99 results about "Virtual power" patented technology

This invention provides a method, apparatus, and equipment for capacity optimization configuration of a wind-gas-storage virtual power plant, belonging to the technical field of virtual power plants. The method includes: filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal; calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal; calculating the minimum rated capacity of the battery energy storage system based on the second frequency signal; calculating the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system; constructing an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant; and solving the objective function using a particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system. This invention addresses the deficiency in current virtual power plant capacity planning configurations that do not consider the output characteristics of wind power, gas turbines, and energy storage batteries, potentially leading to power instability during peak load periods.

This invention discloses a method, system, and storage medium for assessing the adjustable load of a virtual power plant in public buildings. The method includes: acquiring raw influencing factor data, including at least air conditioning system operating parameters reflecting equipment aging and building thermal parameters reflecting building thermophysical characteristics; constructing hysteresis features characterizing thermal inertia based on the air conditioning system operating parameters, and constructing multi-parameter coupling features based on their coupling relationship with building thermal parameters; inputting the obtained data into a pre-trained adjustable load assessment model to obtain the adjustable load assessment result; wherein the model is a multi-model fusion model and integrates a rolling mixed-integer linear programming solver for synchronously generating a full-chain control scheme for the air conditioning system. This invention, by introducing a quantitative expression of the thermal inertia of the air conditioning system and multi-parameter coupling features, achieves an integrated closed loop of assessment and control, significantly improving the response accuracy and overall benefits of public buildings participating in virtual power plants.

This invention relates to a hierarchical voltage regulation method for main and distribution networks that considers the regulation capabilities of virtual power plants. First, a multi-resource aggregation model of the virtual power plant, including distributed generation, energy storage, and flexible loads, is constructed to quantify its active / reactive power regulation potential and dynamic response characteristics. Second, a three-layer coordinated control framework of the main grid, distribution network, and virtual power plant is designed, clarifying the control objectives and responsibilities of each layer and establishing a command decomposition and feedback iteration mechanism between layers. Then, a hierarchical optimization mathematical model is constructed. The upper layer performs global optimization with the objectives of minimizing network losses and voltage deviation, while the lower layer dynamically allocates the internal resource output of the virtual power plant based on an improved particle swarm optimization algorithm, achieving hierarchical coordination through alternating iterations. Compared with existing technologies, this invention has advantages such as effectively reducing voltage deviation, lowering system network losses, and significantly improving the voltage stability and resource utilization efficiency of the main and distribution networks.

This invention relates to a coupled clearing method and apparatus for virtual power plants participating in the spot electricity market, applied in the field of electricity market technology. The method includes: calculating the High-Gross Price Ratio (HGRP) as the threshold for economic compensation of DRVPPs, compensating DRVPPs only when the node marginal price is higher than the HGRP, thus preventing the activation of non-economical DRVPPs during low-price periods, preventing low-cost generation resources from being squeezed out of the market, and ensuring the authenticity and effectiveness of the market clearing price signal; incorporating DRVPPs as a controllable and constrained demand-side adjustment variable into the clearing model, avoiding the failure to reflect the adjustment value due to its inclusion in load forecasting, and overcoming the problem of simply treating it as "negative load" and ignoring response constraints, thereby improving the rationality of the clearing results; and adopting a differentiated economic compensation mechanism based on the node marginal price, linking the adjustment revenue of DRVPPs to the marginal electricity cost of the node, achieving precise matching of DRVPP resources with the actual adjustment needs of the system.

This invention discloses a method for dynamic virtual testing and evaluation of power grids based on digital twins: a virtual power grid dynamic simulator is connected in series between the power grid signal sensors and the controller of the system under test; the host computer transmits the dynamic parameters of the power grid to be simulated to the simulator; the simulator modulates the parameters with the actual power grid signal to generate a power grid signal containing virtual disturbances, which is sent to the controller of the system under test to disturb the operation of the system under test; the simulator can also receive control signals from the system under test and generate control signals containing disturbances, which are sent to the controller of the system under test; simultaneously, the control and output responses under disturbance are collected and analyzed to evaluate the system's stability, controllability, or resilience. The dynamic parameters of the power grid are generated by a digital twin model of the actual power grid; the response of the system under test is fed back to the digital twin model, reflecting the interaction with the power grid after connection. This invention integrates digital twins and virtual simulation to achieve safe, efficient, and low-cost testing and evaluation of grid-connected equipment under dynamic and fault conditions of the power grid.

This invention discloses a privacy-preserving energy-data collaborative optimization method and system for Virtual Power Plants (VPPs), relating to the field of virtual power plant operation optimization technology. The method includes establishing an outer master-slave game model to obtain strategic parameters output by the model; combining these strategic parameters with an inner multi-timescale operation optimization model to obtain operational data; constructing a bidirectional coupling feedback mechanism and a Stackelberg equilibrium mechanism using the strategic parameters and operational data; and employing a distributed collaborative solution method based on the bidirectional coupling feedback mechanism and Stackelberg equilibrium mechanism to obtain the VPP privacy-preserving energy-data collaborative optimization results. This invention constructs a bidirectional closed-loop coupling framework, achieving deep integration and collaborative value-added of data element value and energy physical value.

This invention discloses a distributed bidding method for virtual power plants based on the MADDPG algorithm, belonging to the field of electricity market trading and intelligent optimization technology. The method includes the following steps: Step 1) Virtual power plant system modeling and environment construction: constructing a virtual power plant resource characteristic model; Step 2) Distributed bidding model construction: considering P2P transactions between multiple virtual power plants, constructing a distributed bidding model for the electricity-carbon coupling market; Step 3) Reinforcement learning-based model solving: addressing the multi-agent and strongly coupled characteristics of the proposed distributed bidding model, a solution method based on the MADDPG algorithm is used to solve the model. This invention can achieve efficient collaborative decision-making in bidding problems with strong coupling among multiple agents, high dimensionality, and obvious continuous decision-making characteristics, improving the economic benefits and strategy adaptability of virtual power plants, and has good scalability and engineering application value.

This invention discloses a resource collaborative scheduling and demand response optimization system and method for virtual power plants in industrial parks. It achieves global collaborative scheduling through a two-layer Actor-Critic network architecture, and uses a fusion of particle swarm optimization and fuzzy control to achieve grid-connected stability control. A three-layer fusion optimization architecture is constructed to complete the continuous amplitude calculation of demand response and the generation of equipment-level instructions. A unified algorithm support is provided by an intelligent optimization module. This invention effectively solves the problems of weak adaptability of existing global optimization models for virtual power plants in industrial parks, low level of intelligent decision-making in specialized sub-scenarios, and difficulty in coordinating multi-timescale scheduling and refined execution. It can achieve robust optimization and refined control in uncertain scenarios, balancing system economy, stability, and operational reliability. It is suitable for virtual power plants in industrial parks to efficiently participate in the power market and resource collaborative scheduling.

This invention belongs to the field of virtual power plant scheduling technology, specifically involving a joint scheduling method for multiple virtual power plants considering distributed energy optimization and control. The steps include: constructing a control model for photovoltaic power generation resources within the virtual power plant, a scheduling and control model for the series-connected battery energy storage system, and a short-term power generation scheduling model for multiple virtual power plants; jointly optimizing the intraday operation of multiple virtual power plants; setting the objective function of the short-term power generation scheduling model for multiple virtual power plants with the optimization objectives of minimizing the joint operation cost of multiple virtual power plants and minimizing the renewable energy curtailment rate, and solving it using a simulated annealing algorithm to obtain the optimal scheduling plan for multiple virtual power plants within a short-term scheduling cycle when connected to a distributed energy control strategy. This invention can improve the distributed renewable energy absorption capacity, reduce the operating losses of the energy storage system, and enhance the economy and operational flexibility of joint scheduling of multiple virtual power plants while satisfying the constraints of coordinated operation of multiple virtual power plants.

This invention discloses an energy regulation method and system for virtual power plants based on a predictive model, relating to the field of virtual power plant operation control. The method includes the following steps: collecting energy data from the virtual power plant; integrating wind power data and load data, and extracting features from the integrated data; constructing a preliminary energy dynamic balance prediction model based on predicted generation data of distributed energy sources and predicted demand data of load-side resources; using the preliminary energy dynamic balance prediction model to predict day-ahead load demand and distributed energy; adjusting the operating status of each distributed energy source and load-side resource in real time using the niche gray wolf optimization algorithm according to the preliminary dispatch strategy; dynamically adjusting and optimizing the energy dynamic balance prediction model; and re-predicting load demand and distributed energy using the optimized energy dynamic balance prediction model. This invention can respond to changes in the power system in advance, achieving more efficient energy dispatch.

This invention discloses an optimized scheduling method for virtual power plants with multiple resource types, belonging to the field of virtual power plant technology. This method aims to solve the technical challenges of delayed scheduling response due to the strong volatility of distributed resource output, insufficient adaptability to multiple scenarios, and weak distribution network resilience under renewable energy access. The method establishes a multi-resource power adjustable range model based on real-time data, uses linear scaling and displacement algorithms to generate aggregated power domain boundaries, and adjusts the weights according to the different target types of the virtual power plant. It combines ridge regression conditional expectation correction and sub-Bruker optimization to construct a dynamic risk budget, optimizing generation plans and reserve capacity in the day-ahead phase. In the real-time phase, it responds in stages according to power deviation levels, triggering rapid energy storage compensation or emergency interruption of controllable loads. The model parameters are iteratively corrected through a closed-loop feedback mechanism. This method significantly improves the response accuracy and reliability of virtual power plants in frequency regulation, peak shaving, and self-balancing scenarios.

This invention provides a method and system for optimizing frequency regulation transactions in virtual power plants based on fractional heavy-tailed noise modeling, belonging to the field of power market technology. By systematically collecting and preprocessing grid frequency, market settlement, and generation data from virtual power plants, it achieves deep fusion and effective governance of multi-source data, providing high-quality, structured input for subsequent analysis and significantly improving data-driven decision-making capabilities. Using a voltage characteristic model, the energy and response time distribution required for dynamic and static frequency responses can be calculated separately, enabling quantitative and structured analysis of grid frequency regulation needs. This allows virtual power plants to more accurately assess the matching relationship between their own response potential and system requirements. This provides important technical support for the market-oriented and large-scale application of distributed resources under new power systems.

The application belongs to the technical field of virtual power plants, and discloses a water system air conditioner automatic regulation and control method based on a virtual power plant, and the use steps of the method are as follows: S1, the system is an automatic method, and the air conditioner states of all users connected to the system are read; S2, the indoor and outdoor temperatures are monitored, and a water temperature relationship model is maintained; S3, an air conditioner efficiency model is established and maintained; S4, order monitoring and automatic regulation and control; S5, judgment and operation after water temperature regulation; S6, regulation and control during peak regulation; S7, multi-order processing and multi-thread operation; and S8, automatic regulation and control summary. The application provides an air conditioner automatic regulation and control method, which brings great convenience to central air conditioner order management personnel of virtual power plant transactions. Through the automatic strategy, the management personnel can focus more on business orders, and need not participate in the equipment instruction operation during peak regulation, so that the method effectively reduces human error operation and reduces the risk of poor user experience.

This invention discloses a voltage control method for multi-level, multi-park virtual power plants considering equivalent impedance estimation, belonging to the field of power system automatic voltage control and distributed energy collaborative optimization technology. The method includes the following steps: Step S1: Acquisition of virtual power plant related information and initial state setting; Step S2: Park information estimation; Step S3: Equivalent impedance estimation and collaborative model construction; Step S4: Determination of three-level control strategy; Step S5: Three-dimensional collaborative control; Step S6: Correction of model accuracy. This invention deeply integrates hierarchical decision-making and equivalent impedance estimation technologies, enabling refined voltage control in dynamic scenarios and ensuring the coordinated, stable, and efficient operation of virtual power plants across multiple voltage levels and parks.

The application discloses a network-constructing type VSC transient stability analysis method and system considering internal ring dynamic influence, and comprises the following steps: determining the control system of GFM-VSC, determining active power and virtual power angle based on virtual synchronous power outer ring control; determining port node voltage phase and internal potential amplitude based on the active power and virtual power angle; determining the output current amplitude of GFM-VSC based on the internal potential amplitude and virtual power angle; determining whether the saturation link of GFM-VSC is triggered based on the internal potential amplitude and output current amplitude, and determining the saturation link type when it is determined that the saturation link is triggered; and analyzing the transient stability according to the saturation link type.

The application provides a power transaction method and system for a joint VPP in an energy blockchain environment, and relates to the technical field of virtual power plants. The application classifies producers and consumers and determines energy preferences when conducting a producer-consumer P2P transaction for a joint VPP, better meets the actual transaction demand, improves the satisfaction of the producers and consumers participating in the transaction, thereby improving the enthusiasm of the producers and consumers participating in the transaction and improving the activity of energy sharing for the VPP.

The application discloses an intelligent arrangement method and system based on a variable frequency integrated machine, and relates to the technical field of variable frequency integrated machine arrangement. The method comprises the following steps: acquiring real-time operation data of each variable frequency integrated machine, establishing a virtual power image model, and performing power trend prediction; acquiring relevant operation rhythm sequence of each variable frequency integrated machine and inputting the sequence into a rhythm neural network model to generate a multi-machine rhythm coordination curve and perform rhythm adaptive control between the variable frequency integrated machines; collecting historical energy efficiency performance data of each variable frequency integrated machine, establishing an energy efficiency trust model, and generating a power distribution decision; dividing the operation cycle of each variable frequency integrated machine into time periods, calculating energy efficiency integral values of each time period, and performing time period priority adjustment; collecting external environment parameters and performing cross comparison with a power mapping matrix to calculate an environment disturbance coefficient and trigger a power mapping correction mechanism. The application realizes efficient, stable and robust intelligent arrangement and energy efficiency optimization of a variable frequency integrated machine cluster.

The application belongs to the technical field of load type virtual power plants, and discloses a load type virtual power plant cost settlement method and related devices; wherein the load type virtual power plant cost settlement method comprises the following steps: obtaining original data for deviation cost settlement based on the selected load type virtual power plant and the deviation cost settlement mechanism demand; and performing settlement by using the deviation cost settlement mechanism based on the original data to obtain a deviation cost settlement result; the deviation cost settlement mechanism comprises a medium and long term shortage recovery cost settlement mechanism, a medium and long term excess declaration recovery cost settlement mechanism, a medium and long term curve deviation recovery cost settlement mechanism, and a grid-connected load type virtual power plant planned power deviation recovery cost settlement mechanism. Through the inventive concept of "multi-mechanism coverage and scene-specific accurate settlement", the application solves the existing problems of cost settlement under the background of high proportion of new energy from multiple dimensions such as "scene adaptation, efficiency improvement, risk avoidance".

1. Name of the product in this design: Flexible Control Terminal for Power Load. 2. Application of this design: The power load flexible control terminal refers to a hardware device installed on the user side for collecting, predicting and analyzing, testing, controlling and providing services for power users, load aggregators, virtual power plants, etc. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention discloses a rapid assessment method for the regulation capacity of distributed irrigation resources based on physics-guided deep learning, belonging to the field of power system operation and control. It addresses the challenge of balancing computational efficiency and physical security in large-scale heterogeneous resource aggregation. The method first constructs a ground truth dataset of regulation capacity boundaries generated by the physical environment. Then, it builds a physics-guided aggregation neural network model containing modules such as individual feature encoders. The model is trained using a hybrid loss function with dynamic weights and physical constraints. Finally, an online assessment model is constructed to complete real-time evaluation. This invention also provides a corresponding assessment system, computer equipment, and storage media, enabling millisecond-level accurate and secure aggregation regulation capacity boundary assessment. This supports real-time collaborative control of virtual power plants, improves the regulation and operation efficiency of distribution networks, and contributes to the construction of new power systems.

This invention discloses a distributed robust optimization method for virtual power plants participating in multi-timescale frequency regulation, belonging to the field of power system dispatch and control technology. The method includes the following steps: S1, constructing an uncertain set of renewable energy output for the virtual power plant based on frequency regulation control; S2, determining the frequency regulation cost; S3, constructing a two-stage robust optimization dispatch model; S4, adjusting the frequency regulation operation of the virtual power plant according to the solution results. After applying this method, the virtual power plant can achieve higher operating revenue when facing fluctuations in renewable energy output and load uncertainty, reducing revenue fluctuations in extreme scenarios and improving the stability and reliability of system dispatch. Its dynamic adjustment mechanism enables the virtual power plant to possess decision-making capabilities that combine asymptotic optimality and robustness, providing an implementable dispatch optimization scheme for power grids with a high proportion of renewable energy integration.

The application discloses a security-constrained unit commitment fast solving method, system, equipment and medium based on a space-time graph attention network, which comprises the following steps: inputting graph structure input data into a pre-trained space-time graph attention model for multi-task joint reasoning to obtain start-stop state pre-judgment probability and activity prediction score; fixing the binary decision variable of unit start-stop based on the start-stop state pre-judgment probability, screening the line transmission constraint based on the activity prediction score, constructing a reduced mixed integer linear programming model and solving the model, verifying the feasibility of the whole network power flow after obtaining a candidate scheduling scheme, outputting a final scheduling scheme if the verification is passed, and modifying and re-solving the reduced mixed integer linear programming model based on the violation information if the verification exists constraint violation. The application overcomes the problem of slow solving speed of the traditional method under a large-scale system, and can meet the time requirement of future virtual power plants and distributed power sources participating in power market clearing.

This invention relates to the field of carbon electricity dispatching technology for virtual power plants, and discloses a method and system for carbon electricity dispatching of virtual power plant microgrids based on spatiotemporal loss modeling. The method includes: constructing a spatiotemporal coordinate system for carbon quota loss and defining a loss shadow region by integrating grid impedance data, topology relationships, and delay confirmation cycle parameters; mapping inter-regional dispatch requests to directed dispatch line segments within the coordinate system, and obtaining the loss ratio by calculating the geometric intersection of this line segment and the loss shadow region; combining real-time carbon prices and electricity prices to calculate the net hedging profit of carbon quotas after deducting losses, and determining the generation of dispatch instructions accordingly. This invention integrates physical line losses and time delays into a unified geometric model for quantification, eliminating the mapping deviation between book quotas and actual verified quotas, and ensuring the accurate execution of hedging dispatching.

This invention discloses a rapid voltage calculation method for a radial virtual power plant based on the branch U-S theorem, belonging to the field of power system analysis and calculation technology. The method includes: acquiring the power network topology of the radial virtual power plant; numbering each node in the network layer by layer; collecting the voltage and power of each node and the impedance data of each branch; calculating the voltage of each node layer by layer based on the branch U-S theorem, according to the known head-end voltage and the power flowing into the tail end; and outputting the voltage calculation results for each node. The branch U-S theorem provides an analytical expression for the tail voltage of a single power branch. This invention avoids the complex iterative process required by traditional voltage calculation methods, enabling rapid and accurate calculation of the voltage of each node in a radial virtual power plant, significantly improving computational efficiency, and is suitable for scenarios such as real-time monitoring, rapid dispatching, and market response of virtual power plants.

The application discloses a power grid congestion dredging peak regulation method and device, electronic equipment and storage medium, belongs to the technical field of power system operation control, the method comprises the following steps: obtaining the task queue state, network state parameter, original congestion power flow direction and dynamic regulation parameter of each data center of the power grid; based on the bandwidth and the calculation intensity characteristic generation time translation load and space transfer load, construct the time and space schedulable feasible region, and map to the effective virtual power transmission capacity; the decision model is established and solved with the maximum total net income as the target, and the cross-region computing power migration strategy is obtained; combined with the actual migration rate, the packet loss rate and the time delay jitter, the congestion factor and the response deviation are calculated, the upper limit of the bandwidth is dynamically corrected, the congestion dredging and the peak regulation control are realized. Through the implementation of the application, the problem that the control error accumulates due to the lack of closed-loop feedback mechanism in the cross-region computing power scheduling of the prior art, and the virtual power transmission capacity is difficult to respond accurately on the power grid side can be solved.

This invention provides a post-evaluation method for the operational effectiveness of virtual power plants participating in the full-volume electricity spot market, relating to the fields of power system demand-side management and electricity market operation evaluation. The method includes: acquiring time-series data of the target area within the evaluation window; calculating the output baseline of the virtual power plant participating in spot market transactions using a pre-constructed regression identification model based on a set of influencing factors; obtaining a regulation curve based on the difference between the actual equivalent output of the virtual power plant and its output baseline; calculating the peak-valley reduction between the regional total load curve and the counterfactual load curve constructed based on the regulation curve, and statistically analyzing the energy contribution of the regulation curve during the period corresponding to the target electricity price; and evaluating the operational effectiveness of the virtual power plant participating in the full-volume electricity spot market based on the peak-valley reduction and energy contribution. This invention solves the problem of low reliability in the evaluation results of virtual power plant operational effectiveness in the full-volume electricity spot market scenario.

This invention discloses a multi-market collaborative bidding method for virtual power plants from a low-carbon perspective, belonging to the field of multi-market collaborative bidding for electricity. The method includes the following steps: S1, constructing a five-flow coupling model of electricity, carbon, heat, hydrogen, and green certificates, and classifying virtual power plant types to generate a triple uncertainty scenario; S2, building a three-order nested game bidding model; S3, constructing a joint risk management model, outputting risk costs and robust bidding feasible regions; S4, based on the electricity-carbon supply-demand ratio and robust feasible regions, realizing bidirectional matching and pairing transactions of electricity and carbon among multiple virtual power plants, generating initial bidding schemes for the five markets; S5, coupling the game bidding model, risk costs, and initial bidding schemes to obtain the optimal equilibrium bidding solution for the five markets; S6, feasibility verification and closed-loop optimization. Using the above-mentioned multi-market collaborative bidding method for virtual power plants from a low-carbon perspective, the economic benefits and long-term stable operation of the multi-virtual power plant alliance are maximized under the premise of low-carbon compliance.

This invention provides a method and system for optimizing the configuration and scheduling of energy storage capacity in virtual power plants, relating to the field of virtual power plant energy storage technology. The method includes extrapolating time-period capacity demand sequences based on historical data, calculating and correcting the dynamic adjustable capacity boundaries of energy storage units. By identifying the temporal complementarity between energy storage units, they are divided into collaborative configuration groups and their capacity is aggregated. Based on the matching degree between the aggregation results and grid commands, power allocation and reserve are performed hierarchically for units within each group. When the capacity within a group is insufficient, complementary time periods across groups are identified, and the reserve capacity of other groups is used for compensatory scheduling. This invention achieves refined configuration and collaborative scheduling of energy storage capacity, improving the responsiveness of virtual power plants to grid demands and enhancing their operational economy.