73results about How to "Reduce load peak-to-valley difference" patented technology

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 discloses a V2G technology-based power distribution scheduling control method for electric vehicles. According to the power distribution scheduling control method, besides that the influences of charge and discharge power of the electric vehicles are considered, the limiting factors of the capacity of a power distribution network, technical conditions of the electric vehicles and the like are also considered; the traffic factors of drive routes, charging/discharging access and the like of the electric vehicles are further considered; optimized charging and discharging scheduling strategies, for the electric vehicles, conforming to the reality are comprehensively determined; charging and discharging of the electric vehicles are controlled by a V2G system; and the power distribution scheduling control on the electric vehicles is achieved. The embodiment and related data show that according to the V2G technology-based power distribution scheduling control method for the electric vehicles, the negative effects on the system reliability due to the fact that the electric vehicles are accessed on a large scale can be effectively improved; the load utilization rate of the power distribution network is improved; the load peak and off-peak difference of the power distribution network is reduced; and improvement of power distribution scheduling economical efficiency of the electric vehicle and power distribution network operation economical efficiency is helped.

The invention discloses a control method for ordered charging of an electromobile. The control method comprises the following steps of firstly, establishing a charging load characteristic model of the electromobile by integrating factors of battery characteristics, a charging mode and behaviors and habits of users; secondly, with the goal of minimizing the difference between the peak and the valley of a load, establishing a centralized optimization model according to the minimal variance of load demands at all time intervals and the average load level, enabling the load fluctuation to be minimized, and realizing the stabilization of the load to obtain reference optimized power; thirdly, establishing a distributed optimization model, and optimizing an individual charging scheme according to the reference optimized power obtained by a power grid and a middle manager in the optimizing process of the second step and dynamic electricity price in 24 hours. A cooperative control model is established by adopting a centralized and distributed combined optimized control idea, so that the negative effect of large-scale disordered charging of the electromobile on planning and operation of the power grid is reduced.

The invention provides an urban electric vehicle charging decision-making method based on a typical load dynamic game, which relates to the technical field of charging electric vehicles. According tothe short-term load curve clustering of power network, the typical load types of users in a certain area are obtained, and the ideal charging load curve of electric vehicle is calculated, subject to charge demand constraint, charging time constraint, electric vehicle charging quantity limit, power load curve, distribution network capacity as a boundary condition, the minimum error between the charging load curve and the ideal charging load curve as a constraint condition, the lowest user cost as an objective function, the establishment of optimization equation, and solution, to obtain the charging strategy, through the dynamic game decision-making results to determine whether to charge the electric vehicle. The method provided by the invention can improve the load characteristic of the electric network, minimize the fluctuation of the electric network after the electric vehicle is connected to the electric network, improve the load rate of the electric equipment, and on this basis, minimize the charging cost of the electric vehicle user.

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 an aggregator-based electric vehicle participation receiving-end power grid trough and peak regulation demand response regulation and control method. An aggregator issues a trough and peak regulation demand response invitation from a power grid operator to an electric vehicle, divides the trough and peak regulation demand response invitation into a flexible EV load and a non-flexible EV load according to a participation condition of an EV, and extracts a typical charging mode of the EV from historical charging data through a clustering analysis method for EV load prediction. And based on the load prediction data, the power grid operator establishes a rolling unit combination model considering participation of the electric vehicle in trough and peak regulation demandresponse, and determines a unit power generation plan and a demand response resource scheduling plan. According to the demand response regulation and control method, user willingness and a power griddemand are fully considered, interests of the two parties are effectively coordinated and maximized, and resource scheduling efficiency is improved through clustering analysis. The method can effectively stimulate the electric vehicle to actively participate in trough and peak regulation, reduces the trough and peak regulation pressure of the receiving-end power grid, and decreases operation costof the power grid.

The invention provides a regional integrated energy system energy storage configuration method considering an electric/thermal flexible load, and the method comprises the steps: firstly comprehensively analyzing the composition of the electric load and the thermal transmission characteristics of a heat supply system, and building an electric/thermal flexible load model; then, on the basis of the architecture of the known regional integrated energy system, establishing a model of each energy assembly, meanwhile, setting constraint conditions, including energy balance constraint, energy assembly output constraint, tie line transmission power constraint and energy storage constraint; secondly, considering system energy consumption, investment, operation and maintenance and compensation cost, and establishing an economic single-objective optimization model; and finally, based on the LINGO18.0 software, solving the established energy storage optimal configuration model to obtain the optimal output of each device and the optimal capacities of the storage battery and the heat storage tank. According to the method, the regional comprehensive energy system is subjected to energy storage optimization configuration, and the energy utilization efficiency is improved.

The invention relates to an energy storage system based on rail transport. The energy storage system comprises high and low load fields for load parking, a transport track for load transportation, a power transmission line for providing traction power and transmission brake power generation, a traction/brake unit, a load unit, a storage track, a safety track, a control center and a power grid access module. In the invention, by means of combination of energy storage based on electrified track transport and brake power generation, in the case of excess electricity of the system, an electric traction locomotive carries heavy load from a low place to a high place so as to convert electric energy into potential energy for storage; and in the case of high demand of the electricity of the system, brake power generation is performed during the process that the electric locomotive carries the heavy load from the high place to the low place so as to convert the potential energy into the electric energy and transfer the electric energy to a power grid. The energy storage system has the advantages that energy with super-large and controllable volume and high density can be stored, the operation management method is simple, and the energy cycle efficiency is ultrahigh.

The invention provides a stored-energy capacity configuration method used for stabilizing city load growth. The stored-energy capacity is an electric-energy quantity stored by an energy-storage system installed in a power grid. The method includes the following steps: obtaining typical daily load data of an installation point of the energy-storage system; calculating an output power of the energy-storage system; calculating a energy-storage-cell configuration capacity and an energy-storage rated power; constructing an energy-storage capacity configuration optimization target function; and carrying out an iterative operation until an optimal solution is obtained. The method realizes functions of peak load shifting and load peak and valley difference reduction in a city power distribution network. The method reasonably selects an energy-storage capacity according to a typical daily load curve of an energy-storage access point so that the adaptability is great; electric energy consumption and energy storage investment cost are considered comprehensively and while the voltage distribution of the power distribution network is adjusted, the voltage deviation of the power distribution network is reduced and the loss of the power distribution network is reduced, the investment cost is reduced as much as possible so that waste of energy storage device capacity is prevented; and the method is capable of calculation through use of an iterative method, simple and easy to realize and high in convergence rate.

The invention relates to a big consumer power supply access decision-making method considering load characteristic. The big consumer power supply access decision-making method comprises the steps of defining installation reporting parameters of a big consumer according to electrical equipment information provided in installation reporting of the big consumer; forming an excavating load sample dataon the basis of normalization by virtue of the access big consumer historical power utilization data; clustering the load sample data into an L cluster through a fuzzy C mean value clustering algorithm, wherein the clustering center is the big consumer load characteristic library, and determining the type of the sample data according to the maximum membership principle; defining the load characteristic index according to the installation reporting parameters provided by the big consumer, and processing the load sample data according to the load characteristic index to form a training data set; based on the training data set, excavating a load pattern recognition rule of the big consumer through a classification and regression tree (CART) algorithm; and inputting a load recognition resultinto a big consumer power supply access combination and optimization model, and obtaining a power supply access optimization strategy through optimizing.

The invention provides a comprehensive energy system multi-objective optimization scheduling method considering the human body comfort, and the method comprises the steps: considering the comfort of auser in the optimization scheduling of the comprehensive energy system, building a PMV model according to the perception of a human body for heat, and describing the feeling of the user for heat supply; describing the dissatisfaction degree of the user to the heat supply by predicting the dissatisfaction degree PPD, and determining the value range of the PMV by setting the PDD; and establishing aheating load model and various energy equipment models of the comprehensive energy system; setting constraint conditions, wherein the constraint conditions include a user comfort degree constraint, an energy balance constraint, an equipment output constraint, an electricity purchasing power constraint and a storage battery constraint; establishing a multi-objective optimization model by adoptinga hierarchical sequence method in consideration of various indexes such as cost, carbon emission and primary energy utilization rate; and finally, solving the established optimal scheduling model based on software to obtain an optimal scheduling result of the system and each objective function value. Example analysis verifies the effectiveness of the method in optimal scheduling of the comprehensive energy system.

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 discloses a comprehensive energy storage optimal configuration method and system. The method comprises the steps of giving the energy demands of a system at each time period and the maximum output of an energy production unit in the system at each time period in combination with the architecture of a user-side comprehensive energy system; establishing a physical model of an energy conversion unit and an energy storage unit in the system; and establishing a comprehensive energy storage optimal configuration model with the minimum load variance, the minimum renewable energy sourcereduction amount and the minimum comprehensive energy storage capacity as targets , wherein the optimal solution of the model is the optimal capacity which should be configured for various types of energy storage devices. The method can be applied to user-side comprehensive energy systems of various specific architectures, and through multi-energy complementation and multi-energy flow coordinatedoptimization scheduling, the effects of the user-side comprehensive energy systems on reducing the load peak-valley difference of a power grid and improving the renewable energy consumption capacity are fully excavated. According to the method, electric heating and cooling comprehensive energy storage can be coordinated and matched to promote peak load shifting and renewable energy consumption, and the operation safety and economy of the power system are effectively improved.

The invention discloses a multi-energy park day-ahead economic dispatching method by considering demand response and containing electric vehicles. The method comprises the following steps: firstly, modeling each device in the park according to the determined specific composition of the multi-energy park; then introducing the electric vehicle while considering the demand response; taking the minimum total operating cost of the park as an objective function; considering related constraint conditions; combining a Monte Carlo scene theory; constructing a multi-energy park day-ahead economic dispatch random model by considering the uncertainty of wind power output and photovoltaic output, verifying the contribution advantages of demand response and electric vehicles to multi-energy park economic operation and new energy consumption, and obtaining a multi-energy park day-ahead economic dispatch strategy containing the electric vehicles and by considering the demand response. A demand response mechanism and an electric vehicle are introduced to further improve the new energy consumption capability of the park and enhance the operation economy of the multi-energy park.

The invention discloses an electric vehicle distribution path optimization method supporting a charging and discharging strategy, and the method comprises the steps: firstly building a target functionwhich takes the fixed cost, driving cost, punishment cost and charging and discharging cost of a vehicle as optimization, and then according to the coordinates of a customer point and a charging station, performing path planning by adopting a hybrid algorithm of a genetic algorithm and local search to obtain an optimal path scheme; and finally, according to the optimal path scheme of the electricvehicle distribution path obtained in the step 2, in combination with the time-of-use electricity price, guiding the electricity market to make a charging and discharging decision for profit maximization in a power supply mode of battery exchange of the battery swap station. According to the distribution path optimization method, an improved genetic algorithm (GA-LS) combined with local search isdesigned, the influence of charging and discharging of the electric vehicle on a power grid is considered, and the distribution path optimization method has the advantages of being high in robust stability, high in iteration efficiency, high in solving quality and the like.

The invention discloses a charging pile and a charging pile management system. The charging pile comprises a control module, a first judgment module, a connection state confirmation module, a battery state detection module, a prompting module and a processing module, wherein the first judgment module is used for judging whether a current time is in the valley time period or the peak time period; the connection state confirmation module is connected with a charging interface, and is used for confirming whether equipment to be charged is connected to the charging interface; the battery state detection module is connected with the connection state confirmation module, and is used for detecting battery state of the equipment to be charged after the connection state confirmation module confirms that the equipment to be charged is connected to the charging interface; and the processing module is connected with the control module, the first judgment module, the battery state detection module, a charger and the prompting module. The charging pile and the charging pile management system are conductive to reducing peak and valley difference of loads of a power grid, and are convenient for adjusting power supply pressure of the power grid.

The invention discloses a micro-energy network capacity optimization planning method based on an economy-environment and energy dynamic pricing double-layer game. The method comprises the following steps: designing a framework of the economy-environment and energy dynamic pricing double-layer game micro-energy network capacity optimization planning method, establishing an upper-layer environment-economy evolutionary game planning model, establishing a lower-layer energy dynamic pricing strategy master-slave game model, establishing a multi-energy conversion coupling hub model and the like. Theconflict relation between economic benefits and environmental benefits in the planning problem can be considered, meanwhile, the energy price dynamic characteristics are considered, the conflict benefits between a micro-energy network operator and a user are balanced, and the planning efficiency and economy are improved. The planning total cost can be effectively reduced, the user load peak-valley difference is balanced, the energy utilization efficiency is improved, meanwhile, emission of greenhouse gas CO2 and air pollutant NOx is reduced, and the environmental value is achieved. The methodhas the advantages of scientificity, reasonability, high applicability, good effect and the like.

The invention relates to a hydropower station short-term fine scheduling method, and belongs to the technical field of power generation scheduling of power systems. According to the method, the minimum peak-valley difference of the residual load of a power grid is taken as a target, the influence of reservoir operation constraints, unit operation constraints, power constraints and head changes onthe power generation and water pumping characteristics of the pumped storage hydroelectric generating unit is comprehensively considered, and the purpose of improving the fine level of dispatching isachieved. For nonlinear factors such as a nonlinear multi-objective function, a unit power generation and hydropower constraint, a water turbine dynamic characteristic constraint, a water pump dynamiccharacteristic constraint and a unit operation state constraint in a scheduling model. A corresponding linearization processing strategy is provided, an original model is converted into a standard mixed integer linear programming model, and then the model is solved. The method gives full play to the peak shaving effect of the pumped storage hydropower station in the power grid, improves the refinement level of power grid dispatching, and guarantees the safe and stable operation of the power station.

The invention discloses a DBN power grid load prediction method and device based on generalized demand side resources. The method comprises: establishing a scheduling model based on an electricity price contract for the reducible load LC, the transferable load LS and the energy storage system ES, wherein the model determines an optimal scheduling plan of three generalized demand side resources participating in the power market by means of a load aggregator; on the basis, fusing generalized demand side resource influence factors into a DBN load prediction model, and establishing a DBN short-term load prediction model considering generalized demand side resources; and training and testing the prediction model in combination with historical load data and weather data to obtain a daily load prediction curve of the to-be-tested area. The method is high in prediction precision and good in stability, and can meet the power grid prediction requirements under load big data.

The invention relates to a wind power dissipation scheduling method of cogeneration system based on electricity price-load linkage response. According to the method, a real-time electricity price mechanism is introduced, a new real-time electricity price is generated according to load change, the new real-time electricity price guides a power user to respond, load and real-time electricity price linkage response is achieved so as to guide the market behaviors among the main bodies of the power market and transfer the load across time periods. An electric boiler is installed in a heat load center so as to decouple the restraint of determining the electricity by heat, thereby reducing the heat energy loss in the transmission process. In the process of target optimization, the optimal coordination control of the daily load integral dynamic transfer across time periods and the thermal electrolytic coupling is achieved, so that the difference between the network cost and the auxiliary service cost of the abandoned wind is absorbed to the greatest extent, the wind energy is absorbed to the greatest extent, and meanwhile, the load peak-valley difference is reduced.

The invention discloses a load-storage combined optimization operation method based on demand response. The method comprises the steps of 1 collecting original data; 2 establishing constraint conditions; 3 calculating the power grid power supply load, the demand response implementation cost, the energy storage power station cost and the daily income of the energy storage power station in the ith time period after the response to the time-of-use electricity price based on the constraint condition and the collected original data; 4 establishing a target function based on the calculation result in the step 3; and 5 inputting a known condition into the target function established in the step 4 to obtain a joint optimization result. According to the invention, energy storage compensation is used to respond to the load power of time-of-use electricity price; the total cost of load-storage combined operation is minimized; peak clipping and valley filling can be realized; the load peak-valleydifference of the power grid is reduced; excessive user loads do not need to be reduced; the configuration capacity of energy storage is reduced on the premise of meeting the load requirements of users; the power consumption cost of the users and the construction cost of the power grid are saved; and the utilization efficiency of power system equipment is improved.

The invention discloses a charging load model establishment and node voltage calculation method of an electric vehicle. The method comprises: S1, establishing a load charging model of an electric vehicle; S2, establishing a dynamic load model of a power distribution system; S3, giving access points of all load charging models in the power distribution system; and S4, analyzing the impact of electric vehicle charging on the power distribution system. According to the invention, three kinds of charging load models are established, so that the result is suitable for the actual circumstance and become reliable. And on the basis of calculation of the distribution probability characteristics of node voltages of the three models, the following conclusions are made: firstly, conventional chargingmode has the greatest influence on the operational stability of the distribution network; secondly, with the quick charging mode, the peak-to-valley difference of the load is increased substantially,so that economical operation of the grid is affected greatly; and thirdly, with increasing of the proportion of replacing the battery charging mode, the peak-to-valley difference of the load is reduced and the economical efficiency of the grid operation is increased, and the stability of distribution network operation of the power system is enhanced. Therefore, the charging load model establishment and node voltage calculation method has the great significance in research and development of charging vehicles in the field.

The invention discloses a pricing method and system for electricity prices. The method includes steps of: forecasting the user load capacity and the distributed power supply generation capacity in each hour in the previous day in response to the new electricity price; calculating the difference between the user load capacity and the distributed power supply generation capacity in each hour in response to the new electricity price; and according to the user load capacity, the distributed power supply generation capacity, and the difference between the user load capacity and the distributed power supply generation capacity in each hour in response to the new electricity price, using the particle swarm optimization to optimize the electricity price optimization model under the pre-set agreed conditions to obtain the change amount of the electricity price corresponding to each hour and the maximum return corresponding to one day. Based on the distributed power supply short-term forecast generation capacity and cost and combined with the tou price and the step tariff, the load peak and off-peak difference is reduced, the utilization of renewable energy power generation is improved, the power consumption cost of the user is lowered and the income of the power selling company is increased.

The invention discloses an energy storage site selection constant volume multi-objective optimization method considering reduction of peak-valley difference. According to the method, historical data are analyzed to establish a distributed energy storage site selection constant-volume optimal model considering renewable energy output, the position and capacity of an energy storage power station andthe SOC at each moment are taken as optimization variables, the aims of reducing the energy storage operation cost and reducing the peak-valley difference are taken as targets, and equipment model constraint and system operation constraint are considered; feasible positions of a plurality of energy storage power stations are determined by adopting a branch defining method; the capacity and the state of charge of the energy storage power station at each feasible position are set and initialized, the capacity and the state of charge are input into the model, and solving is carried out by adopting a particle swarm algorithm to obtain optimal data. The method can effectively solve the problem of optimal energy storage site selection and constant volume, reduces the peak-valley difference, andreduces the energy storage operation cost.