126 results about "Demand response" patented technology

The application discloses an algorithm electricity collaborative demand response bidding decision method and system based on large model training tasks, and relates to the technical field of power system dispatching. First, the power grid invitation signal is received and analyzed to construct an invitation signal feature set. Then, the state of the intelligent algorithm center is collected, and the computing power shadow price of each large model training task is calculated; the state space and action space are constructed according to the distribution characteristics of the invitation signal feature set and the computing power shadow price, the PPO algorithm is used to solve the optimal bid, and the optimal bidding strategy is output. If the bidding is successful, the adjusted task set is selected in the order from low to high according to the computing power shadow price of each large model training task, and hierarchical control is performed as needed. Through the hierarchical response mechanism and cost consideration, when interruption must be performed, the large model training task that has just completed the "checkpoint saving" is automatically locked and suspended through the computing power shadow price sorting, the impact on the training progress is minimized, and the safety of the model parameters is ensured.

This invention relates to the field of power system demand response technology, and provides a user demand response method based on master-slave game theory under cost control constraints. The method includes: establishing a demand response model using master-slave game theory, where electricity retailers design time-of-use pricing strategies, and electricity users respond based on a convex utility function; the utility function includes a quantification term for electricity usage satisfaction and a calculation term for electricity costs; the electricity retailers' revenue function includes four functions: user electricity costs, long-term market electricity purchase costs, current market electricity purchase costs, and conditional risk value, coupled with the characteristics of the electricity spot market and risk management requirements; proving the existence of a Nash equilibrium solution using backward induction; and optimizing the strategy based on a slime mold optimization algorithm. This invention provides important support for electricity retailers to formulate flexible pricing mechanisms and tap demand-side resources in terms of cost control for users with high response potential and optimization of peak-valley load differences.

This invention belongs to the field of energy management technology for charging stations and discloses a joint scheduling method for energy management and electricity price response of charging stations with energy storage. The method acquires information on incoming power supply capacity, station basic load, energy storage unit operating status, charging terminal operating status, vehicle energy replenishment demand, and demand response, determining the demand response period and recovery period. Based on the station-level direct replenishment capacity without relying on energy storage units during the recovery period, it determines the maximum direct replenishment capacity, response-reserved replenishment energy, and response-transferred replenishment energy for each vehicle waiting to be charged. Furthermore, it determines the station-level minimum charging demand during the demand response period, the energy storage unit's recovery-reserved capacity, and the reduction boundaries on the vehicle side and energy storage side, generating a joint scheduling instruction, and replenishing the reduced replenishment energy during the recovery period. This method is applicable to demand response and energy replenishment scheduling of charging stations with energy storage.

Embodiments relate to a virtual power plant system based on self-demand response. A system according to an embodiment relates to a virtual power plant system that analyzes in real time at least one of power demand data, power generation amount data, electricity price data, and distributed power information and provides the result of the analysis in order to enable an individual consumer to perform self-demand response (Self-DR) using a user terminal.

This application discloses an appliance power decomposition method and storage medium. The method includes: collecting the raw current signal, total active power data, actual power data of the target appliance, and electricity consumption scenario information from the user's main electricity meter; extracting frequency domain feature vectors from the raw current signal, performing one-hot encoding on the electricity consumption scenario information to obtain scenario feature vectors, and normalizing the total active power data and actual power data; constructing a neural network model including a two-layer sliding window; inputting the processed data into the neural network model for training and updating the model parameters; inputting the bus observable signal at any time into the trained neural network model, and outputting the power decomposition result of the target appliance. The advantage of this application is that it improves the accuracy of appliance power decomposition, providing accurate data support for grid energy management and electricity demand response.

This invention discloses a resource collaborative scheduling and demand response optimization system and method for virtual power plants in industrial parks. It achieves global collaborative scheduling through a two-layer Actor-Critic network architecture, and uses a fusion of particle swarm optimization and fuzzy control to achieve grid-connected stability control. A three-layer fusion optimization architecture is constructed to complete the continuous amplitude calculation of demand response and the generation of equipment-level instructions. A unified algorithm support is provided by an intelligent optimization module. This invention effectively solves the problems of weak adaptability of existing global optimization models for virtual power plants in industrial parks, low level of intelligent decision-making in specialized sub-scenarios, and difficulty in coordinating multi-timescale scheduling and refined execution. It can achieve robust optimization and refined control in uncertain scenarios, balancing system economy, stability, and operational reliability. It is suitable for virtual power plants in industrial parks to efficiently participate in the power market and resource collaborative scheduling.

This invention relates to the field of electric vehicle charging station technology, specifically to an energy control strategy for bidirectional demand response in energy storage-based electric vehicle charging stations. The strategy includes the judgment conditions and execution commands required for the control process. The judgment conditions include: whether a vehicle is charging; peak / valley / normal times; existence of emergency demand from the external power grid; battery charge status; and a comparison between the vehicle's charging power demand, the battery's maximum discharge power, and the maximum power input from the power grid to the charging station. The control strategy primarily selects energy storage charging / discharging control and grid-side power supply control during peak / valley / normal times based on the grid-user bidirectional demand response. This includes grid-connected master-slave control and off-grid islanding control switching according to load size at different times, maximizing user charging needs while minimizing charging costs.

The application discloses a turbine flowmeter impeller rotating system and a dynamic balancing method, relates to the technical field of flow measurement, and comprises a turbine flowmeter shell, a core shaft, two flow guides and a rotating mechanism. A plurality of turbine blades are fixedly connected to the side wall of the core shaft. The flow guides are fixedly connected to the turbine flowmeter shell through a plurality of screws. The rotating mechanism comprises a static cavity formed in the flow guide. The core shaft penetrates into the static cavity at both ends. The static cavity is filled with magnetorheological fluid. A ceramic ball is fixedly connected to the inner wall of the static cavity. A tapered groove is formed in the side wall of the ceramic ball. In the application, the rotating speed of the core shaft is monitored in real time through a rotating speed sensor. The current size of a magnetic induction coil is controlled by using a PLC, the viscosity of the magnetorheological fluid is controlled, the damping force is changed, and the displacement of the core shaft in the axial and radial directions is actively inhibited. The on-demand response mechanism fundamentally solves the stability problem caused by high-speed vibration and water hammer impact.

This application relates to the field of microgrid operation and control technology, and discloses a microgrid operation and scheduling method. The solution includes the following steps: S1: Multi-source data acquisition and high-precision prediction. The economic benefits of this application are improved: operating costs are reduced by 15%-20%, the renewable energy consumption rate is increased from 85% to over 98%, and revenue is increased through peak-valley electricity price differences and demand response; reliability is enhanced: the power supply deficit rate is reduced to below 0.5%, the voltage / frequency deviation is controlled within ±2% / ±0.5Hz, and the critical load guarantee rate is 100% in islanded mode; environmental protection is optimized: carbon emissions are reduced by 20%-30%, which meets the requirements of the "dual carbon" policy and reduces pollutant emissions; wide adaptability: it is compatible with microgrids of different scales and operating modes, the algorithm is robust, and it can cope with large fluctuations in wind and solar / load; engineering feasibility: it adopts mature algorithms and standardized processes, and can be upgraded and transformed based on existing EMS systems, with low deployment costs and quick results.

The application discloses a kind of comprehensive flexible resource reliability optimization methods considering dynamic multi-energy demand response.Comprehensive flexible resource is divided into four kinds of multi-state comprehensive flexible resource, construct the multi-state model of comprehensive flexible resource considering time sequence characteristics, obtain the multi-energy load demand of each comprehensive flexible resource;Respectively, the multi-state model of comprehensive flexible resource reduction, transfer, alternative response considering time sequence characteristics is established, obtain the multi-energy load demand of comprehensive flexible resource after reduction, transfer, alternative response end in each state;Fusion three kinds of multi-state model, establish the multi-state model of total demand response, obtain the amount of demand side operation reserve provided in multiple states, and then calculate the reliability of the amount of demand side operation reserve provided by comprehensive flexible resource.The application adopts multi-state model to more accurately characterize the dynamic characteristics of comprehensive flexible resource, and accurately obtains the operation risk parameters of the amount of demand side operation reserve provided by comprehensive flexible resource.

This disclosure provides a power control system that allows users to choose whether or not to respond to demand response (DR) requests regarding the adjustment of power demand for electrical equipment. [Solution] The power control system according to this disclosure includes electrical equipment used by a power consumer, a management device that receives a DR implementation request from a power supplier regarding the adjustment of power demand and manages the adjustment of power demand at the power consumer using the electrical equipment, and a user terminal device used by the user who is a power consumer. When the management device receives a DR implementation request from a power supplier, it sends an instruction request to the user terminal device asking for instructions on whether or not to comply with the received DR implementation request, and receives an acceptance or rejection instruction from the user regarding whether or not to comply with the received DR implementation request via the user terminal device.

This invention discloses a demand response scheduling method and system that considers the fairness of nodal carbon potential location, belonging to the field of low-carbon optimization scheduling technology. The demand response scheduling method considering the fairness of nodal carbon potential location includes: establishing a comprehensive energy system equipment model and a distribution network carbon emission flow model; establishing a hybrid index model considering the fairness of nodal carbon potential location; establishing a load-side dynamic demand response model based on the hybrid index model to generate carbon price signals; constructing a two-stage low-carbon optimization scheduling model considering the uncertainty of renewable energy output, with system operating costs, nodal carbon potential location fairness index, and carbon emission costs as optimization objectives. The optimal scheduling scheme is obtained through iterative solutions by determining unit output plans in the pre-scheduling stage and adjusting load distribution in the subsequent scheduling stage. This invention reduces system operating costs, achieves refined allocation and fair control of carbon emission responsibility in the comprehensive energy system, and improves the overall low-carbon operation level and the degree of reasonable carbon responsibility sharing of the system.

This invention relates to the field of new energy charging and swapping technology, and discloses a demand response method, system, equipment, and medium for an integrated photovoltaic-storage-charging and swapping station. It constructs a basic model of the integrated photovoltaic-storage-charging and swapping station, and divides the power batteries within the station into several discrete groups according to their state of charge. Then, based on constraints on the number of charging / discharging and swapping operations, a dynamic grouping management model for each discrete group is constructed. Using the basic model and the dynamic grouping management model, a two-layer optimization model is constructed, comprising an outer optimization model aimed at peak shaving and valley filling of the power grid, and an inner scheduling model aimed at maximizing the revenue of the integrated photovoltaic-storage-charging and swapping station. A two-layer iterative solution method based on an adaptive mutated particle swarm optimization algorithm is used to solve the two-layer optimization model, obtaining the optimal demand response strategy to control the execution of the integrated photovoltaic-storage-charging and swapping station. This effectively achieves peak shaving and valley filling of the distribution network load while improving the economic benefits of the charging and swapping station.

The application discloses a power distribution network multi-party collaborative planning method and system based on reinforcement learning and double-layer game, constructs a double-layer Stackelberg game framework, sets a distribution network operator (DSO) as an upper-layer decision maker, sets a union (MSC) composed of distributed power, energy storage and users as a lower-layer follower, and describes a multi-party competition and cooperation relationship; heterogenous reinforcement learning algorithms are innovatively adopted; a proximal policy optimization (PPO) algorithm is adopted for discrete planning decisions (such as line and equipment site selection) of the DSO; a deep deterministic policy gradient (DDPG) algorithm is adopted for continuous operation decisions (such as capacity configuration and demand response) of the MSC; two intelligent agents are collaboratively trained through a hierarchical iterative game mechanism, and finally converge to an equilibrium strategy. The application can effectively coordinate the interests of all parties, guarantee the safety of the power grid, and improve the overall economy and renewable energy consumption capacity of the system.

The present application relates to electric vehicle charging management technical field, specifically relates to a kind of electric vehicle charging coordination control method and system.The method is by constructing the linearization power flow model of radial distribution network, photovoltaic output and electric vehicle charging load are included in unified power flow calculation framework, obtain node voltage and line power distribution;The decision-making process of electric vehicle charging station is modeled as Markov decision process, to each charging station as independent agent, according to the space-time load distribution of charging station, power flow constraint and demand response signal dynamically adjusts charging pricing strategy;While introducing communication network modeling and communication cost quantification mechanism, information interaction overhead is included in reward function design, so that Markov decision process realizes collaborative trade-off between profit maximization and communication resource consumption.The present application realizes the intelligentization, order and efficient charging control of electric vehicle group in vehicle network interaction environment under the condition of traffic, power and communication multi-network coordination.

This invention discloses a method and system for coordinated interaction of residential microgrids based on demand response, comprising: acquiring and analyzing residents' power supply parameters through sensors to obtain coordinated power consumption values ​​and analyze them, selecting cooperative users, analyzing their historical cooperation frequency, and obtaining a coordination ratio; comparing the coordination ratio with a coordination ratio threshold to select proactively responding users and generate a proactive response signal; analyzing the power supply parameters of proactively responding users based on the proactive response signal to obtain unit cost; analyzing the unit cost to obtain a cost deviation ratio; comparing the cost deviation ratio with a cost deviation ratio threshold to determine whether the residential microgrid needs improvement, and if so, generating an improvement signal; analyzing the power supply parameters of proactively responding users based on the improvement signal to obtain a microgrid excellence value, and referring to the residential microgrid with the highest excellence value, improving the residential microgrid that needs improvement. This invention improves the user's initiative in responding to grid demands.

This application relates to a training method, system, and load forecasting method for a building baseline load forecasting model. The method includes: acquiring a candidate feature set and a historical load training set; calling an initial model to predict the training set based on the candidate feature set, obtaining the prediction error, and extracting the gain and coverage indices of the candidate feature set to determine its importance; iteratively eliminating candidate feature sets by combining the prediction error and importance to obtain the optimal features; training a target model using the optimal features and the training set; using the target model to predict the baseline load during demand response, calculating the load reduction based on the actual operating load, and generating corresponding demand response scheduling instructions. This application achieves interactive collaboration between feature selection and the prediction model, improving the prediction accuracy of building baseline load and providing reliable data support for the stable scheduling of power grid physical equipment.

In an electric power supply-demand adjustment system for performing electric power supply-demand adjustment by causing, by using a computer including a processor and memory, customers to control power consumption in accordance with an instruction issued by an aggregator, the processor, on the basis of demand response (DR) event information stored in the memory and including a scheduled event regarding the electric power supply-demand adjustment, and customer profile information stored in the memory and indicating a feature of a time-shiftable process performed by the customers consuming electric power, obtains a combination of processes shifted within an acceptable range, in which combination a power reduction amount fulfills a requested adjustment amount, to generate, for each of the customers, DR request information including a request for supply-demand adjustment of a power adjustment amount determined by the scheduled DR event, and determines a width of the shift to carry out electric power supply-demand adjustment.

The invention discloses a virtual power plant demand response clearing method and system based on a ciphertext grading constraint, and relates to the technical field of virtual power plant demand response clearing, and the method comprises the steps: carrying out the ciphertext packaging of a distributed resource node scheduling related parameter of a virtual power plant, and obtaining a node scheduling ciphertext parameter; scheduling control factors are obtained according to the node scheduling ciphertext parameters and the current demand gap parameters of the power grid, node priorities are determined and layered based on the scheduling control factors, and a ciphertext grading constraint matrix is constructed; according to the ciphertext grading constraint matrix and the demand response instruction, outputting an encrypted clearing result according to a power distribution objective function; and according to the ciphertext grading constraint matrix, a dynamic threshold reconstruction set is output according to a preset key reconstruction safety threshold, key reconstruction is completed based on the dynamic threshold reconstruction set, the encrypted clearing result is decrypted through the reconstructed key, and a scheduling instruction is generated. The method has the beneficial effects that the safety and the flexibility of the clearing calculation process are considered.

The application discloses a heterogeneous energy collaborative prediction and scheduling method considering source-load low-carbon characteristics complementation and matching, relates to the technical field of power system source-load prediction and low-carbon scheduling, and comprises the following steps: based on Laguerre polynomials, pseudo-inverse learning and integrated learning, robust local mean decomposition (RLMD) and weighted permutation entropy (WPE), a wind power (WP), photovoltaic power (PVP) and power load (PL) prediction model is constructed, which takes into account the prediction accuracy and stability; a HECM mathematical model is constructed, the HECM mathematical model framework comprises an integrated flexible operation mode of carbon capture power plant (IFCCPP), a wind farm (WF), a photovoltaic power station (PP), a concentrated solar power (CSP), a load active demand response (LADR), a ladder carbon trading mechanism (LCTM), an objective function and related constraints; in the source-load prediction, the two targets of accuracy and stability are considered simultaneously, a compromise solution is selected in the Pareto frontier through the MORUN algorithm, and the wind power, photovoltaic power and power load prediction results are more suitable for the power system scheduling scene with high robustness requirements.

This invention relates to a distribution network reconfiguration method based on distributed generation and SOP (Standard Operating Procedure) optimization configuration, belonging to the technical field of new energy grid connection and distribution network optimization operation. The method includes: performing site selection and capacity determination on the original distribution network using an improved gray wolf algorithm based on a constructed distributed generation optimization configuration model; updating the distribution network structure to form a new distribution network structure, constructing an SOP optimization configuration model, and optimizing the SOP configuration using an improved sensitivity analysis method; connecting the optimal access location and capacity of the distributed generation obtained from site selection and capacity determination, along with the SOP optimized configuration capacity and location, to the distribution network, constructing a distribution network demand response model; constructing a distribution network reconfiguration model based on the distribution network demand response model, reconfiguring the distribution network, obtaining the distribution network disconnection results, and thus obtaining a new topology. It aims to solve the technical problems of increased voltage fluctuations, increased network losses, and bidirectional power flow caused by a high proportion of distributed generation access to the distribution network.

In a charging station regulation method and device, a storage medium and a computer device provided in the application, if the charging station is in an energy-saving and consumption-reducing mode, the distribution transformer load rate of the charging station is calculated; when the distribution transformer load rate is greater than a distribution transformer load rate threshold, the energy storage device and / or each charging pile in the charging station are regulated according to the size relationship between the storage power and the rated power of the energy storage device corresponding to the charging station; if the charging station is in a regulation demand response mode, the energy storage device and / or each target charging pile are regulated according to the size relationship between the dischargeable time length of the energy storage device and the regulation required time length; when the regulated load of the charging station reaches the regulation required load, or after each target charging pile participates in the regulation, the regulated time length of the charging station does not reach the regulation required time length, the energy storage device and / or each non-target charging pile with a cuttable charging time length not greater than the regulation required time length are regulated. In this way, the regulation requirements of the charging station in different scenarios can be met.

The present application relates to the technical field of smart grid demand response, in particular to a power grid demand response method and system based on dynamic optimization of automatic driving bus driving strategy, vehicle state is collected to determine the station adjustment interval and screen the response trajectory, combined with load monitoring and demand instruction to determine the power grid load limiting period, according to the relationship between the predicted station and the limiting period, the trajectory is classified and labeled as access type, and then the vehicle driving and parking rhythm is adjusted to generate charging time sequence sorting, combined with real-time load verification, the actual access sequence of the vehicle is generated and the execution record is generated. The present application generates the station adjustment range based on the driving state and the electric quantity, arranges the charging sequence combined with the power grid load fluctuation, charges in advance or delays in the limited period, sorts the charging behavior and identifies the abnormality, realizes the accurate matching of charging and load constraint, reduces the peak pressure and improves the electric energy efficiency, guarantees the task completion and reduces the conflict, forms the adaptive mechanism, realizes the dynamic matching optimization of charging and power grid.

This invention discloses a renewable energy dispatching method, device, terminal, and medium based on risk perception. The method predicts photovoltaic (PV) power generation using confidence level, environmental data, and a PV power generation prediction model; constructs a two-layer optimization model to coordinate aggregator costs and building user costs during demand response; and obtains building flexible participation capacity and demand response incentive price by solving the two-layer optimization model. Based on confidence level, PV power generation, building flexible participation capacity, and demand response incentive price, it constructs an aggregator day-ahead bidding profit optimization model and supply and demand constraints, respectively. Based on the aggregator day-ahead bidding profit optimization model and supply and demand constraints, it optimizes the energy storage system's charging and discharging strategy and energy trading strategy. This solves the problem of existing technologies failing to consider the uncertainty of power generation, user participation costs in demand response, and willingness to respond, making it difficult to effectively balance the risks of power shortages and trading benefits, and achieving power dispatch optimization under uncertain conditions.

There is provided an energy transaction platform to facilitate a wholesale↔distribution↔DER owner marketplace for DER-based energy products, including contracting, delivery, and settlement, for example, based on blockchain technology. The platform provides components to enrol and verify DER owners having a DER device. Platform components define and communicate contracts for energy services for bidding by DER owners to facilitate e.g. EV charging, GHG reduction, or demand response goals. Contracts are cleared in response to bidding, for example, by DER owners. The platform triggers contract delivery, which delivery is monitored and confirmed. Settlement is made between contract parties according to confirmed participation. Participation rewards and / or credit tokens are transferrable to DER owners, which can be further transferred. DER owner credibility measures can encourage participation and assist with clearing contracts. The platform can use blockchain smart contracts for events and store data to the blockchain.

This invention discloses an industrial park optimization scheduling method based on the wild horse herd algorithm, comprising: establishing a multi-energy resource model of the park and an objective function oriented towards minimizing the overall expected operating cost of the industrial park; constructing a multivariate joint probability distribution model of wind power, photovoltaic power, and load using the R-Teng Copula method to quantify source-load uncertainty and generate multiple typical scenarios; establishing a two-layer optimization model, with the upper layer taking the minimization of the overall expected operating cost of the industrial park as the global optimization objective, and the lower layer dividing the park into several aggregation units with the minimization of the operating cost of each unit as the local optimization objective; performing sub-problem optimization for each aggregation unit based on the wild horse herd algorithm and ADMM; and achieving optimized scheduling based on the charging and discharging power of energy storage units, the demand response reduction power of flexible units, and the output value of distributed units in each aggregation unit obtained through optimization.

This invention belongs to the field of power transmission and distribution systems, and discloses a method and system for generating nodal baseline load response under transmission and distribution coordination. It integrates the nodal marginal electricity price into a pre-constructed two-layer Steinberg game model, iteratively updates the incentive unit price to obtain the optimal value, and linearizes the exponential and product nonlinear terms in the demand response incentive service quantity of the distribution system operator. Finally, based on the linearization result and the optimal unit price, it solves the game equilibrium to generate the nodal baseline load response. The nodal marginal electricity price can characterize the differences in the geographical distribution of nodes, thus transforming a single incentive signal into a differentiated and precise incentive signal. Simultaneously, the linearization process eliminates the limitations of nonlinear terms on commonly used optimizers through mathematical transformation. This enables the demand response mechanism to adapt to the actual operating needs of the power grid under high renewable energy penetration, improves the accuracy of incentives and the feasibility of optimization, effectively enhances the overall effect of demand response, and promotes its practical application.

The present application relates to the field of long-term power outage strategy optimization of power grid, and particularly relates to a long-term power outage strategy optimization method of power grid, comprising: building a user-power grid interaction platform, the platform supports user access through a mobile terminal or a computer terminal, and the platform has a user terminal module, a power grid terminal module, a solution library module and a two-way feedback module; the method collects user basic information, power outage sensitive information, self-provided conditions and other key data by building the user-power grid interaction platform, realizes accurate capture of the needs of different types of users, such as fully non-power outage hospitals and short-time non-power outage shops, ensures that the power outage strategy fully considers the special power demand of users, reduces the production and life loss of users, accurately responds to the differentiated needs of users, and solves the problem of demand response loss.

This invention proposes a power load forecasting and demand response method and system. It employs a dynamic forecasting mechanism to predict power load data for the next time period. This power load data includes power load forecast data for a target subgrid area where the probability of a peak-valley switching event or a power anomaly event occurring in the next time period is greater than a preset probability threshold. The method also acquires demand response strategy configuration data for controllable electrical equipment configured with demand response strategies within the target subgrid area. Based on the demand response strategy configuration data, the method adjusts the power consumption mode of the controllable electrical equipment to ensure that the power parameters of the target subgrid area are within a safe range in the next time period. This improves the universality of demand response on the power consumption side, effectively enhances the grid's flexible load dispatching capability, suppresses the impact of sudden load changes, balances power demand during peak and valley periods, and significantly improves the power supply quality and economy of the grid.

The present application belongs to the technical field of power grid adaptive load scale measurement, and particularly relates to a kind of adaptive load scale measurement method of sub-industry and sub-region;Through step 1, collect the basic data of power grid electricity load in each region of measurement region;Step 2, the price sensitive industry of each region in the measurement region is screened;Step 3, the peak period of power grid electricity in each region in the measurement region and the comparison of time-of-use electricity price in each period;Step 4, the adaptive load regulation capacity of each region industry in the measurement region is measured;Step 5, the adaptive load scale of the measurement region and each region thereof is measured;It can be judged whether the industry has lifted the load in the peak period of electricity consumption, which helps to screen demand response applications;It can lock the industry load with strong regulation capacity and large scale, and ensure the smooth implementation of demand response through targeted publicity and focus;It can evaluate the total and sub-region response scale of a region, which helps to reasonably develop annual response plan.