Energy storage control method, device, apparatus and storage medium

Abstract

The application belongs to the technical field of energy storage, and discloses an energy storage control method, device, equipment and storage medium. According to the yield loss of each candidate demand in each historical scene, a target demand is determined, then a demand penalty is determined according to the target demand, thereby reducing the risk of yield loss in an extreme scene, then an optimization target of maximizing peak-valley yield and minimizing demand electricity fee is constructed, and then the energy storage charging and discharging power is determined based on the optimization target and the demand penalty, and the energy storage system is controlled based on the energy storage charging and discharging power, so that the energy storage system can realize comprehensive optimization among improving peak-valley yield, reducing demand electricity fee and controlling the risk of yield loss in an extreme scene.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and in particular to an energy storage control method, device, equipment and storage medium. Background Technology

[0002] With the advancement of "dual carbon" targets and the deepening of power market reforms, industrial and commercial energy storage has become a key technology for optimizing power resource allocation due to its advantages such as peak shaving and valley filling, and improved power utilization efficiency. Currently, the main profit models for industrial and commercial energy storage are concentrated in two areas: peak-valley revenue and demand control. Existing industrial and commercial energy storage control schemes still mainly rely on predicting maximum demand or manually setting thresholds in terms of target demand thresholds, that is, determining the target for preventing over-demand or demand control based on the predicted load peak. However, predicted demand can only reflect the possible level of load peak and cannot reflect the actual feasibility of energy storage systems under different target demand constraints, the opportunity cost of charging and discharging, the loss of peak-valley revenue, and the risk of control failure in extreme scenarios. Summary of the Invention

[0003] The main objective of this application is to provide an energy storage control method, device, equipment, and storage medium, aiming to solve the technical problem of how to achieve comprehensive optimization of energy storage systems in terms of improving peak and off-peak revenue, reducing demand electricity costs, and controlling the risk of revenue loss in extreme scenarios.

[0004] To achieve the above objectives, this application provides an energy storage control method, which includes the following steps: For any candidate demand in the candidate demand set, the target demand is determined based on the revenue loss of the candidate demand in each historical scenario. Demand penalty is determined based on the target demand, and energy storage charging and discharging power is determined based on the optimization objective and the demand penalty. The optimization objective is to maximize peak-valley revenue and minimize demand electricity costs. Energy storage control is performed based on the energy storage charging and discharging power.

[0005] Optionally, determining the target demand for any candidate demand in the candidate demand set based on the revenue loss of the candidate demand in various historical scenarios includes: For any candidate demand in the candidate demand set, determine the revenue loss of the candidate demand in each historical scenario; Determine the tail risk of the loss corresponding to the candidate demand based on the aforementioned revenue loss; Determine the minimum value among the tail risks of the loss, and take the candidate demand corresponding to the minimum value as the target demand.

[0006] Optionally, determining the revenue loss of any candidate demand in the candidate demand set under various historical scenarios includes: For any candidate demand in the candidate demand set, determine the optimal energy storage control strategy for the candidate demand under each historical scenario; The optimal comprehensive benefit of the candidate demand under each historical scenario is determined based on the optimal energy storage control strategy. The revenue loss of the candidate demand in each historical scenario is determined based on the optimal comprehensive revenue.

[0007] Optionally, determining the demand penalty based on the target demand and determining the energy storage charging and discharging power based on the optimization target and the demand penalty includes: Determine the demand penalty based on the target demand; Based on peak-valley revenue, the demand penalty, the cost-per-kilowatt-hour penalty, and the optimization objective, a joint optimization model for day-ahead peak-valley revenue and demand control is constructed. The energy storage charging and discharging power is determined by the day-ahead peak-valley revenue and demand control joint optimization model.

[0008] Optionally, determining the energy storage charging and discharging power through the day-ahead peak-valley revenue and demand control joint optimization model includes: Energy storage demand control constraints are constructed based on the target demand. Model constraints are constructed based on energy storage operation constraints, load constraints, and energy storage demand control constraints. Under the constraints of the model, the joint optimization model of day-ahead peak-valley revenue and demand control is solved to obtain the energy storage charging and discharging power.

[0009] Optionally, the energy storage control based on the energy storage charging and discharging power includes: A joint optimization model for intraday peak-valley revenue and demand control is constructed based on peak-valley revenue, demand penalty, cost-per-kilowatt-hour penalty, and time-series adjustment factor. The real-time charging and discharging power is determined based on the energy storage charging and discharging power and the joint optimization model of intraday peak-valley revenue and demand control. Energy storage control is performed based on the real-time charging and discharging power.

[0010] Optionally, determining the real-time charging and discharging power based on the energy storage charging and discharging power and the joint optimization model of intraday peak-valley revenue and demand control includes: Determine the predicted charging and discharging power within a preset time period after the current moment; Select the initial charge and discharge power within the preset time period from the energy storage charge and discharge power, and replace the initial charge and discharge power with the predicted charge and discharge power to obtain the spliced ​​charge and discharge power; The spliced ​​charging and discharging power is input into the intraday peak-valley revenue and demand control joint optimization model to obtain the target charging and discharging power within the preset time period; The real-time charge / discharge power corresponding to the current moment is determined based on the target charge / discharge power.

[0011] Furthermore, to achieve the above objectives, this application also provides an energy storage control device, the energy storage control device comprising: The demand determination module is used to determine the target demand for any candidate demand in the candidate demand set based on the revenue loss of the candidate demand in various historical scenarios. The power determination module is used to determine the demand penalty based on the target demand, and to determine the energy storage charging and discharging power based on the optimization objective and the demand penalty, wherein the optimization objective is to maximize peak and valley revenue and minimize demand electricity costs; An energy storage control module is used to control energy storage based on the energy storage charging and discharging power.

[0012] In addition, to achieve the above objectives, this application also proposes an energy storage control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the energy storage control method described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the energy storage control method described above.

[0014] This application determines a target demand for any candidate demand in the candidate demand set based on the revenue loss of the candidate demand under various historical scenarios. Then, a demand penalty is determined based on the target demand. Finally, the energy storage charging and discharging power is determined based on the optimization objective and the demand penalty. The optimization objective is to maximize peak-valley revenue and minimize demand-based electricity costs. Energy storage control is then performed based on the energy storage charging and discharging power. This application determines the target demand based on the revenue loss of each candidate demand under various historical scenarios, and then determines the demand penalty based on the target demand, thereby reducing the risk of revenue loss in extreme scenarios. Then, it constructs an optimization objective of maximizing peak-valley revenue and minimizing demand-based electricity costs. Based on the optimization objective and the demand penalty, the energy storage charging and discharging power is determined, and the energy storage system is controlled based on the energy storage charging and discharging power. This enables the energy storage system to achieve comprehensive optimization between improving peak-valley revenue, reducing demand-based electricity costs, and controlling the risk of revenue loss in extreme scenarios. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the first embodiment of the energy storage control method of this application; Figure 2 This is a flowchart illustrating the second embodiment of the energy storage control method of this application; Figure 3 This is a flowchart illustrating the third embodiment of the energy storage control method of this application; Figure 4 This is a solution scenario for multiple optimal solutions in an embodiment of the energy storage control method of this application; Figure 5 This is a schematic diagram of the intraday rolling process of an embodiment of the energy storage control method of this application; Figure 6 This is a schematic diagram of the overall process of an embodiment of the energy storage control method of this application; Figure 7 This is a structural block diagram of the first embodiment of the energy storage control device of this application; Figure 8 This is a schematic diagram of the structure of the energy storage control device in the hardware operating environment involved in the embodiments of this application.

[0018] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0020] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0021] It should be noted that the executing entity of this application can be a computing service device with data processing, network communication and program execution functions, such as a cloud server, or an electronic device, energy storage control device, etc., capable of realizing the above functions.

[0022] Based on this, embodiments of this application provide an energy storage control method, referring to... Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of the energy storage control method of this application.

[0023] In this embodiment, the energy storage control method includes the following steps: Step S10: For any candidate demand in the candidate demand set, determine the target demand based on the revenue loss of the candidate demand in each historical scenario.

[0024] Understandably, this embodiment can construct a candidate demand set based on historical load data, historical electricity price data, and energy storage system parameters. This set may include multiple candidate demands. In a feasible embodiment, the demand boundary can be determined based on historical load data, and core candidate demands can be generated within the above boundary based on historical load data. Then, candidate demand can be filtered by combining energy storage system parameters and historical electricity price data, and finally, a candidate demand set is generated.

[0025] It should be understood that, for any candidate demand in the candidate demand set, the target demand can be determined based on the revenue loss of the candidate demand in historical scenarios. In a feasible embodiment, the loss of the candidate demand in low-revenue tail scenarios can be measured using conditional value at risk (CVaR), and the candidate demand with the smallest loss can be used as the target demand. This can reduce the risk of revenue loss in extreme load or unfavorable electricity price scenarios, thereby improving the robustness of energy storage peak-valley revenue and demand control strategies.

[0026] Step S20: Determine the demand penalty based on the target demand, and determine the energy storage charging and discharging power based on the optimization objective and the demand penalty. The optimization objective is to maximize peak and valley revenue and minimize demand electricity costs.

[0027] Understandably, this embodiment does not directly use the predicted demand as the target demand, but rather regards the target demand as a strategy parameter that affects the peak-valley arbitrage revenue and demand management revenue of energy storage. A demand penalty can be determined based on the target demand, and an objective function can be constructed based on the optimization objective and the demand penalty. This objective function is constructed with the goal of maximizing peak-valley arbitrage revenue and minimizing demand charges, thereby enabling the determination of energy storage charging and discharging power based on the objective function.

[0028] Further, in this embodiment, step S20 includes: determining the demand penalty based on the target demand; constructing a joint optimization model of day-ahead peak-valley revenue and demand control based on peak-valley revenue, the demand penalty, the cost-per-kilowatt-hour penalty, and the optimization objective; and determining the energy storage charging and discharging power through the joint optimization model of day-ahead peak-valley revenue and demand control.

[0029] It should be understood that an optimization objective can be adopted, namely, maximizing peak-valley arbitrage revenue and minimizing demand costs. A joint optimization model of day-ahead peak-valley revenue and demand control can be constructed. This model can include three parts: peak-valley arbitrage revenue, demand penalty, and cost-per-kilowatt-hour penalty. The joint optimization model of day-ahead peak-valley arbitrage and demand control is expressed as follows:

[0030] The model consists of three parts: (1) Peak-valley arbitrage profits: ,in and The charging and discharging electricity price for time period t. and (2) Demand penalty: (1) Energy storage charging and discharging power during time period t; ,in Cost per kW based on monthly demand. This indicates that the power supplied to the PCC point exceeds the target demand. It can be the difference between the grid power at the PCC point and the target demand; (3) the cost per kilowatt-hour penalty: , Cost per kilowatt-hour.

[0031] In practical implementation, solving the aforementioned joint optimization model of day-ahead peak-valley arbitrage and demand control yields the day-ahead energy storage charging and discharging power at 96 points. and .

[0032] Furthermore, in this embodiment, determining the energy storage charging and discharging power through the day-ahead peak-valley revenue and demand control joint optimization model includes: constructing energy storage demand control constraints based on the target demand; constructing model constraints according to the energy storage operation constraints, load constraints, and the energy storage demand control constraints; and solving the day-ahead peak-valley revenue and demand control joint optimization model under the model constraints to obtain the energy storage charging and discharging power.

[0033] Understandably, energy storage operation constraints can be constructed, expressed as follows:

[0034]

[0035]

[0036] Equations (1)-(3) above represent power constraints, primarily ensuring that the energy storage system cannot charge and discharge simultaneously, and limiting the charging and discharging power of the energy storage system to within the rated power range. and The rated charging and discharging power is t during the time period.

[0037]

[0038]

[0039] Equations (4)-(5) above represent the energy storage anti-reverse current and anti-overload constraints, meaning that if the load is overloaded, the energy storage cannot be recharged to increase the overload capacity. This is the overload limit; if the load reverses, the storage device cannot discharge, increasing the reverse flow. This is the limit for countercurrent flow.

[0040]

[0041]

[0042]

[0043] Equations (6)-(8) above represent the SOC constraints for energy storage, mainly including the initial SOC constraints, which are the initial conditions for day-ahead rolling optimization. This represents the current state of battery charge. The current actual SOC value read from the Energy Management System (EMS); SOC balance constraints. The state of charge at the end of the t-th time period. This represents the state of charge at the end of the previous time period. This represents the charging power from the previous time period. This represents the discharge power of the previous time period. For charging efficiency, For discharge efficiency, The duration of each time period, For the rated capacity of the energy storage system, The index for the current time step; SOC boundary constraints, For the minimum permissible SOC, This is the highest permissible SOC.

[0044] It should be understood that energy storage demand control constraints can be constructed based on the target demand, expressed as:

[0045]

[0046] In equation (9), The target demand for control (the value is negative). The load of the plant area is negative, so the right side of the inequality represents the power supplied to the grid at the connection point minus the target demand. Furthermore, a negative value for the intermediate variable z indicates the total excess demand, while a positive value for z indicates that there is currently no excess demand.

[0047] By adding formula (10) and the day-ahead peak-valley arbitrage and demand control joint optimization model, the comprehensive weighted objective of maximizing peak-valley arbitrage revenue and minimizing demand electricity cost can be achieved. The peak-valley arbitrage revenue weight is the time-of-use electricity price, and the demand electricity cost weight is the demand electricity price.

[0048] In practical implementation, for the power consumption scenario in the factory area, the adjustment potential of adjustable load is fully explored. Under the premise of not affecting normal production order and meeting production process constraints, the factory area production scheduling plan and energy storage operation strategy are collaboratively optimized. This ultimately generates a day-ahead planned power curve and production scheduling scheme that meets economic and safety requirements, effectively reducing the maximum power demand of industrial and commercial factories. The core inputs of this module have been updated to include: production scheduling time window boundaries, minimum continuous operating time of equipment, and adjustable equipment operating power. The objective is consistent with the day-ahead peak-valley arbitrage and demand control joint optimization model. New load constraints have also been added, including load power balance constraints, expressed as:

[0049] In equation (11), This is the reference power for uncontrollable loads, which typically include rigid electrical loads such as lighting, security, computer rooms, emergency backup, and uninterrupted production equipment in industrial and commercial plants. This value is an input parameter constant. For transferable loads, this typically includes electrical loads with time-transfer characteristics such as production equipment, process auxiliary equipment, and experimental equipment with adjustable scheduling. This value is an optimization variable.

[0050] The load constraints also include a total duration constraint for flexible load operation, expressed as:

[0051]

[0052] In the formula α is a 0-1 binary variable representing the production status (1 indicates that the equipment is working during time period t, and 0 indicates that it is idle); α and β represent the upper and lower limits of the allowed working time, and d represents the total number of time periods required for the equipment to complete all the work of the day. The physical meaning of equation (12) is: the production / experimental equipment must complete the specified working time on the day to ensure the user's needs; the physical meaning of equation (13) is: the equipment must be turned on and executed within the allowed time window.

[0053] The load constraints also include uninterrupted operation constraints, expressed as:

[0054]

[0055] In the above formula, 96 represents the total number of scheduling periods (duration 24h, step size 15min). The physical meaning of formula (14): Once the equipment is started ( If the state changes from 0 to 1, it must run continuously for d time periods until the work is completed to avoid downtime. This is consistent with the actual operating characteristics of equipment such as production plans and plant experiments. In formula (15), the power of the production / experiment equipment is the execution power. Its physical meaning is to convert the 0-1 working state of the equipment into the actual power value, obtain the total power of the load that can be transferred in each time period of the plant, and realize the quantitative mapping of "state-power".

[0056] Step S30: Perform energy storage control based on the energy storage charging and discharging power.

[0057] Understandably, under the constraints of the above model, the joint optimization model of day-ahead peak-valley arbitrage and demand control is solved to obtain the energy storage charging and discharging power, that is, the energy storage day-ahead 96-point scheduling plan power curve, and then the energy storage system is controlled based on the predicted energy storage charging and discharging power.

[0058] This embodiment determines a target demand for any candidate demand in the candidate demand set based on the revenue loss of the candidate demand under various historical scenarios. Then, a demand penalty is determined based on the target demand. Finally, the energy storage charging and discharging power is determined based on the optimization objective and the demand penalty. The optimization objective is to maximize peak-valley revenue and minimize demand-based electricity costs. Energy storage control is then performed based on the energy storage charging and discharging power. This embodiment determines the target demand based on the revenue loss of each candidate demand under various historical scenarios, and then determines the demand penalty based on the target demand, thereby reducing the risk of revenue loss in extreme scenarios. Then, it constructs an optimization objective of maximizing peak-valley revenue and minimizing demand-based electricity costs. Based on the optimization objective and the demand penalty, the energy storage charging and discharging power is determined, and the energy storage system is controlled based on the energy storage charging and discharging power. This enables the energy storage system to achieve comprehensive optimization between improving peak-valley revenue, reducing demand-based electricity costs, and controlling the risk of revenue loss in extreme scenarios.

[0059] refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the energy storage control method of this application.

[0060] Based on the first embodiment described above, in this embodiment, step S10 includes: Step S101: For any candidate demand in the candidate demand set, determine the revenue loss of the candidate demand in each historical scenario.

[0061] Understandably, the historical scenario can be set as follows: , The number of historical cycles indicates the number of the th cycle. For any candidate demand in the candidate demand set, the revenue or loss of that candidate demand in each historical scenario can be determined, which can be in each historical month. The resulting loss of income.

[0062] Further, in this embodiment, step S101 includes: for any candidate demand in the candidate demand set, determining the optimal energy storage control strategy for the candidate demand in each historical scenario; determining the optimal comprehensive benefit of the candidate demand in each historical scenario based on the optimal energy storage control strategy; and determining the benefit loss of the candidate demand in each historical scenario based on the optimal comprehensive benefit.

[0063] It should be understood that the candidate demand set can be assumed to be... , For the first One candidate requirement. For each historical scenario. and the demand for each candidate ,Will The data is input into the energy storage peak-valley arbitrage and demand control optimization model for historical testing to obtain the optimal energy storage control strategy for the candidate demand under various historical scenarios. , For historical scenes Next, select candidate demand The optimal energy storage control strategy for time-limited energy storage. An optimized algorithm for peak-valley arbitrage and demand control. For historical scenes The input data for the model can include equipment parameters of the energy storage system, such as rated power.

[0064] Understandably, candidate demand can be determined based on the optimal energy storage control strategy. In historical context The optimal comprehensive benefit can be determined by the energy storage control strategy, which may include the energy storage operating power curve and the energy storage charging and discharging strategy. The optimal comprehensive benefit may include peak-valley arbitrage benefits and demand reduction benefits.

[0065] In practical implementation, to use Conditional Value at Risk (CVaR), returns need to be converted into loss variables. CVaR refers to the arithmetic mean of all extreme tail losses exceeding VaR at a given confidence level when an asset or portfolio loss exceeds the corresponding Value at Risk (VaR). Candidate Demand In historical context The following profit loss is , For historical scenes The following only considers the accompanying returns of the energy storage peak-valley arbitrage strategy.

[0066] Step S102: Determine the tail risk of the loss corresponding to the candidate demand based on the loss of revenue.

[0067] Understandably, for each candidate demand It is possible to determine the set of gains and losses in historical scenarios. And calculate the corresponding tail risk of loss, the calculation formula is: , Tail risk of revenue loss for candidate demand. Let be the confidence interval. , As an auxiliary variable for VaR, the candidate demand is optimal in the above formula. The VaR quantile for the loss of gain. For example, =13500 represents the candidate demand in the worst 20% of historical scenarios. On average, this decision would result in a loss of 13,500 yuan in revenue.

[0068] Step S103: Determine the minimum value among the tail risks of the loss, and take the candidate demand corresponding to the minimum value as the target demand.

[0069] It should be understood that the tail risk of loss for each candidate demand can be determined, and the minimum value of the tail risk can be identified. The candidate demand corresponding to the minimum value is then used as the target demand. The calculation formula is as follows: ,for Target demand.

[0070] This embodiment sets the target demand using the above method, avoiding the problem that the actual demand cannot be reduced to the target value due to the target demand being too low, resulting in the loss of peak-valley arbitrage benefits. It also avoids the problem that the demand management benefits are insufficient due to the target demand being set too high, thereby achieving a balance between peak-valley arbitrage benefits, demand control benefits, and tail risks in the energy storage system.

[0071] This embodiment determines the revenue loss of any candidate demand in the candidate demand set under various historical scenarios. Then, based on the revenue loss, it determines the tail risk of the corresponding candidate demand, and finally identifies the minimum tail risk. The candidate demand corresponding to this minimum tail risk is then selected as the target demand. This embodiment obtains the comprehensive revenue distribution under different candidate demands through historical benchmarking and selects the candidate demand with the smallest average loss in the worst-case scenario from the historical benchmarking as the target demand. This approach reduces the risk of revenue loss under extreme load or unfavorable electricity price scenarios, thereby improving the robustness of energy storage peak-valley arbitrage and demand control strategies.

[0072] refer to Figure 3 , Figure 3This is a flowchart illustrating the third embodiment of the energy storage control method of this application.

[0073] Based on the above embodiments, in this embodiment, step S30 includes: Step S301: Construct a joint optimization model for intraday peak-valley revenue and demand control based on peak-valley revenue, demand penalty, electricity cost penalty, and time-series adjustment factor.

[0074] Understandably, based on the day-ahead forecast of energy storage charging and discharging power, intraday rolling optimization can be performed, and a joint optimization model of intraday peak-valley revenue and demand control can be constructed, expressed as:

[0075] in, As a timing adjustment factor, its core function is to guide the charging and discharging timing of energy storage by introducing this adjustment factor when multiple equally optimal solutions are obtained from the model. This achieves a scheduling preference of charging as early as possible and discharging as late as possible, reserving sufficient control space for real-time demand regulation and avoiding the problem of demand exceeding the limit and economic decline due to the inability to respond to demand control requirements when the energy storage capacity is exhausted.

[0076] Step S302: Determine the real-time charging and discharging power based on the energy storage charging and discharging power and the joint optimization model of intraday peak-valley revenue and demand control.

[0077] It should be understood that the energy storage charging and discharging power is generated on day D-1, and the 96-point load forecast curve for day D is output once. The real-time charging and discharging power on day D can be determined based on the energy storage charging and discharging power and the joint optimization model of intraday peak-valley revenue and demand control.

[0078] Further, in this embodiment, step S302 includes: determining the predicted charging and discharging power within a preset time period after the current moment; selecting the initial charging and discharging power within the preset time period from the energy storage charging and discharging power, and replacing the initial charging and discharging power with the predicted charging and discharging power to obtain the spliced ​​charging and discharging power; inputting the spliced ​​charging and discharging power into the intraday peak-valley revenue and demand control joint optimization model to obtain the target charging and discharging power within the preset time period; and determining the real-time charging and discharging power corresponding to the current moment based on the target charging and discharging power.

[0079] Understandably, Step 1: Initialize t=0, indicating the start of the first intraday rolling process; Step 2: Use the predicted charging and discharging power (length 96) as the input source and temporarily store it in a list; Step 3: Replace the initial charging and discharging power [t, t+16) data with the ultra-short-term load prediction results (replacement length is 16), i.e., the predicted charging and discharging power, with a preset duration of 16 points, i.e. 4 hours; Step 4: Substitute the spliced ​​charging and discharging power into the intraday peak-valley revenue and demand control joint optimization model to obtain the target charging and discharging power within 4 hours, completing the rolling strategy at time t; Step 5: Increment t by 1 until the 96th solution is completed for the day. For example, if the current time is 0:00, the energy storage charging and discharging power for the day can be determined through the joint optimization model of daily peak-valley revenue and demand control. The initial charging and discharging power from 0:00 to 4:00 is selected from the energy storage charging and discharging power. The predicted charging and discharging power from 0:00 to 4:00 also needs to be calculated. The initial charging and discharging power is replaced with the predicted charging and discharging power to obtain the spliced ​​charging and discharging power, which is the predicted charging and discharging power plus the unreplaced part of the energy storage charging and discharging power. The spliced ​​charging and discharging power is then input into the joint optimization model of daily peak-valley revenue and demand control to obtain the target charging and discharging power from 0:00 to 4:00. Thus, the real-time charging and discharging power at the current time, i.e., 0:00, is selected.

[0080] It should be understood that the day-ahead load data is generated on day D-1, outputting a 96-point load forecast curve at a time. The ultra-short-term load forecast data is generated on day D, at time t-1, outputting 16 points of load forecast data at a time. However, the intraday rolling power planning model requires input of 96-t points of load data (load data from the current time t to the end of the day). Therefore, it is necessary to concatenate the day-ahead load forecast data with the intraday ultra-short-term load forecast data, requiring a total of 96*96 load data points on day D, which are then substituted into the model for solution. During the solution process, to ensure the continuity and executability of the production plan, the production schedule determined by the day-ahead optimization is used... As fixed, known constants are substituted into the model, no further adjustments are made to the production sequence; only the energy storage charging and discharging power is subject to rolling correction. With a rolling cycle of 15 minutes, the optimization model is re-solved to generate the latest planned power curve, thereby achieving dynamic adaptation of the scheduling scheme.

[0081] In the specific implementation, refer to Figure 4 , Figure 4 In one embodiment of the energy storage control method of this application, the solver (Gurobi, Scip) encounters multiple optimal solutions when solving the joint optimization model of intraday peak-valley revenue and demand control, such as... Figure 4As shown in the diagram. A typical scenario is during off-peak electricity prices in the early morning, where energy storage can be charged at any time window, and the objective function value corresponding to different charging times is completely consistent. During peak hours, there are also multiple discharge opportunities, all of which can satisfy the goals of peak-valley arbitrage and demand control. If only the traditional optimal objective is used for solving, the solver will randomly output any set of optimal solutions, failing to distinguish the differences in robustness of the solutions when facing load uncertainties.

[0082] Step S303: Perform energy storage control based on the real-time charging and discharging power.

[0083] It should be understood that, referring to Figure 5 , Figure 5 This is a schematic diagram of the intraday rolling process of an embodiment of the energy storage control method of this application, as shown below. Figure 5 As shown, based on the energy storage planned power generated by the previous day's optimization, i.e., the energy storage charging and discharging power, in order to cope with uncertainties such as load forecast deviation, production condition fluctuations, and sudden power demand in actual operation, a rolling optimization model is further constructed within the day to realize real-time correction and closed-loop control of the energy storage power plan and load scheduling scheme. Input: target demand, equipment parameters, previous day load forecast, previous day production schedule. If t < 96, then read the load forecast data of time period t (ultra-short term) of day D, perform load data splicing, call the rolling planned power model within the day, i.e. the joint optimization model of intra-day peak and valley revenue and demand control, solve and send the planned power of time period t to the local, determine whether the actual load exceeds demand, if not, adjust the charging planned power, and if so, calculate the excess demand value, the energy storage actively discharges to reduce demand, and update the demand value in real time.

[0084] In the specific implementation, refer to Figure 6 , Figure 6 This is a schematic diagram of the overall process of an embodiment of the energy storage control method of this application, as shown below. Figure 6As shown, the model includes the following steps: A target demand determination method based on historical performance-based revenue distribution and conditional value at risk (VaR): This model constructs multiple candidate demands based on historical load data, electricity price data, and energy storage system parameters. It calculates the revenue distribution under different candidate demands through performance-based revenue calculation and uses VaR theory to determine the final target demand. A joint optimization model for day-ahead peak-valley arbitrage and demand control based on flexible load: This module builds a mathematical programming model, comprehensively incorporating next-day time-of-use electricity prices, historical load data, monthly demand control targets, and adjustable load boundary conditions in the plant area. The model solves to generate the day-ahead planned power curve for energy storage and the production schedule for adjustable loads in the plant area, clarifying the charging and discharging power thresholds of the energy storage system at different times, and the start-up, shutdown, and power adjustment schemes for adjustable loads. Intraday peak-valley arbitrage and rolling optimization design for demand control: This module uses ultra-short-term load forecast results as the core input and adopts a rolling solution method every 15 minutes to dynamically adjust the day-ahead planned power curve, promptly correcting scheduling deviations caused by load forecasting errors and sudden operating conditions, effectively improving the robustness of the model.

[0085] This embodiment constructs a joint optimization model for intraday peak-valley revenue and demand control based on peak-valley revenue, demand penalty, levelized cost of electricity (LCOE) penalty, and timing adjustment factors. Then, it determines the real-time charging and discharging power based on the energy storage charging and discharging power and the joint optimization model, and performs energy storage control based on this real-time charging and discharging power. This embodiment, by determining the real-time charging and discharging power based on the energy storage charging and discharging power and the joint optimization model, enables intraday rolling optimization and improves the accuracy of energy storage control.

[0086] Reference Figure 7 , Figure 7 This is a structural block diagram of the first embodiment of the energy storage control device of this application.

[0087] like Figure 7 As shown, the energy storage control device proposed in this application includes: The demand determination module 10 is used to determine the target demand for any candidate demand in the candidate demand set based on the revenue loss of the candidate demand in various historical scenarios. The power determination module 20 is used to determine the demand penalty based on the target demand, and to determine the energy storage charging and discharging power based on the optimization objective and the demand penalty, wherein the optimization objective is to maximize peak and valley revenue and minimize demand electricity costs; The energy storage control module 30 is used to perform energy storage control based on the energy storage charging and discharging power.

[0088] This embodiment determines a target demand for any candidate demand in the candidate demand set based on the revenue loss of the candidate demand under various historical scenarios. Then, a demand penalty is determined based on the target demand. Finally, the energy storage charging and discharging power is determined based on the optimization objective and the demand penalty. The optimization objective is to maximize peak-valley revenue and minimize demand-based electricity costs. Energy storage control is then performed based on the energy storage charging and discharging power. This embodiment determines the target demand based on the revenue loss of each candidate demand under various historical scenarios, and then determines the demand penalty based on the target demand, thereby reducing the risk of revenue loss in extreme scenarios. Then, it constructs an optimization objective of maximizing peak-valley revenue and minimizing demand-based electricity costs. Based on the optimization objective and the demand penalty, the energy storage charging and discharging power is determined, and the energy storage system is controlled based on the energy storage charging and discharging power. This enables the energy storage system to achieve comprehensive optimization between improving peak-valley revenue, reducing demand-based electricity costs, and controlling the risk of revenue loss in extreme scenarios.

[0089] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0090] In addition, for technical details not described in detail in this embodiment, please refer to the energy storage control method provided in any embodiment of this application, which will not be repeated here.

[0091] Based on the first embodiment of the energy storage control device described in this application, a second embodiment of the energy storage control device of this application is proposed.

[0092] In this embodiment, the demand determination module 10 is further configured to determine the revenue loss of any candidate demand in the candidate demand set under each historical scenario; determine the tail risk of loss corresponding to the candidate demand based on the revenue loss; determine the minimum value among the tail risks of loss, and take the candidate demand corresponding to the minimum value as the target demand.

[0093] Furthermore, the demand determination module 10 is also used to determine the optimal energy storage control strategy for any candidate demand in the candidate demand set under each historical scenario; determine the optimal comprehensive benefit of the candidate demand under each historical scenario based on the optimal energy storage control strategy; and determine the benefit loss of the candidate demand under each historical scenario based on the optimal comprehensive benefit.

[0094] Furthermore, the power determination module 20 is also used to determine the demand penalty based on the target demand; construct a day-ahead peak-valley revenue and demand control joint optimization model based on peak-valley revenue, the demand penalty, the cost-per-kilowatt-hour penalty, and the optimization objective; and determine the energy storage charging and discharging power through the day-ahead peak-valley revenue and demand control joint optimization model.

[0095] Furthermore, the power determination module 20 is also used to construct energy storage demand control constraints based on the target demand; construct model constraints according to the energy storage operation constraints, load constraints, and the energy storage demand control constraints; and solve the day-ahead peak-valley revenue and demand control joint optimization model under the model constraints to obtain the energy storage charging and discharging power.

[0096] Furthermore, the energy storage control module 30 is also used to construct a joint optimization model of intraday peak-valley revenue and demand control based on peak-valley revenue, the demand penalty, the cost-per-kilowatt-hour penalty, and the timing adjustment factor; determine the real-time charging and discharging power based on the energy storage charging and discharging power and the joint optimization model of intraday peak-valley revenue and demand control; and perform energy storage control according to the real-time charging and discharging power.

[0097] Furthermore, the energy storage control module 30 is also used to determine the predicted charging and discharging power within a preset time period after the current moment; select the initial charging and discharging power within the preset time period from the energy storage charging and discharging power, and replace the initial charging and discharging power with the predicted charging and discharging power to obtain the spliced ​​charging and discharging power; input the spliced ​​charging and discharging power into the intraday peak-valley revenue and demand control joint optimization model to obtain the target charging and discharging power within the preset time period; and determine the real-time charging and discharging power corresponding to the current moment based on the target charging and discharging power.

[0098] Other embodiments or specific implementations of the energy storage control device of this application can be found in the above-described method embodiments, and will not be repeated here.

[0099] This application provides an energy storage control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the energy storage control method in the above embodiment 1.

[0100] The following is for reference. Figure 8The diagram illustrates a structural schematic of an energy storage control device suitable for implementing embodiments of this application. The energy storage control device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The energy storage control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0101] like Figure 8 As shown, the energy storage control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the energy storage control device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the energy storage control device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows an energy storage control device with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0102] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0103] The energy storage control device provided in this application, employing the energy storage control method described in the above embodiments, can solve the technical problem of comprehensively optimizing the energy storage system to improve peak-valley revenue, reduce demand electricity costs, and control the risk of revenue loss in extreme scenarios. Compared with the prior art, the beneficial effects of the energy storage control device provided in this application are the same as those of the energy storage control method provided in the above embodiments, and other technical features of this energy storage control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0104] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0105] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0106] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the energy storage control method in the above embodiments.

[0107] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0108] The aforementioned computer-readable storage medium may be included in the energy storage control device; or it may exist independently and not assembled into the energy storage control device.

[0109] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the energy storage control device, the energy storage control device: for any candidate demand in the candidate demand set, determines a target demand based on the revenue loss of the candidate demand in various historical scenarios; determines a demand penalty based on the target demand; determines the energy storage charging and discharging power based on the optimization objective and the demand penalty, and performs energy storage control based on the energy storage charging and discharging power, wherein the optimization objective is to maximize peak and valley revenue and minimize demand electricity costs.

[0110] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Python, Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0112] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0113] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described energy storage control method. This addresses the technical problem of comprehensively optimizing the energy storage system to improve peak-valley revenue, reduce demand-based electricity costs, and control the risk of revenue loss in extreme scenarios. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the energy storage control method provided in the above embodiments, and will not be elaborated upon here.

[0114] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. An energy storage control method, characterized in that, The method includes the following steps: For any candidate demand in the candidate demand set, the target demand is determined based on the revenue loss of the candidate demand in each historical scenario. Demand penalty is determined based on the target demand, and energy storage charging and discharging power is determined based on the optimization objective and the demand penalty. The optimization objective is to maximize peak-valley revenue and minimize demand electricity costs. Energy storage control is performed based on the energy storage charging and discharging power.

2. The energy storage control method as described in claim 1, characterized in that, The step of determining the target demand for any candidate demand in the candidate demand set based on the revenue loss of the candidate demand in various historical scenarios includes: For any candidate demand in the candidate demand set, determine the revenue loss of the candidate demand in each historical scenario; Determine the tail risk of the loss corresponding to the candidate demand based on the aforementioned revenue loss; Determine the minimum value among the tail risks of the loss, and take the candidate demand corresponding to the minimum value as the target demand.

3. The energy storage control method as described in claim 2, characterized in that, The step of determining the revenue loss of any candidate demand in the candidate demand set under various historical scenarios includes: For any candidate demand in the candidate demand set, determine the optimal energy storage control strategy for the candidate demand under each historical scenario; The optimal comprehensive benefit of the candidate demand under each historical scenario is determined based on the optimal energy storage control strategy. The revenue loss of the candidate demand in each historical scenario is determined based on the optimal comprehensive revenue.

4. The energy storage control method according to any one of claims 1 to 3, characterized in that, The step of determining the demand penalty based on the target demand and determining the energy storage charging and discharging power based on the optimization target and the demand penalty includes: Determine the demand penalty based on the target demand; Based on peak-valley revenue, the demand penalty, the cost-per-kilowatt-hour penalty, and the optimization objective, a joint optimization model for day-ahead peak-valley revenue and demand control is constructed. The energy storage charging and discharging power is determined by the day-ahead peak-valley revenue and demand control joint optimization model.

5. The energy storage control method as described in claim 4, characterized in that, The determination of energy storage charging and discharging power through the joint optimization model of day-ahead peak-valley revenue and demand control includes: Energy storage demand control constraints are constructed based on the target demand. Model constraints are constructed based on energy storage operation constraints, load constraints, and energy storage demand control constraints. Under the constraints of the model, the joint optimization model of day-ahead peak-valley revenue and demand control is solved to obtain the energy storage charging and discharging power.

6. The energy storage control method according to any one of claims 1 to 3, characterized in that, The energy storage control based on the energy storage charging and discharging power includes: A joint optimization model for intraday peak-valley revenue and demand control is constructed based on peak-valley revenue, demand penalty, cost-per-kilowatt-hour penalty, and time-series adjustment factor. The real-time charging and discharging power is determined based on the energy storage charging and discharging power and the joint optimization model of intraday peak-valley revenue and demand control. Energy storage control is performed based on the real-time charging and discharging power.

7. The energy storage control method as described in claim 6, characterized in that, The determination of real-time charging and discharging power based on the energy storage charging and discharging power and the joint optimization model of intraday peak-valley revenue and demand control includes: Determine the predicted charging and discharging power within a preset time period after the current moment; Select the initial charge and discharge power within the preset time period from the energy storage charge and discharge power, and replace the initial charge and discharge power with the predicted charge and discharge power to obtain the spliced ​​charge and discharge power; The spliced ​​charging and discharging power is input into the intraday peak-valley revenue and demand control joint optimization model to obtain the target charging and discharging power within the preset time period; The real-time charge / discharge power corresponding to the current moment is determined based on the target charge / discharge power.

8. An energy storage control device, characterized in that, The energy storage control device includes: The demand determination module is used to determine the target demand for any candidate demand in the candidate demand set based on the revenue loss of the candidate demand in various historical scenarios. The power determination module is used to determine the demand penalty based on the target demand, and to determine the energy storage charging and discharging power based on the optimization objective and the demand penalty, wherein the optimization objective is to maximize peak and valley revenue and minimize demand electricity costs; An energy storage control module is used to control energy storage based on the energy storage charging and discharging power.

9. An energy storage control device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the energy storage control method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the energy storage control method as described in any one of claims 1 to 7.

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