A method and system for coordinated control of aggregated loads on the demand side

Abstract

This invention discloses a method and system for coordinated control of aggregated load on the demand side. The method includes: acquiring user-side electricity price, market electricity price fluctuations, and user baseline load forecast data; analyzing these to obtain a demand response target; based on the demand response target and combined with user characteristics, selecting corresponding price-based and incentive-based demand response modes, and constructing 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; based on the incentive-based demand response mode, an incentive-based demand-side aggregated load model is constructed; according to the demand response mode and the demand-side aggregated load model, a control strategy for the aggregated load is determined, including adjustment, control, and hybrid strategies; the control strategies are classified according to applicable scenarios to obtain a classification result. This method can improve the renewable energy absorption capacity, reduce system operating costs, and improve the economic efficiency of system operation.

Description

Technical Field

[0001] This invention relates to the field of power grid control technology, and in particular to a method and system for coordinated control of aggregated loads on the demand side. Background Technology

[0002] Load aggregators, aiming to improve the economic efficiency of their electricity sales business through demand response, need to analyze and study the user-side electricity price situation, market price fluctuations, and user-side baseline load before formulating specific strategies. The real-time market electricity price significantly impacts the load aggregator's electricity purchase cost; user-side electricity prices directly determine the load aggregator's electricity sales revenue; and controlling the user's baseline load level not only affects their assessment of resource value but also involves the calculation of compensation amounts after users participate in demand response projects. Therefore, all of the above aspects are fundamental to the load aggregator's demand response decision-making.

[0003] User-side electricity pricing is determined by electricity package agreements signed between load aggregators and users, and is directly linked to the load aggregators' electricity sales revenue. Market price forecasting reflects the fact that changes in electricity prices at any given moment are crucial information reflecting the operational status of the electricity market, directly influencing the behavior of various market participants and thus determining the flow and allocation of different types of resources in the electricity market. User baseline load forecasting is a fundamental element for calculating economic compensation between the implementing entity and participating users based on contractual standards after demand response. Regression and averaging methods are two commonly used methods for determining baseline load. In incentive-based demand response, baseline load is used to calculate load reduction and provide users with appropriate economic compensation. Therefore, accurate user baseline load estimation data is crucial for the implementation of demand response.

[0004] With the rapid development of new energy sources, grid connection has brought new challenges to the power system, such as the volatility and anti-peak-shaving characteristics of new energy output, leading to increased system operating costs. Traditional demand response methods are insufficient to meet the needs of large-scale new energy consumption, requiring more flexible and efficient control methods. Summary of the Invention

[0005] In view of this, in order to overcome the shortcomings of traditional demand response methods in the prior art, which are unable to meet the needs of large-scale renewable energy consumption, the main objective of this invention is to provide a demand-side aggregated load coordinated control method and system for renewable energy consumption. This aims to improve renewable energy consumption capacity, reduce system operating costs, and enhance system operational economy.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a demand-side aggregated load collaborative control method, comprising the following steps:

[0008] Acquire user-side electricity prices, market electricity price fluctuations, and user baseline load forecast data to analyze and obtain demand response targets;

[0009] Based on the aforementioned demand response objectives and combined with user characteristics, corresponding price-based and incentive-based demand response modes are selected, and corresponding demand-side aggregated load models are constructed. Specifically, a price-based demand-side aggregated load model is constructed based on the price-based demand response mode, and an incentive-based demand-side aggregated load model is constructed based on the incentive-based demand response mode.

[0010] Based on the demand response model and the demand-side aggregated load model, the regulation strategy for aggregated load is determined, including adjustment, control and hybrid strategies.

[0011] The control strategies are classified according to their applicable scenarios to obtain the control strategy classification results.

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

[0013] The classification results of the control strategies include: centralized control, decentralized control, hierarchical control, and load broker control.

[0014] Furthermore, the price-based demand-side resource aggregation load model is as follows:

[0015]

[0016] in, This is in response to the actual load demand of demand-side user i during time period t in a price-based demand response project; and Let I represent 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 responding to price-based demand responses.

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

[0018] Under the fixed incentive model, the formula for calculating the economic benefits obtained by users is:

[0019]

[0020] In the formula: Δt is the time length of one control cycle; In response to the actual power consumption of user i at time t; Let i be the baseline power of user i at time t;

[0021] In the flexible incentive model, the load aggregator adopts a multi-level incentive model as the incentive mechanism for rewarding users to participate in the renewable 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; R1 is the level 1 incentive rate; R2 is the level 2 incentive rate; R3 is the level 3 incentive rate; R4 is the level 4 incentive rate; R5 is the level 5 incentive rate; T set_L (i) Set the minimum threshold for the temperature range that user i is allowed to vary; T set_U (i) Set the highest threshold value for the range of temperature values ​​that user i is allowed to vary; Com(i) represents the water heater temperature setting value for user i at time slot t; Com(i) indicates whether user i accepts the temperature setting value exceeding the allowable range of variation, with a value of "1" indicating acceptance and a value of "0" indicating non-acceptance.

[0024] Under the flexible incentive model, the formula for calculating the economic benefits obtained by users is as follows:

[0025]

[0026] in, The economic benefit obtained by the user; Δt is the length of a control cycle; In response to the actual power consumption of daily users at time t; R represents the user's baseline power at time t; R is the incentive rate for the user to participate in demand scheduling at the time slot.

[0027] Furthermore, based on the classification results of the control strategies, demand-side aggregated load coordinated control is implemented, the implementation effect of the control strategies is evaluated, and evaluation results are obtained, including cost reduction, load fluctuation smoothing, and increased renewable energy consumption.

[0028] Furthermore, under the flexible incentive model, load aggregation is divided into different levels according to the degree of user default, and graded compensation rules are formulated.

[0029] The hierarchical compensation rules include:

[0030] Level 1 refers to high-quality resources, with a default rate of less than 3%, and a compensation multiple λ1 = 1.01;

[0031] Level 2 is considered a qualified resource, with a default rate of 3% to 8%, and a compensation multiple λ2 = 1.0;

[0032] Level 3 refers to restricted resources, with a default rate of 8% to 13%, and a compensation multiple λ3 = 0.95;

[0033] Level 4 resources are prohibited, with a default rate exceeding 13%.

[0034] Secondly, embodiments of the present invention provide a demand-side aggregated load coordination control system, comprising:

[0035] The data analysis module is used to acquire user-side electricity price, market electricity price fluctuations, and user baseline load forecast data, and analyze them to obtain demand response targets;

[0036] The demand response module is used to select the corresponding price-based and incentive-based demand response modes based on the demand response objectives and user characteristics, and to construct the corresponding demand-side aggregated load model; wherein, based on the price-based demand response mode, a price-based demand-side aggregated load model is constructed; and based on the incentive-based demand response mode, an incentive-based demand-side aggregated load model is constructed.

[0037] The regulation strategy formulation module is used to determine the regulation strategy of aggregated load based on the demand response mode and the demand-side aggregated load model. The regulation strategy includes adjustment, control and hybrid strategies. The regulation strategy is classified according to the applicable scenario to obtain the regulation strategy classification result.

[0038] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the demand-side aggregated load collaborative control method described in any of the first aspects above.

[0039] Fourthly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the demand-side aggregated load collaborative control method described in any of the first aspects above.

[0040] As can be seen from the above technical solution, compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] Demand-side aggregated load models can quantify the potential of demand response resources, including the number of users, the amount of load that can be reduced, and the potential for load shifting, providing a basis for demand response scheme development. They can predict load curve changes after demand response implementation, such as reduced peak-to-valley differences and smoother load curves, providing a reference for demand response scheme optimization. Analysis of user response characteristics can reveal the degree to which users respond to electricity price signals or economic incentives, providing a basis for selecting demand response modes and determining incentive levels. Analysis of the uncertainty of demand response resources can determine the configuration capacity and charging / discharging strategies of energy storage devices, effectively mitigating the risks arising from user response uncertainty.

[0042] Furthermore, tiered compensation rules can incentivize load aggregators to improve their resource quality, reduce user default risks, and enhance demand response reliability. Through demand-side load aggregation and coordinated control, the system's peak-to-valley difference can be effectively reduced, improving the system's capacity to absorb renewable energy and promoting clean energy development. This can improve system power supply reliability, reduce the risk of power outages, and ensure user electricity needs are met. It can reduce system operating costs, such as lower fuel costs and equipment investment, thus improving system economics. It can reduce user electricity expenses, increase user satisfaction, and promote the optimization of electricity consumption structure. It can also promote the development of the electricity market, such as facilitating electricity market transactions and improving electricity market efficiency. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 This is a flowchart of the demand-side aggregated load collaborative control method of the present invention.

[0045] Figure 2 This is a schematic diagram illustrating the determination of the excitation rate at time t in time slot i according to the present invention;

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

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

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

[0049] Figure 6 This is a schematic diagram of the load curve for a certain day in the region of this invention;

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

[0051] Figure 8 This is a schematic diagram of the response and subsidy status of Category A users under different subsidy standards of the present invention – Scenario 1;

[0052] Figure 9 This is a schematic diagram of the changes in total load of Class A users under different subsidy standards according to the present invention – Scenario 1;

[0053] Figure 10 Is Figure 9 Based on the increase in subsidy standards, the total load of Class A users changes as shown in Scenario 1;

[0054] Figure 11 This is a schematic diagram illustrating the response and subsidy status of Category C users under different subsidy standards in Scenario 1 of the present invention;

[0055] Figure 12 This is a schematic diagram illustrating the changes in the total load of Class C users under different subsidy standards in Scenario 1 of this invention;

[0056] Figure 13 This is a schematic diagram illustrating the response and subsidy status of Category B users under different subsidy standards in Scenario 2 of the present invention;

[0057] Figure 14 This is a schematic diagram illustrating the changes in the total load of Category B users under different subsidy standards in Scenario 2 of this invention;

[0058] Figure 15 This is a schematic diagram illustrating the response and subsidy status of Category C users under different subsidy standards in Scenario 2 of the present invention;

[0059] Figure 16 This is a schematic diagram illustrating the changes in the total load of Class C users under different subsidy standards in Scenario 2 of this invention; Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Demand response (DR), as a flexible method for the interaction and regulation of power generation, grid, and load, enables rapid power regulation and successfully solves the problems of high costs and reduced generation capacity utilization efficiency caused by grid peak shaving from the power source side. However, due to the small capacity and low elasticity of individual demand-side resources, it is often difficult to meet the minimum requirements for participating in DR. In this case, professional load aggregators use demand response DR strategies to guide users to adjust their electricity consumption patterns through economic incentives, coordinating the interaction between the power source and demand sides, and effectively improving the economic efficiency of system operation. Demand response resources bring significant benefits to market operation, such as reducing market price volatility, suppressing the role of market forces, enhancing system operational security, and increasing the return on electricity investment. In the electricity market, demand response is divided into price-based and incentive-based responses according to the response method. To guide user electricity consumption, price-based demand response improves medium- and long-term load characteristics, while incentive-based demand response adjusts short-term load fluctuations.

[0062] Reference Figure 1 As shown in the figure, an embodiment of the present invention discloses a demand-side aggregated load collaborative control method, including the following steps:

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

[0064] S20. Based on the demand response objectives and user characteristics, select the corresponding price-based and incentive-based demand response modes, and construct the corresponding demand-side aggregated load models; wherein, based on the price-based demand response mode, construct a price-based demand-side aggregated load model; and based on the incentive-based demand response mode, construct an incentive-based demand-side aggregated load model.

[0065] S30. Based on the demand response mode and the demand-side aggregated load model, determine the control strategy for aggregated load, including adjustment, control and hybrid strategies.

[0066] S40. Classify the control strategies according to the applicable scenarios to obtain the control strategy classification results.

[0067] In step S10, user-side electricity price, market electricity price fluctuations, and user baseline load forecast data are collected, analyzed, and demand response targets are determined.

[0068] In step S20, based on the demand response objectives and user characteristics, a suitable demand response mode (price-based or incentive-based) is selected, and a corresponding demand-side aggregated load model is constructed.

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

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

[0071] In step S30, based on the demand response model and load aggregation model, the load aggregator's control strategy is formulated, including adjustment, control, and hybrid strategies.

[0072] In step S40, demand response resources are involved in grid peak-shaving optimization operations, including load aggregators participating in incentive-based user demand response grid optimization operation control strategies and load aggregators participating in incentive-based peak-shaving source-load interaction control strategies. Based on the degree of user default, load aggregators are divided into different levels, and graded compensation rules are established to incentivize load aggregators to improve their resource quality.

[0073] The tiered compensation rules include:

[0074] Level 1 refers to high-quality resources, with a default rate of less than 3%, and a compensation multiple λ1 = 1.01;

[0075] Level 2 is considered a qualified resource, with a default rate of 3% to 8%, and a compensation multiple λ2 = 1.0;

[0076] Level 3 refers to restricted resources, with a default rate of 8% to 13%, and a compensation multiple λ3 = 0.95;

[0077] Level 4 resources are prohibited, with a default rate exceeding 13%.

[0078] Tiered compensation rules are an incentive mechanism designed to increase the willingness of load aggregators to participate in demand response and reduce the risk of user default, thereby improving the reliability of demand response. This rule categorizes load aggregators into different levels based on the severity of user default and provides corresponding compensation multipliers to incentivize load aggregators to improve their resource quality.

[0079] This method can improve the absorption capacity of new energy sources, reduce system operating costs, improve system economy, increase user satisfaction with electricity use, and promote the development of the electricity market.

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

[0081] Demand response measures mainly take two forms: First, price demand response (PDR), which uses flexible electricity pricing policies (time-of-use pricing, real-time pricing, peak pricing, etc.) to adjust user electricity prices and encourage users to voluntarily reduce and shift load. Second, incentive demand response (IDR), which uses direct economic incentives to guide users to adjust and optimize their electricity consumption behavior, providing a flexibly dispatchable resource for market cost management and reliability analysis. It incentivizes users to participate in load adjustment through economic compensation or electricity prices based on signed contracts or agreements. Specific projects include interruptible load (IL), direct load control (DLC), demand-side bidding, and emergency demand response (EDR). Incentive demand response includes two modes: fixed incentive mode and flexible incentive mode. The characteristics of the two modes of demand response projects are shown in Table 3-1.

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

[0083]

[0084] As shown above, incentive programs include both mandatory and non-mandatory components. However, considering comfort and privacy, small and medium-sized users, primarily residential users, generally do not favor or even resist participating in mandatory programs directly controlled by load aggregators. Therefore, in the context of market electricity price fluctuations in the electricity sales business, load aggregators, from the perspective of increasing customer participation in demand response, can better guide customers to voluntarily adjust and widely participate in demand response resource organization by providing economic compensation or electricity discounts for non-restrained load reduction behaviors of small and medium-sized users.

[0085] Based on the differences in the electricity consumption behavior of controlled users regulated by load aggregators, their regulation modes can be roughly divided into two categories: indirect regulation mode based on electricity price and direct regulation mode based on contract.

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

[0087] Price-based demand-side resources refer to load resources that respond to market price signals released by demand-side resource aggregation response entities, and also to load resources responding to price-based demand response projects. The goal of the price-based demand response mechanism is to use market prices that reflect potential electricity operation and production costs to guide end-users to change their electricity consumption behavior, so that end-users bear the corresponding price costs, thereby achieving the effective allocation of demand-side resources.

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

[0089]

[0090] In the formula: This is in response to the actual load demand of demand-side user i during time period t in a price-based demand response project; and Let I represent 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 responding to price-based demand responses.

[0091] In a demand response implementation model based on load aggregators, the main stakeholders include the power grid company, load aggregators, and users. There are two levels of incentives: one from the power grid to the load aggregators, and another from the load aggregators to the users. The power grid's incentives for load aggregators primarily encourage their active participation, while the load aggregators' incentives for users mainly encourage user participation by purchasing control over adjustable loads.

[0092] The fixed incentive mode refers to a situation where the power grid applies a fixed incentive to the load aggregator, and the load aggregator applies a fixed incentive to the users. Let E be the fixed incentive rate provided by the power grid to the load aggregator under the fixed incentive mode. g E g The value can be referenced from the demand response subsidy standards of various provinces and cities in my country; the fixed incentive rate for load aggregators is E. a E a The value of E is determined by the load aggregator based on its own actual situation, and different load aggregators may have different values. Generally, E g >E a .

[0093] Under a fixed incentive model, the economic benefit obtained by user i The calculation formula is as follows:

[0094]

[0095] In the formula: Δt is the time length of one control cycle; In response to the actual power consumption of user i at time t; Let be the baseline power of user i at time t.

[0096] The formula for calculating the economic benefit π1 obtained by the load aggregator is as follows:

[0097]

[0098] In the formula: p res (t) represents the response of the load aggregator at time t.

[0099] The flexible incentive model refers to a system where the power grid applies a fixed incentive to load aggregators, while load aggregators apply a flexible incentive to users. The fixed incentive standard applied by the power grid to load aggregators in the flexible incentive model is the same as that in the fixed incentive model. Regarding the flexible incentives applied by load aggregators to users, a multi-level incentive model is adopted as the incentive mechanism for load aggregators to reward users for participating in renewable energy consumption business, as shown in the following formula:

[0100]

[0101] In the formula: RM i (t) represents the incentive rate for user i to participate in demand scheduling at time slot t; R1 is the level 1 incentive rate; R2 is the level 2 incentive rate; R3 is the level 3 incentive rate; R4 is the level 4 incentive rate; R5 is the level 5 incentive rate; T set_L (i) Set the minimum threshold for the temperature range that user i is allowed to vary; T set_U (i) Set the highest threshold value for the range of temperature values ​​that user i is allowed to vary; Com(i) represents the water heater temperature setting value for user i at time slot t; Com(i) indicates whether user i accepts the temperature setting value exceeding the allowable range of variation, with a value of "1" indicating acceptance and a value of "0" indicating non-acceptance.

[0102] from Figure 2 (Examples are given for R1 to R3, a total of three levels.) It can be observed that the more severe the violation of user preferences, the higher the user's incentive level, that is, the higher the incentive rate of the load aggregator for the user.

[0103] Under the flexible incentive model, the economic benefits obtained by user i The calculation formula is as follows:

[0104]

[0105] The formula for calculating the economic benefit π2 obtained by the load aggregator is as follows:

[0106]

[0107] Commonly used control strategies include four modes: centralized control, distributed control, hierarchical control, and load balancing. Diagrams illustrating these four control modes are shown below. Figure 3 As shown.

[0108] Centralized control is similar to the control of current generator sets, with the dispatch center directly issuing commands to the load side. Distributed control, based on the smart grid, uses power electronic equipment to monitor and control the load in real time. While flexible, the control equipment can only reflect local observations and cannot feed back the overall situation to the dispatch center, sometimes leading to under-control or over-control. Load aggregator control modes combine the advantages of both decentralized and centralized control, acting as an intermediary mechanism within the control center. It participates in grid dispatching above and directly coordinates the unequal distribution between users and the grid below. Table 2 shows the applicable scenarios for each control strategy and the shortcomings 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 intelligentization of electrical equipment, and the gradual opening up of the electricity sales market, the proportion of electricity consumption by residents is increasing. New market participants such as load aggregators and service providers have emerged, capable of integrating fragmented residential load resources to participate in demand response, making residential load resources a high-quality demand response resource on the demand side. Therefore, it is necessary to conduct research on strategies for the participation of residential loads in demand response.

[0112] The underlying load coordination and control framework of the load aggregator, such as Figure 4 As shown, it is divided into load aggregator layer, load agent layer, and user equipment layer.

[0113] (1) Resource Aggregator Layer: The aggregator analyzes and summarizes the load resource information within its scope, groups them, and manages them separately by the load agent. It calculates the response potential of each load group and feeds it back to the upper-level dispatch center layer. The aggregator then obtains the target power issued by the upper-level dispatch center.

[0114] (2) Load Agent Layer: On the day before, each load group receives the target power adjustment amount issued by the upper aggregator; on the real-time time scale, the load agent monitors the load in the load group in real time, continuously updates the load status and response potential and other parameters, and conducts secondary negotiation of the target power between load groups based on the control target issued on the day before 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 controllability margin index to the load agent through edge side calculation of the user; 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 market demands and provide high-quality demand response services, load aggregators need to control user load according to certain strategies. Upon receiving a demand response request, load aggregators react quickly, attempting to predict the demand and control user load in a more efficient manner. Load aggregators possess various demand response control resources, including adjustable loads, energy storage devices, and distributed power sources. Through reasonable power flexibility load control strategies, load aggregators can integrate and quantify demand-side resources, helping users flexibly manage their loads, smooth intermittent load fluctuations, reduce system peak-to-valley differences, and, compared to increasing installed capacity, have lower investment costs, resulting in significant social and economic benefits.

[0117] Load aggregators formulate different control strategies based on the physical characteristics of different control resources and the control projects. For large industrial and commercial users, load aggregators generally implement interruptible load (IL) projects. For small and medium-sized users, the control strategies for demand response resources can be divided into three main categories: first, regulation strategies, where load aggregators issue price signals through time-of-use pricing, peak pricing, and peak-hour subsidies to guide users to voluntarily participate in demand response projects; second, control strategies, where load aggregators sign mandatory demand response task contracts with small and medium-sized users to determine the user's participation method and response volume, or use energy management systems to directly control flexible loads (DLC); and third, a combination of regulation and control strategies to achieve targeted control of multiple types of resources.

[0118] The load aggregator's aggregation management of controlled users can adopt... Figure 5 The diagram illustrates a centralized-distributed control architecture. Under this architecture, a large number of end-users directly connect to load aggregators, clustering into controlled units according to load type. Driven by electricity price incentives or control commands, the load aggregators guide or directly control their electricity consumption behavior. In this scenario, the load aggregator acts 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 dispatchable data to the power grid. On the other hand, it collects operating status and price signals from the power grid side and formulates and issues control commands based on dispatch requirements.

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

[0120] The upper layer of the hierarchical control architecture is the dispatch center-aggregator layer. The dispatch center predicts the control period of the system on a day-ahead scale and issues the peak-shaving period to each aggregator a day-ahead. After receiving the control period issued by the dispatch center, each aggregator analyzes and predicts the response potential of the load under its jurisdiction and reports it to the dispatch center. The upper-layer aggregator optimizes the dispatch model to obtain the allocation of each aggregator during the peak-shaving period.

[0121] The lower layer of the hierarchical control architecture is the aggregator-user side layer. On the day-ahead time scale, the aggregator clusters its loads according to model coefficients, forming multiple load groups, which are managed by load agents. Each agent analyzes the response potential of each group during its control period and reports it to the aggregator. Considering load rebound effects and control costs, the aggregator optimizes the peak-shaving combination of multiple load groups within its jurisdiction to form a day-ahead target power allocation plan. On the real-time time scale, the load agents continuously update load operating status parameters and response potential based on the day-ahead time scale, and conduct secondary negotiation of the target power for the load groups based on the control targets issued by the aggregator day-ahead and the updated response potential. Finally, each load group achieves its control objectives according to a state sequence control strategy based on controllable margins.

[0122] In demand response projects, depending on the different ways the power grid company controls demand response resources, the organizational models can be divided into three types:

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

[0124] Mode 2: The power grid company sends a demand response signal to the load aggregator, which then controls the electrical equipment.

[0125] Mode 3: The power grid company sends a demand response signal to the load aggregator, and the user controls the electrical equipment after the load aggregator agrees.

[0126] In terms of response reliability, the reliability of these three modes decreases in that order. The power grid company's demand response agreements include direct agreements between the power grid company and large users, as well as negotiations with load aggregators regarding demand response compensation mechanisms. The power grid company's load aggregators customize demand response subsidy pricing mechanisms based on user response capacity levels, developing multiple plans. Users choose the subsidy pricing package according to their own characteristics, fully leveraging the load aggregator's role as an intermediary between the power grid company and users.

[0127] Because different types of flexible loads differ significantly in their physical models and operating characteristics, load aggregator control methods vary considerably depending on the load type. A hierarchical control strategy employs a dispatch center-aggregator layer at the top. The dispatch center issues peak-shaving periods to each aggregator a day-ahead. After receiving the control periods from the dispatch center, each aggregator analyzes and predicts the response potential of its assigned loads and reports this to the dispatch center. The upper-level aggregators then optimize the dispatch model to determine the allocation of peak-shaving periods for each aggregator. This hierarchical control strategy, with its coordinated efforts between the upper and lower layers, effectively ensures the safe and economical operation of the power grid.

[0128] For example:

[0129] Demand response resources are integrated through power grid companies, load aggregators, and electricity retailers. This is based on the current control methods used by power grid companies to organize demand response resources for optimized power grid operation.

[0130] Select the 24-hour load curves of several residential users in a certain region during the summer, as shown below. Figure 6 As shown.

[0131] Depend on Figure 6 It can be seen that the load curve of the region during the summer weekdays shows a "double peak" characteristic, that is, there are small peaks in electricity consumption at 11:00 am and 8:00 pm to 9:00 pm, while the load level is relatively low in the early morning.

[0132] The peak-valley electricity pricing packages signed between load aggregators and residential users are as follows: 9:00 to 23:00 is the peak period, with a peak period price of 1.095 yuan / kWh; 23:00 to 9:00 the next day is the valley period, with a valley period price of 0.515 yuan / kWh.

[0133] The load curves of 150 residential users show some similarity to the overall regional load curve, also exhibiting a "double-peak" characteristic. Developing demand response measures for these 150 users is of significant practical importance for "peak shaving and valley filling" of the overall load curve. The total load baseline for these 150 users is as follows: Figure 7 As shown.

[0134] The load baselines of the 150 users can be divided into three categories: Category 1 "midday peak" users, Category 2 "evening peak" users, and Category 3 "double peak" users. The overall curve for Category 1 users shows a peak electricity consumption at 12 noon, the overall curve for Category 2 users shows a peak electricity consumption at 9 pm, and the overall curve for Category 3 users shows a double peak electricity consumption at both 12 noon and 9 pm.

[0135] Based on the "double-peak" characteristic of the total load curve in the region where the load aggregator is located, assuming two day-ahead electricity price curves, two scenarios are established: the midday electricity price peak and the evening electricity price peak, denoted as Scenario 1 and Scenario 2, respectively.

[0136] The electricity price curve values ​​are shown in Table 3-5. The peak-valley electricity pricing packages signed between load aggregators and residential users are as follows: 8:00 to 23:00 is the peak period, with a peak price of RMB 1.095 / kWh; 23:00 to 8:00 the next day is the valley period, with a valley price of RMB 0.515 / kWh. When the price difference between the current market real-time electricity price and the peak-valley time-of-use price exceeds a threshold of RMB 0.5 / kWh, the load aggregator initiates an incentive-based demand response. Therefore, the subsidy periods can be further defined: Scenario 1 is 11:00 to 12:00; Scenario 2 is 21:00 to 22:00.

[0137] Table 5. Electricity Price Curves for Scenario 1 and Scenario 2

[0138]

[0139]

[0140] The load baselines of 150 users can be divided into three categories: Category A users (midday peak), Category B users (evening peak), and Category C users (both peak and peak periods). To ensure basic residential electricity consumption, user i is constrained to ensure that its minimum load after participating in demand response is not lower than its minimum load before participation. In Scenario 1 (midday peak pricing), users participating in the demand response project include both Category A and Category C users; in Scenario 2 (evening peak pricing), users include both Category B and Category C users. The overall curve for Category A users shows a peak consumption at 12:00 noon, the overall curve for Category B users shows a peak consumption at 21:00, and the overall curve for Category C users shows a double peak consumption at both 12:00 noon and 21:00. The superimposed curve for the three user categories will inevitably also show a double peak consumption.

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

[0142] In Scenario 1, the "midday peak" electricity pricing scenario, the load aggregator's incentive period is from 11:00 to 12:00. First, the user response model is analyzed, with the subsidy standard increasing from 0 to 0.7 yuan / kWh, with intervals of 0.01 yuan / kWh. The total load reduction of Class A users during the incentive period and the changes in the total subsidy amount issued by the load aggregator are calculated and statistically analyzed. The results are as follows: Figure 8 As shown.

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

[0144] From the perspective of load reduction, the dead zone threshold for incentives for Category A users is 0.29 yuan / kWh, meaning that when the subsidy standard exceeds 0.29 yuan / kWh, some users in Category A begin to adjust their electricity load. The saturation threshold is 0.55 yuan / kWh, indicating that when the subsidy standard is higher than this, the load adjustment of all Category A users has reached the upper limit of the constraint, and the load reduction will no longer increase. Looking at the total subsidy amount issued by the load aggregator, the curve trend is consistent with the load reduction trend within the dead zone and response zone. After entering the saturation zone, since the load reduction has reached the upper limit, the total subsidy amount increases linearly with the subsidy standard, and the slope is the maximum load reduction of 84.560 kW for Category A users.

[0145] Figure 10 As subsidies increased, Category A users gradually reduced their electricity load between 11:00 and 12:00 and shifted it to other time periods. Outside of the subsidized periods, Category A users' activity levels were discretely distributed between 13:00 and 23:00. This resulted in a certain degree of increase in the total load curve for Category A users between 13:00 and 23:00, but due to the dispersed nature of these periods, the increase in load at any single moment was not significant.

[0146] In Scenario 1, the total load reduction and total subsidy amount during the incentive period for Category C users are as follows: Figure 11 As shown.

[0147] The dead zone threshold for incentives for Category C users is 0.37 yuan / kWh, meaning that when the subsidy standard exceeds 0.37 yuan / kWh, some Category C users begin to adjust their electricity load. The saturation threshold is 0.54 yuan / kWh, indicating that when the subsidy standard exceeds this, the load reduction of all Category C users reaches the constraint limit and will not continue to increase. After the total subsidy amount enters the saturation zone, it increases linearly with the subsidy standard, with a slope equal to the maximum load reduction of 62.368 kW for Category C users.

[0148] Similarly, a comparative analysis was conducted on the overall load changes of Category C users relative to the baseline load, such as... Figure 12 As shown, when the subsidy standard increases, Category C users gradually reduce their electricity consumption between 11:00 and 12:00, while load changes occur at other times. Combined with the "bi-peak" characteristic of the Category C user load curve, with peak electricity consumption periods at noon and in the evening, correspondingly, in Scenario 1, the total load curve for Category C users is expected to rebound between 19:00 and 22:00, with the most significant rebound occurring at 21:00. Furthermore, there is also a certain increase in load levels around 10:00 and 13:00 before and after the incentive period, possibly indicating that users have advanced or postponed some of their electricity consumption.

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

[0150] Table 6. Statistics on Revenue Before and After Demand Response Implementation by Load Aggregator in Scenario 1 (Unit: Yuan)

[0151]

[0152] (2) Scene 2

[0153] In Scenario 2, the "evening peak" electricity pricing scenario, the load aggregator's incentive period is from 21:00 to 22:00. Similarly, examining the lower-level rational user response model, the subsidy standard is set to increase from 0 to 0.7 yuan / kWh, with intervals of 0.1 yuan / kWh. The total load reduction of Class B users during the subsidy period and the total subsidy amount issued by the load aggregator are calculated and statistically analyzed. Figure 13 As shown, the dead zone threshold for incentives for Category B users is 0.29 yuan / kWh, indicating that when the subsidy standard exceeds this threshold, a small number of Category B users begin to respond to the incentive signal; the saturation threshold is 0.51 yuan / kWh, meaning that when the subsidy standard is higher than this, the load reduction of all Category B users has reached its limit. Similarly, after the total subsidy amount enters the saturation zone, it increases linearly with the subsidy standard, with a slope equal to the maximum load reduction of 88.239 kW for Category B users.

[0154] Compare and analyze the overall load changes of Category B users relative to the baseline load, such as... Figure 14 As shown, with the increase in subsidy standards, Category B users gradually responded to the subsidy signal, reducing their electricity load from 21:00 to 22:00 and adjusting their electricity consumption schedules for other periods. Category B users are more active in the evening, with their overall load showing a significant rebound around 20:00 and 23:00, which are the times before and after the subsidy period. At the same time, there is also a slight increase in load during the early morning hours.

[0155] In Scenario 2, the total load reduction and total subsidy amount for Category C users during the subsidy period are as follows: Figure 15As shown. Similarly, the overall load change of Class C users relative to the baseline load under scenario 2 is analyzed, as follows: Figure 16 As shown.

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

[0157] Table 7: Statistics on Revenue Before and After Demand Response Implementation by the Scenario 2 Load Aggregator (RMB)

[0158]

[0159] In summary, the main conclusions are as follows: First, under economic incentive signals, user response behavior can be divided into dead zones, response zones, and saturation zones. Furthermore, influenced by factors such as electricity load levels, the thresholds for dead zones and saturation zones, as well as the load transfer periods, differ significantly among different types of user response behavior. Second, in Scenario 1, the optimal subsidy standards for Class A and Class C users are 0.5359 yuan / kWh and 0.4778 yuan / kWh, respectively, resulting in a 31.03% increase in the load aggregator's profit compared to before demand response. In Scenario 2, the optimal subsidy standards for Class B and Class C users are 0.4369 yuan / kWh and 0.4778 yuan / kWh, respectively, with a 21.41% increase in profit. The significant increase in load aggregator profits demonstrates the rationality of the demand response strategy.

[0160] Due to limitations imposed by objective factors and forecasting models, load aggregators inevitably have some bias in their predictions of day-ahead real-time electricity prices. Demand response strategies are formulated based on specific electricity price forecasts, with peak prices during subsidy periods being particularly crucial. Currently, market electricity prices directly determine the electricity purchase costs of load aggregators, and their forecasting biases significantly impact the overall benefits of implementing demand response. Under the same subsidy standard, Table 8 shows the changes in electricity purchase costs and overall benefits for Category C users under different electricity price forecasting biases during peak periods. Table 8 shows that when the electricity price level is underestimated (i.e., the actual value is higher than the forecast), the electricity purchase cost increases, and the peak-valley price difference (the portion of the peak-valley price lower than the real-time price) widens. Consequently, the overall benefits of demand response for load aggregators increase more significantly and are positively correlated with the absolute value of the electricity price forecasting bias. Conversely, when the electricity price level is overestimated (i.e., the actual value is lower than the forecast), the overall benefits decrease as the absolute value of the bias increases. The table also indicates that even considering an electricity price forecasting bias of ±10%, load aggregators still have room for profitability.

[0161] Table 8. Statistics on the Comprehensive Benefits of Category C Users under Different Electricity Price Forecast Deviations During Peak Hours

[0162] Deviation between actual and predicted electricity prices +2％ +5％ +8％ +10％ Actual electricity purchase cost (RMB) 915.40 920.63 925.85 929.34 Overall benefits (yuan) 21.29 24.40 27.51 29.58 Changes in overall benefits relative to the original scenario 10.77％ 26.95％ 43.13％ 53.90％ Deviation between actual and predicted electricity prices -2％ -5％ -8％ -10％ Actual electricity purchase cost (RMB) 908.43 903.20 897.97 894.49 Overall benefits (yuan) 17.15 14.04 10.93 8.86 Changes in overall benefits relative to the original scenario -10.77％ -26.95％ -43.13％ -53.90％

[0163] Based on the same inventive concept, embodiments of the present invention provide a demand-side aggregated load collaborative control system, comprising:

[0164] The data analysis module is used to acquire user-side electricity price, market electricity price fluctuations, and user baseline load forecast data, and analyze them to obtain demand response targets;

[0165] The demand response module is used to select the corresponding price-based and incentive-based demand response modes based on the demand response objectives and user characteristics, and to construct the corresponding demand-side aggregated load model; wherein, based on the price-based demand response mode, a price-based demand-side aggregated load model is constructed; and based on the incentive-based demand response mode, an incentive-based demand-side aggregated load model is constructed.

[0166] The regulation strategy formulation module is used to determine the regulation strategy of aggregated load based on the demand response mode and the demand-side aggregated load model. The regulation strategy includes adjustment, control and hybrid strategies. The regulation strategy is classified according to the applicable scenario to obtain the regulation strategy classification result.

[0167] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned demand-side aggregated load collaborative control method.

[0168] This invention provides another computer device, including 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 above-described demand-side aggregated load collaborative control method.

[0169] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

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

Claims

1. A demand side aggregated load coordinated control method, characterized in that, Includes the following steps: Acquire user-side electricity prices, market electricity price fluctuations, and user baseline load forecast data to analyze and obtain demand response targets; Based on the aforementioned demand response objectives and combined with user characteristics, corresponding price-based and incentive-based demand response modes are selected, and corresponding demand-side aggregated load models are constructed. Specifically, a price-based demand-side aggregated load model is constructed based on the price-based demand response mode, and an incentive-based demand-side aggregated load model is constructed based on the incentive-based demand response mode. Based on the demand response model and the demand-side aggregated load model, the regulation strategy for aggregated load is determined, including adjustment, control and hybrid strategies. The control strategies are classified according to their applicable scenarios to obtain the control strategy classification results; The price-based demand-side resource aggregation load model is as follows: （1） （2） wherein, a demand side user responsive to a price-based demand response program i t a load actual demand power during a time period; a demand side user i a minimum and a maximum of a range of power intervals to which the demand side user is responsive; a set of demand side users responsive to a price-based demand response program;​​ The incentive-based demand-side aggregated load model includes: a fixed incentive mode and a flexible incentive mode; Under the fixed incentive model, the formula for calculating the economic benefits obtained by users is: (3) where: is the length of a control period; is the daily user response; i is the actual power at time t; is the baseline power for user i at time t;E a is the fixed incentive rate of the load aggregator to the user; In the flexible incentive model, the load aggregator adopts a multi-level incentive model as the incentive mechanism for rewarding users to participate in the renewable energy consumption business, as shown in the following formula: (5) In the formula: Indicates time slot t Time users i Incentive rate for participating in demand scheduling; Level 1 incentive rate; The incentive rate is Level 2. The incentive rate is level 3; The incentive rate is level 4. The incentive rate is 5 levels. For users i The minimum threshold for the allowed temperature setting range; For users i The highest threshold value within the allowed range of temperature settings; For time slots t Time users i The water heater temperature setting value; Indicates user i Whether to accept temperature settings exceeding the allowable range of variation; a value of "1" indicates acceptance, and a value of "0" indicates rejection. Under the flexible incentive model, the formula for calculating the economic benefits obtained by users is as follows: （6） in, The economic benefits obtained by users; The duration of one control cycle; In response to daily users' time t The actual power; R represents the user's baseline power at time t; R is the incentive rate for the user to participate in demand scheduling at the time slot.

2. The demand-side aggregated load collaborative control method as described in claim 1, characterized in that, The incentive-based demand response model includes: a fixed incentive model and a flexible incentive model; The classification results of the control strategies include: centralized control, decentralized control, hierarchical control, and load broker control.

3. The demand-side aggregated load collaborative control method as described in claim 1, characterized in that, The classification results of the control strategies are used to implement demand-side aggregated load coordinated control, evaluate the implementation effect of the control strategies, and obtain evaluation results, including cost reduction, load fluctuation smoothing, and increased renewable energy consumption.

4. The demand-side aggregated load collaborative control method as described in claim 1, characterized in that, Under the flexible incentive model, load aggregation is divided into different levels according to the degree of user default, and graded compensation rules are formulated. The hierarchical compensation rules include: Level 1 refers to high-quality resources, with a default rate of less than 3%, and a compensation multiple λ1 = 1.01; Level 2 is considered a qualified resource, with a default rate of 3% to 8%, and a compensation multiple λ2 = 1.0; Level 3 refers to restricted resources, with a default rate of 8% to 13% and a compensation multiple λ3 = 0.95; Level 4 resources are prohibited, with a default rate exceeding 13%.

5. A demand-side aggregated load collaborative control system, characterized in that, The system, employing the demand-side aggregated load collaborative control method as described in any one of claims 1 to 4, comprises: The data analysis module is used to acquire user-side electricity price, market electricity price fluctuations, and user baseline load forecast data, and analyze them to obtain demand response targets; The demand response module is used to select the corresponding price-based and incentive-based demand response modes based on the demand response objectives and user characteristics, and to construct the corresponding demand-side aggregated load model; wherein, based on the price-based demand response mode, a price-based demand-side 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 regulation strategy formulation module is used to determine the regulation strategy of aggregated load based on the demand response mode and the demand-side aggregated load model. The regulation strategy includes adjustment, control and hybrid strategies. The regulation strategy is classified according to the applicable scenario to obtain the regulation strategy classification result.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the demand-side aggregated load collaborative control method as described in any one of claims 1 to 4.

7. 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, wherein the processor executes the program to implement the demand-side aggregated load collaborative control method as described in any one of claims 1 to 4.

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