1221 results about "Demand response" patented technology

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

The invention 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 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 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 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.

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 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 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.

The invention provides a virtual power plant aggregation cost quantification method considering multiple demand response fuzzy dynamic willingness, and belongs to the field of distributed resource aggregation regulation and control. Firstly, a constructed flexible load user dynamic response willingness model quantifies the participation level of flexible loads such as an electric vehicle and an air conditioner cluster under multi-factor driving. And secondly, the constructed wind power / photovoltaic typical scene set can be used for risk quantification considering uncertainty in a virtual power plant aggregation cost calculation risk model. Thirdly, a constructed virtual power plant aggregation cost calculation risk model objective function comprehensively considers resource calling cost, network loss, adjustment deviation penalty and system tail risk loss; and finally, establishing constraint conditions of the virtual power plant aggregation cost calculation risk model, and realizing evaluation of schedulable margins of an electric vehicle cluster and an air conditioner cluster. According to the method, credible input can be provided for the adjustable resource boundary of the virtual power plant, and quantitative representation of the relationship between the aggregation main body adjustment service amount and the cost is realized.

The invention discloses a bidding and incentive combined strategy optimization method, system and device for a source-load double-side peak regulation auxiliary service market, and a storage medium, and belongs to the technical field of electricity market transaction. The method comprises the following steps: constructing a load aggregator and thermal power generating unit-oriented double-market joint optimization model according to parameter data of a peak regulation auxiliary service market and an incentive demand response market; the double-market joint optimization model is formalized into a Markov decision process; constructing a multi-task multi-agent reinforcement learning training framework; and solving the multi-task multi-agent reinforcement learning training framework through an asynchronous training multi-task multi-agent flexible action-evaluation algorithm to obtain a load aggregator bidding and incentive optimal joint strategy and an optimal bidding amount and quotation strategy of the thermal power generating unit. According to the method, game behaviors among multiple market participants and joint decision behaviors of demand-side market subjects in double markets are considered at the same time, and the method is widely applicable to multi-task agent collaborative optimization in the electricity market.

The invention provides a demand response method and system based on dynamic carbon footprint and marginal cost collaborative optimization, and the method comprises the steps: calculating a renewable energy equivalent emission factor in real time based on a full life cycle, and precisely distributing node-level carbon emission responsibilities in combination with power grid power flow reverse tracking; identifying a marginal unit through an optimal power flow, introducing a ramp rate and network loss correction, and constructing a space-time multi-dimensional cost matrix; and designing a carbon-electricity price linkage mechanism, and solving an optimal load adjustment strategy by adopting mixed integer cone programming. According to the method, renewable energy fluctuation and marginal unit influence can be dynamically tracked, accurate carbon emission reduction is realized, the system operation cost is reduced, and meanwhile, the user comfort is guaranteed.

The invention relates to an aggregation optimization method and device based on 5G base station energy storage participation demand response, equipment, a storage medium and a program product, and relates to the technical field of power energy storage. The method can improve the resource utilization efficiency. The method comprises the following steps: cleaning and interpolating abnormal data in original base station parameters of a 5G base station to obtain target base station parameters; according to the future load curve, the future electricity price sequence and the target base station parameters, obtaining a charging and discharging strategy of the 5G base station; determining power purchase cost, frequency modulation income and loss cost according to the charging and discharging strategy, and constructing a multi-target optimization model based on the power purchase cost, the frequency modulation income and the loss cost; and determining a target problem in the multi-target optimization model, solving the target problem through an alternating direction multiplier method to obtain an optimal solution, dynamically adjusting a charging and discharging strategy according to the optimal solution and battery health degree feedback information of the 5G base station, and if the adjusted charging and discharging strategy meets an optimization termination condition, executing optimization result output.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant resource aggregation method for a dynamic peak regulation demand of a power grid, which comprises the following steps of: acquiring multi-source data, preprocessing the multi-source data, and then verifying the data quality; aiming at different resource types including temperature control load, energy storage and charging piles, respectively constructing refined models, setting constraint conditions of the refined models, and solving a resource operation feasible region by applying multi-dimensional space mapping and linear programming; establishing a target function and a constraint condition by taking the lowest cost and the minimum energy abandoning as targets; solving a target function by using a dung beetle optimization algorithm, and screening an optimal resource aggregation scheme by using an entropy weight method; and based on the optimal resource aggregation scheme, dividing peak, valley and normal periods, constructing a four-dimensional peak regulation index, determining a weight by using an analytic hierarchy process, and screening an optimal resource combination in each period to execute scheduling. The method can guarantee the accuracy and high efficiency of the peak regulation demand response of the power grid, and assists in improving the stability of the power system and the renewable energy consumption level.

The invention discloses a virtual power plant optimal scheduling method considering renewable energy, and relates to the technical field of energy management, and the method comprises the steps: constructing a multi-energy cooperative control model, analyzing an operation feature data set, and dynamically adjusting the resource distribution of a virtual power plant; performing time period load alignment on the power in the power generation regulation and control scheme, and generating a user response strategy in combination with an intelligent demand response mechanism; performing combined scheduling on the power generation regulation and control scheme and the user response strategy, and outputting a power generation and utilization combined scheduling report; and the dispatching center executes power generation regulation and control and power utilization guidance on the virtual power plant according to the power generation and power utilization combined dispatching report, and feeds back and updates the multi-energy cooperative control model according to the operation data after execution and the dispatching target in the power generation and power utilization combined dispatching report. According to the invention, the multi-energy cooperative control model is constructed to analyze the operation characteristic data set, the resource distribution of the virtual power plant is dynamically adjusted, and the complementarity and coordination relationship among different energy forms are accurately identified.

The invention relates to the technical field of charging station regulation and control, and discloses a charging station regulation and control method and system considering ordered charging and demand response. The method comprises the steps of processing uncertainty of vehicle arrival time and user departure time through a demand prediction model, constructing a scheduling optimization model to maximize charging station income, and comprehensively considering charging income, V2G discharge income, electricity purchase cost and user waiting penalty. After a power grid demand response signal is received, two-stage decoupling optimization is carried out, the first stage is to optimize a charging pile shutdown strategy, and the second stage is to optimize a vehicle scheduling scheme. The time uncertainty is adaptively adjusted through an affine decision rule, a mixed integer linear programming solver is utilized to obtain an optimal regulation and control instruction of the charging station, and robust scheduling execution is realized. According to the method, the problem that the charging station cannot realize multi-target collaborative optimization scheduling under double challenges of demand prediction uncertainty and power grid demand response is solved, and the robustness and the economic benefit of a charging station scheduling scheme are improved.

The invention discloses a double-layer optimization scheduling method considering electricity-storage-carbon cooperation and user demands, and the method comprises the steps: building a constraint model for the dynamic segmented adjustment of a carbon transaction price along with the carbon emission according to the carbon emission quotas of thermal power, wind power and photovoltaic power and based on a reference carbon transaction cost model; taking the lowest comprehensive power generation cost as a target, jointly optimizing output plans of thermal power, wind power, photovoltaic power and energy storage, coupling the constraint model to limit carbon emission of the thermal power generating unit, and constructing an electricity-storage-carbon collaborative optimization model; a demand response mechanism is constructed based on the user energy consumption mode satisfaction degree and the energy consumption cost satisfaction degree, a user load curve is adjusted and optimized through the electricity price, and a user demand optimization model is established; solving the double-layer optimization model hierarchically by adopting an improved algorithm; and compared with a reference carbon transaction model and a stepped carbon transaction model, the constraint force is stronger, and collaborative optimization of power generation economy, low-carbon property and user satisfaction is realized under the condition of meeting system power balance and power grid security constraint conditions.

The invention provides a data center calculation, electricity and heat collaborative optimization scheduling method and system, and relates to the technical field of comprehensive energy scheduling. According to the scheduling method and system, a collaborative scheduling model of computing power, electric power and thermal power is constructed, and a user side thermal demand response mechanism is introduced, so that mismatching of waste heat supply of a data center and user thermal demand in time and space is dynamically relieved, and the waste heat utilization rate and the overall energy efficiency of the system are remarkably improved; by establishing a joint optimization framework, a complex coupling relationship among computing power, electric power and heating power is accurately described and coordinated, so that the comprehensive operation cost of the data center is minimized on the premise of ensuring the service quality of the workload of the data center; and a large language model-assisted deep learning algorithm is further adopted to solve the scheduling model so as to cope with multiple challenges of workload fluctuation, electricity price change and heat demand uncertainty, and a self-adaptive, intelligent and interpretable scheduling decision is realized.

The invention discloses an electric vehicle charging dynamic pricing method and system based on user consumption psychology, and the method comprises the steps: S1, calculating the response time period and the shortest charging time of a user according to the charging behavior data of an electric vehicle user, and screening the user with the demand response capability; s2, constructing a three-dimensional psychological feature vector based on the user with the demand response capability, performing weighted calculation on a user type decision parameter, and dividing user types; s3, constructing a differentiated comprehensive satisfaction model for different types of users; s4, constructing an electric vehicle charging double-layer pricing model based on a stackelberg game; and making a charging station price strategy, thereby realizing flexible regulation and control of the charging load of the electric vehicle. The method has the advantages of reducing the deviation between the scheduling instruction and the actual response, remarkably improving the regulation and control efficiency of the price signal on the load and the like.

The invention relates to the technical field of power dispatching, in particular to a virtual power plant control system and method. Comprising a data fusion module, a dynamic optimization scheduling module, an intelligent prediction module, a user interaction module, a self-learning adaptive module, a cooperative control module, a safety protection module and a storage unit. Key information is accurately extracted; the dynamic optimization scheduling module is based on a deep reinforcement learning algorithm, fully considers various factors to generate a global optimal strategy, and overcomes the defects of local optimization; the safety protection module constructs a multi-layer protection system to guarantee the safety of the system; the user interaction module covers the functions of information display, demand response, service customization and the like, personalized demands of users are met, complex conditions of the energy market and power grid operation can be handled on the whole, the operation benefit and safety of the virtual power plant are improved, and popularization is facilitated.

The invention discloses a building energy system multi-type demand response operation strategy optimization method and system, and relates to the technical field of building energy management and demand response optimization control, and the method comprises the steps: collecting building user side multi-source information, building a building cooling load prediction structure, and bringing the structure into an optimization scheduling model input system. Constructing a building energy system optimization scheduling equipment model; collecting power grid side information, and dynamically loading an optimal scheduling model for a time-of-use electricity price scene and a peak clipping scene based on judgment of power grid demand response type information of the next day; analyzing an optimization problem of a unified objective function in a double-type response scene, introducing a power reservation coefficient based on a time-of-use electricity price scene, and controlling flexible resource retention; a mixed integer linear programming algorithm is adopted to solve the multi-scene optimization scheduling model, and a corresponding strategy, scheme and power configuration plan are generated; according to the method, flexible dynamic regulation and control and unified scheduling in a multi-response scene are realized, and the strategy adaptability and collaboration are improved.

The invention discloses a regional integrated energy system optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.