2450 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 invention discloses a multi-dimensional time sequence photovoltaic grid-connected harmonic current prediction control method, and particularly relates to the technical field of power system control. Grid-connected original multi-dimensional electrical signals are collected and decomposed into multi-dimensional frequency signals through Fourier transform, the characteristic components of an independent harmonic source are separated through the blind source separation and non-negative matrix factorization technology, and the frequency and amplitude characteristics of the characteristic components are extracted; then, a multi-dimensional time sequence prediction model is constructed through a recurrent neural network, dynamic behaviors of multiple harmonic sources are predicted at the same time, and frequency domain data are reconstructed and frequency aliasing errors are corrected in combination with high-frequency compensation and multi-scale analysis; and finally, inputting a prediction result into a photovoltaic inverter control system, dynamically generating a compensation current opposite to a target harmonic wave, realizing effective offset of the harmonic wave, accurately processing the multi-source harmonic wave problem in a complex power grid environment, and greatly improving the harmonic wave suppression capability, the electric energy quality and the system operation stability of a photovoltaic grid-connected system.

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 provides a new energy storage consumption capability index prediction method, and relates to the field of power system operation, and the method comprises the steps: building a nonlinear mapping relation between solar radiation intensity, photovoltaic module conversion efficiency, a temperature coefficient, an aging attenuation rate factor and power generation efficiency for photovoltaic power generation, so as to dynamically correct a photovoltaic power generation potential output prediction value; based on a potential output prediction result of photovoltaic and wind power generation, solving an energy storage capacity configuration and operation strategy through an intelligent optimization algorithm to maximize the benefit of an energy storage system, and obtaining a dynamic prediction value of an energy storage consumption capability index; and combining prediction results of the statistical model based on the historical data, the dynamic model based on the physical mechanism and the data-driven model based on machine learning by adopting an adaptive weighted fusion strategy. According to the method, accurate prediction of potential output of new energy power generation and dynamic evaluation of the energy storage absorption capability are realized, and effective technical support is provided for new energy grid connection and optimal configuration of an energy storage system.

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 invention relates to the technical field of ship power systems, and discloses a ship comprehensive power system based on composite energy storage, comprising an energy generation module which comprises a diesel generator, a gas turbine and a renewable energy sub-module and is used for generating electric energy; the energy storage management module comprises a lithium battery, a super capacitor and a flywheel energy storage system and is used for storing and releasing electric energy; the energy distribution and scheduling module is used for controlling energy distribution between the energy generation module and the energy storage management module; the load management module is used for adjusting the load power requirement according to the load priority; and the health management and fault monitoring module is used for monitoring the operation states of the energy storage management module and the system in real time. Through cooperative work of multiple energy storage units and combination with optimization control of the energy distribution and scheduling module, characteristics of different energy storage devices are fully exerted. Therefore, loss in the energy conversion process is remarkably reduced, distribution and utilization of electric energy are optimized, and the overall energy utilization efficiency of the system is improved by about 15%-20%.

The invention relates to a shared energy storage virtual inertia compensation control method for wind power cluster collection, and belongs to the technical field of power system control. In order to solve the problem of insufficient cooperative control of a wind power system and an energy storage system in the prior art, a dynamic model is established by obtaining operation parameters, and the dynamic model comprises a wind turbine generator active power control model and an energy storage system charging and discharging model; designing a virtual inertia control strategy, and introducing virtual inertia control to simulate inertia response characteristics of the synchronous generator; shared energy storage coordination control is carried out, and a strategy is designed by using an MPC method according to an inertia demand and an SOC state; establishing a frequency dynamic response model and calculating inertia response time as a virtual inertia control start-stop condition; and optimizing controller parameters and designing locking logic by adopting an intelligent algorithm. The method can improve the system performance and efficiency, enhance the frequency stability, improve the energy utilization efficiency of the energy storage system, optimize the system performance, and improve the system stability and reliability.

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 relates to a virtual power plant master-slave game-based method for participating in optimal scheduling of an electric energy market by a battery swap station. The method comprises the following steps: step 1, collecting historical data of wind power and photovoltaic power generation by a virtual power plant; step 2, predicting wind power and photovoltaic power output of the second day by using a CNN-LSTM-based day-ahead wind and light output prediction model; 3, the virtual power plant sets the confidence level of the conditional value-at-risk; 4, the virtual power plant predicts the conditions of power over-generation, over-use, insufficient supply and the like in the area; and step 5, the virtual power plant analyzes the adjustment capability and the standby capability of the power generation side and the power utilization side, establishes and solves a real-time scheduling model based on the virtual power plant master-slave game, and obtains a charging and discharging service strategy. The method can fully consider the wind and light uncertainty, the charging and discharging requirements of the battery swap station and the power balance requirements of the power system.

The invention relates to the technical field of data management, in particular to an electrical equipment fault prediction system and method based on data fusion, and the system intelligently adjusts a data collection strategy by accurately capturing electromagnetic signal characteristic parameters of an electrical equipment operation environment and scientifically evaluating an electromagnetic interference index. The accuracy and efficiency of data acquisition are effectively improved, meanwhile, by monitoring the operation state parameters of the electrical equipment, the characteristics of the electrical equipment can be deeply extracted, the data acquisition compliance index is calculated, a powerful basis is provided for data preprocessing, finally, the multi-dimensional operation state parameters of the electrical equipment are highly fused, operation degradation factors can be accurately identified, and the accuracy and efficiency of data acquisition are improved. According to the invention, the method achieves the precise prediction of the fault level of the electrical equipment, gives out an early warning in time, remarkably improves the accuracy and timeliness of fault prediction, effectively reduces the operation and maintenance cost, enhances the stability and safety of a power system, and provides a powerful technical guarantee for the intelligent management and maintenance of the electrical equipment.

The invention provides an intelligent electric energy distribution cabinet heat flow field prediction method and system, and relates to the technical field of cabinet heat flow field prediction, and the method comprises the steps: carrying out the clustering analysis of the obtained operation data and material attributes of each region in an intelligent electric energy distribution cabinet, and the element layout information of equipment in the cabinet; respectively establishing a thermal-mechanical coupling model, a fluid dynamic model and an electromagnetic model; and according to the final refined grid model, simulating fluid dynamics by using a motion constraint smoothing particle fluid dynamics method so as to obtain an optimal layout scheme of the internal structure of the intelligent electric energy distribution cabinet. And calculating the optimal layout scheme by using an analog simulation technology to obtain an optimization scheme of air flow and heat transfer in the intelligent electric energy distribution cabinet, and realizing prediction of the heat flow field of the intelligent electric energy distribution cabinet. Accurate prediction and optimization of the heat flow field of the power distribution cabinet are realized, the reliability and safety of a power system are improved, and the accident risk and loss are reduced.

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 relates to the technical field of power system operation and control, in particular to a medium-and-long-term marketization operation bidding strategy for a wind-light-water-storage combination. Aiming at the problems of volatility, large prediction error, limited profit space and the like of wind and light power generation in the medium-and-long-term power market, the invention proposes that wind and light power generation and cascade hydropower are combined to participate in medium-and-long-term market bidding transaction together; wind and light power generation is encouraged to fully participate in the market by introducing a carbon transaction and green certificate transaction mechanism, and a double-layer optimization model is constructed by considering the influence of current market electricity price fluctuation; the upper layer is a combination quantity report and quotation decision-making model, and the lower layer is a market clearing model so as to realize combination market expected total revenue maximization and social welfare maximization; the strategy significantly improves the medium and long term contract competitiveness and profit space of the wind and light new energy, promotes the consumption of the new energy, and has good environmental benefits and economic benefits.

Systems and methods are provided relating to power systems, such as a power grid, including for providing control to a power system by utilizing available flexibility in charging electric vehicles (EVs). The system generates control information for controlling the power system based on predicted power demand in the system during a target time period and based on predicted EV charging curtailment information, which relates to a predicted flexibility in charging EVs while meeting charging goals of the EVs during a target time period. The generated control information includes EV charging scheduling information that utilizes the predicted flexibility in charging EVs by scheduling charging of EVs to curtail or to increase an aggregate charging load of the EVs during the target time period.

The invention discloses a reinforcement learning-based optimal scheduling method for an electric vehicle to participate in an energy storage market, and relates to the technical field of energy management, and the method comprises the steps: constructing a vehicle intelligent agent and an optimization target of the vehicle intelligent agent; dynamically executing the action of each vehicle agent and pre-introducing a reward function for optimizing a target weight coefficient so as to coordinate the charging sequence among the vehicle agents; constructing a schedulable amount solving model of the vehicle intelligent agents, and solving the schedulable amounts of the vehicle intelligent agents in different areas; and taking the schedulable amount of the vehicle intelligent agent as the input of the neural network model, and outputting an optimal charging planning strategy to realize the optimal scheduling of the vehicle intelligent agent cluster. By establishing private car, commercial car and taxi intelligent agents and utilizing the reinforcement learning algorithm to optimize the operation strategy of the electric vehicle, the problem that the electric vehicle is connected to the power grid in a large scale to cause impact on the power system is solved.

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 relates to the field of short-term load prediction and power dispatching control of a power system, in particular to an energy output optimization dispatching method and system considering a random source load virtual power plant, and designs a virtual power plant double-layer optimization dispatching model. The lower-layer model constructs a user load prediction model of an improved Transform, and then a renewable energy output model is established by using a scene generation method; the upper layer model takes the power generation cost minimization of the wind-light storage and combustion virtual power plant as a target, considers constraint conditions such as gas turbine output power, ramp rate, energy storage charging and discharging power, electricity storage quantity and power balance, and proposes a single guide target function comparison algorithm considering random source load to carry out optimization solution on a virtual power plant optimization scheduling target function; and planning and deciding the output of each energy source in the next 24 hours. Experiments show that the new energy consumption level can be remarkably improved under the random source load condition, and better economic benefits are obtained while stable operation of a power distribution system is promoted.

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 provides a charging station capacity optimal planning method considering space-time charging scheduling of an electric vehicle. The method comprises the following steps: carrying out functional region division on a city region with a traffic-power coupling system; counting the number of nodes, node loads and branch impedance in the power distribution network, and road types, road lengths and the number of roads in the traffic network; the method comprises the following steps: acquiring data such as a typical daily road travel time index, a power load and electric vehicle behaviors, and performing ordered charging intention evaluation on electric vehicle users in a region to obtain a charging mode of each electric vehicle; and taking the charging facility capacity of the electric vehicle charging station as an optimization variable, taking the minimum comprehensive cost as a target, establishing a target function, combining the operation constraint and the time-space constraint, establishing an optimization model, and solving to obtain a planning scheme. According to the method, the influence of the charging behavior of the electric vehicle on the running state of the traffic and power system can be deeply excavated, meanwhile, the benefit requirements of multiple related subjects are considered, and remarkable economic benefits are 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 relates to a virtual power plant adjustable capacity boundary construction method considering conditional value-at-risk, and the method comprises the steps: firstly constructing a virtual power plant electric energy dispatching model based on time-of-use electricity price guidance by taking the minimum electricity purchasing cost of a virtual power plant spot market as an objective function; constructing a virtual power plant regulation capacity boundary depicting model by taking maximum output and minimum output of the virtual power plant in the regulation time period as objective functions; the maximum output and the minimum output of the virtual power plant in the adjustment period respectively correspond to the upper limit and the lower limit of the adjustment capacity boundary of the virtual power plant; solving the virtual power plant electric energy scheduling model to obtain a scheduling strategy of the virtual power plant in the electric energy market; and finally, according to a scheduling strategy of the virtual power plant in the electric energy market, performing global optimization on the virtual power plant adjustment space capacity description model to obtain an adjustable capacity boundary of the virtual power plant in an adjustment time period. The invention is beneficial to improving the flexibility of the power system, promoting the green and low-carbon development of the power industry and optimizing the energy structure.

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 relates to the technical field of wind and light energy storage, in particular to a wind and light energy storage optimization method and system for a low-carbon economic park. Comprising the steps that a plurality of energy storage sub-regions are established based on park parameters, an energy storage sub-region sequence A is generated, A = (a1, a2... an), and ai is the ith energy storage sub-region; n is the number of the energy storage sub-regions; establishing an energy storage collaborative model, and setting energy storage equipment parameters in each energy storage sub-region; according to a preset feedback time node, obtaining operation parameters of each energy storage sub-region, and judging whether a correction instruction is generated or not; according to the park parameters, a plurality of energy storage sub-regions are established, and corresponding energy storage modules are established for each energy storage sub-region, so that regional adjustment is carried out on the interior of the park, the normal power supply demand of the interior of the park in the valley period of wind and light power generation is ensured, and the peak clipping and valley filling optimization scheduling efficiency of a power system is improved. And the overall operation and maintenance cost of the energy storage system is reduced.