172results about How to "Reduce peak-to-valley difference" patented technology

The invention provides an intelligent control method and system for interactive home appliances on the basis of time-of-use electricity price response. The method is characterized by comprising the steps that the home appliances are automatically classified according to types of home appliance loads; use habits of the home appliances of users are determined according to historical home appliance running status data collected in 30 days; according to time-of-use electricity price policy information of a receiving power grid, basic load demand ranges corresponding to all time periods are predicted and determined for the users; according to obtained user home appliance use habit characteristics and load settings, controllable degree indexes of the home appliances are calculated, and running status models of the home appliances are determined; according to the controllable degree indexes of the home appliances, dynamic control priorities and a control algorithm of the home appliances are determined, and intelligent control over the home appliances is achieved. The method and system have the advantages that interactivity is good, the receiving degree of the users is high, the users can be guided to reasonably avoid the peak hours for electricity utilization, and electricity utilization cost is remarkably reduced.

The invention relates to a battery energy storage system peak clipping and valley filling real-time control method based on load prediction and belongs to the field of power system automatic control. The control method provided by the invention comprises the following steps of: firstly searching similar history daily load data, carrying out expanding short-term load prediction by adopting a linear regression analysis method, building a battery energy storage system peak clipping and valley filling real-time optimization model, solving the battery energy storage system peak clipping and valley filling real-time optimization model by adopting a dynamic programming algorithm, and obtaining the output power of a battery energy storage system at each moment. The control method provided by the invention comprises battery charging and discharging frequency constraint and discharge depth constraint in the real-time optimization model, is used for researching relation between battery life and the charging and discharging frequency and the relation between the battery life and the discharge depth and is beneficial to prolonging the battery life. Minimum load variance is taken as a target function, the peak-to-valley of a load curve can be reduced, the load curve is smoother while constraint conditions are met, and the peak clipping and valley filling application requirement can be met. Local part of the load curve can be smoother by adopting load smoothness constraint.

The invention discloses a multi-microgrid interconnected operation coordinated scheduling optimization method in consideration of interaction response. The method comprises the following steps: initializing a system and acquiring a related parameter in the day-ahead optimization process, wherein each microgrid independently solves the respective optimization strategy according to the initialization information; and then performing information interaction in a microgrid cluster to share the respectively acquired optimal strategies, and updating the system state information, wherein a system control center judges whether the Nash equilibrium is achieved; entering the intra-day optimization stage, performing short-term prediction on the scenery output and load demand, inputting the short-termprediction data in single microgrid to establish an optimization model, performing rolling optimization on the system and solving the same; selecting all control variables in a control time domain asthe actual scheduling scheme after the solution is finished, waiting for the next optimization period, and repeating the above optimization process. Through the optimization method disclosed by the invention, the consumption capacity on the new energy by the microgrid and the system reliability in the fault can be effectively improved, and the economic benefit of the microgrid is increased to a certain degree.

The invention discloses an electric automobile orderly charging control method based on a peak-valley time-of-use tariff. According to electric automobile charging characteristics and user traveling habits, the charging demand of an electric automobile is obtained through Monte Carlo simulation, an electric automobile orderly charging optimization strategy is established, a two-stage optimizationmodel with the lowest electric automobile total charging cost and the minimum power distribution network load peak-valley difference as targets is established, so that the purposes of the optimal charging cost and power grid load peak-valley difference leveling are achieved, the economical efficiency of a user and the stability of a power grid are guaranteed, finally, the two-stage optimization model is solved through erection of CPLEX based on MATLAB, the solving speed is high, efficiency is relatively high, influence of disorderly charging on the power grid is effectively reduced, and orderly charging control of the electric automobile is achieved.

The invention provides a multiple spatial and temporal scale gradually-advancing load dispatching mode designing method. The method comprises the following steps: extracting the responding characteristics of schedulable load multiple temporal scales; designing a layering load dispatching mode based on load acting; designing the multiple spatial and temporal scale gradually-advancing power load dispatching mode. The multiple spatial and temporal scale gradually-advancing load dispatching mode designing method configures load side sources in day-ahead, intra-day and real time and other temporal scales and promotes the load side sources to participate in the dispatch and the operation of a power grid together with a generating side unit, so as to be favorable to improving the utilization rate of power equipment, promotes a daily load curve to gradually tend to be smooth, reduces the valley-to-peak difference of a system, and has great potentiality in promoting the digesting and saving aspect of large-scale renewable energy sources.

The invention relates to a peak shaving control method based on an air conditioner aggregation model. The peak shaving control method based on the air conditioner aggregation model is characterized in that the peak shaving control method comprises the following steps that (1) a load model of single air conditioner is built according to the energy conservation principle and air conditioner running characteristics; (2) unified dispatch and control are conducted on air conditioner groups through load aggregators, the aggregation model of the air conditioner groups is built, and peak shaving potential of an aggregated air conditioner load is evaluated; (3) in the current market, a minimum electric power company dispatching cost is taken as the objective function, and dispatching plans of next-day peak shaving time periods are made; and (4) in the process of real-time dispatching, the profits of the load aggregators and the comfort level of users are considered together as a whole to achieve optimized control of the aggregated air conditioner load. According to the peak shaving control method based on the air conditioner aggregation model, the technical scheme achieves centralized dispatching control over the air conditioner groups, load-side resources are exploited sufficiently, and the investment pressure of power network facilities is relieved.

The present invention provides a multi-population genetic particle swarm optimization method containing the micro-grid capacity configuration of electric automobiles. The method realizes the energy storage function of an electric automobile on the premise that the electricity demand of the electric automobile can be met. According to the technical scheme of the invention, a multi-target model, with the annual cost, the annual loss of load probability and the peak-valley difference of a load curve as targets, is proposed. Based on the multi-population genetic particle swarm algorithm, a target function is solved out. In this way, the optimal capacity of each unit in a micro-grid system can be obtained precisely. On the premise that the system reliability is ensured and the load fluctuation is stabilized and inhibited, a higher economic benefit is obtained. Through optimizing the micro-grid system containing the electric automobile, the mobile energy-storage device of the electric automobile is utilized to realize the peak-load shifting purpose on the basis that the reliability and the economy of the system are guaranteed. Meanwhile, the peak-valley difference of the system curve is reduced. Not only is the stability of the power system improved, but also the economic benefit is higher. Therefore, the popularization and the utilization of a cleaning device of the electric automobile are facilitated.

The invention discloses an intelligent electric park demand response strategy. At present, applicable subjects established by most of intelligent demand models are single residential users, so that diversity and uncertainty of a practical user group are neglected and involvement of a park formed by aggregation of a plurality of users is avoided. The intelligent electric park demand response strategy is implemented as follows: (1), flexible loads of an intelligent electric park are classified according to different demand responses; (2), corresponding demand response strategy models are established for different flexible loads; (3), a corresponding demand response strategy model is established for the intelligent electric park; and (4), effects of users in the park under different demand response strategies are calculated. According to the intelligent electric park demand response strategy, optimized scheduling is carried out on all demand response resources under different electricity price incentive mechanism effects by using user power cost minimization as an objective, so that the user power cost can be reduced and the load electricity behavior can be improved. Therefore, the intelligent electric park can participate in the demand response under a market and competitive mechanism.

The invention discloses an air conditioning load control strategy making method based on direct load control. According to the air conditioning load control strategy making method, a thermodynamic model of a single air conditioning unit is established and used for representing the relation among air conditioning power, temperature and time; an ideal in-turn control strategy for centralized control of air conditioning loads is analyzed according to the thermodynamic model of the single air conditioner unit and requirements of users for comfort level specified in a direct load control contract, and reduced upper limit values which can be provided by all the air conditioning loads under control are calculated. A decision-making model for the direct control of the air conditioning loads in actual electric power system operation is established. Through optimization of the control strategy of all the air conditioning loads under control, the deviation between actual force exerted by the air conditioning loads and ideal reduction quantity is minimized. Moreover, continuous starting and closing time of the air conditioning loads is used for replacing constraint conditions of a real-time room temperature representation air conditioning in-turn control strategy.

The invention relates to a method for controlling distributed power resources of active power distribution networks. The method is characterized in that unified dispatching and partition controlling effects are realized by the method, and the method includes steps of (1), determining a power coordination control scheme for the power distribution networks; (2), determining power coordination control objectives of the power distribution networks; (3), judging whether a power coordination control target is a peak load shifting control objective or not; (4), implementing the peak load shifting control objective; (5), implementing a protocol load curve operation control objective. The control objectives include the protocol load curve operation control objective and the peak load shifting control objective. The method has the advantages that a resource coordination control mode for the active power distribution networks is provided by the method and conforms to principles of 'unified dispatching and hierarchical control' of the traditional power transmission-distribution networks, and the power coordination control scheme is provided for establishing a distributed power capacity management system and is suitable for regions, micro-grids and tie lines.

The invention provides an ordered charging scheduling method for an electric vehicle. The method comprises the steps of calculating out energy consumption of the electric vehicle in a current work condition according to a pre-established driving range predicting model of the electric vehicle; calculating out a driving range of the electric vehicle according to the energy consumption of the electric vehicle in the current work condition and a current SOC; calculating out a charging quantity of the electric vehicle according to a pre-established electric vehicle power battery service life model when a proportion between the driving range of the electric vehicle and a predicted driving range of the electric vehicle before next charging is smaller than a set threshold value; scheduling a charging process of the electric vehicle according to the charging quantity and departure time of the electric vehicle and a current load condition of a power distribution network by taking the minimum difference between peak and valley of the local-area power distribution network as an optimizing target. According to the ordered charging scheduling method, meeting a driving behavior characteristic of a driver is used as a base, the service life of a power battery of the electric vehicle is prolonged by reasonably guiding the electric vehicle to charge and discharge, the difference between peak and valley of a network load is reduced, and stable operation of a power network is guaranteed.

The invention discloses a method for participating in automatic generation control by controllable loads. Aiming at the conditions of increased adjustment complexity of a power grid and more diversified regulation and control means caused by the novel controllable loads in the power grid, the scattered controllable loads are aggregated and are enabled to be equivalent to virtual adjustable units whose outputs are negative values, parameters including an adjusting threshold and an adjusting range thereof are automatically calculated, the controllable loads are brought into a conventional automatic generation control system and participate in the adjustment of the power grid with other energies together, the automatic generation control is developed towards automatic power generation and consumption combined control, centralized cooperative control of the adjustable units of the whole grid is realized, the comprehensive regulation and control effect of the power grid is achieved to the maximum, and technical means is provided for interaction of the energies, the grid, and the loads.

The invention relates to the technical field of wind power generation, in particular to a wind power generation-combined ordered charge and discharge coordinated control system of a pure electric vehicle. The system is characterized in that: information of each charging station is transmitted to a power grid dispatching center, the power grid dispatching center judges the state of the current power system to generate a corresponding charging control mode, and then, the information is transmitted to a charging station concentrated coordination monitoring system to carry out information interaction; the power gird dispatching center transmits a pitch peak instruction to each dynamic battery, namely a dynamic battery group, through a communication network of the charging station concentrated coordination monitoring system; and the dynamic battery group receives the pitch peak instruction to realize pitch peak and feeds back residual electric quantity to a power grid at the same time. According to the system disclosed by the invention, an advantage complementation relation among wind power generation, charge and discharge of the electric vehicle and power gird stabilization is realized and coordinated; the acceptability of wind and electricity is improved; the harmonic pollution caused by disordered charge of the pure electric vehicles and the adverse influence on the power grid are reduced to the greatest extent, and the utilization efficiency of wind and electricity and the stabilization level of the power grid are improved.

The invention discloses a residential energy local network (RELN) energy optimization control method under the consideration of demand side management. The method comprises the following steps: S1, inputting an electricity utilization state and demand information of a user; S2, forecasting a regenerable energy output; S3, recording state information of an energy storage system in an RELN; S4, dynamically optimizing the energy of the RELN; S5, in the current time period, performing specific electricity utilization, idling or discharge operation on all family electrical appliances, PEVs (plug-in electric vehicles) and the energy storage system according to an electricity utilization scheme obtained in the step S4, transmitting scheduling information by adopting an AMI (advanced measurement infrastructure); S6, receiving and updating operation time and energy consumption of residential loads as well as state of charge information of the PEVs and the energy storage system by a RELN energy management center via the AMI; S7, repeating the steps S1 to S6 until the optimization range is ended. According to the RELN energy optimization control method under the consideration of demand side management, dispatching decisions are made for all the residential electrical appliances, the PEVs and the energy storage system based on the target that the operation cost of the RELN is minimized, so that energy optimization control over the RELN is realized.

The invention provides an electric vehicle charging guidance method considering road-network-vehicle, which considers the charging demand difference of different types of electric vehicles, performs optimized guidance scheduling on the electric vehicles in slow charging and fast charging modes from two aspects of day-ahead and real-time. The method comprises the following steps: establishing an electrical traffic cooperative control architecture; formulating a charging station selection and navigation strategy; formulating a time-sharing and zoning electricity price to guide quick charging; and establishing a double-layer optimization scheduling model to guide slow charging. According to the method, on the premise that the safety and economy of a power distribution system are guaranteed, the satisfaction degree of power consumers is considered, and different charging requirements, time and space and other factors are considered to research the guide strategy and operation mode of the electric vehicle. Analysis of the charging station and system load conditions before and after optimization shows that the method enables the system load to be gentler than that in a free charging mode, plays a role in peak clipping and valley filling to a certain extent, and improves the operation safety and economy of a power system.

The invention discloses an active power distribution network scheduling method combined with the V2G technology, and belongs to the technical field of power system economic scheduling. The schedulingmethod uses the V2G technology to control the charging and discharging of electric vehicles, considers the influence of electric vehicle access on the active power of an line exit to perform day-aheadscheduling on the distributed energy and stored energy on the basis of taking the electric vehicle as an energy storage system, performs real-time scheduling on operating distributed power generationunits according to the various types of distributed energy output costs, performs real-time scheduling of the stored energy with minimum energy storage output fluctuation cost, realizes the consumption of distributed energy, improves the utilization rate of renewable energy, controls the charging and discharging of electric vehicles through the V2G system, performs load shifting on the power gridon the premise of meeting the travel of electric vehicle users, reduces the peak-to-valley difference of the power distribution network, and improves the economy and stability of grid operation.

The invention relates to a sending end power grid power source structure planning method considering local load peak regulation capacity. A power source structure planning model is established in combination with various actual constraint conditions such as peak regulation constraints on the basis of considering wind and light storage peak regulation capacity and local load peak regulation capacity. The method comprises the following steps: establishing a power source structure planning model considering local load peak regulation capacity; obtaining a unit maintenance plan, considering to addthe power and local load peak regulation capacity of an energy storage wind power plant and a solar power station, and carrying out random production simulation of wind, solar energy, water, fire andpumping storage power generation; and obtaining hybrid particle swarm optimization to solve the power source structure planning model, and thus obtaining an optimal power source structure planning scheme. Compared with the prior art, the method has the advantages that users and electric automobiles are considered to serve as local load to participate electric power system peak regulation actively, the difference between peak and valley of the system can be reduced effectively and the difficulty of electric power system peak regulation is relieved.

The invention provides a hybrid energy storage capacity optimal allocation method for an AC / DC hybrid microgrid, and belongs to the field of the AC / DC hybrid microgrid. The hybrid energy storage capacity optimal allocation method comprises the steps: establishing a hybrid energy storage capacity two-layer optimal allocation model; power allocation verification based on set empirical mode decomposition; and allocation of the hybrid energy storage system capacity. The power commands of the lithium battery and the supercapacitor are obtained by the filter order. The energy storage life and the converter loss are comprehensively considered with the rated power and the rated capacity of the lithium battery and supercapacitor as the optimization variables and the annual integrated cost of the system as the goal, and an adaptive particle swarm optimization algorithm is applied to perform optimal solution so as to obtain the optimal annual integrated cost of the system and the corresponding rated power and rated capacity of the lithium battery and the supercapacitor. The problem of tie-line power fluctuation caused by wind-optical output fluctuation and load fluctuation in the AC / DC hybridmicrogrid can be solved.

The invention discloses a regional power grid source network load storage multivariate coordination ubiquitous scheduling control system, which comprises a decision-making layer, a micro-grid coordination control layer, and an equipment layer. The decision-making layer is used for acquires operation data of each control object device from the micro-grid coordination control layer, predicting renewable energy output and load requirements in a region, completing peak regulation and frequency modulation, reserve capacity coordination control and power transaction security check, carrying out day-ahead and intra-day collaborative optimization scheduling on each control object, and issuing a control target to the micro-grid coordination control layer. The micro-grid coordination control layer is used for realizing uploading of operation data of equipment in the micro-grid to the decision-making layer, realizing the internal coordination control and economic dispatch of the micro-grid, decomposing the decision-making layer control instruction and issuing the instruction to the equipment layer. The equipment layer is used for acquiring related equipment operation data and uploading the data to the micro-grid coordination control layer; executing a control instruction of the micro-grid coordination control layer; and island operation of the equipment in emergency is supported. According to the invention, global coordination optimization control under a multi-dimensional target can be realized, and the new energy acceptance capability and the power grid operation economy are improved.

The invention discloses an automatic demand response evaluation system and method for demand side management. The system comprises a data acquisition module, a user demand response implementation effect evaluation module, a user simulation settlement module, a user assessment record module and a regional power grid response capability evaluation module. Demand response data can be reasonably evaluated, a basis is provided for improving demand response, the implementation effect of demand response can be better improved continuously, and the vital function of demand response on regional energy optimization is fully achieved.

The invention relates to a microgrid energy storage optimal configuration method containing an electric automobile. On the basis of a multi-objective particle swarm algorithm, the method serves as a method used for multi-objective nonlinear optimization. On the time scale of power grid economic dispatching, a configuration model which takes minimum daily operation maintenance cost and microgrid peak load regulation cost and maximum daily comprehensive income and electric automobile total revenue as an objective is constructed; and through a linear weighting method, a multi-objective function is converted into a single-objective function, and finally, the single-objective function is optimized by the particle swarm algorithm. Therefore, an energy storage capacity which needs to be configured by a system can be accurately calculated, meanwhile, the control quantity of each controllable element in the microgrid is obtained, the peak load regulation capability of the microgrid is improved,the energy storage capacity configuration investment of the microgrid is effectively lowered, meanwhile, the economic efficiency of microgrid operation and positivity for accessing the electric automobile into the power grid to carry out ordered charging and discharging can be improved, and the peak and valley difference of the microgrid system is lowered.

The invention relates to a method for optimizing a transmitting end grid power supply structure in consideration of channel constraints, comprising the following steps of acquiring unit data, load related data and price information data; establishing a power supply structure optimization model in consideration of channel constraints; obtaining a unit overhaul plan, and in consideration of adding an energy storage wind farm, the output of a solar power plant, a local peak load regulation capacity and a key section transmission capacity, performing random production simulation of wind, solar, water, fire and pumped storage power generation; and solving the power supply structure optimization model by using a hybrid particle swarm optimization algorithm to obtain an optimal power supply structure optimization solution. Compared with the prior art, the method considers the transmission capacity constraint of the key section, ensures that the newly-introduced unit does not limit the outputdue to insufficient section transmission capacity, and ensures the economical efficiency and rationality of a planning scheme.

The invention relates to a multi-agent based electricity utilization strategy capable of enabling residential users to participate in automated demand response. The multi-agent based electricity utilization strategy is characterized by designing a corresponding multi-Agent analog system and interaction mechanism according to the characteristic that the residential users participate in automated demand response. An electricity utilization habit satisfaction degree and an electricity utilization cost satisfaction degree are comprehensively considered, and aiming to maximize a user comprehensive satisfaction degree, an electricity utilization plan decision model is established; in order to achieve peak clipping and valley filling, a multi-target optimization model of a load centralized merchant Agent is established, and aiming to minimize a peak-valley difference of a balance load curve and maximize an average satisfaction degree of all users participating in automated demand response, an electricity utilization scheme is corrected; and during real-time operation, a power grid Agent releases a real-time demand response signal to carry out real-time regulation on the corrected electricity utilization scheme, and finally, proposes an index variable for evaluating an automated demand response behavior. The multi-agent based electricity utilization strategy can guide users who pay attention to electricity utilization cost to obviously reduce electricity utilization cost on the premise of not influencing daily life.

The invention discloses a multi-strategy coordinated power grid optimization operation method based on response priorities. The multi-strategy coordinated power grid optimization operation method comprises the steps of: S1, acquiring a power grid daily basic load curve, predicting total energy required for charging an electric vehicle, and acquiring peak / flat / valley electricity price information; S2, establishing orderly charging optimization control models including a multi-target optimization model and an orderly charging control information base; S3, calculating electric vehicle charging start time and number of access allowed electric vehicles, and issuing relevant information to electric vehicle users; S4, and judging whether the electric vehicle is allowed to charge immediately after the user receives a notification, so that fluctuations of an output building integral load curve are greatly reduced, the optimized load region is stable, and the peak and valley difference is significantly reduced.

The invention relates to a day-ahead optimal scheduling method for a comprehensive energy system containing heat accumulating type electric heating. The method is characterized by comprising the following steps: 1, proposing a comprehensive energy system operation optimization objective function containing heat accumulating type electric heating on a power grid interaction level according to income and operation characteristics of heat accumulating type electric heating, and determining the comprehensive energy system operation optimization objective function containing heat accumulating typeelectric heating and constraint conditions thereof; and 2, solving the comprehensive energy system operation optimization objective function containing heat accumulating type electric heating in the step 1 by adopting a simplex algorithm according to the characteristics of the model so as to obtain a planned electric heating load balance scheduling curve of the system and hourly operation output of various heating and energy storage devices. The load impact on the power grid can be effectively reduced, the peak-valley difference of the power grid is reduced, the use efficiency of electric heating equipment is improved, and the heating operation cost of a user is greatly reduced.

The invention provides a power distribution network time-of-use power price optimizing method taking reliability into consideration. The power distribution network time-of-use power price optimizing method is a time-of-use power price forming method which takes user reliability, power supply company benefit and user cost into consideration at the same time. On the basis of user need elasticity, the time-of-use power price optimizing method combining reliability compensation of users is provided, a stable particle swarm optimizing algorithm is adopted for optimized solving, and the time-of-use power price forming method is used for forming of time-of-use power price of a power distribution network. The power distribution network time-of-use power price optimizing method can be adjusted according to different regions and different user types, thereby having high adaptability.

The invention discloses a photovoltaic charging station charging control method and device based on weather prediction. The method comprises: acquiring a weather prediction data set; adjusting the maximum energy storage discharge power of an energy storage battery in the photovoltaic charging station on that day according to the weather prediction data set so as to determine the initial maximum energy storage discharge power; according to a preset step length, dividing the initial energy storage maximum discharge power to obtain a plurality of discharge powers, acquiring historical data of thepower distribution network; predicting a load curve of the power distribution network on that day; in each preset time period of the current day, according to the load curve, the in-station vehicle charging demand data and the photovoltaic power generation data of the photovoltaic charging station, obtaining the power purchase cost of each power grid when power is purchased from the power distribution network under each discharge power, and taking the discharge power corresponding to the lowest power purchase cost of the power grid as the maximum energy storage discharge power of the currentday; and according to the energy storage maximum discharge power of the day and the current time period, orderly charging the vehicles in the station based on a time-phased charging strategy.

The invention relates to an electric vehicle charging load random connecting control system and method of charging pile self-decision, and belongs to the field of electric vehicle intelligent charging. The system comprises a control decision generator and a plurality of intelligent charging piles. The control decision generator obtains the electricity price and load information of a current control area, calculates the probability distribution of corresponding charging start moments of users of different charging time durations according to a load curve by means of the designed random connecting method, and issues control instructions to all the intelligent charging piles, and the charging piles determine the final charging start time according to the probabilities of the connecting users.The method has a good peak-load shifting effect, can restrain the power grid load fluctuation and reduce the charging cost of the users of electric vehicles; the whole control process is independently completed by the device in the control area, concentrated type communication and control are not needed, implementation is simple and convenient, and the system is low in cost and suitable for orderly charging of residential areas with large-scale private electric vehicles.

The invention relates to an intra-regional electric vehicle ordered charging control method. The method comprises the steps of analyzing trip characteristics of electric vehicles based on the trip chain theory, collecting trip chain information of the number of N electric vehicles in a region, obtaining the battery state of charge of each electric vehicle at an initial moment of a user and obtaining the charging time and charging place of each electric vehicle through analysis; obtaining operating state information of a power grid, and updating a conventional load curve; and finally, aiming to minimize the load difference between peak and valley of a power distribution network, and determining the optimal charging time period and the optimal charging duration of each electric vehicle by using the genetic algorithm. According to the invention, the strategy to control the ordered charging of the electric vehicles can be made, the trip chain theory is used for predicting the charging load, the authenticity of the electric vehicle load prediction is improved, the availability of the control strategy is improved, and the effects of reducing the difference between peak and valley and the load fluctuation are achieved.

The invention relates to a park energy internet optimization operation method considering response behavior uncertainty, and the method comprises the following steps: 1) constructing an improved pricetype demand response uncertainty model, and obtaining an optimal time-of-use electricity price strategy for electricity price adjustment; 2) constructing a response model of the excitable load, and realizing peak clipping and valley filling together with the interruptible load; 3) constructing a transferable and translatable load response model, and reducing response uncertainty by establishing aprice reward and punishment mechanism; and 4) taking the lowest park energy internet operation cost as an objective function, constructing a park energy internet optimization scheduling model, solving the optimization scheduling model, and guiding the demand side to participate in load response according to a result. Compared with the prior art, the method has the advantages of quickness, reliability, high economy, comprehensive consideration of uncertainty of price type and excitation type demand response, peak clipping and valley filling effects and the like.