1320 results about "Electricity system" patented technology

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention discloses a virtual power plant intelligent regulation and control method and system based on artificial intelligence, and belongs to the technical field of electric power system intelligent regulation and control, and the virtual power plant intelligent regulation and control method based on artificial intelligence comprises the following steps: S1, aggregating equipment side data, desensitizing to generate topological codes, and constructing time scale matrix synchronization; s2, constructing a dynamic model by equipment parameters, and mapping real-time data to output a difference map; s3, adding equipment constraints, building a multi-objective function, optimizing a strategy and performing correlation analysis; s4, a wind and light fluctuation overrun trigger RL strategy and an abnormal switching base line generate a mixed instruction; s5, locally verifying the instruction, and correcting and feeding back parameters if the prediction is out of limit; s6, generating a three-dimensional thermodynamic diagram, and displaying an association report and a historical record by AR; s7, aggregating the data to reconstruct the training set, locally fine-tuning the strategy network and performing incremental updating; the method has the beneficial effects that the regulation and control pain point of the virtual power plant is systematically solved, the operation and maintenance cost is reduced, the new energy consumption capability is improved, and the equipment out-of-limit risk is reduced.

The invention discloses an electrical work ticket intelligent generation method and device, a medium and equipment in the technical field of power systems, and aims to solve the problems of serious module decoupling, dependence on manual connection and insufficient intelligent generation capability in the prior art. The method comprises the following steps: generating a required digital knowledge base and an electrical work task description generation work ticket based on a pre-constructed work ticket by utilizing a multi-model collaborative generation architecture; the multi-model collaborative generation architecture comprises a rule driving module used for calling a corresponding template based on task element matching; the task execution unit is used for executing a specific subtask according to a pre-deployed lightweight model or rule engine; and the large language model is used for performing semantic understanding and reasoning according to the electrical work task description, calling the rule driving module and the task execution unit according to a semantic understanding and reasoning result, and generating a work ticket in combination with a digital knowledge base required by work ticket generation. The method has both the accuracy of structured knowledge recognition and the flexibility of natural language generation.

The invention discloses a new energy medium and long term stochastic programming method and system coupled with an electro-hydrogen water regulation power supply, and the method comprises the steps: building a power supply planning model with the minimum planning operation cost as an optimization target; bringing the new energy power supply and energy storage adjusting equipment operation model and the new energy consumption rate upper and lower limit constraint expression into a power supply planning model; in consideration of the randomness of fluctuation at two sides of a source and a load in power supply planning, based on the obtained power system operation scene set S and the power supply planning model, dividing into an investment decision-making layer and an operation simulation layer according to a time period influenced by a decision, and constructing a power supply planning two-stage stochastic optimization model based on a typical scene; and solving the power supply planning two-stage stochastic optimization model under a medium and long term planning framework, and determining the investment planning installed capacity of the adjusting power supply and the new energy in the planning level year. According to the invention, economy of medium and long term planning and power supply capacity configuration of new energy can be improved, and reasonable consumption and flexible adjustment of new energy electric power are promoted.

The invention relates to a power distribution network two-stage recovery method based on an electric vehicle and mobile energy storage, and belongs to the technical field of disaster prevention and reduction and emergency recovery of a power system. According to the technical scheme, the method is divided into two stages of recovery: stage 1 pre-disaster pre-configuration and stage 2 post-disaster collaborative recovery; establishing a power distribution network line vulnerability evaluation model, and generating a fault scene in extreme weather based on a wind speed-fault probability curve; constructing a charging station and maintenance station pre-configuration optimization model, and determining the optimal site selection in the traffic network by adopting integer nonlinear programming; establishing a post-disaster recovery mixed integer programming model, coordinating and dispatching the electric vehicle, the mobile energy storage system and the maintenance team, and realizing collaborative optimization of load recovery and line repair; and designing a traffic network and power distribution network coupling mechanism, quantifying a system toughness index and optimizing a resource scheduling strategy. Through multi-resource cooperative scheduling, the post-disaster recovery capability of the power distribution network is remarkably improved, and an innovative solution is provided for improving the toughness of the power distribution network in extreme weather.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

Systems and methods for battery system management and monitoring apparatus(es) and equipment using a sensor cluster are described. The systems and methods can be used to automatically repair or otherwise address actual and predicted failure modes of the apparatus(es), which include electrical systems, power systems, energy storage systems, and other systems.

The invention provides a load frequency control method for a multi-region combined power system considering wind power integration, and the method comprises the following steps: building a combined state space model of the multi-region combined power system, comprising a state space sub-model of a wind power region, a state space sub-model of a thermal power region and tie line power deviation constraint relationships among the regions; a combined control topology of the multi-area combined power system is established, the combined control topology comprises a sub-control topology of a wind power area, a sub-control topology of a thermal power area and a tie line power deviation feedback topology, and the tie line power deviation feedback topology comprises feedback devices which are the total number of the wind power area and the thermal power area; and controlling equipment in the wind power area and the thermal power area to carry out power grid load frequency adjustment by using a control instruction output by the combined control topology. According to the technical scheme, dynamic response speed characteristics of wind power and thermal power and comprehensive frequency modulation requirements are considered, and optimal load frequency adjustment is carried out on the multi-region power system.

The invention provides a carbon ceramic resistor and a preparation method thereof, and relates to the technical field of special material production of power systems and electrical equipment. The preparation method comprises the steps that carbon-containing electric conductor raw materials are mixed to obtain a first mixed raw material, and the carbon-containing electric conductor raw materials comprise, by mass, 15 parts of gluconic acid, 10 parts of aluminum oxide and 55 parts of deionized water; performing hydrothermal reaction on the first mixed raw material to obtain a carbon-containing conductor precursor; the carbon-containing conductor precursor is carbonized in a protective atmosphere, a carbon-containing conductor of a core-shell structure is obtained, and the carbon-containing conductor takes aluminum oxide as an inner core and takes a conductive carbon material formed by gluconic acid as a conductive outer shell; mixing the carbon-containing electric conductor with the core-shell structure with a carbon ceramic matrix raw material to obtain a resistor blank; and sintering the resistor blank to obtain the carbon ceramic resistor. The uniformity, the stability and the reliability of the carbon ceramic resistor are improved, so that the carbon ceramic resistor can better meet the application requirements of ultrahigh-voltage and extra-high-voltage alternating-current power transmission systems.

The invention discloses a multi-time-scale coordination control device and method for a novel electric power system, and relates to the technical field of electric power automation. Aiming at the problems of difficulty in multi-source data collaboration, complexity in control strategy optimization and insufficient scene adaptability in a novel power system, the device realizes flexible deployment and mobile operation through a high-strength aluminum alloy frame integrated modular functional assembly; the environment monitoring module is used for high-frequency collection of multi-parameter environment data, and equipment operation safety is guaranteed; a plurality of communication protocols are supported by means of a multi-time scale control module, and distributed energy equipment is seamlessly connected; based on three core units of a data processing center module, a scene simulation unit and a cooperative control center module, second-to-hour-level electric parameter data generation, complex scene simulation verification and intelligent generation, execution and optimization of a control strategy are realized; and finally, under the complex working condition of high-proportion new energy access, the control precision, the operation stability and the comprehensive benefits of the power system are remarkably improved.

The invention belongs to the technical field of novel power system photovoltaic and energy storage system optimization configuration, and discloses an optical storage system optimization method based on carbon emission and line loss rate collaborative modeling, which comprises the following steps: constructing a double-layer planning structure of an upper layer planning model and a lower layer operation model, a carbon emission calculation model and an improved line loss rate calculation model are introduced into the lower layer, joint optimization of configuration and operation is realized through parameter coupling, and the charge and discharge efficiency loss power consumption of the energy storage device is introduced as an independent parameter in line loss rate calculation, so that the line loss calculation precision is improved; and solving by adopting an improved particle swarm optimization algorithm, and objectively sorting candidate schemes in combination with an information entropy method and a TOPSIS comprehensive evaluation method. A simulation result based on an IEEE33 node power distribution system shows that the model can effectively reduce carbon emission and line loss rate, improves node voltage level, and has good convergence and engineering applicability.

The invention discloses an excitation load grounding fault detection method, system and device based on EEMD and Hilbert spectrum analysis and a medium, and belongs to the technical field of power system energy storage equipment, and the method comprises the steps: collecting and preprocessing an electric signal, and constructing a discretization time domain data sequence; performing iterative decomposition on the sequence to obtain multiple groups of components; time domain structure analysis is carried out, and target components containing fault mutation features are screened; frequency domain transformation is carried out on the target component to construct spectrum distribution, and normalization processing is carried out; and then time-frequency joint transformation is carried out, energy distribution and energy entropy indexes are calculated, and diagnosis is completed. According to the method, modal aliasing is suppressed by using EEMD adaptive noise injection and a set average strategy, accurate separation of high-frequency transient disturbance and low-frequency harmonic waves is realized, multi-dimensional feature combined diagnosis is formed by combining frequency domain screening and time-frequency analysis, and the fault detection precision and reliability are improved.

The invention belongs to the technical field of power system operation control, and discloses a virtual power plant aggregation operation optimization method considering flexible and adjustable resource hierarchical and partitioned regulation, which is characterized in that dynamic clustering and clustering are performed on a distributed power supply based on an improved K-medoids algorithm, and response characteristic analysis is performed on an electric vehicle, an energy storage load and a temperature control load; by establishing a self-adaptive layering and partitioning mechanism, dynamic layer region boundary adjustment is realized. An optimization model with the lowest cost as the target is constructed, the optimal resource scheduling scheme is solved by comprehensively considering various cost factors and operation constraint conditions, and efficient configuration of resources and effective control of the cost are achieved; and the adjustable load and the energy storage resource are guided to participate in system regulation and control, so that the peak-valley difference of the system is reduced with relatively low regulation and control cost. According to the method, the resource scheduling efficiency is effectively improved, high-quality peak regulation auxiliary service is provided for the power grid, and efficient utilization of distributed energy is assisted.

The invention discloses a power system random scheduling method based on wind-solar joint probability distribution and double-layer dynamic optimization. The method comprises the following steps: firstly, constructing a wind and light output joint probability distribution model by adopting nonparametric kernel density estimation and a Frank Copula function so as to accurately describe space-time correlation; secondly, generating a wind-solar combined output scene through Monte Carlo simulation, and performing reduction by adopting a k-means + + clustering algorithm to obtain a typical scene set; secondly, establishing a double-layer optimization model, wherein the upper layer takes the minimum net load variance as a target to coordinate wind and light storage output stabilizing fluctuation; and the lower layer optimizes the output of the traditional unit by taking the minimum operation cost of the system as a target. Finally, a dynamic climbing constraint mechanism is introduced, the net load fluctuation standard deviation output by the upper layer is converted into a dynamic constraint threshold value of the climbing rate of the lower layer unit, collaborative optimization of economical efficiency and stability is achieved, and the dispatching robustness of the high-proportion renewable energy power system is remarkably improved.

The invention discloses a bidirectional communication load node control method and system for power demand response, and relates to the technical field of load control. The bidirectional communication load node control method and system oriented to the power demand response comprises the following steps: S1, acquiring load control communication data, establishing a load control communication database, and preprocessing the load control communication data; s2, evaluating the impact risk of the load breaking behavior of each node on the electrical system, and constructing a load breaking candidate node list and a node number index table; s3, constructing a scheduling priority sequence and executing a load breaking operation, evaluating the disturbance risk of each round of load breaking behavior on the stability of the power system, adjusting a load breaking interval in combination with an evaluation result, and updating the node number index table and the load breaking execution list; and S4, analyzing unit power impact sensitivity characteristics, identifying strategy sensitive nodes, and updating a threshold parameter table. The problems of loop current abrupt change and protection maloperation caused by neglecting node power difference in load breaking control are solved.

The invention discloses an electric vehicle charging load data virtual acquisition method and system based on a conditional diffusion model, and a storage medium, and relates to the technical field of power system operation optimization and artificial intelligence, and the method comprises the steps: constructing a dual-channel feature extraction network, and outputting conditional information; injecting conditional information into the conditional diffusion model by using a cross attention mechanism for de-noising; introducing a self-supervised alignment loss function, and constructing a self-supervised regularization mechanism with a stable interpolation result; and training the conditional diffusion model according to the historical charging load data set, and performing reverse denoising on the target missing data fragment. The method has the advantages that data features are comprehensively captured, accurate guidance of condition information is achieved, output stability is effectively improved, and a high-fidelity interpolation result highly consistent with real data distribution is generated through reverse denoising.

A microgrid island division method based on a cumulative foreground theory and a transfer learning thought comprises the following steps: step 1, aiming at a day-ahead decision-making scene, taking income maximization in a scheduling period as a core target, integrating short-term renewable energy output prediction and electricity price fluctuation data, and constructing a capacity optimization configuration model; 2, constructing a double-layer model, constructing a transaction decision model by the upper-layer model by taking energy storage power station revenue maximization as a target, and determining a power station quotation strategy; the lower layer model takes the sum of the electric energy cost and the frequency modulation service cost as the target to construct a combined clearing model of the electric energy and the frequency modulation market; and 3, solving the double-layer model constructed in the step 2. According to the method, energy storage can participate in an electric energy and frequency modulation market through strategic quotation under a reasonable market mechanism, and an energy storage power station is stimulated to optimize resource allocation, so that the frequency modulation pressure of a power system is reduced, the energy storage economic benefit is improved, and the stability and resource utilization efficiency of the power market are further improved.

The invention relates to the technical field of power system assessment, and discloses a power system vulnerability assessment method and system considering extreme weather influence, and the method comprises the steps: building a line fault probability model of a power system in extreme weather to determine the line time-varying fault probability under dynamic weather influence; generating a typical scene through a time sequence relativity matching generative adversarial network model with gradient penalty and Monte Carlo simulation; taking the generated typical scene as input, simulating cascading failure evolution through an alternating current cascading failure model, and outputting a cascading failure evolution path; and based on a fault evolution result, through a spectrogram theory and a Bayesian network, in combination with severity analysis, structural vulnerability evaluation and causal-probability pre-judgment, multi-level vulnerability quantification of the power system is realized, and the problems that single-level evaluation is insufficient and causal association is ignored in the prior art are solved. And the risk assessment precision and the disaster prevention capability of the power system in extreme weather are improved.

This document describes, among other things, a vehicle power system that includes a traction battery, a six-phase inverter divided into two groups of six switches, a six-phase motor, and a pair of switches. In drive mode, the switches configure the inverter to connect the traction battery and the motor. In plug-in mode, the system reconfigures so that one group of switches connects an AC source to the motor, while the other group connects the motor to the traction battery.

The invention belongs to the field of power system operation and control, and particularly relates to an operation control method for a wind-light hydrogen storage multi-energy complementary system, which comprises the following steps: performing empirical mode decomposition on an original output sequence to obtain an IMF component, training parameters by combining a hidden Markov chain model and a forward-backward algorithm and solving an optimal state sequence, and fusing a predicted value by using Bayesian estimation to obtain an optimal state sequence; outputting a wind and light output and load demand global prediction value; by taking the minimum comprehensive operation cost, the minimum carbon emission intensity and the minimum power supply shortage rate as targets, obtaining equipment, power balance and hydrogen storage capacity constraints through a constraint verification algorithm, and constructing a multi-target optimization model; a scene trigger function identifies four types of typical scenes, a chaos particle swarm optimization algorithm is improved to solve a model, and density peak clustering is carried out to obtain a global scheduling scheme containing output of an electrolytic cell and a fuel cell and energy storage charge and discharge power. According to the invention, through scene adaptive optimization and intelligent clustering screening, the randomness and volatility of wind and light output are dealt with.

The invention discloses a carbon emission factor calculation method adaptive to a gateway meter and a functional module, and belongs to the technical field of power system carbon emission, and the method comprises the following steps: collecting real-time carbon emission power flow data and upstream and downstream position information of a power system by using a carbon emission measurement function of the gateway meter; filling missing data; aiming at a source side carbon emission factor calculation function module, establishing a thermal power generating unit carbon emission model of a thermal power generating unit carbon emission quantification algorithm based on time sequence feature enhancement; calculating grid-side and load-side carbon emission factors; according to different positions of a gateway meter, a carbon emission factor calculation function module with corresponding carbon metering factor calculation and communication functions is designed, calculated carbon emission factors are uploaded to a cloud electric carbon platform, and regional node hour-level carbon emission factors are displayed on a local carbon emission factor calculation function module. According to the method, the accuracy, the real-time performance and the reliability of the regional carbon emission data are remarkably improved by constructing the MS-CBAM-CNN cascade architecture.

The invention relates to the technical field of power system demand side management and optimization operation, and discloses an electric heating load master-slave game collaborative optimization method based on AP clustering and adjustable potential modeling, which comprises the following steps: establishing a directly-heated electric heating indoor heat transfer model to analyze a single user to obtain load characteristics of heating users; a decentralized electric heating load refined aggregation model is constructed, and decentralized heating users are divided into different clusters; an adjustable potential quantification model is constructed, and the adjustable electric quantity and the adjustable potential of each cluster are obtained; and a heating load group optimization operation model based on the master-slave game is constructed, power grid purchase and sale electricity price information, distributed power supply cost information, adjustable electric quantity and adjustable potential are input, and a power utilization behavior adjustment strategy is obtained. According to the method, through refined house type clustering, master-slave game framework design and an improved solution algorithm, precise mining and optimized operation of the heating load group regulation potential are realized, the peak regulation pressure of a power grid is effectively reduced, and the comfort of a user is considered.

The invention discloses a light storage charging station electric vehicle ordered charging control method based on bilevel planning, and the method comprises the steps: firstly constructing a bilevel planning model of a light storage charging station, and enabling the upper layer planning model to be used for optimizing the overall energy scheduling of the charging station; the lower-layer planning model performs optimization scheduling on the charging behavior of the electric vehicle in the charging process according to the energy distribution result of the upper-layer planning model; and then, iteratively solving the bilayer planning model by adopting a dynamic alternating direction multiplier method, taking the output of the upper layer planning model as the input constraint of the lower layer planning model, and feeding back the result to the upper layer planning model by the lower layer planning model until the target function value of the upper layer planning and the lower layer planning converges or reaches a preset number of iterations, and the orderly charging power of the electric vehicle is obtained, and a control strategy is dynamically adjusted in case of operation deviation. According to the method, through collaborative optimization of upper-layer planning and lower-layer planning, the overall operation benefit of the power system is optimized, and a flexible charging strategy is provided for an electric vehicle user.

The invention relates to a lightning trip risk assessment method and system for a current collection line of a wind power plant station, and belongs to the technical field of lightning protection of a power system, and the method comprises the following steps: collecting a multi-dimensional data set of a target wind power plant station, the multi-dimensional data set comprising lightning activity parameters, landform data, equipment structure parameters and historical lightning fault data; constructing a three-dimensional electromagnetic transient model of the current collection line, wherein the three-dimensional electromagnetic transient model of the current collection line integrates an electromagnetic transient program EMTP counterattack simulation model, a time domain finite difference method FDTD induction lightning shielding model and a terrain correction electrical geometric model EGM; calculating lightning trip-out rates based on the multi-dimensional data set and the three-dimensional electromagnetic transient model of the current collection line, wherein the lightning trip-out rates comprise an impact trip-out rate, a shielding failure trip-out rate and an induction lightning trip-out rate under fan shielding; and carrying out risk grading on the tower according to the lightning trip-out rate, establishing a terrain-tower type coupling risk assessment system, and outputting a risk assessment result.

The invention relates to an electric energy and flexible climbing collaborative optimization method considering energy storage, belongs to the field of power system optimization, and solves the problems that energy storage is difficult to effectively participate in flexible climbing of a power system, and the requirement of the system for flexible adjustment capability under the background of new energy permeability improvement cannot be met in the existing method. And the wind and light abandoning and load shedding electric quantity is large. Comprises: obtaining day-ahead power system operation data of a target power grid; based on the day-ahead operation data of the power system, the deterministic and non-deterministic climbing requirements of the power system are calculated; based on the deterministic and non-deterministic climbing demands of the power system, calculating to obtain a flexible climbing demand of the power system; based on the flexible climbing requirement of the power system, an electric energy and flexible climbing collaborative optimization model with the goal of minimizing the total loss of the coal consumption and the energy storage operation loss of the thermal power generating unit is constructed; and solving the electric energy and flexible climbing collaborative optimization model based on a set constraint condition by using a solver to obtain a thermal power generating unit output plan and an energy storage charging and discharging scheme.

The invention relates to an electro-hydrogen conversion new energy base capacity planning configuration optimization method, belongs to the technical field of new energy, and solves the technical problems of seasonal imbalance, excessive conservative traditional robust optimization, imperfect evaluation system and insufficient multi-energy flow coupling optimization in existing new energy base capacity configuration. Comprising the steps of obtaining historical data in a new energy base scheduling period; generating a typical scene set based on the historical operation data, constructing a confidence interval of the wind and light output uncertainty of the new energy base, and obtaining a confidence robustness gamma value; constructing an electricity-hydrogen collaborative capacity optimization model with the goal of maximizing the comprehensive efficiency of the new energy base power system; converting the electricity-hydrogen collaborative capacity optimization model into a robust optimization model based on the confidence interval and the confidence robustness gamma value of the wind-solar output uncertainty; and solving the robust optimization model by using a CPLEX solver to obtain a new energy base capacity planning configuration scheme. And reliable low-carbon operation of the new energy base is realized.

The invention belongs to the technical field of multi-energy micro-grid system optimization scheduling, and particularly relates to a multi-energy micro-grid distribution robust low-carbon economic scheduling method based on deep learning, electronic equipment and a medium. According to the method, a mathematical model of a multi-energy micro-grid system is established according to coupling characteristics of various energy sources among power systems. In order to improve the economical efficiency and the low-carbon property of the system, a load demand response mechanism and a carbon transaction mechanism are adopted, and an electric heating load demand response model and a reward and punishment type stepped carbon transaction model are constructed. In order to solve the wind and light uncertainty of the integrated energy system and improve the robustness of the system, a scene set of uncertain variables is generated by using a conditional generative adversarial network in deep learning, and the generated scenes are clustered by using a K-means clustering method to obtain typical scenes. In order to obtain more real probability distribution, a fluctuation range of a typical scene is constrained by using a comprehensive norm, and a probability distribution fuzzy set of uncertain variables is obtained. And based on the constructed fuzzy set, the demand response model and the reward and punishment type stepped carbon transaction model, a two-stage distribution robust low-carbon economic optimization model of the multi-energy microgrid is established, in the first stage, an energy storage equipment start-stop plan of the system is determined, and in the second stage, an initial plan is adjusted and supplemented after uncertainties are revealed. And finally, carrying out iterative solution on the established model by utilizing a column and constraint generation method to obtain an optimal scheduling scheme, thereby ensuring the low-carbon property, the economical efficiency and the robustness of the system.

The invention provides a high-proportion new energy power system operation mode identification method. The method comprises the steps of obtaining time sequence operation simulation data of a high-proportion new energy power system; determining a plurality of characteristic indexes of a daily operation scene of the high-proportion new energy power system according to the time sequence operation simulation data; performing clustering analysis on each feature index to obtain a clustering analysis result; and determining a typical operation mode of the high-proportion new energy power system according to a clustering analysis result and a pre-constructed typical operation mode screening rule. Compared with a traditional method, the method not only can dynamically adapt to a complex operation environment brought by high-proportion new energy access, but also can remarkably improve the identification capability of potential risks in an extreme operation mode, and extracts a typical operation mode with representativeness and risk revealing capability.

The invention relates to a power plant unit maintenance optimization method considering multiple uncertain factors of a power market, and belongs to the technical field of power system operation management. The method comprises the following steps: firstly, obtaining spot market electricity price and new energy output data in each time period of a historical year in a power plant; a random scene set considering spot market electricity price and new energy output is established based on historical data, time-varying features are described through data segmentation, state space division and a non-uniform Markov model, and the random scene set is generated by using a Monte Carlo method; and finally, taking maximization of the total expected income of the power plant as an objective function, constructing an optimization model in combination with preset constraint conditions of the unit, inputting the random scene set into the optimization model for solving, and outputting an optimized unit operation and maintenance strategy which comprises maintenance plans of wind power, photovoltaic and thermal power units and corresponding output plans. Multiple uncertain factors in the power market can be effectively handled, and the operation and maintenance benefits of a power plant are improved.

This invention relates to the field of power system generation scheduling and discloses a method for risk analysis and control of pre-flood energy storage in cascade hydro-wind-solar complementary systems. Taking the pre-flood energy storage as a constraint, a dry-season drawdown model and a flood-season water storage operation rule are established to define simulation criteria. A comprehensive set of indicators, including dry-season power shortages, flood-season water spillage, inadequate year-end energy storage, and wind and solar power curtailment, is constructed to quantify multi-stage, multi-source operational risks. By coupling Monte Carlo simulation with fuzzy membership functions, the method characterizes multidimensional uncertainty scenarios and their probabilities, and analyzes the quantitative relationship between pre-flood energy storage and system benefits, risk probabilities as well as losses. Simulation results demonstrate that precise risk characterization coupled with proper storage control increases annual generation by 580 million kWh while reducing average risk-induced losses by 42%, demonstrating substantial practicality.