Independent energy storage day-ahead market transaction decision optimization method and system

Abstract

The invention discloses an independent energy storage day-ahead market transaction decision optimization method and system, and relates to the technical field of power grid regulation and control, and the method comprises the steps: constructing an electricity price change response model, constructing an excitation compensation response model, and constructing a hybrid response model; constructing a multi-objective decision optimization function according to the maximum independent energy storage profit, the minimum demand response cost and the highest user satisfaction degree, and constructing a transaction decision optimization model through the multi-objective decision optimization function, an electricity price change response model, an excitation compensation response model and a mixed response model; and inputting current day-ahead market transaction information and current load data to the transaction decision optimization model, solving the transaction decision optimization model according to the multi-objective decision optimization function, and obtaining an independent energy storage day-ahead market transaction decision. The method has the beneficial effects that the cost and profit of the user side and the independent energy storage side are balanced, and the suitability and rationality of decision making are improved.

Description

Technical Field

[0001] This application relates to the field of power grid control technology, and in particular to a method and system for optimizing day-ahead market trading decisions for independent energy storage. Background Technology

[0002] Driven by the "dual carbon" goals, the large-scale grid integration of renewable energy has significantly increased uncertainty on both the source and load sides, placing higher demands on the power system's flexible adjustment capabilities. Against this backdrop, optimized strategies for independent energy storage under demand response mechanisms have become a key technological path to ensure the safe and stable operation of the power grid.

[0003] Against the backdrop of the rapid evolution and continuous improvement of the current power system, demand response guides users to actively adjust the flexible resources on the demand side to alleviate the peak load pressure on the power grid and achieve "peak shaving and valley filling", thereby optimizing the system's operating status.

[0004] In real life, the reduction or increase of load will inevitably have a certain impact on users' normal production and life, thereby affecting their electricity experience, reducing the utility of electricity use, and causing user dissatisfaction. In fact, each user has their own electricity usage habits, and increasing or decreasing the load will bring about changes in users' psychology, which makes users less willing to adjust their electricity load.

[0005] Based on their willingness to participate in electricity demand response and whether they experience dissatisfaction, users can be divided into three categories: (1) Price-sensitive users. These users will actively adjust their electricity consumption behavior according to changes in market prices. They tend to have the lowest electricity price when meeting their daily electricity needs. They are generally residential users. They shift their electricity demand to periods with lower electricity prices by adjusting the temperature of their air conditioners or the charging time of their electric vehicles. Since these users actively participate in demand response based on price differentiation, their user satisfaction is generally high. (2) Contractual users. These users are sensitive to compensation prices and tend to sign annual or quarterly interruptible load agreements to lock in stable income. They are generally industrial and commercial users with large-scale production equipment or interruptible production lines who can quickly reduce their load. Since these users are mostly involuntary, their user satisfaction is generally low. (3) Fixed users. These users do not participate in demand response. They operate according to their own electricity needs and do not change their load curve. Their load is always the baseline load.

[0006] However, the use of a single demand response mechanism in related technologies has significant limitations, making it impossible to balance the operational needs of the energy supply side and the cost needs of the user side. The effectiveness of a single price-based demand response is highly dependent on users' price elasticity. Different user groups (such as residents and industries) have different sensitivities to electricity prices, resulting in insufficient stability of load shifting. Moreover, when the fluctuation range of electricity prices is too large, it can easily lead to a decline in users' electricity experience and discourage their willingness to participate in the long term. In particular, it has a weak ability to regulate rigid loads and is difficult to meet the system's deep peak shaving needs. A single incentive-based demand response faces the dilemma of high costs: the marginal benefits of incentive subsidies are diminishing, and over-reliance on direct compensation can easily lead to incentive involution, pushing up the overall demand response cost; at the same time, without long-term guidance from price signals, user response behavior is easily driven by short-term incentives, lacks the autonomy to participate continuously, and is difficult to form stable load adjustment resources. Summary of the Invention

[0007] This application addresses the technical problem in existing technologies where the use of a single demand response mechanism fails to balance the operational needs of the energy supply side and the cost needs of the user side. It provides an optimization method and system for day-ahead market trading decisions for independent energy storage. By constructing multiple models with different responses to reflect user load adjustments with varying sensitivities, and combining a multi-objective function that includes energy supply side profits, user costs, and user satisfaction, the system solves day-ahead market trading decisions for independent energy storage while adapting to actual user load adjustments. This balances the demands of both ends and improves the accuracy and adaptability of day-ahead market trading decisions for independent energy storage.

[0008] To achieve the aforementioned technical objectives, this application provides a technical solution: an optimization method for day-ahead market trading decisions for independent energy storage, comprising the following steps: calculating peak-valley load shifting rates under different electricity price changes based on historical load data; calculating demand response loads and corresponding load shifting costs for different time periods based on peak-valley load shifting rates and corresponding electricity price information; constructing an electricity price change response model; constructing an incentive compensation response model based on user peak-shaving and valley-filling response amounts and corresponding unit compensation amounts; constructing a hybrid response model based on the synergistic effect of electricity prices and incentive mechanisms, combining the electricity price change response model and the incentive compensation response model; constructing a multi-objective decision optimization function based on the maximum independent energy storage profit, minimum demand response cost, and highest user satisfaction; constructing a trading decision optimization model using the multi-objective decision optimization function, the electricity price change response model, the incentive compensation response model, and the hybrid response model; inputting current day-ahead market trading information and current load data into the trading decision optimization model; solving the trading decision optimization model based on the multi-objective decision optimization function to obtain the day-ahead market trading decision for independent energy storage.

[0009] Furthermore, the calculation of peak-valley load transfer rate under different electricity price changes based on historical load data includes: calculating the ratio of the power transferred from peak to valley periods to the original peak load based on the load data during peak hours and the load data during valley periods, and obtaining the peak-valley load transfer rate.

[0010] Furthermore, the step of calculating the demand response load and corresponding load transfer cost for different time periods based on the peak-valley load transfer rate and the corresponding electricity price information includes: dividing the load transfer stage according to the price difference gradient distribution characteristics in the electricity price information; constructing the load migration relationship based on the load transfer stage and the peak-valley load transfer rate; and calculating the demand response load and corresponding load transfer cost for different time periods based on the load migration relationship and the electricity price changes for different time periods.

[0011] Furthermore, the step of constructing an incentive compensation response model based on user peak shaving and valley filling response volume and corresponding unit compensation amount includes: establishing a first gradient relationship between peak shaving response volume and unit compensation amount corresponding to peak periods; establishing a second gradient relationship between valley filling response volume and unit compensation amount corresponding to valley periods; calculating interruption load and compensation cost based on the first gradient relationship and the second gradient relationship, and constructing an incentive compensation response model.

[0012] Furthermore, the construction of the hybrid response model based on the synergistic effect of electricity price and incentive mechanism, combined with the electricity price change response model and the incentive compensation response model, includes: constructing a synergistic effect coefficient based on the interaction between electricity price and incentive mechanism during peak and off-peak periods; and calculating the adjusted load and total synergistic cost under hybrid demand response based on the synergistic effect coefficient, combined with the electricity price change response model and the incentive compensation response model, to construct the hybrid response model.

[0013] Furthermore, the construction of the synergy effect coefficient based on the interaction between electricity price and incentive mechanism during peak and off-peak periods includes: calculating the synergy response coefficient for peak periods based on the maximum load transfer rate and the total incentive intensity during peak periods; and calculating the synergy response coefficient for off-peak periods based on the maximum load transfer rate and the total incentive intensity during off-peak periods.

[0014] Furthermore, it also includes: constructing a user satisfaction model based on the dissatisfaction cost coefficient and the load change ratio.

[0015] Furthermore, the construction of the multi-objective decision optimization function based on the maximum independent energy storage profit, the minimum demand response cost, and the highest user satisfaction includes: calculating the independent energy storage profit based on the day-ahead market electricity price, the day-ahead market winning bid volume of independent energy storage, the unit compensation amount for independent energy storage participating in peak shaving and valley filling, the operating data of independent energy storage power stations, and the load data in which independent energy storage participates; calculating the demand response cost based on the load transfer cost, compensation cost, and total collaborative cost; and calculating the user satisfaction level based on the user satisfaction model and user load data.

[0016] Furthermore, the step of solving the trading decision optimization model based on the multi-objective decision optimization function to obtain the day-ahead market trading decision for independent energy storage also includes: using a non-dominated sorting genetic algorithm to iteratively solve the trading decision optimization model based on the current day-ahead market trading information and the current load data to obtain a set of non-dominated solutions; calculating the membership degree of the non-dominated solutions in the set of non-dominated solutions based on the fuzzy membership function, and outputting the day-ahead market trading decision for independent energy storage using the non-dominated solution with the maximum comprehensive membership degree.

[0017] Another technical solution provided in this application is an independent energy storage day-ahead market transaction decision optimization system, used to implement the method described above, including: a first model construction unit, used to calculate the peak-valley load transfer rate under different electricity price changes based on historical load data, calculate the demand response load and corresponding load transfer cost for different time periods based on the peak-valley load transfer rate and the corresponding electricity price information, and construct an electricity price change response model corresponding to the electricity price change; a second model construction unit, used to construct an incentive compensation response model based on the user's peak shaving and valley filling response amount and the corresponding unit compensation amount; a third model construction unit, used to construct a hybrid response model based on the synergistic effect of electricity price and incentive mechanism, combined with the electricity price change response model and the incentive compensation response model; a comprehensive model construction unit, used to construct a multi-objective decision optimization function based on the maximum independent energy storage profit, the minimum demand response cost, and the highest user satisfaction, and construct a transaction decision optimization model using the multi-objective decision optimization function, the electricity price change response model, the incentive compensation response model, and the hybrid response model; and a decision output unit, used to call the transaction decision optimization model, input the current day-ahead market transaction information and the current load data into the transaction decision optimization model, solve the transaction decision optimization model according to the multi-objective decision optimization function, and obtain the independent energy storage day-ahead market transaction decision.

[0018] The beneficial effects of this application are as follows: 1. The electricity price change response model reflects the guiding role of electricity price changes on load changes during peak and off-peak periods; the incentive compensation response model quantifies the correlation between compensation amount and peak shaving and valley filling response; and the hybrid response model integrates the synergistic effect of price and incentives. The electricity price change response model, incentive compensation response model, and hybrid response model are constructed to correspond to users with different electricity consumption responses, improving the accuracy of demand response behavior prediction. Furthermore, by utilizing the maximum energy storage profit, minimum response cost, and highest user satisfaction, multi-objective optimization is performed based on the demand response model, outputting an independent day-ahead market trading decision that simultaneously balances economy, cost, and user experience, improving the adaptability and rationality of the decision.

[0019] 2. By using both linear and nonlinear collaborative cost coefficients, the linear cost growth when the adjustment amount is small and the nonlinear cost increase due to factors such as conflict when the adjustment amount is large can be reflected simultaneously, thereby improving the accuracy of calculating the total collaborative cost of hybrid demand response. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the method for optimizing day-ahead market trading decisions for independent energy storage in this application.

[0021] Figure 2 This is a diagram illustrating how users respond to electricity prices.

[0022] Figure 3A This is a schematic diagram of the first gradient relationship corresponding to the peak period of this application.

[0023] Figure 3B This is a schematic diagram of the second gradient relationship corresponding to the trough period of this application.

[0024] Figure 4 This is a schematic diagram of a non-dominated solution in one embodiment of the independent energy storage day-ahead market trading decision optimization method of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] like Figure 1 As shown in the first embodiment of this application, the method for optimizing day-ahead market trading decisions for independent energy storage includes the following steps: Calculate the peak-valley load transfer rate under different electricity price changes based on historical load data, calculate the demand response load and corresponding load transfer cost for different time periods based on the peak-valley load transfer rate and the corresponding electricity price information, and construct an electricity price change response model corresponding to electricity price changes; An incentive compensation response model is constructed based on the user's peak shaving and valley filling response volume and the corresponding unit compensation amount. A hybrid response model is constructed based on the synergistic effect of electricity prices and incentive mechanisms, combined with an electricity price change response model and an incentive compensation response model. A multi-objective decision optimization function is constructed based on the maximum independent energy storage profit, the minimum demand response cost, and the highest user satisfaction. A transaction decision optimization model is then constructed using the multi-objective decision optimization function, the electricity price change response model, the incentive compensation response model, and the hybrid response model. Input the current day-ahead market transaction information and current load data into the transaction decision optimization model, solve the transaction decision optimization model according to the multi-objective decision optimization function, and obtain the independent energy storage day-ahead market transaction decision.

[0027] In this embodiment, the electricity price change response model reflects the guiding role of electricity price changes on load changes during peak and off-peak periods; the incentive compensation response model quantifies the correlation between compensation amount and peak shaving and valley filling response; and the hybrid response model integrates the synergistic effect of price and incentives. These three models—electricity price change response model, incentive compensation response model, and hybrid response model—are constructed to correspond to users with different electricity consumption responses, improving the accuracy of demand response behavior prediction. Furthermore, multi-objective optimization is performed based on the demand response model using the principles of maximizing energy storage profit, minimizing response cost, and maximizing user satisfaction. This outputs an independent day-ahead market trading decision for energy storage that simultaneously balances economy, cost, and user experience, improving the adaptability and rationality of the decision.

[0028] Specifically, calculating the peak-valley load shift rate under different electricity price changes based on historical load data includes: The peak-to-valley load transfer rate is obtained by calculating the ratio of the power transferred from peak to off-peak hours to the original peak load based on the load data during peak and off-peak hours.

[0029] like Figure 2 As shown, the user's responsiveness to the electricity price signal exhibits a three-stage approximately linear characteristic. When the threshold is lower, the user is in the response cutoff zone because the electricity price signal does not change significantly, and the user hardly responds. Within the response range of the electricity price, the user's responsiveness is approximately linearly correlated with the intensity of the electricity price incentive. When the change in the electricity price signal exceeds the upper threshold, the user's responsiveness is constrained by the law of diminishing marginal utility, enters the rigid constraint zone, and the user stops responding.

[0030] Based on the assumption of rigid constraints in electricity consumption, and assuming the existence of a time-of-use (TOU) pricing mechanism, obtaining the corresponding electricity price information is known as obtaining the TOU pricing mechanism. Price-sensitive user groups exhibit load migration characteristics under the TOU pricing mechanism. Assuming that total user electricity consumption remains constant, and only load shifting occurs in the time dimension, the peak-valley load shift rate is: ; in, This indicates the load shift rate from peak electricity price periods to off-peak electricity price periods. Indicates the slope of the response region. Indicates the change in electricity price. This indicates the threshold for changes in electricity prices within the cutoff zone. This represents the threshold for changes in electricity prices in the saturation zone. This indicates the maximum load transfer rate.

[0031] In this embodiment, the electricity price change stage where users are insensitive to electricity price changes is defined as the cutoff region, the electricity price change stage where users' response sensitivity changes linearly with electricity price changes is defined as the saturation region, and the electricity price change stage where users' response reaches its maximum value and electricity price changes do not cause changes in users' response is defined as the saturation region. The load transfer rate changes in different electricity price change stages reflect the fluctuations in users' response sensitivity to electricity price changes, and then user load response is predicted based on user response sensitivity, thereby improving the accuracy of user load response prediction.

[0032] Based on peak-valley load shifting rates and corresponding electricity price information, the demand response load and corresponding load shifting costs for different time periods are calculated, including: The load transfer phases are divided based on the price difference gradient distribution characteristics in the electricity price information. The load migration relationship is constructed based on the load transfer phases and peak-valley load transfer rates. The demand response load and corresponding load transfer costs for different time periods are calculated based on the load migration relationship and the electricity price changes at different times.

[0033] Based on the price difference gradient distribution characteristics of the time-of-use (TOU) pricing mechanism contained in the electricity price information, the load transfer stages affected by the TOU mechanism mainly include peak-to-normal period transfer, peak-to-valley period transfer, and normal-to-valley period transfer. The load migration relationship can be constructed based on the load transfer stages and peak-to-valley load transfer rates as follows: ; ; ; in, express Response to changes in electricity prices over time to load shifting values. This indicates the load transfer rate during peak-to-normal periods. This indicates the load transfer rate during peak-valley periods. This indicates the load transfer rate during the off-peak period. This represents the average value of the original load during peak hours. This represents the average value of the original load during off-peak hours. This represents the average value of the original load during the off-peak period. Indicates peak hours. Indicates off-peak hours. Indicates a low point in time. express Load values ​​after demand response during the time period express Load values ​​before demand response during a given period.

[0034] In this embodiment, the load transfer rate is calculated by determining whether the change in electricity price at time t corresponds to the cutoff zone, saturation zone, or response zone. Then, the load migration relationship of the corresponding time period is matched according to whether time t corresponds to the peak period, off-peak period, or valley period.

[0035] Then, the load transfer cost is calculated based on the calculated load transfer value in response to electricity price changes: ; in, Indicates load transfer costs. express Electricity price changes over a period of time , This indicates a trading cycle.

[0036] When users shift their electricity consumption from peak hours to off-peak hours due to changes in electricity prices, they need to compensate for the extra revenue generated from their off-peak electricity consumption, resulting in load shifting costs.

[0037] The incentive compensation response model is constructed based on the user peak-shaving and valley-filling response volume and the corresponding unit compensation amount, including: Establish the first gradient relationship between peak shaving response volume and unit compensation amount during peak periods; A second gradient relationship is established between the valley-filling response amount and the unit compensation amount during the valley period; The interruption load and compensation cost are calculated using the first gradient relationship and the second gradient relationship, and an incentive compensation response model is constructed.

[0038] Incentive-based demand response (ITR) employs reward measures and direct economic compensation to incentivize users to change their electricity consumption behavior and optimize load curves. ITR is primarily used for interruptible loads. It incentivizes users to sign response agreements with power companies by implementing preferential policies, guiding them to reduce unnecessary loads during peak hours and encourage the use of renewable energy during off-peak hours, thereby reducing operating costs and ensuring stable grid operation.

[0039] In this embodiment, by establishing a tiered demand response compensation mechanism for peak hours and a tiered demand response compensation mechanism for off-peak hours, interruptible load users are guided to achieve the goal of peak shaving and valley filling. Multiple incentive intervals are set in peak and off-peak hours, allowing various users to adjust the response time and response amount more flexibly according to their own actual situation.

[0040] like Figure 3A As shown, the first gradient relationship between peak-shaving response and unit compensation amount during peak hours is as follows: ; in, This indicates the unit compensation amount for peak-shaving electricity. This indicates that peak load reduction can be achieved by decreasing the peak response time for users during peak load periods. Indicates the benchmark electricity price. This represents the peak-shaving excitation coefficient. This indicates the reference value for peak reduction response.

[0041] During peak hours, when users reduce their electricity consumption by more than different thresholds, the compensation amount increases progressively to incentivize users to increase their peak shaving response.

[0042] like Figure 3B As shown, the second gradient relationship between the valley-filling response and the unit compensation amount during the valley period is as follows: ; in, This indicates the compensation amount per unit of off-peak electricity consumption. This represents the peak-filling response volume of users during off-peak hours, and is generally a negative value. This represents the valley filling incentive coefficient. This indicates the reference value for the valley filling response.

[0043] During off-peak hours, when a user's electricity consumption exceeds different thresholds, the compensation amount increases progressively to incentivize the user to increase their off-peak response.

[0044] The interruption load is calculated using the first gradient relationship and the second gradient relationship as follows: ; in, This represents the interrupted load during time period t.

[0045] For responses exceeding each threshold, the compensation cost is calculated in segments using the corresponding coefficients. ; in, This indicates the cost of compensation.

[0046] Based on the synergistic effect of electricity prices and incentive mechanisms, a hybrid response model is constructed by combining an electricity price change response model and an incentive compensation response model, including: A synergistic effect coefficient is constructed based on the interaction between electricity prices and incentive mechanisms during peak and off-peak periods; Based on the synergy effect coefficient, combined with the electricity price change response model and the incentive compensation response model, the adjusted load and total synergy cost under the mixed demand response are calculated, and a mixed response model is constructed.

[0047] The hybrid response model reflects the synergistic effect of simultaneously using time-of-use electricity price fluctuations to guide users to adjust their load and using incentive compensation to ensure demand response. This covers adjustment needs that cannot be met by a single demand response mechanism and avoids the failure of a single demand response mechanism when demand elasticity is insufficient.

[0048] In this embodiment, by integrating the dual mechanisms of electricity price signals and incentive compensation, the electricity price signals drive users to pursue the minimization of electricity costs, while the incentive compensation covers the users' participation costs. Through these dual economic levers, the willingness of users to respond is further enhanced.

[0049] Among them, the synergistic effect coefficient, constructed based on the interaction between electricity prices and incentive mechanisms during peak and off-peak periods, includes: The coordinated response coefficient during peak hours is calculated based on the maximum load transfer rate and the total excitation intensity during peak hours. The collaborative response coefficient for off-peak periods is calculated based on the maximum load transfer rate and the total excitation intensity during off-peak periods.

[0050] Specifically, the synergy coefficient is: ; in, Represents the collaborative response coefficient; This represents a three-tiered incentive ladder.

[0051] In this embodiment, The total incentive intensity during peak and off-peak periods is calculated by using the incentive amount and incentive coefficient in the three stages of the incentive process. The degree of synergy between price guidance and incentive is quantified by the ratio of the maximum load transfer rate to the total incentive intensity.

[0052] Specifically, based on the synergy effect coefficient combined with the electricity price change response model and the incentive compensation response model, the adjusted load and total synergy cost under the mixed demand response are calculated as follows: ; ; in, express Adjusting load after mixed demand response at any given time. This represents the total cost of price-incentive hybrid collaboration. This represents the cost coefficient of linear synergy. This represents the nonlinear collaborative cost coefficient.

[0053] In this embodiment, by using both linear and nonlinear collaborative cost coefficients, the linear cost growth when the adjustment amount is small and the nonlinear cost increase due to factors such as conflict when the adjustment amount is large are simultaneously reflected, thereby improving the accuracy of calculating the total collaborative cost of hybrid demand response.

[0054] In this embodiment, the method for optimizing day-ahead market trading decisions for independent energy storage further includes: A user satisfaction model is constructed based on the dissatisfaction cost coefficient and the load change ratio.

[0055] Demand response may require users to adjust their electricity consumption behavior during specific periods, reducing load during peak hours or increasing load during off-peak hours to ensure stable grid operation. Because load shifting forces users to go against their usual electricity usage habits, it can lead to dissatisfaction. User satisfaction stems from users' subjective feelings after adjusting their electricity consumption behavior; it plays a crucial role in users' enthusiasm for participating in demand response and ultimately affects the effectiveness of its implementation. Therefore, the user satisfaction model is as follows: ; in, Indicating user satisfaction, when When expressing the most dissatisfaction, At that time, he expressed his greatest satisfaction; express The change in load at any given time. express User baseline load at any given time This represents the cost coefficient for dissatisfaction.

[0056] In this embodiment, the dissatisfaction cost coefficient can be obtained based on expert experience or calculated based on historical load data and historical user rating data.

[0057] A multi-objective decision optimization function is constructed based on the maximum independent energy storage profit, the minimum demand response cost, and the highest user satisfaction, including: The profit of independent energy storage is calculated based on the day-ahead market electricity price, the day-ahead market winning bid volume of independent energy storage, the unit compensation amount for independent energy storage participating in peak shaving and valley filling, the operating data of independent energy storage power stations, and the load data of independent energy storage participation. Calculate the demand response cost based on load transfer costs, compensation costs, and total coordination costs. User satisfaction is calculated based on a user satisfaction model and user load data.

[0058] The trading decision optimization model is constructed using multi-objective decision optimization functions, electricity price change response models, incentive compensation response models, and hybrid response models, including: The demand response cost in the multi-objective decision optimization function is calculated using the load transfer cost output by the electricity price change response model, the compensation cost output by the incentive compensation response model, and the collaborative total cost output by the hybrid response model, and a transaction decision optimization model is constructed.

[0059] In this embodiment, a multi-objective decision optimization function is constructed by considering independent energy storage profit, demand response cost, and user satisfaction. This ensures that the transaction decision obtained from the optimization solution balances the operational needs of the independent energy storage end and the experience needs of the user end. Simultaneously, in the demand response cost calculation, three types of demand response models are integrated, and the demand response cost for each user is calculated specifically based on their corresponding response sensitivity, ensuring the adaptability of the decision output.

[0060] Specifically, based on the day-ahead market electricity price, the day-ahead market winning bid volume of independent energy storage, the unit compensation amount for independent energy storage participating in peak shaving and valley filling, the operating data of independent energy storage power stations, and the load data of independent energy storage participation, the profit of independent energy storage is calculated as follows: ; in, Indicates the profit of independent energy storage. This represents the total number of typical scenarios. Typical scenarios Below The daily market electricity price at any given time, Typical scenarios Below The amount of electricity won in the market by independent energy storage at any time recently. Typical scenarios The unit compensation amount for independent energy storage participating in peak shaving. Typical scenarios The unit compensation amount for independent energy storage participating in valley filling. Typical scenarios Below Load changes involving independent energy storage at any given time Typical scenarios Below The unit power operation and maintenance cost of independent energy storage at any given time. This indicates the operating power of an independent energy storage power station.

[0061] The demand response cost is calculated based on load transfer costs, compensation costs, and total coordination costs: ; in, Indicates demand response cost, This indicates that logical variables are enabled for load transfer costs. Typical scenarios The load transfer cost under This indicates that logical variables are used to indicate compensation costs. Typical scenarios The compensation cost below, This indicates that the total collaborative cost uses a logical variable. Typical scenarios The total cost of collaboration.

[0062] In this embodiment, , , The value can be 0 or 1. A value of 0 indicates that this cost is not used in the calculation of demand response costs, while a value of 1 indicates that this cost is used in the calculation of demand response costs. Since the three demand response mechanisms do not occur simultaneously, the corresponding activation constraint is constructed as follows: .

[0063] Based on the user satisfaction model and user workload data, the user satisfaction level is calculated as follows: ; in, Indicates user satisfaction level. Typical scenarios User load changes Typical scenarios The user baseline load.

[0064] Based on the maximum independent energy storage profit, minimum demand response cost, and highest user satisfaction, a multi-objective decision optimization function is constructed as follows: ; Where Z represents the multi-objective decision optimization function.

[0065] In this embodiment, typical scenarios are pre-constructed based on Monte Carlo simulation and k-means clustering algorithm to obtain power grid operation data under different scenarios.

[0066] Specifically, constructing a multi-objective decision optimization function based on the maximum independent energy storage profit, the minimum demand response cost, and the highest user satisfaction also includes: Energy storage constraints are constructed based on the energy storage performance of independent energy storage. Construct node balance constraints based on power grid balance relationships; Demand response constraints are constructed based on the upper and lower limits of load adjustment.

[0067] Energy storage constraints are constructed based on the total charging and discharging capacity, maximum charging and discharging capacity, upper and lower limits of state of charge, charged capacity, and charging and discharging efficiency of independent energy storage power stations. Node balancing constraints are constructed based on the principle that the sum of user load changes and the charging and discharging capacity of independent energy storage power stations equals the net load demand of the power grid. Demand response constraints are constructed based on the upper limit of load adjustment.

[0068] Furthermore, the current market transaction information and current load data are input into the transaction decision optimization model. The model is then solved according to the multi-objective decision optimization function to obtain the day-ahead market transaction decision for independent energy storage. During the solution process, the independent energy storage profit, demand response cost, and user satisfaction are continuously balanced to achieve collaborative optimization among multiple stakeholders.

[0069] As a second embodiment of this application, the method of solving the trading decision optimization model based on the multi-objective decision optimization function to obtain the day-ahead market trading decision for independent energy storage further includes: The non-dominated sorting genetic algorithm is used to iteratively solve the trading decision optimization model based on the current day-to-day market trading information and the current load data to obtain a set of non-dominated solutions; The membership degree of non-dominated solutions in the non-dominated solution set is calculated based on the fuzzy membership function, and the non-dominated solution with the maximum comprehensive membership degree is used to output the day-ahead market trading decision for independent energy storage.

[0070] Specifically, in the iterative solution of the multi-objective decision optimization function in the trading decision optimization model using the Non-Dominated Sorting Genetic Algorithm (NSGA-II algorithm) based on current market trading information and current load data, the energy storage charging and discharging capacity and typical scenarios are taken into account. Below The load changes involving independent energy storage at any given time and the user load changes are used as decision variables, and the following iterative solution steps are performed: S1: Input the current day-ahead market transaction information, current load data, and key parameters of the NSGA-II algorithm, including day-ahead electricity price, baseline load, renewable energy output forecast, crossover probability, genetic probability, and maximum number of iterations; S2: Encoding and population initialization. Each individual is encoded as a real number vector of length 4T, containing decision variables for all time periods. N individuals are randomly generated to ensure that the initial decision variables are all greater than 0. S3: Fitness calculation, which calculates the independent energy storage profit, demand response cost, and user satisfaction of each individual. S4: Non-dominated sorting, which stratifies all individuals according to their dominance relationship. The first layer is the non-dominated solution, i.e., the Pareto front, the second layer is the solution dominated by the first layer, and so on. S5: Crowding degree calculation, calculates the crowding degree of individuals within the same non-dominated layer, in order to maintain the diversity of solutions; S6: Selection, Crossover, and Mutation. Based on non-dominated ranking and crowding, select superior individuals to enter the next generation. Then, simulated binary crossover is used to generate offspring. Multinomial or Gaussian mutation is performed on the offspring to maintain diversity. S7: Termination condition: reaching the maximum number of iterations or Pareto front convergence after several consecutive generations without significant changes.

[0071] The output provides a set of nondominated solutions from the Pareto front, demonstrating the trade-offs between energy storage profits, system costs, and user satisfaction.

[0072] To formulate optimal charging and discharging strategies for independent energy storage under different demand response mechanisms, it is necessary to comprehensively consider specific circumstances and select a suitable optimal compromise solution from a set of Pareto optimal solutions. Therefore, in this embodiment, based on fuzzy set theory, a fuzzy membership function is used to represent the membership degree corresponding to each objective function value in each Pareto solution, thereby finding the solution with the highest comprehensive membership degree that can be selected during the trading process.

[0073] Specifically, for the first Calculate the standardized membership degree for each objective function. .

[0074] Calculate the maximum independent energy storage profit and the highest user satisfaction using the following formulas: ; in, Indicates the first The current value of the objective function. Indicates the first The minimum value of an objective function. Indicates the first The maximum value of each objective function.

[0075] Calculate the minimum demand response cost using the following formula: .

[0076] Then, for each non-dominated solution, calculate the average membership degree of all its objective functions. The non-dominated solution with the highest average membership degree is taken as the optimal compromise solution. The energy storage charging and discharging capacity and typical scenarios corresponding to the optimal compromise solution are then considered. Below The independent energy storage system participates in load changes and user load changes at any given time to make day-ahead market trading decisions.

[0077] In this embodiment, the solution is performed using the MATLAB 2022b simulation platform based on the YALMIP and GUROBI solvers. The interruptible loads participating in the tiered demand response are divided into three levels, with compensation prices of 150 RMB / MWh, 200 RMB / MWh, and 300 RMB / MWh for each level. The amount of interruptible loads invoked does not exceed 5% of the total load. The synergistic effect cost coefficients are 50 RMB / MWh and 0.2 RMB / MWh, respectively. The NSGA-II algorithm has a population of 50, 200 iterations, a crossover probability of 0.9, and a mutation probability of 0.1.

[0078] like Figure 4 As shown, based on the above parameter settings, each point on the graph represents a non-dominated solution, meaning that the optimal solution is the one that performs better on all objectives simultaneously when no other solution can do so, demonstrating the game-theoretic relationship between the three objective functions. The trends in the graph show that the optimal compromise solution for price-based demand response lies in the region of moderate profit, low cost, and relatively high satisfaction. This is mainly because price-based demand response guides user behavior through electricity price signals, resulting in lower compensation costs, but limited scheduling flexibility, moderate profits, and higher user satisfaction due to proactive response. The optimal compromise solution for incentive-based demand response lies in the region of low profit, high cost, and low satisfaction. This is because incentive-based demand response forces users to respond to demand, leading to large load adjustments, increased opportunities for energy storage charging and discharging, and improved profits. However, it incurs high compensation costs, significantly impacting user electricity consumption behavior and resulting in lower satisfaction. The hybrid approach lies in the middle region between profit and cost, with moderate user satisfaction. This is achieved by combining price and incentive strategies to balance cost and effectiveness. Next, based on the optimal compromise solutions for each type of demand response in the Pareto front, we will analyze the charging and discharging strategies for independent energy storage participating in day-ahead trading.

[0079] It should be noted that in this embodiment, typical scenarios in electricity market transactions are generated in advance based on historical grid operation data to quantify the impact of uncertainty on the electricity market. In this way, during the process of optimizing the day-ahead market transaction decision for independent energy storage, the pre-constructed typical scenarios are used to obtain renewable energy output, unit compensation amount, peak-valley load transfer rate, etc. under the current scenario.

[0080] As a second embodiment of this application, the independent energy storage day-ahead market trading decision optimization system includes: The first model building unit is used to calculate the peak-valley load transfer rate under different electricity price changes based on historical load data, and to calculate the demand response load and corresponding load transfer cost for different time periods in combination with the current time-of-use electricity pricing mechanism, thereby building an electricity price change response model corresponding to electricity price changes. The second model building unit is used to build an incentive compensation response model based on the user's peak shaving and valley filling response volume and the corresponding unit compensation amount. The third model building unit is used to construct a hybrid response model based on the synergistic effect of electricity price and incentive mechanism, combined with the electricity price change response model and the incentive compensation response model. The integrated model building unit is used to construct a multi-objective decision optimization function based on the maximum independent energy storage profit, the minimum demand response cost, and the highest user satisfaction. The transaction decision optimization model is constructed using the multi-objective decision optimization function, the electricity price change response model, the incentive compensation response model, and the hybrid response model. The decision output unit is used to call the trading decision optimization model, input the current day-ahead market trading information and the current load data into the trading decision optimization model, solve the trading decision optimization model according to the multi-objective decision optimization function, and obtain the independent energy storage day-ahead market trading decision.

[0081] The first model building unit, the second model building unit, and the third model building unit are connected to the decision output unit.

[0082] In this embodiment, during periods of severe price fluctuations due to extreme weather or supply-demand imbalances, the proportion of incentive-based demand response in the trading decision optimization model is adjusted to increase peak load reduction rates. A high-compensation mechanism quickly activates users' load reduction capabilities, achieving short-term load reduction targets and ensuring reliable grid regulation during emergencies. During periods of stable electricity prices, the trading decision optimization model increases the proportion of price-based response, guiding users to spontaneously adjust their electricity consumption behavior based on time-of-use pricing signals. This reduces direct compensation costs while maintaining user satisfaction, making it suitable for normalized market operations. Secondly, differentiated compensation designs are implemented based on the response characteristics and demand preferences of different user groups. Sensitive users should adopt a high-price-based proportion model, encouraging them to shift flexible loads to off-peak periods through peak-valley price discounts and electricity consumption suggestions. These users have a low tolerance for forced adjustments, and frequent interruptions should be avoided to maintain satisfaction. For contract-based users, a high-incentive model should be adopted, combining production process flexibility to implement load interruptions or reductions during high-price periods, maximizing peak shaving effects while balancing economic benefits and grid demand. Energy storage systems need to dynamically adjust their operating modes according to demand response types to improve the adaptability of day-ahead market trading decisions for independent energy storage.

[0083] The specific embodiments described above are preferred embodiments of the independent energy storage day-ahead market transaction decision optimization method and system of this application, and are not intended to limit the specific implementation scope of this application. The scope of this application includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. An optimization method for day-ahead market trading decisions for independent energy storage, characterized by: Includes the following steps: Calculate the peak-valley load transfer rate under different electricity price changes based on historical load data, and calculate the demand response load and corresponding load transfer cost for different time periods based on the peak-valley load transfer rate and the corresponding electricity price information, and construct an electricity price change response model. An incentive compensation response model is constructed based on the user's peak shaving and valley filling response volume and the corresponding unit compensation amount. A hybrid response model is constructed based on the synergistic effect of electricity prices and incentive mechanisms, combined with an electricity price change response model and an incentive-based compensation response model. A multi-objective decision optimization function is constructed based on the maximum independent energy storage profit, the minimum demand response cost, and the highest user satisfaction. A transaction decision optimization model is then constructed using the multi-objective decision optimization function, the electricity price change response model, the incentive compensation response model, and the hybrid response model. Input the current day-ahead market transaction information and current load data into the transaction decision optimization model, solve the transaction decision optimization model according to the multi-objective decision optimization function, and obtain the independent energy storage day-ahead market transaction decision.

2. The method for optimizing day-ahead market trading decisions for independent energy storage as described in claim 1, characterized in that: The calculation of peak-valley load shift rate under different electricity price changes based on historical load data includes: The peak-to-valley load transfer rate is obtained by calculating the ratio of the power transferred from peak to off-peak hours to the original peak load based on the load data during peak and off-peak hours.

3. The method for optimizing day-ahead market trading decisions for independent energy storage as described in claim 2, characterized in that: The calculation of demand response load and corresponding load transfer costs for different time periods based on peak-valley load transfer rates and corresponding electricity price information includes: The load transfer phases are divided based on the price difference gradient distribution characteristics in the electricity price information. The load migration relationship is constructed based on the load transfer phases and peak-valley load transfer rates. The demand response load and corresponding load transfer costs for different time periods are calculated based on the load migration relationship and the electricity price changes at different times.

4. The method for optimizing day-ahead market trading decisions for independent energy storage as described in claim 1, characterized in that: The construction of the incentive compensation response model based on the user peak-shaving and valley-filling response volume and the corresponding unit compensation amount includes: Establish the first gradient relationship between peak shaving response volume and unit compensation amount during peak periods; A second gradient relationship is established between the valley-filling response amount and the unit compensation amount during the valley period; The interruption load and compensation cost are calculated using the first gradient relationship and the second gradient relationship, and an incentive compensation response model is constructed.

5. The method for optimizing day-ahead market trading decisions for independent energy storage as described in claim 1, 2, 3, or 4, characterized in that: The construction of a hybrid response model based on the synergistic effect of electricity prices and incentive mechanisms, combined with an electricity price change response model and an incentive-based compensation response model, includes: A synergistic effect coefficient is constructed based on the interaction between electricity prices and incentive mechanisms during peak and off-peak periods; Based on the synergy effect coefficient, combined with the electricity price change response model and the incentive compensation response model, the adjusted load and total synergy cost under the mixed demand response are calculated, and a mixed response model is constructed.

6. The method for optimizing day-ahead market trading decisions for independent energy storage as described in claim 5, characterized in that: The construction of the synergistic effect coefficient based on the interaction between electricity prices and incentive mechanisms during peak and off-peak periods includes: The coordinated response coefficient during peak hours is calculated based on the maximum load transfer rate and the total excitation intensity during peak hours. The collaborative response coefficient for off-peak periods is calculated based on the maximum load transfer rate and the total excitation intensity during off-peak periods.

7. The method for optimizing day-ahead market trading decisions for independent energy storage as described in claim 1, characterized in that: Also includes: A user satisfaction model is constructed based on the dissatisfaction cost coefficient and the load change ratio.

8. The method for optimizing day-ahead market trading decisions for independent energy storage as described in claim 4, characterized in that: The multi-objective decision optimization function constructed based on the maximum independent energy storage profit, the minimum demand response cost, and the highest user satisfaction includes: The profit of independent energy storage is calculated based on the day-ahead market electricity price, the day-ahead market winning bid volume of independent energy storage, the unit compensation amount for independent energy storage participating in peak shaving and valley filling, the operating data of independent energy storage power stations, and the load data of independent energy storage participation. Calculate the demand response cost based on load transfer costs, compensation costs, and total coordination costs. User satisfaction is calculated based on a user satisfaction model and user load data.

9. The method for optimizing day-ahead market trading decisions for independent energy storage as described in claim 1, characterized in that: The step of solving the trading decision optimization model based on the multi-objective decision optimization function to obtain the day-ahead market trading decision for independent energy storage also includes: The non-dominated sorting genetic algorithm is used to iteratively solve the trading decision optimization model based on the current day-to-day market trading information and the current load data to obtain a set of non-dominated solutions; The membership degree of non-dominated solutions in the non-dominated solution set is calculated based on the fuzzy membership function, and the non-dominated solution with the maximum comprehensive membership degree is used to output the day-ahead market trading decision for independent energy storage.

10. An independent energy storage day-ahead market trading decision optimization system, used to implement the method as described in any one of claims 1 to 9, characterized in that: include: The first model building unit is used to calculate the peak-valley load transfer rate under different electricity price changes based on historical load data, and to calculate the demand response load and corresponding load transfer cost for different time periods based on the peak-valley load transfer rate and the corresponding electricity price information, thereby building an electricity price change response model. The second model building unit is used to build an incentive compensation response model based on the user's peak shaving and valley filling response volume and the corresponding unit compensation amount. The third model building unit is used to construct a price-incentive hybrid demand response model by combining the synergistic effect of electricity prices and incentive mechanisms with price-based demand response models and incentive compensation response models. The integrated model building unit is used to construct a multi-objective optimization function based on the maximum independent energy storage profit, the minimum demand response cost, and the highest user satisfaction. The transaction decision optimization model is constructed using the multi-objective optimization function, the price-based demand response model, the incentive compensation response model, and the price-incentive hybrid demand response model. The decision output unit is used to call the trading decision optimization model, input the current day-ahead market trading information and the current load data into the trading decision optimization model, and use the genetic algorithm to solve the trading decision optimization model to obtain the independent energy storage day-ahead market trading decision.

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