Method and device for formulating strategy of demand control and peak-valley arbitrage

Abstract

The invention discloses a demand control and peak-valley arbitrage strategy making method and device. The method comprises the following steps: acquiring historical power consumption data; determining a plurality of predicted power consumption demands according to the historical power consumption data; determining a target probability density function according to the plurality of predicted power demands; and determining a target control strategy according to the target probability density function, wherein the target control strategy is a control strategy that maximum demand control and peak-valley arbitrage income are obtained through the energy storage system and the power consumption does not exceed a target demand threshold. Error distribution of power demand prediction is considered, uncertainty in power demand prediction is quantified, a target control strategy masters an accurate target demand threshold value, maximum demand control and peak-valley arbitrage income can be obtained within the target demand threshold value, and power demand prediction accuracy is improved. Therefore, the economic benefits obtained by the energy storage system in peak valley arbitrage and demand control are improved.

Description

Technical Field

[0001] The present application relates to the field of information processing technologies, and particularly relates to a method and device for formulating a demand control and peak-valley arbitrage strategy. Background Art

[0002] When industrial enterprises use energy storage systems for peak-valley arbitrage and demand control, it is necessary to determine a reasonable electricity demand to maximize the economic benefits of the energy storage system. In the prior art, a deterministic method is used to calculate the electricity demand, and a control strategy that meets the electricity demand is generated accordingly. Due to the uncertainty of the electricity demand and the unpredictable factors in system operation not being considered, the accuracy of the generated control strategy is low, resulting in additional demand costs or losses of peak-valley arbitrage benefits in the actual application of the energy storage system, and thus the economic benefits of the energy storage system are low. Therefore, how to improve the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control has become a technical problem to be further solved. Summary of the Invention

[0003] The present application proposes a method and device for formulating a demand control and peak-valley arbitrage strategy to solve the problem of low economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control, and improve the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control.

[0004] In a first aspect, an embodiment of the present application provides a method for formulating a demand control and peak-valley arbitrage strategy, which is applied to a server in an energy storage system. The method includes:

[0005] Obtain historical electricity data, where the historical electricity data is used to characterize the historical electricity consumption of the electricity load;

[0006] Determine multiple predicted electricity demands according to the historical electricity data, where the multiple predicted electricity demands are used to characterize the predicted electricity demand of the electricity load for each hour within a target period, and the target period is used to characterize the time period for electricity billing;

[0007] Determine a target probability density function according to the multiple predicted electricity demands, where the target probability density function is used to characterize the probability distribution of the errors of the multiple predicted electricity demands;

[0008] Determine a target control strategy according to the target probability density function, where the target control strategy is a control strategy that maximizes the demand control and peak-valley arbitrage benefits through the energy storage system and the electricity consumption does not exceed a target demand threshold, and the target demand threshold is used to characterize the maximum electricity consumption of the electricity load within the target period.

[0009] In a possible embodiment, determining the target probability density function according to the plurality of predicted electricity demands includes: determining a plurality of verified electricity consumptions according to the historical electricity data, where the plurality of verified electricity consumptions are used to indicate the actual electricity consumption of the electricity load per hour in at least one target sub-period of the target period; determining the target probability density function according to the plurality of verified electricity consumptions and the plurality of predicted electricity demands.

[0010] In a possible embodiment, determining the target probability density function according to the plurality of verified electricity consumptions and the plurality of predicted electricity demands includes: determining a plurality of first target matrices according to the plurality of verified electricity consumptions and the plurality of predicted electricity demands, where the plurality of first target matrices are used to characterize the deviation degree of the plurality of predicted electricity demands relative to the plurality of verified electricity consumptions in the at least one target sub-period; determining a second target matrix according to the plurality of first target matrices, where the second target matrix is used to characterize the deviation degree of the plurality of predicted electricity demands relative to the plurality of verified electricity consumptions in the target period; determining the target probability density function according to the second target matrix.

[0011] In a possible embodiment, determining the target control strategy according to the target probability density function includes: performing at least one demand threshold generation and strategy verification operation according to the target probability density function until the generated control strategy contains a solution that satisfies the target demand threshold, to obtain the target control strategy. The demand threshold generation and strategy verification operation includes the following steps: determining the target demand threshold according to the target probability density function; determining a candidate control strategy according to the target demand threshold; judging whether the candidate control strategy contains a solution that satisfies the target demand threshold; if not, then re-performing the demand threshold generation and strategy verification operation; if so, then determining the candidate control strategy as the target control strategy.

[0012] In a possible embodiment, determining the target demand threshold according to the target probability density function includes: obtaining a preset allowable error confidence level; determining a target confidence interval according to the allowable error confidence level and the target probability density function; determining a real-time demand threshold according to the target confidence interval, where the real-time demand threshold is used to characterize the maximum electricity demand of the electricity load in the current sub-period of the at least one target sub-period; determining a plurality of first demand thresholds according to the real-time demand threshold, where the plurality of first demand thresholds are used to characterize the maximum electricity demands of the electricity load in a plurality of sub-periods of the target period, and the plurality of sub-periods include the at least one target sub-period; determining the target demand threshold according to the plurality of first demand thresholds.

[0013] In a possible embodiment, determining a candidate control strategy according to the target demand threshold includes: determining a target optimization function according to the target demand threshold, where the target optimization function maximizes the demand control benefit and the peak-valley arbitrage benefit as optimization objectives; obtaining preset optimization constraint conditions; and determining the candidate control strategy according to the target optimization function and the optimization constraint conditions.

[0014] In a possible embodiment, determining the target optimization function according to the target demand threshold includes: obtaining a preset penalty factor, where the penalty factor is used to penalize the electricity demand exceeding the target demand threshold; determining a first target function according to the target demand threshold and the penalty factor, where the first target function is used to calculate the demand control benefit; obtaining a preset second target function, where the second target function is used to calculate the peak-valley arbitrage benefit; and determining the target optimization function according to the first target function and the second target function.

[0015] In a second aspect, an embodiment of the present application provides a strategy formulation device for demand control and peak-valley arbitrage, which is applied to a server in an energy storage system. The device includes:

[0016] A first receiving unit, configured to obtain historical electricity consumption data, where the historical electricity consumption data is used to characterize the historical electricity consumption situation of the electricity load.

[0017] A first processing unit, configured to determine a plurality of predicted electricity demands according to the historical electricity consumption data, where the plurality of predicted electricity demands are used to characterize the electricity demand of the predicted electricity load for each hour within a target period, and the target period is used to characterize the time period for electricity billing; determine a target probability density function according to the plurality of predicted electricity demands, where the target probability density function is used to characterize the probability distribution of the errors of the plurality of predicted electricity demands; and determine a target control strategy according to the target probability density function, where the target control strategy is a control strategy that maximizes the demand control and peak-valley arbitrage benefits through the energy storage system and the electricity consumption does not exceed the target demand threshold, and the target demand threshold is used to characterize the maximum electricity consumption of the electricity load within the target period.

[0018] In a third aspect, an embodiment of the present application provides a server, including a processor, a memory, and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the processor, and the programs include instructions for performing the steps in the method according to any one of the first aspects.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program / instruction is stored, and when the computer program / instruction is executed by a processor, the steps of the method according to any one of the first aspects are implemented.

[0020] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement part or all of the steps of the method described in any one of the first aspects of the embodiments of the present application.

[0021] It can be seen that in the present application, the server obtains historical power consumption data, which is used to characterize the historical power consumption situation of the power consumption load; determines multiple predicted power consumption demands according to the historical power consumption data, and the multiple predicted power consumption demands are used to characterize the power consumption demands of the predicted power consumption load for each hour within a target period, and the target period is used to characterize the time period for power consumption billing; determines a target probability density function according to the multiple predicted power consumption demands, and the target probability density function is used to characterize the probability distribution of the errors of the multiple predicted power consumption demands; determines a target control strategy according to the target probability density function, and the target control strategy is a control strategy for obtaining the maximum demand control and peak-valley arbitrage benefits through an energy storage system and the power consumption not exceeding a target demand threshold, and the target demand threshold is used to characterize the maximum power consumption of the power consumption load within the target period. By determining the target probability density function that characterizes the probability distribution of the errors of the multiple predicted power consumption demands, and then determining the target control strategy that can obtain the maximum demand control and peak-valley arbitrage benefits and the power consumption does not exceed the target demand threshold according to the target probability density function. In this way, the error distribution of the predicted power consumption demand is considered, the uncertainty in the power consumption demand prediction is quantified, so that the target control strategy masters the accurate target demand threshold, and can obtain the maximum demand control and peak-valley arbitrage benefits within the target demand threshold, thereby improving the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 is a schematic structural diagram of an energy storage system provided by an embodiment of the present application;

[0024] Figure 2 is a schematic structural diagram of a power consumption system provided by an embodiment of the present application;

[0025] Figure 3 is a schematic structural diagram of a server in an energy storage system provided by an embodiment of the present application;

[0026] Figure 4It is a schematic flowchart of a method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application;

[0027] Figure 5 It is a schematic flowchart of another method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application;

[0028] Figure 6 It is a schematic diagram of a scenario of a method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application;

[0029] Figure 7 It is a schematic flowchart of yet another method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application;

[0030] Figure 8 It is a schematic flowchart of still another method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application;

[0031] Figure 9 It is a block diagram of functional units of a device for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application;

[0032] Figure 10 It is a block diagram of functional units of another device for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application;

[0033] Figure 11 It is a block diagram of the structure of a server provided by an embodiment of the present application. Detailed implementation manners

[0034] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0035] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0036] Reference to "embodiment" in this application means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0037] The "and / or" in the embodiments of the present application describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone; A and B exist simultaneously; B exists alone. Among them, A and B may be singular or plural.

[0038] In the embodiments of the present application, the symbol " / " may represent an "or" relationship between the front and back associated objects. In addition, the symbol " / " may also represent a division sign, that is, perform a division operation. For example, A / B may represent A divided by B.

[0039] The "at least one (item)" or its similar expression in the embodiments of the present application refers to any combination of these items, including any combination of single item (item) or plural items (items), and refers to one or more, and multiple refers to two or more. For example, at least one (item) of a, b or c may represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b and c. Among them, each of a, b, c may be an element or a set containing one or more elements.

[0040] The "equal to" in the embodiments of the present application can be used in conjunction with "greater than" and is applicable to the technical solutions adopted when it is greater than, and can also be used in conjunction with "less than" and is applicable to the technical solutions adopted when it is less than. When "equal to" is used in conjunction with "greater than", it is not used in conjunction with "less than"; when "equal to" is used in conjunction with "less than", it is not used in conjunction with "greater than".

[0041] To better understand the solutions of the embodiments of the present application, the terminal devices, related concepts and backgrounds that may be involved in the embodiments of the present application will be introduced below.

[0042] CPLEX solver: A mathematical programming solver that can handle various optimization models such as linear programming, integer programming, mixed integer programming, and quadratic programming, and uses efficient algorithms such as the simplex method, interior point method, branch and bound method, and cutting plane method, with accuracy, stability, and also supports parallel and distributed computing.

[0043] With the rapid development of renewable energy, the national power system faces a huge challenge of imbalance between supply and demand. However, using battery energy storage is an effective method to solve power consumption.

[0044] For large power-consuming industrial enterprises and other electricity users, the two-part tariff method is generally adopted, that is, the basic electricity price and the electricity consumption price are charged together. The electricity consumption price is calculated based on the actual electricity consumption of the enterprise, and this part of the cost is paid according to the actual consumed electricity and the corresponding electricity consumption price, which can reflect the electricity cost in the cost of industrial enterprises; the basic electricity price is calculated according to the transformer capacity or the maximum demand of industrial enterprises. Whether the enterprise actually uses electricity or how much electricity it uses, as long as it occupies the power supply capacity resources, it needs to pay the basic electricity fee, which can reflect the capacity cost in the cost of industrial enterprises, that is, the fixed cost part.

[0045] For industrial enterprises with high-power electricity consumption, an energy storage system can be used for peak-valley arbitrage and a reasonable demand control method can be adopted to achieve reasonable demand management and maximize economic benefits.

[0046] Currently, when industrial enterprises use energy storage systems for peak-valley arbitrage and demand control, they need to determine a reasonable electricity demand to maximize the economic benefits of the energy storage system. In the existing technology, a deterministic method is used to calculate the electricity demand, and a control strategy that meets the electricity demand is generated accordingly. Due to the lack of consideration of the uncertainty of electricity demand and the unpredictable factors in system operation, the accuracy of the generated control strategy is low, resulting in additional demand costs or losses of peak-valley arbitrage benefits in the actual application of the energy storage system, and the economic benefits of the energy storage system are low. Therefore, how to improve the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control has become a technical problem that needs to be further solved.

[0047] To solve the above problems, the embodiments of the present application provide a method and device for formulating a demand control and peak-valley arbitrage strategy. The method obtains historical electricity consumption data, which is used to characterize the historical electricity consumption situation of the electricity load; determines multiple predicted electricity demands based on the historical electricity consumption data, and the multiple predicted electricity demands are used to characterize the electricity demand of the predicted electricity load per hour; determines a target probability density function based on the multiple predicted electricity demands, and the target probability density function is used to characterize the probability distribution of the errors of the multiple predicted electricity demands; determines a target control strategy based on the target probability density function, and the target control strategy is a control strategy that obtains the maximum demand control and peak-valley arbitrage benefits through the energy storage system and the electricity consumption does not exceed the target demand threshold. The target demand threshold is used to characterize the maximum electricity consumption of the electricity load within the target period, and the target period is used to characterize the time period for electricity billing.

[0048] Please refer to Figure 1 , Figure 1 which is a schematic structural diagram of an energy storage system provided by the embodiments of the present application. As Figure 1As shown in the figure, the energy storage system 100 includes an energy storage battery 110 and a server 120. The energy storage battery 110 is communicatively connected to the server 120. The energy storage battery 110 can be a single energy storage battery or an energy storage battery pack composed of multiple energy storage batteries. The server 120 can be a single server or a server cluster composed of multiple servers.

[0049] During the daily use of the energy storage system 100, the server 120 obtains historical power consumption data, which is used to characterize the historical power consumption situation of the power consumption load; determines multiple predicted power consumption demands based on the historical power consumption data, and the multiple predicted power consumption demands are used to characterize the power consumption demands of the predicted power consumption load per hour; determines a target probability density function based on the multiple predicted power consumption demands, and the target probability density function is used to characterize the probability distribution of the errors of the multiple predicted power consumption demands; determines a target control strategy based on the target probability density function, and the target control strategy is a control strategy for obtaining the maximum demand control and peak-valley arbitrage benefits through the energy storage system and the power consumption not exceeding the target demand threshold. The target demand threshold is used to characterize the maximum power consumption of the power consumption load within the target period, and the target period is used to characterize the time period for power consumption billing.

[0050] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a power consumption system provided by an embodiment of the present application. As Figure 2 shown, the power consumption system 200 includes a transformer 210, an energy storage system 100, and a power consumption load 220. The transformer 210 is used to adjust the power of the electric energy obtained from the power grid or transmitted to the power grid. The energy storage system 100 is communicatively connected to the power consumption load 220 through the server 120. The energy storage system 100 is communicatively connected to the transformer 210 through the server 120. The energy storage system 100 is connected to the power grid through the transformer 210. The power consumption load 220 is connected to the power grid through the transformer 210. The power consumption load 220 can be a single power consumption load or a power consumption load group composed of multiple power consumption loads.

[0051] Among them, the transformer 210 is used to adjust the power of the electric energy obtained from the power grid or input to the power grid. Specifically, it can: transmit electric energy to the power consumption load 220 at a load rate that meets the power consumption load 220, transmit electric energy to the energy storage system 100 at a battery charging power that meets the energy storage system 100, and transmit electric energy to the power grid at a transmission power that meets the power grid.

[0052] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a server in an energy storage system provided by an embodiment of the present application. As Figure 3As shown in the figure, the server 120 includes a processor 310 and a memory 320, and the processor 310 is communicatively connected to the memory 320. Among them, one or more programs are stored in the memory 320, and the one or more programs are configured to be executed by the processor 310. The functions of the one or more programs are to obtain historical power consumption data, which is used to characterize the historical power consumption situation of the power consumption load; to determine a plurality of predicted power consumption demands according to the historical power consumption data, and the plurality of predicted power consumption demands are used to characterize the power consumption demands of the predicted power consumption load per hour; to determine a target probability density function according to the plurality of predicted power consumption demands, and the target probability density function is used to characterize the probability distribution of the errors of the plurality of predicted power consumption demands; to determine a target control strategy according to the target probability density function, and the target control strategy is a control strategy that obtains the maximum demand control and peak-valley arbitrage benefits through the energy storage system and the power consumption does not exceed the target demand threshold. The target demand threshold is used to characterize the maximum power consumption of the power consumption load within the target period, and the target period is used to characterize the time period of power consumption billing.

[0053] The following introduces a method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application.

[0054] Please refer to Figure 4 , Figure 4 FIG. is a schematic flowchart of a method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application, which is applied to a server 120 in an energy storage system 100 as shown in Figure 1 The energy storage system 100 includes an energy storage battery 110 and a server 120. The energy storage battery 110 is communicatively connected to the server 120. The energy storage battery 110 may be a single energy storage battery or an energy storage battery pack composed of multiple energy storage batteries. The server 120 may be a single server or a server cluster composed of multiple servers. As shown in Figure 4 The method includes the following steps:

[0055] Step S410, obtaining historical power consumption data.

[0056] Among them, the historical power consumption data is used to characterize the historical power consumption situation of the power consumption load.

[0057] Among them, the historical power consumption data includes the historical hourly power consumption of the power consumption load, the historical daily production plan, the historical ambient temperature, the historical ambient humidity, the historical power consumption price and the load nature. The load nature is label information used to indicate the power consumption type of the corresponding distributed load resource. The power consumption types include industrial power consumption, commercial power consumption, and residential power consumption.

[0058] Step S420, determining a plurality of predicted power consumption demands according to the historical power consumption data.

[0059] Among them, the multiple predicted power consumption demands are used to characterize the predicted power consumption demand of each hour of the power load.

[0060] Among them, the determining of the multiple predicted power consumption demands according to the historical power consumption data may specifically be: determining a training data set according to the historical power consumption data, where the training data set is time series data; determining the multiple predicted power consumption demands according to the training data set and a preset power consumption demand prediction model.

[0061] Among them, the power consumption demand prediction model includes a long short-term memory network. The determining of the multiple predicted power consumption demands according to the training data set and a preset power consumption demand prediction model may specifically be: training the long short-term memory network using the training data set to obtain the multiple predicted power consumption demands output by the long short-term memory network.

[0062] Step S430, determining a target probability density function according to the multiple predicted power consumption demands.

[0063] Among them, the target probability density function is used to characterize the probability distribution of the errors of the multiple predicted power consumption demands.

[0064] In a possible embodiment, the determining of the target probability density function according to the multiple predicted power consumption demands includes: determining multiple verified power consumptions according to the historical power consumption data, where the multiple verified power consumptions are used to indicate the actual power consumption of each hour of the power load within at least one target sub-period in the target period; determining the target probability density function according to the multiple verified power consumptions and the multiple predicted power consumption demands.

[0065] Among them, the multiple verified power consumptions may specifically be the historical power consumption per hour in the historical power consumption data.

[0066] Among them, the multiple verified power consumptions are time series data.

[0067] It can be seen that in this example, the stored historical probability density function is updated according to the verified power consumption determined from the real-time power consumption data and the currently predicted predicted power consumption demand, so as to obtain a target probability density function with higher accuracy. Then, according to the target probability density function, a target control strategy that can obtain the maximum demand control and peak-valley arbitrage benefits and the power consumption does not exceed the target demand threshold is determined. In this way, the error distribution of the predicted power consumption demand is considered, the uncertainty in the power consumption demand prediction is accurately quantified, the target demand threshold mastered by the target control strategy is more accurate, and the maximum demand control and peak-valley arbitrage benefits can be obtained within the target demand threshold, thereby improving the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control.

[0068] In a possible embodiment, the determining of the target probability density function based on the multiple verified power consumption amounts and the multiple predicted power consumption demands includes: determining multiple first target matrices based on the multiple verified power consumption amounts and the multiple predicted power consumption demands, where the multiple first target matrices are used to characterize the deviation degree of the multiple predicted power consumption demands relative to the multiple verified power consumption amounts within the at least one target sub-period; determining a second target matrix based on the multiple first target matrices, where the second target matrix is used to characterize the deviation degree of the multiple predicted power consumption demands relative to the multiple verified power consumption amounts within the target period; and determining the target probability density function based on the second target matrix.

[0069] Wherein, the at least one target sub-period is the sub-periods that have elapsed and are in progress within the target period.

[0070] Wherein, the determining of the multiple first target matrices based on the multiple verified power consumption amounts and the multiple predicted power consumption demands may specifically be: determining a first time window based on the target period, where the first time window is a set of time data composed of each hour within each target sub-period; and determining the multiple first target matrices based on the multiple verified power consumption amounts, the multiple predicted power consumption demands, and the first time window.

[0071] Wherein, the determining of the multiple first target matrices based on the multiple verified power consumption amounts, the multiple predicted power consumption demands, and the first time window may specifically be: determining the first target matrix based on the difference between the multiple verified power consumption amounts and the multiple predicted power consumption demands corresponding to the first time window. For example: the system time is the jth day of the kth month in 2025, the target period may be the month to which the system time belongs, that is, the kth month, the at least one target sub-period is each day (including the jth day) that has elapsed and is in progress within the kth month, and the first time window may be: h = [h1…,h i , then the multiple predicted power consumption demands on the jth day in the kth month may be The multiple verified power consumption amounts on the jth day in the kth month may be Then the first target matrix on the jth day in the kth month is In this way, the first target matrix for each day in the kth month is obtained, and thus the multiple first target matrices are obtained.

[0072] Among them, determining the second target matrix according to the multiple first target matrices may specifically be: determining a second time window according to the target period, where the second time window is a set of time data composed of each target sub-period within the target period; determining the second target matrix according to the multiple first target matrices and the second time window. For example: The second time window may be, for example, d = [d1…, d j , and the second target matrix is determined according to the first target matrices of each day that has elapsed and is in progress in the k-th month, that is, the multiple first target matrices of the k-th month. Then the second target matrix may be, for example,

[0073] Among them, determining the target probability density function according to the second target matrix includes: performing kernel density estimation on the second target matrix to obtain the target probability density function. For example, it may be determined by the following formula:

[0074]

[0075] Among them, f(ε) is the target probability density function, λ represents the standard deviation of the second target matrix E, is the average value of the second target matrix E, and N is the number of elements of the second target matrix E.

[0076] Among them, the target probability density function is specifically used to characterize the probability distribution of the errors of the multiple predicted electricity demands in at least one target sub-period within the target period. For example, the target probability density function characterizes the probability distribution of the errors of the predicted electricity demands of each day in the k-th month.

[0077] Among them, please refer to Figure 5 , Figure 5 is a schematic flowchart of another method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application. As Figure 5 shown, another method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application includes the following steps:

[0078] Step S431, determining a plurality of verified electricity consumptions according to the historical electricity consumption data.

[0079] Among them, the plurality of verified electricity consumptions are used to indicate the actual electricity consumption of the electrical load per hour within the target period.

[0080] Step S4321, determining a plurality of first target matrices according to the plurality of verified electricity consumptions and the plurality of predicted electricity demands.

[0081] Among them, the multiple first target matrices are used to characterize the deviation degree of the multiple predicted electricity demands relative to the multiple verified electricity consumptions within at least one target sub-period in the target period.

[0082] Step S4322: Determine a second target matrix according to the multiple first target matrices.

[0083] Among them, the second target matrix is used to characterize the deviation degree of the multiple predicted electricity demands relative to the multiple verified electricity consumptions within the target period.

[0084] Step S4323: Determine the target probability density function according to the second target matrix.

[0085] Among them, please refer to Figure 6 , Figure 6 is a schematic diagram of a scenario of a method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application. As Figure 6 shown, the target probability density function determined according to the second target matrix presents a Gaussian distribution in a coordinate system with the vertical coordinate being the probability density and the horizontal coordinate being the average percentage error (normalized).

[0086] It can be seen that in this example, multiple first target matrices are determined according to multiple verified electricity consumptions and multiple predicted electricity consumptions, and then a second target matrix is determined according to the multiple first target matrices, so as to determine a target probability density function according to the second target matrix, enabling the target probability density function to characterize the probability distribution of the errors of the predicted electricity demands in at least one target sub-period in the target period, and then determining a target control strategy that can obtain the maximum demand control and peak-valley arbitrage benefits and the electricity consumption does not exceed the target demand threshold according to the target probability density function. In this way, considering the error distribution of the predicted electricity demands, the uncertainty in the electricity demand prediction is accurately and comprehensively quantified, making the target demand threshold grasped by the target control strategy more accurate, and being able to obtain the maximum demand control and peak-valley arbitrage benefits within the target demand threshold, thereby improving the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control.

[0087] Step S440: Determine a target control strategy according to the target probability density function.

[0088] Among them, the target control strategy is a control strategy that can obtain the maximum demand control and peak-valley arbitrage benefits through the energy storage system and the electricity consumption does not exceed the target demand threshold, and the target demand threshold is used to characterize the maximum electricity consumption of the electricity load within the target period.

[0089] Among them, the target control strategy is used to control the energy storage system to perform power scheduling.

[0090] In a possible embodiment, determining the target control strategy according to the target probability density function includes: performing at least one demand threshold generation and policy verification operation according to the target probability density function until the generated control strategy contains a solution that satisfies the target demand threshold, to obtain the target control strategy. The demand threshold generation and policy verification operation includes the following steps: determining the target demand threshold according to the target probability density function; determining a candidate control strategy according to the target demand threshold; judging whether the candidate control strategy contains a solution that satisfies the target demand threshold; if not, re-performing the demand threshold generation and policy verification operation; if so, determining the candidate control strategy as the target control strategy.

[0091] Among them, judging whether the candidate control strategy contains a solution that satisfies the target demand threshold may specifically be: solving the candidate control strategy to obtain at least one solution; judging whether there is a solution that satisfies the target demand threshold among the at least one solution.

[0092] Among them, solving the candidate control strategy to obtain at least one solution, for example: using a CPLEX solver to solve the candidate control strategy to obtain the at least one solution.

[0093] Among them, please refer to Figure 7 , Figure 7 is a schematic flowchart of another method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application. As Figure 7 shown, another method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application includes the following steps:

[0094] Step S410, obtaining historical power consumption data.

[0095] Among them, the historical power consumption data is used to characterize the historical power consumption situation of the power consumption load.

[0096] Step S420, determining multiple predicted power consumption demands according to the historical power consumption data.

[0097] Among them, the multiple predicted power consumption demands are used to characterize the power consumption demand of the predicted power consumption load for each hour within the target period, and the target period is used to characterize the time period for power consumption billing.

[0098] Step S431, determining multiple verified power consumptions according to the historical power consumption data.

[0099] Among them, the multiple verified power consumptions are used to indicate the actual power consumption of the power consumption load for each hour within the target period.

[0100] Step S432: Determine the target probability density function according to the multiple verified power consumption amounts and the multiple predicted power demand amounts.

[0101] Step S441: Perform at least one power demand threshold generation and policy verification operation according to the target probability density function until the generated control policy contains a solution that satisfies the target power demand threshold, and obtain the target control policy.

[0102] Among them, step S441 includes the following steps:

[0103] Step S4411: Determine the target power demand threshold according to the target probability density function;

[0104] Step S4412: Determine the candidate control policy according to the target power demand threshold;

[0105] Step S4413: Determine whether the candidate control policy contains a solution that satisfies the target power demand threshold;

[0106] If not, return to S4411;

[0107] If so, determine the candidate control policy as the target control policy.

[0108] It can be seen that in this example, by performing at least one power demand threshold generation and policy verification operation according to the target probability density function, the generated target control policy can obtain the maximum power demand control and peak-valley arbitrage benefits and the power consumption does not exceed the target power demand threshold. In this way, considering the error distribution of the predicted power demand, the uncertainty in the power demand prediction is accurately and comprehensively quantified, making the target power demand threshold mastered by the target control policy more accurate, and being able to obtain the maximum power demand control and peak-valley arbitrage benefits within the target power demand threshold, thereby improving the economic benefits obtained by the energy storage system in peak-valley arbitrage and power demand control.

[0109] In a possible embodiment, the determining the target power demand threshold according to the target probability density function includes: obtaining a preset allowable error confidence level; determining a target confidence interval according to the allowable error confidence level and the target probability density function; determining a real-time power demand threshold according to the target confidence interval, where the real-time power demand threshold is used to represent the maximum power demand of the current sub-period in which the power load is currently located in the at least one target sub-period; determining a plurality of first power demand thresholds according to the real-time power demand threshold, where the plurality of first power demand thresholds are used to represent the maximum power demands of a plurality of sub-periods of the power load in the target period, and the plurality of sub-periods include the at least one target sub-period; and determining the target power demand threshold according to the plurality of first power demand thresholds.

[0110] Among them, the allowable error confidence level is an empirical value obtained by statistically analyzing the multiple verified power consumption amounts and the multiple predicted power demand amounts.

[0111] Among them, the target confidence interval can be, for example:

[0112]

[0113] Among them, δ is the allowable error confidence level.

[0114] Among them, determining the real-time demand threshold according to the target confidence interval can specifically be: determining the maximum value within the target confidence interval as the real-time demand threshold.

[0115] Among them, determining multiple first demand thresholds according to the real-time demand threshold can specifically be: determining at least one predicted demand threshold according to the real-time demand threshold and a preset demand threshold prediction model; obtaining the historical demand threshold within the target period; determining the historical demand threshold, the real-time demand threshold, and the at least one predicted demand threshold as the first demand threshold to obtain the multiple first demand thresholds.

[0116] Among them, the at least one predicted demand threshold is the demand threshold for at least one future sub-period predicted, and the at least one future sub-period refers to other sub-periods in the multiple sub-periods except the at least one target sub-period. For example: the multiple sub-periods are each day in month k, the at least one target sub-period is each day (including the j-th day) that has been experienced and is being experienced in month k, and the at least one future sub-period is each day (excluding the j-th day) in month k that has not been experienced.

[0117] Among them, determining at least one predicted demand threshold according to the real-time demand threshold and a preset demand threshold prediction model can specifically be: rolling and updating the real-time demand threshold into the preset demand threshold prediction model so that the demand threshold prediction model outputs the predicted demand threshold for the next sub-period to obtain the at least one predicted demand threshold.

[0118] Among them, the historical demand threshold is the demand threshold determined in the historical sub-periods other than the current sub-period in the at least one target sub-period. Each time the current sub-period ends and enters the next sub-period, the current sub-period is determined as the historical sub-period, and the next sub-period in the multiple sub-periods is added to the at least one target sub-period.

[0119] Among them, determining the target demand threshold according to the multiple first demand thresholds can specifically be according to the following formula:

[0120] Τ m =max{Τm , Τ1, …, Τ j , …, Τ j+n}

[0121] where Τ m is the target demand threshold value.

[0122] It can be seen that in this example, the target confidence interval is determined according to the allowable error confidence level and the target probability density function, and then the real-time demand threshold value of the current sub-cycle is determined according to the target confidence interval, and then a plurality of first demand threshold values are determined according to the real-time demand threshold value, so as to determine the target demand threshold value according to the plurality of first demand threshold values, so that the generated target control strategy can obtain the maximum demand control and peak-valley arbitrage benefits and the electricity consumption does not exceed the target demand threshold value. In this way, the error distribution of the predicted electricity demand is considered, and the uncertainty in the electricity demand prediction is accurately and comprehensively quantified, so that the target demand threshold value mastered by the target control strategy is more accurate, and the maximum demand control and peak-valley arbitrage benefits can be obtained within the target demand threshold value, thereby improving the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control.

[0123] In a possible embodiment, determining the candidate control strategy according to the target demand threshold value includes: determining a target optimization function according to the target demand threshold value, where the target optimization function takes the maximization of the demand control benefit and the peak-valley arbitrage benefit as the optimization goal; obtaining a preset optimization constraint condition; and determining the candidate control strategy according to the target optimization function and the optimization constraint condition.

[0124] Among them, the optimization constraint conditions include source-load balance constraint, battery attenuation constraint, and battery capacity and power constraint.

[0125] Among them, the demand control benefit is used to represent the reduced electricity cost for demand control.

[0126] Among them, the source-load balance constraint is used to achieve the conservation of the power input by the energy storage system to the power grid, the power consumed by the electrical load, and the total load of the energy storage system. The source-load balance constraint can be, for example:

[0127] P in = P load + P ESS

[0128] Among them, P in is the active power input by the power grid, P load is the sum of the active powers consumed by the electrical load; P ESS is the active power output by the energy storage system.

[0129] Among them, the battery degradation constraint is used to limit the charge and discharge costs of the energy storage system in each sub-cycle. The battery degradation constraint can be, for example:

[0130]

[0131] Among them, C APV is the investment and operation cost of the energy storage system, with the unit of year; C op is the operation and maintenance cost of the energy storage system, with the unit of year; C PV is the initial investment cost of the energy storage system; r is the discount rate; T ESS is the lifespan of the energy storage system.

[0132] Among them, the battery capacity and power constraint means that the energy stored in the energy storage battery in the energy storage system is restricted by the charging power and energy consumption. Specifically, it can be that the state of charge (SOC) of the energy storage system is restricted by the upper and lower limits of charge and discharge and the chronological continuity. And, within a charge and discharge operation cycle, the SOC value at the starting moment is equal to the SOC value at the ending moment. The battery capacity and power constraint can be, for example:

[0133]

[0134] SOC min ≤SOC(t)≤SOC max

[0135] SOC(0)=SOC(T)

[0136] Among them, SOC(t) is the SOC value of the energy storage system at time t; P(t) is the active power output of the energy storage system; η is the charge and discharge efficiency of the energy storage system; SOC min and SOC max are the lower and upper limits of charge and discharge of the energy storage system respectively. SOC(0) and SOC(T) are the SOC values of the energy storage system at the initial moment and the ending moment respectively; T is a charge and discharge operation cycle.

[0137] It can be seen that in this example, the target optimization function is determined according to the target demand threshold, and then the candidate control strategy is determined according to the set optimization constraint conditions and the target optimization function. Based on this, the target control strategy that can maximize the demand control and peak-valley arbitrage benefits and the electricity consumption does not exceed the target demand threshold can be obtained. And, the feasibility and stability of the obtained target control strategy are higher, and the maximum demand control and peak-valley arbitrage benefits can be obtained within the target demand threshold, thereby improving the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control.

[0138] In a possible embodiment, determining the target optimization function according to the target demand threshold includes: obtaining a preset penalty factor, where the penalty factor is used to penalize the electricity demand exceeding the target demand threshold; determining a first target function according to the target demand threshold and the penalty factor, where the first target function is used to calculate the demand control benefit; obtaining a preset second target function, where the second target function is used to calculate the peak-valley arbitrage benefit; and determining the target optimization function according to the first target function and the second target function.

[0139] Among them, determining the first target function according to the target demand threshold and the penalty factor can be, for example:

[0140]

[0141] Among them, B1 is the first target function, p am is the demand electricity price in units of power, Τ m is the target demand threshold, and P is the maximum demand value without considering the output of the energy storage system; is the actual maximum demand of the target period, and ρ is the penalty factor.

[0142] Among them, when it is detected that the real-time electricity consumption in the current sub-period exceeds the target demand threshold, update the real-time electricity consumption to the target demand threshold.

[0143] Among them, the second target function can be, for example:

[0144]

[0145] Among them, B2 is the second target function, P dis (t) and p cha (t) are the peak shaving and valley filling discharge power and charging power respectively, p peak and p valley are the peak time electricity price and valley time electricity price respectively, T d and T c are the peak shaving duration and valley filling duration respectively, C loss is the daily charge and discharge cost of the energy storage system.

[0146] Among them, determining the target optimization function according to the first target function and the second target function can be specifically: determining the sum of the first target function and the second target function as the target optimization function.

[0147] Among them, please refer to Figure 8 , Figure 8 is a schematic flowchart of another method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application. As Figure 8As shown in the figure, another method for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of the present application includes the following steps:

[0148] Step S410: Obtain historical electricity consumption data.

[0149] Among them, the historical electricity consumption data is used to characterize the historical electricity consumption situation of the electricity load.

[0150] Step S420: Determine multiple predicted electricity demands according to the historical electricity consumption data.

[0151] Among them, the multiple predicted electricity demands are used to characterize the electricity demand of the predicted electricity load for each hour within the target period, and the target period is used to characterize the time period for electricity billing.

[0152] Step S431: Determine multiple verified electricity consumptions according to the historical electricity consumption data.

[0153] Among them, the multiple verified electricity consumptions are used to indicate the actual electricity consumption of the electricity load for each hour within the target period.

[0154] Step S4321: Determine multiple first target matrices according to the multiple verified electricity consumptions and the multiple predicted electricity demands.

[0155] Among them, the multiple first target matrices are used to characterize the deviation degree of the multiple predicted electricity demands relative to the multiple verified electricity consumptions within at least one target sub-period in the target period.

[0156] Step S4322: Determine a second target matrix according to the multiple first target matrices.

[0157] Among them, the second target matrix is used to characterize the deviation degree of the multiple predicted electricity demands relative to the multiple verified electricity consumptions within the target period.

[0158] Step S4323: Determine the target probability density function according to the second target matrix.

[0159] Step S441: Perform at least one demand threshold generation and strategy verification operation according to the target probability density function until the generated control strategy contains a solution that meets the target demand threshold, and obtain the target control strategy.

[0160] Among them, step S441 includes the following steps:

[0161] Step S4411a: Obtain a preset allowable error confidence level.

[0162] Step S4411b: Determine a target confidence interval according to the allowable error confidence level and the target probability density function.

[0163] Step S4411c, determining a real-time demand threshold according to the target confidence interval.

[0164] Wherein, the real-time demand threshold is used to characterize the maximum power consumption demand of the current sub-cycle in which the power consumption load is currently located in the at least one target sub-cycle.

[0165] Step S4411d, determining a plurality of first demand thresholds according to the real-time demand threshold.

[0166] Wherein, the plurality of first demand thresholds are used to characterize the maximum power consumption demands of a plurality of sub-cycles of the power consumption load in the target cycle, and the plurality of sub-cycles include the at least one target sub-cycle.

[0167] Step S4411e, determining the target demand threshold according to the plurality of first demand thresholds.

[0168] Step S4412a, determining a target optimization function according to the target demand threshold.

[0169] Wherein, the target optimization function takes the maximization of demand control benefits and peak-valley arbitrage benefits as the optimization goal.

[0170] Step S4412b, obtaining preset optimization constraint conditions.

[0171] Step S4412c, determining the candidate control strategy according to the target optimization function and the optimization constraint conditions.

[0172] Step S4413, determining whether the candidate control strategy contains a solution that satisfies the target demand threshold.

[0173] If not, return to S4411a;

[0174] If so, determine the candidate control strategy as the target control strategy.

[0175] It can be seen that in this example, a first target function for calculating demand control benefits and a second target function for calculating peak-valley arbitrage benefits are determined according to the target demand threshold, and a target optimization function is determined according to the first target function and the second target function. In this way, demand control and peak-valley arbitrage are combined into a dual-objective optimization, and a penalty mechanism is introduced, so that the maximum demand control and peak-valley arbitrage benefits can be obtained within the target demand threshold, thereby improving the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control.

[0176] It can be seen that in this application, the server obtains historical power consumption data, which is used to characterize the historical power consumption situation of the power load; determines multiple predicted power consumption demands according to the historical power consumption data, and the multiple predicted power consumption demands are used to characterize the power consumption demands of the predicted power load for each hour within the target period, and the target period is used to characterize the time period for power consumption billing; determines the target probability density function according to the multiple predicted power consumption demands, and the target probability density function is used to characterize the probability distribution of the errors of the multiple predicted power consumption demands; determines the target control strategy according to the target probability density function, and the target control strategy is a control strategy that obtains the maximum demand control and peak-valley arbitrage benefits through the energy storage system and the power consumption does not exceed the target demand threshold, and the target demand threshold is used to characterize the maximum power consumption of the power load within the target period. By determining the target probability density function that characterizes the probability distribution of the errors of the multiple predicted power consumption demands, and then determining the target control strategy that can obtain the maximum demand control and peak-valley arbitrage benefits and the power consumption does not exceed the target demand threshold according to the target probability density function, in this way, the error distribution of the predicted power consumption demand is considered, the uncertainty in the power consumption demand prediction is quantified, so that the target control strategy masters the accurate target demand threshold, and can obtain the maximum demand control and peak-valley arbitrage benefits within the target demand threshold, thereby improving the economic benefits obtained by the energy storage system in peak-valley arbitrage and demand control.

[0177] The above mainly introduces the solution of the embodiment of this application from the perspective of the execution process on the method side. It can be understood that in order for the controller to implement the above functions, it includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments provided in this article, this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but this implementation should not be considered to exceed the scope of this application.

[0178] Consistent with the above-described embodiments, please refer to Figure 9 , Figure 9 is the functional unit composition block diagram of a device for formulating a demand control and peak-valley arbitrage strategy provided by an embodiment of this application, as shown in Figure 9As shown, the strategy formulation device 900 for demand control and peak-valley arbitrage includes: a first receiving unit 901, configured to obtain historical power consumption data, where the historical power consumption data is used to characterize the historical power consumption situation of the power consumption load; a first processing unit 902, configured to determine multiple predicted power consumption demands according to the historical power consumption data, where the multiple predicted power consumption demands are used to characterize the predicted power consumption demand of the power consumption load for each hour within a target period, and the target period is used to characterize the time period for power consumption billing; determine a target probability density function according to the multiple predicted power consumption demands, where the target probability density function is used to characterize the probability distribution of the errors of the multiple predicted power consumption demands; determine a target control strategy according to the target probability density function, where the target control strategy is a control strategy for obtaining maximized demand control and peak-valley arbitrage benefits through the energy storage system and the power consumption not exceeding a target demand threshold, and the target demand threshold is used to characterize the maximum power consumption of the power consumption load within the target period.

[0179] In a possible embodiment, in terms of determining the target probability density function according to the multiple predicted power consumption demands, the first processing unit 902 is specifically configured to: determine multiple verified power consumptions according to the historical power consumption data, where the multiple verified power consumptions are used to indicate the actual power consumption of the power consumption load for each hour within at least one target sub-period within the target period; determine the target probability density function according to the multiple verified power consumptions and the multiple predicted power consumption demands.

[0180] In a possible embodiment, in terms of determining the target probability density function according to the multiple verified power consumptions and the multiple predicted power consumption demands, the first processing unit 902 is specifically configured to: determine multiple first target matrices according to the multiple verified power consumptions and the multiple predicted power consumption demands, where the multiple first target matrices are used to characterize the deviation degree of the multiple predicted power consumption demands relative to the multiple verified power consumptions within the at least one target sub-period; determine a second target matrix according to the multiple first target matrices, where the second target matrix is used to characterize the deviation degree of the multiple predicted power consumption demands relative to the multiple verified power consumptions within the target period; determine the target probability density function according to the second target matrix.

[0181] In a possible embodiment, in terms of determining the target control strategy according to the target probability density function, the first processing unit 902 is specifically configured to: perform at least one demand threshold generation and policy verification operation according to the target probability density function until the generated control strategy contains a solution that meets the target demand threshold, so as to obtain the target control strategy. The demand threshold generation and policy verification operation includes the following steps: determining the target demand threshold according to the target probability density function; determining a candidate control strategy according to the target demand threshold; judging whether the candidate control strategy contains a solution that meets the target demand threshold; if not, then re-performing the demand threshold generation and policy verification operation; if so, then determining the candidate control strategy as the target control strategy.

[0182] In a possible embodiment, in terms of determining the target demand threshold according to the target probability density function, the first processing unit 902 is specifically configured to: obtain a preset allowable error confidence level; determine a target confidence interval according to the allowable error confidence level and the target probability density function; determine a real-time demand threshold according to the target confidence interval, where the real-time demand threshold is used to represent the maximum electricity demand of the current sub-cycle in which the electricity load is currently located in the at least one target sub-cycle; determine a plurality of first demand thresholds according to the real-time demand threshold, where the plurality of first demand thresholds are used to represent the maximum electricity demands of a plurality of sub-cycles of the electricity load in the target cycle, and the plurality of sub-cycles include the at least one target sub-cycle; determine the target demand threshold according to the plurality of first demand thresholds.

[0183] In a possible embodiment, in terms of determining a candidate control strategy according to the target demand threshold, the first processing unit 902 is specifically configured to: determine a target optimization function according to the target demand threshold, where the target optimization function maximizes the demand control benefit and the peak-valley arbitrage benefit; obtain preset optimization constraint conditions; determine the candidate control strategy according to the target optimization function and the optimization constraint conditions.

[0184] In a possible embodiment, in terms of determining the target optimization function according to the target demand threshold, the first processing unit 902 is specifically configured to: obtain a preset penalty factor, where the penalty factor is used to penalize the electricity demand exceeding the target demand threshold; determine a first target function according to the target demand threshold and the penalty factor, where the first target function is used to calculate the demand control benefit; obtain a preset second target function, where the second target function is used to calculate the peak-valley arbitrage benefit; determine the target optimization function according to the first target function and the second target function.

[0185] It can be understood that since the method embodiments and the apparatus embodiments are different presentation forms of the same technical concept, the content of the method embodiments in this application should be synchronously adapted to the apparatus embodiments, which will not be elaborated here.

[0186] In the case of adopting an integrated unit, as Figure 10 shown, Figure 10 is a block diagram of the functional units of another strategy formulation apparatus for demand control and peak-valley arbitrage provided by an embodiment of this application. In Figure 10 shown, the strategy formulation apparatus 900 for demand control and peak-valley arbitrage includes: a processing module 1012 and a communication module 1011. The processing module 1012 is used to control and manage the operations of a strategy formulation apparatus 900 for demand control and peak-valley arbitrage. For example, it executes the steps of the first receiving unit 901 and the first processing unit 902, and / or is used to execute other processes of the technologies described herein. The communication module 1011 is used to support the interaction between a strategy formulation apparatus 900 for demand control and peak-valley arbitrage and other devices. As Figure 10 shown, the strategy formulation apparatus 900 for demand control and peak-valley arbitrage may further include a storage module 1013, and the storage module 1013 is used to store the program code and data of the strategy formulation apparatus 900 for demand control and peak-valley arbitrage.

[0187] Among them, the processing module 1012 may be a processor or a controller. For example, it may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure of this application. The processor may also be a combination that realizes computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on. The communication module 1011 may be a transceiver, an RF circuit or a communication interface, etc. The storage module 1013 may be a memory.

[0188] Among them, all relevant contents of each scenario involved in the above method embodiments can be cited in the function descriptions of the corresponding functional modules, which will not be elaborated here. The above strategy formulation apparatus 900 for demand control and peak-valley arbitrage can all execute the Figure 4 shown strategy formulation method for demand control and peak-valley arbitrage.

[0189] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0190] Figure 11 is a block diagram of the structure of a server provided by an embodiment of the present application. As Figure 11 shown, the server 120 may include one or more of the following components: a processor 310, and a memory 320 coupled to the processor 310, where the memory 320 may store one or more computer programs 321, and the one or more computer programs 321 may be configured to implement the methods described in the above embodiments when executed by the one or more processors 310.

[0191] The processor 310 may include one or more processing cores. The processor 310 connects various parts within the entire server 120 using various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 320, and by invoking data stored in the memory 320, it performs various functions of the server 120 and processes data. Optionally, the processor 310 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 310 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing display content; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 310 and may be implemented separately through a communication chip.

[0192] The memory 320 may include random access memory (RAM) and may also include read-only memory (ROM). The memory 320 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 320 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for implementing at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created during the use of the server 120.

[0193] It can be understood that the server 120 may include more or fewer structural elements than those shown in the above structural block diagram, which will not be limited here. An embodiment of the present application provides a computer-readable storage medium, on which computer programs / instructions are stored. When the computer programs / instructions are executed by a processor, the steps of the method according to any possible embodiment are implemented.

[0194] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0195] In several embodiments provided by the present application, it should be understood that the disclosed methods, devices, and systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0196] The unit described as a separate component may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0197] In addition, each functional unit in various embodiments of the present invention can be integrated in a processing unit, or each unit can be physically included separately, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a combination of hardware and software functional units.

[0198] The integrated unit implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional units stored in a storage medium include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute some steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, magnetic disks, optical discs, volatile memories, or non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM), etc., all of which are media that can store program code.

[0199] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various modifications and alterations, including combinations of the above different functions and implementation steps, including software and hardware implementation manners, all within the protection scope of the present invention.

Claims

1. A method for formulating a demand control and peak-valley arbitrage strategy, characterized in that, A server applied to an energy storage system, the method comprising: Obtain historical power consumption data, which is used to characterize the historical power consumption situation of the power consumption load; Determine a plurality of predicted power consumption demands according to the historical power consumption data, where the plurality of predicted power consumption demands are used to characterize the power consumption demand of the predicted power consumption load for each hour within a target period, and the target period is used to characterize the time period for electricity billing; Determine a target probability density function according to the plurality of predicted power consumption demands, where the target probability density function is used to characterize the probability distribution of the errors of the plurality of predicted power consumption demands; Determine a target control strategy according to the target probability density function, where the target control strategy is a control strategy that obtains the maximum demand control and peak-valley arbitrage benefits through the energy storage system and the power consumption does not exceed a target demand threshold, and the target demand threshold is used to characterize the maximum power consumption of the power consumption load within the target period.

2. The method according to claim 1, characterized in that, The determining the target probability density function according to the plurality of predicted power consumption demands includes: Determine a plurality of verified power consumptions according to the historical power consumption data, where the plurality of verified power consumptions are used to indicate the actual power consumption of the power consumption load for each hour within at least one target sub-period of the target period; Determine the target probability density function according to the plurality of verified power consumptions and the plurality of predicted power consumption demands.

3. The method according to claim 2, wherein The determining the target probability density function according to the plurality of verified power consumptions and the plurality of predicted power consumption demands includes: Determine a plurality of first target matrices according to the plurality of verified power consumptions and the plurality of predicted power consumption demands, where the plurality of first target matrices are used to characterize the deviation degree of the plurality of predicted power consumption demands relative to the plurality of verified power consumptions within the at least one target sub-period; Determine a second target matrix according to the plurality of first target matrices, where the second target matrix is used to characterize the deviation degree of the plurality of predicted power consumption demands relative to the plurality of verified power consumptions within the target period; Determine the target probability density function according to the second target matrix.

4. The method according to claim 3, characterized in that The determining the target control strategy according to the target probability density function includes: Perform at least one demand threshold generation and strategy verification operation according to the target probability density function until the generated control strategy contains a solution that satisfies the target demand threshold, and the demand threshold generation and strategy verification operation includes the following steps: Determine the target demand threshold according to the target probability density function; Determine a candidate control strategy according to the target demand threshold; Judge whether the candidate control strategy contains a solution that satisfies the target demand threshold; If not, then re-perform the demand threshold generation and strategy verification operation; If so, then determine the candidate control strategy as the target control strategy.

5. The method according to claim 4, characterized in that, The determining the target demand threshold according to the target probability density function includes: Obtain a preset allowable error confidence level; Determine a target confidence interval according to the allowable error confidence level and the target probability density function; Determine a real-time demand threshold according to the target confidence interval, where the real-time demand threshold is used to represent the maximum power consumption demand of the current sub-cycle in which the power consumption load is currently located in the at least one target sub-cycle; Determine a plurality of first demand thresholds according to the real-time demand threshold, where the plurality of first demand thresholds are used to represent the maximum power consumption demands of the power consumption load in a plurality of sub-cycles in the target cycle, and the plurality of sub-cycles include the at least one target sub-cycle; Determine the target demand threshold according to the plurality of first demand thresholds.

6. The method according to claim 5, characterized in that, The determining of the candidate control strategy according to the target demand threshold includes: Determine a target optimization function according to the target demand threshold, where the target optimization function aims to maximize the demand control benefit and the peak-valley arbitrage benefit; Obtain preset optimization constraint conditions; Determine the candidate control strategy according to the target optimization function and the optimization constraint conditions.

7. The method according to claim 5, characterized in that The determining of the target optimization function according to the target demand threshold includes: Obtain a preset penalty factor, where the penalty factor is used to penalize the power consumption demand exceeding the target demand threshold; Determine a first target function according to the target demand threshold and the penalty factor, where the first target function is used to calculate the demand control benefit; Obtain a preset second target function, where the second target function is used to calculate the peak-valley arbitrage benefit; Determine the target optimization function according to the first target function and the second target function.

8. A strategy formulation device for demand control and peak-valley arbitrage, characterized in that, Applied to a server in an energy storage system, the device includes: A first receiving unit, configured to obtain historical power consumption data, where the historical power consumption data is used to represent the historical power consumption situation of the power consumption load; A first processing unit, configured to determine a plurality of predicted power consumption demands according to the historical power consumption data, where the plurality of predicted power consumption demands are used to represent the predicted power consumption demands of the power consumption load per hour in a target cycle, and the target cycle is used to represent the time cycle for power consumption billing; determine a target probability density function according to the plurality of predicted power consumption demands, where the target probability density function is used to represent the probability distribution of the errors of the plurality of predicted power consumption demands; determine a target control strategy according to the target probability density function, where the target control strategy is a control strategy that maximizes the demand control and the peak-valley arbitrage benefit through the energy storage system and the power consumption does not exceed the target demand threshold, and the target demand threshold is used to represent the maximum power consumption of the power consumption load in the target cycle.

9. A server, characterized in that, Comprising a processor, a memory, and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the processor, and the programs include instructions for performing the steps in the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Stored thereon with computer programs / instructions, where when the computer programs / instructions are executed by the processor, the steps of the method according to any one of claims 1-7 are implemented.

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