A user-side energy storage optimal deployment method fusing demand response in a capacity market environment
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-23
- Publication Date
- 2026-08-11
AI Technical Summary
[0004]针对现有技术中对电力市场电价特征考虑不充分、未协同融合需求响应与容量市场参与、用户侧储能研究不足、优化目标单一且未兼顾系统效益的问题，本申请提供一种容量市场环境下融合需求响应的用户侧储能优化部署方法，能够实现用户经济收益最大化、提升电力系统运行效益、适配容量市场与实时电价环境
1、本申请相较于传统的储能配置方案，创新性地构建了融合需求响应与储能调节的DR-ESS优化框架，首次将容量市场合同约束、现货市场实时电价波动与需求响应深度融合，解决了现有技术基于分时电价假设的局限性，更贴合实际电力市场运行场景；通过外层全寿命周期净收益最大化与内层日内收益最大化的双层优化结构结合遗传算法与fmincon工具箱的分层求解策略，既保障了全局最优搜索能力，又提升了约束优化问题的求解效率，实现了长期配置与短期运行的协同；同时，该方法在提升用户全寿命周期经济收益的基础上，还能有效削减系统峰值负荷、降低电网投资压力、增强电力系统供电可靠性，兼顾用户利益与系统效益；
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Abstract
Description
Technical Field
[0001] This invention relates to the technical field of energy storage optimization configuration in power systems, and in particular to a user-side energy storage optimization deployment method that integrates demand response in a capacity market environment. Background Technology
[0002] Currently, with the rapid evolution of China's power market and the continuous increase in the proportion of renewable energy installed capacity, the demand for flexible regulation and operational stability in the power system has significantly increased. The uncertainty and volatility of renewable energy output exacerbate the difficulty of grid dispatch, making the capacity market an increasingly important institutional choice for ensuring power supply security. It can provide flexible capacity support for intermittent renewable energy while stimulating investment in conventional generating capacity, ensuring sufficient reserve resources during peak load periods. Energy storage systems (ESS), with their advantages of rapid response and flexible dispatch, are considered a potential key player in the future capacity market. Researching the rational allocation of user-side energy storage in a capacity market environment is of great significance for improving user benefits, enhancing system stability, and promoting renewable energy consumption.
[0003] Existing energy storage optimization schemes are mostly based on time-of-use pricing assumptions, lacking sufficient consideration of the real-time fluctuation characteristics of spot market electricity prices. They are difficult to adapt to the actual operating environment of the electricity market and do not adequately consider the characteristics of electricity market prices. Furthermore, they do not combine user demand response (DR) resources with the scenario of energy storage systems participating in the capacity market, failing to fully leverage the benefits of synergistic optimization between the two and failing to integrate demand response and capacity market participation. They mostly focus on grid-side energy storage or wind-storage and photovoltaic-storage microgrid systems, with insufficient coverage of deployment schemes for user-side energy storage to coordinate with demand response in the capacity market. Alternatively, they only aim at renewable energy consumption or a single economic indicator, failing to achieve synergistic optimization of multiple objectives such as user benefits, system peak load reduction, and grid investment pressure reduction. The optimization objectives are singular and do not take into account system benefits. Summary of the Invention
[0004] To address the problems in existing technologies such as insufficient consideration of electricity market pricing characteristics, lack of coordination and integration of demand response and capacity market participation, inadequate research on user-side energy storage, and singular optimization objectives without taking into account system benefits, this application provides a user-side energy storage optimization deployment method that integrates demand response in a capacity market environment. This method can maximize user economic benefits, improve the operational efficiency of the power system, and adapt to the capacity market and real-time electricity price environment.
[0005] Firstly, the aforementioned inventive objective of this application is achieved through the following technical solutions: A method for optimizing the deployment of user-side energy storage with integrated demand response in a capacity market environment, the method comprising: Obtain key electricity price data from capacity market contracts, construct a demand response model based on the key electricity price data to conduct user electricity demand response analysis, and output the user's daily-scale electricity load curve; Based on the electricity load curve, a dual analysis is performed to maximize net profit and daily revenue over the entire life cycle on the user side. Based on the analysis results, a two-layer energy storage optimization configuration model is constructed. The current energy storage charging and discharging strategy is obtained, the strategy is optimized through the energy storage two-layer optimization configuration model, and the number of charging and discharging times and the charging and discharging depth after strategy optimization are counted to obtain charging and discharging data. The net benefit of energy storage over its entire life cycle is calculated based on the charge and discharge data. The rated charge and discharge power and rated capacity of the energy storage are iteratively updated based on the calculation results. When the preset iteration termination condition is reached, the optimal energy storage deployment strategy is output.
[0006] In a preferred embodiment, this application can be further configured as follows: obtaining key electricity price data from capacity market contracts, constructing a demand response model based on the key electricity price data to perform user electricity demand response analysis, and outputting a daily-scale electricity load curve for users, specifically including: Calculate the difference in electricity load before and after the real-time electricity price response based on the key electricity price data, perform user self-elastic demand response analysis based on the difference in electricity load, and construct a user self-elastic demand response sub-model. Define the cross elasticity matrix of user electricity demand, obtain incentive subsidies and penalties in capacity market contracts as price variables to derive user cross elasticity demand response, and construct a user cross elasticity demand response sub-model. By integrating the user self-elastic demand response sub-model and the user cross-elastic demand response sub-model, a real-time electricity price integrated demand response model that takes into account capacity market contracts is constructed. Based on the real-time electricity price integrated demand response model, user electricity demand response analysis is performed on the key electricity price data, and a daily-scale electricity load curve for users is constructed by using the electricity consumption time and the corresponding electricity demand.
[0007] In a preferred embodiment, this application can be further configured as follows: calculating the difference in user electricity load before and after the real-time electricity price response based on the key electricity price data, performing user self-elastic demand response analysis based on the electricity load difference, and constructing a user self-elastic demand response sub-model, specifically including: The expression for calculating the electrical load difference is as follows: (1) in, This represents the difference in electricity load for a user before and after the real-time electricity price response. , These represent the user's electricity load before and after responding to the real-time electricity price; The total subsidy price cost obtained by the user through demand response at each time point is calculated, and the expression for calculating the total subsidy price cost is as follows: (2) in, Indicates the user's position in the first month. The total subsidy price cost obtained through demand response at all times. This indicates that the user signed the contract on the [date] based on the capacity market settlement results. The incentive subsidy price obtained per hour for reducing unit load; The total penalty payable by a user for failing to reduce unit load in accordance with the capacity market contract is calculated, and the total penalty calculation expression is as follows: (3) in, This represents the total penalty payable by a user for failing to reduce unit load in accordance with the aforementioned capacity market contract. Indicates user number The penalty payable for unit load that is not reduced at all times. The first requirement of the capacity market contract The amount of load reduction at any given time; The user's benefit after the self-resilient response is calculated, and the benefit expression is as follows: (4) in, This indicates the first time after the user performs a self-resilient response. Total revenue at each moment This indicates the benefits that users gain from consuming electricity. Indicates the first Electricity price at any given time; The partial derivative of the revenue expression is calculated, and the user electricity load after the self-elastic demand response is analyzed based on the partial derivative result. Based on the analysis result, a user self-elastic demand response sub-model is constructed. The expression of the user self-elastic demand response sub-model is as follows: (5) in, This indicates the user's electricity load after considering the user's self-elastic demand response. Represents the 24-hour elasticity matrix matrix elements, , These represent the node where the user is located at the th position. The electricity price at a given time and the day-ahead real-time electricity price, This indicates that, according to the capacity market contract user, in the [number]th [year]... The incentive subsidy price obtained per unit of load should be reduced every hour. This represents the penalty that must be paid for reducing a unit of load.
[0008] In a preferred embodiment, this application may be further configured as follows: the partial derivative processing of the revenue expression, the analysis of user electricity load after self-elastic demand response based on the partial derivative result, and the construction of a user self-elastic demand response sub-model based on the analysis result, further includes: The partial derivative of the user's self-resilient response benefit is shown in the following expression: (6) Substituting the total subsidy price and the total penalty, we get: (7) The benefits of user self-elastic response are calculated and partial derivatives are performed. The expression for the benefit function is as follows: (8) The partial derivative expression of the benefit function is shown below: (9) in, This indicates the electrical energy consumed by the user before initiating a self-resilient response. The gains obtained , These represent the user's position in the [number]th month. Real-time electricity price at any given time and real-time electricity price a day before the current day. This represents the elasticity matrix composed of the electrical load at each moment of the user's self-elastic response within a daily timescale. Element.
[0009] In a preferred embodiment, this application can be further configured as follows: defining the user's electricity demand cross-elasticity matrix, obtaining incentive subsidies and penalties from capacity market contracts as price variables to derive the user's cross-elasticity demand response, and constructing a user cross-elasticity demand response sub-model, specifically including: The cross-elasticity matrix of user electricity demand is defined as follows: (10) in, Indicates the first Time and the A matrix representing the cross-elastic response of user electricity demand at any given time. Indicates the user's position in the first month. The current day's real-time electricity price. Indicates the first The electricity load before responding to user demand at any given moment. , They represent the first Power load after real-time user demand response Find the partial derivative of, and in the first... Real-time electricity price Find the partial derivative; Based on the aforementioned user electricity demand cross-elasticity matrix, the user's electricity demand after responding to real-time electricity prices is analyzed. The expression for the electricity demand is as follows: (11) in, This indicates the user's electricity load after responding to the real-time electricity price. , They represent the first Real-time electricity price at any given time and real-time electricity price a day before the current day. Indicates the first Price variables at any given time; The incentive subsidies and penalties in the capacity market contract are obtained. These incentive subsidies and penalties are used as price variables, and combined with the electricity demand expression, a user cross-elasticity demand response sub-model is derived. The expression of the user cross-elasticity demand response sub-model is shown below: (12) in, This indicates that the user, according to the signed capacity market contract, on the [date / period]... The incentive subsidy price per unit load should be reduced every hour. This indicates that the user, according to the signed capacity market contract, on the [date / period]... The penalty payable for failing to reduce unit load at all times.
[0010] In a preferred embodiment, this application can be further configured as follows: the fusion of the user self-resilient demand response sub-model and the user cross-resilient demand response sub-model to construct a real-time electricity price integrated demand response model taking into account capacity market contracts specifically includes: The calculation expression for the real-time electricity price integrated demand response model is shown below: (13) in, This indicates the electricity demand of users within a given day, based on signed capacity market contracts, after achieving maximum benefit through demand response. Indicates the first Electricity load before responding to user demand at any given moment; This represents the user-independent demand response sub-model. This represents the user cross-elasticity demand response sub-model.
[0011] In a preferred embodiment, this application can be further configured as follows: the step of performing a dual analysis of maximizing net profit over the entire life cycle of the user side and maximizing intraday revenue based on the electricity load curve, and constructing a two-layer energy storage optimization configuration model based on the analysis results, specifically includes: Based on the electricity load curve, a user-side lifecycle net profit maximization analysis is performed, and an outer optimization sub-model is constructed. The expression for maximizing the lifecycle net profit is as follows: (14) in, This represents the net revenue over the entire lifecycle of energy storage. This indicates the amount of electricity cost reduction throughout the entire life cycle of energy storage. This represents the net revenue that users obtain from executing the contract plan throughout the entire life cycle of energy storage. This represents the reduction in transformer costs resulting from the decrease in annual peak load throughout the entire lifespan of the energy storage system. , These represent the operation and maintenance costs throughout the entire life cycle of energy storage. and one-time fixed investment costs; Based on the user load curve, an analysis of maximizing intraday revenue is performed, and an inner-layer optimization sub-model is constructed. The expression for maximizing intraday revenue is as follows: (15) in, This represents the maximum daily benefit achieved by users after responding to demand by operating the energy storage system. This represents the total daily revenue earned by a user from executing contracts within a daily timeframe. This represents the total daily penalty payable by a user for failing to fulfill their contractual obligations within a given period. This represents the total daily electricity cost for users after demand response and energy storage operations are performed on a daily scale. Based on the outer optimization sub-model and the inner optimization sub-model, a dual-layer energy storage optimization configuration model is constructed.
[0012] In a preferred embodiment, this application can be further configured such that: the step of building a dual-layer energy storage optimization configuration model based on the outer layer optimization sub-model and the inner layer optimization sub-model also includes: The energy storage configuration optimization constraints are applied to the two-layer energy storage optimization model. These constraints include power balance constraints and user deployment cost constraints. The power balance constraint conditions are as follows: (16) in, Indicates the user side The power exchanged with the power grid at all times Indicates the first The charging and discharging power of the energy storage system at any given time. , The charging and discharging power of the battery are respectively expressed. The user deployment cost constraints are as follows: (17) in, This represents the maximum user investment cost; The energy storage operation strategy optimization constraints are applied to the energy storage two-layer optimization configuration model. The energy storage operation strategy optimization constraints include battery charging and discharging power constraints, battery state of charge constraints, and capacity market contract constraints. The battery charging and discharging power constraints are as follows: (18) in, , Indicates the energy storage system in the first The maximum charging and discharging power at any given moment; The battery state-of-charge constraints are as follows: (19) in, Indicates the rated capacity of the energy storage system. This represents the value at the end of one energy storage scheduling cycle. , These represent the initial state of charge and the final state of charge of the energy storage system, respectively. , These represent the minimum and maximum states of charge of the energy storage system, respectively. , They represent , State of charge at time t, Indicates the time difference within the scheduling period; The capacity market contract constraints are as follows: (20) in, , They represent the first The load to be reduced by users executing contracts and the load to be reduced for unfulfilled contracts. This indicates the amount of load reduction required by capacity market contracts.
[0013] In a preferred embodiment, this application can be further configured as follows: obtaining the current energy storage charging and discharging strategy, optimizing the strategy through the energy storage two-layer optimization configuration model, and statistically analyzing the number of charging and discharging cycles and the charging and discharging depth after strategy optimization to obtain charging and discharging data, specifically includes: Obtain the energy storage charging and discharging strategy of the current energy storage system, and optimize the energy storage charging and discharging strategy with the goal of maximizing the intraday revenue in the energy storage two-layer optimization configuration model. The number of charge / discharge cycles and the depth of charge / discharge of the energy storage system after the statistical strategy optimization are summarized to obtain the charge / discharge data of the energy storage system.
[0014] In a preferred embodiment, this application can be further configured as follows: The calculation of the net benefit over the entire lifecycle of energy storage based on the charge / discharge data, the iterative updating of the rated charge / discharge power and rated capacity of the energy storage based on the calculation results, and the output of the optimal energy storage deployment strategy when a preset iteration termination condition is met, specifically including: The net benefit of energy storage throughout its entire life cycle is calculated using a dual-layer energy storage optimization configuration model. Based on the calculation results, a genetic algorithm is used to iteratively update the rated charge and discharge power and rated capacity of the energy storage. When the preset iteration termination condition is met, the rated charge and discharge power and rated capacity of the energy storage in the last iteration are used as the optimal deployment parameters to optimize the energy storage operation system and output the optimal energy storage deployment strategy.
[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. Compared with traditional energy storage configuration schemes, this application innovatively constructs a DR-ESS optimization framework that integrates demand response and energy storage regulation. It is the first to deeply integrate capacity market contract constraints, real-time electricity price fluctuations in the spot market, and demand response, overcoming the limitations of existing technologies based on time-of-use pricing assumptions and better aligning with actual electricity market operation scenarios. By combining a two-layer optimization structure of maximizing net revenue over the outer life cycle and maximizing intraday revenue in the inner layer with a hierarchical solution strategy using genetic algorithms and the fmincon toolbox, it not only ensures global optimal search capabilities but also improves the solution efficiency of constrained optimization problems, achieving synergy between long-term configuration and short-term operation. At the same time, this method can effectively reduce system peak load, reduce grid investment pressure, and enhance power system supply reliability while improving the economic benefits of users over the entire life cycle, thus balancing user interests and system benefits. 2. This application proposes a real-time electricity price integrated demand response model based on capacity market contracts: it integrates self-elasticity and cross-elasticity, fully considers the real-time fluctuations of spot market electricity prices, capacity market subsidies and default penalties, and accurately simulates user load response behavior; 3. This application constructs a two-layer optimization configuration framework for energy storage that takes into account the capacity market: the outer layer optimizes energy storage power and capacity with the goal of maximizing net benefits over the entire life cycle, while the inner layer formulates charging and discharging strategies with the goal of maximizing daily benefits, thereby achieving synergistic optimization of long-term configuration and short-term operation; 4. This application adopts a hierarchical solution scheme combining genetic algorithm and fmincon toolbox: the outer layer searches for the globally optimal energy storage configuration parameters through genetic algorithm, and the inner layer uses effective set algorithm to efficiently solve the intraday charging and discharging strategy, which is suitable for solving nonlinear mixed integer optimization problems; 5. This application achieves multi-objective synergistic optimization: while improving users' economic benefits, it effectively reduces the system's peak load, reduces grid investment pressure, enhances the power supply reliability of the power system, and adapts to the power system operation needs under the high penetration rate of new energy. Attached Figure Description
[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.
[0017] Figure 1 This is a flowchart illustrating the implementation of the user-side energy storage optimization deployment method in a capacity market environment that integrates demand response, as described in this embodiment.
[0018] Figure 2 This is a flowchart illustrating the implementation of the demand response model for user electricity demand response analysis in this embodiment.
[0019] Figure 3 This is a flowchart illustrating the implementation of the dual-layer energy storage optimization configuration model in this embodiment.
[0020] Figure 4 This is a flowchart illustrating the strategy optimization implementation in this embodiment.
[0021] Figure 5 This is a flowchart illustrating the deployment optimization implementation in this embodiment. Detailed Implementation
[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0023] It should be understood that, when used in this specification, the terms “comprising” and “including” indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.
[0024] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” are intended to include the plural forms.
[0025] It should also be further understood that the term "and / or" as used in this specification refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.
[0026] In one embodiment, such as Figure 1 As shown, this application discloses a user-side energy storage optimization deployment method that integrates demand response in a capacity market environment, specifically including the following steps: S1: Obtain key electricity price data from capacity market contracts, construct a demand response model based on the key electricity price data to conduct user electricity demand response analysis, and output the user's daily-scale electricity load curve.
[0027] Specifically, capacity market contract parameters are obtained by parsing capacity market contracts, and user load, real-time electricity prices, and energy storage equipment parameters are acquired in real time during contract execution. Figure 2 As shown, step S1 includes: S11: Calculate the difference in electricity load before and after the real-time electricity price response based on key electricity price data, conduct user self-elastic demand response analysis based on the difference in electricity load, and construct a user self-elastic demand response sub-model.
[0028] Specifically, the expression for calculating the difference in electrical load is as follows: (1) in, This represents the difference in electricity load for a user before and after the real-time electricity price response. , These represent the user's electricity load before and after responding to the real-time electricity price.
[0029] The total subsidy price cost obtained by the user through demand response at each time point is calculated using the following expression: (2) in, Indicates the user's position in the first month. The total subsidy price cost obtained through demand response at all times. This indicates that the user signed the contract on the [date] based on the capacity market settlement results. The incentive subsidy price obtained per unit load should be reduced every hour at any given time.
[0030] The total penalty payable by a user for failing to reduce unit load in accordance with the capacity market contract is calculated using the following formula: (3) in, This represents the total penalty a user must pay for failing to reduce unit load in accordance with capacity market contracts. Indicates user number The penalty payable for unit load that is not reduced at all times. The first requirement of the capacity market contract The amount of load reduction at any given time.
[0031] The user's benefit after the self-resilient response is calculated using the following expression: (4) in, This indicates the first time after the user performs a self-resilient response. Total revenue at each moment Indicates the user's electricity consumption The benefits that can be obtained Indicates the first Electricity prices at any given time.
[0032] The partial derivative of the revenue expression is calculated, and the user electricity load after the self-elastic demand response is analyzed based on the partial derivative results. Based on the analysis results, a user self-elastic demand response sub-model is constructed. The expression of the user self-elastic demand response sub-model is shown below: (5) in, This indicates the user's electricity load after considering the user's self-elastic demand response. Represents the 24-hour elasticity matrix matrix elements, , These represent the node where the user is located at the th position. The electricity price at a given time and the day-ahead real-time electricity price, This indicates that, according to the capacity market contract user, in the [number]th [year]... The incentive subsidy price obtained per unit of load should be reduced every hour. This represents the penalty that must be paid for reducing a unit of load.
[0033] This embodiment also includes: The partial derivative of the user's self-resilient response benefit is shown in the following expression: (6) Substituting the total subsidy price and the total penalty, we get: (7) The benefits of user self-elastic response are calculated and partial derivatives are performed. The benefit function expression is shown below: (8) The partial derivative expression of the benefit function is shown below: (9) in, This indicates the electrical energy consumed by the user before initiating a self-resilient response. The gains obtained , These represent the user's position in the [number]th month. Real-time electricity price at any given time and real-time electricity price a day before the current day. This represents the elasticity matrix composed of the electrical load at each moment of the user's self-elastic response within a daily timescale. Element.
[0034] Based on the derivation of the user load expression based on formula (9), the user power load after considering self-elasticity is analyzed, and formula (5) is obtained.
[0035] S12: Define the cross-elasticity matrix of user electricity demand, obtain incentive subsidies and penalties in capacity market contracts as price variables to derive the user cross-elasticity demand response, and construct a user cross-elasticity demand response sub-model.
[0036] Specifically, the cross-elasticity matrix of user electricity demand is defined as follows: (10) in, Indicates the first Time and the A matrix representing the cross-elastic response of user electricity demand at any given time. Indicates the user's position in the first month. The current day's real-time electricity price. Indicates the first The electricity load before responding to user demand at any given moment. , They represent the first Power load after real-time user demand response Find the partial derivative of, and in the first... Real-time electricity price Find the partial derivative.
[0037] Assuming the demand function is a linear function, then It is a constant. , It is a positive integer, and .
[0038] Based on the cross-elasticity matrix of user electricity demand, we analyze the user's electricity demand after responding to real-time electricity prices. The electricity demand expression is as follows: (11) in, This indicates the user's electricity load after responding to the real-time electricity price. , They represent the first Real-time electricity price at any given time and real-time electricity price a day before the current day. Indicates the first Price variables at any given time.
[0039] By obtaining the incentive subsidies and penalties from capacity market contracts and using them as price variables, and combining them with the electricity demand expression, a user cross-elasticity demand response sub-model is derived. The expression of the user cross-elasticity demand response sub-model is shown below: (12) in, This indicates that the user, according to the signed capacity market contract, on the [date / period]... The incentive subsidy price per unit load should be reduced every hour. This indicates that the user, according to the signed capacity market contract, on the [date / period]... The penalty payable for failing to reduce unit load at all times.
[0040] Formula (11) considers the user demand response situation for 24 hours a day. Combined with the derivation of self-elastic demand, the incentive subsidies and penalties in the contract are included in the price variables to derive formula (12) to construct the user cross-elastic demand response sub-model.
[0041] S13: Integrate the user self-elastic demand response sub-model and the user cross-elastic demand response sub-model to construct a real-time electricity price integrated demand response model that takes into account capacity market contracts.
[0042] Specifically, the calculation expression for the real-time electricity price integrated demand response model is as follows: (13) in, This indicates the electricity demand of users within a given day, based on signed capacity market contracts, after achieving maximum benefit through demand response. Indicates the first Electricity load before responding to user demand at any given moment; This represents the user-independent demand response sub-model. This represents the user cross-elasticity demand response sub-model.
[0043] Formula (13) represents the electricity demand that a user should obtain within 24 hours based on the capacity market contract and the maximum benefit that demand response should provide.
[0044] S14: Based on the real-time electricity price integrated demand response model, conduct user electricity demand response analysis on key electricity price data, and construct the user's daily-scale electricity load curve with the electricity consumption time and the corresponding electricity demand.
[0045] Specifically, by using a real-time electricity price comprehensive demand response analysis model and combining it with a real-time electricity price driving mechanism, the dynamic response behavior of users to electricity price signals is simulated, and a daily-scale (24-hour) electricity load curve for users is constructed using the electricity consumption time and the corresponding electricity demand as coordinates.
[0046] S2: Based on the electricity load curve, perform dual analysis to maximize net profit and intraday revenue over the entire life cycle on the user side, and construct a dual-layer energy storage optimization configuration model based on the analysis results.
[0047] Specifically, in this embodiment, the electricity load curve after demand response adjustment is used as input to establish a two-layer optimization model that maximizes the net revenue over the outer life cycle and the intraday revenue within the inner layer, as follows: Figure 3 As shown, it specifically includes: S21: Based on the electricity load curve, perform a user-side lifecycle net profit maximization analysis, construct an outer optimization sub-model, and the expression for maximizing lifecycle net profit is shown below: (14) in, This represents the net revenue over the entire lifecycle of energy storage. This indicates the amount of electricity cost reduction throughout the entire life cycle of energy storage. This represents the net revenue that users obtain from executing the contract plan throughout the entire life cycle of energy storage. This represents the reduction in transformer costs resulting from the decrease in annual peak load throughout the entire lifespan of the energy storage system. , These represent the operation and maintenance costs throughout the entire life cycle of energy storage. And one-time fixed investment costs.
[0048] In this embodiment, the reduction in electricity costs over the entire lifecycle of energy storage mainly includes the reduction in electricity costs for daily user consumption and the reduction in electricity costs for monthly user capacity. ,in, This indicates the electricity cost per user on a daily basis. The calculation expressions for user capacity electricity charges on a monthly scale are as follows: (twenty one) (twenty two) in, Indicates user number Year The reduction in electricity consumption and electricity costs per day Indicates user number Year Monthly reduction in capacity electricity costs, Indicates the number of days in a year. Indicates the number of months in a year. Indicates energy storage lifespan. Indicates the discount rate. This indicates the inflation rate.
[0049] The formula for calculating the net revenue obtained by users from executing the contract plan throughout the entire life cycle of energy storage is as follows: (twenty three) in, Indicates user number Year The total revenue obtained after the contract requires the reduction of response load. Indicates user number Year The total penalty payable for breach of contract and failure to reduce load.
[0050] Since transformers and energy storage devices have different lifecycles, and considering the time-dependent nature of funds, the calculation expression for the reduction in transformer costs due to the decrease in annual peak load over the entire lifecycle of energy storage is as follows: (twenty four) in, The transformer cost reduction resulting from the decrease in the user's annual peak load is expressed by the following formula: (25) in, This indicates the ratio of transformer installation costs to equipment costs. This indicates the unit cost of transformers for industrial users. This indicates the annual load peak reduction rate. , This represents the user's original maximum annual load. This represents the maximum annual load after user participation in demand response and energy storage regulation. Indicates the transformer load rate. Indicates the power factor. This indicates the transformer's lifespan.
[0051] The total life-cycle cost of user-side energy storage includes operation and maintenance costs. and one-time fixed investment costs The calculation expressions for both are as follows: (26) (27) in, , These are the rated capacity and rated charge / discharge power of the energy storage system, respectively. This indicates the cost per unit capacity of energy storage. This indicates the cost per unit of charge / discharge power of energy storage. This represents the annual operating and maintenance cost per unit charge / discharge power of an energy storage system.
[0052] S22: Based on the user load curve, perform intraday revenue maximization analysis and construct an inner-layer optimization sub-model. The intraday revenue maximization expression is shown below: (15) in, This represents the maximum daily benefit achieved by users after responding to demand by operating the energy storage system. This represents the total daily revenue earned by a user from executing contracts within a daily timeframe. This represents the total daily penalty payable by a user for failing to fulfill their contractual obligations within a given period. This represents the total daily electricity cost incurred by a user after demand response and energy storage operations are performed on a daily scale.
[0053] The formula for calculating the total daily revenue earned by a user from executing a contract within a daily timeframe is as follows: (28) The formula for calculating the total daily penalty for a user's failure to perform a contract within a given day is as follows: (29) in, , The first The load reduced by users executing capacity contracts and the load of unfulfilled capacity schedule contracts.
[0054] A user's daily electricity bill is calculated by adding up the electricity bills for each time period, i.e.: (30) (31) in, Indicates the first Electricity costs at all times.
[0055] S23: Based on the outer and inner optimization sub-models, a dual-layer optimization configuration model for energy storage is built.
[0056] Specifically, the outer optimization sub-model is used to optimize energy storage configuration, while the inner optimization sub-model is used to optimize energy storage operation strategy. The two sub-models are combined to build a dual-layer optimization configuration model for energy storage.
[0057] This embodiment also includes: S24: Apply energy storage configuration optimization constraints to the two-layer energy storage optimization model. The energy storage configuration optimization constraints include power balance constraints and user deployment cost constraints. The power balance constraint conditions are as follows: (16) in, Indicates the user side The power exchanged with the power grid at all times Indicates the first The charging and discharging power of the energy storage system at any given time, in this embodiment, during centralized discharge. For positive, , The charging and discharging power of the battery are respectively represented.
[0058] The user deployment cost constraints are as follows: (17) in, This represents the maximum user investment cost.
[0059] S25: Apply energy storage operation strategy optimization constraints to the two-layer energy storage optimization configuration model. The energy storage operation strategy optimization constraints include battery charging and discharging power constraints, battery state of charge constraints, and capacity market contract constraints.
[0060] The battery charging and discharging power constraints are as follows: (18) in, This indicates that the daily charging and discharging power of the energy storage system is conserved. Indicates the energy storage system in the first The maximum charging and discharging power at any given moment.
[0061] The battery state-of-charge constraints are as follows: (19) in, Indicates the rated capacity of the energy storage system. This represents the value at the end of one energy storage scheduling cycle. , These represent the initial state of charge and the final state of charge of the energy storage system, respectively. , These represent the minimum and maximum values of the state of charge (SOC) of the energy storage system, respectively, indicating that the SOC of the energy storage system remains within the extreme range during operation. , They represent , State of charge at time t, This indicates the time difference within a scheduling cycle; to ensure continuous operation, the state of charge of the energy storage system must remain consistent at the beginning and end of a scheduling cycle.
[0062] The capacity market contract constraints are as follows: (20) in, , They represent the first The load to be reduced by users executing contracts and the load to be reduced for unfulfilled contracts. This indicates the amount of load reduction required by capacity market contracts.
[0063] S3: Obtain the current energy storage charging and discharging strategy, optimize the strategy through the energy storage two-layer optimization configuration model, and count the number of charging and discharging cycles and the charging and discharging depth after strategy optimization to obtain charging and discharging data.
[0064] Specifically, such as Figure 4 As shown, step S3 includes: S31: Obtain the energy storage charging and discharging strategy of the current energy storage system, and optimize the energy storage charging and discharging strategy with the goal of maximizing intraday revenue in the energy storage two-layer optimization configuration model.
[0065] Specifically, the charging and discharging strategy of the current energy storage system is obtained, and the charging and discharging strategy is solved and optimized in a hierarchical manner with the goal of maximizing daily revenue using the fmincon toolbox.
[0066] S32: Statistically analyze the number of charge and discharge cycles and the depth of charge and discharge of the energy storage system after optimization of the statistical strategy, and summarize the charge and discharge data of the energy storage system.
[0067] Specifically, the number of charge-discharge cycles and the depth of charge-discharge of the energy storage system during the statistical strategy optimization process are summarized to obtain the charge-discharge data of the energy storage system.
[0068] S4: Calculate the net benefit of energy storage throughout its entire life cycle based on charge and discharge data. Iterate and update the rated charge and discharge power and rated capacity of energy storage based on the calculation results. When the preset iteration termination condition is reached, output the optimal energy storage deployment strategy.
[0069] Specifically, such as Figure 5 As shown, step S4 includes: S41: The net benefit of energy storage throughout its entire life cycle is calculated by using a dual-layer energy storage optimization configuration model. Based on the calculation results, a genetic algorithm is used to iteratively update the rated charging and discharging power and rated capacity of the energy storage.
[0070] Specifically, the net benefit of energy storage throughout its entire life cycle is calculated using a dual-layer energy storage optimization configuration model. Based on the net benefit of energy storage throughout its entire life cycle, the rated charging and discharging power and rated capacity of the energy storage are iteratively updated using a genetic algorithm.
[0071] S42: When the preset iteration termination condition is met, the rated charge and discharge power and rated capacity of the energy storage in the last iteration are used as the optimal deployment parameters to optimize the energy storage operation system and output the optimal energy storage deployment strategy.
[0072] Specifically, when the preset iteration termination conditions are met, such as reaching the maximum number of iterations or reaching the maximum net benefit of the energy storage over its entire life cycle, the rated charge and discharge power iteration value and rated capacity iteration value of the energy storage in the last iteration are used as the optimal deployment parameters to optimize the energy storage operation strategy and output the optimal energy storage deployment strategy.
[0073] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0074] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application of the technical solution and the constraints involved. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.
[0075] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.
[0076] 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.
[0077] 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, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.
Claims
1. A method for optimizing the deployment of user-side energy storage in a capacity market environment that integrates demand response, characterized in that, The method includes: Obtain key electricity price data from capacity market contracts, construct a demand response model based on the key electricity price data to conduct user electricity demand response analysis, and output user daily-scale electricity load curves; Based on the electricity load curve, a dual analysis is performed to maximize net profit and daily revenue over the entire life cycle on the user side. Based on the analysis results, a two-layer energy storage optimization configuration model is constructed. The current energy storage charging and discharging strategy is obtained, the strategy is optimized through the energy storage two-layer optimization configuration model, and the number of charging and discharging times and the charging and discharging depth after strategy optimization are counted to obtain charging and discharging data. The net benefit of energy storage over its entire life cycle is calculated based on the charge and discharge data. The rated charge and discharge power and rated capacity of the energy storage are iteratively updated based on the calculation results. When the preset iteration termination condition is reached, the optimal energy storage deployment strategy is output. 2.The method of claim 1, wherein The process of acquiring key electricity price data from capacity market contracts, constructing a demand response model based on this key electricity price data to perform user electricity demand response analysis, and outputting daily-scale electricity load curves for users specifically includes: Calculate the difference in electricity load before and after the real-time electricity price response based on the key electricity price data, perform user self-elastic demand response analysis based on the difference in electricity load, and construct a user self-elastic demand response sub-model. Define the cross elasticity matrix of user electricity demand, obtain incentive subsidies and penalties in capacity market contracts as price variables to derive user cross elasticity demand response, and construct a user cross elasticity demand response sub-model. By integrating the user self-elastic demand response sub-model and the user cross-elastic demand response sub-model, a real-time electricity price integrated demand response model that takes into account capacity market contracts is constructed. Based on the real-time electricity price integrated demand response model, user electricity demand response analysis is performed on the key electricity price data, and a daily-scale electricity load curve for users is constructed by using the electricity consumption time and the corresponding electricity demand.
3. The method of claim 2, wherein, The step involves calculating the difference in user electricity load before and after the real-time electricity price response based on the key electricity price data, performing user self-elastic demand response analysis based on the electricity load difference, and constructing a user self-elastic demand response sub-model, specifically including: The expression for calculating the electrical load difference is as follows: （1） wherein, represents the difference of the electricity load of the user before and after the real-time electricity price response, , respectively represents the electricity load of the user before and after the real-time electricity price response; The total subsidy price cost obtained by the user through demand response at each time point is calculated, and the expression for calculating the total subsidy price cost is as follows: （2） wherein, represents the total subsidy price cost obtained by the user through demand response at the hourly reduction unit load at the represents the incentive subsidy price obtained by the user signing a contract according to the capacity market settlement result at the hourly reduction unit load at the The total penalty payable by a user for failing to reduce unit load in accordance with the capacity market contract is calculated, and the total penalty calculation expression is as follows: （3） wherein, represents the total penalty paid by the user for not making the unit load reduction according to the capacity market contract, represents the penalty paid by the user for not reducing the unit load at the time instant, represents the load reduction amount required by the capacity market contract at the time instant; The user's benefit after the self-resilient response is calculated, and the benefit expression is as follows: （4） wherein, represents the total revenue at the time after the user has responded elastically, represents the benefit that the user can obtain from consuming electrical energy, represents the electricity price at the time . The partial derivative of the revenue expression is calculated, and the user electricity load after the self-elastic demand response is analyzed based on the partial derivative result. Based on the analysis result, a user self-elastic demand response sub-model is constructed. The expression of the user self-elastic demand response sub-model is as follows: （5） in, This indicates the user's electricity load after considering the user's self-elastic demand response. Represents the 24-hour elasticity matrix matrix elements, , These represent the node where the user is located at the th position. The electricity price at a given time and the day-ahead real-time electricity price, This indicates that, according to the capacity market contract user, in the [number]th [year]... The incentive subsidy price obtained per unit load should be reduced every hour. This represents the penalty that must be paid for reducing a unit of load.
4. The user-side energy storage optimization deployment method integrating demand response in a capacity market environment as described in claim 3, characterized in that, The step of taking partial derivatives of the revenue expression, analyzing the user electricity load after self-elastic demand response based on the partial derivative results, and constructing a user self-elastic demand response sub-model based on the analysis results, further includes: The partial derivative of the user's self-resilient response benefit is shown in the following expression: （6） Substituting the total subsidy price and the total penalty, we get: （7） The benefits of user self-elastic response are calculated and partial derivatives are performed. The expression for the benefit function is as follows: （8） The partial derivative expression of the benefit function is shown below: （9） in, This indicates the electrical energy consumed by the user before initiating a self-resilient response. The gains obtained , These represent the user's position in the [number]th month. Real-time electricity price at any given time and real-time electricity price a day before the current day. This represents the elasticity matrix composed of the electrical load at each moment of the user's self-elastic response within a daily timescale. Element.
5. The method of claim 3, wherein, The process involves defining the cross-elasticity matrix of user electricity demand, obtaining incentive subsidies and penalties from capacity market contracts as price variables to derive the user cross-elasticity demand response, and constructing a user cross-elasticity demand response sub-model, specifically including: The cross-elasticity matrix of user electricity demand is defined as follows: （10） in, Indicates the first Time and the A matrix representing the cross-elastic response of user electricity demand at any given time. Indicates the user's position in the first month. The current day's real-time electricity price. Indicates the first The electricity load before responding to user demand at any given moment. , They represent the first Power load after real-time user demand response Find the partial derivative of , and in the . Real-time electricity price Find the partial derivative; Based on the aforementioned user electricity demand cross-elasticity matrix, the user's electricity demand after responding to real-time electricity prices is analyzed. The expression for the electricity demand is as follows: （11） in, This indicates the user's electricity load after responding to the real-time electricity price. , They represent the first Real-time electricity price at any given time and real-time electricity price a day before the current day. Indicates the first Price variables at any given time; The incentive subsidies and penalties in the capacity market contract are obtained. These incentive subsidies and penalties are used as price variables, and combined with the electricity demand expression, a user cross-elasticity demand response sub-model is derived. The expression of the user cross-elasticity demand response sub-model is shown below: （12） in, This indicates that the user, according to the signed capacity market contract, on the [date / period]... The incentive subsidy price per unit load should be reduced every hour. This indicates that the user, according to the signed capacity market contract, on the [date / period]... The penalty payable for failing to reduce unit load at all times.
6. The method of claim 5, wherein, The method of integrating the user-independent demand response sub-model and the user-cross-independent demand response sub-model to construct a real-time electricity price integrated demand response model that takes into account capacity market contracts specifically includes: The calculation expression for the real-time electricity price integrated demand response model is shown below: （13） in, This indicates the electricity demand of users within a given day, based on signed capacity market contracts, after achieving maximum benefit through demand response. Indicates the first Electricity load before responding to user demand at any given moment; This represents the user-independent demand response sub-model. This represents the user cross-elasticity demand response sub-model. 7.The method of claim 1, wherein, The process involves a dual analysis based on the electricity load curve to maximize both the net profit over the entire lifecycle of the user and the daily revenue. Based on the analysis results, a two-layer energy storage optimization configuration model is constructed, specifically including: Based on the electricity load curve, a user-side lifecycle net profit maximization analysis is performed, and an outer optimization sub-model is constructed. The expression for maximizing the lifecycle net profit is as follows: （14） in, This represents the net revenue over the entire lifecycle of energy storage. This indicates the amount of electricity cost reduction throughout the entire life cycle of energy storage. This represents the net revenue that users obtain from executing the contract plan throughout the entire life cycle of energy storage. This represents the reduction in transformer costs resulting from the decrease in annual peak load throughout the entire lifespan of the energy storage system. , These represent the operation and maintenance costs throughout the entire life cycle of energy storage. and one-time fixed investment costs; Based on the user load curve, an analysis of maximizing intraday revenue is performed, and an inner-layer optimization sub-model is constructed. The expression for maximizing intraday revenue is as follows: （15） in, This represents the maximum daily benefit achieved by users after responding to demand by operating the energy storage system. This represents the total daily revenue earned by a user from executing a contract within a daily timeframe. This represents the total daily penalty payable by a user for failing to fulfill their contractual obligations within a given period. This represents the total daily electricity cost for users after demand response and energy storage operations are performed on a daily scale. Based on the outer optimization sub-model and the inner optimization sub-model, a dual-layer energy storage optimization configuration model is constructed. 8.The method of claim 7, wherein, The step of building a dual-layer energy storage optimization configuration model based on the outer layer optimization sub-model and the inner layer optimization sub-model further includes: The energy storage configuration optimization constraints are applied to the two-layer energy storage optimization model. These constraints include power balance constraints and user deployment cost constraints. The power balance constraint conditions are as follows: （16） in, Indicates the user side The power exchanged with the power grid at all times Indicates the first The charging and discharging power of the energy storage system at any given time. , The charging and discharging power of the battery are respectively expressed. The user deployment cost constraints are as follows: （17） wherein, represents the maximum user investment cost; The energy storage operation strategy optimization constraints are applied to the energy storage two-layer optimization configuration model. The energy storage operation strategy optimization constraints include battery charging and discharging power constraints, battery state of charge constraints, and capacity market contract constraints. The battery charging and discharging power constraints are as follows: （18） in, , Indicates the energy storage system in the first The maximum charging and discharging power at any given moment; The battery state-of-charge constraints are as follows: （19） in, Indicates the rated capacity of the energy storage system. This represents the value at the end of one energy storage scheduling cycle. , These represent the initial state of charge and the final state of charge of the energy storage system, respectively. , These represent the minimum and maximum states of charge of the energy storage system, respectively. , They represent , State of charge at time t, Indicates the time difference within the scheduling period; The capacity market contract constraints are as follows: （20） wherein, , denote the load that the user performs contract curtailment at the time and the load that is to be curtailed by the user for the unfulfilled contract, respectively, denote the load curtailment amount required by the capacity market contract. 9.The method of claim 1, wherein, The process of obtaining the current energy storage charging and discharging strategy, optimizing the strategy using the energy storage two-layer optimization configuration model, and statistically analyzing the number of charging and discharging cycles and the depth of charging and discharging after strategy optimization to obtain charging and discharging data specifically includes: Obtain the energy storage charging and discharging strategy of the current energy storage system, and optimize the energy storage charging and discharging strategy with the goal of maximizing the intraday revenue in the energy storage two-layer optimization configuration model. The number of charge / discharge cycles and the depth of charge / discharge of the energy storage system after the statistical strategy optimization are summarized to obtain the charge / discharge data of the energy storage system. 10.The method of claim 1, wherein, The calculation of net lifecycle benefits of energy storage based on the charge and discharge data, the iterative updating of rated charge and discharge power and rated capacity of energy storage based on the calculation results, and the output of the optimal energy storage deployment strategy when a preset iteration termination condition is reached specifically include: The net benefit of energy storage throughout its entire life cycle is calculated using a dual-layer energy storage optimization configuration model. Based on the calculation results, a genetic algorithm is used to iteratively update the rated charge and discharge power and rated capacity of the energy storage. When the preset iteration termination condition is met, the rated charge and discharge power and rated capacity of the energy storage in the last iteration are used as the optimal deployment parameters to optimize the energy storage operation system and output the optimal energy storage deployment strategy.