1801 results about "Electricity system" patented technology

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

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 distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The present application discloses a calculation method for multi-scale electric-carbon energy efficiency optimization for a power system, which includes designing a carbon flow calculation improvement model of the power system that takes into account a photovoltaic cluster auxiliary service, converting a power lossy transmission network of the power system equivalent to a lossless power consumption network, and calculating a carbon flow of the power system that takes into account a photovoltaic reactive power cluster auxiliary service; designing a decision-making system for low-carbon operation of the power system based on a coupling of electric-carbon energy efficiency, and calculating a correlation between electric quantity and the carbon flow of the power system that takes into account the photovoltaic cluster auxiliary service; the output varies in the range of 0-1, larger values represent a stronger correlation between electric-carbon energy efficiency.

The invention discloses a mobile energy storage pre-layout and dynamic scheduling system and method oriented to toughness improvement of a power distribution network, and belongs to the technical field of power systems and intelligent power grids. In the pre-disaster stage, a two-stage robust optimization model fusing photovoltaic output uncertainty is constructed, and the configuration cost of mobile energy storage, the load reduction risk and the traffic accessibility of key nodes are comprehensively considered to generate an optimal deployment scheme; in a post-disaster stage, based on a traffic network state and a multi-source output characteristic, a multi-source collaborative dynamic scheduling model is constructed, a migration path, a charging and discharging strategy and charge state control of mobile energy storage are optimized in real time, and power supply loss of a key load is minimized. The system realizes rapid power supply recovery and safe operation in the island power grid by cooperatively controlling distributed resources such as mobile energy storage, electric vehicles, photovoltaic and diesel generators and the like. According to the invention, the quick response capability and recovery efficiency of the power distribution network under extreme disasters are significantly improved.

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 a collaborative optimization scheduling method and system considering energy storage frequency modulation reserve. The method comprises the following steps: acquiring basic data of a power system; based on the basic data, a collaborative optimization scheduling model considering energy storage frequency modulation reserve is established, and the collaborative optimization scheduling model takes the minimum sum of the coal consumption cost, the unit start-stop cost and the wind curtailment cost of the thermal power generating unit as a scheduling target under multiple constraint conditions; the multiple constraint conditions comprise a lithium battery power constraint, a charging and discharging state constraint, an SOC constraint, a frequency modulation response constraint, a frequency safety constraint, a frequency modulation standby constraint, a flywheel energy recovery constraint, a thermal power generating unit operation constraint and a power balance constraint; solving the collaborative optimization scheduling model to obtain an optimal scheduling scheme of the power system; by comprehensively utilizing the advantages of a traditional unit, flywheel energy storage and lithium battery energy storage, the safety and operation economy of the system during frequency disturbance are improved.

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 virtual power plant optimization system and method, and the system comprises a virtualization platform module which is used for integrating distributed energy, energy storage equipment, an electric vehicle, a flexible load and other resources, and achieves the virtualization and centralized management of the resources; the dynamic resource aggregation module is used for dynamically matching resource characteristics with power system requirements; the market transaction module is used for optimizing the earnings of the virtual power plant; and the core algorithm module is used for optimizing the load prediction precision. The market transaction module implements two-stage market bidding based on a multi-agent game model, analyzes the uncertainty of quotation of competitors through a multi-scene simulation technology, optimizes a dynamic transaction strategy, and achieves the maximization of spot market benefits and the controllability of risks. Through cooperative work of a virtualization platform module, a dynamic resource aggregation module, a market transaction module, a core algorithm module, a security management module and the like, efficient management of resources and maximization of market benefits are realized.

The invention discloses a low-carbon flexible coal-fired unit power control system and method, and relates to the technical field of power systems, and the system comprises a cascade waste heat recovery module, a boiler deep regulation and control module, a thermal coordination module, an environmental protection and emission reduction module, an intelligent operation scheduling module and an energy efficiency monitoring module. The gradient waste heat recovery module carries out gradient recovery on flue gas heat energy according to temperature zone grades; the boiler depth regulation and control module is used for regulating combustion air supply and heat absorption paths to realize low-load stable combustion; the thermal coordination module dynamically distributes thermal loads between the multiple heat sources and the thermal units; the environmental protection and emission reduction module jointly controls desulfurization, denitration and wastewater resourceful treatment; the intelligent operation scheduling module generates a load regulation and control instruction and coordinates multi-module response; the energy efficiency monitoring module collects and analyzes energy consumption and emission indexes and assists strategy optimization. The energy efficiency utilization and emission control capability of the unit under the deep peak regulation condition is improved, and flexible, efficient and low-carbon operation is achieved.

The invention discloses a power system state analysis method based on large-scale distributed energy access, and relates to the technical field of power distribution networks. The method comprises the following steps: collecting basic and historical equipment operation data of a power distribution network, and constructing a virtual power plant model; real-time equipment operation data are collected, and the virtual power plant model is corrected; constructing a photovoltaic output prediction model by using the real-time photovoltaic power generation data, outputting a prediction track and calculating a photovoltaic output fluctuation ratio; constructing a fuel output prediction model based on real-time load demand and fuel reserve data, obtaining a fuel output track and calculating stability, and obtaining a power generation side reliability index by combining a photovoltaic output volatility; according to the energy storage charging and discharging power and the controllable load adjusting quantity, energy storage stability and controllable load stability are calculated respectively, and a power grid side stability index is obtained; and fusing the power generation side reliability index and the power grid side stability index, and calculating a power distribution network state evaluation index. According to the method, the state of the power distribution network is identified through a multi-dimensional evaluation mechanism.

The invention discloses an electric vehicle charging pile fault remote detection method and device, electronic equipment and a medium, and relates to the technical field of computers. The method comprises the following steps: determining an associated charging pile set in a target area, wherein the associated charging pile set and a current charging pile belong to the same power system; acquiring first operation data of the current charging pile and second operation data corresponding to each associated charging pile in the associated charging pile set; calculating a fault evaluation value corresponding to each fault evaluation index of the current charging pile according to the first operation data and each second operation data; calculating a real-time score corresponding to the current charging pile according to each fault evaluation value and different weights corresponding to each fault evaluation index; obtaining historical operation data of the target charging pile set, and calculating a fault score threshold value of the fault charging pile according to the historical operation data; and comparing the real-time score with a fault score threshold value, and determining whether the current charging pile has a fault or not. The fault detection efficiency of the charging pile can be improved.

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 provides a charging pile energy consumption scheduling method based on a load prediction model, and the method comprises the steps: carrying out the data collection and preprocessing, obtaining multi-source data, such as charging, equipment, environment and electricity price, from a charging station management system, and cleaning abnormal values; and then using grey correlation analysis to determine weights of factors influencing energy consumption, constructing a grey prediction model, and combining preprocessing and weight distribution data to predict future charging pile load demands. And the control center receives a load prediction result and vehicle reservation information, preliminarily plans a charging distribution scheme according to the real-time state of the charging pile, and optimizes scheduling by taking the minimization of the total charging cost and consideration of the user satisfaction and the stability of the power system as targets through a simulated annealing algorithm. During operation, an actual state and an external environment are continuously monitored, a charging plan is rapidly adjusted when deviation occurs, finally, vehicle charging bills of all departments are generated according to an execution scheme, and system performance indexes are regularly evaluated to realize continuous optimization. The method can effectively reduce energy consumption cost and guarantee efficient and stable operation of the charging station.

The invention relates to the technical field of boiler combustion optimization, and discloses an Agent-driven pulverized coal boiler combustion optimization closed-loop control method and system, and the method comprises the steps: obtaining combustion data in the combustion process of a pulverized coal boiler; according to the combustion data and a preset combustion task, generating an initial combustion control strategy through a multi-agent layered cooperation mode of the power system; according to the combustion data and an initial combustion control strategy, optimizing the initial combustion control strategy by predicting the combustion state of the boiler in a preset future time period to obtain an optimized combustion strategy, and guiding the boiler to carry out combustion adjustment so as to carry out closed-loop control on the combustion optimization process of the gas turbine; through closed-loop control over the combustion optimization process of the pulverized coal boiler, different combustion tasks can be responded in time, the control strategy is automatically adjusted according to the combustion tasks and the combustion state of the boiler, the control performance is continuously improved, the combustion state meets the task requirement all the time, and stable operation of the gas turbine is guaranteed.

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.

The invention provides an island energy system optimization scheduling method considering ammonia-to-electricity waste heat power generation, and the method employs an independent island integrated energy system containing an ammonia-to-electricity system to execute a weekly-day-intra-day multi-stage operation optimization scheduling strategy, and the independent island integrated energy system is a system considering the ammonia supply demand. Wind energy and solar energy are used as energy input, energy is supplied to seawater desalination equipment and an electricity-to-ammonia system, and the electric load requirements of island residents are met; meanwhile, seawater desalination equipment is used as a power demand response resource to increase the water-energy coupling relationship of the system model, and cross-time storage and utilization of fresh water resources are realized through a reservoir, so that an ammonia-hydrogen power ship which is used for meeting daily travel demands of island residents is used as an ammonia load; an electricity-to-ammonia system and an ammonia storage tank are adopted to realize effective consumption of island wind and light resources; according to the invention, the long-period energy supply reliability of the island can be improved, and the problem of uncertainty of wind energy and light energy resources of the island can be effectively solved.

The invention discloses a VR interaction method and system for virtual disassembly and assembly teaching of a three-electric system of a new energy automobile. The invention relates to the technical field of VR teaching. And loading a 3D model and a scene configuration file, updating a virtual scene in real time, constructing a scene topological structure, recording a component relationship, and initializing security rules and process configuration. Loading the 3D model and the scene configuration file by using an asynchronous loading technology based on a GPU (Graphics Processing Unit); updating the virtual scene in real time and prompting information; the father-son hierarchical relation of the components is recorded through the tree-shaped data structure, the disassembly and assembly process is controlled based on the constructed behavior tree, and it is ensured that students operate according to the correct sequence and steps. Safety rules and flow configuration are built in, for example, high-voltage electricity must be cut off firstly when the battery is disassembled and assembled, and safety accidents caused by misoperation are effectively avoided. And when the operation of the student is illegal, the state machine is triggered to roll back to the latest legal state, so that the student is helped to correct errors in time, and the smooth operation of the disassembly and assembly process is ensured.

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 relates to the technical field of power systems, and particularly discloses a multi-energy coupling system optimal scheduling method considering grid-connected safety, and the method comprises the steps: constructing a multi-energy coupling system architecture model; the multi-energy coupling system architecture model comprises a renewable energy power generation unit, a traditional energy power generation unit, an energy storage unit, an energy conversion unit and a combined cooling heating and power generation unit; constructing a distributed energy storage optimization operation model based on the multi-energy coupling system architecture model; the constraint conditions comprise charging and discharging constraint, charge state constraint and peak clipping constraint of battery energy storage; carrying out output constraint on the energy conversion equipment; electric vehicle charging station operation constraint; new energy power generation constraint; power balance constraint; public network line transmission power constraint; constraining transmission power of an internal distribution network line; and solving the distributed energy storage optimization operation model by adopting an optimization algorithm to obtain an optimization scheduling scheme. The method has the advantage that scientific and reliable technical support is provided for efficient operation of the multi-energy coupling system.

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 relates to the field of power systems and automation thereof, in particular to a multi-energy system collaborative operation optimization method based on improved particle swarm optimization and a related device. Modeling according to the operation characteristics of the multiple energy systems to obtain a multi-energy system model; optimizing the multi-energy system model based on particle swarm optimization, and calculating and generating an energy scheduling result by taking system operation cost, energy utilization efficiency and pollutant emission as optimization targets; and distributing output priorities of different energies according to an energy scheduling result, and generating an energy storage system operation strategy based on the output priorities. According to the invention, the comprehensive power system model of photovoltaic, wind power, hydroelectric, thermal power and energy storage systems is constructed, and according to the operation characteristics of each energy device, the physical characteristics and dynamic interaction relationships of different energy sources can be accurately simulated. According to the model, equipment constraint conditions and real-time operation parameters are fully considered, high-precision basic data are provided for optimal scheduling, and the practicability and reliability of the model are remarkably enhanced.

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 multi-energy power system optimization scheduling method based on improved robust optimization, and relates to the technical field of power system scheduling. Minimizing the total operation cost of the power system as a total objective function; establishing constraint conditions for the total target, wherein the constraint conditions comprise thermal power generating unit constraint, renewable energy system constraint and energy storage system constraint; establishing a robust optimization scheduling model according to the total objective function and each constraint condition, and processing robust optimization by adopting a Benders decomposition method to obtain a decision variable meeting the objective function; and adjusting a scheduling plan according to the decision variable to realize optimal scheduling of the power system. Introducing a constraint condition of multi-energy overall balance; an adjustment factor is introduced to control the balance between robustness and economy, the uncertainty influence of wind power photovoltaic fluctuation is dynamically controlled through an uncertainty influence factor, Benders are introduced, a large-scale problem is decomposed into a main problem and a sub-problem, the calculation complexity is reduced while efficient solving is achieved, and the method is suitable for large-scale real-time scheduling.