8 results about "Power generation scheduling" patented technology

This invention belongs to the field of virtual power plant scheduling technology, specifically involving a joint scheduling method for multiple virtual power plants considering distributed energy optimization and control. The steps include: constructing a control model for photovoltaic power generation resources within the virtual power plant, a scheduling and control model for the series-connected battery energy storage system, and a short-term power generation scheduling model for multiple virtual power plants; jointly optimizing the intraday operation of multiple virtual power plants; setting the objective function of the short-term power generation scheduling model for multiple virtual power plants with the optimization objectives of minimizing the joint operation cost of multiple virtual power plants and minimizing the renewable energy curtailment rate, and solving it using a simulated annealing algorithm to obtain the optimal scheduling plan for multiple virtual power plants within a short-term scheduling cycle when connected to a distributed energy control strategy. This invention can improve the distributed renewable energy absorption capacity, reduce the operating losses of the energy storage system, and enhance the economy and operational flexibility of joint scheduling of multiple virtual power plants while satisfying the constraints of coordinated operation of multiple virtual power plants.

The present application relates to the technical field of management platform, especially to a maintenance plan and power generation scheduling collaborative optimization system and method based on deep reinforcement learning. The method is: collecting power grid multi-source heterogeneous data, standardizing and extracting features of the multi-source heterogeneous data, and constructing a multi-dimensional state feature vector; constructing a deep reinforcement learning model; inputting the real-time generated multi-dimensional state feature vector into the trained deep reinforcement learning model to output a comprehensive decision vector; generating a maintenance work order and a power generation scheduling instruction based on the comprehensive decision vector, and issuing and executing; collecting power grid actual operation data after the instruction is issued and executed, calculating the evaluation results based on the preset evaluation index system, and adjusting the parameters of the deep reinforcement learning model according to the evaluation results to drive the continuous optimization of the model. The present application improves the collaboration of maintenance plan and power generation scheduling, improves the efficiency of power grid operation, guarantees the safety and stability of power supply, and reduces the operation and maintenance cost.

The application discloses a photovoltaic power station power prediction method based on prior knowledge guided genetic programming, first, obtaining training data preprocessed by symbol normalization, learning photovoltaic power generation symbol rules through self-supervised pre-training of a Transformer model, and outputting a prior symbol vector of a target function; a double fitness collaborative optimization mechanism containing numerical fitting accuracy and physical mechanism similarity is designed, and the prior vector is used to strengthen and select individuals with high physical matching degree; a Pareto multi-objective optimization is used to balance the double-dimensional conflict, the prior knowledge is transferred to the whole evolution process of genetic programming, and finally the optimal explainable prediction model is extracted from the Pareto frontier solution set. The application solves the problems of traditional genetic programming evolution blindness, easy falling into local optimum and poor physical consistency of generated model, realizes explicit physical explainability of the model while ensuring prediction accuracy, and can be widely applied to power generation scheduling and operation optimization of photovoltaic power stations.

The application belongs to the field of power grid dispatching, and specifically discloses a new energy power generation dispatching method facing zero examination quantity constraint. The core of realizing zero examination of the application lies in constructing an economic optimization model allowing active wind curtailment and energy storage cooperation. Traditional methods usually only rely on energy storage to smooth fluctuations, and may fail due to insufficient capacity under extreme fluctuations. The scheme internalizes the examination cost as a cost, and simultaneously takes the wind curtailment instruction and energy storage control as optimization variables, so that the model can autonomously weigh between economy and compliance. The introduced zero examination quantity constraint sets an insurmountable boundary for active power change, and the wind curtailment variable in the model ensures that when the fluctuation may exceed the limit, the system can cooperate with energy storage through the most economical wind curtailment decision to ensure that the grid-connected power strictly meets the standard. Compared with the prior art, through the wind curtailment and energy storage cooperation mechanism, a complete and feasible path for realizing zero examination is provided, and the grid-connected certainty and safety are fundamentally improved.

The application discloses a power system generation scheduling strategy determination method and device and a storage medium. The method comprises the following steps: determining a plurality of operation scenes in a target power system, and determining generation scheduling strategies corresponding to the plurality of operation scenes respectively; determining a similar scene with the maximum similarity to a current scene in the plurality of operation scenes, and a similar scheduling strategy corresponding to the similar scene; and obtaining a target scheduling strategy corresponding to the current scene based on the similar scheduling strategy. The application solves the technical problem that the decision efficiency of the power system is not ideal in the related art.

The present application relates to the technical field of water and electricity dispatching, and particularly relates to a water and electricity cluster power generation dispatching optimization method and system based on water regime prediction. Multi-source prediction data of water, wind and light are loaded first, multi-dimensional joint scene trees are obtained through multi-source uncertainty joint scene generation, and joint distribution fuzzy sets are constructed accordingly; meanwhile, a multi-element dynamic constraint set is formed by loading a cascade water and electricity fine coupling model and various constraint data, a multi-time scale nested distribution robust rolling optimization model is constructed and executed based on the two, unit load distribution instructions are obtained, and finally a dispatching decision scheme is output, so that multi-dimensional optimization of water and electricity cluster dispatching is realized.

The application discloses a kind of based on kalina cycle's work heat exchange network optimization method, belong to work heat exchange network technical field, including S1 obtain process stream parameter, the property parameter of kalina cycle and other economic data;S2 by comparing the import and export temperature and import and export pressure of stream, the stream is classified;S3 the heat capacity flow rate and enthalpy of each stream are calculated;S4 construct work heat exchange network and the mixed integer nonlinear programming model (including objective function and constraint condition) of kalina (kalina) cycle coupling;S5 with annual total cost TAC minimization as objective function, utilize solver to solve model, obtain optimal work heat exchange network structure and operating parameter.The application adopts above-mentioned one kind based on kalina cycle's work heat exchange network optimization method, can determine optimal heat integration scheme, work exchange configuration and power generation scheduling strategy, realize multi-energy collaborative optimization, to significantly improve the economy and energy efficiency of system.