15results about How to "Smooth load fluctuation" patented technology

The invention provides a micro-grid robust optimization scheduling method considering a segmented multi-interval uncertainty set, and the method comprises the steps: carrying out the segmentation fitting of the source load prediction power and error data of wind, light and loads based on a Gaussian mixture model, and combining with a box-type interval uncertainty set to build a multi-interval uncertainty set of power segmentation segments; the uncertainties are used for describing wind, light and load uncertainties; constructing a microgrid robust optimization scheduling model considering the segmented multi-interval uncertainty set and the carbon emission by taking the minimization of the operating cost and the carbon emission cost of the microgrid as a target; and solving the micro-grid robust optimization scheduling model by adopting a column sum constraint generation algorithm. According to the method, the problems of uncertainty modeling and carbon emission of the micro-grid can be solved, the conservative property of a robust optimization scheduling strategy is reduced, and the economical efficiency of micro-grid scheduling is improved.

The invention discloses a user side optical storage integrated system full life cycle economy evaluation and capacity optimization configuration method, and relates to the technical field of power system energy configuration. Establishing a full-life-cycle economic evaluation model of the photovoltaic power generation system and the energy storage system, wherein the full-life-cycle economic evaluation model covers the full life cycles of the photovoltaic power generation system and the energy storage system; a multi-target evaluation function with the capacity of a photovoltaic power generation system and the capacity of an energy storage system as decision variables and with the goals of saving the power utilization cost of a user, stabilizing load fluctuation and improving the photovoltaic power generation consumption rate of the user side is established, and then the capacity configuration problem of the user side photovoltaic power storage integrated system is optimized and solved through a sea image optimization algorithm. According to the invention, the economical efficiency of the optical storage integrated system can be effectively improved, the load fluctuation can be stabilized, the photovoltaic power generation consumption rate is improved, and the application prospect is wide.

The invention provides an evolutionary game-based optical storage charging station V2G pricing method and system considering carbon transaction, a storage medium and electronic equipment, and relates to the technical field of V2G. The V2G pricing method is oriented to the V2G pricing problem of the CSO of the EV user and the charging station operator. The method comprises the following steps: firstly, adopting Monte Carlo simulation to obtain EV user data, and simulating a decision behavior of an EV user in a real scene; the Minkowski method is adopted to aggregate feasible power areas of the CSO, the total power boundary and the capacity boundary of the CSO in the day-ahead stage are derived, and comprehensive modeling is carried out on the EV user and the CSO; then, on the premise of initializing a pricing strategy, CSO, charging and discharging power and a price scheme are selected for a finite rationality EV user through an evolutionary game; secondly, taking stepped carbon transactions into consideration, and performing pricing optimization by adopting an improved multi-target whale optimization algorithm among the CSOs to realize maximization of economic benefits and social benefits; and finally, returning the pricing strategy to the EV user, carrying out multiple iterations to realize convergence, and finally outputting a global optimal V2G price.

The invention relates to the technical field of power distribution networks, in particular to an electrified traffic-power distribution network dynamic double-layer optimization scheduling method, which comprises the following steps of: constructing a target function of a power distribution network optimization scheduling model by taking the minimum sum of active power loss of a power distribution network in all time periods within scheduling time as a target; constructing a power distribution network optimization scheduling model constraint; on the basis that the traffic network is in a mixed DUE state under the balanced path departure rate, constructing a dynamic balance constraint condition in the traffic network optimization scheduling model; constructing a dynamic equilibrium differential variational inequality in the traffic network optimization scheduling model; and constructing an electrified traffic-power distribution network dynamic double-layer optimization scheduling model by taking the power distribution network optimization scheduling model as an upper-layer model and the traffic network scheduling optimization model as a lower-layer model. According to the invention, by controlling the charging electricity price time and space distribution, the comprehensive travel cost of the user is controlled to realize ordered charging, the traffic flow distribution is optimized, the power distribution network loss is effectively reduced, the flexible resources of double networks are fully utilized, and the load fluctuation is stabilized.

The invention discloses a geothermal heat supply system integrating electricity price response and a heat storage scheduling strategy, belongs to the technical field of geothermal heat supply, and can solve the problems that an existing single geothermal heat supply system is low in efficiency and poor in stability. The system comprises a deep geothermal well; a photo-thermal module; the temperature commutator comprises an inlet end and a plurality of outlet ends; the deep geothermal well and the photo-thermal module are both communicated with the inlet end; the temperature reverser is used for detecting the temperature of the heat exchange fluid at the inlet end and controlling the outflow direction of the heat exchange fluid according to the temperature; the heat storage module comprises a heat storage heating unit and a heat storage water supply unit which are connected to different outlet ends of the temperature reverser, and the heat storage temperature of the heat storage heating unit is higher than that of the heat storage water supply unit; and the control module is used for controlling the deep geothermal well and the photo-thermal module to store heat to the heat storage module in the valley electricity period of the heat consumption valley period and controlling the heat storage module to release heat to the user side in the peak electricity period of the heat consumption peak period. The device is used for geothermal heat supply.

The present application relates to the technical field of power grid, disclose a kind of energy scheduling method of power grid peak shaving, comprising the following steps: S1, establish gas network hydrogen doping model, the relationship between hydrogen doping ratio and hydrogen doping cost is calculated by gas network hydrogen doping model;S2, establish risk assessment model, calculate the risk loss caused by source and load volatility;S3, establish peak shaving model, calculate peak shaving cost;S4, establish multi-zone scheduling model, with the minimum total cost as the goal, under the constraint condition, the model is solved using alternating multiplier algorithm, the upper limit value of gas network hydrogen doping is optimized.The risk assessment model of the present application quantifies the risk brought by source and load uncertainty to the system, can provide reference for decision makers, improve the accuracy of scheduling strategy, the peak shaving model involves the equipment in hydrogen energy comprehensive utilization link in the peak shaving of power system, suppresses load fluctuation, realizes peak clipping and valley filling, changes the proportion of peak shaving cost by adjusting economic coefficient, realizes different peak shaving effect.

The invention relates to a method for designing the charging electricity price of an electric vehicle for exciting new energy consumption, and the method comprises the following steps: evaluating the charging behavior of the electric vehicle, and calculating the charging load of the electric vehicle; dividing peak and valley periods of new energy power supply through a membership function; calculating an elastic response relational expression of the electric vehicle load demand to the electricity price, and calculating the charging cost; taking the minimum new energy power supply total amount and the minimum charging cost as targets, constructing a consumption model, and setting a target function and a constraint condition of the consumption model; and resolving the consumption model through a genetic algorithm to obtain an electric vehicle charging electricity price adjustment design scheme. Compared with the prior art, the method has the advantages that the influence of the electricity price on the load change of the power grid can be predicted in combination with the fluctuation change of the new energy power generation capacity, and the consumption of the new energy is facilitated.

The invention discloses a power dispatching method, device and equipment for an optical storage micro-grid system and a medium, and belongs to the technical field of power dispatching. The method comprises the following steps: acquiring source network load storage resource data of a micro-grid system; based on the source network load storage resource data of the micro-grid system, obtaining the off-grid power of a tie line between the micro-grid system and the large power grid; according to the off-grid power of the tie line, selecting a photovoltaic maximum consumption strategy or a tie line control strategy to perform power dispatching of the micro-grid system; according to the tie line control strategy, the off-grid power of a tie line is adjusted by controlling one or more of photovoltaic equipment, energy storage equipment and load equipment. According to the power dispatching method provided by the invention, the load impact of the micro-grid system on a large power grid can be reduced, the utilization rate of energy storage equipment is improved, power generation and load fluctuation are stabilized, various resource configurations of source grid load storage in the micro-grid system are optimized, and the stability and economical efficiency of operation of the micro-grid system are improved.

The application provides a dynamic priority charging and discharging scheduling method and system based on community V2X vehicle network interaction, obtains vehicle battery state, travel regularity, community load and power grid state data through V2X communication, jointly evaluates vehicle dynamic priority and scheduling time window, and forms power cell division based on load demand and vehicle adaptability. Then, the conflict is resolved by combining the deterministic travel plan, an emergency adjustment set is generated, power optimization distribution is completed within the power grid safety boundary, and a scheduling instruction is output. Thus, the aggregation, evaluation, grading and optimized scheduling of community electric vehicle energy storage resources are realized, the electric vehicle cluster is converted into a controllable distributed energy storage system under the premise of guaranteeing the travel demand of the vehicle owner, which not only supports the power grid and stabilizes the fluctuation, but also creates benefits.

The application discloses a user-side hybrid energy storage optimal configuration method considering demand response and carbon cost, and belongs to the field of energy storage system optimal configuration. The optimal configuration method comprises the following steps: obtaining typical original load data of a hybrid energy storage user to be configured; obtaining load reduction of the user load after the load participates in demand response based on an electricity quantity and electricity price elasticity matrix, correcting the typical original load data to obtain load data after demand response; introducing a VSQF frequency domain decomposition algorithm to decouple the load data after demand response into high-frequency components and low-frequency components; substituting the high-frequency components into an upper-layer multi-objective optimal configuration model to output high-frequency fluctuation suppression results and a super capacitor configuration scheme; substituting the low-frequency components combined with the high-frequency fluctuation suppression results into a lower-layer multi-objective optimal configuration model to output a storage battery configuration scheme, and obtaining a final hybrid energy storage configuration scheme.

This invention proposes a two-stage optimization method for distribution networks integrating electric vehicles (EVs) and distributed generation. In the first stage, an orderly scheduling of EVs is achieved by establishing a user participation dispatch probability model and a dynamic electricity price model considering load. The optimized EV load is then superimposed onto the distribution network reconfiguration optimization model in the second stage. Finally, network reconfiguration and distributed generation output are used to reduce network losses and node voltage deviations across the entire distribution network. The proposed method reduces peak-to-valley load differences. Considering user participation rates, it not only reduces user charging costs but also improves node voltage levels and reduces network losses.

This invention belongs to the field of power technology, specifically relating to a method, system, equipment, and medium for regulating the power of electric vehicle charging piles. The method includes the following steps: acquiring historical power grid operation data for each distribution area and establishing an objective function; obtaining a Pareto optimal solution set using a multi-objective optimization method based on particle swarm optimization algorithm according to the objective function; calculating the per-unit voltage values ​​of all distribution network nodes in each time period based on the Pareto optimal solution set; selecting solutions where no voltage crossover occurs at the per-unit voltage values ​​of the distribution network nodes, calculating the corresponding total voltage deviation; selecting the solution with the smallest total voltage deviation as the optimal solution for day-ahead optimization; and controlling the charging and discharging of the charging piles according to the optimal solution for day-ahead optimization. Accuracy is improved by filtering the Pareto solution set obtained from the multi-objective optimization results.

The power system real-time scheduling method and system considering energy storage differentiated response comprise: updating wind power and photovoltaic output and load prediction data every preset time interval in a scheduling period; a robust optimization model containing new energy prediction error uncertainty and energy storage differentiated response is established, including constructing an objective function, robust operation constraints of different types of energy storage power stations, system power balance constraints and line transmission capacity constraints; a robust dual method is used to convert the robust optimization problem containing interval uncertainty into a deterministic mixed integer linear programming problem; according to the wind power and photovoltaic output and load prediction data in each scheduling period, the robust optimization model of the power system real-time scheduling is solved every other scheduling period, and the real-time power instructions of two types of energy storage and conventional units are output. The present application can fully exert the regulation flexibility and comprehensive benefits of different types of energy storage power stations, can adapt to different regional new energy fluctuation characteristics, and can improve the new energy consumption capacity.

This application relates to a photovoltaic-storage-charging scheduling control system and method. The system includes: photovoltaic modules, energy storage modules, charging equipment, measurement modules, a scheduling controller, and a cloud platform. The scheduling controller executes the following steps: based on charging load prediction and photovoltaic power generation prediction algorithms, it processes a historical data feature database and outputs prediction results for a future period; the historical data feature database is generated by processing historical data, time period information, and meteorological data; based on the prediction results, it obtains the energy storage capacity required during off-peak periods and the energy storage capacity required during parity periods, and schedules the photovoltaic modules, energy storage modules, and charging equipment based on the energy storage capacity required during off-peak periods and the energy storage capacity required during parity periods. This achieves the linkage between energy storage capacity planning and weather conditions, fully utilizes historical data, time period information, and meteorological data to predict energy storage, enabling the full absorption of photovoltaic surplus on sunny days and effectively smoothing load fluctuations on rainy days, avoiding energy waste and grid impact.

The invention belongs to the technical field of power grid energy storage management, and provides a rapid capacity expansion and reconstruction method of a modular energy storage system. Comprising the following steps: obtaining participation records of each modular group in the modular energy storage system on power grid side peak regulation in a plurality of power grid historical operation stages, performing power grid side peak regulation participation analysis and power grid side peak regulation demand trend analysis, and identifying a module group to be expanded; based on the capacity data of each modular group and the maximum charging and discharging rate data, correlation analysis is carried out, and a modular group capacity-charging rate coupling model and a modular group capacity-discharging rate coupling model are constructed; and obtaining charge and discharge rate data of the to-be-expanded module group when participating in power grid side peak regulation for multiple times, carrying out volatility analysis, predicting a target charge rate and a target discharge rate, and obtaining an expansion upper limit of the to-be-expanded module group by combining the modular group capacity-charge rate coupling model and the modular group capacity-discharge rate coupling model. And accurate capacity expansion of the modular energy storage system is realized.