1366 results about "Virtual power plant" patented technology

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 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.

The invention relates to the technical field of power system fault recovery, in particular to a power distribution network fault transfer optimization method fusing a knowledge base under the participation of a virtual power plant, and the method comprises the steps: firstly modeling a power distribution network fault transfer process into a Markov decision process to construct a power grid environment model, and then extracting power grid topological features through a graph neural network; the method comprises the following steps: extracting and fusing time sequence features in combination with a Transform structure, then introducing expert knowledge to carry out imitation learning, providing an initial strategy for an intelligent agent, then adopting PPO and DQN cooperative training to optimize an intelligent agent strategy, finally aggregating distributed energy with the help of a virtual power plant, realizing resource coordination and fault load transfer, and dynamically correcting the strategy through closed-loop feedback. Therefore, dynamic adaptability, resource cooperation efficiency and strategy reliability of power distribution network fault recovery are improved, power supply recovery time is shortened, and safe and stable operation of a power grid is guaranteed.

The invention discloses a virtual power plant global optimization scheduling method, system and device based on cloud edge collaboration, and a storage medium, and belongs to the technical field of power system scheduling. The method comprises the following steps: based on a cloud edge coordinated regulation and control framework comprising a cloud layer, an edge layer and an end side layer, taking minimization of the total operation cost of a system as a target, comprehensively considering a power balance constraint, a main network interaction constraint, a distribution network transmission constraint, a distributed resource operation constraint, an energy storage equipment constraint and a renewable energy consumption constraint; establishing a global optimization scheduling model; edge collaborative optimization is realized by adopting an alternating direction multiplier method, a global coupling problem is decomposed into local optimization sub-problems and a cloud coordination problem of each region, and aggregation power information is sent to the cloud after the local optimization sub-problems are solved in parallel in each region; and the cloud performs global coordination optimization to generate an optimal scheduling strategy, and issues a scheduling instruction to the edge layer to control the actual operation of the distributed power supply, the energy storage equipment and the controllable load, thereby realizing the collaborative optimization scheduling of the virtual power plant. The problems that in virtual power plant large-scale distributed resource coordination optimization, calculation complexity is high, communication burden is heavy, and real-time performance and global optimality are difficult to consider at the same time are effectively solved.

The invention discloses a virtual power plant intelligent regulation and control method and system based on artificial intelligence, and belongs to the technical field of electric power system intelligent regulation and control, and the virtual power plant intelligent regulation and control method based on artificial intelligence comprises the following steps: S1, aggregating equipment side data, desensitizing to generate topological codes, and constructing time scale matrix synchronization; s2, constructing a dynamic model by equipment parameters, and mapping real-time data to output a difference map; s3, adding equipment constraints, building a multi-objective function, optimizing a strategy and performing correlation analysis; s4, a wind and light fluctuation overrun trigger RL strategy and an abnormal switching base line generate a mixed instruction; s5, locally verifying the instruction, and correcting and feeding back parameters if the prediction is out of limit; s6, generating a three-dimensional thermodynamic diagram, and displaying an association report and a historical record by AR; s7, aggregating the data to reconstruct the training set, locally fine-tuning the strategy network and performing incremental updating; the method has the beneficial effects that the regulation and control pain point of the virtual power plant is systematically solved, the operation and maintenance cost is reduced, the new energy consumption capability is improved, and the equipment out-of-limit risk is reduced.

The invention relates to the technical field of virtual power plant scheduling, and discloses a distributed collaborative optimization scheduling method for a virtual power plant. The method includes collecting an operating state data set of a target virtual power plant. And performing distributed collaborative model construction processing on the operation state data set to generate collaborative scheduling features covering power distribution balance degree, constraint matching closeness and interactive response sensitivity. And calling a pre-trained optimization scheduling model to carry out multi-target collaborative optimization processing on the collaborative scheduling features to obtain an optimization scheduling result and a key collaborative region identifier. And based on the association relationship between the load demand fluctuation sequence and the equipment adjustment capability, performing operation environment compensation correction processing on the optimization scheduling result, and generating a corrected result. And generating a virtual power plant scheduling strategy set including a power transfer path adjustment scheme and an energy storage equipment configuration processing scheme according to the key cooperative region identifier. According to the method, distributed energy resources are effectively integrated through multi-dimensional collaborative optimization and dynamic correction.

The invention relates to the technical field of electric power intelligent management, and discloses a cooperative scheduling method and system for a virtual power plant, and the method comprises the following steps: S1, collecting the real-time data of each distributed power supply, each load and an energy storage system in the virtual power plant, and carrying out the ultra-short-term prediction, and obtaining a prediction parameter; s2, dynamically calculating the dynamic operation boundary of the energy storage system based on the real-time state of the energy storage system; s3, on the day before the current operation day, generating a pre-scheduling plan through collaborative decision making of a multi-target fuzzy satisfaction function; s4, in the current running day, taking the pre-scheduling plan as a reference, updating boundaries and prediction parameters in a rolling manner, and generating a real-time scheduling instruction through model prediction; and S5, monitoring the deviation between the actual output of each resource and the real-time scheduling instruction in real time, and when the deviation exceeds a threshold value, starting a collaborative deviation compensation mechanism to carry out power balance. According to the invention, fine cooperative scheduling of different types of distributed resources can be realized in a complex environment with high uncertainty.

The invention discloses a virtual power plant participated deep reinforcement learning power distribution network load recovery method and system, and relates to the technical field of power distribution network dispatching and virtual power plant cooperative control, and the method comprises the steps: firstly collecting the resource data of a distributed photovoltaic system, an energy storage system and a controllable load in a virtual power plant, and constructing a virtual power plant adjustable capability model; abstracting the power distribution network into an undirected topological graph through a clustering algorithm, dynamically partitioning the undirected topological graph, and allocating exclusive intelligent agents and corresponding virtual power plant resources to each region; a decision framework based on multi-agent deep reinforcement learning is established, a centralized training and distributed execution mode is adopted, a power grid and virtual power plant resource state is combined to output an action decision, and a multi-dimensional reward function is designed; and meanwhile, a non-key action shielding mechanism is introduced, and the action of the agents in the non-fault area is constrained through fault mask vectors, so that interference is reduced, and multi-agent load recovery in which the virtual power plant participates is realized.

The invention relates to the technical field of distributed energy aggregation cooperative regulation and control, and discloses a virtual power plant hierarchical cooperative intelligent scheduling method and system based on multi-objective optimization, and the method comprises the steps: obtaining the operation parameters and prediction data of each subsystem of a virtual power plant, generating an energy storage system hour-level charge state target trajectory, and carrying out the prediction of the target trajectory; establishing an energy storage system charge state constraint budget pool; generating a budget allocation table; monitoring a short-term budget allowance state, when the short-term budget allowance state is lower than a safety threshold value, borrowing a part of budget from a long-term budget for redistribution, and dynamically adjusting a power amplitude limit value of deviation correction according to charge state constraint tensity; and when the accumulated deviation exceeds the autonomous correction capability or the constraint tensity reaches a critical value, generating an interlayer deviation report and triggering global re-optimization, and adjusting the budget distribution proportion of the next period according to the actual budget consumption condition at the end of the hour-level period. According to the invention, the continuous optimization and self-learning capability of the hierarchical scheduling strategy are realized.

The invention relates to the technical field of electric power control, in particular to a multi-virtual power plant collaborative scheduling method and system based on mixed games and carbon transactions. The method comprises the following steps: acquiring parameters of each virtual power plant, reading a carbon quota allocation scheme and a carbon transaction market price released by a power grid, and establishing a virtual power plant operation cost expression containing a carbon transaction cost; constructing a non-cooperative game model, and obtaining a preliminary optimal output strategy of each virtual power plant by taking minimization of the operation cost of the virtual power plant as a target; the operation state of the virtual power plant is detected, if the risk that renewable energy consumption is insufficient or the total carbon emission exceeds the standard exists, a cooperative optimization mechanism is triggered, a plurality of virtual power plants are selected to form a joint optimization group, and the joint optimization group is regarded as an independent participant to obtain an optimal output strategy again; and issuing the optimized scheduling instruction to each virtual power plant for execution according to the optimal output strategy, and participating in carbon market transaction according to the actual carbon emission and the quota difference in the settlement period.

The invention provides a resource collaborative scheduling system and method for a virtual power plant. The method comprises the following steps: determining space-time probability distribution of wind and light output in the virtual power plant through historical meteorological data and historical illumination data; determining space-time load distribution of an electric vehicle cluster in the virtual power plant, and constructing a source-load interaction scene set under multiple space-time scales in the virtual power plant by fusing the space-time probability distribution and the space-time load distribution; determining a multi-objective optimization function of the virtual power plant according to the price demand signal of the electric energy service market and the source-load interaction scene set; and performing optimization solution on the multi-objective optimization function to obtain a collaborative scheduling plan of the virtual power plant, decomposing the collaborative scheduling plan into a control instruction sequence, and issuing the control instruction sequence to a local controller of each distributed resource. According to the scheme of the invention, a multi-target optimization system considering the operation benefit and the renewable energy power abandonment rate can be constructed through the source-load interaction scene under multiple spatial-temporal scales, so that the closed-loop management and control of the resource scheduling of the virtual power plant can be realized.

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 provides an urban virtual power plant operation method and device based on an intelligent agent architecture, relates to the field of artificial intelligence, and solves the problem that in the prior art, operation strategies related to a virtual power plant are mostly based on static rules and manually set optimization models. And rapid and efficient response is difficult to realize in a multi-participant, multi-constraint condition and dynamic electricity market environment. The method comprises the following steps: collecting multiple pieces of first data in an urban energy system in an urban virtual power plant; performing arrangement and task scheduling on the first data on the basis of data types required by the plurality of agents to execute tasks, determining the first data required to be processed by each agent, and calling the plurality of agents to process the first data required to be processed; and generating a regulation and control decision and a market transaction strategy of the urban virtual power plant based on the processing results output by the plurality of agents and the operation constraint conditions. The method is used in the operation process of the urban virtual power plant.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant optimal scheduling system and method, and the system comprises a data obtaining module, an edge calculation module, a prediction module, a scheduling controller, a topology reconstruction module, and an intelligent terminal device cluster. According to the invention, the edge computing module carries out localization processing and prediction on the sensing data, so that rapid generation and issuing of a scheduling scheme are realized, and the problem of response delay caused by network transmission and centralized computing of a traditional centralized architecture is avoided, thereby supporting millisecond scheduling feedback and improving the scheduling efficiency. The real-time response capability under the sudden load fluctuation or fault condition is remarkably improved, a multi-dimensional perception and prediction mechanism is constructed based on an LSTM neural network prediction model, the recognition and trend prediction capability of the system on meteorological disturbance, equipment aging and operation abnormity is enhanced, the intelligent level of the virtual power plant system is improved, and the real-time performance of the virtual power plant system is improved. The system can dynamically generate an optimal scheduling strategy to ensure stable operation of the virtual power plant under various working conditions.

The invention belongs to the technical field of virtual power plants, and particularly relates to a distribution network virtual power plant aggregation control method based on a disperse complex adaptive system, which comprises the following steps: constructing a distribution network disperse complex adaptive system architecture comprising a tail end resource layer, a local autonomy layer and a global collaboration layer; setting a dynamic aggregation index of the virtual power plant, and calculating the dynamic aggregation index of the virtual power plant based on the operation data of the distributed resources in the local autonomous layer; a virtual power plant dynamic aggregation algorithm based on a greedy strategy is adopted, and a virtual power plant is formed through aggregation; a virtual power plant voltage control strategy based on key node selection is constructed, in the local autonomy layer, each virtual power plant preferentially controls internal resources to realize autonomy, and in the global collaboration layer, key node voltage in each virtual power plant is adjusted to realize collaboration control among the virtual power plants. According to the method, the adjustment potential of the distributed resources can be fully mined, so that the distributed resources can better participate in power grid adjustment, and resource utilization and power grid benefit maximization are realized.

The invention discloses a virtual power plant capacity configuration and regulation operation optimization method, which comprises the following steps: constructing an aggregation model and physically consistent digital twinning, establishing a linearized power distribution network model containing voltage and power flow constraints, and depicting resource efficiency and time delay characteristics; forming a time-varying uncertainty set through quantile calibration and set drift constraint; establishing a capacity-operation joint double-layer optimization model, realizing capacity configuration under the constraint of the whole life cycle cost, and obtaining a rolling scheduling strategy through distributed robust optimization; before issuing, control shielding and formalized constraint are adopted to ensure the safety of the power grid; a multi-variety collaborative quotation is generated on the market side through opportunity constraint and risk measurement; hierarchical adaptive re-optimization is realized based on a trigger criterion, and a twin model is continuously calibrated by using a hardware-in-the-loop experiment; model updating is realized by adopting federated learning and differential privacy; executing degradation control under an abnormal condition, and performing smooth rollback after recovery; and finally, the capacity and operation parameters are evaluated and corrected through performance and service life linkage.

The invention belongs to the technical field of novel electric power system operation control and trust management, and particularly discloses a virtual power plant full-process trusted aggregation method and system based on a hierarchical trust chain. Differentiated hierarchical trust chain models of a data acquisition link, a scheduling control link and a market transaction link are constructed respectively; through real-time credibility evaluation and adaptive weight adjustment, credibility calculation of multi-agent collaborative decision is realized. According to the invention, a trust network formed by a plurality of efficient, safe and transparent virtual power plant trust chains is constructed, solid technical support is provided for fair competition, intelligent scheduling and reliable operation of a power market, and efficient interaction and stable operation of a power system are ensured.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant regulation and control method and system for realizing new energy consumption, and the system comprises a multi-source heterogeneous data collection module, an intelligent prediction analysis module, a resource aggregation modeling module, an optimization decision module, a block chain cooperation module, and a digital twinborn evaluation module. Multi-time-scale coupling prediction is carried out on new energy output and load demand through the deep space-time convolutional neural network, fluctuation and intermittency characteristics of new energy can be described, short-term and ultra-short-term prediction precision is improved, wind curtailment and light curtailment rate and load reduction risk are reduced, and the prediction efficiency is improved. According to the method, high matching between a virtual power plant scheduling plan and an actual operation condition is guaranteed, a flexible resource feature matrix is constructed, and distributed energy storage, interruptible load and electric vehicle multi-element resources are subjected to refined modeling and aggregation, so that a virtual unit capable of being efficiently scheduled can be formed, the resource utilization efficiency is improved, and the overall scheduling cost is reduced.

The invention provides a virtual power plant dynamic response scheduling method and system based on a cognitive spectrum network, which can improve the reliability, self-adaptive capability calculation efficiency and risk management and control capability of a virtual power plant communication system, and relates to the technical field of virtual power plants, the method comprises the following steps: establishing a cognitive spectrum self-organizing network; collecting current operation state data of each power resource under the virtual power plant and historical operation state data under different working conditions; obtaining a resource response characteristic of each power resource; outputting a preliminary optimization result; converting the preliminary optimization result into a credible optimization result; generating a system risk partition topology report; a market risk hedging strategy capable of being automatically executed is generated; and generating a final scheduling instruction containing the power set value, the execution time, the hedging operation and the risk constraint condition of each resource. According to the method, a feasible technical path is provided for large-scale efficient utilization of distributed resources under the background of the energy internet.

The invention discloses a power grid photovoltaic output and load sequence modeling method, system and device and a storage medium, and the method comprises the steps: comprehensively utilizing the multi-scale feature extraction capability of a time-frequency decomposition technology, the time sequence dependence modeling capability of a long and short-term memory network, and the global hyper-parameter optimization capability of a Bayesian optimization algorithm; and carrying out collaborative modeling and prediction on the photovoltaic output and the power load under a unified framework. By introducing a source load time-delay correlation analysis and probability interval construction mechanism, point prediction results and uncertainty intervals of photovoltaic, load and net load can be output at the same time, and a set of source load integrated prediction system with high prediction precision, strong robustness and reliable interval characterization capability is constructed. The method can improve the precision and reliability of photovoltaic power and load prediction, also can reduce the risk in power system scheduling, optimizes the energy storage configuration strategy, and especially has wide popularization potential and application prospects in the scenes of new energy grid-connected operation, intelligent micro-grid and virtual power plant management and the like.

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 provides a virtual power plant group resource scene adaptive scheduling method and system, and a storage medium, and the method comprises the steps: building a typical external feature model of a virtual power plant based on the resource characteristics and core parameters of different types of distributed resources; generating a feasible region of the single equipment based on power constraint, electric quantity constraint and climbing constraint of the single equipment in the virtual power plant, and aggregating the feasible region of the single equipment to form an aggregated feasible region of the virtual power plant; based on a typical external feature model of the virtual power plant and different service scene requirements, dynamically adjusting response capability index weights in different service scenes, and based on an aggregation feasible region of the virtual power plant, constructing a virtual power plant dynamic aggregation model adapted to multiple scenes; and solving the dynamic aggregation model of the virtual power plant by taking minimization of the power generation cost of the virtual power plant as a target to obtain an optimal scheduling scheme of the virtual power plant.

The invention discloses a virtual power plant voltage-reactive power coordination control method based on fusion of a graph neural network and deep reinforcement learning, and the method comprises the steps: explicitly introducing a topological structure of a power distribution network into the state representation of deep reinforcement learning in the form of graph data, and extracting and processing the graph structure data through the graph neural network; topological correlation and dynamic interaction among nodes in a power grid are accurately captured; and the extracted graph features and the traditional system state quantity are fused and input to the deep Q network for learning and decision making, so that the optimal coordination control of the distributed resources in the virtual power plant and the power distribution network equipment is output. The virtual power plant voltage-reactive power control method and device aim at solving the problem that in the prior art, perception on a power grid structure is insufficient, and the decision-making precision and robustness of virtual power plant voltage-reactive power control and the adaptability in a large-scale complex power distribution network are remarkably improved.

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 embodiment of the invention provides a virtual power plant terminal access control system based on dynamic permission, and the system comprises an identity authentication module which is configured to collect hardware invariant features and firmware signature information of terminal equipment and generate an encryption certificate; the trust evaluation module is in communication connection with the identity authentication module and is configured to continuously monitor communication behavior characteristics and service instruction characteristics of the terminal and output a dynamic trust score; the dynamic authority management module is in data connection with the trust evaluation module and is configured to adjust the access authority level in real time according to the mapping relation of the dynamic trust score in a preset authority level interval; the strategy engine module is configured to generate an access control instruction when abnormal behavior characteristics are detected; and the behavior monitoring module interacts with the strategy engine module and the dynamic authority management module and is configured to execute blocking operation and feed back an execution result to the log auditing module. And the hardware invariant features and the dynamic behavior features are subjected to fusion evaluation, so that the terminal credibility quantification precision is improved.

The invention discloses a virtual power plant bidding optimization method fusing LLM knowledge reasoning and MAPPO, and the method comprises the steps: constructing the operation state input of a virtual power plant participating in a spot market, and guiding LLM to carry out the semantic extraction of historical market clearing data, energy storage state, electricity price trend and bidding rules through a Prompt mechanism; and forming a task stage semantic state and a structured reward function, and embedding the task stage semantic state and the structured reward function into an Actor-Critic network of the MAPPO. A semantic state generated by the LLM is introduced into the Actor network to serve as auxiliary input, and the semantic state and the environment state jointly generate a quotation action; structured semantic rewards are introduced into the Critic network, and the perception ability of value estimation on market rules is improved. A market clearing result is fed back to the intelligent agent to form a revenue signal, the revenue signal and the reward generated by the LLM are superposed to participate in dominant function calculation, and finally strategy convergence is completed under a PPO cutting optimization mechanism.

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 relates to the technical field of power system automatic management, in particular to a virtual power plant optimal scheduling method and system.The virtual power plant optimal scheduling method comprises the steps that real-time multi-mode state data of all nodes are collected through an intelligent electric meter and a sensor, and a time sequence input tensor is constructed; a prediction tensor is generated based on the power system resource state and historical operation data, and abnormity is identified through difference operation with the real-time tensor; constructing an abnormal propagation map and identifying key linkage nodes; generating a candidate disposal path and an edge mapping strategy according to the key node data; testing a response index of each path in the virtual environment, collecting residual data, and selecting an optimal path; scheduling a processing sequence based on node priorities; and dynamically updating model parameters and a scoring function structure according to execution feedback. According to the method, the problem that tiny anomalies are difficult to perceive under the condition of complex power demands is solved, and model correction and strategy updating can be carried out according to disturbance response residual errors and simulation deviations after actual execution.

The invention belongs to the technical field of virtual power plants, and particularly relates to a multi-physics field coupled virtual power plant energy storage health state monitoring and predicting method, which comprises the following steps: acquiring electrochemical parameters and thermodynamic parameters of an energy storage facility in real time, preprocessing the acquired data, and storing the preprocessed data into a database; calculating the current SOH of the energy storage facility, constructing a physical information neural network (PINN), and predicting the SOH of the energy storage facility based on the current SOH and historical data in the database; displaying a curve graph of data acquired in real time, the current SOH, an SOH prediction chart and alarm information in a visual mode; and with maximization of SOH and minimization of energy loss and thermal risk as targets, a Pareto optimization problem is constructed, and an optimal charging and discharging strategy is solved. According to the method, multi-parameter perception, physical mechanism and deep learning are fused, and high-precision real-time monitoring and prediction of the energy storage health state of the virtual power plant can be realized.

The invention discloses an adjustable resource unified data external characteristic modeling, regulation and control method and system oriented to various transaction scenes. The method comprises the following steps: collecting real-time data of an internal adjustable resource of a virtual power plant; the climbing time, the duration, the response capacity and the responsible frequency are quantized and defined as a group of external characteristic parameters, an adjustable resource data external characteristic model is constructed, adjustable resources are classified according to the external characteristic parameters of the model, and an adjustable resource characteristic library is established; in combination with the response characteristic and the load state of each adjustable resource, generating an optimal regulation and control strategy meeting the regulation and control requirements, and allocating a corresponding regulation and control instruction to each adjustable resource; each adjustable resource adjusts power output according to the regulation and control instruction, and meanwhile, the calling cost of each adjustable resource is calculated; in combination with the real-time state and the adjustment cost minimization target, the regulation sequence and proportion are optimized; according to the method, the economy and flexibility of the virtual power plant in multi-scene regulation can be effectively improved, and meanwhile, the accuracy of power grid frequency modulation response and the system stability are enhanced.