence factor, the temperature adaptability influence factor, the efficiency influence factor and the cycle life influence factor; based on the distribution network load data and the operating condition analysis results, the weights of each indicator in the response speed influence factor, the power density influence factor and the temperature adaptability influence factor are dynamically adjusted to obtain the construction cost adaptability factor; according to the performance and adaptability of the energy storage system during the operation and maintenance process, the weights of each indicator in the efficiency influence factor and the cycle life influence factor are dynamically adjusted to obtain the operation and maintenance adaptability factor.

[0055] In one embodiment, the construction cost adaptability factor is expressed as:

[0056] η build.i =ω1α resp.i +ω2β pow.i +ω3ε temp.i

[0057] In the formula, α resp.i , β pow.i , γ temp.i are the response speed influencing factor, power density influencing factor, and temperature adaptability influencing factor of energy storage system i respectively; w1, w2, and w3 are the weights of the response speed influencing factor, the power density influencing factor, and the temperature adaptability influencing factor respectively;

[0058] The expression of operation and maintenance adaptability factor is:

[0059] η op.i =ω4δ eff.i +ω5δ life.i

[0060] In the formula, δ eff.i , δ life.i are the efficiency influencing factor and cycle life influencing factor of energy storage system i respectively; w4 and w5 are the efficiency influencing factor weight and cycle life influencing factor weight respectively.

[0061] In one embodiment, the full life cycle economic benefit model unit constructs a benefit analysis objective function based on the adaptability factors of different types of energy storage systems and takes the optimal full life cycle economic benefit as the optimization goal, and establishes a constraint condition set based on power balance, energy storage system charging and discharging power limits, life cycle limits and safe operation constraints. When constructing the full life cycle economic benefit model based on the benefit analysis objective function and the constraint condition set, the full life cycle cost is established based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, and the benefit analysis objective function is established with the goal of minimizing the full life cycle cost; a constraint condition set is established based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage equipment, the life cycle of the energy storage equipment and the safe operation of the distribution network and the energy storage equipment; and a full life cycle economic benefit model is constructed based on the benefit analysis objective function and the constraint condition set.

[0062] In one embodiment, based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, the full life cycle cost is established, and with the goal of minimizing the full life cycle cost, the benefit analysis objective function is established, including:

[0063] Based on the purchase cost, installation cost and commissioning cost of the equipment, combined with the construction cost adaptability factor of the energy storage system, calculate the construction investment cost of the energy storage system; based on the power management, equipment maintenance and system operation and maintenance costs, combined with the operation and maintenance cost adaptability factor of the energy storage system, analyze the operation and maintenance cost of the energy storage system; based on the recycling and dismantling costs of the energy storage equipment, solve the decommissioning cost of the energy storage system; use the life cycle assessment method to obtain environmental impact data, transform the environmental impact data through the environmental cost factor, and establish the environmental cost of the energy storage system; based on the environmental cost of the energy storage system, combine the construction investment cost, operation and maintenance cost and decommissioning cost of the energy storage system to construct the full life cycle cost, and establish the benefit analysis objective function based on the full life cycle cost;

[0064] The expression of the benefit analysis objective function is:

[0065] F=λ build C build +λ op C op +λ ret C ret +λ en C env

[0066] In the formula, C build is the construction investment cost, C op is the operation and maintenance cost, C ret is the decommissioning cost, C env is the environmental protection cost, λ buildis the weight of construction investment cost, λ op is the weight of operation and maintenance cost, λ ret is the weight of decommissioning cost, λ en The weight of environmental protection cost.

[0067] In one embodiment, based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage device, the life cycle of the energy storage device, and the safe operation of the distribution network and the energy storage device, a constraint condition set is established, including:

[0068] Based on the total load and power generation of the distribution network, a grid load balance constraint is established; based on the charging and discharging power of the energy storage system, a charge and discharge limit constraint is constructed; based on the number of charge and discharge cycles of the energy storage device, a life cycle constraint is established; based on the voltage of the distribution network hub node and the operating temperature range of the energy storage device, a safe operation constraint is constructed, and the grid load balance constraint, charge and discharge limit constraint and life cycle constraint are combined to obtain a constraint condition set.

[0069] In one embodiment, the expression of the grid load balance constraint is:

[0070]

[0071] Where P g (t) represents the power generated by the power supply at time t, P n (t) represents the power generated by renewable energy, P load (t) represents the active power demand of the load at time t, P ess,i (t) represents the charging and discharging power of energy storage system i at time t;

[0072] The expression of charge and discharge limit constraint is:

[0073]

[0074] Where P ess Indicates the charging and discharging power of energy storage system i; and are the lower and upper limits of power respectively;

[0075] The expression of the life cycle constraint is:

[0076] N(t)(i)≤N max (i)

[0077] Where N max (i) is the maximum number of charge and discharge cycles of energy storage device i, and N(t)(i) is the number of charge and discharge cycles of energy storage system i to time t;

[0078] The expression of the safe operation constraint is:

[0079] Vmin ≤V(t)≤V max

[0080] f min ≤f(t)≤f max

[0081] T min ≤T(t)≤T max

[0082] Where V(t) is the voltage of the distribution network hub node at time t; f(t) is the frequency of the distribution network hub node at time t; T i (t) is the temperature of energy storage system i at time t; V min 、V max is the minimum safety range and maximum safety range of voltage; f min 、f max is the minimum and maximum safe range of frequency; T min (i) T max (i) is the minimum operating temperature range and maximum operating temperature range of energy storage device i.

[0083] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0084] In one embodiment, a computer device includes a memory and a processor, wherein a computer program is stored in the memory, and the steps of the above method are implemented when the processor executes the computer program.

[0085] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0086] In one embodiment, a computer program is stored on a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0087] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0088] (1) The present invention proposes a method for optimizing the configuration of an energy storage system through a full life cycle economic benefit analysis, which can reduce the overall economic cost and improve the energy utilization efficiency and stability of the distribution network.

[0089] (2) The present invention is applicable to fields such as smart grids and microgrids, and is particularly suitable for power systems that generate electricity from large-scale renewable energy.

[0090] (3) By optimizing the energy storage configuration, the present invention can effectively balance the grid load, improve the renewable energy absorption capacity, reduce power loss, and reduce the overall operating cost of the system.

[0091] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0093] Figure 1 is a flow chart of a method for optimizing an energy storage system based on benefit analysis according to an exemplary embodiment;

[0094] Figure 2 is a specific implementation diagram of an energy storage system optimization method based on benefit analysis according to an exemplary embodiment;

[0095] Figure 3 It is a schematic diagram of the composition of the full life cycle economic benefit analysis of multiple types of energy storage systems in an energy storage system optimization method based on benefit analysis according to an exemplary embodiment;

[0096] Figure 4 is a flow chart of a genetic algorithm suitable for calculating configurations of multiple types of energy storage systems in an energy storage system optimization method based on benefit analysis according to an exemplary embodiment;

[0097] Figure 5 is a structural block diagram of an energy storage system optimization system based on benefit analysis according to an exemplary embodiment;

[0098] Figure 6 The diagram is a schematic structural diagram of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0099] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0100] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0101] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0102] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0103] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0104] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0105] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0106] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0107] Figure 1-Figure 4 An embodiment of an energy storage system optimization method based on benefit analysis of the present invention is shown.

[0108] In this optional embodiment, the energy storage system optimization method based on benefit analysis includes:

[0109] Step S101: setting adaptability factors of different types of energy storage systems based on distribution network load data, operating conditions and basic performance data of energy storage systems;

[0110] Step S103: According to the adaptability factors of different types of energy storage systems, taking the optimal economic benefit of the whole life cycle as the optimization goal, constructing a benefit analysis objective function, and establishing a constraint condition set according to power balance, energy storage system charging and discharging power limit, life cycle limit and safe operation constraint, and constructing a whole life cycle economic benefit model based on the benefit analysis objective function and the constraint condition set;

[0111] Step S105: using a multi-population genetic algorithm to solve the full life cycle economic benefit model and obtain optimal configuration solutions for different types of energy storage systems.

[0112] In this optional embodiment, the types of energy storage systems include lead-acid batteries, lithium batteries, compressed air energy storage, flywheel energy storage, supercapacitors and hydrogen energy storage;

[0113] The basic performance data of the energy storage system include energy density, power density, life cycle, efficiency, charging and discharging characteristics, response speed and operating temperature;

[0114] The adaptability factors of the energy storage system include the construction cost adaptability factor and the operation and maintenance cost adaptability factor.

[0115] Specifically, the performance data of the energy storage system is shown in Table 1.

[0116] Table 1 Energy storage system performance data

[0117]

[0118]

[0119] In this optional embodiment, based on the distribution network load data, operating conditions and basic performance data of the energy storage system, the adaptability factors of different types of energy storage systems are set, including:

[0120] According to the requirements of the target scenario, set the adaptability factors of different types of energy storage systems, including response speed influence factor, power density influence factor, temperature adaptability influence factor, efficiency influence factor and cycle life influence factor;

[0121] Based on the distribution network load data and operating condition analysis results, the weights of various indicators in the response speed influencing factor, power density influencing factor and temperature adaptability influencing factor are dynamically adjusted to obtain the construction cost adaptability factor;

[0122] Specifically, the analysis of the load data and operating conditions of the distribution network includes: (1) collecting load data, obtaining load data over a period of time in the distribution network, and determining load fluctuation rate, peak frequency, average load and other characteristics; (2) operating condition evaluation, analyzing historical data of ambient temperature and other operating conditions to determine the temperature adaptability requirements of the energy storage system and the impact of the operating environment on the equipment.

[0123] According to the performance and adaptability of the energy storage system during operation and maintenance, the weights of various indicators in the efficiency influencing factor and the cycle life influencing factor are dynamically adjusted to obtain the operation and maintenance adaptability factor.

[0124] In this optional embodiment, the expression of the construction cost adaptability factor is:

[0125] η build.i =ω1α resp.i +ω2β pow.i +ω3ε temp.i

[0126] In the formula, α resp.i , β pow.i , γ temp.iare the response speed influencing factor, power density influencing factor, and temperature adaptability influencing factor of energy storage system i respectively; w1, w2, and w3 are the weights of the response speed influencing factor, the power density influencing factor, and the temperature adaptability influencing factor respectively;

[0127] Specifically, the specific indicators under the construction cost adaptability factor consider response speed, power density, and temperature adaptability range. Based on the load data and operating conditions of the regional distribution network, the weights of various indicators in the adaptability factor are dynamically adjusted. In this embodiment, taking lead-acid batteries as an example, the corresponding energy storage system construction cost adaptability factor is:

[0128] η bulid.aci =ω1α resp.aci +ω2β pow.aci +ω3γ temp.aci

[0129] α resp.aci =β pow.aci =γ temp.aci =1

[0130] In the formula, α resp.aci , β pow.aci , γ temp.aci They are the response speed influencing factor, power density influencing factor and temperature adaptability influencing factor of lead-acid batteries respectively.

[0131] Specifically, the expressions of the response speed influencing factor, power density influencing factor, and temperature adaptability influencing factor of the energy storage system i are:

[0132]

[0133] Where, T i , P i , R i are the response speed, power density, and temperature adaptability range of energy storage system i, T aci , P aci , R aci They are the response speed, power density and temperature adaptability range of lead-acid batteries respectively.

[0134] The expression of operation and maintenance adaptability factor is:

[0135] η op.i =ω4δ eff.i +ω5δ life.i

[0136] In the formula, δ eff.i ,δ life.i are the efficiency influencing factor and cycle life influencing factor of energy storage system i respectively; w4 and w5 are the efficiency influencing factor weight and cycle life influencing factor weight respectively.

[0137] Specifically, the operation and maintenance adaptability factor is used to measure the performance and adaptability of the energy storage system during long-term operation and maintenance. Specific indicators under the operation and maintenance adaptability factor include system efficiency and cycle life.

[0138] Specifically, the efficiency factor affects the energy loss and operating cost of the system. A high-efficiency system reduces energy loss and lowers operating costs. The cycle life factor measures the durability and replacement frequency of the system in long-term use. A longer cycle life reduces maintenance and replacement costs. Similarly, taking the lead-acid battery as the benchmark, let δ eff.aci =δ life.aci =1, we get:

[0139]

[0140] In the formula, λ i , τ i are the operating efficiency and cycle life of energy storage system i respectively.

[0141] In this optional embodiment, according to the adaptability factors of different types of energy storage systems, the optimal economic benefit of the entire life cycle is taken as the optimization goal, and a benefit analysis objective function is constructed. According to power balance, energy storage system charging and discharging power limit, life cycle limit and safe operation constraint, a constraint condition set is established. Based on the benefit analysis objective function and the constraint condition set, a full life cycle economic benefit model is constructed, including:

[0142] Based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, the full life cycle cost is established, and the benefit analysis objective function is established with the goal of minimizing the full life cycle cost;

[0143] Establish a set of constraints based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage equipment, the life cycle of the energy storage equipment, and the safe operation of the distribution network and the energy storage equipment;

[0144] Based on the benefit analysis objective function and constraint condition set, a full life cycle economic benefit model is constructed.

[0145] In this optional embodiment, based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, the full life cycle cost is established, and with the goal of minimizing the full life cycle cost, the benefit analysis objective function is established, including:

[0146] Based on the purchase cost, installation cost and commissioning cost of the equipment, combined with the construction cost adaptability factor of the energy storage system, calculate the construction investment cost of the energy storage system;

[0147] Analyze the operation and maintenance costs of the energy storage system based on the power management, equipment maintenance and system operation and maintenance costs, combined with the operation and maintenance cost adaptability factor of the energy storage system;

[0148] Based on the recycling and removal costs of energy storage equipment, the decommissioning costs of energy storage systems are solved;

[0149] Use life cycle assessment methods to obtain environmental impact data, transform environmental impact data through environmental cost factors, and establish the environmental cost of the energy storage system;

[0150] Based on the environmental cost of the energy storage system, the full life cycle cost is constructed by combining the construction investment cost, operation and maintenance cost, and decommissioning cost of the energy storage system. Based on the full life cycle cost, the benefit analysis objective function is established.

[0151] The expression of the benefit analysis objective function is:

[0152] F=λ build C build +λ op C op +λ ret C ret +λ en C env

[0153] In the formula, C build is the construction investment cost, C op is the operation and maintenance cost, C ret is the decommissioning cost, C env is the environmental protection cost, λ build is the weight of construction investment cost, λ op is the weight of operation and maintenance cost, λ ret is the weight of decommissioning cost, λ en The weight of environmental protection cost.

[0154] Specifically, the construction investment cost of the energy storage system, including the cost of equipment purchase, installation and commissioning, is expressed as:

[0155]

[0156] In the formula, C equip,i is the equipment purchase cost of energy storage type i, C in,i is the installation cost of energy storage type i, C co,i K is the commissioning cost of energy storage type i. i is the capacity of energy storage type i, η build.i is the construction cost adaptability factor of energy storage type i.

[0157] Specifically, the operation and maintenance costs of the energy storage system include power management, equipment maintenance, and system operation and maintenance costs, expressed as:

[0158]

[0159] In the formula, C man,i is the electricity management cost of energy storage type i, C rep,i is the maintenance cost of energy storage type i, C maint,i is the operating cost of energy storage type i, η op,i is the operation and maintenance cost adaptability factor of energy storage type i.

[0160] Specifically, the decommissioning cost of the energy storage system, taking into account the recovery and removal of the energy storage equipment, is expressed as:

[0161]

[0162] Where η ret,i is the decommissioning cost adaptability factor of energy storage type i. base,ret.i is the recovery and dismantling cost of energy storage type i.

[0163] Specifically, the environmental protection cost uses the life cycle assessment (LCA) method to quantify the environmental impact of various energy storage systems during the entire process of production, use and retirement, and then converts these impacts into environmental protection costs. In order to comprehensively evaluate the impact of energy storage systems on the environment throughout their entire life cycle, their life cycle can be divided into three stages: production, use and retirement.

[0164] Specifically, the a production stage covers the extraction and processing of raw materials, the manufacturing process of the energy storage system, and the impact of the transportation process on the environment. This stage mainly considers greenhouse gas emissions, energy consumption, and emissions of other pollutants.

[0165] Specifically, b. Use phase: Analyze the energy conversion efficiency, energy loss and related emissions of the energy storage system during operation. Mainly includes the energy consumption and emissions caused by the system's power management and heat dissipation process.

[0166] Specifically, the c decommissioning stage involves the recycling and disposal process after the life of the energy storage system ends, evaluating the treatment methods of waste materials, the possibility of recycling, and the environmental burden generated during the disposal process, such as the leakage of hazardous substances and the risk of pollution.

[0167] Specifically, in the above embodiment, the environmental impact data obtained through LCA analysis mainly include: a. Carbon emissions, the amount of carbon dioxide emissions generated during the entire life cycle, which is used to evaluate the impact of greenhouse gas emissions; b. Pollutant emissions, such as SO2, NO xThe impact of emissions of other pollutants during production, operation and decommissioning and the pollution control on the environment.

[0168] Specifically, these environmental impact data are converted into economic costs through environmental cost factors, and the conversion expression is:

[0169]

[0170] Where η env,i is the environmental protection cost adaptability factor of energy storage type i, C CO2.i is the carbon emission of energy storage type i, C pollu.i is the environmental pollutant treatment cost of carbon emissions of energy storage type i, is the unit carbon transaction cost, μ pollu.i is the pollutant control cost factor.

[0171] In this optional embodiment, based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage device, the life cycle of the energy storage device, and the safe operation of the distribution network and the energy storage device, the constraint condition set is established, including:

[0172] Based on the total load and power generation of the distribution network, establish the grid load balance constraints;

[0173] Construct charging and discharging limit constraints based on the charging and discharging power of the energy storage system;

[0174] Establish life cycle constraints based on the number of charge and discharge cycles of energy storage devices;

[0175] According to the voltage of the distribution network hub node and the operating temperature range of the energy storage equipment, the safe operation constraints are constructed, and the constraint condition set is obtained by combining the grid load balance constraints, charge and discharge limit constraints and life cycle constraints.

[0176] In this optional embodiment, the expression of the power grid load balance constraint is:

[0177]

[0178] Where P g (t) represents the power generated by the power supply at time t, P n (t) represents the power generated by renewable energy, P load (t) represents the active power demand of the load at time t, P ess,i (t) represents the charging and discharging power of energy storage system i at time t;

[0179] Specifically, the expression of the charge and discharge limit constraint is:

[0180]

[0181] 1-Dmax ≤S(t)≤1

[0182] Where P ess Indicates the charging and discharging power of energy storage system i, in kilowatts (kW); and are the lower and upper limits of power respectively; D max Indicates the maximum discharge depth of the battery;

[0183] Specifically, the life cycle constraint of energy storage equipment takes into account the service life of the equipment. Usually, the service life is related to the charge and discharge cycle and the retirement time of the equipment, ensuring that the equipment will not continue to operate beyond its life cycle to avoid instability of the energy storage system. The expression of the life cycle constraint is:

[0184] N(t)(i)≤N max (i)

[0185] Where N max (i) is the maximum number of charge and discharge cycles of energy storage device i, and N(t)(i) is the number of charge and discharge cycles of energy storage system i to time t.

[0186] Specifically, the safe operation constraints of the distribution network and energy storage equipment ensure that each device operates under safe and stable conditions to avoid overload, equipment damage or system crash; the expression of the safe operation constraint is:

[0187] V min ≤V(t)≤V max

[0188] f min ≤f(t)≤f max

[0189] T min ≤T(t)≤T max

[0190] Where V(t) is the voltage of the distribution network hub node at time t (unit: V); f(t) is the frequency of the distribution network hub node at time t (unit: Hz); T i (t) is the temperature of energy storage system i at time t (unit: °C); V min 、V max is the minimum and maximum safety range of voltage (unit: V); f min 、f max is the minimum and maximum safe range of frequency (unit: Hz); T min (i) T max (i) is the minimum operating temperature range and maximum operating temperature range of energy storage device i (unit: °C).

[0191] In this optional embodiment, a multi-population genetic algorithm is used to solve the full life cycle economic benefit model to obtain optimal configuration solutions for different types of energy storage systems.

[0192] Specifically, the full life cycle economic benefit model is solved using a multi-population genetic algorithm (MPGA), which can simulate the natural selection process, and conduct global optimization search under complex constraints through the parallel evolution of multiple populations and genetic operations (such as selection, crossover, mutation, etc.), so as to avoid falling into the problem of local optimal solutions, and ensure that the obtained configuration scheme has strong adaptability and robustness while meeting economic, technical and environmental constraints. Specifically, the optimization process of the genetic algorithm includes the steps of generating the initial population, fitness evaluation, selecting crossover mutation, extracting the optimal solution, and finally obtaining the energy storage system configuration scheme that can maximize the economic benefits. The MPGA algorithm flow chart is shown below. Figure 4 As shown, the following steps are included:

[0193] (1) Initialize the population: Input system data and randomly generate multiple populations based on the feasible configuration range and constraints of the energy storage system. Each population represents a possible energy storage system configuration scheme. i} as a chromosome, each decision variable k j ={k j (1),k j (2),…,k j (N)} is encoded into an N-bit binary code to form a chromosome segment of length N. These initial configuration schemes cover the capacity allocation, charging and discharging strategies and power scheduling of different types of energy storage devices to ensure the diversity of solutions. The gene encoding of the individual contains information such as the capacity, scheduling strategy and power allocation of various types of equipment in the energy storage system.

[0194] (2) Fitness evaluation: For each individual in the population, its fitness is evaluated to determine its quality. The fitness function is usually a comprehensive evaluation index related to economic benefits, environmental impact and system reliability. In the present invention, the fitness evaluation comprehensively considers the construction cost, operation cost, and decommissioning cost of the energy storage system, as well as environmental benefits and system response speed.

[0195] (3) Selection operation: According to the evaluation results of the fitness function, individuals with higher fitness are selected as parents for reproduction. The genetic information of the better individuals is passed on to the next generation. Through selection, the population can be concentrated in the solution space with better performance, thereby accelerating the search process.

[0196] (4) Crossover operation: Perform a crossover operation on the parent individuals to generate new individuals. In the present invention, the crossover operation recombines information such as capacity allocation and charge / discharge strategies according to the configuration characteristics of the energy storage system to generate diverse child individuals, thereby enhancing the breadth and diversity of the search.

[0197] (5) Mutation operation: Perform mutation operations on offspring individuals to introduce new solution spaces. Mutation operations can be to randomly change parameters such as the capacity, scheduling strategy, or charge and discharge power of the energy storage system to prevent the algorithm from falling into a local optimal solution and maintain the global nature of the search.

[0198] (6) Constraint correction: In the evolutionary process of each generation, if there are individuals that do not meet the constraints (such as energy storage device power limit, load balance constraints, etc.), they will be adjusted to the valid range through correction operations. The correction operation can be to readjust the configuration that does not meet the conditions so that it meets all the constraints. This process ensures that the final optimization result meets the actual application requirements.

[0199] (7) Termination condition: The iterative process will stop when the preset termination condition is met. The termination condition is when the maximum number of iterations is reached or when the optimal solution between multiple generations does not change significantly. After termination, the individual with the best fitness is selected as the final energy storage system configuration solution.

[0200] (8) Result output: Ultimately, the optimal configuration solution solved by the multi-population genetic algorithm will include the capacity allocation of each energy storage device, the scheduling strategy, and the charging and discharging power of each energy storage device in different time periods. These configuration solutions can effectively improve the economic benefits, environmental friendliness, and system reliability of the energy storage system and meet the load requirements of the power grid.

[0201] Figure 5 An embodiment of an energy storage system optimization system based on benefit analysis of the present invention is shown.

[0202] In this optional embodiment, the energy storage system optimization system based on benefit analysis includes:

[0203] Adaptability factor unit 201, used to set adaptability factors of different types of energy storage systems based on distribution network load data, operating conditions and basic performance data of energy storage systems;

[0204] The full life cycle economic benefit model unit 203 is used to construct a benefit analysis objective function based on the adaptability factors of different types of energy storage systems and the optimal economic benefit of the full life cycle as the optimization goal, and to establish a constraint condition set based on power balance, energy storage system charging and discharging power limit, life cycle limit and safe operation constraint, and to construct a full life cycle economic benefit model based on the benefit analysis objective function and the constraint condition set;

[0205] The genetic algorithm solving unit 205 is used to solve the full life cycle economic benefit model using a multi-population genetic algorithm to obtain optimal configuration solutions for different types of energy storage systems.

[0206] In this optional embodiment, the types of energy storage systems include lead-acid batteries, lithium batteries, compressed air energy storage, flywheel energy storage, supercapacitors and hydrogen energy storage;

[0207] The basic performance data of the energy storage system include energy density, power density, life cycle, efficiency, charging and discharging characteristics, response speed and operating temperature;

[0208] The adaptability factors of the energy storage system include the construction cost adaptability factor and the operation and maintenance cost adaptability factor.

[0209] In this optional embodiment, when the adaptability factor unit 201 sets the adaptability factors of different types of energy storage systems based on the distribution network load data, operating conditions and basic performance data of the energy storage system, the adaptability factors of different types of energy storage systems are set according to the requirements of the target scenario, including the response speed influence factor, the power density influence factor, the temperature adaptability influence factor, the efficiency influence factor and the cycle life influence factor; based on the distribution network load data and the operating condition analysis results, the weights of each indicator in the response speed influence factor, the power density influence factor and the temperature adaptability influence factor are dynamically adjusted to obtain the construction cost adaptability factor; according to the performance and adaptability of the energy storage system during the operation and maintenance process, the weights of each indicator in the efficiency influence factor and the cycle life influence factor are dynamically adjusted to obtain the operation and maintenance adaptability factor.

[0210] In this optional embodiment, the expression of the construction cost adaptability factor is:

[0211] η build.i =ω1α resp.i +ω2β pow.i +ω3ε temp.i

[0212] In the formula, α resp.i , β pow.i , γ temp.i are the response speed influencing factor, power density influencing factor, and temperature adaptability influencing factor of energy storage system i respectively; w1, w2, and w3 are the weights of the response speed influencing factor, the power density influencing factor, and the temperature adaptability influencing factor respectively;

[0213] The expression of operation and maintenance adaptability factor is:

[0214] η op.i =ω4δ eff.i +ω5δ life.i

[0215] In the formula, δ eff.i ,δ life.i are the efficiency influencing factor and cycle life influencing factor of energy storage system i respectively; w4 and w5 are the efficiency influencing factor weight and cycle life influencing factor weight respectively.

[0216] In this optional embodiment, the full life cycle economic benefit model unit 203 constructs a benefit analysis objective function based on the adaptability factors of different types of energy storage systems and takes the optimal full life cycle economic benefit as the optimization goal, and establishes a constraint condition set based on power balance, energy storage system charging and discharging power limits, life cycle limits and safe operation constraints. When constructing the full life cycle economic benefit model based on the benefit analysis objective function and the constraint condition set, the full life cycle cost is established based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, and the benefit analysis objective function is established with the goal of minimizing the full life cycle cost; a constraint condition set is established based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage equipment, the life cycle of the energy storage equipment and the safe operation of the distribution network and the energy storage equipment; and a full life cycle economic benefit model is constructed based on the benefit analysis objective function and the constraint condition set.

[0217] In this optional embodiment, based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, the full life cycle cost is established, and with the goal of minimizing the full life cycle cost, the benefit analysis objective function is established, including:

[0218] Based on the purchase cost, installation cost and commissioning cost of the equipment, combined with the construction cost adaptability factor of the energy storage system, calculate the construction investment cost of the energy storage system; based on the power management, equipment maintenance and system operation and maintenance costs, combined with the operation and maintenance cost adaptability factor of the energy storage system, analyze the operation and maintenance cost of the energy storage system; based on the recycling and dismantling costs of the energy storage equipment, solve the decommissioning cost of the energy storage system; use the life cycle assessment method to obtain environmental impact data, transform the environmental impact data through the environmental cost factor, and establish the environmental cost of the energy storage system; based on the environmental cost of the energy storage system, combine the construction investment cost, operation and maintenance cost and decommissioning cost of the energy storage system to construct the full life cycle cost, and establish the benefit analysis objective function based on the full life cycle cost;

[0219] The expression of the benefit analysis objective function is:

[0220] F=λ build C build +λ op C op +λ ret C ret +λ en C env

[0221] In the formula, C build is the construction investment cost, C op is the operation and maintenance cost, C ret is the decommissioning cost, C env is the environmental protection cost, λ build is the weight of construction investment cost, λ op is the weight of operation and maintenance cost, λ ret is the weight of decommissioning cost, λ en The weight of environmental protection cost.

[0222] In this optional embodiment, based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage device, the life cycle of the energy storage device, and the safe operation of the distribution network and the energy storage device, the constraint condition set is established, including:

[0223] Based on the total load and power generation of the distribution network, a grid load balance constraint is established; based on the charging and discharging power of the energy storage system, a charge and discharge limit constraint is constructed; based on the number of charge and discharge cycles of the energy storage device, a life cycle constraint is established; based on the voltage of the distribution network hub node and the operating temperature range of the energy storage device, a safe operation constraint is constructed, and the grid load balance constraint, charge and discharge limit constraint and life cycle constraint are combined to obtain a constraint condition set.

[0224] In this optional embodiment, the expression of the power grid load balance constraint is:

[0225]

[0226] Where P g (t) represents the power generated by the power supply at time t, P n (t) represents the power generated by renewable energy, P load (t) represents the active power demand of the load at time t, P ess,i (t) represents the charging and discharging power of energy storage system i at time t;

[0227] The expression of charge and discharge limit constraint is:

[0228]

[0229] Where P ess Indicates the charging and discharging power of energy storage system i; and are the lower and upper limits of power respectively;

[0230] The expression of the life cycle constraint is:

[0231] N(t)(i)≤N max (i)

[0232] Where Nmax (i) is the maximum number of charge and discharge cycles of energy storage device i, and N(t)(i) is the number of charge and discharge cycles of energy storage system i to time t;

[0233] The expression of the safe operation constraint is:

[0234] V min ≤V(t)≤V max

[0235] f min ≤f(t)≤f max

[0236] T min ≤T(t)≤T max

[0237] Where V(t) is the voltage of the distribution network hub node at time t; f(t) is the frequency of the distribution network hub node at time t; T i (t) is the temperature of energy storage system i at time t; V min 、V max is the minimum safety range and maximum safety range of voltage; f min 、f max is the minimum and maximum safe range of frequency; T min (i) T max (i) is the minimum operating temperature range and maximum operating temperature range of energy storage device i.

[0238] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0239] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0240] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0241] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0242] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0243] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for optimizing an energy storage system based on benefit analysis, characterized in that: The energy storage system optimization method based on benefit analysis includes: Based on the distribution network load data, operating conditions and basic performance data of the energy storage system, set the adaptability factors of different types of energy storage systems; According to the adaptability factors of different types of energy storage systems, the optimization goal is to optimize the economic benefits of the entire life cycle, and to build a benefit analysis objective function. In addition, a constraint condition set is established based on power balance, energy storage system charging and discharging power limits, life cycle limits, and safe operation constraints. Based on the benefit analysis objective function and constraint condition set, a full life cycle economic benefit model is built. A multi-population genetic algorithm is used to solve the full life cycle economic benefit model and obtain the optimal configuration scheme for different types of energy storage systems.

2. The energy storage system optimization method based on benefit analysis according to claim 1, characterized in that: The types of energy storage systems include lead-acid batteries, lithium batteries, compressed air energy storage, flywheel energy storage, supercapacitors and hydrogen energy storage; The basic performance data of the energy storage system include energy density, power density, life cycle, efficiency, charging and discharging characteristics, response speed and operating temperature; The adaptability factor of the energy storage system includes a construction cost adaptability factor and an operation and maintenance cost adaptability factor.

3. The energy storage system optimization method based on benefit analysis according to claim 1 is characterized in that: The adaptability factors of different types of energy storage systems are set based on the distribution network load data, operating conditions and basic performance data of the energy storage system, including: According to the requirements of the target scenario, set the adaptability factors of different types of energy storage systems, including response speed influence factor, power density influence factor, temperature adaptability influence factor, efficiency influence factor and cycle life influence factor; Based on the distribution network load data and operating condition analysis results, the weights of various indicators in the response speed influencing factor, power density influencing factor and temperature adaptability influencing factor are dynamically adjusted to obtain the construction cost adaptability factor; According to the performance and adaptability of the energy storage system during operation and maintenance, the weights of various indicators in the efficiency influencing factor and the cycle life influencing factor are dynamically adjusted to obtain the operation and maintenance adaptability factor.

4. The energy storage system optimization method based on benefit analysis according to claim 3 is characterized in that: The expression of the construction cost adaptability factor is: or build.i =ω1a resp.i +ω2β pow.i +ω3e temp.i In the formula, α resp.i , β pow.i , γ temp.i are the response speed influencing factor, power density influencing factor, and temperature adaptability influencing factor of energy storage system i respectively; w1, w2, and w3 are the weights of the response speed influencing factor, the power density influencing factor, and the temperature adaptability influencing factor respectively; The expression of the operation and maintenance adaptability factor is: or op.i =ω4δ eff.i +ω5d life.i In the formula, δ eff.i , δ life.i are the efficiency influencing factor and cycle life influencing factor of energy storage system i respectively; w4 and w5 are the efficiency influencing factor weight and cycle life influencing factor weight respectively.

5. The energy storage system optimization method based on benefit analysis according to claim 3 is characterized in that: According to the adaptability factors of different types of energy storage systems, the optimization goal is to optimize the economic benefits of the entire life cycle, construct a benefit analysis objective function, and establish a constraint condition set based on power balance, energy storage system charging and discharging power limit, life cycle limit and safe operation constraint. Based on the benefit analysis objective function and the constraint condition set, the full life cycle economic benefit model is constructed, including: Based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, the full life cycle cost is established, and the benefit analysis objective function is established with the goal of minimizing the full life cycle cost; Establish a set of constraints based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage equipment, the life cycle of the energy storage equipment, and the safe operation of the distribution network and the energy storage equipment; Based on the benefit analysis objective function and constraint condition set, a full life cycle economic benefit model is constructed.

6. The energy storage system optimization method based on benefit analysis according to claim 5 is characterized in that: The full life cycle cost is established based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, and the benefit analysis objective function is established with the goal of minimizing the full life cycle cost, including: Based on the purchase cost, installation cost and commissioning cost of the equipment, combined with the construction cost adaptability factor of the energy storage system, calculate the construction investment cost of the energy storage system; Analyze the operation and maintenance costs of the energy storage system based on the power management, equipment maintenance and system operation and maintenance costs, combined with the operation and maintenance cost adaptability factor of the energy storage system; Based on the recycling and removal costs of energy storage equipment, the decommissioning costs of energy storage systems are solved; Use life cycle assessment methods to obtain environmental impact data, transform environmental impact data through environmental cost factors, and establish the environmental cost of the energy storage system; Based on the environmental cost of the energy storage system, the full life cycle cost is constructed by combining the construction investment cost, operation and maintenance cost, and decommissioning cost of the energy storage system. Based on the full life cycle cost, the benefit analysis objective function is established. The expression of the benefit analysis objective function is: F=λ build C build +λ op C op +λ ret C ret +λ en C env In the formula, C build is the construction investment cost, C op is the operation and maintenance cost, C ret is the decommissioning cost, C env is the environmental protection cost, λ build is the weight of construction investment cost, λ op is the weight of operation and maintenance cost, λ ret is the weight of decommissioning cost, λ en The weight of environmental protection cost.

7. The energy storage system optimization method based on benefit analysis according to claim 5 is characterized in that: The constraint condition set established according to the load balance of the distribution network, the charging and discharging characteristics of the energy storage device, the life cycle of the energy storage device, and the safe operation of the distribution network and the energy storage device includes: Based on the total load and power generation of the distribution network, establish the grid load balance constraints; Construct charging and discharging limit constraints based on the charging and discharging power of the energy storage system; Establish life cycle constraints based on the number of charge and discharge cycles of energy storage devices; According to the voltage of the distribution network hub node and the operating temperature range of the energy storage equipment, the safe operation constraints are constructed, and the constraint condition set is obtained by combining the grid load balance constraints, charge and discharge limit constraints and life cycle constraints.

8. The energy storage system optimization method based on benefit analysis according to claim 7 is characterized in that: The expression of the power grid load balance constraint is: Where P g (t) represents the power generated by the power supply at time t, P n (t) represents the power generated by renewable energy, P load (t) represents the active power demand of the load at time t, P ess,i (t) represents the charging and discharging power of energy storage system i at time t; The expression of the charge and discharge limit constraint is: Where P ess Indicates the charging and discharging power of energy storage system i; and are the lower and upper limits of power respectively; The expression of the life cycle constraint is: N(t)(i)≤N max (i) Where N max (i) is the maximum number of charge and discharge cycles of energy storage device i, and N(t)(i) is the number of charge and discharge cycles of energy storage system i to time t; The expression of the safe operation constraint is: V min ≤V(t)V max f min ≤f(t)f max T min ≤T(t)T max Where V(t) is the voltage of the distribution network hub node at time t; f(t) is the frequency of the distribution network hub node at time t; T i (t) is the temperature of energy storage system i at time t; V min 、V max is the minimum safety range and maximum safety range of voltage; f min 、f max is the minimum and maximum safe range of frequency; T min (i) T max (i) is the minimum operating temperature range and maximum operating temperature range of energy storage device i.

9. An energy storage system optimization system based on benefit analysis, characterized in that: The energy storage system optimization system based on benefit analysis includes: An adaptability factor unit, used to set adaptability factors of different types of energy storage systems based on distribution network load data, operating conditions and basic performance data of the energy storage system; The full life cycle economic benefit model unit is used to construct a benefit analysis objective function based on the adaptability factors of different types of energy storage systems and the optimal economic benefit of the full life cycle as the optimization goal. It also establishes a constraint condition set based on power balance, energy storage system charging and discharging power limits, life cycle limits and safe operation constraints. Based on the benefit analysis objective function and the constraint condition set, a full life cycle economic benefit model is constructed. The genetic algorithm solving unit is used to solve the full life cycle economic benefit model using a multi-population genetic algorithm to obtain the optimal configuration solutions for different types of energy storage systems.

10. The energy storage system optimization system based on benefit analysis according to claim 9, characterized in that: The types of energy storage systems include lead-acid batteries, lithium batteries, compressed air energy storage, flywheel energy storage, supercapacitors and hydrogen energy storage; The basic performance data of the energy storage system include energy density, power density, life cycle, efficiency, charging and discharging characteristics, response speed and operating temperature; The adaptability factor of the energy storage system includes a construction cost adaptability factor and an operation and maintenance cost adaptability factor.

11. The energy storage system optimization system based on benefit analysis according to claim 9, characterized in that: When setting the adaptability factors of different types of energy storage systems based on the distribution network load data, operating conditions and basic performance data of the energy storage system, the adaptability factor unit sets the adaptability factors of different types of energy storage systems according to the requirements of the target scenario, including the response speed influence factor, the power density influence factor, the temperature adaptability influence factor, the efficiency influence factor and the cycle life influence factor; based on the distribution network load data and the operating condition analysis results, dynamically adjusts the weights of each indicator in the response speed influence factor, the power density influence factor and the temperature adaptability influence factor to obtain the construction cost adaptability factor; according to the performance and adaptability of the energy storage system during the operation and maintenance process, dynamically adjusts the weights of each indicator in the efficiency influence factor and the cycle life influence factor to obtain the operation and maintenance adaptability factor.

12. The energy storage system optimization system based on benefit analysis according to claim 11, characterized in that: The expression of the construction cost adaptability factor is: or build.i =ω1a resp.i +ω2β pow.i +ω3e temp.i In the formula, α resp.i , β pow.i , γ temp.i are the response speed influencing factor, power density influencing factor, and temperature adaptability influencing factor of energy storage system i respectively; w1, w2, and w3 are the weights of the response speed influencing factor, the power density influencing factor, and the temperature adaptability influencing factor respectively; The expression of the operation and maintenance adaptability factor is: or op.i =ω4δ eff.i +ω5d life.i In the formula, δ eff.i ,δ life.i are the efficiency influencing factor and cycle life influencing factor of energy storage system i respectively; w4 and w5 are the efficiency influencing factor weight and cycle life influencing factor weight respectively.

13. The energy storage system optimization system based on benefit analysis according to claim 11, characterized in that: The full life cycle economic benefit model unit constructs a benefit analysis objective function based on the adaptability factors of different types of energy storage systems and takes the optimal full life cycle economic benefit as the optimization goal, and establishes a constraint condition set based on power balance, energy storage system charging and discharging power limits, life cycle limits and safe operation constraints. When constructing the full life cycle economic benefit model based on the benefit analysis objective function and the constraint condition set, the full life cycle cost is established based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, and the benefit analysis objective function is established with the goal of minimizing the full life cycle cost; a constraint condition set is established based on the load balance of the distribution network, the charging and discharging characteristics of the energy storage equipment, the life cycle of the energy storage equipment and the safe operation of the distribution network and the energy storage equipment; and a full life cycle economic benefit model is constructed based on the benefit analysis objective function and the constraint condition set.

14. The energy storage system optimization system based on benefit analysis according to claim 13, characterized in that: The full life cycle cost is established based on the construction investment cost, operation and maintenance cost, decommissioning cost and environmental protection cost of the energy storage system, and the benefit analysis objective function is established with the goal of minimizing the full life cycle cost, including: Based on the purchase cost, installation cost and commissioning cost of the equipment, combined with the construction cost adaptability factor of the energy storage system, calculate the construction investment cost of the energy storage system; based on the power management, equipment maintenance and system operation and maintenance costs, combined with the operation and maintenance cost adaptability factor of the energy storage system, analyze the operation and maintenance cost of the energy storage system; based on the recycling and dismantling costs of the energy storage equipment, solve the decommissioning cost of the energy storage system; use the life cycle assessment method to obtain environmental impact data, transform the environmental impact data through the environmental cost factor, and establish the environmental cost of the energy storage system; based on the environmental cost of the energy storage system, combine the construction investment cost, operation and maintenance cost and decommissioning cost of the energy storage system to construct the full life cycle cost, and establish the benefit analysis objective function based on the full life cycle cost; The expression of the benefit analysis objective function is: F=λ build C build +λ op C op +λ ret C ret +λ en C env In the formula, C build is the construction investment cost, C op is the operation and maintenance cost, C ret is the decommissioning cost, C env is the environmental protection cost, λ build is the weight of construction investment cost, λ op is the weight of operation and maintenance cost, λ ret is the weight of decommissioning cost, λ en The weight of environmental protection cost.

15. The energy storage system optimization system based on benefit analysis according to claim 13, characterized in that: The constraint condition set established according to the load balance of the distribution network, the charging and discharging characteristics of the energy storage device, the life cycle of the energy storage device, and the safe operation of the distribution network and the energy storage device includes: Based on the total load and power generation of the distribution network, a grid load balance constraint is established; based on the charging and discharging power of the energy storage system, a charge and discharge limit constraint is constructed; based on the number of charge and discharge cycles of the energy storage device, a life cycle constraint is established; based on the voltage of the distribution network hub node and the operating temperature range of the energy storage device, a safe operation constraint is constructed, and the grid load balance constraint, charge and discharge limit constraint and life cycle constraint are combined to obtain a constraint condition set.

16. The energy storage system optimization system based on benefit analysis according to claim 15, characterized in that: The expression of the power grid load balance constraint is: Where P g (t) represents the power generated by the power supply at time t, P n (t) represents the power generated by renewable energy, P load (t) represents the active power demand of the load at time t, P ess,i (t) represents the charging and discharging power of energy storage system i at time t; The expression of the charge and discharge limit constraint is: Where P ess Indicates the charging and discharging power of energy storage system i; and are the lower and upper limits of power respectively; The expression of the life cycle constraint is: N(t)(i)≤N max (i) Where N max (i) is the maximum number of charge and discharge cycles of energy storage device i, and N(t)(i) is the number of charge and discharge cycles of energy storage system i to time t; The expression of the safe operation constraint is: V min ≤V(t)≤V max f min ≤f(t)≤f max T min ≤T(t)T max Where V(t) is the voltage of the distribution network hub node at time t; f(t) is the frequency of the distribution network hub node at time t; T i (t) is the temperature of energy storage system i at time t; V min 、V max is the minimum safety range and maximum safety range of voltage; f min 、f max is the minimum and maximum safe range of frequency; T min (i) T max (i) is the minimum operating temperature range and maximum operating temperature range of energy storage device i.

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