A method for coordinated optimization of independent energy storage configuration and operation settlement considering capacity support capability

Abstract

The application discloses a kind of independent energy storage configuration and operation settlement coordination optimization method considering capacity support capability, belong to power system energy storage planning and power market optimization technical field.The method is with independent energy storage investment configuration as object, based on target area wind power, photovoltaic and load actual operation data constructs multi-scenario operation environment, and on the basis of given energy storage investment offer decision, in turn carry out market operation response solution, operation settlement income calculation and high net load duration capacity support capability evaluation, establish the feedback update relationship between energy storage configuration scheme, market operation result, settlement income and capacity support shortage.Through the above process, independent energy storage configuration scheme considering investment economy, operation feasibility, market income capacity and sustained capacity support capability can be formed.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage planning and power market optimization technology, specifically to a collaborative optimization method for independent energy storage configuration and operation settlement that takes into account capacity support capabilities. Background Technology

[0002] With the continuous expansion of installed capacity of new energy sources such as wind power and photovoltaics, the uncertainty and volatility of both the power system's source and load sides have significantly increased. New energy output is characterized by randomness, intermittency, and anti-peak shaving, while load demand varies significantly with seasonal, time-of-day, and regional energy consumption structures. This puts the system under greater pressure for peak shaving, reserve, and real-time balancing under conditions of high new energy penetration.

[0003] Energy storage resources, with their rapid charging and discharging, flexible adjustment, and energy time-shifting capabilities, are gradually becoming important resources for improving the absorption capacity of new energy sources, enhancing system flexibility, and supporting capacity guarantees.

[0004] However, existing energy storage configuration methods often focus on site selection and capacity determination or minimizing system operating costs, typically treating energy storage operating revenue as a simplified exogenous parameter, lacking a detailed description of market operation processes and settlement mechanisms. While some methods consider energy storage participation in the electricity market or ancillary services market, they often treat day-ahead operation, standby deployment, and real-time dispatch separately, failing to uniformly describe their coupling relationships.

[0005] Furthermore, under conditions of high renewable energy penetration, energy storage not only needs to participate in market operations to generate revenue, but also needs to have the ability to continuously supply energy during periods of high net load. For independent energy storage, if it only pursues short-term market operation revenue and frequently participates in charging, discharging, and real-time dispatch, it may result in insufficient available power at the beginning of periods of high net load. Even if its rated power is high, it will be difficult to continuously provide supporting power during continuous peak periods.

[0006] Furthermore, energy storage investment and configuration are significantly scenario-dependent. Wind power, solar power, and load data vary across different regions, and the charging and discharging requirements, backup support requirements, real-time call frequency, and capacity fulfillment pressures of energy storage also differ under different operating scenarios. Without constructing multi-scenario operating environments based on the actual historical wind power, solar power, and load data of the target region, it is difficult to accurately assess the future profitability and investment recovery capability of energy storage configuration solutions in that region.

[0007] Therefore, it is necessary to propose a collaborative optimization method for independent energy storage configuration and operation settlement that takes into account capacity support capabilities. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a method for the coordinated optimization of independent energy storage configuration and operation settlement that takes into account capacity support capabilities. This method can yield an independent energy storage configuration scheme that balances investment economics, operational feasibility, real-time response capabilities, and continuous energy supply capabilities with high net load, thereby achieving coordinated optimization among energy storage configuration, market operation response, settlement revenue, and continuous capacity support capability evaluation.

[0009] The present invention adopts the following technical solution.

[0010] This invention provides a method for coordinated optimization of independent energy storage configuration and operation settlement, taking into account capacity support capability, comprising the following steps:

[0011] S1: Acquire operational scenario data, basic power system parameters, and energy storage technology and economic parameters for the target area, and construct a set of typical operational scenarios based on the operational scenario data for the target area;

[0012] S2: Based on the typical operating scenario set and energy storage technology and economic parameters, construct an independent energy storage upper-layer investment pricing model, and solve it to obtain the energy storage investment pricing decision for the current round;

[0013] S3: Based on the current round of energy storage investment bidding decisions, target area operation scenario data, power system basic parameters and energy storage technical and economic parameters, construct a multi-scenario lower-level market operation response model, and solve for the optimal operation results and market operation settlement revenue;

[0014] S4: Based on the optimal operating results and the continuous characteristics of peak net load in the scenario, establish a continuous capacity support capability evaluation model to calculate the capacity support deficit of energy storage in various typical operating scenarios.

[0015] S5: Feed the market operation settlement revenue and capacity support deficit back to the independent energy storage upper-layer investment pricing model to update the energy storage investment pricing decision;

[0016] S6: Repeat S2 to S5 until the energy storage investment pricing decision and comprehensive evaluation objectives meet the iteration termination conditions, and output the independent energy storage configuration results, operation settlement results and continuous capacity support evaluation results to achieve collaborative optimization of independent energy storage configuration and operation settlement.

[0017] Optionally, the target area operation scenario data includes target area load time-series data, wind power output time-series data, and photovoltaic output time-series data. The typical operation scenario set constructed in S1 based on the target area operation scenario data includes:

[0018] The load time series data, wind power output time series data, and photovoltaic output time series data of the target area in the historical period are divided into daily scene samples by day;

[0019] For each daily scene sample, calculate the net load time series curve and extract source load matching features, net load peak features, net load valley features, net load ramp features, net load fluctuation features, and high net load persistence features to form a daily scene feature vector;

[0020] After normalizing the daily scene feature vectors, a clustering method is used to aggregate the scenes. The actual daily scene sample closest to the cluster center in each cluster is taken as the typical operating scene, and the number of actual daily scene samples contained in the cluster is taken as the representative number of days of the corresponding typical operating scene.

[0021] Optionally, in S2, the independent energy storage upper-layer investment pricing model aims to maximize the overall return for independent energy storage investors, and its objective function is:

[0022] ,

[0023] In the formula, This is the capital recovery factor. For energy storage investment costs, For the scene The market operation settlement revenue is calculated by the multi-scenario lower-level market operation response model. This is the capacity support shortfall penalty coefficient, used to characterize the strength of suppression against insufficient peak-hour support from energy storage. The annual equivalent capacity support deficit is obtained from the continuous capacity support capability evaluation model. Typical operating scenarios The number of days represented;

[0024] Under preset constraints, the energy storage investment pricing decision variables that maximize the objective function value are obtained and used as the energy storage investment pricing decision for the current round.

[0025] The decision variables for energy storage investment pricing are expressed as follows:

[0026] ,

[0027] In the formula, Indicates energy storage unit Whether to build, and Indicates its candidate access node Build energy storage For energy storage units Rated power capacity, For energy storage units Rated energy capacity; and Energy storage units Quotation parameters for day-to-day and real-time markets.

[0028] Optionally, in S3, based on the current round of energy storage investment bidding decisions, target area operation scenario data, power system basic parameters, and energy storage technical and economic parameters, a multi-scenario lower-level market operation response model is constructed to solve for the optimal operation results and market operation settlement revenue, including:

[0029] A standard market clearing model is constructed for each typical operating scenario. The standard market clearing model aims to minimize the sum of the clearing costs of conventional unit bids, energy storage bids, and wind and solar curtailment penalties.

[0030] Based on the current round of energy storage investment pricing decisions, reference operating results and price response bases are obtained based on the standard market clearing model;

[0031] Under the constraints of node balance, upstream and downstream reserve demand, line power flow, and conventional unit and energy storage operation, an energy storage operation-settlement model is constructed and used as a multi-scenario lower-level market operation response model.

[0032] Based on the reference operating results and price response, the multi-scenario lower-level market operation response model is solved to obtain the optimal operating results and market operation settlement revenue.

[0033] Optionally, the clearing cost function of the standard market clearing model is:

[0034]

[0035] In the formula, For the scene Lower the clearing cost function value. For conventional units A unified unit price coefficient; For conventional units During the period the day-to-day effort; , The units During the period Upper and lower backup deployment quantities; For the unit During the period Alternate call; For energy storage units The benchmark price; The penalty cost for units that abandon wind and solar power; For nodes During the period The amount of wind and solar power wasted For a set of nodes, This is a collection of conventional generating units.

[0036] Optionally, the energy storage operation-settlement model can be expressed as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] In the formula, For system operators in the scenario The total payment cost, i.e., the lower-level payment function, For the scene The set of clearing variables, The constraint corresponds to the set of multipliers. The set of multipliers corresponding to inequality constraints. To be the Lagrangian function of the clearing cost function, Indicate the feasible region for clearing. To find the partial derivative operator; It is a subset of price variables, and ; , They are respectively with Directly related subsets of inequality multipliers and subsets of equality multipliers, and ; This is the upper bound of the multipliers of the inequality; , These are the reference operating results obtained from the standard market clearing model and the reference price and reference equation multiplier corresponding to the price response basis; , These are the trust domain radius of the price variable and the trust domain radius of its related equation multiplier, respectively.

[0045] Optional, lower-level payment function Expressed as follows:

[0046]

[0047] in, For the scene The set of clearing variables, This refers to the set of nodes where conventional generating units are located. For energy storage units access node During the period The day-ahead settlement price variable, For conventional units access node During the period The day-ahead settlement price variable, For conventional units During the period of recent efforts, For energy storage units During the period The discharge power, , These are the upper reserve settlement price variable and the lower reserve settlement price variable, respectively. , The units During the period The upper and lower backup deployment quantities, , For energy storage units During the charging period The upper and lower backup deployment quantities, , For energy storage units During the discharge state in the time period Upper standby deployment quantity, lower standby deployment quantity For conventional units access node During the period Real-time settlement price variables, For energy storage units access node During the period Real-time settlement price variables, For the unit During the period Alternate call, , For energy storage units Real-time call volume during charging and discharging states. The penalty cost for units that curtail wind and solar power. For nodes During the period The amount of wind and solar power wasted For time step.

[0048] Optionally, the variables of the lower-level payment function can be divided into scheduling variable blocks. Multiplier variable block The BCD method is used to solve the problem alternately, specifically as follows:

[0049] In the In the BCD iteration, the multiplier variable block is fixed, and the scheduling variable block is solved. During the solution process, a proximal regularization term is introduced into the scheduling variable block to obtain the scheduling block subproblem:

[0050]

[0051] In the formula, For the first The near-end regularization parameter of the scheduling variable block in the next iteration. , Let these represent the scheduling variables for the (k+1)th and kth iterations, respectively. This represents the block of multiplier variables for the k-th iteration;

[0052] In obtaining Then, fix the scheduling variable block, solve the multiplier variable block, and obtain the multiplier block subproblem;

[0053] Before each round of BCD iteration, the corresponding subproblem is solved with the parameters of the previous round as the initial values. If the value of the lower-level pay function in the (k+1)th iteration does not meet the preset sufficient descent condition after the iteration update, the corresponding proximal regularization parameter is amplified and the solution is resolved.

[0054] Repeat the above iterations until the preset solution convergence condition is met.

[0055] Optionally, the optimal operating results include the optimal real-time state of charge trajectory of energy storage. In S4, based on the optimal operating results and the continuous characteristics of peak net load in the scenario, a continuous capacity support capability evaluation model is established to calculate the capacity support deficit of energy storage under various typical operating scenarios, including:

[0056] S4.1: Calculate the net load and the net load average, determine the peak net load time, and determine the peak net load duration period as the peak net load duration characteristic of the scenario;

[0057] S4.2: Determine the capacity support demand power based on the continuous characteristics of peak net load in the scenario, net load, and average net load;

[0058] S4.3: The continuous capacity support capability evaluation model aims to minimize the total capacity support deficit during the peak net load period. The optimal real-time state of charge at the beginning of the peak net load period of energy storage is used as the initial physical state of the continuous capacity support capability evaluation model. Under the constraints of rated power, recursive relationship of available continuous support power state, and capacity support demand, the continuous capacity support capability evaluation model is solved to obtain the capacity support deficit of energy storage under various typical operating scenarios.

[0059] Optionally, the continuous capacity support capability evaluation model can be expressed as follows:

[0060] ,

[0061] in, For the scene Lower energy storage continuously supports power variation A set; For the scene The energy storage below can support changes in electricity volume. A set; For the scene Capacity support for the missing variable A set; For the scene The total capacity below supports the shortfall. For the scene During the peak net load period, For time step;

[0062] The recursive relationship constraint of the continuous support charge state can be expressed as follows:

[0063]

[0064]

[0065] in, Indicates energy storage unit In the scene Next period It can continuously support the power state variable; Indicates energy storage unit In the scene Next period It can continuously support the power state variable; For time step; For energy storage discharge efficiency;

[0066] Capacity support demand constraints are expressed as follows:

[0067]

[0068]

[0069] In the formula, For the scene Next period The capacity support deficit is used to characterize the portion of the energy storage's inadequacy in supporting the continuous high net load demand during that period. To determine the capacity to support the required power, the larger of the difference between the net load and the average net load and 0 is used as the capacity to support the required power.

[0070] Compared with the prior art, the beneficial effects of the present invention include at least the following:

[0071] This invention introduces a continuous capacity support capability evaluation based on real-time SOC (State of Charge) trajectory during the energy storage configuration optimization process. It takes into account the energy storage quota capacity and power, market operation results, and high net load continuous support requirements in a coordinated manner, so that the energy storage configuration scheme has both market profitability and high net load continuous energy supply capability.

[0072] This invention employs a coordinated iterative mechanism among energy storage configuration, market operation, settlement revenue, and continuous capacity support capability evaluation. It can simultaneously obtain energy storage investment decisions, market operation response results, and net load peak period support capability evaluation results, avoiding decision-making biases caused by individual optimization configuration or individual simulation operation. Attached Figure Description

[0073] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments 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. Wherein:

[0074] Figure 1 This is a schematic diagram of an independent energy storage optimization configuration method that takes into account operational response and continuous capacity support according to the present invention.

[0075] Figure 2 This is a schematic diagram of the net load curve in a different typical scenario in the application examples of this invention;

[0076] Figure 3 This is a schematic diagram illustrating the response of electricity price changes to energy storage operation in a typical scenario of the application of this invention.

[0077] Figure 4 This is a schematic diagram comparing the continuous capacity support deficit of different schemes in an application example of the present invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0079] like Figure 1As shown, this invention provides a method for coordinated optimization of independent energy storage configuration and operation settlement, taking into account capacity support capabilities, including the following steps:

[0080] S1: Obtain target area operation scenario data, power system basic parameters and energy storage technology and economic parameters, and construct a typical operation scenario set based on the target area operation scenario data.

[0081] Specifically, in S1, the target area operation scenario data includes target area load time-series data, wind power output time-series data, photovoltaic output time-series data, day-ahead forecast data, real-time scenario data, dispatch period sets, and time steps. Power system basic parameters include grid topology parameters, line capacity parameters, and conventional unit operating parameters. Energy storage techno-economic parameters include energy storage candidate access nodes, energy storage unit power investment cost, energy storage unit energy investment cost, energy storage charge / discharge efficiency, upper bound of energy storage power capacity, upper bound of energy storage energy capacity, energy storage operation boundary parameters, and capacity support shortfall penalty coefficient.

[0082] Specifically, the power system fundamental parameters are used to construct the standard market clearing model and energy storage operation-settlement model in step S3, including the construction of day-ahead node power balance constraints, real-time node power balance constraints, line power flow constraints, conventional unit output constraints, and reserve constraints. Energy storage techno-economic parameters are used to construct the energy storage investment pricing decision model in step S2, as well as the energy storage operation constraints in step S3 and the continuous capacity support capability evaluation constraints in step S4. Specifically, energy storage candidate access nodes, energy storage unit power investment cost, energy storage unit energy investment cost, energy storage power capacity upper bound, and energy storage energy capacity upper bound are used for energy storage access status, power capacity, and energy capacity decisions; energy storage charge / discharge efficiency and energy storage operation boundary parameters are used for energy storage charge / discharge power constraints and state of charge constraints; and the capacity support deficit penalty coefficient is used for the construction of continuous capacity support deficit feedback and comprehensive evaluation objectives.

[0083] The typical operating scenario set is constructed as follows:

[0084] (1) Using the natural day as the basic sample unit, the load time series data, wind power time series data and photovoltaic time series data in the historical period of the target area are divided into daily scene samples. The time scale of each daily scene sample is 24 hours and the time resolution is 1 hour, corresponding to the set of scheduling periods. ,in, Indicates the number of days Each scheduling period has a time step of [number]. ;

[0085] (2) Calculate the net load time series curve for each daily scene sample, and extract source load matching features, net load peak features, net load valley features, net load ramp features, net load fluctuation features and high net load continuity features to form a daily scene feature vector;

[0086] (3) After normalizing the daily scene feature vector, a clustering method is used to aggregate the scenes. The actual daily scene sample closest to the cluster center in each cluster is taken as the typical running scene, and the set of typical running scenes is denoted as . ,in, This represents any typical operating scenario; the actual number of daily samples contained in this cluster is taken as the corresponding typical operating scenario. The representative number of days is denoted as .

[0087] This invention constructs multi-scenario operating environments based on historical data of actual wind power, photovoltaic power, and load in the target area, which can more accurately assess the revenue performance and investment recovery capability of energy storage configuration schemes in the future operation of the area.

[0088] S2: Based on the typical operating scenario set and energy storage technology and economic parameters, construct an independent energy storage upper-layer investment pricing model, and solve it to obtain the energy storage investment pricing decision for the current round.

[0089] Specifically, in S2, the independent energy storage upper-layer investment pricing model takes independent energy storage investors as the decision-making body and is used to determine the construction location, capacity scale, and market pricing parameters of independent energy storage.

[0090] Let the set of energy storage units be... , Indicates the energy storage unit index. For energy storage units Candidate access nodes for energy storage. Variables for energy storage investment pricing decisions. include:

[0091] (1)

[0092] In the formula, Indicates energy storage unit Whether to build, and Indicates its candidate access node Build energy storage; For energy storage units Rated power capacity; For energy storage units Rated energy capacity; and Energy storage units The pricing parameters in the day-ahead and real-time markets are determined independently by independent energy storage investment and operation entities within the allowed range. These values ​​affect the volume of energy storage bids and market operation settlement revenue through the lower-level market clearing process.

[0093] The independent energy storage upper-layer investment pricing model aims to maximize the overall returns for independent energy storage investors. It comprehensively considers energy storage investment costs, market operation settlement revenue, and capacity support shortfall penalties. Its objective function is:

[0094] (2)

[0095] In the formula, Let Y be the capital recovery factor, and the lifespan of the energy storage project be Y years. ; The discount rate; For energy storage investment costs; For the scene The market operation settlement revenue is calculated based on the response results of the lower-level market. It is the capacity support shortfall penalty coefficient, used to characterize the suppression strength of insufficient support for peak energy storage; The annual equivalent capacity support deficit is obtained from the sustainable capacity support capability evaluation model established by S4. Typical operating scenarios The number of days represented.

[0096] Energy storage investment costs Represented as:

[0097] (3)

[0098] In the formula, , Energy storage units The unit power investment cost and unit energy investment cost.

[0099] Scene The market operation settlement revenue includes day-ahead electricity settlement revenue, reserve capacity settlement revenue, and real-time upward dispatch settlement revenue, expressed as:

[0100] (4)

[0101] In the formula, , Energy storage units access node During the period The day-ahead settlement price variable and the real-time settlement price variable; , These are the upper standby settlement price variable and the lower standby settlement price variable, respectively. , Energy storage units During the period Charging power and discharging power; , Energy storage units During the charging period The upper and lower backup deployment quantities, , Energy storage units During the discharge state in the time period Upper and lower backup deployment quantities; , Energy storage units Real-time call volume during charging and discharging; Indicates the time step.

[0102] The independent energy storage upper-layer investment pricing model includes the following constraints:

[0103] (5)

[0104] (6)

[0105] (7)

[0106] (8)

[0107] (9)

[0108] (10)

[0109] In the formula, , Energy storage units The upper bound of power and the upper bound of energy capacity; This represents the maximum number of energy storage facilities that can be built. , Energy storage units During the period Price coefficients in the current and real-time markets; , Energy storage units During the period The upper limit of the pricing parameters in the day-ahead market and the upper limit of the pricing parameters in the real-time market can be set according to the market pricing rules, the energy storage operating cost level, or the pricing strategy boundaries of the investment and operation entity.

[0110] Understandably, under the constraints of formulas (5)-(10), the goal is to find the energy storage investment pricing decision variables that maximize the objective function value of formula (2). .

[0111] S3: Based on the current round of energy storage investment bidding decisions, target area operation scenario data, power system basic parameters and energy storage technical and economic parameters, construct a multi-scenario lower-level market operation response model, and solve for the optimal operation result and market operation settlement revenue.

[0112] Specifically, in step S3, given the current round of energy storage investment bidding decisions, a reference operating result and price response basis are obtained based on the standard clearing model. While retaining operational physical constraints and energy storage state constraints, an energy storage operation-settlement model is constructed. This model serves as a multi-scenario lower-level market operation response model. Solving this model yields energy storage charging and discharging power, state of charge trajectory, scenario price response results, and market operation settlement revenue. This provides the operating response and settlement revenue of energy storage configuration schemes in multiple scenarios, providing a basis for subsequent continuous capacity support evaluation and configuration scheme updates.

[0113] S3 specifically includes:

[0114] S3.1: Given the current round of energy storage investment bidding decisions, obtain reference operating results and price response basis based on the standard clearing model.

[0115] Let the first The energy storage investment pricing decision obtained from the on-board investment pricing model is as follows: Given the upper-level investment pricing decision Then, first construct the scene. The standard market clearing model aims to minimize the sum of the clearing costs of conventional generating unit bids, energy storage bids, and wind / solar curtailment penalties. Its clearing cost function is:

[0116] (11)

[0117] In the formula, For the scene Lower the clearing cost function value. For conventional units A unified unit price coefficient; For conventional units During the period the day-to-day effort; , The units During the period Upper and lower backup deployment quantities; For the unit During the period Alternate call; For energy storage units The benchmark price; The penalty cost for units that abandon wind and solar power; For nodes During the period The amount of wind and solar power wasted For a set of nodes, This is a collection of conventional generating units.

[0118] It should be noted that in the standard market clearing cost function, energy storage charging behavior participates in power balancing as load-side electricity demand, and is not separately included in the quotation cost as a supply service purchased by the system operator from energy storage; the real-time upward adjustment of energy storage call volume is included in the real-time call quotation cost as a real-time balancing service; the real-time downward adjustment of energy storage call volume is a withdrawal of the established plan and is not separately included in the lower layer.

[0119] The clearing cost function is used in the standard market clearing model. Its goal is to find the clearing result with the minimum operating cost based on the unit bid, energy storage bid, and system operation constraints, i.e., equations (13)-(16), as well as line power flow constraints, conventional unit and energy storage operation constraints. The clearing result includes the set of clearing variables. Price variable subset Reference Price Reference equation multipliers .

[0120] Record the scene clearing variable set for:

[0121] (12)

[0122] in, For energy storage units During the period Scene The charging and discharging state variables under these conditions; and These are the day-ahead state of charge and real-time state of charge for energy storage, respectively. , They are nodes During the period The day-ahead and real-time phase angles; , These represent the day-ahead and real-time wind and solar power absorption capacities, respectively.

[0123] S3.2: Under the conditions of retaining the physical constraints of operation and the energy storage state constraints, namely the node balance constraints, the upstream and downstream reserve demand constraints, the line power flow constraints, and the conventional unit and energy storage operation constraints, an energy storage operation-settlement model is constructed. The energy storage operation-settlement model is used as the multi-scenario lower-level market operation response model. Based on the reference operation results and price response given in S3.1, the multi-scenario lower-level market operation response model is solved to obtain the energy storage charging and discharging power, the state of charge trajectory, the scenario price response results, and the market operation settlement revenue.

[0124] Let the original clearing feasible region be denoted as , It consists of the following constraints:

[0125] (1) Day-ahead and real-time node balance constraints

[0126] (13)

[0127] (14)

[0128] In the formula, This refers to the set of nodes where conventional generating units are located. For nodes During the period The scenery was beautiful before the day's contribution value; For nodes During the period The day-ahead load forecast; The line admittance matrix; For the unit During the period The next standby call; , For energy storage units During the period Backup call quantity under charging and discharging states; For nodes During the period The scenery actually reflects strength; For nodes During the period The actual load value; For formula - The corresponding dual multipliers, Represents a node During the period The day-ahead settlement price variable, Represents a node During the period The real-time settlement price variable; ":" is a constraint specifier used to indicate that the dual multiplier of the constraint on the left side of the symbol is the expression on the right side of the symbol.

[0129] (2) Constraints on backup requirements

[0130] (15)

[0131] (16)

[0132] In the formula, For time period Required upper and lower backup capacity , The dual multipliers corresponding to equations (15)-(16), i.e., equation Middle and lower standby settlement price variables.

[0133] It should be noted that this invention introduces dual variables or price response variables corresponding to the constraints to characterize the marginal price and settlement price response relationship of the constraints, and is used to calculate the settlement revenue of market operation.

[0134] (3) Power flow constraints of the line

[0135] Including upper and lower limits of line power flow and reference phase angle constraints

[0136] (4) Operational constraints of conventional generating units and energy storage

[0137] This includes constraints on the output and ramp-up of conventional generating units, constraints on the deployment and dispatch of conventional generating units as backup power, constraints on the charging and discharging power of energy storage, constraints on the state of charge of energy storage, constraints on the deployment and dispatch of energy storage as backup power, and constraints on the consumption and curtailment of wind and solar power.

[0138] Let the set of the above equality constraints be . The set of inequality constraints is The set of multipliers corresponding to the equality constraint is The inequality constraint corresponds to the set of multipliers as follows: Then the Lagrangian function of the standard market clearing cost function will be... Represented as:

[0139] (17)

[0140] in, Representing a scene The set of clearing variables, Representing a scene The set of energy storage pricing parameters, , express transpose, express The transpose of .

[0141] It should be noted that the purpose of constructing the Lagrange function is to extract the marginal correlation between the operating variables and the price response variables from the standard market clearing model. The resulting gradient condition is used to constrain the price response variables in the subsequent run-settlement response model, ensuring that the price variables in the payment function have a standard clearing basis and preventing the settlement price from arbitrarily changing outside of the operating constraints. The correlation between the clearing variables and the price response variables is established through the Lagrange function and gradient condition of the standard clearing model.

[0142] Furthermore, the operation-settlement model is constructed as follows:

[0143] (18)

[0144] (19)

[0145] (20)

[0146] (twenty one)

[0147] (twenty two)

[0148] (twenty three)

[0149] (twenty four)

[0150] In the formula, For system operators in the scenario The total payment cost, i.e., the lower-level payment function, To find the partial derivative operator; It is a subset of price variables, and ; , They are respectively formulas In the expansion and Directly related subsets of inequality multipliers and subsets of equality multipliers, and ; This is the upper bound of the multipliers of the inequality; , These are the reference price and reference equation multiplier corresponding to the standard clearing reference solution, respectively; that is, the optimal solution of the corresponding multiplier obtained by solving the standard clearing solution. , These are the trust domain radius of the price variable and the trust domain radius of its related equation multiplier, respectively.

[0151] It can be represented as:

[0152] (25)

[0153] Understandably, the payment function used in the run-settlement response model represents the total settlement cost paid by the system operator to market participants such as conventional generating units and energy storage, given the clearing operation outcome and price response outcome. Since electricity market settlement payments are typically determined by the clearing price, reserve price, and the awarded volume, the actual payment amount by the system operator is not equivalent to the bid cost in the standard clearing model. Therefore, using only the standard clearing cost function as the lower-level objective is insufficient to reflect the settlement payment level corresponding to the operation outcome.

[0154] Based on this, this invention further constructs an operation-settlement response model on top of the standard market clearing model. The standard clearing model provides reference operation results and reference price response results. The payment function, based on these reference results, calculates the total settlement payment cost for the system operator using variables such as the clearing price, reserve price, unit output, energy storage charging and discharging power, and reserve call-up. Simultaneously, to avoid excessive deviation of the price response results from the standard clearing results, this invention sets a trust domain constraint centered on the reference price for price response variables directly related to the settlement price. This allows for the characterization of the settlement payment level corresponding to the operation results while preserving the physical feasibility of market clearing.

[0155] Because the lower-level payment function includes price-quantity bilinearity, the lower-level response problem is non-convex. Therefore, the lower-level variables are divided into scheduling variable blocks. Multiplier variable block The BCD (Block Coordinate Descent) method is used to solve the problem alternately.

[0156] In the In the next BCD iteration, the multiplier block is fixed. Solve the block of scheduling variables. At this point, the payment function... Regarding scheduling variables It is a convex function. To avoid excessively large step sizes in adjacent iterations, a proximal regularization term is introduced into the scheduling variable block, resulting in the scheduling block subproblem:

[0157] (26)

[0158] Equation (19) (27)

[0159] In the formula, For the first The near-end regularization parameter of the scheduling variable block in the next iteration. Let represent the set of clearing variables in the (k+1)th iteration, i.e., the scheduling variables in the (k+1)th iteration. This represents the block of multiplier variables in the k-th iteration.

[0160] In obtaining Then, fix the scheduling variable block, solve the multiplier variable block, and write the multiplier block subproblem as:

[0161] (28)

[0162] Equations (20)-(24) (29)

[0163] In the formula, Let the multiplier variable be the variable in the (k+1)th iteration. For the proximal parameters of the multiplier variable block in the equation, represents the proximal parameters of the inequality multiplier variable block.

[0164] To ensure stable descent of the block coordinates, an adaptive backtracking update mechanism is used for the near-end regularization parameters. Let the... Scene after the next iteration The overall target value is:

[0165] (30)

[0166] Before each iteration, the corresponding subproblem is solved using the parameters from the previous iteration as initial values. If the updated parameters fail to achieve the desired result... The following conditions must be met for a sufficient descent:

[0167] (31)

[0168] Then, amplify the corresponding proximal parameters using the following formula and solve again:

[0169] (32)

[0170] in, Indicates the first During the nth iteration The values ​​of each variable block, ; For the scene The set of clearing variables; The set of multipliers corresponding to equality constraints; The set of multipliers corresponding to inequality constraints; To satisfy the formula The smallest non-negative integer, This is the retrospective amplification factor.

[0171] The solution for lower-level block coordinate descent is considered to have converged when the following conditions are met:

[0172] (33)

[0173] (34)

[0174] In the formula, The convergence tolerance for solving the lower-level BCD.

[0175] The scenario is obtained through the above solution. The optimal running result is the optimal set of clearing variables. Price response results Market operation settlement revenue and the optimal state of charge trajectory of energy storage The market operation settlement revenue and the optimal real-time state of charge trajectory of energy storage are used for revenue feedback in step S5 and net load peak continuous power supply capacity assessment in step S4, respectively.

[0176] S4: Based on the optimal operating results and the continuous characteristics of peak net load in the scenario, establish a continuous capacity support capability evaluation model to calculate the capacity support deficit of energy storage in each typical operating scenario of the typical operating scenario set.

[0177] The characteristics of peak net load in a scenario are not limited to the duration of peak net load, peak net load, time of peak occurrence, duration of high net load near the peak, and net load level during the duration.

[0178] Specifically, in step S4, a continuous capacity support capability evaluation model is established based on the real-time state-of-charge trajectory of energy storage obtained from the lower-level market operation response model. This model is used to evaluate the actual energy support capability of energy storage during periods of sustained high net load. S4 specifically includes:

[0179] S4.1: Calculate the net load and the net load average, determine the peak net load time, and determine the peak duration of the net load.

[0180] First, based on typical operating scenarios Real-time load and real-time scenery output Calculate the net load of the target area :

[0181] (35)

[0182] Then, calculate the scenario. Average net load under :

[0183] (36)

[0184] in, Represents the set of scheduling periods. This indicates the number of scheduling periods.

[0185] Further, define the scene. Peak net load time :

[0186] (37)

[0187] by Centered on the search, continuously satisfy the forward and backward conditions. The time period is used to obtain a continuous high net load period that includes the peak net load time. Its start time is... and end time They are respectively:

[0188] (38)

[0189] (39)

[0190] Then the scene Peak net load duration Defined as:

[0191] (40)

[0192] Its duration is .

[0193] S4.2: Based on the duration of peak net load Net load and net load average Determine the capacity to support the required power .

[0194] After determining the duration of peak net load, define the capacity support demand power. for:

[0195] (41)

[0196] in, .

[0197] For energy storage units Introducing energy storage to continuously support power variables and available continuous support power state variables .in, Indicates energy storage unit In the scene Next period Power available for continuous support during peak net load periods; Indicates energy storage unit The amount of electricity that can be used to support discharge during the evaluation of continuous capacity support capability.

[0198] S4.3: Under the constraints of (42)-(47), the scenario is solved with the objective of minimizing the total capacity support deficit during the peak net load period. Set of power variables for continuous support of energy storage Scene The following is a set of available energy storage variables that can support electricity. Scene The set of capacity support deficit variables is used to obtain the capacity support deficit in each scenario. .

[0199] The real-time state-of-charge trajectory of the energy storage obtained in step S3 is used as the initial physical state for evaluating the continuous capacity support capability. After deducting the minimum safe charge, the available support charge at the start of the peak is obtained. :

[0200] (42)

[0201] In the formula, The energy storage unit obtained in step S3 exist Optimal real-time battery level at any given moment; For energy storage units The minimum safe energy level is determined by the energy storage operation boundary parameters.

[0202] Energy storage continuously supports power variables Meets rated power constraints:

[0203] (43)

[0204] In the formula, For energy storage units Rated power capacity; Index for scheduling periods; Index for typical scenarios.

[0205] During the peak net load period, the recursive relationship of the available continuous support power status is as follows:

[0206] (44)

[0207] (45)

[0208] in, Indicates energy storage unit In the scene Next period It can continuously support the power state variable; Indicates energy storage unit In the scene Next period It can continuously support the power state variable; For time step; This refers to the energy storage and discharge efficiency.

[0209] For each time period within the peak net load period, energy storage continuously supports power variables. capacity support deficit Together, we can meet the capacity support requirements:

[0210] (46)

[0211] (47)

[0212] In the formula, For the scene Next period The capacity support deficit is used to characterize the portion of the energy storage that is insufficient to support the demand for continuous high net load during this period.

[0213] Under the above constraints, the deficit is supported by the total capacity during the peak net load period. With the objective of minimizing, we solve the sub-model for evaluating sustainable capacity support capabilities, and obtain the following results. , , :

[0214] (48)

[0215] st (42)-(47) (49)

[0216] in, For the scene The set of power variables that the energy storage continuously supports; For the scene The set of available energy storage variables that can support energy storage; For the scene The set of variables supporting the capacity shortage; For the scene The total capacity below supports the shortfall.

[0217] S4.4: The capacity support deficit under each typical operating scenario is converted into the number of representative days to obtain the annual equivalent capacity support deficit. :

[0218] (50)

[0219] S5: Feed the market operation settlement revenue obtained in S3 and the capacity support deficit under each typical operation scenario obtained in S4 back to the independent energy storage upper-layer investment pricing model in S2 to update the energy storage investment pricing decision.

[0220] Specifically, in step S5, the lower-level running results obtained from the current round are... and equivalent capacity support deficit Feedback is sent to the upper layer, and by formula Calculate the market revenue of standalone energy storage Solve the independent energy storage upper-layer investment pricing model to obtain the decision variables for the new round of upper-layer investment pricing. .

[0221] S6: Repeat steps S2 to S5 until the energy storage investment pricing decision and comprehensive evaluation objectives meet the iteration termination conditions, and output the independent energy storage configuration results, operation settlement results, and continuous capacity support evaluation results.

[0222] Specifically, in step S6, in the first... After all iterations are completed, the iteration termination condition can be expressed as:

[0223] (51)

[0224] (52)

[0225] (53)

[0226] In the formula, For the (r+1)th round of energy storage units Whether to build or not The rth round of energy storage units Whether to build or not Let be the normalized vector of the continuous variable part of the energy storage investment bidding decision in the (r+1)th round. Let be the normalized vector of the continuous variable part of the energy storage investment bidding decision in the r-th round. This represents the target value for the comprehensive evaluation in the (r+1)th round; For the first Round comprehensive evaluation target value; , These are the convergence tolerances for continuous decision variables and the convergence tolerances for comprehensive evaluation objectives, which can be preset according to the solver's numerical accuracy and computational efficiency requirements.

[0227] Represented as:

[0228] ,

[0229] in, , Energy storage units The upper bound of power and the upper bound of energy capacity; , Energy storage units During the period The upper limit of the quotation parameters in the daytime market and the upper limit of the quotation parameters in the real-time market.

[0230] Represented as:

[0231] ,

[0232] in, This is the capital recovery factor. For the cost of energy storage investment in the rth round, For the settlement of profits in the rth round of market operation, This is to support the shortfall in the equivalent capacity of the rth round of the year.

[0233] The independent energy storage configuration results include energy storage access nodes, energy storage power capacity, and energy storage capacity, namely:

[0234]

[0235] in, For energy storage units The optimal access state. For energy storage units The optimal power capacity, For energy storage units The optimal energy capacity.

[0236] The operational settlement results include energy storage charging power, energy storage discharging power, energy storage state of charge trajectory, price response results, and market operational settlement revenue under various typical operational scenarios, namely:

[0237]

[0238] in, and Energy storage units In typical operating scenarios Time period The optimal charging power and optimal discharging power; For energy storage units The optimal real-time state-of-charge trajectory; For the scene The optimal price response result is as follows; For the scene The optimal market operation settlement profit.

[0239] The continuous capacity support evaluation results include the capacity support deficit under each typical operating scenario and the annual equivalent capacity support deficit, namely:

[0240]

[0241] in, Typical operating scenario The capacity support shortfall, The annual equivalent capacity support deficit is calculated based on the representative days of each typical operating scenario.

[0242] Therefore, the final output includes independent energy storage configuration schemes, operation settlement response results under typical operating scenarios, and continuous capacity support capability evaluation results, which are used to characterize the comprehensive optimization effect of energy storage configuration schemes in terms of investment economy, operation adaptability, and continuous capacity support capability.

[0243] This invention unifies the energy storage configuration decision in step S2, the multi-scenario market operation simulation in step S3, the market settlement revenue, and the continuous capacity support capability evaluation in step S4 into a unified model. This enables the energy storage configuration scheme to not only meet static investment constraints, but also to verify its operational feasibility, market profitability, and high net load continuous energy supply capability under multi-scenario operation conditions in the target area.

[0244] Specifically, MATLAB was used to write the independent energy storage optimization configuration method, which takes into account operational response and continuous capacity support, as shown in steps S1 to S6 of this invention, and Gurobi was called to solve the constructed optimization model. The following mainly demonstrates the technical effects of this invention with application example data.

[0245] Implementation results:

[0246] This application example uses the IEEE-30 node system as the test object. This system comprises six conventional thermal power units with a total load of 283.4MW. Based on the original system, a 70MW wind farm is connected at node 21, and a 44MW photovoltaic power station is connected at node 17. The candidate nodes for energy storage connection are nodes 15, 17, and 21. The application example uses annual load, actual wind power, and photovoltaic output data from a region in Jiangsu Province to construct a typical operating scenario. The net load characteristics of each typical scenario are as follows: Figure 2 As shown. By Figure 2 It is evident that different typical scenarios exhibit significant differences in net load levels, peak periods, and peak-to-valley differences, reflecting the impact of wind and solar power output and load variations on system operation. The method described in this invention enables synergistic optimization of energy storage configuration, operational response, and continuous capacity support capabilities.

[0247] To verify the effectiveness of the method of this invention, this application example sets up three comparison schemes: Scheme 1 is the baseline scheme without energy storage; Scheme 2 is the energy storage configuration scheme without feedback of continuous capacity support evaluation results; Scheme 3 is the scheme of this invention. All schemes are solved under the same grid structure, source-load data, energy storage candidate access nodes, and energy storage device parameters.

[0248] Figure 3 This reflects the response of the invention's solution to electricity price changes and energy storage operation in scenario 4. According to... Figure 3It can be seen that when energy storage is charged during periods of low electricity price, the net output of energy storage is negative; when it releases electricity during periods of high electricity price or high net load, the net output of energy storage is positive. This result shows that the method of the present invention can form an hourly operating response of energy storage based on electricity price changes and system operating status under typical scenarios, and provide an operational status basis for capacity support evaluation during periods of continuous high net load.

[0249] Figure 4 This reflects the persistent capacity support shortfall of different solutions under various typical operating scenarios. According to Figure 4 It can be seen that, compared with Scheme 1 and Scheme 2, Scheme 3 can reduce the continuous capacity support deficit in all typical scenarios. After adjusting for scenario probabilities, the average annual continuous capacity support deficit is 27.86MW for Scheme 1, 23.34MW for Scheme 2, and reduced to 21.01MW for Scheme 3. This result indicates that feeding back the continuous capacity support evaluation results to the energy storage configuration model can improve the continuous support capability of energy storage under different typical operating scenarios.

[0250] Furthermore, Scheme 2 yields an energy storage configuration of 28.4MW / 56.9MWh at node 17, with an annual operating settlement revenue of RMB 28.7826 million and a net annual comprehensive revenue of RMB 843,500. The scheme of this invention yields an energy storage configuration of 44.7MW / 89.4MWh at node 15, with an annual operating settlement revenue of RMB 45.2975 million and a net annual comprehensive revenue of RMB 13.9221 million. This demonstrates that feeding back the continuous capacity support evaluation results to the energy storage configuration model can enhance the support capacity during periods of high net load while simultaneously improving overall revenue.

[0251] As can be seen from the results of this application example, the method of the present invention can synergistically consider the energy storage configuration results, operation response results, and continuous capacity support capability evaluation results, so that the resulting energy storage configuration scheme takes into account the investment economy, operation adaptability, and continuous capacity support capability, thereby avoiding decision-making biases caused by energy storage configuration based solely on static investment parameters or single operation results.

[0252] 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 the present invention.

[0253] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0254] All parts not covered in this invention are the same as or can be implemented using existing technologies.

[0255] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for coordinated optimization of independent energy storage configuration and operation settlement considering capacity support capability, characterized in that: Includes the following steps: S1: Acquire operational scenario data, basic power system parameters, and energy storage technology and economic parameters for the target area, and construct a set of typical operational scenarios based on the operational scenario data for the target area; S2: Based on the typical operating scenario set and energy storage technology and economic parameters, construct an independent energy storage upper-layer investment pricing model, and solve it to obtain the energy storage investment pricing decision for the current round; S3: Based on the current round of energy storage investment bidding decisions, target area operation scenario data, power system basic parameters and energy storage technical and economic parameters, construct a multi-scenario lower-level market operation response model, and solve for the optimal operation results and market operation settlement revenue; S4: Based on the optimal operating results and the continuous characteristics of peak net load in the scenario, establish a continuous capacity support capability evaluation model to calculate the capacity support deficit of energy storage in various typical operating scenarios. S5: Feed the market operation settlement revenue and capacity support deficit back to the independent energy storage upper-layer investment pricing model to update the energy storage investment pricing decision; S6: Repeat S2 to S5 until the energy storage investment pricing decision and comprehensive evaluation objectives meet the iteration termination conditions, and output the independent energy storage configuration results, operation settlement results and continuous capacity support evaluation results to achieve collaborative optimization of independent energy storage configuration and operation settlement.

2. The method for coordinated optimization of independent energy storage configuration and operation settlement considering capacity support capability as described in claim 1, characterized in that, The target area operation scenario data includes target area load time series data, wind power output time series data, and photovoltaic output time series data. The typical operation scenario set constructed in S1 based on the target area operation scenario data includes: The load time series data, wind power output time series data, and photovoltaic output time series data of the target area in the historical period are divided into daily scene samples by day; For each daily scene sample, calculate the net load time series curve and extract source load matching features, net load peak features, net load valley features, net load ramp features, net load fluctuation features, and high net load persistence features to form a daily scene feature vector; After normalizing the daily scene feature vectors, a clustering method is used to aggregate the scenes. The actual daily scene sample closest to the cluster center in each cluster is taken as the typical operating scene, and the number of actual daily scene samples contained in the cluster is taken as the representative number of days of the corresponding typical operating scene.

3. The method for coordinated optimization of independent energy storage configuration and operation settlement considering capacity support capability as described in claim 1, characterized in that, In S2, the independent energy storage upper-layer investment pricing model aims to maximize the overall return for independent energy storage investors. Its objective function is: ， In the formula, This is the capital recovery factor. For energy storage investment costs, For the scene The market operation settlement revenue is calculated by the multi-scenario lower-level market operation response model. This is the capacity support shortfall penalty coefficient, used to characterize the strength of suppression against insufficient peak-hour support from energy storage. The annual equivalent capacity support deficit is obtained from the continuous capacity support capability evaluation model. Typical operating scenarios The number of days represented; Under preset constraints, the energy storage investment pricing decision variables that maximize the objective function value are obtained and used as the energy storage investment pricing decision for the current round. The decision variables for energy storage investment pricing are expressed as follows: ， In the formula, Indicates energy storage unit Whether to build, and Indicates its candidate access node Build energy storage For energy storage units Rated power capacity, For energy storage units Rated energy capacity; and Energy storage units Quotation parameters for day-to-day and real-time markets.

4. The method for coordinated optimization of independent energy storage configuration and operation settlement considering capacity support capability as described in claim 1, characterized in that, In S3, based on the current round of energy storage investment bidding decisions, target area operation scenario data, power system basic parameters, and energy storage technical and economic parameters, a multi-scenario lower-level market operation response model is constructed. The optimal operation result and market operation settlement revenue are obtained by solving this model, including: A standard market clearing model is constructed for each typical operating scenario. The standard market clearing model aims to minimize the sum of the clearing costs of conventional unit bids, energy storage bids, and wind and solar curtailment penalties. Based on the current round of energy storage investment pricing decisions, reference operating results and price response bases are obtained based on the standard market clearing model; Under the constraints of node balance, upstream and downstream reserve demand, line power flow, and conventional unit and energy storage operation, an energy storage operation-settlement model is constructed and used as a multi-scenario lower-level market operation response model. Based on the reference operating results and price response, the multi-scenario lower-level market operation response model is solved to obtain the optimal operating results and market operation settlement revenue.

5. The method for coordinated optimization of independent energy storage configuration and operation settlement taking into account capacity support capability as described in claim 4, characterized in that, The clearing cost function of the standard market clearing model is: ， In the formula, For the scene Lower the clearing cost function value. For conventional units A unified unit price coefficient; For conventional units During the period the day-to-day effort; , The units During the period Upper and lower backup deployment quantities; For the unit During the period Alternate call; For energy storage units The benchmark price; The penalty cost for units that abandon wind and solar power; For nodes During the period The amount of wind and solar power wasted For a set of nodes, This is a collection of conventional generating units.

6. The method for coordinated optimization of independent energy storage configuration and operation settlement taking into account capacity support capability as described in claim 4, characterized in that, The energy storage operation-settlement model is expressed as follows: ， In the formula, For system operators in the scenario The total payment cost, i.e., the lower-level payment function, For the scene The set of clearing variables, The constraint corresponds to the set of multipliers. The set of multipliers corresponding to inequality constraints. To be the Lagrangian function of the clearing cost function, Indicate the feasible region for clearing. To find the partial derivative operator; It is a subset of price variables, and ; , They are respectively with Directly related subsets of inequality multipliers and subsets of equality multipliers, and , , ; This is the upper bound of the multipliers of the inequality; , These are the reference operating results obtained from the standard market clearing model and the reference price and reference equation multiplier corresponding to the price response basis; , These are the trust domain radius of the price variable and the trust domain radius of its related equation multiplier, respectively.

7. The method for coordinated optimization of independent energy storage configuration and operation settlement taking into account capacity support capability as described in claim 6, characterized in that, Lower-level payment function Expressed as follows: ， in, For the scene The set of clearing variables, This refers to the set of nodes where conventional generating units are located. For energy storage units access node During the period The day-ahead settlement price variable, For conventional units access node During the period The day-ahead settlement price variable, For conventional units During the period of recent efforts, For energy storage units During the period The discharge power, , These are the upper reserve settlement price variable and the lower reserve settlement price variable, respectively. , The units During the period The upper and lower backup deployment quantities, , For energy storage units During the charging period The upper and lower backup deployment quantities, , For energy storage units During the discharge state in the time period Upper standby deployment quantity, lower standby deployment quantity For conventional units access node During the period Real-time settlement price variables, For energy storage units access node During the period Real-time settlement price variables, For the unit During the period Alternate call, , For energy storage units Real-time call volume during charging and discharging states. The penalty cost for units that curtail wind and solar power. For nodes During the period The amount of wind and solar power wasted For time step.

8. The method for coordinated optimization of independent energy storage configuration and operation settlement taking into account capacity support capability as described in claim 7, characterized in that, Divide the variables of the lower-level payment function into scheduling variable blocks. Multiplier variable block The BCD method is used to solve the problem alternately, specifically as follows: In the In the BCD iteration, the multiplier variable block is fixed, and the scheduling variable block is solved. During the solution process, a proximal regularization term is introduced into the scheduling variable block to obtain the scheduling block subproblem: ， In the formula, For the first The near-end regularization parameter of the scheduling variable block in the next iteration. , Let these represent the scheduling variables for the (k+1)th and kth iterations, respectively. This represents the block of multiplier variables for the k-th iteration; In obtaining Then, fix the scheduling variable block, solve the multiplier variable block, and obtain the multiplier block subproblem; Before each round of BCD iteration, the corresponding subproblem is solved with the parameters of the previous round as the initial values. If the value of the lower-level pay function in the (k+1)th iteration does not meet the preset sufficient descent condition after the iteration update, the corresponding proximal regularization parameter is amplified and the solution is resolved. Repeat the above iterations until the preset solution convergence condition is met.

9. The method for coordinated optimization of independent energy storage configuration and operation settlement considering capacity support capability as described in claim 1, characterized in that, The optimal operating results include the optimal real-time state of charge trajectory of energy storage. In S4, based on the optimal operating results and the continuous characteristics of peak net load in the scenario, a continuous capacity support capability evaluation model is established to calculate the capacity support deficit of energy storage under various typical operating scenarios, including: S4.1: Calculate the net load and the net load average, determine the peak net load time, and determine the peak net load duration period as the peak net load duration characteristic of the scenario; S4.2: Determine the capacity support demand power based on the continuous characteristics of peak net load in the scenario, net load, and average net load; S4.3: The continuous capacity support capability evaluation model aims to minimize the total capacity support deficit during the peak net load period. The optimal real-time state of charge at the beginning of the peak net load period of energy storage is used as the initial physical state of the continuous capacity support capability evaluation model. Under the constraints of rated power, recursive relationship of available continuous support power state, and capacity support demand, the continuous capacity support capability evaluation model is solved to obtain the capacity support deficit of energy storage under various typical operating scenarios.

10. The method for coordinated optimization of independent energy storage configuration and operation settlement taking into account capacity support capability as described in claim 9, characterized in that, The sustainable capacity support capability evaluation model is expressed as follows: ， in, For the scene Lower energy storage continuously supports power variation A set; For the scene The energy storage below can support changes in electricity volume. A set; For the scene Capacity support for the missing variable A set; For the scene The total capacity below supports the shortfall. For the scene During the peak net load period, For time step; The recursive relationship constraint of the continuous support charge state can be expressed as follows: ， in, Indicates energy storage unit In the scene Next period It can continuously support the power state variable; Indicates energy storage unit In the scene Next period It can continuously support the power state variable; For time step; For energy storage discharge efficiency; Capacity support demand constraints are expressed as follows: ， In the formula, For the scene Next period The capacity support deficit is used to characterize the portion of the energy storage's inadequacy in supporting the continuous high net load demand during that period. To determine the capacity to support the required power, the larger of the difference between the net load and the average net load and 0 is used as the capacity to support the required power.

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