An optimization decision method for distributed lithium battery energy storage participating in power market

Abstract

The application discloses an optimization decision method for distributed lithium battery energy storage participating in a power market, and relates to the technical field of the power market. The method comprises the following steps: constructing a profit model and a cost model of the distributed lithium battery energy storage participating in the power market, constructing a profit function of the distributed lithium battery energy storage participating in the power market based on the profit model and the cost model, and determining the power purchase capacity and the power sale capacity of the distributed lithium battery energy storage participating in the power market and the bidding capacity of the distributed lithium battery energy storage in the power market when the profit function is maximum under the constraint conditions of the state of charge of the distributed lithium battery energy storage and the self characteristics of the distributed lithium battery energy storage. The method solves the technical problem that the existing distribution strategy is not good, so that the distribution strategy cannot well guide the distributed battery to enter the power market.

Description

Technical Field

[0001] This invention relates to the field of electricity market technology, and in particular to an optimized decision-making method for distributed lithium-ion battery energy storage to participate in the electricity market. Background Technology

[0002] Energy storage can effectively address issues such as insufficient grid regulation capacity and increased difficulty in frequency stabilization caused by a high proportion of renewable energy. Unlike traditional generation-side and load-side resources, energy storage has physical characteristics such as limited energy and rapid response, and its participation in the electricity market also has unique mechanisms. Choosing appropriate allocation strategies can enable the market to better allocate energy storage resources.

[0003] Traditional research on energy storage participation in the electricity market has largely focused on the generation side of large-scale energy storage, primarily studying the supply of energy storage systems for renewable energy integration, with less consideration given to the economics of energy storage operation in the electricity market. In recent years, research on energy storage on the distribution network side has also emerged, such as electric vehicles (distributed batteries) connecting to the grid and generating revenue through energy arbitrage and frequency regulation. However, these studies on the distribution network side typically do not differentiate the parameters of distributed batteries, nor do they consider the differences in allocation strategies for the optimization process of distributed batteries of different types, operating conditions, and technical characteristics entering the electricity market. This results in allocation strategies that cannot effectively guide distributed batteries into the electricity market. Summary of the Invention

[0004] This invention provides an optimized decision-making method for distributed lithium-ion battery energy storage to participate in the electricity market. It addresses the technical problem that existing allocation strategies do not consider the differences in the types, operating conditions, and technical characteristics of distributed batteries entering the electricity market, resulting in allocation strategies that cannot effectively guide distributed batteries to enter the electricity market.

[0005] This invention provides an optimized decision-making method for distributed lithium-ion battery energy storage to participate in the electricity market, comprising:

[0006] S1: Based on the bidding capacity and market electricity price of distributed lithium-ion energy storage participating in the electricity market, construct a profit model for distributed lithium-ion energy storage participating in the electricity market;

[0007] S2: Construct a cost model for distributed lithium-ion energy storage to participate in the electricity market based on the operating costs, depreciation costs, and maintenance costs of distributed lithium-ion energy storage participating in the electricity market;

[0008] S3: Construct a profit function for distributed lithium-ion battery energy storage to participate in the electricity market based on the profit model and the cost model;

[0009] S4: Determine the maximum value of the profit function based on the state of charge constraints and inherent characteristic constraints of distributed lithium-ion energy storage.

[0010] S5: Output the decision model for distributed lithium-ion energy storage participating in the electricity market when the profit function reaches its maximum value; wherein, the decision model includes the capacity of distributed lithium-ion energy storage to purchase electricity from the electricity market, the capacity to sell electricity from the electricity market, and the capacity to bid in the electricity market.

[0011] Preferably, the profit model includes an energy market profit model and a frequency regulation market profit model.

[0012] Preferably, the profit model of the energy market is as follows:

[0013]

[0014]

[0015]

[0016]

[0017] in, This represents the profitability of lithium-ion battery storage in the energy market at time t under scenario s. This represents the electricity price in the energy market at time t under scenario s. Δt represents the bidding capacity of lithium-ion battery storage in the energy market at time t, and Δt represents the bidding time interval. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the electricity sales capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the electricity purchase capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the discharge efficiency of the v-th lithium-ion battery. This represents the charging efficiency of the v-th lithium-ion battery storage device. This represents the electricity sales power of the v-th lithium-ion battery storage unit in the energy market at time t. Let V represent the power purchased by the v-th lithium-ion energy storage unit in the energy market at time t, where V represents the total number of lithium-ion energy storage units and v represents the v-th lithium-ion energy storage unit.

[0018] Preferably, the frequency modulation market profit model includes a capacity profit model and a performance profit model;

[0019] The capacity profitability model is as follows:

[0020]

[0021]

[0022] in, This indicates the capacity profitability of lithium battery storage in the frequency regulation market at time t under scenario s. This represents the capacity electricity price in the frequency regulation market at time t under scenario s. Score represents the bidding capacity of lithium-ion battery storage in the frequency regulation market at time t. perf This indicates the overall performance evaluation score of lithium battery energy storage in the frequency regulation market. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the frequency regulation market at time t.

[0023] The performance-based profitability model is as follows:

[0024]

[0025]

[0026] in, This indicates the performance and profitability of lithium-ion battery storage in the frequency regulation market at time t under scenario s. This represents the performance-based electricity price in the frequency regulation market at time t under scenario s. Score represents the frequency modulation market bidding capacity at time t. perf This indicates the overall performance evaluation score of lithium battery energy storage in the frequency regulation market. Represents the mileage ratio at time t under scenario s, where RegD represents the fast response frequency modulation signal and RegA represents the slow response frequency modulation signal;

[0027] The specific profit model for the frequency modulation market is as follows:

[0028]

[0029] in, This indicates the profitability of lithium-ion battery storage in the frequency regulation market at time t under scenario s. This indicates the capacity profitability of lithium battery storage in the frequency regulation market at time t under scenario s. This represents the performance and profitability of lithium battery storage in the frequency regulation market at time t under scenario s.

[0030] Preferably, the cost model includes a lithium battery energy storage operation cost model, a lithium battery energy storage depreciation cost model, and a lithium battery energy storage maintenance cost model.

[0031] Preferably, the lithium battery energy storage operating cost model is as follows:

[0032]

[0033] in, λ represents the operating cost of lithium-ion battery energy storage at time t. op β represents the unit operating cost of lithium-ion battery energy storage. t This represents the average duration of upward and downward adjustments to the bidding capacity within the bidding time interval Δt. This represents the electricity sales capacity of lithium-ion battery storage in the energy market at time t. This represents the capacity of lithium-ion battery storage to purchase electricity in the energy market at time t. This represents the bidding capacity of lithium-ion battery storage in the frequency regulation market at time t.

[0034] Preferably, the lithium battery energy storage depreciation cost model is as follows:

[0035]

[0036]

[0037] In the formula, This represents the depreciation cost of lithium-ion battery energy storage at time t. This represents the depreciation cost of the v-th lithium-ion battery storage unit at time t, where V represents the total number of lithium-ion battery storage units, and v represents the v-th lithium-ion battery storage unit. This represents the fixed cost of the v-th lithium-ion energy storage device. The slope of the aging rate of the v-th lithium-ion battery storage unit is represented by a dimensionless quantity. The horizontal axis represents the total energy throughput of the v-th lithium-ion battery storage unit, and the vertical axis represents the change in the maximum energy stored by the v-th lithium-ion battery storage unit. This represents the electricity sales capacity of the v-th lithium-ion battery storage unit in the energy market at time t. β represents the energy purchase capacity of the v-th lithium-ion battery storage at time t in the energy market, Δt represents the bidding time interval, and β represents the energy purchase capacity of the v-th lithium-ion battery storage at time t. t This represents the average duration of upward and downward adjustments to the bidding capacity within the bidding time interval Δt. This represents the bidding capacity of lithium-ion battery storage in the frequency regulation market at time t. This represents the discharge efficiency of the v-th lithium-ion battery. B represents the charging efficiency of the v-th lithium-ion battery storage device. v,max This represents the initial maximum storage capacity of the v-th lithium-ion battery energy storage device. This represents the fixed cost of the v-th lithium-ion battery storage unit.

[0038] Preferably, the lithium battery energy storage maintenance cost model is as follows:

[0039] c m =λ m p max

[0040]

[0041] Among them, c m λ represents the maintenance cost of lithium-ion battery energy storage. m p represents the maintenance cost per unit of energy charged or discharged in lithium-ion battery energy storage. max p represents the total power of lithium battery energy storage. v,max This represents the maximum power of the v-th lithium-ion battery storage unit.

[0042] Preferably, the profit function is as follows:

[0043]

[0044] Where S represents the total number of scenes, s represents the s-th scene, and γ s Let represent the probability of scenario s occurring, T represent the time period for lithium-ion battery energy storage to participate in the electricity market, and t represent time t within the time period for lithium-ion battery energy storage to participate in the electricity market. This represents the profitability of lithium-ion battery storage in the energy market at time t under scenario s. This indicates the profitability of lithium-ion battery storage in the frequency regulation market at time t under scenario s. This represents the operating cost of lithium-ion battery energy storage at time t. c represents the depreciation cost of lithium-ion battery energy storage at time t. m This indicates the maintenance cost of lithium-ion battery energy storage.

[0045] Preferably, the state of charge constraint specifically includes:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] Among them, SOC t,v This represents the state of charge of the v-th lithium-ion battery at time t. This represents the state of charge limit of the v-th lithium-ion battery storage device. The state of charge (SOC) represents the upper limit of the v-th lithium-ion battery energy storage unit, α represents the self-discharge rate of the lithium-ion battery energy storage unit, and SOC is the maximum state of charge. t-1,v This represents the state of charge of the v-th lithium-ion battery at time t-1. This represents the discharge efficiency of the v-th lithium-ion battery. This represents the charging efficiency of the v-th lithium-ion battery storage device. This represents the electricity sales power of the v-th lithium-ion battery storage unit in the energy market at time t. β represents the power purchased by the v-th lithium-ion battery storage unit in the energy market at time t, Δt represents the bidding time interval, and β t This represents the average duration of upward and downward adjustments to the bidding capacity within the bidding time interval Δt. This represents the bidding power of the v-th lithium-ion battery storage unit in the frequency regulation market at time t. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the energy market at time t. h represents the bidding capacity of the v-th lithium-ion battery storage at time t in the frequency regulation market. reg Indicates the time required to provide spin-off standby service. Let h be the required reserve capacity of the v-th lithium-ion battery energy storage owner at time t. user Let V represent the backup time required by the owner of the lithium-ion battery storage, V represent the total number of lithium-ion battery storage units, v represent the v-th lithium-ion battery storage unit, T represent the time period for lithium-ion battery storage to participate in the electricity market, and t represent time t within the time period for lithium-ion battery storage to participate in the electricity market.

[0057] Preferably, the self-characteristic constraints specifically include:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] in, This represents the electricity sales capacity of lithium-ion battery storage in the energy market at time t. This represents the capacity of lithium-ion battery storage to purchase electricity in the energy market at time t. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the energy market at time t. p represents the bidding capacity of the v-th lithium-ion battery storage at time t in the frequency regulation market. v,max σ represents the maximum power of the v-th lithium-ion energy storage unit, σ represents the ratio of the capacity that the lithium-ion energy storage unit needs to increase / decrease to the unit capacity when winning a bid for a unit capacity in the frequency regulation market, V represents the total number of lithium-ion energy storage units, v represents the v-th lithium-ion energy storage unit, T represents the time period for the lithium-ion energy storage unit to participate in the electricity market, and t represents time t within the time period for the lithium-ion energy storage unit to participate in the electricity market.

[0064] As can be seen from the above technical solutions, the present invention provides an optimized decision-making method for distributed lithium-ion battery energy storage to participate in the electricity market. Its advantages lie in that by constructing a profit model and a cost model for distributed lithium-ion battery energy storage to participate in the electricity market, and constructing a profit function for distributed lithium-ion battery energy storage to participate in the electricity market based on the profit model and the cost model, it considers the boundary differences of lithium-ion battery energy storage to participate in the electricity market under different scenarios, and also considers the differences in the characteristics of lithium-ion battery energy storage entering the electricity market due to different types, operating conditions, and technical characteristics. With the state of charge constraint and its own characteristic constraint of distributed lithium-ion battery energy storage as constraints, it determines the power purchase capacity, power sales capacity and bidding capacity of distributed lithium-ion battery energy storage in the electricity market corresponding to the maximum of the profit function. This solves the technical problem that the existing allocation strategy is inadequate, which leads to the allocation strategy not being able to guide distributed batteries to enter the electricity market well. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart illustrating an optimized decision-making method for distributed lithium-ion battery energy storage participating in the electricity market, provided as an example of the application. Detailed Implementation

[0067] This invention provides an optimized decision-making method for distributed lithium-ion battery energy storage to participate in the electricity market. It solves the technical problem that existing allocation strategies do not consider the differences in the types, operating conditions, and technical characteristics of distributed batteries entering the electricity market, which leads to the allocation strategies not being able to effectively guide distributed batteries to enter the electricity market.

[0068] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0069] Embodiment 1 of this application provides an optimized decision-making method for distributed lithium-ion battery energy storage to participate in the electricity market. Please refer to [link to relevant documentation]. Figure 1 In Example 1, the method includes:

[0070] S1: Based on the bidding capacity and market electricity price of distributed lithium-ion energy storage participating in the electricity market, construct a profit model for distributed lithium-ion energy storage participating in the electricity market.

[0071] In step S1, the electricity market includes the energy market and the frequency regulation market. The profit model for distributed lithium-ion battery storage participating in the electricity market specifically includes: the energy market profit model for distributed lithium-ion battery storage participating in the energy market and the frequency regulation market profit model for distributed lithium-ion battery storage participating in the frequency regulation market. The energy market profit model and the frequency regulation market profit model together constitute the profit model for lithium-ion battery storage participating in the electricity market.

[0072] The specific energy market profitability model for distributed lithium-ion battery energy storage participating in the energy market is as follows:

[0073]

[0074]

[0075]

[0076]

[0077] in, This represents the profitability of lithium-ion battery storage in the energy market at time t under scenario s. This represents the electricity price in the energy market at time t under scenario s. Δt represents the bidding capacity of lithium-ion battery storage in the energy market at time t, and Δt represents the bidding time interval. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the electricity sales capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the electricity purchase capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the discharge efficiency of the v-th lithium-ion battery. This represents the charging efficiency of the v-th lithium-ion battery storage device. This represents the electricity sales power of the v-th lithium-ion battery storage unit in the energy market at time t. Let V represent the power purchased by the v-th lithium-ion energy storage unit in the energy market at time t, where V represents the total number of lithium-ion energy storage units and v represents the v-th lithium-ion energy storage unit.

[0078] Furthermore, the profitability of distributed lithium-ion battery energy storage participating in the frequency regulation market includes two parts: capacity profitability and performance profitability. That is, the frequency regulation market profitability model includes a capacity profitability model and a performance profitability model.

[0079] The specific capacity profitability model for distributed lithium-ion battery energy storage participating in the frequency regulation market is as follows:

[0080]

[0081]

[0082] in, This indicates the capacity profitability of lithium battery storage in the frequency regulation market at time t under scenario s. This represents the capacity electricity price in the frequency regulation market at time t under scenario s. Score represents the bidding capacity of lithium-ion battery storage in the frequency regulation market at time t. perf This indicates the overall performance evaluation score of lithium battery storage in the frequency regulation market. This represents the bidding capacity of the v-th lithium-ion energy storage unit in the frequency regulation market at time t.

[0083] The specific performance and profitability model for distributed lithium-ion battery energy storage participating in the frequency regulation market is as follows:

[0084]

[0085]

[0086] in, This indicates the performance and profitability of lithium-ion battery storage in the frequency regulation market at time t under scenario s. This represents the performance-based electricity price in the frequency regulation market at time t under scenario s. Score represents the frequency modulation market bidding capacity at time t. pref This indicates the overall performance evaluation score of lithium battery storage in the frequency regulation market. RegD represents the mileage ratio at time t under scenario s, RegA represents the fast response frequency modulation signal, and RegD represents the slow response frequency modulation signal.

[0087] The overall profit model for distributed lithium-ion battery energy storage participating in the frequency regulation market is as follows:

[0088]

[0089] in, This represents the profitability of lithium-ion battery storage in the frequency regulation market at time t under scenario s. This indicates the capacity profitability of lithium battery storage in the frequency regulation market at time t under scenario s. This represents the performance and profitability of lithium battery storage in the frequency regulation market at time t under scenario s.

[0090] The above-mentioned quantification of the profitability of lithium battery energy storage participating in the energy market and frequency regulation market can provide more accurate guidance for lithium battery energy storage to participate in the electricity market.

[0091] S2: Based on the operating costs, depreciation costs, and maintenance costs of distributed lithium-ion energy storage participating in the electricity market, construct a cost model for distributed lithium-ion energy storage participating in the electricity market.

[0092] The costs of lithium-ion battery energy storage participating in the electricity market mainly include the operating costs of lithium-ion battery energy storage, the depreciation costs of lithium-ion battery energy storage, and the maintenance costs of lithium-ion battery energy storage.

[0093] It is understandable that when lithium-ion energy storage participates in the electricity market, it needs to be operated and maintained to ensure its normal operation throughout its lifespan. The costs incurred in operation and maintenance usually include the costs of testing, installation, loss, shutdown, manpower, inspection and repair of lithium-ion energy storage. In this embodiment, it specifically includes three types: operating costs, depreciation costs and maintenance costs.

[0094] The operating cost of lithium-ion battery energy storage participating in the electricity market is calculated using a lithium-ion battery energy storage operating cost model, which is as follows:

[0095]

[0096] in, λ represents the operating cost of lithium-ion battery energy storage at time t. op β represents the unit operating cost of lithium-ion battery energy storage. t This represents the average duration of upward and downward adjustments to the bidding capacity within the bidding time interval Δt. This represents the electricity sales capacity of lithium-ion battery storage in the energy market at time t. This represents the capacity of lithium-ion battery storage to purchase electricity in the energy market at time t. This represents the bidding capacity of lithium-ion battery storage in the frequency regulation market at time t.

[0097] The depreciation cost of lithium-ion battery energy storage participating in the electricity market is calculated using the lithium-ion battery energy storage depreciation cost model, which is as follows:

[0098]

[0099]

[0100] In the formula, This represents the depreciation cost of lithium-ion battery energy storage at time t. This represents the depreciation cost of the v-th lithium-ion battery storage unit at time t, where V represents the total number of lithium-ion battery storage units, and v represents the v-th lithium-ion battery storage unit. This represents the fixed cost of the v-th lithium-ion energy storage device. The slope of the aging rate of the v-th lithium-ion battery storage unit is represented by a dimensionless quantity. The horizontal axis represents the total energy throughput of the v-th lithium-ion battery storage unit, and the vertical axis represents the change in the maximum energy stored by the v-th lithium-ion battery storage unit. This represents the electricity sales capacity of the v-th lithium-ion battery storage unit in the energy market at time t. β represents the energy purchase capacity of the v-th lithium-ion battery storage at time t in the energy market, Δt represents the bidding time interval, and β represents the energy purchase capacity of the v-th lithium-ion battery storage at time t. tThis represents the average duration of upward and downward adjustments to the bidding capacity within the bidding time interval Δt. This represents the bidding capacity of lithium-ion battery storage in the frequency regulation market at time t. This represents the discharge efficiency of the v-th lithium-ion battery. B represents the charging efficiency of the v-th lithium-ion battery storage device. v,max This represents the initial maximum storage capacity of the v-th lithium-ion battery energy storage device. This represents the fixed cost of the v-th lithium-ion battery storage unit.

[0101] The maintenance cost of lithium-ion battery energy storage participating in the electricity market is calculated using the lithium-ion battery energy storage maintenance cost model, which is as follows:

[0102] c m =λ m p max

[0103]

[0104] Among them, c m λ represents the maintenance cost of lithium-ion battery energy storage. m p represents the maintenance cost per unit of energy charged or discharged in lithium-ion battery energy storage. max p represents the total power of lithium battery energy storage. v,max This represents the maximum power of the v-th lithium-ion battery storage unit.

[0105] The above-mentioned quantification of the costs of lithium-ion battery energy storage participating in the energy market and frequency regulation market can provide more accurate guidance for lithium-ion battery energy storage to participate in the electricity market.

[0106] S3: Construct a profit function for distributed lithium-ion battery energy storage to participate in the electricity market based on the profit model and the cost model.

[0107] By considering the total profit and total cost of lithium-ion energy storage participating in the electricity market, a profit maximization objective function for lithium-ion energy storage participating in the electricity market is constructed.

[0108] Specifically, the profit function is as follows:

[0109]

[0110] Where S represents the total number of scenes, s represents the s-th scene, and γ s Let represent the probability of scenario s occurring, T represent the time period for lithium-ion battery energy storage to participate in the electricity market, and t represent time t within the time period for lithium-ion battery energy storage to participate in the electricity market. This represents the profitability of lithium-ion battery storage in the energy market at time t under scenario s. This represents the profitability of lithium-ion battery storage in the frequency regulation market at time t under scenario s. This represents the operating cost of lithium-ion battery energy storage at time t. c represents the depreciation cost of lithium-ion battery energy storage at time t. m This indicates the maintenance cost of lithium-ion battery energy storage.

[0111] S4: Determine the maximum value of the profit function by taking the state of charge constraint and its own characteristic constraint of distributed lithium battery energy storage as constraints.

[0112] It is understandable that the profit function in step S3 is based on the total profit and total cost of lithium battery energy storage participating in the energy market and frequency regulation market. Therefore, when calculating the maximum value of the above profit function, it is necessary to consider the constraints when energy storage participates in the energy market and frequency regulation market. The above constraints include the state of charge constraint and the inherent characteristic constraint of lithium battery energy storage.

[0113] Specifically, the aforementioned state of charge constraints include:

[0114] (1) Charge state boundary constraints for the protection of lithium battery energy storage:

[0115]

[0116] Among them, SOC tv This represents the state of charge of the v-th lithium-ion battery at time t. This represents the state of charge limit of the v-th lithium-ion battery storage device. This represents the upper limit of the state of charge of the v-th lithium-ion energy storage device.

[0117] (2) State of charge constraints for lithium-ion battery energy storage when participating in the frequency regulation market:

[0118] Upper limit constraint for peak adjustment:

[0119]

[0120] Among them, SOC t,v This represents the state of charge of the v-th lithium-ion battery at time t. β represents the upper limit of the state of charge of the v-th lithium-ion battery storage device. t This represents the average duration of upward and downward adjustments to the bidding capacity within the bidding time interval Δt. This represents the bidding power of the v-th lithium-ion battery storage unit in the frequency regulation market at time t. This represents the charging efficiency of the v-th lithium-ion battery storage unit.

[0121] Lower limit constraint for peak reduction:

[0122]

[0123] Among them, SOC t,v This represents the state of charge of the v-th lithium-ion battery at time t. β represents the state of charge limit of the v-th lithium-ion battery storage device. t This represents the average duration of upward and downward adjustments to the bidding capacity within the bidding time interval Δt. This represents the bidding power of the v-th lithium-ion battery storage unit in the frequency regulation market at time t. This represents the charging efficiency of the v-th lithium-ion battery storage unit.

[0124] (3) State of charge constraints when lithium battery energy storage participates in the energy market:

[0125] Electricity purchase capacity upper limit constraint:

[0126]

[0127] Among them, SOC t,v This represents the state of charge of the v-th lithium-ion battery at time t. This represents the upper limit of the state of charge of the v-th lithium-ion battery storage device. This represents the charging efficiency of the v-th lithium-ion battery storage device. Δt represents the power purchased by the v-th lithium-ion battery storage unit in the energy market at time t, and Δt represents the bidding time interval.

[0128] Lower limit constraint on electricity sales capacity:

[0129]

[0130] Among them, SOC t,v This represents the state of charge of the v-th lithium-ion battery at time t. This represents the state of charge limit of the v-th lithium-ion battery storage device. This represents the discharge efficiency of the v-th lithium-ion battery. Let t represent the power sold by the vth lithium-ion battery storage unit in the energy market at time t, and Δt represent the bidding time interval.

[0131] (4) Capacity constraints for lithium battery energy storage to participate in bidding for energy markets and frequency regulation markets:

[0132]

[0133] Among them, SOC t,v This represents the state of charge of the v-th lithium-ion battery at time t. This represents the state of charge limit of the v-th lithium-ion battery storage device. This represents the upper limit of the state of charge of the v-th lithium-ion battery storage device. Δt represents the bidding capacity of lithium battery storage in the energy market at time t, and Δt represents the bidding time interval.

[0134] (5) Constraints arising from the combined bidding capacity and reserved backup capacity of lithium-ion energy storage in the frequency regulation market:

[0135]

[0136]

[0137] Among them, SOC t,v This represents the state of charge of the v-th lithium-ion battery at time t. This represents the upper limit of the state of charge of the v-th lithium-ion battery storage device. This represents the discharge efficiency of the v-th lithium-ion battery. This represents the charging efficiency of the v-th lithium-ion battery storage device. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the energy market at time t. h represents the bidding capacity of the v-th lithium-ion battery storage at time t in the frequency regulation market. reg Indicates the time required to provide spin-off standby service. Let h be the required reserve capacity of the v-th lithium-ion battery energy storage owner at time t. user denoted by , V represents the total number of lithium-ion energy storage units, v represents the v-th lithium-ion energy storage unit, T represents the time period for lithium-ion energy storage to participate in the electricity market, t represents time t within the time period for lithium-ion energy storage to participate in the electricity market, and Δt represents the bidding time interval.

[0138] The state of charge of the v-th lithium-ion battery at time t can be calculated using the following formula:

[0139]

[0140]

[0141] Among them, SOC t,v Let α represent the state of charge (SOC) of the v-th lithium-ion battery at time t, and let α represent the self-discharge rate of the lithium-ion battery. t-1,v This represents the state of charge of the v-th lithium-ion battery at time t-1. This represents the discharge efficiency of the v-th lithium-ion battery. This represents the charging efficiency of the v-th lithium-ion battery storage device. This represents the electricity sales power of the v-th lithium-ion battery storage unit in the energy market at time t. β represents the power purchased by the v-th lithium-ion battery storage unit in the energy market at time t, Δt represents the bidding time interval, and β t This represents the average duration of upward and downward adjustments to the bidding capacity within the bidding time interval Δt. Let V represent the bidding power of the v-th lithium-ion energy storage unit in the frequency regulation market at time t, where V represents the total number of lithium-ion energy storage units, v represents the v-th lithium-ion energy storage unit, T represents the time period for lithium-ion energy storage to participate in the electricity market, and t represents time t within the time period for lithium-ion energy storage to participate in the electricity market.

[0142] S5: Output the decision model for distributed lithium-ion energy storage participating in the electricity market when the profit function reaches its maximum value; wherein, the decision model includes the capacity of distributed lithium-ion energy storage to purchase electricity from the electricity market, the capacity to sell electricity from the electricity market, and the capacity to bid in the electricity market.

[0143] Steps S1 to S4 above quantify the profitability of distributed energy storage in the energy market and frequency regulation market. They also consider the operating costs, depreciation costs, and maintenance costs of distributed energy storage in the electricity market. Based on the relationship between profitability and costs, a profit function for distributed lithium-ion battery energy storage participating in the electricity market is constructed. Using the state-of-charge constraints and inherent characteristic constraints of distributed lithium-ion battery energy storage in the energy market and frequency regulation market as constraints, the maximum value of the above profit function is determined, i.e., when the profit of lithium-ion battery energy storage in the electricity market is maximized, as well as the capacity purchased and sold by distributed lithium-ion battery energy storage and the bidding capacity in the frequency regulation market.

[0144] When the above-mentioned profits are maximized, the optimal operating strategy for distributed lithium-ion energy storage at time t is the capacity of electricity purchased and sold from the energy market, as well as the bidding capacity in the frequency regulation market.

[0145] This invention provides an optimized decision-making method for distributed lithium-ion battery energy storage (Li-Nd.) participating in the electricity market. It constructs a profit model and a cost model for Li-Nd., and based on these models, a profit function for Li-Nd., is built. This method considers the differences in Li-Nd., including different scenarios, types, operating conditions, and technical characteristics, when Li-Nd., it uses the state-of-charge (SOC) constraints and inherent characteristics of distributed Li-Nd., and determines the maximum profit function, corresponding to the purchased and sold capacity of distributed Li-Nd., as well as the bidding capacity in the frequency regulation market. This solves the technical problem that existing allocation strategies are inadequate, leading to their failure to effectively guide distributed batteries into the electricity market.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0149] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0150] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0151] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimized decision-making method for distributed lithium-ion battery energy storage participating in the electricity market, characterized in that, include: S1: Based on the bidding capacity and market electricity price of distributed lithium-ion energy storage participating in the electricity market, construct a profit model for distributed lithium-ion energy storage participating in the electricity market; S2: Construct a cost model for distributed lithium-ion energy storage to participate in the electricity market based on the operating costs, depreciation costs, and maintenance costs of distributed lithium-ion energy storage participating in the electricity market; S3: Construct a profit function for distributed lithium-ion battery energy storage to participate in the electricity market based on the profit model and the cost model; S4: Determine the maximum value of the profit function based on the state of charge constraints and inherent characteristic constraints of distributed lithium-ion energy storage. S5: Output the decision model for distributed lithium-ion battery energy storage to participate in the electricity market when the profit function reaches its maximum value; wherein, the decision model includes the capacity of distributed lithium-ion battery energy storage to purchase electricity from the electricity market, the capacity to sell electricity from the electricity market, and the capacity to bid in the electricity market; The charge state constraints specifically include: in, This represents the state of charge of the v-th lithium-ion battery at time t. This represents the state of charge limit of the v-th lithium-ion battery storage device. This represents the upper limit of the state of charge of the v-th lithium-ion battery storage device. This indicates the self-discharge rate of lithium-ion battery energy storage. This represents the state of charge of the v-th lithium-ion battery at time t-1. This represents the discharge efficiency of the v-th lithium-ion battery. This represents the charging efficiency of the v-th lithium-ion battery storage device. This represents the electricity sales power of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the power purchased by the v-th lithium-ion battery storage unit in the energy market at time t. Indicates the time interval between bidding processes. This indicates the bidding capacity during the bidding interval. The average duration of internal upward and downward adjustments, This represents the bidding power of the v-th lithium-ion battery storage unit in the frequency regulation market at time t. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the frequency regulation market at time t. Indicates the time required to provide spin-off standby service. Let be the required reserve capacity of the v-th lithium-ion battery energy storage owner at time t. The value represents the backup time required by the owner of the lithium battery energy storage, V represents the total number of lithium battery energy storage units, v represents the v-th lithium battery energy storage unit, T represents the time period for lithium battery energy storage to participate in the electricity market, and t represents time t within the time period for lithium battery energy storage to participate in the electricity market. The self-characteristic constraints specifically include: in, This represents the electricity sales capacity of lithium-ion battery storage in the energy market at time t. This represents the capacity of lithium-ion battery storage to purchase electricity in the energy market at time t. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the frequency regulation market at time t. σ represents the maximum power of the v-th lithium-ion energy storage unit, σ represents the ratio of the capacity that the lithium-ion energy storage unit needs to increase / decrease to the unit capacity when winning a bid for a unit capacity in the frequency regulation market, V represents the total number of lithium-ion energy storage units, v represents the v-th lithium-ion energy storage unit, T represents the time period for the lithium-ion energy storage unit to participate in the electricity market, and t represents time t within the time period for the lithium-ion energy storage unit to participate in the electricity market.

2. The optimized decision-making method for distributed lithium-ion battery energy storage participating in the electricity market according to claim 1, characterized in that, The profit model includes an energy market profit model and a frequency regulation market profit model.

3. The optimized decision-making method for distributed lithium-ion battery energy storage participating in the electricity market according to claim 2, characterized in that, The specific profit model for the energy market is as follows: in, This represents the profitability of lithium-ion battery storage in the energy market at time t under scenario s. This represents the electricity price in the energy market at time t under scenario s. This represents the bidding capacity of lithium-ion battery storage in the energy market at time t. Indicates the time interval between bidding processes. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the electricity sales capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the electricity purchase capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the discharge efficiency of the v-th lithium-ion battery. This represents the charging efficiency of the v-th lithium-ion battery storage device. This represents the electricity sales power of the v-th lithium-ion battery storage unit in the energy market at time t. Let V represent the power purchased by the v-th lithium-ion energy storage unit in the energy market at time t, where V represents the total number of lithium-ion energy storage units and v represents the v-th lithium-ion energy storage unit.

4. The optimized decision-making method for distributed lithium-ion battery energy storage participating in the electricity market according to claim 2, characterized in that, The frequency modulation market profitability model includes a capacity profitability model and a performance profitability model. The capacity profitability model is as follows: in, This indicates the capacity profitability of lithium battery storage in the frequency regulation market at time t under scenario s. This represents the capacity electricity price in the frequency regulation market at time t under scenario s. This represents the bidding capacity of lithium-ion battery storage in the frequency regulation market at time t. This indicates the overall performance evaluation score of lithium battery storage in the frequency regulation market. This represents the bidding capacity of the v-th lithium-ion battery storage unit in the frequency regulation market at time t. The performance-based profitability model is as follows: in, This indicates the performance and profitability of lithium-ion battery storage in the frequency regulation market at time t under scenario s. This represents the performance-based electricity price in the frequency regulation market at time t under scenario s. This represents the frequency modulation market bidding capacity at time t. This indicates the overall performance evaluation score of lithium battery storage in the frequency regulation market. This represents the mileage ratio at time t under scenario s. Indicates a fast response frequency modulation signal. Indicates a slow-response frequency modulation signal; The specific profit model for the frequency modulation market is as follows: in, This represents the profitability of lithium-ion battery storage in the frequency regulation market at time t under scenario s. This indicates the capacity profitability of lithium battery storage in the frequency regulation market at time t under scenario s. This represents the performance and profitability of lithium battery storage in the frequency regulation market at time t under scenario s.

5. The optimized decision-making method for distributed lithium-ion battery energy storage participating in the electricity market according to claim 1, characterized in that, The cost model includes a lithium battery energy storage operation cost model, a lithium battery energy storage depreciation cost model, and a lithium battery energy storage maintenance cost model.

6. The optimized decision-making method for distributed lithium-ion battery energy storage participating in the electricity market according to claim 5, characterized in that, The specific lithium battery energy storage operation cost model is as follows: in, This represents the operating cost of lithium-ion battery energy storage at time t. This represents the unit operating cost of lithium-ion battery energy storage. This indicates the bidding capacity during the bidding interval. The average duration of internal upward and downward adjustments, This represents the electricity sales capacity of lithium-ion battery storage in the energy market at time t. This represents the capacity of lithium-ion battery storage to purchase electricity in the energy market at time t. This represents the bidding capacity of lithium-ion battery storage in the frequency regulation market at time t.

7. The optimized decision-making method for distributed lithium-ion battery energy storage participating in the electricity market according to claim 5, characterized in that, The specific lithium battery energy storage depreciation cost model is as follows: In the formula, This represents the depreciation cost of lithium-ion battery energy storage at time t. This represents the depreciation cost of the v-th lithium-ion battery storage unit at time t, where V represents the total number of lithium-ion battery storage units, and v represents the v-th lithium-ion battery storage unit. This represents the fixed cost of the v-th lithium-ion energy storage device. The slope of the aging rate of the v-th lithium-ion battery storage unit is represented by a dimensionless quantity. The horizontal axis represents the total energy throughput of the v-th lithium-ion battery storage unit, and the vertical axis represents the change in the maximum energy stored by the v-th lithium-ion battery storage unit. This represents the electricity sales capacity of the v-th lithium-ion battery storage unit in the energy market at time t. This represents the electricity purchase capacity of the v-th lithium-ion battery storage unit in the energy market at time t. Indicates the time interval between bidding processes. This indicates the bidding capacity during the bidding interval. The average duration of internal upward and downward adjustments, This represents the bidding capacity of lithium-ion battery storage in the frequency regulation market at time t. This represents the discharge efficiency of the v-th lithium-ion battery. This represents the charging efficiency of the v-th lithium-ion battery storage device. This represents the initial maximum storage capacity of the v-th lithium-ion battery energy storage device. This represents the fixed cost of the v-th lithium-ion battery storage unit.

8. The optimized decision-making method for distributed lithium-ion battery energy storage participating in the electricity market according to claim 5, characterized in that, The specific lithium battery energy storage maintenance cost model is as follows: in, This indicates the maintenance cost of lithium-ion battery energy storage. This represents the maintenance cost per unit of energy charged or discharged in lithium-ion battery energy storage. This indicates the total power of lithium battery energy storage. This represents the maximum power of the v-th lithium-ion battery storage unit.

9. The optimal decision-making method for distributed lithium-ion battery energy storage participating in the electricity market according to claim 1, characterized in that, The profit function is specifically as follows: Where S represents the total number of scenes, and s represents the s-th scene. Let represent the probability of scenario s occurring, T represent the time period for lithium-ion battery energy storage to participate in the electricity market, and t represent time t within the time period for lithium-ion battery energy storage to participate in the electricity market. This represents the profitability of lithium-ion battery storage in the energy market at time t under scenario s. This indicates the profitability of lithium-ion battery storage in the frequency regulation market at time t under scenario s. This represents the operating cost of lithium-ion battery energy storage at time t. This represents the depreciation cost of lithium-ion battery energy storage at time t. This indicates the maintenance cost of lithium-ion battery energy storage.

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