30results about How to "Optimizing Load Profiles" 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 an electric automobile optimal peak-valley time-of-use pricing method giving consideration to the owner satisfaction degree. The electric automobile optimal peak-valley time-of-use pricing method specifically comprises the following steps of (1) establishing probability models of a last journey ending moment and a daily running mileage; (2) establishing demand response models for charging and discharging of electric automobiles, including (2a) a demand response model of an A-type electric automobile, (2b) a demand response model of a B-type electric automobile and (2c) a demand response model of a C-type electric automobile; (3) establishing an optimal peak-valley time-of-use electrovalence solving model. Charging and discharging power models of the electric automobiles are established according to two factors influencing the power demands of the electric automobiles, namely charging beginning moment and the daily running mileage, then an optimal peak-valley time-of-use electrovalence solving scheme applicable to the charging and discharging of the electric automobiles is established based on the electric automobile owner satisfaction degree and power grid benefits, and investment on peak-load units and lines is reduced to some extent.

The invention discloses a power grid safety optimization dispatching method considering demand response. The method comprises the following steps: establishing a dynamic excitation compensation mechanism, and carrying out modeling on demand response behaviors of a user according to load shedding and transfer features; establishing a calculation model of power grid reliability index loss-of-load probability and expected energy not supplied; converting a reliability index into an economic index and integrating the index into an objective function, and establishing a day-ahead economic dispatch model with the total operation cost minimum being as an objective; establishing constraint conditions to enable an electric power system to meet system constraints, unit constraints and security constraints; and carrying out solving through calculation software to obtain an optimal day-ahead economic dispatch result. The demand response and reliability index are integrated into day-ahead dispatching of the electric power system, and the optimal dispatching model considering dynamic excitation compensation and expected power failure loss is established, thereby coordinately solving the problem of unit commitment of a generation power side and the problem of supply-demand interaction of a demand side and realizing economy and security of operation of a power grid.

The invention discloses an active distribution network failure recovery strategy considering inside and outside games. A failure recovery model considering the inside and outside double games is established through taking balance of interests of supply and requisitioning parties in a recovery process as the principle. In the outside game, an optimized time-of-use price is made through the game between a power grid and a user, so that the load peak is suppressed to reduce the pressure of recovery at the load peak. A dynamic game is solved through adoption of an inverse regression algorithm; in the inside game, collaborative recovery is carried out on an electric car, a distributed power supply and a controllable load based on an optimized load curve in order to realize maximum community of interest of the power grid and the user in the failure recovery. A modified ant colony algorithm is called to solve a failure recovery problem, and then each recovery scheme is taken as a game participant to transformer the failure recovery problem into a problem of the game between multiple recovery schemes. The power losses and the lowest node voltage value are taken as evaluation indexes, and the optimized scheme, namely a Nash equilibrium solution, is obtained through the games.

The invention relates to an electric vehicle dual-scale charging guiding method and system considering users' satisfaction degree. According to the method, electric vehicles are divided into an immediate charging cluster and a time-delay charging cluster according to charging time frame requirements from uses, and satisfaction degree functions in different scales are established; in the time scalelayer, a model is established by considering power grid factors and user charging cost factors, and a load curve is adjusted by guiding the start charging time of the electric vehicles; in the spacescale layer, a model is established by considering charging station factors and user traveling cost factors, and the charging station selection of each vehicle is planned, so that the charging stationoperation efficiency is improved; and electric vehicle charging behavior guiding for users on time and space scales is performed according to the solved optimal solution. Compared with the prior art,optimization and balance utilization of the load curve, the traffic condition and charging station equipment are realized while users' satisfaction degree is satisfied.

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 relates to a home user intelligent power use mode scheduling method. The method comprises the steps of constructing the classification model of a home user load, constructing the satisfaction model of a user to a home appliance, constructing a user power consumption cost model, constructing the overall average satisfaction model of the user, constructing an intelligent power dispatching overall model, and solving the constructed model through CPLEX and obtaining the scheduling scheme which is most suitable for a home user intelligent power use mode. Through the detailed classification of the home user load, the load can be reasonably and efficiently scheduled, a real-time power pricing mechanism is used to carry out requirement response of a user, on the basis of considering photovoltaic power generation, the problems of electricity cost and satisfaction concerned by the user are comprehensively considered, the economic efficiency of the user is improved.

The invention relates to a demand response-based intelligent control method for a home energy management system. The method comprises the following steps that initial parameters are input, and a population is randomly generated by an initialization operator; a target function for spending the least electricity fee is built, constraint conditions are set, and a fitness model is constructed; the fitness model is solved to obtain the equipment states in all the scheduling time periods, and storage battery charging and discharging are conducted according to the equipment states in the scheduling time periods; a selection operation and a crossover operation are conducted in sequence, whether the population meets the constraint conditions or not is judged, and if not, variation is conducted after population repairing; if yes, variation is directly conducted; and whether the population obtained after variation meets the constraint conditions or not is judged, and if not, population repairing is conducted. Compared with the prior art, the method has the advantages of adopting peak-load shifting to transfer part of the distributed energy generating capacity, being more flexible in application, adopting the repairing method to meet the constraint conditions, being more suitable for some complex non-linear integer model problems and the like.

The invention discloses a distributed wind storage system optimization method considering source-network-load multi-party interests. The method comprises the following steps of: taking a maximum operation cost difference value of a power distribution network operator before and after grid connection of a wind storage system as an optimization target; taking the maximum internal return rate of thedistributed power supply investor as the target; taking the maximum revenue of the user after performing the demand side response as the target; based on a multi-objective optimization planning modelof three interest subjects of a power distribution network operator, a distributed power supply investor and a user, a distributed wind storage system scheduling strategy is adopted, and the interestsof the power distribution network operator, the distributed power supply investor and the user are optimized under the condition that the reactive power regulation capability of the wind storage system is fully considered. In the distributed power supply planning process, considering the source-network-load three-party interest plan, the project has more practical engineering significance, the benefits of the distributed power supply investors can be improved, and the utilization rate of renewable energy sources is increased.

The invention relates to a unit combination calculation method considering large-scale wind power consumed by electric vehicles. A wind curtailment cost model and a direct purchase model of wind powerand a charging and discharging cost model and a battery aging cost model of electric vehicles are established by taking the emergency demand response of conventional thermal power generating units, awind power plant, plug-in electric vehicles and a load side as a research object, a demand response model is established, a unit combination model considering large-scale wind power consumed by electric vehicles with the objective of minimum total operation cost of the system is established on the basis, and the problem is solved by an optimization algorithm based on teaching and learning. According to the invention, the charging and discharging power, the charging and discharging efficiency, the battery aging cost and the corresponding total charging and discharging cost of plug-in electricvehicles are studied, the influence of participation of the electric vehicles in scheduling on the total operation cost of the system is analyzed, and the self-elasticity and mutual elasticity of theload are considered in the demand response, so that an emergency demand response plan is formulated, the total operation cost of the system can be remarkably reduced, and the load curve is optimized.

The invention discloses an intelligent electricity consumption oriented continuous task type load energy control method. The method comprises the following steps: constructing a continuous task type load model; constructing an energy control optimized decision model; solving the continuous task type load model and the energy control optimized decision model. According to the method, the use time of the load in the high electricity price time period is shortened, and the load is transferred to the time period when the distributed energy resource electricity generation is prominent, so that the total load curve can be improved; a part of distributed energy resource electricity generating capacity is transferred by a peak-clipping valley-filling manner of an energy storage device, on the one hand, the proportion of elimination on the spot of the distributed energy resource electricity generating capacity can be increased, the wasteful power grid access manner of the surplus electric quantity can be avoided, and the better economic efficiency can be obtained; on the other hand, the adverse effects of the distributed power supply on the power grid access can be reduced, and the method is favorable to the safe and stable operation of the power grid.

The invention discloses a user comprehensive energy system optimal scheduling method considering energy storage multi-type services, belongs to the technical field of power system scheduling, and specifically comprises the steps of proposing a concept of demand management and a logic behavior framework of the energy storage multi-type services in a long-time scale; establishing a comprehensive energy system economic dispatching model; establishing constraint conditions to enable the comprehensive energy system to meet power balance constraints, power grid exchange power, reducible load constraints and equipment operation constraints; establishing an intra-day scheduling model taking minimum total operation cost as a target; establishing intraday constraints. And an optimal month-before-daycoordinated scheduling result is obtained. According to the invention, the problem of how to realize the highest economic benefit through the combination of demand management, energy arbitrage, participation in demand response and provision of standby power by the energy storage power station is solved, the service function of energy storage of different time scales in the system is fully excavated, and the schedulability and economy of the operation of the comprehensive energy system are improved.

The invention discloses a power distribution network day-ahead dispatching method and device considering carbon cost and multiple uncertainties, and belongs to the technical field of power systems and automation. The method comprises the steps that a column and constraint generation algorithm is adopted to solve a day-ahead economic dispatching model, and a solving result is obtained; the day-ahead economic dispatching model takes comprehensive minimization of user electricity purchase cost, demand response cost and carbon cost as a target function, constraint conditions are power balance constraint, demand response constraint and uncertainty constraint, and optimization variables are demand response participating positions, demand response power, response moments and time lengths and deviation variables; and formulating a day-ahead economic dispatching plan according to a solving result. According to the method, the optimal scheduling scheme can be obtained under the condition that the system meets the power balance constraint, the demand response constraint and the uncertainty constraint by adopting the day-ahead economic scheduling model considering the carbon cost and the multiple uncertainties.

The invention relates to a processing method for optimizing connecting information of new consumers of a power distribution network. The processing method for the optimizing connecting information of the new consumers of the power distribution network comprises the following steps of (1) carrying out industry classification on the new power consumers , meanwhile, carrying out analysis on the connected power distribution network, (2) carrying out matching on the new consumers and the power distribution network according to a different consumer combination optimization algorithm, and (3) obtaining the optimizing connecting information of the new consumers. Compared with the prior art, the processing method for the optimizing connecting information of the new consumers of the power distribution network has the advantages of improving operating efficiency of power generation and supply devices, reducing operating cost of a power system, reducing consumption of resources, being beneficial to environmental protection and the like.

The invention discloses a day-ahead scheduling optimization method considering a source load bilateral elastic space. The day-ahead scheduling optimization method comprises the following steps: firstly, establishing a mathematical model of a power grid in a region including a thermal power generating unit, a wind generating set, a rigid load and a flexible load; establishing a corresponding MDP mathematical model for the dynamic scheduling problem of the power grid in the region containing the elastic schedulable resources on the two sides of the power grid source load; and finally, adopting a near-end strategy optimization algorithm in deep reinforcement learning to carry out strategy solution on the MDP mathematical model, wherein an obtained optimization strategy can select a reasonable action scheme according to the actual operation state of the power grid at the scheduling moment, and optimal scheduling of the power grid is realized. According to the invention, the elastic schedulable resources in the power system can be fully utilized to solve the peak regulation demand problem of the power grid, the consumption of new energy is promoted, and the safe and stable operation of the power system is ensured.

The embodiment of the invention discloses a power supply method using location-side energy storage configuration, which comprises the following steps: S1, analyzing a user-side energy storage form, and dividing the user-side energy storage form into three categories of machinery, electromagnetism and electrochemistry; S2, analyzing the cost income condition of the energy storage system, analyzingthe investment cost and expected income of the energy storage according to the energy storage of the investment user side, and determining the type and scale of the investment energy storage; S3, according to the energy storage configuration model and the solving method set in the step S2, based on analysis of energy storage installed on the user side, establishing an energy storage capacity optimization configuration and cost benefit model; S4, formulating an electricity selling price capable of customizing energy storage configuration according to the analysis in the step S3; and S5, determining the configuration of the energy storage capacity according to the analysis of the actual case. An electricity selling company shares the energy storage cost into the user electricity prices, thepackage electricity prices are reduced along with increase of the contract years, and the user investment energy storage pressure is reduced.

The invention provides an electric vehicle charging method and system. The method comprises the steps: obtaining position information of a node where an electric vehicle is located in a current trip chain and first state-of-charge information of the electric vehicle at the current moment; determining a possible decision behavior of the electric vehicle at each of other nodes in the trip chain according to the first state-of-charge information and the current node position information; acquiring second state-of-charge information of the charging pile at the current moment, and determining the state-of-charge transition probability of the charging pile according to the second state-of-charge information and the possible decision behavior; and determining an optimal decision-making behavior of the electric vehicle based on the minimum peak-valley difference of the total load curve of the power distribution network according to the state-of-charge transition probability of the charging pile, and controlling the charging of the electric vehicle according to the optimal decision-making behavior. The method can optimize the load curve of the power distribution network.