3881 results about "Power load" patented technology

The invention discloses a power load prediction method based on a two-channel cross attention network, and belongs to the technical field of time sequence prediction in deep learning. The method comprises the following steps of obtaining a power load data set, performing preprocessing on the power load data set, decomposing power load data into a plurality of local components by applying VMD, iteratively optimizing VMD parameters through an APSO algorithm, and screening a subsequence with the lowest complexity to be combined with original features to serve as model input; and then constructing a dual-channel cross attention network model, processing time sequence data by using a parallel mechanism of a BiLSTM channel and a Transform channel, enhancing the feature expression ability through an improved cross attention layer, and performing power load prediction by using an MLP fused KAN network. According to the method, the time sequence prediction of the power load is carried out by constructing the two-channel cross attention network, the prediction precision and robustness are high, and the accuracy of load time sequence prediction is remarkably improved.

The invention relates to the technical field of power systems, provides an association rule analysis-based power load prediction method and system, and aims to solve the problem of hidden fault response lag caused by lack of an equipment health state and load fluctuation dynamic coupling mechanism in the prior art. And the problem of load prediction compensation deviation caused by insufficient weight quantization of the fault propagation path is solved. The method comprises the following steps: generating equipment state data according to a vibration spectrum and an insulation aging index of power equipment; performing fusion analysis, generating an equipment health degree evaluation index, and establishing a dynamic influence model of the equipment abnormal event on the power grid load fluctuation according to the association rule; identifying potential abnormal equipment, performing logic mapping, and generating a fault propagation path weight; and dynamically adjusting according to the weight to obtain an adjusted power load predicted value. According to the technical scheme provided by the invention, equipment vibration and insulation aging data are fused, a health assessment and fault propagation model is constructed, and load prediction is dynamically corrected to prevent and control power grid risks.

A system and method for efficiency and revitalization of power plant assets allows for existing fossil fuel burning power plants to operate in tandem with renewable power sources and to operate at a consistent base load. The system and method reduce fossil fuel burning power plant power / load cycling and improve efficiency, increase power plant useful lifetime, and reduce emissions. The system and method allow for substantially consistent power output regardless of power demand and power supply generation from existing fossil fuel burning power plants and renewable energy sources supplying a common grid.

The invention discloses a virtual power plant collaborative optimization operation method based on distributed energy, and the method achieves the real-time monitoring of the operation state of a distributed energy resource through the precise modeling of the distributed energy resource, and predicts the change of the power load demand. A multi-objective optimization model and an advanced optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, are utilized, so that the virtual power plant can dynamically adjust energy supply and load demands, and economic benefit maximization, energy efficiency optimization and carbon emission minimization are realized. The method also considers the energy efficiency and economic benefits of the virtual power plant, and improves the comprehensive benefits of the system through comprehensive analysis and optimization. Through effective interaction with the power grid, the virtual power plant can provide peak regulation, frequency modulation and standby services, a demand response mechanism is implemented, and operation of the power grid is further optimized. The method has the beneficial effects of improving the energy utilization efficiency, enhancing the stability of a power grid, optimizing economic benefits, promoting environmental protection, improving the flexibility and adaptability of the system and supporting policies and market mechanisms.

The invention discloses a power load prediction method based on multi-source heterogeneous data feature fusion, system equipment and a medium, and relates to the technical field of power system load prediction, and the method comprises the steps: obtaining a power load prediction data set, and carrying out the preprocessing; performing input feature processing on the static data, the graph data and the time series data based on a multi-layer perceptron, a graph convolutional network and a gating loop unit network, and fusing the processed features based on a feature splicing method to obtain comprehensive feature information; based on full-connection neural network construction, taking the comprehensive features as input, inputting training set data into a model, and performing iterative optimization according to a model effect to adjust model parameters and hyper-parameters; and based on the parameter optimization adjustment model, using the evaluated optimal model to predict the power load in the future time period. The method is superior to a traditional method in precision and stability, can better adapt to complex and changeable load prediction scenes, and has wide application prospects and remarkable actual value.

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention discloses a computing power load stabilizing and energy-saving management system based on a flywheel energy storage system, and belongs to the technical field of energy storage and management, and the system comprises a data collection module which is used for collecting and calculating a real-time current harmonic spectrum of a cluster and server rack vibration data to generate an electric energy quality characteristic spectrum; the prediction modeling module is used for establishing a prediction model output computing power demand dynamic distribution matrix based on multi-dimensional space-time correlation; the optimization control module is used for analyzing a peak load phase in the power demand dynamic distribution matrix to generate a dynamic coordination control strategy; and the power execution module is used for executing the dynamic coordination control strategy to adjust the phase parameter of the reverse electromotive force of the flywheel so as to offset the voltage harmonic component of the server bus. Harmonic phase compensation is realized through dynamic reverse electromotive force modulation, the calculation load prediction precision can be effectively improved, and the energy utilization efficiency and the equipment operation reliability are remarkably improved.

The invention provides a machine room overall electrical scheme configuration method and system for cabinet layout, and relates to the technical field of electrical design, and the method comprises the steps: building a machine room three-dimensional digital model, and carrying out the function partitioning; optimizing the cabinet layout and determining the maximum capacity; constructing an electricity load prediction model; uPS load balancing is carried out by adopting deep reinforcement learning; designing an intelligent bus duct wiring and circuit breaker access scheme; constructing an equipment topological graph and collecting operation parameters; extracting equipment association features based on the graph attention network; and identifying an abnormal power consumption mode and optimizing a regulation and control scheme.

The invention discloses a power load prediction method and system based on a meteorological prediction model, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining historical power load data and corresponding multi-dimensional meteorological data, constructing a meteorological coupling effect analysis model, calculating a dynamic coupling weight matrix among the meteorological parameters through an attention mechanism network; performing feature fusion on the coupling weight matrix and the multi-dimensional meteorological data through a gating circulation unit to generate a space-time coupling feature vector; establishing a power load prediction model, and extracting a short-term fluctuation feature and a long-term trend feature of the power load from the space-time coupling feature vector based on a deep residual network architecture and a parallel convolution channel; the dynamic coupling weight matrix is updated based on prediction error back propagation, and adaptive adjustment of the meteorological parameter coupling relation is achieved. According to the method, dynamic fusion of the spatial-temporal characteristics is realized through the cross gating unit, so that the model can perform power load prediction by using spatial-temporal information, and the prediction precision is improved.

The invention relates to a control method and system for a photovoltaic energy storage system, and the method comprises the steps: obtaining the real-time irradiance data of the photovoltaic energy storage system, carrying out the power generation prediction of an irradiance region, and obtaining a dynamic power generation prediction value; acquiring charge state parameters of a photovoltaic energy storage system and aggregated power load demand data of a target power utilization area, and performing energy storage scheduling analysis in combination with the dynamic power generation power prediction value to obtain a reference energy storage scheduling strategy; acquiring energy consumption equipment parameters of the target power utilization area, and performing equipment scheduling control with the reference energy storage scheduling strategy to obtain an equipment-level control instruction set; and performing grid-connected mode and off-grid mode collaborative analysis on the dynamic generation power prediction value, the reference energy storage scheduling strategy and the equipment-level control instruction set to obtain an energy storage global control strategy. According to the invention, multi-level collaborative optimization can be realized, and the economical efficiency and sustainability of the system are improved while stable power supply is guaranteed.

The invention discloses an industrial and commercial park electrical load power prediction method, and relates to the technical field of electrical load prediction, and the method comprises the steps: obtaining park electrical equipment power data, and obtaining a standardized time sequence power matrix through normalization preprocessing; performing wavelet denoising and empirical mode decomposition on the power matrix, and separating a power trend component from a fluctuation component; constructing a trend prediction sub-model by using a long-short term memory network, and constructing a fluctuation prediction sub-model by using a seasonal ARIMA model; carrying out weighted combination on the two sub-models through an integrated learning method, and constructing a comprehensive prediction model; and finally, based on the historical prediction error, setting an adaptive threshold value for constraint processing, and outputting a final prediction result. According to the method, power data features are adaptively separated through a multi-level decomposition strategy, a dual-model collaborative prediction mechanism is adopted, environmental factors are introduced to improve prediction precision, a self-adaptive correction mechanism is designed to ensure result reliability, and accuracy and stability of park power load prediction are remarkably improved.

The invention relates to the technical field of computing power load balancing, and discloses a computing power node load balancing method and device, equipment and a storage medium. The computing power node load balancing method comprises the steps that a target computing power task is read, task feature information is extracted, and the computing power resource quantity needed by the task is calculated through a prediction model; calculating the load of each node in real time, and screening candidate nodes meeting the resource margin; and evaluating the matching degree of the candidate nodes and the tasks, and selecting an optimal node to perform task allocation. According to the method, the task resource demand is accurately predicted through the prediction model, the optimal node is dynamically screened in combination with the real-time load, the problems that a traditional strategy cannot sense the instantaneous load, the calculation complexity is high and cross-cloud adaptation is difficult are solved, the load balancing efficiency is improved, the system calculation overhead is reduced, and meanwhile the cross-cloud environment adaptability is enhanced.

The invention discloses a city expansion area transformer substation layout method and system based on an artificial intelligence algorithm. The method comprises the following steps: predicting future electrical load demand data of an area by mining an association rule between geographic space data and historical load data of the city expansion area; analyzing the prediction data by using a large language model, partitioning the city expansion area according to an analysis result, obtaining each sub-area and load difference data thereof, combining the data with historical power distribution network grid structure data of the city expansion area, and processing the data through the large language model to obtain an initial layout scheme of the transformer substation; by simulating the power supply capacity of each sub-region at different load growth rates, the initial layout scheme of the transformer substation is optimized, an optimization scheme is obtained and executed, and the optimal layout of the transformer substation is obtained by comprehensively applying artificial intelligence algorithms such as a large language model and a machine learning algorithm dynamic planning algorithm. Accurate prediction of the electrical load demand of the urban expansion area and effective optimization of the transformer substation layout scheme are realized.

The invention relates to the technical field of power operation and maintenance, in particular to a power system operation and maintenance method and system of an intelligent power distribution cabinet for weak current control. The method comprises the following steps: acquiring power operation data and sensing data of a power distribution cabinet, and analyzing a topological structure of a power system to obtain a dynamic topological structure of the power system; performing potential load anomaly analysis on the sensing data of the power distribution cabinet to obtain load anomaly node data of the power system; performing load anomaly influence structure division based on the load anomaly node data of the power system to obtain load anomaly influence nodes; performing abnormal node classification on the load abnormal influence nodes according to the sensing data of the power distribution cabinet to obtain three-phase power load offset nodes and three-phase power improper wiring nodes; and performing power distribution reconstruction on the three-phase power load offset node and the three-phase power improper wiring node to obtain abnormal node power distribution data. According to the invention, the weak current control efficiency and the power distribution energy efficiency can be improved.

The invention relates to an electrical load prediction and optimization regulation and control method and system for high-energy-consumption equipment, and solves the problems of inaccurate load prediction, single regulation and control means and difficulty in dynamic adaptation of the high-energy-consumption equipment, and the method comprises the steps: collecting multi-source data of the high-energy-consumption equipment in real time, constructing a dynamic equipment collaborative causal graph after preprocessing, and extracting key constraints; inputting the data and the constraints into the dynamic digital sample model to obtain a system state simulation result; based on the result, a multi-objective optimization regulation and control strategy is generated and executed by using a meta-learning + reinforcement learning decision framework; and collecting actual data comparison deviation, starting hierarchical federated learning when a threshold value is exceeded, grouping and aggregating similar experiences according to a causal graph topology, and dynamically calibrating model parameters and a decision framework. The method has the following effects that accurate load prediction and multi-target cooperative regulation and control of the high-energy-consumption equipment are achieved, working condition changes are dynamically adapted, the cost is reduced, and continuous production and the service life of the equipment are guaranteed.

The invention discloses a power load prediction method and device based on multi-factor influence, and particularly relates to the technical field of power load prediction. Historical weather data and power load data are collected, preprocessing and feature extraction are carried out, temperature change deviation features and power load lag features are extracted and input into a prediction model for training, the accuracy of a prediction result is evaluated, and when the prediction accuracy is low, the prediction result is subjected to prediction. The abnormal degree is further analyzed and predicted, and the power dispatching strategy is adjusted in time, so that the violent fluctuation of the load demand is dealt with, the stable operation of the power grid is ensured, the accuracy of power load prediction is effectively improved, and the risk of overload or collapse of the power grid is reduced especially in the case of extreme weather.

The invention provides an intelligent power grid load dynamic monitoring method and system, and the method comprises the steps: collecting real-time power consumption data, and generating a real-time power consumption data sequence; constructing a time sequence model based on historical synchronous data, generating an expected power load data sequence, and comparing the real-time power consumption data sequence with the expected power load data sequence to obtain a load trend prediction result; analyzing by applying a self-adaptive dynamic clustering algorithm based on a graph neural network, generating a configuration scheme of a clustering center and a clustering number, determining an optimal power distribution scheme in combination with a bilevel programming model and a robust optimization algorithm, and evaluating the risk of the optimal power distribution scheme to generate a power distribution strategy; based on a power distribution strategy, a demand side management strategy based on the game theory is formulated to generate an intelligent load response strategy so as to realize dynamic load monitoring; according to the invention, the management level of the smart power grid is improved, and a solid foundation is provided for future energy management and optimal scheduling.

The invention discloses a power system load dynamic optimization method based on reinforcement learning. The method comprises the following steps: S1, collecting power system data to construct a state space; s2, constructing a hierarchical reinforcement learning model based on the state space, and dividing a high-level decision and a low-level execution task; s3, training a high-level decision model, and outputting a scheduling task target category instruction in a high-level state; s4, training a low-layer execution model, and outputting a control action in combination with a current node state and a high-layer instruction; s5, introducing an evolutionary mechanism to generate a strategy population and optimizing a low-layer execution model; s6, fusing an evolutionary mechanism and a strategy gradient to synchronously optimize individuals with excellent performance; s7, deploying the trained model to a power dispatching system; s8, performing model parameter fine tuning based on scheduling feedback; and S9, continuously applying the fine-tuned model to load scheduling control. According to the invention, power load accurate scheduling and strategy efficient adaptive optimization are realized, and system responsiveness and operation stability are improved.

The invention provides a power load long time sequence prediction method and a related device, and relates to the technical field of power load prediction. The method comprises the steps of collecting power load time sequence data; preprocessing the power load time sequence data; decomposing the preprocessed power load time sequence data into a seasonal component and a trend component through a mobile decomposition method; inputting the trend components into a trend clustering enhancement network, performing channel clustering on the trend components through a K-means clustering algorithm, applying independent linear transformation to each clustering group, and extracting long-term trend features; inputting the seasonal components into a seasonal causal attention network, and extracting short-term seasonal features through a causal mask mechanism and a multi-head attention mechanism; fusing the long-term trend features and the short-term seasonal features to generate fused features; and inputting the fusion features into a decoder, and outputting a power load prediction result through a full connection layer. The change trend of the power load data is fully considered, and the prediction precision of the power load long sequence is improved.

The invention discloses a carbon emission monitoring method and system for a data center, and the method comprises the steps: collecting the power generation proportion data of different energy types in real time, predicting the power supply change trend of renewable energy in combination with meteorological information, calculating the power supply proportion of each energy type, and constructing a dynamic weight matrix; constructing a time sequence prediction model by using historical and real-time renewable energy data, and predicting the future renewable energy power supply capability; carbon emission factors and priorities of different data centers are calculated, a task migration feasibility matrix is constructed, calculation tasks are dynamically migrated to a low-carbon emission data center according to a scheduling revenue function, a task scheduling path is optimized, cross-regional data transmission time delay and energy consumption are reduced, and cross-regional optimization scheduling of computing power is achieved; a multi-dimensional time sequence prediction model is constructed, the carbon emission trend is predicted, and carbon emission abnormity is monitored in real time. According to the invention, the problem of matching of computing power load and renewable energy of a data center can be solved.

The embodiment of the invention provides a power system short-term load prediction method and device, equipment, a medium and a product. The method comprises the following steps: acquiring historical power load data and historical meteorological parameter data in a historical time period and meteorological parameter data of a to-be-predicted day; according to the historical meteorological parameter data and the meteorological parameter data of the to-be-predicted day, determining a time period corresponding to a target similar day of each time period in the to-be-predicted day, and forming a historical day sample set based on the historical power load data and the historical meteorological parameter data of the time period corresponding to the target similar day; training an improved multi-layer feedforward neural network model based on the historical day sample set to obtain a trained target load prediction model; and inputting the meteorological parameter data of the to-be-predicted day into the target load prediction model to obtain a power load prediction value of each time period in the to-be-predicted day, and accumulating the power load prediction values of all time periods in the to-be-predicted day to obtain a power load prediction value of the to-be-predicted day. By using the method, the power load prediction precision is improved.

The invention discloses a power load prediction method based on heterogeneous ensemble learning and an attention mechanism. The method comprises the following steps: acquiring historical data of a power load, the historical data at least comprising time sequence data; preprocessing the historical data to obtain processed feature data; based on the feature data, an integrated learning model is constructed, and the integrated learning model at least comprises a combination of multiple base learners; predicting the feature data through the integrated learning model to obtain a preliminary prediction result; and introducing an attention mechanism to carry out weighted adjustment on the preliminary prediction result, and generating a final power load prediction value. According to the method, the nonlinear and complex modes of the power load are effectively captured, the prediction performance is remarkably improved, and accurate decision support is provided for intelligent operation of a power system.

The invention discloses an electric energy meter error prediction method and system based on big data analysis, and relates to the technical field of electric power measurement, and the method comprises the steps: firstly reading the device attribute, the voltage fluctuation threshold value and the power utilization load threshold value of a target electric energy meter, dividing parameters into a plurality of voltage fluctuation intervals and load proportion intervals, and constructing a plurality of power utilization scenes; thirdly, obtaining a sample training set based on the power big data by taking device attributes and power consumption scenes as constraints, and training through a BP neural network to obtain a plurality of error predictors; and finally, by establishing an error prediction model, inputting a real-time voltage fluctuation coefficient, an electricity load proportion and an environment factor, and outputting a prediction error coefficient of the electric energy meter. The technical problem that an existing electric energy meter is inaccurate in error prediction in a complex environment is solved, and the technical effect that metering of the electric energy meter is more accurate under different voltage fluctuation, environment conditions and load conditions is achieved.

The invention discloses an electric power system short-term load prediction method based on TCN-BIGRU-CBAM, and the method comprises the steps: obtaining a standard load time series data set as input data, and carrying out the preprocessing of the input data; establishing a time convolutional network TCN model, and extracting a long-term dependency relationship in the time sequence; establishing a two-way gating cycle unit network BIGRU, and outputting two-way time characteristics; constructing a convolutional block attention module (CBAM), and obtaining a four-dimensional feature map subjected to channel and space attention enhancement; and establishing a full connection layer, mapping the time sequence characteristics to a scalar through linear transformation, and outputting a power load prediction value of a target time step. According to the method, the problems of information loss, difficulty in high-dimensional feature extraction and the like of a traditional short-term load prediction model in complex time sequence modeling are effectively solved, the precision and the stability of short-term load prediction are remarkably improved, and the method has relatively high practical value and popularization potential.

The invention provides a multi-modal multi-scale feature fusion-based power load prediction method and system, and relates to the technical field of power load prediction. According to the method, a cross-modal attention mechanism is adopted to fuse multi-scale load time sequence features and satellite image features; decoding the fusion features through a multi-step prediction decoder to obtain a multi-step power load prediction result; the acquisition mode of the satellite image features comprises the following steps: performing feature extraction on a satellite image based on a GRVT model to obtain the satellite image features; the GRVT model includes a number of stacked gated residual encoder network elements. According to the method, satellite image feature extraction is carried out based on the GRVT model, the global information processing advantage of Transform is utilized, the single-layer information retaining capability of a residual connection mechanism is also utilized, the two are combined to efficiently extract satellite image features, the satellite image features are fused during power load prediction, and the situation that the data type is single, and the prediction efficiency is high is avoided. And the precision of power load prediction is improved.

The invention discloses a rural power grid user power load prediction method and system based on a liquid neural network. The method comprises the following steps: firstly, collecting rural power grid user power load historical data including multi-dimensional features such as weather and agricultural modes, and carrying out data preprocessing; then constructing liquid neurons based on biological neuron dynamics, and modeling the state of the liquid neurons through a differential equation; thirdly, constructing a liquid neural network based on liquid neurons, improving the characterization capability of multi-scale time sequence data through multi-level time constant setting and time gating residual connection, and supplementing network initial information in combination with a multi-layer perceptron architecture; completing model training by using the time sequence data set; the actual application performance of the model is tested based on the test data and the actual application scene; and finally, deploying the model to practical application, and carrying out power load prediction on rural power grid users. According to the method, the expression capability of the model for the multi-scale time sequence data is improved, and high-precision rural power grid user power load prediction is realized.

The invention relates to the technical field of power load prediction, and discloses a power load prediction method and system based on a VMD and MoE system, and the method comprises the steps: inputting original power load data into a trained VMD decomposer, dynamically adjusting a modal number K value and a penalty factor # imgabs0 # value according to a power load fluctuation level, and decomposing the data into K modal components; thirdly, inputting the modal components into the trained MoE system, distributing M groups of expert models and weights for the modal components through a gating network, and adjusting the number of the expert models of the high-frequency and low-frequency modal components according to the fluctuation level; and finally, at least one group of expert models is selected from the M groups of expert models, prediction results are fused through weighted summation based on the selected model and the weight thereof, and a final power load prediction value is obtained. Through the method, the precision and robustness of power load prediction are improved.

The invention discloses a power load model parameter identification method based on improved deep reinforcement learning, and belongs to the technical field of power load identification. The method comprises the following steps: determining a to-be-identified power load parameter based on a complete comprehensive load model considering power distribution network impedance and reactive compensation; then designing a power load parameter identification model based on a bidirectional gating cycle unit depth deterministic strategy gradient algorithm of priority experience playback; circuit data are collected for model training, and the trained model is used for parameter identification. On the basis of an existing power system mechanism model and a data driving model, the load model parameters are identified by improving the deep reinforcement learning model, and verification shows that the method can accurately identify the power system load model parameters, can improve the fitting precision of the power system load model, and has a good application prospect. The operation strategy of the power grid is optimized, and the operation cost is reduced.

The invention relates to the technical field of electric power system control, in particular to an electric power resource intelligent control system based on digital twinning, which comprises a data acquisition module, a digital twinning model module, an electric power dispatching module, a fault early warning module and a three-dimensional visualization module. According to the method, the load demand prediction model is established based on the hybrid RNN, the RNN and full-connection network hybrid neural network model is constructed, the historical time sequence, the time feature, the historical statistical feature and the similarity feature are learned, and the problem that the power load demand is difficult to predict due to renewable energy source grid connection and power demand increase is solved; a power dispatching decision is made through a multi-objective function optimization algorithm based on an improved particle swarm, the particle swarm optimization algorithm is improved by adding a variation strategy for high-fitness particles, the problem that the particle swarm optimization algorithm is prone to falling into a local optimal solution in the practice process is solved, and therefore the problem that power dispatching of multi-objective optimization is difficult to achieve is solved.

The invention relates to the technical field of power data processing, in particular to an energy storage configuration optimization method, which comprises the following steps: acquiring new energy output time sequence data and computing power load characteristic data; generating a space-time correlation coupling evaluation result of the new energy output volatility and the computing power load volatility; inputting a result into a hybrid power supply double-layer optimization model, recursively correcting parameters through a two-stage collaborative solution algorithm, and outputting a Pareto optimal solution set; a computing power task elastic regulation and control mechanism is embedded, and the task priority is dynamically adjusted according to the energy storage charge state and the new energy output level to generate a scheduling strategy; finally, an energy storage configuration scheme and a dynamic scheduling strategy are output, and collaborative optimization of cost effectiveness and power supply reliability is achieved. The method breaks through the coupling conflict of the economic target and the robust constraint in the traditional bilevel planning, remarkably reduces the energy storage configuration cost, and improves the system stability.