Excitation strategy optimization method for power demand response and related device

Abstract

The invention discloses an incentive strategy optimization method for power demand response and a related device. The method comprises the following steps: constructing a demand response evaluation index set; determining a target time-of-use electricity price strategy in the ladder-shaped series time-of-use electricity price strategies according to the economic feasibility of the industrial user and the power supply side and the utilization efficiency of the renewable energy sources; determining electric charge expenditure according to a target time-of-use electricity price strategy and the electricity purchase quantity of the industrial user; correcting the initial incentive integral after the industrial user participates in the power demand response to obtain a corrected incentive integral; constructing an objective function of a multi-objective optimization model according to the electric charge expenditure, the corrected excitation integral and at least one evaluation index in the demand response evaluation index set; determining constraint conditions of the multi-objective optimization model; and solving the objective function according to constraint conditions to obtain an incentive strategy for improving the participation degree of the industrial users. The method can improve the enthusiasm of industrial users to participate in power demand response.

Description

Technical Field

[0001] The present application relates to the field of power demand response, and in particular to an incentive strategy optimization method for power demand response and related devices. Background Art

[0002] Electricity demand response refers to the situation where, when electricity market prices rise or fall significantly, or when there is a risk to system safety and reliability, electricity users change their inherent electricity consumption patterns, reduce or increase electricity consumption, thereby promoting the balance of electricity supply and demand, maintaining system reliability and improving system operating efficiency.

[0003] In recent years, with the development of economy and society, the important role of industrial users in participating in electricity demand response has been recognized by the industry. At present, there are many factors affecting industrial users' participation in electricity demand response, and the actual response is complex. At present, most of them are calculated or predicted by formulating mandatory measures or using policy subsidies, which cannot fully tap the response potential of industrial users, and thus cannot ensure the smooth implementation of demand response. Summary of the invention

[0004] The embodiments of the present application provide an incentive strategy optimization method for power demand response and related devices to increase the enthusiasm of industrial users to participate in power demand response.

[0005] In a first aspect, an embodiment of the present application provides an incentive strategy optimization method for power demand response, including: Constructing a demand response evaluation indicator set, wherein the demand response evaluation indicator set includes economic related indicators, social related indicators and environmental related indicators; Determine the target time-of-use electricity price strategy in the ladder series of time-of-use electricity price strategies based on the economic feasibility of industrial users and power supply sides, as well as the utilization efficiency of renewable energy; Determining electricity expenses according to the target time-of-use electricity price strategy and the amount of electricity purchased by the industrial user, the amount of electricity purchased by the industrial user being determined according to the adjustment strategy formulated by the industrial user for participating in the electricity demand response; and correcting the initial incentive points of the industrial user after participating in the electricity demand response to obtain corrected incentive points; Constructing an objective function of a multi-objective optimization model according to the electricity fee expenditure, the modified incentive points and at least one evaluation index in the demand response evaluation index set; Determining constraints of the multi-objective optimization model; The objective function is solved according to the constraint conditions to obtain an incentive strategy for improving the participation of the industrial users.

[0006] In a second aspect, an embodiment of the present application provides an incentive strategy optimization device for power demand response, including: A first construction unit is used to construct a demand response evaluation indicator set, wherein the demand response evaluation indicator set includes economic related indicators, social related indicators and environment related indicators; A first determination unit is used to determine a target time-of-use electricity price strategy in a ladder series of time-of-use electricity price strategies according to the economic feasibility of industrial users and power supply sides, and the utilization efficiency of renewable energy; a second determination unit, configured to determine the electricity fee expenditure according to the target time-of-use electricity price strategy and the purchased electricity amount of the industrial user, the purchased electricity amount of the industrial user being determined according to the adjustment strategy formulated by the industrial user for participating in the electricity demand response; and to correct the initial incentive points of the industrial user after participating in the electricity demand response to obtain corrected incentive points; A second construction unit is used to construct an objective function of a multi-objective optimization model according to the electricity fee expenditure, the modified incentive points and at least one evaluation index in the demand response evaluation index set; A third determining unit, used to determine the constraint conditions of the multi-objective optimization model; A solving unit is used to solve the objective function according to the constraint conditions to obtain an incentive strategy for improving the participation of the industrial users.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory storing execution instructions, wherein the memory stores one or more programs; when the processor executes the execution instructions stored in the memory, the processor executes the method described in the first aspect.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing an energy data management program, including execution instructions. When a processor of an electronic device executes the execution instructions, the processor executes the method described in the first aspect.

[0009] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0010] It can be seen that in an embodiment of the present application, a demand response evaluation index set is first constructed, and the demand response evaluation index set includes economic-related indicators, social-related indicators and environmental-related indicators; then, a target time-of-use electricity price strategy is determined in a ladder series of time-of-use electricity price strategies based on the economic feasibility of industrial users and the power supply side, and the utilization efficiency of renewable energy; then, the electricity bill expenditure is determined based on the target time-of-use electricity price strategy and the power purchase amount of the industrial user, and the power purchase amount of the industrial user is determined based on the adjustment strategy formulated by the industrial user to participate in the electricity demand response; and, the initial incentive points of the industrial user after participating in the electricity demand response are corrected to obtain the corrected incentive points; then, the objective function of the multi-objective optimization model is constructed based on the electricity bill expenditure, the corrected incentive points and at least one evaluation indicator in the demand response evaluation index set; the constraints of the multi-objective optimization model are determined; finally, the objective function is solved according to the constraints to obtain an incentive strategy for improving the participation of the industrial users. This application uses a non-single form of ladder series time-of-use electricity price strategy, and by considering the economic feasibility of multiple parties and the utilization efficiency of renewable energy, it determines a time-of-use electricity price strategy that is highly matched with the current production scenario of industrial users. The time-of-use electricity price strategy is used to calculate electricity expenditure, and the revised point incentive and a comprehensive set of evaluation indicators based on the economic, social and environmental factors of industrial users' response to electricity demand are superimposed to form a multi-objective optimization strategy for industrial users to participate in demand-side response, to measure the willingness of industrial users to participate in demand-side response, and then mobilize the demand-side response potential of industrial users in different production scenarios, thereby increasing the enthusiasm of industrial users to participate in electricity demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 This is a system architecture diagram of an incentive strategy optimization system provided by an embodiment of the present application; Figure 2 is a flow chart of an incentive strategy optimization method for power demand response provided in an embodiment of the present application; Figure 3 is a flow chart of another method for optimizing an incentive strategy for power demand response provided in an embodiment of the present application; Figure 4 It is a functional unit composition block diagram of an incentive strategy optimization device for power demand response provided by an embodiment of the present application; Figure 5 It is a functional unit composition block diagram of another incentive strategy optimization device for power demand response provided by an embodiment of the present application; Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" 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 includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0015] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0016] Currently, most methods are used to calculate or predict the response behavior of industrial users by formulating mandatory measures or using policy subsidies. This fails to fully tap the response potential of industrial users and thus fails to ensure the smooth implementation of demand response.

[0017] In view of the above problems, an embodiment of the present application provides an incentive strategy optimization method for power demand response and a related device. The embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0018] See also Figure 1 , Figure 1 1 is a system architecture diagram of an incentive strategy optimization system provided by an embodiment of the present application. Figure 1As shown, the incentive strategy optimization system 100 includes a data acquisition module 101, an electricity price adjustment module 102, an analysis module 103, a construction module 104 and a solution module 105, and each module is connected to each other in communication, wherein the data acquisition module 101 is used to collect multi-party data when industrial users participate in power demand response, and transmit the collected data to the construction module 104, wherein the multi-party data includes the response data of industrial users and the loss data of orders that are not completed due to the participation of industrial users in the response, the economic price subsidy data provided by the policy, and the emission data of pollutants that can be reduced by the power generation party, etc.; the electricity price The adjustment module 102 is used to establish different time-of-use electricity price strategies under different production scenarios, and then determine the time-of-use electricity price strategy that matches the current production scenario of the industrial user based on the economic feasibility and environmental protection of multiple parties, and transmit the determined time-of-use electricity price strategy to the analysis module 103; the analysis module 103 is used to calculate the electricity expenditure according to the matching time-of-use electricity price strategy, and at the same time correct the initial incentive points, and construct an objective function for achieving the lowest possible electricity expenditure and the strongest possible points incentive based on the electricity expenditure and the corrected incentive points, and solve the objective function, and transmit the solution result to the construction module 104. The construction module 104 is used to construct an economic, social, and environmental protection index evaluation system based on the multi-party data transmitted by the data acquisition module 101, and to construct the objective function of the multi-objective optimization model based on the solution results transmitted by the analysis module 103, taking into account the marginal diminishing effect, and combining the index evaluation system to construct a power balance constraint and an adjustable load constraint for industrial users; the solution module 105 can be a solver, which is used to solve the objective function and constraint conditions constructed by the construction module 104 to obtain an incentive strategy that meets the current production scenario of the industrial user.

[0019] See also Figure 2 , Figure 2 is a flow chart of an incentive strategy optimization method for power demand response provided by an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps: S210, construct a demand response evaluation indicator set.

[0020] Among them, the demand response evaluation indicator set includes economic related indicators, social related indicators and environmental related indicators.

[0021] Specifically, the economic-related indicators include subsidy indicators and loss indicators, the social-related indicators include peak shaving indicators and smoothing indicators, and the environment-related indicators include multiple environmental protection indicators. The subsidy indicator is determined based on the response subsidy and the number of responses, response power, and response duration in the response data; the loss indicator is determined based on the production plan of the industrial user during the power demand response period and the response time in the response data; the peak shaving indicator is determined based on the load adjusted by the industrial user during the peak power consumption period during the power demand response period; the smoothing indicator is determined based on the load adjusted by the industrial user to balance the load curve within the response time; the multiple environmental protection indicators are determined based on the emissions of multiple pollutants corresponding to the target power generation; the target power generation is used to indicate the power generation that can be reduced by the target generator set on the power supply side after the industrial user participates in the power demand response; and the target generator set is used to indicate a generator set that will generate at least one of the multiple pollutants during the power generation process.

[0022] Among them, industrial users are users who use electricity in industrial production activities. When responding on the demand side, industrial users are not only sensitive to electricity expenses and economic incentives, but also need to consider their own response costs. In addition, industrial users are also controlled by the power grid dispatching department and the environmental protection department. The actual response process is relatively complicated. Therefore, considering the various factors affecting the participation of industrial users in demand-side response, a comprehensive demand response evaluation indicator set with economic, social and environmental protection characteristics is constructed.

[0023] Among them, the subsidy index is used to indicate the economic subsidy obtained by users after participating in demand-side response. Specifically, the response subsidy refers to the subsidy unit price per power each time the user participates in demand-side response. Furthermore, the subsidy index The calculation process can refer to formula 1: , where N is the number of times the user participates in the demand side response during the demand side response time period, is the user's i-th response power, is the duration of the user's i-th participation in demand-side response, It is the subsidy unit price per power when the user participates in demand-side response for the i-th time.

[0024] Among them, the loss index is used to indicate the user's own utility loss caused by the load participating in the demand-side response, such as the reduction in production efficiency caused by load reduction, and the amount of orders that are not completed during the response period. Specifically, the amount of orders that are not completed due to the change in production plan due to participation in the demand-side response is determined based on the production plan and response time of the industrial user during the power demand response period. Furthermore, the loss index The calculation process can refer to formula 2: ,in, It is the amount of orders that are less completed due to changes in production plans caused by participating in demand-side response, where i=1,2,3...., and the order amount is increasing, for example, S2 is less than S3.

[0025] Among them, the peak shaving index is used to indicate the impact on avoiding power shortages. It is the load's ability to participate in responding to the local power system's peak power consumption to alleviate power shortages, reflecting the load's ability to participate in the peak shaving response. The load amount adjusted by industrial users during the peak power consumption period of the response period is determined based on the difference between the original load amount and the load amount after demand response. The original load amount is the power consumption of industrial users before any demand response measures are taken. It can be obtained by analyzing the historical power consumption data of industrial users, or by monitoring the power consumption of industrial users. Specifically, the peak shaving index The calculation process can refer to formula 3: ,in, During the peak electricity consumption period, is the original load at time t, is the load after industrial users respond to demand at time t.

[0026] Among them, the smoothing index is used to indicate the impact on changing the load distribution, which is to improve the local load distribution by means of peak shaving and valley filling, and reflects the ability to smooth the load curve. The calculation process can refer to formula 4: , where T is the time period during which the load participates in demand-side response.

[0027] Among them, the industrial load demand-side response can greatly reduce the power generation of power plants on the power supply side, thereby reducing the emission of various pollutants caused by power generation. For example, thermal power generation requires a large amount of fossil energy, which emits carbon dioxide CO2, sulfur dioxide SO2, nitric oxide NO, nitrogen dioxide NO2 and other pollutants, posing a threat to the environment. Therefore, based on the reduced power generation, the emission reduction of various pollutants is calculated to construct multiple environmental protection indicators. Specifically, the multiple environmental protection indicators include CO2 emission reduction, SO2 emission reduction, NO emission reduction, NO2 emission reduction, and can also be the emission reduction of other pollutants, which are not limited here. Furthermore, CO2 emission reduction 、SO2 emission reduction 、NO emission reduction 、NO2 emission reduction The calculation process can refer to formula 5: ,in, To reduce the amount of power generation, The amount of CO2 emitted per unit of electricity generated, The amount of SO2 emitted per unit of electricity generated, is the amount of NO emitted per unit of electricity generated, The amount of NO2 emitted per unit of electricity generated.

[0028] It can be seen that in the embodiments of the present application, by constructing a comprehensive set of demand response evaluation indicators, the regulation capability and potential of industrial loads in demand-side response can be more accurately evaluated, thereby more effectively guiding industrial users to participate in grid regulation.

[0029] S220, determining a target time-of-use electricity price strategy in the ladder series time-of-use electricity price strategy according to the economic feasibility of industrial users and the power supply side, and the utilization efficiency of renewable energy.

[0030] Among them, in order to encourage industrial users to reasonably transfer loads, a time-of-use electricity price strategy of "peak, flat, valley" mode can be adopted. After the implementation of time-of-use electricity prices, industrial users will adjust the operating hours of factories and other facilities accordingly, so as to reduce the electricity costs of industrial users themselves. It is possible to consider using non-single form of time-of-use electricity prices in combination with the actual production of industrial users, so as to better promote large industrial users such as cement plants to participate in demand-side response and mobilize their demand-side response potential in different production scenarios. Based on this, a ladder-shaped time-of-use electricity price strategy is established, in which the ladder-shaped time-of-use electricity price strategy is formed by changing the peak-valley electricity price difference, thereby forming a series of different time-of-use electricity price strategies. Specifically, the sum of the peak-valley electricity prices in a single time-of-use electricity price strategy is consistent with the sum of the peak-valley electricity prices before adjustment, and the flat section electricity price remains unchanged.

[0031] Among them, the power supply side is the part of the power system responsible for power generation, including various types of power plants such as thermal power plants, hydropower stations, wind farms, photovoltaic power stations, etc. The power supply side first provides a set of time-of-day electricity price strategies to large industrial users, and then large industrial users adjust their loads based on the set of time-of-day electricity price strategies, and feed back the adjusted planned loads to the power grid, through which electricity is transmitted. Then, the power supply side screens the time-of-use electricity price strategy based on the electricity cost of large industrial users, that is, selects and retains the time-of-use electricity price strategy that reduces the user's electricity cost, and combines the units based on the retained time-of-use electricity price strategy, so as to reasonably arrange the operation combination of various types of generator sets; the power supply side continues to screen the retained time-of-use electricity price strategy based on the available time-of-use strategy conditions, obtains at least one time-of-use electricity price strategy, and marks it with an available label, wherein the available time-of-use strategy condition is: whether the sum of the power supply side operating cost under the time-of-use electricity price and the reduction in the electricity consumption cost of large industrial users after the adoption of the time-of-use electricity price is greater than the power supply side operating cost when the basic electricity price is used, and the available time-of-use electricity price strategy that meets this condition is finally selected as the time-of-use electricity price strategy for the large industrial user in at least one available time-of-use electricity price strategy, that is, the target time-of-use electricity price strategy. Among them, the abandonment of wind and light is mainly caused by factors such as lagging grid construction, insufficient peak-shaving capacity, and volatility of renewable energy. Minimizing wind and solar power curtailment means that the losses caused by the inability to fully utilize wind power and photovoltaic power generation due to various reasons are reduced to a minimum.

[0032] S230, determining the electricity expense according to the target time-of-use electricity price strategy and the power purchase amount of the industrial user; and correcting the initial incentive points of the industrial user after participating in the power demand response to obtain corrected incentive points.

[0033] The amount of electricity purchased by the industrial user is determined according to an adjustment strategy formulated by the industrial user to participate in the electricity demand response.

[0034] Specifically, the response data includes a response time, and the method of determining the electricity expense according to the target time-of-use electricity price strategy and the amount of electricity purchased by the industrial user includes: determining the electricity prices corresponding to different time periods within the response time according to the target time-of-use electricity price strategy; and obtaining the adjustment strategy formulated by the industrial user for participating in the power demand response; determining a plurality of different adjustment spaces for the load power of the industrial user in the different time periods according to the adjustment strategy; obtaining the power generation power and power generation efficiency corresponding to the target power generation method of the industrial user in the different time periods, the target power generation method being used to realize the on-site consumption of electric energy; determining the amount of electricity purchased by the industrial user in different time periods according to the plurality of different adjustment spaces for the load power and the power generation power and power generation efficiency corresponding to the target power generation method in the different time periods; and determining the amount of electricity purchased by the industrial user in different time periods according to the electricity prices and The electricity bill expenditure is determined according to the amount of electricity purchased in different time periods; the initial incentive points of the industrial users after participating in the electricity demand response are corrected to obtain the corrected incentive points, including: obtaining the number of industrial users who queried points within a preset time period, the initial incentive points, the first deviation power at the previous scheduling moment, and the second deviation power at the previous scheduling moment, the initial incentive points are the incentive points at the current scheduling moment, the first deviation power is used to indicate the difference between the actual electricity consumption of the industrial users and the planned power generation, and the second deviation power is used to indicate the difference between the actual daily load curve of the industrial users and the preset minimum output curve; the initial incentive points are corrected according to the number of industrial users who queried points, the first deviation power at the previous scheduling moment, and the second deviation power at the previous scheduling moment to obtain the corrected incentive points.

[0035] Among them, by reasonably arranging the production process and equipment start and stop, a larger range of load regulation can be achieved, thereby determining the adjustable load power at a certain moment, that is, the adjustment space for the load power. Among them, the power generation methods that can realize the local consumption of electric energy can include rooftop photovoltaic power generation, distributed wind power, biomass power generation, geothermal power generation, etc. Specifically, rooftop photovoltaic power generation is installed on the top of the building, and the generated electric energy can be directly supplied to the electrical equipment in the building, reducing the loss of electric energy during transmission and realizing local consumption; in some areas with good wind energy resources, small distributed wind power projects can be built, and after power generation, it can be directly supplied to nearby users or local power grids to realize local consumption; some areas are rich in biomass resources such as agricultural waste and forestry residues, and these resources can be used to generate electricity by building biomass power plants; some areas are rich in biomass resources such as agricultural waste and forestry residues, and these resources can be used to generate electricity by building biomass power plants.

[0036] It is assumed that the current target power generation method for industrial users is rooftop photovoltaic power generation. Then the rooftop photovoltaic power generation power and rooftop photovoltaic power generation efficiency are obtained, and the amount of electricity that industrial users need to purchase from the power grid is obtained in combination with the adjustment space, so as to obtain the electricity expense based on the electricity price and the amount of electricity purchased. Specifically, the electricity expense The calculation process can refer to Formula 6: , in, For industrial users who need to purchase electricity from the grid, is the adjustable load power at time t, is the rooftop photovoltaic power generation power, is the rooftop photovoltaic power generation efficiency, T is the total time of participating demand-side response, is the power that industrial users need to purchase from the grid at time t, is time t The time-of-use electricity price of the power grid during the time-of-use electricity price stage, n is the number of time-of-use electricity price segments, which can be 4.

[0037] Among them, the integral incentive can quantify the specific value of the contribution of industrial users to demand-side response. The power grid company pre-determines the amount of integral incentives based on the source-load deviation power of the regional power grid. The integral incentive is based on the deviation power between the typical daily load of industrial users measured in the regional power grid in a certain season and the regional conventional minimum output curve. Determine, among which, ,in, is the conventional minimum output of the region at time t, is the total power load of industrial users in the regional power grid at time t. For example, it is assumed that the thermal power units in the region will generate electricity at 50% of their rated capacity to provide a stable power supply, and the actual output of wind power generation and photovoltaic power generation constitutes the conventional minimum output of the region.

[0038] Furthermore, since there is a lag in the participation of industrial users in demand-side response, and in the actual process of industrial electricity consumption, there may be sudden changes in load or failures in power generation equipment, etc., it is necessary to adjust the credit amount within this time period in a timely manner. By referring to the number of industrial users who queried credits on the previous day, combined with the real-time status of industrial users' electricity consumption, the real-time credits at the scheduling time of the day are corrected. Among them, the preset time period can be determined according to the scheduling time, and the current scheduling time can be scheduled in units of days. The number of industrial users who queried credits within the preset time period can be directly derived from the query platform. Specifically, the correction process of the real-time credits at the scheduling time of the day can refer to Formula 7: ,in, is the initial value of the integral at scheduling time t, i.e., the initial incentive integral, is the corrected incentive score, The number of industrial users who queried their points on the previous day. is the total number of industrial users, The proportional coefficient of the degree of influence of the integral is adjusted in real time. is the source-load deviation power of the actual power consumption of the total industrial users at time t-1 (for load dump or power generation equipment failure, etc.), that is, the first deviation power at the previous scheduling time, It is the deviation power at time t-1 between the typical daily load curve of industrial users in the regional power grid and the regional conventional minimum output curve on a typical day in a certain season, that is, the second deviation power at the previous scheduling moment.

[0039] S240: constructing an objective function of a multi-objective optimization model according to the electricity expenditure, the corrected incentive points and at least one evaluation index in the demand response evaluation index set.

[0040] Specifically, the objective function of the multi-objective optimization model is constructed based on the electricity expenditure, the revised incentive points and at least one evaluation indicator in the demand response evaluation indicator set, including: standardizing the electricity expenditure and the revised incentive points respectively to obtain target electricity expenditure and target incentive points; using the minimum difference between the first data and the second data as the first objective function, the first data is the product of the target electricity expenditure and a first preset weight, the second data is the product of the target incentive points and a second preset weight, the first objective function is used to determine the state in which the industrial user is most willing to participate in the electricity demand response; solving the first objective function to obtain first reference data; constructing the objective function of the multi-objective optimization model based on the first reference data and at least one evaluation indicator in the demand response evaluation indicator set.

[0041] Among them, electricity expenditure is calculated based on a highly matching time-of-use electricity price strategy, and point incentives are taken into consideration to measure the willingness of industrial users to participate in demand-side response.

[0042] Among them, the reference values ​​of electricity expenditure and the corrected incentive points are determined respectively, and the obtained electricity expenditure is divided by the reference value corresponding to the electricity expenditure to obtain the per-unit value of the electricity expenditure, that is, the target electricity expenditure. Similarly, the corrected incentive points are divided by the reference value corresponding to the corrected incentive points to obtain the per-unit value of the corrected incentive points, that is, the target incentive points. The first preset weight and the second preset weight are both in the range of [0,1]. Specifically, the first objective function Refer to formula 8: ,in, is the first preset weight, is the second preset weight, Target electricity expenditure, Build incentive points for your goals.

[0043] Among them, the objective function is minimized through a single objective solution form, that is, the electricity expenditure is as low as possible and the point incentive is as strong as possible, so as to obtain the situation when industrial users are most willing to participate in demand-side response.

[0044] Specifically, the objective function of the multi-objective optimization model is constructed based on the first reference data and at least one evaluation indicator in the demand response evaluation indicator set, including: determining at least one target evaluation indicator in the demand response evaluation indicator set, and setting multiple preference coefficients for each target evaluation indicator, wherein the target evaluation indicator is an evaluation indicator with marginal effect in the demand response evaluation indicator set; standardizing the first reference data and each target evaluation indicator respectively to obtain first target data corresponding to the first reference data and second target data corresponding to each target evaluation indicator; using the minimum difference between the third data and the fourth data as the objective function of the multi-objective optimization model, wherein the third data is the product of the first target data and a third preset weight, and the fourth data is the sum of each fifth data in at least one fifth data, and a single fifth data is determined according to the fourth preset weight, the second target data corresponding to the single target evaluation indicator, and multiple preference coefficients corresponding to the single target evaluation indicator.

[0045] The evaluation index is selected from the demand response evaluation index set based on actual experience, and can be selected based on the power consumption characteristics of industrial users. The standardization process can be determined by selecting a reference value, and the data obtained by standardization can be a per-unit value.

[0046] Among them, the decision variables of the objective function can be set as the start and stop status and power of the industrial user's production line, and a multi-objective optimization model for the industrial user's demand-side response can be constructed, where the time-of-use electricity price and integral incentives reflect the willingness of industrial users. The multi-objective optimization model is to optimize the index system focus on the indicators under the conditions of meeting the demand-side response and ensuring the willingness of industrial users. The multi-objective optimization problem is transformed into a single-objective optimization problem through weighted fuzzy linearization. Among them, the initial objective function You can refer to formula 9: ,in, is the first reference data, is the weighted coefficient corresponding to the first indicator, is the per-unit value of the first indicator, is the weighted coefficient corresponding to the second indicator, is the per-unit value of the second indicator, where the first indicator and the second indicator are two different indicators in the demand response evaluation indicator set. When the indicator value is larger, the effect is better, and the weighting coefficient takes the value [0,1]; when the indicator value is smaller, the effect is better, and the weighting coefficient takes the value [-1,0].

[0047] Among them, multiple preference coefficients can be set. In this embodiment, two preference coefficients are set for example.

[0048] Furthermore, due to the linear weighting, there may be a problem of ignoring the decreasing utility of the sub-goal boundary, which may lead to a distorted solution result. In order to further correct the solution result of the objective function, in the initial objective function On the basis of , a quadratic function type utility function is used to reflect the boundary utility decreasing relationship of the corresponding sub-goals. Specifically, when the determined indicators include only one indicator with the boundary utility decreasing characteristic, the objective function of the multi-objective optimization model is Refer to formula 10: ,in, is the per-unit value of the indicator with marginal utility decreasing characteristics, and is the preference coefficient of Q3 indicator, It is the weighting coefficient of Q3 indicator.

[0049] Furthermore, if the determined indicators include multiple indicators with marginal utility decreasing characteristics, the objective function of the multi-objective optimization model can be: The utility function of formula 10 is further subtracted from the utility functions of other indicators with diminishing marginal utility characteristics.

[0050] Specifically, the objective function of the multi-objective optimization model is constructed according to the first reference data and at least one evaluation indicator in the demand response evaluation indicator set, including: determining multiple evaluation indicators in the demand response evaluation indicator set, the multiple evaluation indicators include at least one target evaluation indicator and at least one other evaluation indicator, and setting multiple preference coefficients for each target evaluation indicator, the target evaluation indicator is an evaluation indicator with marginal effect in the demand response evaluation indicator set, and the other evaluation indicators are evaluation indicators without marginal effect in the demand response evaluation indicator set; standardizing the first reference data and each evaluation indicator in the multiple evaluation indicators respectively to obtain the first target data corresponding to the first reference data and the second target data corresponding to each evaluation indicator; using the minimum difference between the sixth data and the seventh data as the objective function of the multi-objective optimization model, the sixth data is the product of the first target data and the fifth preset weight, the seventh data is the sum of at least one eighth data and at least one ninth data, the single eighth data is the product of the second target data corresponding to the single other evaluation indicator and the sixth preset weight, and the single ninth data is determined according to the seventh preset weight, the second target data corresponding to the single target evaluation indicator, and the multiple preference coefficients corresponding to the single target evaluation indicator.

[0051] Among them, the multiple evaluation indicators may include one or more target evaluation indicators and one or more other evaluation indicators. In this embodiment, an example is given for one target evaluation indicator and one other evaluation indicator. Specifically, for the case where the determined indicators include an indicator with a marginal utility decreasing characteristic and an indicator without a marginal utility decreasing characteristic, the objective function of the multi-objective optimization model can refer to Formula 11: , is the per-unit value of other evaluation indicators, is the fifth preset weight, is the sixth preset weight, and is the preference coefficient, is the per-unit value of the target evaluation indicator.

[0052] S250: Determine the constraint conditions of the multi-objective optimization model.

[0053] Specifically, the constraint conditions include power balance constraint and adjustable load constraint. The adjustable load constraint includes boundary constraint of transferable load power, minimum continuous operation time constraint of production equipment, minimum continuous time constraint and maximum continuous time constraint of load reduction of industrial users, and boundary range constraint of total daily electricity consumption of industrial users. The power balance constraint is: ,in, is the operating power of the industrial user's production line i at time t, N is the total number of the industrial user's production lines, is the photovoltaic power generation power of the industrial user at time t, is the interactive power between the industrial user and the power supply side at time t; the boundary constraint of the transferable load power is: ,in, is the maximum value of the transferable load power, is the minimum value of transferable load power, is the transferable load power, is the state variable of load transfer at time t; the minimum continuous operation time constraint of the production equipment is: ,in, The minimum continuous operation time of the production equipment, is the state variable of load transfer at time t-1, and t0 is the time when load transfer starts; the minimum continuous time constraint and maximum continuous time constraint of the industrial user in reducing load are: ,in, is the state variable of load reduction at time t, is the state variable of load reduction at time t-1, The minimum continuous operating time of the industrial users under load reduction, is the maximum continuous operation time of the industrial user when reducing load; the boundary range constraint of the total daily electricity consumption of the industrial user is: ,in, is the maximum reduction ratio coefficient of daily electricity consumption, is the total daily electricity consumption, The maximum value of daily electricity consumption.

[0054] Among them, in view of the power balance constraint, the factory buildings of industrial users in the industrial park can install rooftop photovoltaics to solve part of the factory buildings' own electricity consumption, reduce electricity costs, and adopt a mode of self-generation and self-use, and the surplus is connected to the grid. The power balance constraint must be met, that is, the load power of the industrial user production line, the interactive power of the factory roof photovoltaics and the grid is in a power balance state. Specifically, the power balance constraint does not take into account the heat loss of production electricity.

[0055] Among them, the state variable of load transfer takes the value of 0 or 1. If load transfer is in progress at time t, vt=1, otherwise it is 0, and the same applies to load reduction ut.

[0056] S260, solving the objective function according to the constraint conditions to obtain an incentive strategy for improving the participation of the industrial users.

[0057] Among them, under the conditions of satisfying the power balance constraint and the adjustable load constraint, the objective function is solved to minimize the objective function, thereby obtaining an incentive strategy that meets the user's actual production situation, and providing a response strategy from the perspective of industrial users to reduce the response cost and increase the response benefit, thereby mobilizing the demand-side response potential of industrial users in different production scenarios.

[0058] Among them, the commercial solver Cplex can be called through the modeling toolbox yalmip to solve the objective function.

[0059] It can be seen that in the embodiment of the present application, by using a non-single form of ladder series time-of-use electricity price strategy, and by considering the economic feasibility of multiple parties and the utilization efficiency of renewable energy, a time-of-use electricity price strategy with a high degree of matching with the current production scenario of the industrial user is determined, and the time-of-use electricity price strategy is used to calculate the electricity expenditure, and the revised point incentive and a comprehensive set of evaluation indicators based on the economic, social and environmental factors of the industrial user's response to electricity demand are superimposed to form a multi-objective optimization strategy for industrial users to participate in demand-side response, so as to measure the willingness of industrial users to participate in demand-side response, and then mobilize the demand-side response potential of industrial users in different production scenarios, thereby increasing the enthusiasm of industrial users to participate in electricity demand response.

[0060] In one possible embodiment, see Figure 3 , Figure 3 is a flow chart of another method for optimizing an incentive strategy for power demand response provided by an embodiment of the present application, such as Figure 3 As shown in the figure, considering the various factors affecting the participation of industrial users in demand-side response, an economic, social and environmental indicator evaluation system is constructed, among which the economic indicators include policy subsidies and response costs, the social indicators include the impact on avoiding power shortages and the impact on changing load distribution, and the environmental indicators include CO2 emission reduction, SO2 emission reduction, NO emission reduction and NO2 emission reduction. At the same time, according to the existing time-of-use electricity price mechanism, the peak-valley electricity price difference can be gradually changed to form a series of different time-of-use electricity prices and obtain a ladder-shaped time-of-use electricity price strategy. Then, the time-of-use electricity price strategy is matched according to the actual generation scenario of industrial users, and the electricity expenditure is calculated. At the same time, considering the hysteresis of industrial users participating in demand-side response, as well as the possible sudden changes in load or failures of power generation equipment, the points obtained from the initial query are corrected to obtain real-time incentive points. Furthermore, the electricity expenditure calculated by the matching time-of-use electricity price strategy is superimposed on the real-time point incentive to measure the willingness of industrial users to participate in demand-side response. The willingness of industrial users to participate in demand-side response is guaranteed by minimizing electricity expenditure and making the point incentive as strong as possible. The comprehensive indicator evaluation system and industrial users' willingness are combined with a weighted fuzzy linearization multi-objective model to form a multi-objective optimization strategy model for industrial users to participate in demand-side response taking into account the boundary diminishing effect. In the process of solving the objective function of the multi-objective optimization strategy model, the power balance constraint and the adjustable load constraint are met to obtain a multi-objective optimization strategy.

[0061] It can be seen that in the embodiments of the present application, by measuring the user's response willingness in multiple aspects, the effect of reducing the response cost of industrial users and increasing the response benefits is achieved, thereby increasing the enthusiasm of industrial users to participate.

[0062] For the above embodiments, please refer to Figure 4 , Figure 4 is a functional unit block diagram of an incentive strategy optimization device for power demand response provided by an embodiment of the present application, such as Figure 4As shown, the incentive strategy optimization device 40 for power demand response includes: a first construction unit 41, which is used to construct a demand response evaluation index set, and the demand response evaluation index set includes economic related indicators, social related indicators and environmental related indicators; a first determination unit 42, which is used to determine the target time-of-use electricity price strategy in the ladder series time-of-use electricity price strategy according to the economic feasibility of the industrial user and the power supply side, and the utilization efficiency of renewable energy; a second determination unit 43, which is used to determine the electricity fee expenditure according to the target time-of-use electricity price strategy and the purchased electricity amount of the industrial user, and the purchased electricity amount of the industrial user is determined according to the adjustment strategy formulated by the industrial user for participating in the power demand response; and, correcting the initial incentive points of the industrial user after participating in the power demand response to obtain the corrected incentive points; a second construction unit 44, which is used to construct the objective function of the multi-objective optimization model according to the electricity fee expenditure, the corrected incentive points and at least one evaluation indicator in the demand response evaluation index set; a third determination unit 45, which is used to determine the constraint conditions of the multi-objective optimization model; a solving unit 46, which is used to solve the objective function according to the constraint conditions to obtain an incentive strategy for improving the participation of the industrial user.

[0063] Among them, the economic-related indicators include subsidy indicators and loss indicators, the social-related indicators include peak shaving indicators and smoothing indicators, and the environment-related indicators include multiple environmental protection indicators. The subsidy indicator is determined according to the response subsidy and the number of responses, response power, and response duration in the response data; the loss indicator is determined according to the production plan of the industrial user during the power demand response period and the response time in the response data; the peak shaving indicator is determined according to the load adjusted by the industrial user during the peak power consumption period during the power demand response period; the smoothing indicator is determined according to the load adjusted by the industrial user to balance the load curve within the response time; the multiple environmental protection indicators are determined according to the emissions of multiple pollutants corresponding to the target power generation; the target power generation is used to indicate the power generation that can be reduced by the target generator set on the power supply side after the industrial user participates in the power demand response; the target generator set is used to indicate a generator set that will generate at least one of the multiple pollutants during the power generation process.

[0064] The constraints include power balance constraints and adjustable load constraints. The adjustable load constraints include boundary constraints of transferable load power, minimum continuous operation time constraints of production equipment, minimum continuous time constraints and maximum continuous time constraints of load reduction of industrial users, and boundary range constraints of total daily electricity consumption of industrial users. The power balance constraints are: ,in, is the operating power of the industrial user's production line i at time t, N is the total number of the industrial user's production lines, is the photovoltaic power generation power of the industrial user at time t, is the interactive power between the industrial user and the power supply side at time t; the boundary constraint of the transferable load power is: ,in, is the maximum value of the transferable load power, is the minimum value of transferable load power, is the transferable load power, is the state variable of load transfer at time t; the minimum continuous operation time constraint of the production equipment is: ,in, The minimum continuous operation time of the production equipment, is the state variable of load transfer at time t-1, and t0 is the time when load transfer starts; the minimum continuous time constraint and maximum continuous time constraint of the industrial user in reducing load are: ,in, is the state variable of load reduction at time t, is the state variable of load reduction at time t-1, The minimum continuous operating time of the industrial users under load reduction, is the maximum continuous operation time of the industrial user when reducing load; the boundary range constraint of the total daily electricity consumption of the industrial user is: ,in, is the maximum reduction ratio coefficient of daily electricity consumption, is the total daily electricity consumption, The maximum value of daily electricity consumption.

[0065] In a possible embodiment, the response data includes a response time. In determining the electricity expense according to the target time-of-use electricity price strategy and the amount of electricity purchased by the industrial user, the second determination unit 43 is specifically used to: determine the electricity prices corresponding to different time periods within the response time according to the target time-of-use electricity price strategy; and obtain the adjustment strategy formulated by the industrial user to participate in the power demand response; determine a plurality of different adjustment spaces for the load power of the industrial user in the different time periods according to the adjustment strategy; obtain the power generation power and power generation efficiency corresponding to the target power generation method of the industrial user in the different time periods, the target power generation method being used to realize the local consumption of electric energy; determine the amount of electricity purchased by the industrial user in different time periods according to the plurality of different adjustment spaces of the load power and the power generation power and power generation efficiency corresponding to the target power generation method in the different time periods; and determine the amount of electricity purchased by the industrial user in different time periods according to the different adjustment spaces of the load power and the power generation power and power generation efficiency corresponding to the target power generation method in the different time periods; and determine the amount of electricity purchased by the industrial user in different time periods according to the different adjustment spaces of the load power and the power generation power and power generation efficiency corresponding to the target power generation method in the different time periods. The electricity expense is determined by the electricity price corresponding to the same period and the amount of electricity purchased in different periods; the initial incentive points of the industrial users after participating in the power demand response are corrected to obtain the corrected incentive points, including: obtaining the number of industrial users who queried points within a preset period, the initial incentive points, the first deviation power at the previous scheduling moment, and the second deviation power at the previous scheduling moment, the initial incentive points are the incentive points at the current scheduling moment, the first deviation power is used to indicate the difference between the actual electricity consumption of the industrial users and the planned power generation, and the second deviation power is used to indicate the difference between the actual daily load curve of the industrial users and the preset minimum output curve; the initial incentive points are corrected according to the number of industrial users who queried points, the first deviation power at the previous scheduling moment, and the second deviation power at the previous scheduling moment to obtain the corrected incentive points.

[0066] In one possible embodiment, in terms of constructing the objective function of the multi-objective optimization model based on the electricity expenditure, the revised incentive points and at least one evaluation indicator in the demand response evaluation indicator set, the second construction unit 44 is specifically used to: standardize the electricity expenditure and the revised incentive points respectively to obtain target electricity expenditure and target incentive points; use the minimum difference between the first data and the second data as the first objective function, the first data is the product of the target electricity expenditure and a first preset weight, the second data is the product of the target incentive points and a second preset weight, the first objective function is used to determine the state in which the industrial user is most willing to participate in the electricity demand response; solve the first objective function to obtain first reference data; construct the objective function of the multi-objective optimization model based on the first reference data and at least one evaluation indicator in the demand response evaluation indicator set.

[0067] In a possible embodiment, in terms of constructing the objective function of the multi-objective optimization model based on the first reference data and at least one evaluation indicator in the demand response evaluation indicator set, the second construction unit 44 is specifically used to: determine at least one target evaluation indicator in the demand response evaluation indicator set, and set multiple preference coefficients for each target evaluation indicator, wherein the target evaluation indicator is an evaluation indicator with marginal effects in the demand response evaluation indicator set; perform standardization on the first reference data and each target evaluation indicator respectively to obtain first target data corresponding to the first reference data and second target data corresponding to each target evaluation indicator; use the minimum difference between the third data and the fourth data as the objective function of the multi-objective optimization model, wherein the third data is the product of the first target data and a third preset weight, and the fourth data is the sum of each fifth data in at least one fifth data, and a single fifth data is determined according to the fourth preset weight, the second target data corresponding to the single target evaluation indicator, and multiple preference coefficients corresponding to the single target evaluation indicator.

[0068] In a possible embodiment, in terms of constructing the objective function of the multi-objective optimization model according to the first reference data and at least one evaluation indicator in the demand response evaluation indicator set, the second construction unit 44 is specifically used to: determine multiple evaluation indicators in the demand response evaluation indicator set, the multiple evaluation indicators include at least one target evaluation indicator and at least one other evaluation indicator, and set multiple preference coefficients for each target evaluation indicator, the target evaluation indicator is an evaluation indicator with a marginal effect in the demand response evaluation indicator set, and the other evaluation indicator is an evaluation indicator without a marginal effect in the demand response evaluation indicator set; standardize the first reference data and each evaluation indicator in the multiple evaluation indicators respectively to obtain the first target data corresponding to the first reference data and the second target data corresponding to each evaluation indicator; use the minimum difference between the sixth data and the seventh data as the objective function of the multi-objective optimization model, the sixth data is the product of the first target data and the fifth preset weight, the seventh data is the sum of at least one eighth data and at least one ninth data, the single eighth data is the product of the second target data corresponding to the single other evaluation indicator and the sixth preset weight, and the single ninth data is determined according to the seventh preset weight, the second target data corresponding to the single target evaluation indicator, and the multiple preference coefficients corresponding to the single target evaluation indicator.

[0069] It can be understood that since the method embodiment and the device embodiment are different presentation forms of the same technical concept, the content of the method embodiment part in this application should be synchronously adapted to the device embodiment part and will not be repeated here.

[0070] In the case of integrated units, see Figure 5 , Figure 5 is a functional unit block diagram of another incentive strategy optimization device for power demand response provided by an embodiment of the present application, such as Figure 5 As shown, the incentive strategy optimization device 40 for power demand response includes: a processing module 402 and a communication module 401. The processing module 402 is used to control and manage the actions of the incentive strategy optimization device 40 for power demand response, for example, to execute the steps of the first construction unit 41, the first determination unit 42, the second determination unit 43, the second construction unit 44, the third determination unit 45 and the solution unit 46, and / or to execute other processes of the technology described herein. The communication module 401 is used for the interaction between the incentive strategy optimization device 40 for power demand response and other devices. Figure 5 As shown, the device 40 for optimizing an incentive strategy for responding to electric power demand may further include a storage module 403, and the storage module 403 is used to store program codes and data of the device 40 for optimizing an incentive strategy for responding to electric power demand.

[0071] Among them, the processing module 402 can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic boxes, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication module 401 can be a transceiver, an RF circuit or a communication interface, etc. The storage module 403 can be a memory.

[0072] All relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here. The above incentive strategy optimization device 40 for power demand response can execute the above Figure 2 The incentive strategy optimization method for power demand response is shown.

[0073] See also Figure 6 , Figure 6 is a schematic diagram of the structure of an electronic device proposed in an embodiment of the present application, such as Figure 6As shown, the electronic device 60 includes a processor 610, a memory 620, a communication interface 630 and one or more programs 621. The one or more programs 621 are stored in the memory and are configured to be executed by the processor. When the program is executed, it includes part or all of the steps of any one of the incentive strategy optimization methods for power demand response recorded in the method embodiments. The processor, memory and communication interface are interconnected and complete communication with each other.

[0074] The memory may be a volatile memory such as a dynamic random access memory (DRAM), or a non-volatile memory such as a mechanical hard disk. The memory is used to store a set of executable program codes, and the processor is used to call the executable program codes stored in the memory, and may execute part or all of the steps of any of the incentive strategy optimization methods for power demand response described in the above-mentioned embodiment of the incentive strategy optimization method for power demand response.

[0075] It can be seen that the electronic device 60 described in the embodiment of the present application first constructs a demand response evaluation index set, which includes economic-related indicators, social-related indicators and environmental-related indicators; then determines the target time-of-use electricity price strategy in the ladder series time-of-use electricity price strategy according to the economic feasibility of the industrial user and the power supply side, and the utilization efficiency of renewable energy; then determines the electricity bill expenditure according to the target time-of-use electricity price strategy and the purchased electricity volume of the industrial user, and the purchased electricity volume of the industrial user is determined according to the adjustment strategy formulated by the industrial user to participate in the electricity demand response; and, corrects the initial incentive points of the industrial user after participating in the electricity demand response to obtain the corrected incentive points; then constructs the objective function of the multi-objective optimization model according to the electricity bill expenditure, the corrected incentive points and at least one evaluation indicator in the demand response evaluation index set; determines the constraints of the multi-objective optimization model; finally, solves the objective function according to the constraints to obtain an incentive strategy for improving the participation of the industrial user. This application uses a non-single form of ladder series time-of-use electricity price strategy, and by considering the economic feasibility of multiple parties and the utilization efficiency of renewable energy, it determines a time-of-use electricity price strategy that is highly matched with the current production scenario of industrial users. The time-of-use electricity price strategy is used to calculate electricity expenditure, and the revised point incentive and a comprehensive set of evaluation indicators based on the economic, social and environmental factors of industrial users' response to electricity demand are superimposed to form a multi-objective optimization strategy for industrial users to participate in demand-side response, to measure the willingness of industrial users to participate in demand-side response, and then mobilize the demand-side response potential of industrial users in different production scenarios, thereby increasing the enthusiasm of industrial users to participate in electricity demand response.

[0076] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0077] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0078] It should be noted that, for the sake of simplicity, the aforementioned method implementations are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by the present application.

[0079] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device implementation described above is only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0081] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0082] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0083] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. According to this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0084] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0085] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. An incentive strategy optimization method for power demand response, It is characterized in that include: Constructing a demand response evaluation indicator set, wherein the demand response evaluation indicator set includes economic related indicators, social related indicators and environmental related indicators; Determine the target time-of-use electricity price strategy in the ladder series of time-of-use electricity price strategies based on the economic feasibility of industrial users and power supply sides, as well as the utilization efficiency of renewable energy; Determining electricity expenses according to the target time-of-use electricity price strategy and the amount of electricity purchased by the industrial user, the amount of electricity purchased by the industrial user being determined according to the adjustment strategy formulated by the industrial user for participating in the electricity demand response; and correcting the initial incentive points of the industrial user after participating in the electricity demand response to obtain corrected incentive points; Constructing an objective function of a multi-objective optimization model according to the electricity fee expenditure, the modified incentive points and at least one evaluation index in the demand response evaluation index set; Determining constraints of the multi-objective optimization model; The objective function is solved according to the constraint conditions to obtain an incentive strategy for improving the participation of the industrial users.

2. The method according to claim 1, It is characterized in that The response data includes the response time, and the electricity fee expenditure is determined according to the target time-of-use electricity price strategy and the amount of electricity purchased by the industrial user, including: Determine the electricity prices corresponding to different time periods within the response time according to the target time-of-use electricity price strategy; and obtain the adjustment strategy formulated by the industrial user for participating in the power demand response; determine a plurality of different adjustment spaces for the load power of the industrial user in the different time periods according to the adjustment strategy; obtain the power generation power and power generation efficiency corresponding to the target power generation mode of the industrial user in the different time periods, the target power generation mode is used to realize the local consumption of electric energy; determine the amount of electricity purchased by the industrial user in different time periods according to the plurality of different adjustment spaces of the load power and the power generation power and power generation efficiency corresponding to the target power generation mode in the different time periods; Determine the electricity fee expenditure according to the electricity prices corresponding to the different time periods and the amount of electricity purchased in the different time periods; The step of correcting the initial incentive points of the industrial user after participating in the power demand response to obtain the corrected incentive points includes: Obtain the number of industrial users who query points within a preset time period, the initial incentive points, the first deviation power at the previous scheduling moment, and the second deviation power at the previous scheduling moment, wherein the initial incentive points are the incentive points at the current scheduling moment, the first deviation power is used to indicate the difference between the actual power consumption of the industrial user and the planned power generation, and the second deviation power is used to indicate the difference between the actual daily load curve of the industrial user and the preset minimum output curve; The initial incentive score is corrected according to the number of industrial users whose scores are queried, the first deviation power at the last scheduling moment, and the second deviation power at the last scheduling moment to obtain the corrected incentive score.

3. The method according to claim 2, It is characterized in that The objective function of the multi-objective optimization model is constructed according to the electricity fee expenditure, the modified incentive points and at least one evaluation index in the demand response evaluation index set, including: Standardizing the electricity expense and the corrected incentive points respectively to obtain target electricity expense and target incentive points; The first objective function is to minimize the difference between the first data and the second data, wherein the first data is the product of the target electricity fee expenditure and a first preset weight, and the second data is the product of the target incentive points and a second preset weight. The first objective function is used to determine the state in which the industrial user has the highest willingness to participate in the power demand response; Solving the first objective function to obtain first reference data; The objective function of the multi-objective optimization model is constructed according to the first reference data and at least one evaluation index in the demand response evaluation index set.

4. The method according to claim 3, It is characterized in that The constructing the objective function of the multi-objective optimization model according to the first reference data and at least one evaluation indicator in the demand response evaluation indicator set includes: Determining at least one target evaluation indicator in the demand response evaluation indicator set, and setting a plurality of preference coefficients for each target evaluation indicator, wherein the target evaluation indicator is an evaluation indicator having a marginal effect in the demand response evaluation indicator set; Standardizing the first reference data and each target evaluation indicator respectively to obtain first target data corresponding to the first reference data and second target data corresponding to each target evaluation indicator; The minimum difference between the third data and the fourth data is used as the objective function of the multi-objective optimization model, the third data is the product of the first target data and the third preset weight, the fourth data is the sum of each fifth data in at least one fifth data, and the single fifth data is determined according to the fourth preset weight, the second target data corresponding to the single target evaluation indicator, and multiple preference coefficients corresponding to the single target evaluation indicator.

5. The method according to claim 3, It is characterized in that The constructing the objective function of the multi-objective optimization model according to the first reference data and at least one evaluation indicator in the demand response evaluation indicator set includes: Determine a plurality of evaluation indicators in the demand response evaluation indicator set, wherein the plurality of evaluation indicators include at least one target evaluation indicator and at least one other evaluation indicator, and set a plurality of preference coefficients for each target evaluation indicator, wherein the target evaluation indicator is an evaluation indicator having a marginal effect in the demand response evaluation indicator set, and the other evaluation indicator is an evaluation indicator having no marginal effect in the demand response evaluation indicator set; Standardizing the first reference data and each of the multiple evaluation indicators respectively to obtain first target data corresponding to the first reference data and second target data corresponding to each of the evaluation indicators; The minimum difference between the sixth data and the seventh data is used as the objective function of the multi-objective optimization model, the sixth data is the product of the first target data and the fifth preset weight, the seventh data is the sum of at least one eighth data and at least one ninth data, the single eighth data is the product of the second target data corresponding to a single other evaluation indicator and the sixth preset weight, and the single ninth data is determined according to the seventh preset weight, the second target data corresponding to the single target evaluation indicator, and multiple preference coefficients corresponding to the single target evaluation indicator.

6. The method according to any one of claims 1 to 5, It is characterized in that The economic-related indicators include subsidy indicators and loss indicators, the social-related indicators include peak shaving indicators and smoothing indicators, and the environment-related indicators include multiple environmental protection indicators. The subsidy indicator is determined based on the response subsidy and the number of responses, response power, and response duration in the response data. The loss indicator is determined based on the production plan of the industrial user during the power demand response period and the response time in the response data. The peak shaving indicator is determined based on the load adjusted by the industrial user during the peak power consumption period during the power demand response period. The smoothing indicator is determined based on the load adjusted by the industrial user to balance the load curve within the response time. The multiple environmental protection indicators are determined based on the emissions of multiple pollutants corresponding to the target power generation. The target power generation is used to indicate the power generation that can be reduced by the target generator set on the power supply side after the industrial user participates in the power demand response. The target generator set is used to indicate a generator set that will generate at least one of the multiple pollutants during the power generation process.

7. The method according to any one of claims 1 to 5, It is characterized in that The constraints include power balance constraints and adjustable load constraints, wherein the adjustable load constraints include boundary constraints of transferable load power, minimum continuous operation time constraints of production equipment, minimum continuous time constraints and maximum continuous time constraints of load reduction of the industrial users, and boundary range constraints of the total daily electricity consumption of the industrial users; The power balance constraint is: ,in, is the operating power of the industrial user's production line i at time t, N is the total number of the industrial user's production lines, is the photovoltaic power generation power of the industrial user at time t, is the interaction power between the industrial user and the power supply side at time t; The boundary constraint of the transferable load power is: ,in, is the maximum value of the transferable load power, is the minimum value of transferable load power, is the transferable load power, is the state variable of load transfer at time t; The minimum continuous operation time constraint of the production equipment is: ,in, The minimum continuous operation time of the production equipment, is the state variable of load transfer at time t-1, t 0 is the moment when load transfer starts; The minimum continuous time constraint and maximum continuous time constraint of the industrial users in reducing load are: ， in, is the state variable of load reduction at time t, is the state variable of load reduction at time t-1, The minimum continuous operation time of the industrial users under load reduction, The maximum continuous operating time of the industrial user under load reduction; The boundary range constraint of the total daily electricity consumption of the industrial user is: ,in, is the maximum reduction ratio coefficient of daily electricity consumption, is the total daily electricity consumption, The maximum value of daily electricity consumption.

8. An incentive strategy optimization device for power demand response, It is characterized in that include: A first construction unit is used to construct a demand response evaluation indicator set, wherein the demand response evaluation indicator set includes economic related indicators, social related indicators and environment related indicators; A first determination unit is used to determine a target time-of-use electricity price strategy in a ladder series of time-of-use electricity price strategies according to the economic feasibility of industrial users and power supply sides, and the utilization efficiency of renewable energy; a second determination unit, configured to determine the electricity fee expenditure according to the target time-of-use electricity price strategy and the purchased electricity amount of the industrial user, the purchased electricity amount of the industrial user being determined according to the adjustment strategy formulated by the industrial user for participating in the electricity demand response; and to correct the initial incentive points of the industrial user after participating in the electricity demand response to obtain corrected incentive points; A second construction unit is used to construct an objective function of a multi-objective optimization model according to the electricity fee expenditure, the modified incentive points and at least one evaluation index in the demand response evaluation index set; A third determining unit, used to determine the constraint conditions of the multi-objective optimization model; A solving unit is used to solve the objective function according to the constraint conditions to obtain an incentive strategy for improving the participation of the industrial users.

9. An electronic device, It is characterized in that The method comprises a processor and a memory storing execution instructions, wherein the memory stores one or more programs; when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, It is characterized in that An energy data management program is stored, including execution instructions. When a processor of an electronic device executes the execution instructions, the processor executes the method according to any one of claims 1-7.

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