9results about How to "Smooth load fluctuation" patented technology

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 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.