1513 results about "Demand response" patented technology

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

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

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

Disclosed in the present invention are a low-carbon operation method and system for an integrated electric-thermal energy system under a demand response. The method comprises: collecting data in a combined heat and power generation unit, establishing a low-carbon demand response model on a demand side, and constructing an integrated electric-thermal energy system model; on the basis of the integrated electric-thermal energy system model, establishing an optimal scheduling model that satisfies an operational constraint condition and aims to maximize the revenue of an energy supply side; establishing a carbon emission flow model to calculate a carbon emission flow, mapping the carbon emission of the energy supply side to a load side, and establishing the low-carbon demand response model on the demand side by maximizing the consumer surplus as an objective; and using a genetic algorithm to solve the optimal scheduling model that aims to maximize the revenue of the energy supply side, and the low-carbon demand response model on the demand side, obtaining a low-carbon optimization scheduling plan of the integrated electric-thermal energy system, and implementing scheduling of the integrated electric-thermal energy system. According to the present invention, the carbon emission of the integrated electric-thermal energy system is reduced, the cost of energy conservation and emission reduction is reduced, and the low carbon performance and the economical efficiency are both considered.

The invention relates to the field of building automation, discloses a control method and system for a low-carbon energy-saving building system, and aims to solve the problems of insufficient multi-source data integration, dynamic response lag and the like, a distributed sensor network is adopted to collect environment, equipment and energy data in real time, a dynamic energy efficiency index matrix is generated through spatial-temporal feature fusion, and the dynamic energy efficiency index matrix is subjected to dynamic energy efficiency analysis. In combination with a deep reinforcement learning model, carbon emission, energy consumption cost and comfort are collaboratively optimized, and the system adopts a cloud edge collaboration architecture: an edge terminal realizes equipment-level millisecond response, cloud digital twin global optimization is performed, and a heterogeneous gateway and an energy router complete multi-protocol equipment linkage and energy scheduling. The innovative technology comprises air conditioner variable air volume control, illumination self-adaptive dimming, elevator colony and ant colony scheduling and actual measurement display, the comprehensive energy consumption of the system is reduced by 32.7%, the demand response reaches the second level, the PMV thermal comfort index is stabilized to be + / -0.5, and a building cluster is supported to participate in a virtual power plant. And an intelligent building low-carbon integrated scheme is provided.

The invention discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

The present invention belongs to the technical field of building integrated energy. Disclosed is a virtual energy storage-considered double-layer optimization scheduling method for a building integrated energy system, the method comprising: constructing an energy hub-based low-carbon building integrated energy system containing wind-solar energy storage and energy conversion devices; comprehensively analyzing characteristics of loads of the system to improve the demand response capability thereof; further providing a double-layer optimization model containing an upper-layer energy operator pricing layer and a lower-layer building user optimization layer, building virtual energy storage and building user comfort indicators being considered in said model to improve the system scheduling flexibility so as to construct an overall user satisfaction indicator; and finally, solving the double-layer optimization model to optimize device contributes, demand responses and electricity purchasing and selling plans of the building integrated energy system, so as to obtain an optimal scheduling policy. The present invention can finely regulate and control various loads of the building integrated energy system, thus improving the energy utilization efficiency, alleviating the power supply pressure of the system, and achieving the purposes of energy conservation and emission reduction of buildings.

The invention provides a photovoltaic energy storage collaborative optimization scheduling prediction method and system based on reinforcement learning, and relates to the technical field of photovoltaic energy storage, and the method comprises the steps: constructing a multi-target evaluation system containing peak-valley difference minimization and demand response income, and combining a hierarchical strategy selection mechanism of adaptive weight to determine an optimal scheduling strategy, and parameter optimization and rolling prediction are carried out by using the dual Q network action value evaluation model, so that optimal scheduling of the energy storage system is realized. The economic benefit of the photovoltaic energy storage system can be effectively improved, the peak-valley difference of the power grid is reduced, and the stability and reliability of system operation are improved.

The invention relates to the technical field of power system optimization, and discloses a regional building group source network load storage demand response optimization method. Comprising the following steps of multi-source heterogeneous data fusion collection and intelligent preprocessing, power utilization behavior spatial-temporal characteristic deep mining, multi-dimensional response potential dynamic evaluation modeling, multi-target layered optimization decision generation, personalized excitation strategy self-adaptive generation and closed-loop cooperative regulation execution and feedback. According to the method, user strategy updating is simulated through a replication dynamic equation of an evolutionary game, efficient search of excitation parameters is realized by combining a Bayesian optimization Gaussian process and an expectation improvement function, a user group strategy evolution rule can be dynamically captured, parameters such as electricity price discount and subsidy gradient are accurately optimized in a limited sampling range, and the method is suitable for large-scale popularization and application. A'behavior modeling-data optimization 'closed loop is formed, users are stimulated to participate in demand response, optimal configuration of power resources is realized, and the flexibility and economy of the system are improved.

The invention provides an active power distribution network electric vehicle V2G optimization method and system considering user will, and belongs to the technical field of power distribution network operation scheduling, and the method comprises the steps: obtaining the real-time basic load, photovoltaic output and electric vehicle charging and discharging power data of power distribution network operation; calculating the real-time net load of the power distribution network based on the acquired data; based on the real-time net load, dividing the time-of-use electricity price by using a membership function; determining a demand response price upper limit; within the interval of the upper limit and the lower limit of the demand response price, the discharge electricity price of the electric vehicle user participating in the demand response is determined according to the time-of-use electricity price and the peak clipping compensation coefficient; whether the V2G condition is met or not is inspected according to whether the electric vehicle networking time preset by the user according to the travel demand is greater than the charging and discharging time or not; if yes, dispatchable electric vehicle discharge power is obtained; and constructing an active power distribution network electric vehicle V2G optimization scheduling model, and solving the optimization scheduling model to obtain the optimal values of the discharge electricity price, the actual scheduling discharge power and the photovoltaic consumption rate.

The invention discloses a power grid operation control method for realizing source-grid-load-storage cooperation, and relates to the technical field of power grid operation control, and the method comprises the steps: carrying out the ultra-short-term prediction of power grid operation data through LSTM, ARIMA, VAE, MDP and other models, covering the new energy output, power flow change, load demand and energy storage state, and carrying out the fusion of the day-ahead prediction and real-time prediction data, thereby achieving the real-time prediction of the power grid operation data. Optimizing a prediction result by using a weighting method; in the aspect of scheduling optimization, a scheduling optimization model is established according to prediction data, a day-ahead scheduling plan is calculated, the operation cost of a power grid is minimized, and meanwhile, source-side wind and light output, grid-side power flow regulation and control, load-side demand response and a storage-side charging and discharging strategy are optimized; scheduling optimization is introduced, so that the output of a conventional unit is reduced in a wind power high output period, and the new energy consumption rate is improved; in the real-time scheduling stage, dynamic optimization is carried out by adopting MPC in combination with a day-ahead scheduling plan and real-time prediction data; and when the wind power output is excessive, the energy storage equipment is controlled to absorb redundant electric energy.

The invention discloses an intelligent water supply management system and method based on wind-light-water multi-source data fusion, and relates to the technical field of electric digital data processing. The system comprises a time alignment deviation quantification module, a time alignment regulation and control module, a data fusion timeliness response quantification module and a timeliness response regulation and control module. According to the method, the time alignment deviation quantization result is obtained by quantizing the multi-source data time parameters, whether time alignment regulation is carried out or not is judged, if yes, the timeliness response parameters are obtained after regulation, and if not, the timeliness response parameters are directly obtained and quantized to obtain the data fusion timeliness response quantization result; and whether PLC scanning interval regulation and monitoring verification are carried out or not is judged based on the data fusion timeliness response quantification result, so that the timeliness of water supply management demand response is improved, and the problem that in the prior art, the water supply management execution response flexibility is poor due to the fact that the time difference of multi-source data processing is not fully considered is solved.

The invention discloses a multi-target demand response optimization method considering low carbon and user satisfaction, which belongs to the technical field of demand side management and optimization scheduling, and comprises the following steps: randomly generating an initial scheduling solution set containing a population 1 and a population 2, establishing a multi-target scheduling model simultaneously including power balance constraint, user comfort constraint and resource capacity constraint; an infeasible solution in the population 1 is processed through an adaptive penalty function, the population 2 is optimized through a feasibility priority strategy, and the population is evolved and updated in combination with crossover, variation, tournament selection and environment selection mechanisms; and evaluating a multi-target fitness value of a scheduling solution in each generation of iteration, screening a Pareto frontier solution set, continuously updating the Pareto frontier solution set until a termination condition is met, and selecting a compromise optimization scheduling scheme from the final Pareto solution set. According to the method, low carbon, cost and user satisfaction are integrated, a dual population optimization algorithm is adopted, resource coordinated scheduling is achieved, and system efficiency and environmental benefits are remarkably improved.

The invention relates to the technical field of participation of iron and steel industry loads in power grid interaction, and provides a network load game peak regulation optimization scheduling method and system for iron and steel industry loads, and the method comprises the steps: analyzing and obtaining an adjustable potential interval of key production equipment based on the specific load characteristics of the iron and steel industry loads; fine modeling is carried out on the adjustable potential of the iron and steel industry load, production factor constraint conditions are considered, and the adjustable capacity of different devices and multi-production-line combinations is obtained through analysis; and finally, analyzing behaviors of peak regulation strategy participants and formulators, and constructing a master-slave game optimization scheduling model by using the Stackelberg game and solving the model. According to the method, the enthusiasm of the short-process iron and steel industry load participating in power grid peak regulation can be reasonably mobilized, the load income of the iron and steel industry is increased while the peak-valley difference of the power grid load is effectively reduced, and theoretical guidance and reference can be provided for participation of the iron and steel industry load in demand response, interaction with the power grid, optimal scheduling and the like.

The invention relates to a hydrogen-containing comprehensive energy system low-carbon economic dispatching method based on a near-end strategy optimization algorithm. The method comprises the following steps: constructing an equipment model, a carbon transaction mechanism model and a comprehensive demand response model of an electricity-heat-gas-hydrogen comprehensive energy system; based on the equipment model, the carbon transaction mechanism model and the comprehensive demand response model, determining an objective function and constraint conditions of low-carbon economic dispatching of the hydrogen-containing comprehensive energy system; a Markov decision process is used for describing the uncertainty problem of the hydrogen-containing integrated energy system, a state space, an action space and a reward function are determined, a near-end strategy optimization algorithm is adopted for solving, and output of each device is optimized and dispatched. Compared with the prior art, the new energy consumption can be effectively promoted, the economical efficiency and the low-carbon property of the system are improved, real-time scheduling is carried out according to random fluctuation of energy output, and there is no need to depend on accurate prediction or uncertainty modeling of source and load output.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The invention discloses a reinforcement learning-based mobile vehicle charging and storage dynamic scheduling method and system, and solves the problems of insufficient scheduling flexibility and low peak-valley electricity price utilization rate of a fixed charging facility of an existing parking lot. A dynamic environment model is constructed, the real-time SOC of the mobile charging and storage vehicle, the position topological relation and the charging demand space-time distribution are integrated, and a deep reinforcement learning algorithm is adopted to train an intelligent body to generate a multi-dimensional collaborative optimization strategy. According to the method, a charging / discharging time sequence, a task path and energy distribution are autonomously planned, a reward function mechanism fusing dynamic path cost and energy constraint is innovatively designed, a multi-vehicle asynchronous collaborative decision framework is established, and dual targets of charging demand response efficiency and operation cost optimization are achieved. According to the method, an MCSV hardware embedded system which supports an ROS2 communication protocol and has a real-time sensor data processing capability is deployed, so that effective transition from a theoretical strategy to actual application is realized.

The invention belongs to the technical field of building heat supply and intelligent power grid demand response control, and particularly relates to an intelligent power grid-oriented heat pump dynamic preheating and demand response control method, which comprises the following steps of: identifying building thermal characteristic parameters, introducing real-time environment variables such as external meteorological parameters and the like and thermal resistance and thermal capacity characteristics of a building, excessive or insufficient preheating caused by only relying on a timer is effectively avoided, and the balance of accurate heat supply and energy conservation is achieved; by adjusting the preheating starting time and the peak period temperature control strategy, the heat pump can dynamically respond according to the electricity price or the power grid demand signal, and the peak load is translated or reduced, so that the dual purposes of economic benefits, energy conservation and emission reduction are achieved; and formulating a peak period control strategy and formulating a non-peak period control strategy. According to the control method, actual thermal characteristics and external meteorological conditions of a building are taken as cores, load reduction in the peak period of a power grid and indoor thermal comfort are taken as double targets, and preheating time self-adaption, demand response flexibility and system operation reliability are achieved at the same time.

The invention provides an intelligent power grid energy optimization management method and system, and relates to the technical field of data processing, and the method comprises the steps: 1, synchronously collecting photovoltaic output fluctuation data, user load demand data and power grid frequency state data, so as to obtain an original monitoring data set; step 2, performing spatial-temporal feature mapping on the original monitoring data set, extracting a multi-dimensional feature vector, determining a dominant feature shaft system based on covariance analysis, and calculating a dynamic coupling degree between the feature shaft systems to divide a regulation and control domain; time sequence key characteristic quantities are selected in a regulation and control domain, a load response state transition path is constructed, a user behavior correction coefficient is deduced according to the load response state transition path, dynamic load balance is achieved through a multi-period rolling strategy, and a load distribution scheme is generated. According to the invention, through real-time dynamic regulation and control of power supply output, an excitation electricity price signal is generated and user demand response is linked, so that intelligent power grid energy supply and demand balance optimization is realized.

The invention discloses a regulation and control strategy for a water-wind-light-storage integrated multi-energy complementary system in an extreme high-temperature scene, and relates to the technical field of optimal scheduling and toughness evaluation of the multi-energy complementary system. The method comprises the following steps: acquiring meteorological and equipment parameters, and constructing a performance model of photovoltaic, wind power, thermal power and power transmission facilities under an extreme high temperature condition; an objective function is established based on the minimum operation cost of the multi-energy complementary system, and constraint conditions are established according to supply and demand balance, equipment operation, peak regulation margin and load reduction; performing optimization scheduling on the system through an optimization tool in combination with a demand response strategy to obtain a multi-energy collaborative operation scheme; a toughness evaluation model based on a comprehensive hierarchy-entropy weight method is adopted, the toughness of the system is quantitatively analyzed from the dimensions of economy, flexibility, safety and the like, and toughness scores and improvement suggestions are output. The economic operation efficiency and the anti-risk capability of the multi-energy complementary system under the extremely high temperature condition are effectively improved, and scientific support is provided for safe and stable operation of the multi-energy complementary system.

Provided are a method and apparatus for optimizing an energy storage system, and a device and a storage medium. The method comprises: on the basis of an energy storage configuration to be solved, unit energy storage configuration cost, demand response cost to be acquired, net load fluctuation value to be acquired, cost discount rate and energy storage service life of an energy storage system to be optimized, constructing an energy storage configuration objective function; on the basis of a total demand response duration, demand response load, system output value, electricity price to be solved and unit clean energy curtailment cost of the energy storage system to be optimized, constructing a demand response objective function; on the basis of the energy storage configuration objective function, energy storage configuration constraints, the demand response objective function and demand response constraints, jointly solving the energy storage configuration to be solved and the electricity price to be solved to obtain a target energy storage configuration and a target electricity price, so as to realize the optimization of the energy storage system to be optimized. Thus, the operating costs are reduced and clean energy integration is also maximized.

The invention discloses a distributed robust optimization scheduling method and system for a solar photovoltaic photo-thermal comprehensive utilization system, and belongs to the field of comprehensive utilization of renewable energy sources. The method comprises the steps of obtaining an electrical load and a thermal load based on an integrated load prediction model; obtaining typical sunlight photovoltaic and photo-thermal based on scene clustering analysis; based on photovoltaic, photo-thermal, electrical load and thermal load, an objective function and a constraint condition for optimal scheduling are set for the solar photovoltaic photo-thermal comprehensive utilization system, and based on the objective function and the constraint condition, a two-stage distribution robust optimal scheduling model is obtained by combining a comprehensive demand response mechanism; and solving the two-stage distribution robust optimization scheduling model by adopting a column and constraint generation algorithm to obtain a two-stage distribution robust optimization scheduling result. The method obviously improves the utilization rate of renewable energy sources, reduces the operation cost and carbon emission of the system, and is suitable for building energy systems in solar energy-enriched areas.

The invention discloses an integrated energy system low-carbon scheduling method considering energy-carbon coupling, and the method is based on a carbon emission flow theory, is combined with the strong fitting capability of a neural network, proposes a carbon flow constraint learning method, converts a complex mapping relation between power flow and carbon flow into mixed integer linear constraint, and achieves the low-carbon scheduling of an integrated energy system. And effective embedding of the carbon flow constraint in the optimization model is realized. Meanwhile, in order to reduce the structural complexity of the neural network, a sparse training strategy is introduced, the model parameter scale is effectively compressed, a ReLU activation function is linearized through an improved large-M method, and a cut plane constraint is introduced to gradually tighten a feasible region, so that the solving efficiency of an optimization model is remarkably improved. And finally, embedding the carbon flow constraint model into the optimal scheduling problem of the integrated energy system, exciting the carbon emission reduction consciousness of the load side, and promoting the load side to perform low-carbon energy consumption adjustment by guiding the demand response behavior of the load side based on the carbon signal of the load side, thereby realizing low-carbon scheduling under energy-carbon coordination and reducing the overall carbon emission level of the system.

The invention provides a virtual power plant scheduling method and system based on distributed resource operation domain description, and relates to the technical field of virtual power plant optimization scheduling, and the method comprises the steps: determining a virtual power plant distributed resource operation domain; considering the influence of demand response load active power and reactive power adjustment on the virtual power plant output model, and constructing a comprehensive load model; dividing a feasible region of a variable in a bilinear term in the comprehensive load model into several non-intersecting regions by adopting a segmented McCormick relaxation method, and converting the regions into a mixed integer linear programming form; the operation constraint condition of the virtual power plant is considered, a mixed integer linear programming form is combined, boundary points of the operation domain of the virtual power plant are solved, and the range of the distributed resource operation domain of the virtual power plant is depicted; and in the distributed resource operation domain range of the virtual power plant, constructing a virtual power plant optimization scheduling model by taking the sum of the total operation cost and the demand response compensation as a target, and applying the obtained optimal operation scheduling strategy to the virtual power plant for execution.

The invention relates to a collaborative optimization scheduling method and system based on low-carbon demand response, and the method comprises the following steps: constructing a source-network-load-carbon storage emission flow metering model, and obtaining the carbon emission data of each part during the operation of a power system; obtaining a dynamic carbon emission factor according to the carbon emission data of each part during the operation of the power system, issuing the dynamic carbon emission factor to a user side, guiding a user to change the power utilization habit, and achieving the dynamic transfer of the power utilization load; a stepped carbon transaction mechanism is formulated according to the power utilization load transfer condition of the user, and the user is stimulated to optimize the carbon emission cost through carbon quota distribution and carbon price grading; and constructing a multi-objective optimization problem with minimum carbon emission intensity and minimum total cost of the power system, and solving the multi-objective optimization problem through a near-end strategy optimization PPO algorithm to obtain an optimal scheduling strategy.

The invention discloses a collaborative planning operation scheduling method and system for a power distribution network and a micro-grid, and the method comprises the steps: obtaining the historical operation data of the power distribution network and the micro-grid, and obtaining typical scene features according to the historical operation data; the typical scene features comprise typical power features; according to the typical scene features and a preset power grid cooperative operation requirement, a constraint condition and a target function are established, and a cooperative planning operation model of the power distribution network and the micro-grid is obtained; the collaborative planning operation model comprises a power grid planning layer and a power grid operation layer; performing linearization conversion on the collaborative planning operation model, and solving the linearized collaborative planning operation model to obtain a configuration and scheduling strategy; and performing configuration and scheduling operation on the power distribution network and the micro-grid by applying the configuration and scheduling strategy. By adopting the embodiment of the invention, the demand response capability of flexible resources can be mined, the economical efficiency of investment is improved, the optimal configuration of the power distribution network and the micro-grid is realized, and meanwhile, the method also has relatively high safety.

The application discloses a shared energy storage system optimization method and system for comprehensive energy microgrids, which take into account a shared energy storage-multi-microgrid system model invested by a third party. The comprehensive energy microgrids are regional energy systems integrating multiple energy sources and adjustable loads, including CHP, heat storage, power storage and distributed renewable energy. Shared energy storage is invested by a third part, serve as a power price publisher, and is limited by power price boundaries. The method takes into account a carbon emission and trading model to reduce carbon emission. Costs of the microgrids include a power / gas buying cost, a demand response cost, a carbon trading cost, and an operating cost of CHP units. Finally, a minimum cost solution is resolved by a MILP algorithm. The invention realizes microgrid scheduling and improves the economic efficiency and environmental friendliness of the comprehensive energy microgrids.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.

The invention relates to the technical field of power dispatching management and control, and discloses a power distribution network power dispatching method based on a virtual power plant AI large model and demand response, and the method comprises the steps: collecting the operation data of a power distribution network, and constructing a feature vector; an AI large model is adopted to calculate and predict load output, and joint uncertainty information is output; calculating a system power unbalance amount, and generating a scheduling strategy; issuing a scheduling instruction corresponding to the scheduling strategy and executing the scheduling instruction; comprehensive performance evaluation indexes are calculated, and whether a performance reduction reason diagnosis mechanism is started or not is judged; according to the method, the AI large model is adopted, the load power, the photovoltaic output and the wind power output are predicted at the same time through a multi-task learning strategy, and the correlation among multiple variables is fully utilized; by constructing a multi-objective optimization model, comprehensively considering economy, safety and reliability and adopting an improved particle swarm optimization algorithm for solving, coordinated optimization configuration of demand response resources is realized, power grid fluctuation is effectively reduced, and power supply reliability is improved.

The invention discloses a distribution line load prediction and optimal scheduling method and system, and relates to the technical field of intelligent scheduling of power systems, and the method comprises the steps: generating a time-aligned multi-source fusion input data set; constructing a mixed time sequence load prediction model, and introducing a weighted quantile loss function in a model training process; constructing a joint probability distribution model of the renewable energy output and demand response participation rate, and sampling joint probability distribution; constructing a rolling time domain power distribution network optimization scheduling model; a two-layer mixed strategy is adopted to deal with uncertainty, an optimization problem is decomposed into a plurality of sub-problems, and an alternating direction multiplier method with adaptive penalty parameters is used for distributed solution. According to the method, a multi-objective optimization scheduling model is established in a rolling time domain, and dynamic closed-loop optimization is realized; and by introducing a two-layer hybrid solving strategy and an ADMM distributed algorithm with an adaptive penalty parameter, the calculation efficiency and expandability are remarkably improved while the global consistency is ensured.