Demand-side aggregation load cooperative control method and system

Abstract

The invention discloses a demand side aggregation load cooperative control method and system, and the method comprises the steps: obtaining user side electricity price, market electricity price fluctuation and user baseline load prediction data, and carrying out the analysis to obtain a demand response target; selecting corresponding price type and excitation type demand response modes based on the demand response target in combination with user characteristics, and constructing a corresponding demand side aggregation load model; wherein a price-type demand side resource aggregation load model is constructed based on a price-type demand response mode; based on the excitation type demand response mode, constructing an excitation type demand side aggregation load model; according to the demand response mode and the demand side aggregation load model, a regulation and control strategy of the aggregation load is determined, and the regulation and control strategy comprises a regulation type strategy, a control type strategy and a mixed type strategy; and classifying the regulation and control strategies according to an applicable scene to obtain a regulation and control strategy classification result. According to the method, the new energy consumption capability can be improved, the system operation cost is reduced, and the system operation economy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid regulation and control, and particularly to a method and system for collaborative control of aggregated loads on the demand side. Background Art

[0002] Load aggregators carry out demand response in order to improve the economic benefits of power sales business. Before formulating specific strategies, it is necessary to conduct corresponding analysis and research on the electricity price situation on the user side, the fluctuations of market electricity prices, and the baseline load on the user side. The real-time market electricity price significantly affects the power purchase cost of load aggregators; the electricity price on the user side directly determines the power sales revenue of load aggregators; and controlling the baseline load level of users not only affects their assessment of resource value, but also involves the calculation of compensation amounts after users participate in demand response projects. Therefore, the above aspects are all basic links in the demand response decision-making of load aggregators.

[0003] The electricity price situation on the user side is generated by the load aggregator and the user signing an electricity usage package agreement, which is directly related to the power sales revenue of the load aggregator. Market electricity price forecasting reflects that the change of electricity price at each moment is the key information reflecting the operation state of the power market, which will directly affect the behavior activities of various market players, and thus determines the flow and allocation process of various types of resources in the power market. User baseline load forecasting is a basic element for the implementing entity and participating users to calculate economic compensation according to contract standards after demand response. The regression method and the average value method are two commonly used current methods for determining the baseline load. In incentive-based demand response, the load reduction amount is calculated using the baseline load, and appropriate economic compensation is given to users. Therefore, accurate user baseline load estimation data is crucial for the implementation of demand response.

[0004] With the rapid development of new energy, grid connection brings new challenges to the power system, such as the volatility and reverse peak regulation characteristics of new energy output, resulting in problems such as increased system operation costs. Traditional demand response methods are difficult to meet the demand for large-scale new energy consumption, and more flexible and efficient control methods are needed. Summary of the Invention

[0005] In view of this, in order to overcome the shortcoming that traditional demand response methods in the above-mentioned prior art are difficult to meet the demand for large-scale new energy consumption, the main purpose of the present invention is to provide a method and system for collaborative control of aggregated loads on the demand side for new energy consumption. To improve the new energy consumption capacity, reduce the system operation cost, and improve the economic efficiency of system operation.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a method for collaborative control of aggregated loads on the demand side, including the following steps:

[0008] Obtain the electricity price on the user side, the fluctuation of the market electricity price, and the predicted data of the user's baseline load, and analyze to obtain the demand response target;

[0009] Based on the demand response target, combined with user characteristics, select the corresponding price-based and incentive-based demand response modes, and construct the corresponding aggregated load model on the demand side; among them, based on the price-based demand response mode, construct a price-based aggregated load model of demand-side resources; based on the incentive-based demand response mode, construct an incentive-based aggregated load model on the demand side;

[0010] According to the demand response mode and the aggregated load model on the demand side, determine the regulation strategy for the aggregated load, and the regulation strategy includes adjustment type, control type, and hybrid type strategies;

[0011] Classify the regulation strategy according to the applicable scenarios to obtain the classification result of the regulation strategy.

[0012] Furthermore, the incentive-based demand response mode includes: fixed incentive mode and flexible incentive mode;

[0013] The classification result of the regulation strategy includes: centralized control, decentralized control, hierarchical control, and load agent control.

[0014] Furthermore, the price-based aggregated load model of demand-side resources is:

[0015]

[0016] Among them, is the actual demand power of the load of demand-side user i at time t in response to the price-based demand response project; and are respectively the minimum and maximum values of the range of the power that demand-side user i can respond to; I is the set of demand-side users responding to the price-based demand response.

[0017] Furthermore, the incentive-based aggregated load model on the demand side includes: fixed incentive mode and flexible incentive mode;

[0018] In the fixed incentive mode, the calculation formula for the economic benefit obtained by the user is:

[0019]

[0020] In the formula: Δt is the time length of a control cycle; is the actual power of user i at time t on the response day; is the baseline power of user i at time t;

[0021] In the flexible incentive model, regarding the flexible incentives adopted by the load aggregator for users, a multi-level incentive model is used as the incentive mechanism for the load aggregator to reward users for participating in new energy consumption business, as shown in the following formula:

[0022]

[0023] In the formula: RM i (t) represents the incentive rate for user i to participate in demand scheduling at time slot t; R 1 is the first-level incentive rate; R 2 is the second-level incentive rate; R 3 is the third-level incentive rate; R 4 is the fourth-level incentive rate; R 5 is the fifth-level incentive rate; T set_L (i) is the lowest threshold of the temperature setting range allowed to vary for user i; T set_U (i) is the highest threshold of the temperature setting value range allowed to vary for user i; is the water heater temperature setting value of user i at time slot t; Com(i) represents whether user i accepts that the temperature setting value exceeds the allowed variation range, with the value "1" indicating acceptance and the value "0" indicating non-acceptance;

[0024] In the flexible incentive model, the calculation formula for the economic benefit obtained by the user is:

[0025]

[0026] Among them, is the economic benefit obtained by the user; Δt is the time length of a control cycle; is the actual power of the user at time t on the response day; is the baseline power of the user at time t; R is the incentive rate for the user to participate in demand scheduling at the time slot.

[0027] Furthermore, for the classification result of the regulation strategy, implement the coordinated control of the aggregated load on the demand side, evaluate the implementation effect of the regulation strategy, and obtain the evaluation result, including cost reduction, load fluctuation suppression, and increased new energy consumption.

[0028] Furthermore, under the flexible incentive model, according to the user's default degree, the load aggregation is divided into different levels, and a hierarchical compensation rule is formulated;

[0029] The hierarchical compensation rule includes:

[0030] The first level is high-quality resources, with a default percentage lower than 3%, and the compensation multiple λ 1 = 1.01;

[0031] The second level is qualified resources, with a default percentage between 3% and 8%, and the compensation multiple λ2 = 1.0;

[0032] For the third level which is restricted resources, the default percentage is between 8% and 13%, and the compensation multiple λ 3 = 0.95;

[0033] For the fourth level which is prohibited resources, the default percentage exceeds 13%.

[0034] In a second aspect, an embodiment of the present invention provides an aggregated load collaborative control system on the demand side, including:

[0035] A data analysis module, configured to obtain user-side electricity price, market electricity price fluctuations, and user baseline load prediction data, and analyze to obtain a demand response target;

[0036] A demand response module, configured to select corresponding price-based and incentive-based demand response modes based on the demand response target and in combination with user characteristics, and construct a corresponding aggregated load model on the demand side; wherein, based on the price-based demand response mode, construct a price-based aggregated load model of demand-side resources; based on the incentive-based demand response mode, construct an incentive-based aggregated load model on the demand side;

[0037] A regulation strategy formulation module, configured to determine an aggregated load regulation strategy according to the demand response mode and the aggregated load model on the demand side, where the regulation strategy includes adjustment type, control type, and hybrid type strategies; classify the regulation strategies according to the applicable scenarios to obtain a regulation strategy classification result.

[0038] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, it implements the demand-side aggregated load collaborative control method according to any one of the above first aspects.

[0039] In a fourth aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the program, it implements the demand-side aggregated load collaborative control method according to any one of the above first aspects.

[0040] It can be seen from the above technical solutions that compared with the prior art, the beneficial effects of the present invention are:

[0041] Through the demand-side aggregated load model, the potential of demand response resources can be quantified, including the number of users, the amount of load that can be curtailed, the load transfer potential, etc., providing a basis for formulating demand response programs. It can predict the changes in the load curve after the implementation of demand response, such as the reduction of the peak-valley difference and the smoothing of the load curve, providing a reference for optimizing demand response programs. By analyzing the response characteristics of users, it is possible to understand the degree of response of users to electricity price signals or economic incentives, providing a basis for selecting demand response models and determining the intensity of incentives. By analyzing the uncertainty of demand response resources, the configuration capacity and charge-discharge strategy of energy storage devices can be determined to effectively avoid the risks brought by the uncertainty of user responses.

[0042] In addition, through the hierarchical compensation rule, it is possible to encourage load aggregators to improve the quality of their own resources, reduce the risk of user default, and improve the reliability of demand response. Through the coordinated control of demand-side aggregated loads, the peak-valley difference of the system can be effectively reduced, the system's ability to absorb new energy can be improved, and the development of clean energy can be promoted. It can improve the power supply reliability of the system, reduce the risk of system power outages, and ensure the electricity demand of users. It can reduce the system operation cost, such as reducing fuel costs and equipment investment, and improve the economy of the system. It can reduce the electricity bill expenditure of users, improve the user's satisfaction with electricity use, and promote the optimization of the electricity consumption structure. It can also promote the development of the electricity market, such as promoting electricity market transactions and improving the efficiency of the electricity market. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0044] Figure 1 It is a flowchart of the coordinated control method for aggregated loads on the demand side of the present invention.

[0045] Figure 2 It is a schematic diagram for determining the incentive rate of user i at time slot t of the present invention;

[0046] Figure 3 It is a schematic diagram of four flexible load control modes of the present invention;

[0047] Figure 4 It is a schematic diagram of the load coordination control architecture of the present invention;

[0048] Figure 5 It is a schematic diagram of the centralized-distributed control architecture of the F load aggregator of the present invention;

[0049] Figure 6It is a schematic diagram of the daily load curve in a certain area of the present invention;

[0050] Figure 7 It is a schematic diagram of the total load baseline of 150 residential users of the present invention;

[0051] Figure 8 It is a schematic diagram of the response and subsidy situation of Class A users under different subsidy standards of the present invention - Scenario 1;

[0052] Figure 9 It is a schematic diagram of the total load change of Class A users under different subsidy standards of the present invention - Scenario 1;

[0053] Figure 10 It is on Figure 9 the basis of, with the increase of the subsidy standard, the schematic diagram of the total load change of Class A users - Scenario 1;

[0054] Figure 11 It is a schematic diagram of the response and subsidy situation of Class C users under different subsidy standards in Scenario 1 of the present invention;

[0055] Figure 12 It is a schematic diagram of the total load change of Class C users under different subsidy standards in Scenario 1 of the present invention;

[0056] Figure 13 It is a schematic diagram of the response and subsidy situation of Class B users under different subsidy standards in Scenario 2 of the present invention;

[0057] Figure 14 It is a schematic diagram of the total load change of Class B users under different subsidy standards in Scenario 2 of the present invention;

[0058] Figure 15 It is a schematic diagram of the response and subsidy situation of Class C users under different subsidy standards in Scenario 2 of the present invention;

[0059] Figure 16 It is a schematic diagram of the total load change of Class C users under different subsidy standards in Scenario 2 of the present invention; Detailed implementation manners

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0061] Demand response (DR), as a method for flexible regulation of the interaction among power sources, grids, and loads in the power system, can achieve rapid power regulation and successfully solve the problems of high costs and reduced utilization efficiency of power generation capacity caused by peak shaving from the power supply side. However, due to the small capacity of individual demand-side resources and relatively low flexibility levels, it is often difficult to meet the minimum requirements for participating in DR. In this case, professional load aggregators use DR strategies to guide users to adjust their electricity consumption patterns through economic incentives, coordinate the interaction between the power supply side and the demand side, and effectively improve the economic efficiency of system operation. Demand response resources bring significant benefits to market operation, such as reducing the volatility of market electricity prices, suppressing the effects of market power, enhancing the security of system operation, and increasing the returns on power investment. In the power market, demand response is classified into price-based and incentive-based according to the response method. To guide users' electricity consumption, price-based demand response is used to improve medium- and long-term load characteristics, and incentive-based demand response is used to adjust short-term load fluctuations.

[0062] Referring to Figure 1 As shown, an embodiment of the present invention discloses a collaborative control method for aggregated loads on the demand side, including the following steps:

[0063] S10. Obtain user-side electricity prices, market electricity price fluctuations, and user baseline load prediction data, and analyze to obtain demand response targets;

[0064] S20. Based on the demand response targets and combined with user characteristics, select corresponding price-based and incentive-based demand response modes, and construct corresponding demand-side aggregated load models; among them, based on the price-based demand response mode, construct a price-based demand-side resource aggregated load model; based on the incentive-based demand response mode, construct an incentive-based demand-side aggregated load model;

[0065] S30. Determine the regulation strategies for the aggregated loads according to the demand response modes and demand-side aggregated load models, and the regulation strategies include adjustment-based, control-based, and mixed strategies;

[0066] S40. Classify the regulation strategies according to the applicable scenarios to obtain the classification results of the regulation strategies.

[0067] Among them, in step S10, collect user-side electricity prices, market electricity price fluctuations, and user baseline load prediction data, and analyze to determine the demand response targets.

[0068] In step S20, according to the demand response targets and user characteristics, select appropriate demand response modes (price-based or incentive-based), and construct corresponding demand-side aggregated load models.

[0069] Price-based demand response: Construct a price-based demand-side resource aggregated load model.

[0070] Incentive-based demand response: Construct an incentive-based aggregated load model on the demand side.

[0071] In step S30, according to the demand response mode and the load aggregation model, formulate the regulation strategies for the load aggregator, including regulation-based, control-based, and hybrid strategies.

[0072] In step S40, involve the demand response resources in the peak regulation and optimal operation of the power grid, including the regulation strategies for the load aggregator to participate in the incentive-based user demand response grid optimization operation and the source-load interaction regulation strategies for the load aggregator to participate in incentive-based peak regulation. According to the degree of user default, divide the load aggregators into different levels and formulate hierarchical compensation rules to encourage the load aggregators to improve the quality of their own resources.

[0073] Among them, the hierarchical compensation rules include:

[0074] The first level is high-quality resources, with a default percentage lower than 3%, and the compensation multiple λ 1 = 1.01;

[0075] The second level is qualified resources, with a default percentage between 3% and 8%, and the compensation multiple λ 2 = 1.0;

[0076] The third level is restricted resources, with a default percentage between 8% and 13%, and the compensation multiple λ 3 = 0.95;

[0077] The fourth level is prohibited resources, with a default percentage exceeding 13%.

[0078] The hierarchical compensation rule is an incentive measure aimed at improving the enthusiasm of load aggregators to participate in demand response and reducing the user default risk, thereby enhancing the reliability of demand response. This rule divides the load aggregators into different levels according to the degree of user default and gives corresponding compensation multiples to encourage the load aggregators to improve the quality of their own resources.

[0079] This method can improve the new energy consumption capacity, reduce the system operation cost, improve the system economy, improve the user's electricity consumption satisfaction, and promote the development of the electricity market, etc.

[0080] The technical solution of the present invention will be described in detail below:

[0081] Among them, there are mainly the following two forms of demand response measures: one is price demand response (PDR), which adjusts the user electricity price level through flexible electricity price policies (time-of-use electricity price, real-time electricity price, peak electricity price, etc.) to encourage users to voluntarily reduce and transfer loads. The other is incentive demand response (IDR), which uses direct economic incentives to guide users to adjust and optimize their electricity consumption behaviors, providing a flexibly dispatchable resource for the cost management and reliability analysis of the market. It uses economic compensation or electricity price methods according to signed contracts or agreements to encourage users to participate in load adjustment. Specific projects include interruptible load (IL), direct load control (DLC), demand-side bidding, and emergency demand response (EDR), etc. Incentive demand response includes two modes: fixed incentive mode and flexible incentive mode. The characteristics of demand response projects in the two modes are shown in Table 3-1.

[0082] Table 1 Characteristics of Traditional Demand Response Incentive Projects

[0083]

[0084] As can be seen from the above, incentive projects include mandatory and non-mandatory ones. Considering comfort and privacy, small and medium-sized users represented by residents generally do not tend to or even resist participating in mandatory projects directly controlled by load aggregators. Therefore, when load aggregators are dealing with market electricity price fluctuations in the electricity sales business, from the perspective of improving customers' enthusiasm for participating in demand response, a demand response resource organization method that provides certain economic compensation or electricity bill discounts for the non-binding load reduction behaviors of small and medium-sized users, thereby guiding customers to independently adjust and widely participate, has stronger practical feasibility.

[0085] According to the differences in the ways load aggregators regulate the electricity consumption behaviors of controlled users, their regulation modes can be roughly divided into two categories: an indirect regulation mode based on electricity price and a direct regulation mode based on contracts.

[0086] Price-based and incentive-based demand-side resources need to establish their optimized aggregation response objectives according to specific demand response projects.

[0087] Price-based demand-side resources refer to the load resources that respond to the market price signals issued by the aggregators of demand-side resources, or the load resources that respond to price-based demand response projects. The goal of the price-based demand response mechanism is to use market prices that reflect the potential production costs of power operation to guide end-users on the demand side to change their electricity consumption behaviors, and let end-users bear the corresponding price costs to achieve the effective allocation of demand-side resources.

[0088] The price-based demand-side resource aggregation model is as follows:

[0089]

[0090] Where: is the actual demand power of the load of demand-side user i in response to the price-based demand response project at time t; and are the minimum and maximum values of the range of the responsive power of demand-side user i respectively; I is the set of demand-side users who respond to the price-based demand response.

[0091] Under the implementation mode of demand response based on load aggregators, the main stakeholders involved include power grid companies, load aggregators, and users. There are two levels of incentives: from the power grid to load aggregators and from load aggregators to users. The power grid's incentive to load aggregators is mainly to encourage load aggregators to actively participate, and the load aggregator's incentive to users is mainly to motivate users to participate and purchase the controllable load control rights of users.

[0092] The fixed incentive mode means that the power grid adopts a fixed incentive mode for load aggregators, and load aggregators adopt a fixed incentive mode for users. Under the fixed incentive mode, let the fixed incentive rate of the power grid to load aggregators be E g , E g 's value can refer to the demand response subsidy standards of each province and city in China; the fixed incentive rate of load aggregators is E a , E a 's value is determined by the load aggregator according to its own actual situation. For different load aggregators, its value may be different. Generally, E g > E a .

[0093] Under the fixed incentive mode, the economic benefit obtained by a certain user i is calculated as follows:

[0094]

[0095] Where: Δt is the time length of a control cycle; is the actual power of user i at time t on the response day; is the baseline power of user i at time t.

[0096] The economic benefit π obtained by the load aggregator 1 is calculated as follows:

[0097]

[0098] where: p res (t) is the response volume of the load aggregator at the accounting time t.

[0099] The flexible incentive mode means that the power grid adopts a fixed incentive mode for the load aggregator, and the load aggregator adopts a flexible incentive mode for users. The fixed incentive standard adopted by the power grid for the load aggregator in the flexible incentive mode is the same as the fixed incentive standard adopted by the power grid for the load aggregator in the fixed incentive mode. In the flexible incentive mode, regarding the flexible incentive adopted by the load aggregator for users, a multi-level incentive model is adopted as the incentive mechanism for the load aggregator to reward users for participating in the new energy consumption business, as shown in the following formula:

[0100]

[0101] where: RM i (t) represents the incentive rate for user i to participate in demand scheduling at time slot t; R 1 is the first-level incentive rate; R 2 is the second-level incentive rate; R 3 is the third-level incentive rate; R 4 is the fourth-level incentive rate; R 5 is the fifth-level incentive rate; T set_L (i) is the lowest threshold of the allowable temperature setting range for user i; T set_U (i) is the highest threshold of the allowable temperature setting value range for user i; is the water heater temperature setting value of user i at time slot t; Com(i) indicates whether user i accepts that the temperature setting value exceeds the allowable change range, where the value "1" means acceptance and the value "0" means non-acceptance.

[0102] From Figure 2 (illustrating R 1 ~R 3 , a total of three levels), it can be observed that the more serious the violation of user preferences, the higher the incentive level of the user, that is, the higher the incentive rate of the load aggregator for the user.

[0103] Under the flexible incentive mode, the economic benefit obtained by a certain user i is calculated as follows:

[0104]

[0105] The economic benefit π obtained by the load aggregator2 The calculation formula is as follows:

[0106]

[0107] Common control strategies include four control modes: centralized control, decentralized control, hierarchical control, and load aggregator control. Schematic diagrams of the four controls are as Figure 3 shown.

[0108] Centralized control is similar to the current control of generator sets, directly issuing commands from the dispatching center to the load end for control. Decentralized control is based on the smart grid, and through power electronic equipment, it monitors and controls the load in real time. Although the control method is flexible, the control equipment can only reflect local observables and cannot feedback the overall situation to the dispatching center. Therefore, under-control or over-control sometimes occurs. The load aggregator control mode is an intermediate agency that combines the advantages of decentralized and centralized control. It participates in grid dispatching above and directly coordinates the unequal problems between users and the grid below. It can be seen from Table 2 the applicable scenarios of each control strategy and the deficiencies of different control strategies.

[0109] Table 2 Comparison of Different Control Strategies

[0110]

[0111] With the gradual popularization of electricity substitution on the user side, the intelligence of electrical equipment, and the gradual liberalization of the power sales side market, the proportion of electricity consumption on the residential side has increased. New participants such as load aggregators and service providers have emerged in the market, which can integrate scattered residential load resources to participate in demand response, making residential load resources high-quality demand response resources on the demand side. Therefore, it is necessary to carry out research on the strategy of residential load on the demand side participating in demand response.

[0112] The load coordination control framework at the bottom layer of the load aggregator is as Figure 4 shown, which is divided into the load aggregator layer, the load agent layer, and the user equipment layer.

[0113] (1) Resource aggregator layer: The aggregator analyzes and summarizes the load resource information within its scope on a daily basis, groups them, and manages them separately by load agents, calculates the response potential of each load group, and feeds it back to the upper dispatching center layer. On a daily basis, it obtains the target power issued by the upper dispatching center.

[0114] (2) Load agent layer: On a daily basis, each load group obtains the target power adjustment amount issued by the upper aggregator; on a real-time time scale, the load agent monitors the load within the load group in real time, continuously updates parameters such as the load status and response potential, and conducts secondary negotiation of the target power between load groups according to the control target issued on a daily basis and the updated response potential.

[0115] (3) User Equipment Layer: On a real-time time scale, load equipment collects information such as load parameters and operating status through devices such as smart sockets or smart switches, and uploads parameters such as controllable margin indicators to the load agent through edge-side computing of users; and calls the load in the form of direct load control according to the control objectives assigned by the load agent.

[0116] To respond to requests from the market and provide high-quality demand response services, the load aggregator needs to control user loads according to certain strategies. After receiving the demand response request, the load aggregator reacts quickly, tries to predict the request, and controls user loads in a more effective way. The load aggregator has various demand response control resources such as adjustable loads, energy storage devices, and distributed power sources. Through reasonable regulation strategies for flexible electric loads, the load aggregator can integrate and quantify demand-side resources, help users flexibly manage their loads, suppress intermittent load fluctuations, reduce the peak-valley difference of the system, and has low investment costs compared with increasing installed capacity, with good social and economic benefits.

[0117] The load aggregator formulates different control strategies according to the physical characteristics and regulation items of different control resources. The load aggregator generally implements the interruptible load (IL) project for large industrial and commercial users, and the regulation strategies for the demand response resources of small and medium-sized users can be divided into three categories. One is the adjustment type strategy, that is, the load aggregator issues price signals through time-of-use electricity prices, peak electricity prices, peak-hour subsidies, etc. to guide users to voluntarily participate in the demand response project; the second is the control type strategy, that is, the load aggregator signs a mandatory demand response task contract with small and medium-sized users to determine the participation method and response volume of users, or directly controls the flexible load through an energy management system (direct load control, DLC); the third is to combine the adjustment type and control type strategies to achieve targeted regulation of multiple types of resources.

[0118] The load aggregator's aggregated management of controlled users can adopt Figure 5 the centralized-distributed control architecture shown in the figure. Under this architecture, a large number of end-users are directly connected to the load aggregator and clustered into controlled units according to load types. Under the incentive of electricity prices or regulation instructions, the load aggregator guides or directly controls their electricity consumption behaviors. At this time, the load aggregator serves as an interaction platform between users and the power grid. On the one hand, it collects basic parameters, operating status, and electricity consumption expectations of user-side equipment and uploads schedulable data to the power grid; on the other hand, it collects operating status and price signals on the grid side and formulates and issues regulation instructions according to scheduling requirements.

[0119] The centralized - distributed control architecture reduces the difficulty of collecting, transmitting, and processing big data compared with the centralized control method where the grid dispatching center directly controls each independent load, thus making it feasible for application in the research and analysis of large - scale power systems. On the other hand, compared with the distributed control method where intelligent terminals directly control each independent load, this architecture suppresses over - response or under - response to local signals, avoiding conflicts in electricity - using behaviors and deterioration of the overall control effect.

[0120] The upper layer of the hierarchical control architecture is the dispatching center - aggregator layer. The dispatching center predicts the regulation period of the system at the day - ahead scale and issues the peak - shaving period to each aggregator on a day - ahead basis. After receiving the regulation period issued by the dispatching center, each aggregator analyzes and predicts the response potential of the loads under its jurisdiction and reports it to the dispatching center. Through the aggregator optimization scheduling model in the upper layer, the allocation volume of each aggregator during the peak - shaving period is obtained.

[0121] The lower layer of the hierarchical control architecture is the aggregator - user side layer. On the day - ahead time scale, the aggregator clusters and groups the loads under its jurisdiction according to the model coefficients to form multiple load groups, which are managed by load agents. Each agent analyzes the response potential within the regulation period of each group and reports it to the aggregator. The aggregator performs peak - shaving optimization and combination on multiple load groups within its jurisdiction considering the load rebound effect and control cost to form a day - ahead target power allocation plan. On the real - time time scale, the load agent continuously updates the load operation state parameters and response potential on the basis of the day - ahead scale, and conducts secondary negotiation on the target power of the load group according to the control target issued by the aggregator on a day - ahead basis and the updated response potential. Finally, each load group realizes the regulation target according to the state - sequence control strategy based on the controllable margin.

[0122] In demand response projects, according to the different control methods of power grid companies for demand response resources, the organizational models can be divided into three types, namely:

[0123] Model 1: The power grid company directly controls the electrical equipment and adjusts the corresponding equipment parameters according to the load reduction demand.

[0124] Model 2: The power grid company sends demand response signals to load aggregators, and the load aggregators control the electrical equipment.

[0125] Model 3: The power grid company sends demand response signals to load aggregators. After the load aggregators agree, the users themselves control the electrical equipment.

[0126] In terms of the reliability of the response, the reliability of these three modes decreases in turn. The demand response protocols of the power grid company for users' demand response include directly signing agreements with large users by the power grid company and negotiating demand response compensation mechanisms with load aggregators. The load aggregators of the power grid company customize the demand response subsidy price mechanism according to the response capacity of users, formulate multiple sets of plans, and users choose subsidy price mechanism packages according to their own characteristics, giving full play to the agency role of load aggregators between the power grid company and users.

[0127] Due to the large differences in physical models and operating characteristics among different types of flexible loads, there are significant differences in the control methods of load aggregators according to the types of loads. For the hierarchical control strategy, the upper layer is the dispatching center - aggregator layer. The dispatching center issues peak shaving periods to each aggregator on a daily basis. After each aggregator receives the regulation period issued by the dispatching center, it analyzes and predicts the response potential of the loads under its jurisdiction and reports it to the dispatching center. Through the upper-layer aggregator optimal dispatching model, the allocation volume of each aggregator during the peak shaving period is obtained. This hierarchical control strategy coordinates with each other between the upper and lower layers, effectively ensuring the safe and economic operation of the power grid.

[0128] For example:

[0129] Demand response resources are integrated through the power grid company, load aggregators, and electricity retailers. Take the current control method of the power grid company organizing demand response resources to participate in the optimal operation of the power grid.

[0130] Select the 24-hour load curves of several residential users in a certain area in summer as Figure 6 shown.

[0131] It can be Figure 6 seen that the load curves on summer weekdays in this area show the characteristics of "double peaks", that is, there are small peaks in electricity consumption at 11:00 noon and from 20:00 to 21:00 in the evening, and the load level is relatively low in the early morning.

[0132] The peak-valley electricity price package signed by the load aggregator and residential users is: from 9:00 to 23:00 is the peak period, and the electricity price during the peak period is 1.095 yuan / kWh; from 23:00 to 9:00 the next day is the valley period, and the electricity price during the valley period is 0.515 yuan / kWh.

[0133] Select 150 residential users whose load curves have a certain similarity to the total regional load curve and also show the characteristics of "double peaks", with a total of 150 residential users. Conducting demand response for these residential users has important practical significance for "peak shaving and valley filling" of the overall load curve. The total load baseline of 150 users is as Figure 7 shown.

[0134] The load baselines of 150 users can be divided into three categories: the first category of "noon peak" users, the second category of "evening peak" users, and the third category of "double peak" users. For the first category of users, the overall curve shows a power consumption peak at 12:00 noon; for the second category of users, the overall curve shows a power consumption peak at 21:00 in the evening; for the third category of users, the overall curve shows double power consumption peaks at 12:00 noon and 21:00 in the evening.

[0135] According to the "double peak" characteristics of the total load curve in the area where the load aggregator is located, assuming two day-ahead electricity price curves, two scenarios of noon electricity price peak and evening electricity price peak are established, denoted as Scenario 1 and Scenario 2 respectively.

[0136] The electricity price curve values are shown in Table 3-5. The peak-valley electricity price package signed between the load aggregator and residential users is as follows: the peak period is from 8:00 to 23:00, and the electricity price during the peak period is 1.095 yuan / kWh; the valley period is from 23:00 to 8:00 the next day, and the electricity price during the valley period is 0.515 yuan / kWh. It is set that when the price difference between the real-time electricity price in the day-ahead market and the peak-valley time-of-use electricity price is greater than the threshold of 0.5 yuan / kWh, the load aggregator starts incentive-based demand response. Therefore, the subsidy period can be further demarcated: Scenario 1 is from 11:00 to 12:00; Scenario 2 is from 21:00 to 22:00.

[0137] Table 5 Electricity price curve value table for Scenario 1 and Scenario 2

[0138]

[0139]

[0140] The load baselines of 150 users can be divided into three categories: the first category of "noon peak" users, denoted as Category A users, the second category of "evening peak" users denoted as Category B users, and the third category of "double peak" users denoted as Category C users. To ensure the basic living electricity consumption of users, it is restricted that the minimum load of user i after participating in demand response is not lower than the minimum load before participation. In the "noon peak type" electricity price scenario of Scenario 1, the users participating in the demand response project include Category A users and Category C users; in the "evening peak type" electricity price scenario of Scenario 2, the participating users are Category B users and Category C users. For the first category of users, the overall curve shows a power consumption peak at 12:00 noon; for the second category of users, the overall curve shows a power consumption peak at 21:00 in the evening; for the third category of users, the overall curve shows double power consumption peaks at 12:00 noon and 21:00 in the evening. The superimposed curve of the three categories of users must also be a double power consumption peak.

[0141] The significance of clustering the baseline load is that if the load aggregator considers implementing demand response, but in fact there are time requirements for the reduction of user load, the differences among users should be taken into account, and the most suitable users should be selected during the specific required time periods.

[0142] In Scenario 1, the "Wufeng-type" electricity price scenario, the incentive period for the load aggregator is from 11:00 to 12:00. First, analyze the user response model. Set the subsidy standard to increase from 0 to 0.7 yuan / kWh, with an interval of 0.01 yuan / kWh. Calculate and count the total load reduction of Class A users during the incentive period, as well as the change in the total subsidy amount paid by the load aggregator. The results are as Figure 8 shown.

[0143] When the subsidy standards are set to 0.4 yuan / kWh, 0.5 yuan / kWh, and 0.6 yuan / kWh respectively, compare and analyze the overall load change of Class A users relative to the baseline load, as Figure 9 shown.

[0144] From the perspective of load reduction, the dead zone threshold for the incentive of Class A users is 0.29 yuan / kWh, that is, when the subsidy standard is greater than 0.29 yuan / kWh, some users in Class A start to adjust their electricity consumption loads; the saturation threshold is 0.55 yuan / kWh, indicating that when the subsidy standard is higher than this value, the load adjustments of all Class A users have reached the constraint upper limit, and the load reduction amount will no longer increase. From the perspective of the total subsidy amount paid by the load aggregator, in the dead zone and response areas, the curve trend is consistent with the load reduction amount trend. After entering the saturation area, since the load reduction amount has reached the upper limit, the total subsidy amount increases linearly with the subsidy standard, and the slope is the maximum load reduction amount of Class A users, 84.560 kW.

[0145] Figure 10 In , as the subsidy standard increases, Class A users gradually reduce their electricity consumption loads from 11:00 to 12:00 and transfer them to other periods. Except for the subsidy period, the moments with high activity of Class A users are discretely distributed from 13:00 pm to 23:00 pm. This causes the total load curve of Class A users to increase to a certain extent at 13:00 to 23:00, but due to the dispersion of moments, the increase in load at a single moment is not obvious.

[0146] In Scenario 1, the total load reduction and total subsidy amount during the incentive period of Class C users are as Figure 11 shown.

[0147] The dead zone threshold for the incentive of Class C users is 0.37 yuan / kWh, that is, when the subsidy standard is greater than 0.37 yuan / kWh, some users in Class C start to adjust their electricity consumption loads; the saturation threshold is 0.54 yuan / kWh, indicating that when the subsidy standard is higher than this value, the load reduction amount of all Class C users reaches the constraint upper limit and does not continue to increase. After the total subsidy amount enters the saturation area, it increases linearly with the subsidy standard, and the slope is the maximum load reduction amount of Class C users, 62.368 kW.

[0148] Similarly, the overall load change of type C users relative to the baseline load is compared and analyzed, as Figure 12 shown. When the subsidy standard is increased, type C users gradually reduce their electricity consumption activities from 11:00 to 12:00, and the load also changes during other periods. Considering the "double-peak" characteristic of the load curve of type C users, the active electricity consumption periods are at noon and in the evening. Correspondingly, in Scenario 1, it is expected that the total load curve of type C users will show a load rebound from 19:00 to 22:00 in the evening, which is most obvious at 21:00. In addition, at 10:00 and 13:00 around the incentive moment, the load level also increases to some extent, which may be due to users advancing or delaying some of their electricity consumption behaviors.

[0149] Under the optimal subsidy standard, the costs and benefits before and after demand response are calculated, and the results are shown in Table 6.

[0150] Table 6 Statistical Table of the Revenue of the Load Aggregator before and after Implementing Demand Response in Scenario 1 Unit: Yuan

[0151]

[0152] (2) Scenario 2

[0153] In the "peak in the evening" electricity price scenario of Scenario 2, the incentive period for the load aggregator is from 21:00 to 22:00. Similarly, the lower-layer rational user response model is considered. The subsidy standard is set to increase from 0 to 0.7 yuan / kWh at an interval of 0.1 yuan / kWh. The total load reduction of type B users during the subsidy period and the total subsidy amount issued by the load aggregator are calculated and statistically analyzed, as Figure 13 shown. The dead zone threshold for the incentive of type B users is 0.29 yuan / kWh, indicating that when the subsidy standard is greater than this threshold, a small number of type B users start to respond to the incentive signal; the saturation threshold is 0.51 yuan / kWh, that is, when the subsidy standard is higher than this value, the load reduction of all type B users has reached the upper limit. Similarly, after the total subsidy amount enters the saturation area, it increases linearly with the subsidy standard, and the slope is the maximum load reduction of type B users, which is 88.239 kW.

[0154] The overall load change of type B users relative to the baseline load is compared and analyzed, as Figure 14 shown. As the subsidy standard increases, type B users gradually respond to the subsidy signal, reduce their electricity load from 21:00 to 22:00, and adjust their electricity consumption arrangements during other periods. Type B users are more active in the evening, and their overall load will show a relatively obvious rebound at 20:00 and 23:00, that is, around the subsidy period. At the same time, the load value also increases slightly during the early morning period.

[0155] Under Scenario 2, the total load reduction of type C users during the subsidy period and the total subsidy amount are as Figure 15As shown. Similarly, analyze the overall load change of type C users relative to the baseline load in scenario 2, as Figure 16 shown.

[0156] Calculate the revenue of the load aggregator before and after demand response in scenario 2. The results are shown in Table 7.

[0157] Table 7 Statistical table of the revenue of the load aggregator in scenario 2 before and after implementing demand response / yuan

[0158]

[0159] In summary, the main conclusions can be obtained: First, under the economic incentive signal, the user response behavior can be divided into a dead zone, a response zone, and a saturation zone, and affected by factors such as the electricity load level, there are obvious differences in the dead zone and saturation zone thresholds and the load transfer period of the response behavior of different types of users; Second, the optimal subsidy standards for type A and type C users in scenario 1 are 0.5359 yuan / kWh and 0.4778 yuan / kWh respectively, and the profit of the load aggregator increases by 31.03% compared with that before demand response; The optimal subsidy standards for type B and type C users in scenario 2 are 0.4369 yuan / kWh and 0.4778 yuan / kWh respectively, and the profit increases by 21.41%. The significant increase in the profit of the load aggregator proves the rationality of the demand response strategy.

[0160] Limited by objective factors and prediction models, there must be a certain deviation in the load aggregator's prediction of the real-time electricity price in the day-ahead market. The demand response strategy is formulated under specific electricity price prediction results. Among them, the peak electricity price during the subsidy period is particularly important. The current market electricity price directly determines the electricity purchase cost of the load aggregator, and its prediction deviation will inevitably affect the comprehensive benefits of implementing demand response to a large extent. Under the same subsidy standard, Table 8 shows the changes in the electricity purchase cost and comprehensive benefits of type C users with different electricity price prediction deviations during the peak period. It can be seen from Table 8 that when the electricity price level is underestimated, that is, the actual value is higher than the predicted value, the electricity purchase cost increases, and the price difference between the purchase and sale during the peak period, that is, the part where the peak-valley electricity price is lower than the real-time electricity price, is widened. Therefore, the comprehensive benefits of the load aggregator implementing demand response increase more significantly, and are positively correlated with the absolute value of the electricity price prediction deviation; On the contrary, when the electricity price level is overestimated, that is, the actual value is lower than the predicted value, the comprehensive benefits decrease as the absolute value of the deviation increases. This table also shows that considering the electricity price prediction deviation reaches ±10%, the load aggregator still has room for profit.

[0161] Table 8 Statistical table of the comprehensive benefits of type C users with different electricity price prediction deviations during the peak period

[0162] Deviation of the actual electricity price value relative to the predicted value +2％ +5％ +8％ +10％ Actual electricity purchase cost (yuan) 915.40 920.63 925.85 929.34 Comprehensive benefit (yuan) 21.29 24.40 27.51 29.58 Change in comprehensive benefit relative to the original scenario 10.77％ 26.95％ 43.13％ 53.90％ Deviation of the actual electricity price value relative to the predicted value -2％ -5％ -8％ -10％ Actual electricity purchase cost (yuan) 908.43 903.20 897.97 894.49 Comprehensive benefit (yuan) 17.15 14.04 10.93 8.86 Change in comprehensive benefit relative to the original scenario -10.77％ -26.95％ -43.13％ -53.90％

[0163] Based on the same inventive concept, an embodiment of the present invention provides a coordinated control system for aggregated load on the demand side, including:

[0164] A data analysis module, configured to obtain user-side electricity prices, market electricity price fluctuations, and user baseline load prediction data, and analyze to obtain demand response targets;

[0165] A demand response module, configured to select corresponding price-based and incentive-based demand response modes based on the demand response targets and in combination with user characteristics, and construct a corresponding aggregated load model on the demand side; wherein, based on the price-based demand response mode, a price-based aggregated load model of demand-side resources is constructed; based on the incentive-based demand response mode, an incentive-based aggregated load model on the demand side is constructed;

[0166] A regulation strategy formulation module, configured to determine a regulation strategy for the aggregated load according to the demand response mode and the aggregated load model on the demand side, where the regulation strategy includes adjustment-based, control-based, and hybrid strategies; classify the regulation strategies according to applicable scenarios to obtain a classification result of the regulation strategies.

[0167] An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned coordinated control method for aggregated load on the demand side is implemented.

[0168] An embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the above-mentioned coordinated control method for aggregated load on the demand side is implemented.

[0169] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0170] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for collaborative control of aggregated loads on the demand side, characterized in that: The following steps are involved: Obtain user-side electricity prices, market electricity price fluctuations and user baseline load forecast data, and analyze and obtain demand response targets; Based on the demand response target and in combination with user characteristics, corresponding price-based and incentive-based demand response modes are selected, and corresponding demand-side aggregated load models are constructed; wherein, based on the price-based demand response mode, a price-based demand-side resource aggregated load model is constructed; and based on the incentive-based demand response mode, an incentive-based demand-side aggregated load model is constructed; Determine a control strategy for the aggregated load according to the demand response mode and the demand-side aggregated load model, wherein the control strategy includes a regulating strategy, a control strategy, and a hybrid strategy; The control strategies are classified according to applicable scenarios to obtain control strategy classification results.

2. A method for collaborative control of aggregated loads on the demand side as claimed in claim 1, characterized in that: The incentive-based demand response mode includes: a fixed incentive mode and a flexible incentive mode; The classification results of the control strategies include: centralized control, decentralized control, hierarchical control and load agent control.

3. A method for coordinated control of aggregated loads on the demand side as claimed in claim 1, characterized in that: The price-based demand-side resource aggregation load model is: in, The actual power demand of the load of the demand-side user i in time period t in response to the price-based demand response project; and are the minimum and maximum values ​​of the power range that demand-side user i can respond to respectively; I is the set of demand-side users who respond to price-based demand response.

4. A method for coordinated control of aggregated loads on the demand side as claimed in claim 1, characterized in that: The incentive-type demand-side aggregated load model includes: a fixed incentive mode and a flexible incentive mode; Under the fixed incentive model, the calculation formula for the economic benefits obtained by users is: Where: Δt is the time length of a control cycle; is the actual power of user i at time t on the response day; is the baseline power of user i at time t; In the flexible incentive model, regarding the flexible incentives adopted by load aggregators for users, a multi-level incentive model is adopted as an incentive mechanism for load aggregators to reward users for participating in new energy consumption business, as shown in the following formula: Where: RM i (t) represents the incentive rate of user i participating in demand scheduling at time slot t; R1 is the incentive rate for level 1; R2 is the incentive rate for level 2; R3 is the incentive rate for level 3; R4 is the incentive rate for level 4; R5 is the incentive rate for level 5; T set_L (i) the lowest threshold of the temperature setting range allowed to be changed by user i; T set_U (i) the highest threshold of the temperature setting value range allowed to be changed by user i; is the water heater temperature setting value of user i at time slot t; Com(i) indicates whether user i accepts the temperature setting value exceeding the allowed range of variation, with a value of "1" indicating acceptance and a value of "0" indicating rejection; Under the flexible incentive model, the calculation formula for the economic benefits obtained by users is: in, is the economic benefit obtained by the user; Δt is the length of a control cycle; is the actual power of the daily user at time t; is the baseline power of the user at time t; R is the incentive rate for the user to participate in demand scheduling at the time slot.

5. A method for coordinated control of aggregated loads on the demand side as claimed in claim 1, characterized in that: The control strategy classification results are used to implement demand-side aggregate load collaborative control, evaluate the implementation effect of the control strategy, and obtain evaluation results, including cost reduction, load fluctuation smoothing and increased new energy consumption.

6. A method for coordinated control of aggregated loads on the demand side as claimed in claim 4, characterized in that: Under the flexible incentive mode, load aggregation is divided into different levels according to the degree of user default, and hierarchical compensation rules are formulated; The hierarchical compensation rules include: Level 1 is high-quality resources, with a default percentage of less than 3% and a compensation multiple λ1=1.01; Level 2 is qualified resources, with a default percentage of 3% to 8% and a compensation multiple λ2 = 1.0; Level 3 is limited resources, with a default percentage of 8% to 13% and a compensation multiple λ3 = 0.95; Level 4 is prohibited resources, with a default percentage exceeding 13%.

7. A demand-side aggregate load collaborative control system, characterized in that: include: Data analysis module, used to obtain user-side electricity prices, market electricity price fluctuations and user baseline load forecast data, and analyze and obtain demand response targets; A demand response module is used to select corresponding price-based and incentive-based demand response modes based on the demand response target and in combination with user characteristics, and to construct corresponding demand-side aggregated load models; wherein, based on the price-based demand response mode, a price-based demand-side resource aggregated load model is constructed; and based on the incentive-based demand response mode, an incentive-based demand-side aggregated load model is constructed; The control strategy formulation module is used to determine the control strategy of the aggregated load according to the demand response mode and the demand-side aggregated load model, and the control strategy includes adjustment type, control type and hybrid strategy; the control strategy is classified according to the applicable scenario to obtain the control strategy classification result.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for collaborative control of aggregated loads on the demand side as described in any one of claims 1 to 6 above is implemented.

9. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the demand-side aggregated load collaborative control method described in any one of claims 1 to 6.

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