2746 results about "Virtual power plant" patented technology

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant load prediction and dynamic adjustment optimization system and method, and the system comprises a data collection module, a preprocessing module, a prediction module, an adjustment module and a verification module. The data acquisition module acquires real-time operation data and power market signals of distributed energy nodes; the preprocessing module performs standardization processing on the data through a quantum space-time alignment and anomaly reconstruction technology, and extracts strong correlation vectors of meteorological features and loads; the prediction module adopts an adaptive noise complete set empirical mode decomposition algorithm to separate a trend term, a periodic term and a residual component of a load sequence, and the adjustment module constructs a multi-target optimization model. Efficient aggregation of distributed resources, high-precision load prediction in a meteorological sudden change scene and cooperation of multi-market dynamic scheduling strategies are realized; and the clean energy consumption capability and the virtual power plant market response efficiency are improved.

The invention discloses a virtual power plant peak regulation optimization scheduling method and system, electronic equipment and a medium, and the method comprises the steps: obtaining multi-source data of distributed energy, energy storage equipment and a load side, and carrying out the cleaning and normalization processing; utilizing an LSTM neural network and an XGBoost model to carry out short-term and medium-and-long-term load prediction respectively; based on the prediction result and the resource state, generating a scheduling optimization strategy through a deep Q network (DQN) algorithm; according to an optimization strategy, distributed energy power generation, energy storage charging and discharging and load side response are dynamically managed, and multi-resource cooperative control is achieved; and through a feedback optimization module, DQN model parameters and strategy updating are automatically adjusted according to monitoring data, and the scheduling effect is optimized. The system comprises a data acquisition module, a data preprocessing module, a load prediction module, a scheduling decision module, a resource control module, and a monitoring and feedback optimization module. The method has the beneficial effects that the load prediction precision is improved, the resource scheduling is optimized, the operation cost is reduced, and the method is suitable for scheduling management of a multi-source energy system.

The invention provides a virtual DPU power plant simulation fault restoration method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the preprocessing of collected DPU power plant operation data, including noise reduction, time sequence alignment and abnormal point elimination, carrying out the data quality evaluation, training a fault feature mapping model based on the processed data, and carrying out the fault restoration of the DPU power plant. The model is used for recognizing abnormal clusters in real time, a fault evolution path is searched and determined in combination with a conditional random field and a Monte Carlo tree, an optimal path is determined through a particle filtering algorithm, fault root causes are determined in combination with causal analysis, spectral clustering and a Bayesian network, and a fault diagnosis report is generated.

The invention discloses an intelligent scheduling method for a comprehensive virtual power plant, and relates to the technical field of power plant intelligent scheduling, and the method comprises the steps: carrying out the redundancy elimination filtering and unified time sequence calibration of multi-node sensing data of a cold and hot energy station, energy storage equipment and the like through the deployment of an edge side high-concurrency collection module, generating a high-quality time sequence fusion sequence, and screening schedulable resources; on-line model correction is realized through an error dynamic evaluation mechanism, a multi-stage scheduling strategy is generated by adopting an improved particle swarm algorithm aiming at the targets of economy, reliability and environmental protection, and an optimal instruction is output in combination with an equipment constraint condition. Through priority control and real-time monitoring index evaluation deviation, a rolling correction and standby strategy is triggered, equipment-level fault risk quantification and self-healing control are realized, abnormity is rapidly isolated, and redundant resources are started. Through data-prediction-scheduling-execution closed-loop cooperation, the resource utilization rate, the scheduling precision and the system fault-tolerant capability are significantly improved, and the scheduling capability of source heterogeneous resources is enhanced.

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.

The invention provides an optical storage charging and discharging station aggregation control and optimization method based on a virtual power plant, and aims to solve the problems of multi-target collaborative optimization, dynamic resource response and uncertainty robustness. By introducing a Markov decision process and an adaptive clustering algorithm, the system can dynamically aggregate photovoltaic, energy storage and charging pile resources according to equipment characteristics, and power dispatching is optimized. A multi-objective optimization model is adopted, economical, technical and environmental objectives are combined, a dynamic weight factor is introduced, and optimal scheduling is generated in combination with a fuzzy decision theory. And real-time compensation is carried out by adopting a rolling time domain control framework and deep reinforcement learning, so that the scheduling precision and the response speed are improved. The edge computing and cloud collaboration mechanism reduces the communication load through a lightweight federated learning model, and improves the scheduling response efficiency. According to the invention, the scheduling efficiency of the optical storage charging station can be obviously improved, the operation cost is reduced, the system stability is improved, and the system has good adaptability and expandability.

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.

The invention provides an air conditioner energy-saving control method and system based on a virtual power plant, and the method comprises the steps: determining an air conditioner energy-saving control model corresponding to a plurality of air conditioner devices through obtaining an operation parameter set and real-time power grid load data of the plurality of air conditioner devices in a target region, and carrying out the energy-saving control of the plurality of air conditioner devices according to the real-time power grid load data and load prediction data; and determining a power grid load fluctuation threshold value of the target area, dynamically optimizing the operation parameter set of the plurality of air conditioning equipment through a parameter optimization model, generating an optimized equipment operation parameter subset, and adjusting temperature set values and operation modes of the plurality of air conditioning equipment in a future time period. And generating an air conditioner group control strategy corresponding to a future time period, obtaining operation feedback data, updating weight parameters of the load prediction model according to a deviation value between actual power grid load data and load prediction data, calibrating an optimization rule of the parameter optimization model, and generating a calibrated model. According to the invention, the fine regulation and control capability and the energy coordination efficiency of the air conditioner group can be improved.

The invention provides an air conditioner dispatching method and device based on virtual power plant response. The method comprises the steps that a real-time operation parameter set of all air conditioner terminals in a target area in a preset time window is obtained; performing feature alignment processing on the real-time operation parameter set and a dynamic regulation and control instruction issued by a virtual power plant to generate a joint input feature; the combined input features are input into a pre-trained multi-target optimization model, hierarchical weight distribution is conducted on the combined input features through a feature fusion layer in the multi-target optimization model, an air conditioner regulation and control priority sequence is generated, and a target scheduling strategy is generated based on the priority sequence; and the target scheduling strategy is issued to each air conditioner terminal in the target area to be executed, a feedback parameter set after execution is collected in real time, and incremental parameter updating is conducted on the multi-target optimization model through the feedback parameter set. According to the invention, the accuracy, real-time performance and sustainability of air conditioner terminal scheduling can be improved.

The invention relates to the technical field of virtual power plants, in particular to a multi-target scheduling optimization method and system for a virtual power plant to participate in a power market, and the method comprises the steps: carrying out the dynamic integration of distributed energy, an adjustable load and an energy storage unit in the virtual power plant, and constructing a resource model; establishing a distributed resource identity authentication and data storage mechanism; constructing a multi-objective optimization function, comprehensively considering a plurality of objectives such as resource utilization rate, carbon emission and load balance, and designing a dynamic weight adjustment mechanism; calculating a multi-objective optimization result, determining the optimal output and operation state of virtual power plant resources, and generating a virtual power plant optimization scheduling strategy; and cooperative optimization of internal resource scheduling and external power grid scheduling of the virtual power plant is realized by combining a game theory and a swarm intelligence algorithm. According to the method, the utilization efficiency of distributed resources can be improved, source-network load-storage coordination and new energy consumption are promoted, and the flexibility and comprehensive benefits of a power system are enhanced.

The invention provides a virtual power plant platform source network load storage equipment real-time monitoring and optimization method and system, and relates to the technical field of virtual power plant intelligent optimization scheduling, and the method comprises the steps: collecting equipment operation data, carrying out the noise reduction and feature compression to obtain feature data, and recognizing the equipment fault type based on the feature data; carrying out decoupling reconstruction on the feature data in time and space dimensions to obtain an equipment incidence matrix, constructing an equipment topological graph based on the equipment incidence matrix and a credible fault type, carrying out bidirectional tracking on the equipment topological graph according to a fault diffusion probability, and generating a fault propagation path; grading and sorting the equipment on the fault propagation path, and outputting a fault influence prediction result and an equipment priority list; and carrying out cooperative scheduling optimization on the equipment in the virtual power plant by adopting a hierarchical reinforcement learning algorithm to realize cooperative scheduling optimization of the equipment in the virtual power plant.

The invention relates to the field of power plant control, discloses a virtual power plant control method, system and equipment based on a neural network, and is used for solving the core problems of high data dependence, low topology safety and difficulty in multi-scale collaboration in traditional virtual power plant control. According to the virtual power plant control method based on the neural network, a correction instruction set, a joint estimation value and a topology constraint matrix are input into a neural network controller, and a cooperative control signal is output through singular perturbation decoupling of a fast-varying subsystem and a slow-varying subsystem. And the cooperative control signal is issued to the distributed power supply inverter, the energy storage converter and the intelligent switch, and meanwhile, an execution result is monitored in real time and fed back to the phase space reconstruction module, so that closed-loop control is formed. By constructing the Lyapunov candidate function and calculating the virtual damping coefficient, the transient stability, real-time persistent homologous analysis and topology self-healing instruction generation of the system are enhanced, and the self-healing capability and the fault-resistant capability of the system are improved.

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

The invention discloses a multi-agent-based virtual power plant intelligent control method and system, and the method comprises the steps: dividing a virtual power plant into a plurality of sub-virtual power plants, deploying an agent in each sub-virtual power plant, collecting a local resource state through each agent, and predicting a load demand and the output of a distributed power supply, a hierarchical control unit is adopted to carry out collaborative optimization among the sub-virtual power plants according to a prediction result, and an upper-layer optimization control module constructs a linear programming model according to the prediction result and solves the linear programming model to obtain an initial scheduling scheme; and the lower-layer reinforcement learning control module performs local adjustment on the preliminary scheduling scheme according to a multi-agent depth deterministic strategy gradient algorithm to obtain a decision scheme. Based on a distributed control strategy of a multi-agent architecture, the fault-tolerant capability and reliability of the system are improved, a hierarchical control architecture is adopted, global optimization and local adjustment are organically combined, and efficient coordination and real-time adjustment capability of global resources are realized.

The invention discloses a hierarchical collaborative management method and system for a virtual power plant based on multi-modal deep learning, and the method comprises the steps: constructing a four-dimensional data collection system, and achieving privacy enhancement preprocessing through federated learning and a differential privacy technology; a Bi-LSTM and a heterogeneous graph neural network are adopted to construct a three-mode deep fusion model, the weight is dynamically adjusted in combination with an environment-user dual-drive attention mechanism, and the load prediction precision and the space resource utilization rate are improved; a multi-target scheduling strategy is generated based on a five-dimensional target function and an improved DDPG algorithm, and physical feasibility is ensured through digital twinborn pre-verification; efficient execution and excitation transparency are realized through edge layer FPGA + NPU hardware acceleration and block chain evidence storage; and constructing a user participation ecology by using a natural language interaction strategy engine and a stepped incentive mechanism. The power grid economy, the equipment reliability and the user participation degree are remarkably improved, and intelligent upgrading of the virtual power plant is promoted.

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 relates to the technical field of electric power energy storage system planning analysis, and discloses an electric power system planning operation optimization method, system and device and a storage medium. The method comprises the following steps: collecting electrical and equipment states and environmental parameters for feature extraction, and evaluating the health state of equipment; grouping the distributed energy sources based on health state evaluation and weather early warning information to form a virtual power plant resource pool; performing task decomposition according to the characteristics of the resource pool, and formulating a collaborative scheduling strategy; arranging a maintenance plan according to the equipment state and the scheduling strategy; and optimizing system operation parameters based on the scheduling strategy and the maintenance plan to obtain a balance control scheme. According to the invention, under the condition of considering the health state of the equipment and the influence of extreme weather, the dynamic balance of the minimization of the operation cost of the power distribution network and the maximization of renewable energy consumption is realized through the intelligent arrangement technology of the virtual power plant group.

The invention relates to an intelligent scheduling method and system for a virtual power plant, and aims to improve the precision and efficiency of distributed resource scheduling. The method comprises the following steps: monitoring the state of each distributed resource node of a virtual power plant, and collecting real-time output, charge state and communication quality indexes to obtain a resource state data set; and performing power prediction according to the resource state data set, calculating power prediction deviation in real time, and triggering online correction to obtain a power prediction sequence. And inputting the resource state data set and the power prediction sequence into an improved bee algorithm, and generating a target scheduling scheme of the virtual power plant through neighborhood search containing a prediction deviation correction term and probability selection based on communication reliability. And based on the target scheduling scheme, establishing a three-layer progressive optimization architecture, and realizing multi-time scale coordination through time coupling constraint to obtain a distributed resource power control instruction. By optimizing the resource scheduling scheme, the scheduling efficiency and the system reliability of the virtual power plant in a variable environment are improved.

Disclosed is a system and a method for blockchain-based virtual power plant management, relating to the field of virtual power plants. The system mainly includes three modules: an event-driven based demand rapid response and flexible resource optimal allocation module configured to adjust power supply and demand in real time according to power demand information and available resource information of an industrial park; a blockchain-based data collaboration module configured to establish a data sharing and transaction environment among various participants of a virtual power plant; and a visualization management module configured to provide a user interface for displaying, in real time, operational status and power transactions of the virtual power plant in the user interface. The system can effectively manage virtual power plants, enhancing the efficiency and transparency of power supply.

The invention discloses a virtual power plant system design method based on multilevel federated learning, and relates to the technical field of power system planning, and the design method comprises the steps: designing and constructing a virtual power plant system architecture comprising an energy resource module, a local controller, a regional coordinator, a central coordinator and a regional block chain network. Intelligent prediction and optimal control of distributed energy resources are realized by using a deep learning framework and a model training algorithm, the intelligent level of the system is improved, automatic management of energy transaction and execution of an intelligent contract are realized by using a block chain technology, transparency and security of the transaction are ensured, and multi-level federated learning is adopted, so that the efficiency of the system is improved. According to the invention, distributed cooperation of data distributed processing and model training is realized, risks of data leakage and privacy invasion are effectively avoided, cooperative work of a local controller and a regional coordinator is cooperated, intelligent management and optimal scheduling of distributed energy resources are realized, and manual intervention and management cost are reduced.

The invention discloses a reinforcement learning-based virtual power plant response optimization scheduling system and method, and relates to the technical field of virtual power plant intelligent scheduling. The system comprises an environment modeling module, an intelligent agent module, a multi-agent coordination module and a self-adaptive optimization module which are respectively used for constructing a multi-dimensional state space and a layered action space, generating and optimizing an action strategy based on an Actor-Critic network, executing a scheduling instruction through a layered multi-agent structure and realizing conflict consensus, and dynamically adapting to state space change in combination with incremental learning and meta-learning mechanisms. The system and the method have the advantages of fine state modeling, efficient action response, adaptive strategy updating, stable agent coordination and the like, and can keep the continuity, the stability and the optimality of a scheduling strategy in an operation environment in which multi-source heterogeneous power resources participate in scheduling cooperatively, market rules change frequently and load fluctuation is violent.

The invention discloses an adjustable load security access method for a virtual power plant, and relates to the technical field of power system automation, and the method comprises the following steps: in the operation process of a virtual power plant scheduling system, after an upper-layer scheduling center issues a unified adjustment instruction according to an operation demand, all access nodes are monitored in real time, and the adjustment instruction is sent to the upper-layer scheduling center; the method comprises the following steps: receiving a scheduling instruction from an access node, collecting response behavior data of the access node to the scheduling instruction, preprocessing the collected original response data of a single node, and carrying out unified coding and formatted storage on historical and current response behaviors according to node identification and scheduling time sequence information to construct a structured data set. According to the method, accurate identification and dynamic correction of the adjustable load frequency response offset risk in the virtual power plant are realized, the heterogeneous load perception and control capability is improved, frequency staggering resonance and scheduling failure caused by inconsistent response are avoided, the system cooperative adjustment stability and the load safety access reliability are enhanced, and the method has a good application prospect.

The invention relates to the technical field of virtual power plant operation monitoring, and discloses a virtual power plant distributed resource cluster anomaly monitoring system, method and equipment, and the system comprises a resource node data collection module, an anomaly trend score calculation module, an association graph model construction module, a single node influence calculation module and a comprehensive anomaly early warning output module. In the prior art, a single-point alarm mode depends on a fixed threshold value or a rule base, and especially under the conditions that distributed resources in a virtual power plant are complex in type and space coupling and controller sharing exist between nodes, accurate identification and dynamic early warning of trend anomalies, cooperative faults and potential propagation paths are difficult to realize. According to the method and the device, the multi-dimensional parameter modeling and the graph neural network anomaly propagation analysis are fused, so that the anomaly in the distributed resource cluster is identified and dynamically evaluated, and the early warning accuracy and the intelligent operation and maintenance efficiency of the virtual power plant are improved.

The invention relates to the technical field of energy storage power station combined operation and maintenance management, and provides a virtual power plant-oriented energy storage power station combined operation and maintenance management system, which comprises a dynamic sensing module for acquiring battery cluster temperature difference distribution, power converter switch transient characteristics and grid-connected point harmonic spectrum data of an energy storage power station in real time through a multi-dimensional sensor network; the collaborative decision-making module is used for dynamically generating a cross-station power interaction constraint rule based on the virtual power plant topological structure; the impedance reconstruction module adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; the state balancing module is used for realizing charge state dynamic balance and power margin collaborative distribution among the multiple energy storage power stations; and the secure communication module is used for ensuring secure transmission of control instructions among the modules by adopting a layered encryption protocol. The operation stability, the power distribution flexibility and the equipment safety of the virtual power plant can be improved, and the service life of the energy storage equipment is prolonged.

The invention discloses a virtual power plant scheduling method based on a large language model and deep reinforcement learning, and belongs to the technical field of virtual power plant scheduling. Comprising the following steps: constructing a virtual power plant multi-agent cloud edge collaborative scheduling framework based on large language model driving; predicting wind power, photovoltaic power and load power based on a large language model; constructing a mathematical model of virtual power plant optimization scheduling; converting the virtual power plant optimization scheduling model into a Markov game process in combination with a large language model; performing initialization training on the strategy network of the edge layer intelligent agent by adopting imitation learning to obtain a pre-trained edge layer intelligent agent strategy network; and based on the pre-training strategy network of the boundary layer intelligent agent, combining with a large language model and adopting an improved multi-agent near-end strategy optimization algorithm to solve a scheduling strategy.

The invention relates to the field of power system dispatching control, in particular to a virtual power plant collaborative optimization dispatching method, system and device based on multiple spatial-temporal scales and a storage medium. The method comprises the following steps: acquiring real-time supply and demand data of a multi-energy data source, constructing a dynamic operation data set by adopting distributed data acquisition, and performing time sequence analysis on the data set to extract a multi-energy fluctuation feature set; the fluctuation feature set constructs a network topology model in a spatial dimension, and a resource allocation weight of each energy node is determined through graph calculation to generate a resource allocation optimization scheme; when the real-time demand fluctuation exceeds a threshold value, a reinforcement learning algorithm is adopted to carry out optimization adjustment to obtain a real-time scheduling instruction set; in combination with real-time data of the electricity market, an optimized economic signal set is obtained through multi-objective optimization, and an equipment control instruction set is generated by adopting distributed control; and performing real-time monitoring by utilizing edge calculation according to the equipment control instruction set, and dynamically updating the scheduling instruction set through adaptive adjustment based on the system operation deviation to obtain a final resource optimization configuration scheme.

The invention discloses a virtual power plant-based power distribution area collaborative governance system and method, and relates to the technical field of power system automation and intelligent power distribution networks, and the system comprises a dynamic autonomous partition governance unit which is used for dividing a power distribution area node into a plurality of subunits, correcting abnormal states in the subunits based on a power mutual aid strategy between adjacent subunits; the multi-time-scale collaborative optimization unit is used for constructing a second-level, minute-level and hour-level multi-time-scale collaborative mechanism and coordinating control targets of the power distribution area at different time scales; the edge-cloud collaborative decision-making unit is used for complementing the edge side real-time control strategy and the cloud global strategy; and the elastic resource pool unit is used for calculating the elastic contribution degree of the resources and formulating priorities, and calling the resources according to the priorities to meet load requirements. By integrating the virtual power plant technology, the edge calculation, the digital twinning and the multi-time scale optimization strategy, the intelligent and efficient treatment of the power distribution area is realized.

The invention relates to the technical field of smart power grids, in particular to a hybrid game-based virtual power plant optimization operation method under multiple markets, and the method comprises the steps: building an electricity-certificate-carbon multi-market coupling transaction framework, deeply analyzing a master-slave game relationship between a virtual power plant operation platform and a VPP alliance, and building a price guidance-based double-layer interaction mechanism; according to the method, research is carried out aiming at the condition that a virtual power plant belongs to different interest subjects, a resource complementation-based alliance cooperation model is proposed, and a VPP alliance cooperation optimization framework considering demand side load elasticity is established by introducing a refined model of a flexible load; and establishing a virtual power plant operation platform and VPP alliance mixed game optimization model. Solving a master-slave game stage by adopting a double-layer iterative algorithm; and solving a cooperative game stage in combination with an alternating direction multiplier method. The virtual power plant is guided to participate in multi-market cooperative regulation and control through a mixed game mechanism, safe and stable operation of the power system is ensured, and the operation economy and environmental protection property are improved.

The invention relates to a resource allocation method and system based on a virtual power plant, and belongs to the technical field of electric power, and the method comprises the steps: respectively obtaining electric power basic data corresponding to an area covered by the virtual power plant in different historical time periods; analyzing the power basic data to obtain power analysis data corresponding to the power basic data in different historical time periods; constructing an initial power prediction model according to the power analysis data; acquiring a fluctuation coefficient corresponding to the electric power prediction model to correct the initial electric power prediction model to obtain a target electric power prediction model; and performing supply-demand balance prediction based on the target power prediction model and the power data corresponding to the current time period, and performing power resource allocation according to a prediction result. According to the invention, the electric power resources can be effectively scheduled and allocated.

The invention relates to the technical field of collaborative optimization, in particular to a virtual power plant power generation-utilization-price collaborative optimization system based on an AI large model, and the system comprises a load confidence matching module, a resource stability mapping module, a source-load capacity coupling module, an electricity price interval adjustment module and a comprehensive regulation and control linkage module. According to the method, the confidence interval prediction of the load demand is realized based on the hybrid neural network modeling of the load behavior data and the equipment temperature control characteristic sequence, and the scheduling matching confidence is measured according to the boundary overlapping condition of the prediction interval and the power generation response characteristic; a stability screening mechanism for adjusting resources is constructed in combination with the output fluctuation ratio and the equipment inertia characteristic, the controllability of load adjustment and the real-time performance of source side response are improved, the price adjustment rhythm is corrected through an electricity price response delay factor, dynamic closed-loop linkage between load adjustment and price guidance is achieved, and the load adjustment efficiency is improved. The execution priority is dynamically updated under the condition that multiple response conditions are matched, and the certainty of resource scheduling and the sensitivity of response are improved.