247 results about "Virtual power plant" 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.

The present application relates to a kind of carbon emission dynamic constraints under virtual power plant aggregation flexibility characterization method, belong to power system operation and optimization technical field, a kind of carbon emission dynamic constraints under virtual power plant aggregation flexibility characterization method, including the following steps: S1: establish distributed energy and carbon emission model;S2: construct two-stage adaptive robust optimization problem;S3: initialize uncertain scene, solve main problem to obtain aggregated power candidate boundary;S4: solve sub-problem, verify robust feasibility and search the most adverse uncertainty scene;S5: main-sub problem iteration whether convergence to robust flexible aggregated boundary;S6: output power flexibility aggregation result and be used for distribution network dispatching control, the present application constructs carbon-electricity collaborative coupling characterization framework, solve the core problem that existing technology does not system into carbon emission constraint, realize the deep coupling of carbon emission constraint and virtual power plant regulation capacity.

This invention discloses a joint clearing method and system for the electricity spot market that considers the generalized energy storage characteristics of electric vehicle aggregators. Based on the operational characteristics and physical constraints of large-scale electric vehicle access to the electricity spot market, and combined with the theory of generalized energy storage efficiency, an embedded constraint model is constructed, including charging and discharging power boundaries, energy state temporal coupling, and terminal electricity demand. This enables quantitative modeling and unified scheduling of the flexibility resources of electric vehicle aggregators. Data cleaning and parameter normalization methods are used to map the characteristics of dispersed electric vehicle clusters to standardized virtual power plant parameters. State equation constraints are introduced into the traditional DC optimal power flow and unit combination model. Combining mixed-integer linear programming algorithms and dual pricing theory, the optimal coordination strategy between conventional unit output and electric vehicle charging and discharging is solved, and the low-cost clearing and nodal marginal price of the spot market are calculated. The method of this invention can improve the level of renewable energy consumption and the economy and security of grid operation.

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.

The application discloses a virtual power plant cluster cooperative regulation method and system based on dynamic aggregation, belongs to the technical field of virtual power plant resource regulation, and through double analysis on distributed resource basic characteristics and response characteristics, accurately captures the heterogeneity difference of the distributed resources, constructs a dynamic feature library to homogenize and classify the heterogeneous resources, improves the adaptability of resource aggregation, avoids the limitation that static aggregation is difficult to cope with resource dynamic changes, covers the fluctuation scene of the resources through dynamic aggregation, enhances the robustness of the virtual power plant cluster in response to uncertainty, thereby reduces the scheduling deviation, takes the optimal comprehensive benefit as the target, improves the new energy consumption rate, realizes the cooperative optimization of cluster operation economy, safety and low carbon, and reduces the comprehensive operation cost of the system.

Embodiments relate to a virtual power plant system based on self-demand response. A system according to an embodiment relates to a virtual power plant system that analyzes in real time at least one of power demand data, power generation amount data, electricity price data, and distributed power information and provides the result of the analysis in order to enable an individual consumer to perform self-demand response (Self-DR) using a user terminal.

The application provides a virtual power plant real-time simulation method and system based on digital twinning, relates to the field of virtual power plant real-time simulation, and comprises the following steps: constructing a state transition graph, distinguishing a deterministic path and a random path based on transition probability entropy value, respectively establishing a main twin model and a disturbance twin model, generating a tree-shaped operation trajectory bundle, calculating an intersection of an envelope boundary and an operation constraint boundary to identify a risk moment, and generating a control instruction according to the intersection. The application can realize intelligent prediction and real-time control of distributed power supply, and improve the operation reliability of a virtual power plant.

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 technology, specifically a method for accessing a virtual power plant with multi-source heterogeneous resources. First, resources are categorized by functional attributes, core operating parameters and constraints of each resource are collected, and a mathematical model is constructed based on these parameters. Next, based on resource type, heterogeneous resource communication protocols are identified and parsed, and adaptive protocol conversion is achieved through a gateway. Then, the data is standardized according to unified rules to form a standardized dataset. Subsequently, access permissions are configured hierarchically based on resource type, and encrypted redundant communication links are deployed based on the standardized dataset. Finally, secure resource access is achieved through qualification review and verification activation. Based on this, this invention, through a robust security mechanism, enhances the security and stability of the entire access process and operation, ensuring reliable data transmission and system operation.

The application discloses a virtual power plant cooperative control method based on multi-agent layered reinforcement learning, and a layered multi-agent system (MAS) processes complex tasks through hierarchical cooperation and distributed decision among agents, and specifically comprises the following steps: S1, an attribute information of a single agent is acquired by a bottom execution layer, specific equipment is directly controlled, and an inverter output power is adjusted or a load transfer instruction is executed according to a middle management layer; S2, a middle management layer receives an instruction of a top coordination layer, a sub-group is mixedly divided according to a region or a resource type, the instruction of the top coordination layer is executed, and real-time data is fed back; S3, the top coordination layer serves as a global decision center, is responsible for power market strategy formulation and resource aggregation optimization, and finally outputs a structured decision instruction and dynamic feedback; after the virtual power plant (VPP) receives the structured decision instruction and dynamic feedback output by the top coordination layer, layered processing and dynamic optimization are carried out. The application can obviously reduce delay and reduce load.

The application discloses a kind of virtual power plant dynamic support capacity quantification and boundary measurement method based on inverse optimization, it is related to power system operation and control field, first, the physical model of virtual power plant multi-time period dynamic optimal power flow based on second-order cone relaxation is constructed;Second, with the minimum of virtual power plant grid connection point limited measurement data residual as the goal, a composite inverse optimization model containing feasible region compactness penalty is constructed;Then, using the asymmetric moving average sequential updating algorithm with attenuation, the equivalent power boundary and virtual energy capacity of the system are accurately inversed from multi-scenario operation data;Finally, the target response time length is explicitly introduced, and the state-dependent dynamic flexibility envelope considering instantaneous energy state constraints is constructed.The application overcomes the limitation of bottom-layer parameter black box, and quantifies the dynamic support potential of virtual power plant under multi-time scale in detail, providing a defense over-limit decision basis for safe dispatch.

This invention relates to the field of power engineering management and control and virtual power plant operation collaboration technology, specifically to a big data-based intelligent management and control system and method for power engineering. It collects and evaluates the quality of multi-source heterogeneous data from power engineering sites to obtain reliable data for engineering risk prediction, while simultaneously extracting feature data related to virtual power plant operation. Furthermore, it identifies and aggregates dispatchable load resources at the engineering site to form standardized virtual load packages. Combining the operational demand signals of the virtual power plant with the engineering risk status, it generates dispatch decision schemes through collaborative optimization calculations. Blockchain technology is used to ensure the reliable storage of dispatch instructions, execution, and result data, and based on this, automatically calculates collaborative benefits. This effectively solves the problem in existing technologies where power engineering management and control data are disconnected from virtual power plant operation, preventing a shift from passive risk warning to proactive value mining.

The present application relates to the technical field of zero-carbon factory operation and maintenance central control, and particularly relates to a zero-carbon factory intelligent operation and maintenance central control system and method, the system comprising a data platform, a digital twin construction module, a carbon trajectory real-time measurement module, a multi-target collaborative optimization engine, an edge-side active controller, a virtual power plant aggregation interaction module, a twin deduction and backtracking diagnosis module and a visual human-computer interaction interface, the carbon trajectory real-time measurement module comprising a dynamic emission factor library, and the multi-target collaborative optimization engine comprising a source-side intelligent agent, a load-side intelligent agent, a storage-side intelligent agent and a coordination intelligent agent. The present application can realize real-time carbon footprint tracking and transparent management, through multi-agent game, realize multi-target dynamic collaborative optimization of cost, carbon emission and green electricity consumption, improve response speed and reliability in the face of unexpected situations, so that the zero-carbon factory can not only realize internal low-carbon operation, but also obtain additional income by providing flexible services for the power grid.

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.

The application provides a kind of multi-element solid energy resource and the virtual power plant electric-carbon collaborative scheduling method, device and system of CCUS park, method includes: the construction of heterogeneous resource unified aggregation model library for virtual power plant;Based on each type of model in model library, the electric-carbon collaborative multi-time scale optimization scheduling model is constructed with the total running net income maximization of park virtual power plant in scheduling period as objective function, and with carbon emission budget as core constraint;Using distributed robust optimization method, the multiple uncertainty of scheduling model is improved;Based on the alternate direction multiplier method, the improved scheduling model is distributedly solved, the optimal scheduling instruction of each resource is generated, and the instruction is issued through the virtual power plant intelligent control platform.The application can realize the optimal scheduling of the deep cooperation of virtual power plant electric power flow, heat flow and carbon flow, significantly improve the economy, flexibility and robustness of system operation under the premise of guaranteeing the park net zero carbon target.

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.

The application discloses a kind of virtual power plant energy storage optimization configuration methods for reliability improvement, the method first establishes electric-gas coupling virtual power plant system model, constructs the multi-dimensional reliability index system including self-balancing probability, total missing load expectation and energy storage adequacy probability, then the sequential Monte Carlo method is used to simulate the whole year time sequence of new energy unit random failure, and combined with analytic hierarchy process and maximum minimum normalization method, the comprehensive evaluation model of reliability and economy is constructed, finally with energy storage capacity as decision variable, with the maximum of comprehensive score as target, global optimization is carried out under a variety of operating constraints using particle swarm optimization algorithm, and the optimal energy storage configuration scheme is obtained;The application realizes the reliability improvement and economic balance of energy storage configuration under complex operating environment through dynamic fault modeling and multi-objective collaborative optimization mechanism, greatly enhances the accommodation capacity and energy supply safety of virtual power plant to high proportion of renewable energy.

This invention discloses a method for balancing the interests of multiple stakeholders in a virtual power plant based on a two-layer game model, belonging to the field of power system optimization and operation technology. The method includes the following steps: constructing an upper-layer asymmetric evolutionary game model, introducing strategy entropy and habituation coefficients, simulating the long-term cooperative tendency strategy evolution of stakeholders such as distributed power sources, flexible loads, and energy storage systems, and outputting an evolutionarily stable strategy; inputting the evolutionarily stable strategy as a modulation parameter into a lower-layer stochastic Stackelberg game model, using a typical scenario method to handle uncertainty, and optimizing short-term scheduling plans and internal electricity prices; allocating cooperative surplus based on fuzzy Shapley values, with staker membership determined by historical cooperative tendencies; verifying execution using blockchain smart contracts, and implementing automatic punishment through a dynamic penalty function linking "deviation degree and credit score". This method achieves dynamic and reliable interest balance among multiple stakeholders on both long and short-term scales, significantly improving the stability and economy of the virtual power plant alliance.

The application discloses a virtual power plant operation data intelligent optimization method and system based on the Internet of Things, relates to the technical field of Internet of Things energy monitoring, and acquires energy transmission data sets and energy load data sets after preprocessing and maps to each corresponding node of the virtual power plant through real-time collection of energy operation data of each distributed node layer by arranging power sensors on the distributed node layer of the virtual power plant; energy flow state evaluation is carried out through the construction of an energy flow level deviation index Neq; when there is an anomaly in the energy flow level, a spatial distance matrix Dl(x, i) of the virtual power plant topology is extracted and a comprehensive operation coordination index Eop is constructed to evaluate the residual error distribution state. When the energy flow of the virtual power plant cannot converge naturally, the energy flow Φadj is calculated and distributed to dynamically correct the energy flow of each node, and the energy storage equipment is controlled to switch between the charging and discharging modes. The method realizes dynamic coordination and abnormal self-recovery control of the virtual power plant operation data.

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 discloses a method for quantifying and calculating the dynamic support capacity and boundaries of a virtual power plant based on inverse optimization, relating to the field of power system operation and control. First, this invention constructs a multi-time-scale dynamic optimal power flow physical model of a virtual power plant based on second-order cone relaxation. Second, with the objective of minimizing the residuals of finite measurement data at the virtual power plant's grid connection point, a composite inverse optimization model including a feasible region compactness penalty is constructed. Then, an asymmetric moving average sequential update algorithm with decay is used to accurately deduce the equivalent power boundary and virtual energy capacity of the system from multi-scenario operating data. Finally, a target response time is explicitly introduced to construct a state-dependent dynamic flexibility envelope that takes into account instantaneous energy state constraints. This invention overcomes the black-box limitation of underlying parameters, refines the quantification of the dynamic support potential of the virtual power plant across multiple time scales, and provides a basis for decision-making to prevent limit violations in safe dispatching.