542 results about "Virtual power plant" patented technology

A software platform in communication with networked distributed energy resource energy storage apparatus, configured to deliver various specific applications related to offset demand monitoring, methods of virtual power plant and orchestration, load shaping services, methods of reducing demand at aggregated level, prioritizing computer programs related to virtual energy pool, energy cloud controllers methods, charge discharge orchestration plans of electric vehicles, distributed energy resources, machine learning predictive algorithms, value optimizing algorithms, autonomous sensing event awareness, mode selection methods, capacity reservation monitoring, virtual power plant methods, advanced DER-ES apparatus features, energy management system for governing resources and methods, aggregated energy cloud methods, load shaping methods, marginal cost cycle-life degradation, load shaping API, forward event schedule, on demand request, and load service state request methods. Various rules, constraints of predictive algorithms for signal inputs to determine incremental storage cycles, cycle life degradation marginal cost, iterative and forward event schedule development, and load control.

The invention discloses a smart management system for energy management and electricity transaction in a virtual power plant and a method for optimizing operation thereof. A distributive energy unit serves as terminal energy and an information source apparatus of the virtual power plant smart management system, and thus a distributive resource system is formed. The virtual power plant smart management system is composed of an operation data collection and management module, an enterprise resource management, prediction and resource deployment combination module, an electricity selling plan optimization module, a transaction platform module, a real-time scheduling optimization module, and a control scheduling execution and synchronous simulation function module. According to the invention, distributive energy information management which is featured by a distributive power generation and energy storage apparatus and a flexible load device is adopted. An entire system architecture is provided for implementing all necessary function modules, such as prediction, resource combination management and deployment optimization, power supply and scheduling optimization, market transaction, real-time monitoring and scheduling. The system and method of the invention have great potential in new energy sources, distributive power generation and electricity sell, and in future power system for realizing efficient demand side power management, and in power market environment after power reform.

A system and method for operating a distributed power generation system comprising one or more Local Production Units (LPPUs) is provided. The method includes the steps of receiving and storing data relating to the operating performance of a plurality of LPPUs, receiving and storing data relating to the local power consumption for the plurality of LPPUs, determining the available aggregate quantity of excess power generation capacity for the plurality of LPPUs based upon the operating performance data and the power consumption data, communicating to at least one power purchasing entity an offer to sell at least part of the available aggregate quantity of excess power generation capacity, receiving from at least one power purchasing entity an order to purchase, and communicating with the plurality of LPPUs to provide each LPPU with instructions regarding the quantity of power to be produced. The system includes means for performing each of these steps.

Disclosed is a test system for determining a frequency response of a virtual power plant connected to a utility grid and including a plurality of distinct power plants. The test system includes a test sequence module for providing a frequency test sequence. The frequency test sequence includes a set of frequency test values; and an injection unit for injecting values of the frequency test sequence simultaneously to nodes of the virtual power plant. A node of the virtual power plant includes any of: a power production unit of a power plant, a power storage unit of a power plant, a plant controller of a power plant. Further described are a virtual power plant and a method of testing a frequency response of a virtual power plant.

The invention discloses a virtual power plant optimization scheduling method based on a master-slave game strategy. The method comprises the following steps: 1, analyzing and constructing a virtual power plant model containing multiple load types; 2, establishing a virtual power plant transaction model based on an existing power market transaction mechanism; 3, establishing a risk cost model considering uncertain factors; 4, carrying out optimal scheduling on the virtual power plant by adopting a master-slave game strategy; And 5, solving the optimized scheduling model through a reinforcementlearning algorithm. According to the method, the volatility of distributed energy output in the virtual power plant and the uncertainty of load prediction are considered, an economic dispatching modelof the virtual power plant in the power market is constructed, and a master-slave game strategy and a reinforcement learning algorithm are adopted for optimization solution, so that the purposes of improving the production benefits of the virtual power plant and reducing the load power purchase cost are achieved.

The invention provides an energy block chain network and an operation and scheduling method of a virtual power plant based on the network. According to an energy block network EBN, all generating units G form a power supply index block chain CPSIB according to mapping alpha. A power transaction set T is formed by the transaction process of all generating units G and power units P, a power transaction block chain CETB is formed according to mapping beta, and in the process of forming T, all generating units G and power units P complete a transaction according to a concrete contract. According to the energy block chain network and the operation and scheduling method, an energy block chain network model is introduced into the operation and dispatching process of the virtual power plant, while the overall efficiency of the virtual power plant is improved, the virtual power plant obtains an information security guarantee of a higher degree by the cryptographic characteristics of the block chain.

The invention discloses an adaptive robust scheduling optimization method for a virtual power plant. The method adopts adaptive robust scheduling optimization to process the output uncertainty of renewable energy sources, and considers the day-ahead and real-time two-stage scheduling of the virtual power plant. A model established with the adaptive robust scheduling optimization method is a three-layer optimization model. In order to solve the problem, the method comprises the following steps that: firstly, importing an auxiliary variable, dividing the model into a single-layer main problem and double-layer sub problems; secondly, through a duality theory, converting the double-layer sub problems into the single-layer problem; and finally, adopting a column sum constraint generation method, and solving the main problem and the sub problems through alternating iteration until the gap of two problems is converged into an acceptable range. Compared with statistic robust optimization, themethod is characterized in that the balance situation of the regulation strategy and the real-time market of each polymerization unit in the virtual power plant after the output of the renewable energy sources is obtained is considered, the fluctuation of renewable energy sources can be effectively stabilized, and the economic benefit of the virtual power plant is improved.

The invention discloses a virtual power plant multi-object optimizing scheduling method in consideration of a load side and a power supply side. The virtual power plant multi-object optimizing scheduling method in consideration of the load side and the power supply side comprises establishing a VPP multi-object optimizing scheduling model in consideration of coordinated operation of the load side and the power supply side and solving of the VPP multi-object optimizing scheduling model in consideration of the load side and the power supply side. According to the virtual power plant multi-object optimizing scheduling method in consideration of the load side and the power supply side, new energy consumption improvement capabilities of the load-side resource and the power-supply-side resource are comprehensively considered; and furthermore system operation cost and environment cost are reduced.

The invention provides a virtual power plant combined heat and power scheduling robust optimization model. A model aggregation unit comprises a distributed generating set, a wind turbine generator set, a photovoltaic set, a combined heat and power (CHP) set, a boiler, electric energy storage, heat energy storage, an electric load and a heat load. Participation of the CHP set in the SRM (Spinning Reserve Market) situation is considered. Aiming at the facing uncertain problem of a virtual power plant (VPP) and resulting risks, robust optimization (RO) is utilized to process uncertainty of the EM electricity price, the SRM electricity price, the wind power capacity, the photovoltaic capacity, the electric load and the heat load, and risk quantification indexes are established, and thus robustness and economical efficiency of a RO model are balanced. The model provided by the invention well solves the existing combined heat and power scheduling optimization model establishment problem of the VPP when participating EM and SRM at the same time, and improves flexibility of decision making, and thus the profit of the VPP is increased. Meanwhile, the introduction of the RO model effectively reduces system risks, and thus the effective reference is provided for a decision maker to select a proper robust factor.

The invention discloses a virtual power plant stratified random optimized dispatching method. First, a virtual power plant two-layer coordinated optimization dispatching model which comprises an upper layer virtual power plant layer and a lower layer micro power grid layer is established, wherein micro power grid optimized dispatching models in the lower layer are chance constraint models, in the models, an empirical distribution function is used for describing the power-out probability distribution of an uncontrollable micro power supply in an independent state, and according to the uncontrollable micro power supply, a histogram is combined to select a Copula function to establish a joint probability distribution model. Then, a sampling average approximation method and a KKT optimality condition are used for converting the virtual power plant two-layer coordinated optimization dispatching model to a single-layer deterministic model to be solved, and optimal dispatching is carried out on a virtual power plant. According to the method, coordinated operation of a plurality of micro power grids in the virtual power plant can be considered at the same time, probability distribution and Copula correlation analysis can be used for fully considering the influence of uncontrollable micro power supply randomness and correlation on optimized dispatching, and virtual power plant coordinated random optimized dispatching can be achieved.

The invention discloses a carbon capture virtual plant scheduling optimization method, comprising the steps of S1, constructing a dual carbon amount model of a carbon capture unit for a thermal power plant, the dual carbon amount module being used for representing an absorption link and a regeneration link of a liquid storage type carbon capture unit in a decoupling manner; S2, constructing a virtual power plant aggregated with the thermal power plant and a clean energy power plant, and determining a scheduling optimization objective function and constraints of the virtual power plant by maximizing the net income of the virtual power plant; and S3, according to the predicted output of the clean energy power plant, carrying out scheduling optimization on operating parameters of the virtual power plant through the dual carbon amount model. The method of the invention has the advantages of enabling the power plant to obtain better operation benefits, maximizing the net income during the scheduling period, obtaining more significant carbon emission reduction benefits and higher economic benefits, and having better economy and low carbon performance.

The invention provides a method and system for bidding and profit distribution of a virtual power plant considering the uncertainty. The method includes the steps of establishing, based on a multi-agent model, a distributed power supply bidding strategy within a virtual power plant considering the uncertainty and independence; establishing, according to the distributed power supply bidding strategy within the virtual power plant, a virtual power plant benefit distribution strategy of an improved Shapley value based on risk factors; and validating the virtual power plant benefit distribution strategy. The method and system for the bidding and profit distribution of the virtual power plant according to the invention participate in the bidding of the power market with the goal of maximizing the overall profit on the basis of guaranteeing the load demand and standby demand of the virtual power plant, and at the same time ensure the stability of the cooperation of various distributed powersupplies within the virtual power plant.

The invention discloses a power two-stage interactive optimization scheduling system of a virtual power plant in a haze environment, and specifically relates to a distributed energy resource optimization scheduling method. The method comprises the following steps: A, establishing a photovoltaic power generation prediction and load prediction system considering the influence of the haze environment; B, establishing a power system two-stage interactive scheduling system considering a virtual power plant; C, establishing a mathematical model considering two-stage interactive scheduling of the virtual power plant in the haze environment; D, improving the artificial bee colony algorithm; and E, carrying out solving based on an optimization model of the improved artificial bee colony algorithm. With the system, the energy crisis can be effectively relieved, and the environment can be protected. As new energy and the smart grid technology develop, it is difficult for a power system to directly schedule a lot of grid-connected DERs. The embodiment demonstrates the validity of the model and the feasibility of the algorithm, embodies the superiority of the aggregation of various DERs in power system scheduling, proves the influence of the haze environment on photovoltaic output, load prediction and scheduling, and provides a feasible reference for grid optimization scheduling.

The invention discloses a virtual power plant day-ahead optimal scheduling system and method considering wind power uncertainty. The method comprises the steps: obtaining the next-day wind power fulloutput prediction of a virtual power plant and the next-day thermal load demand prediction of a borne region, formulating a next-day power generation plan, and reporting the next-day power generationplan to a power grid to obtain a next-day output task of the virtual power plant; establishing a virtual power plant energy supply balance model; determining a virtual power plant spinning reserve capacity of the heat reserve; performing wind power uncertainty processing on the virtual power plant power supply balance constraint to reduce the influence of wind power uncertainty on a day-ahead scheduling plan; establishing an objective function with the maximum daily income of the virtual power plant as an objective, setting constraints and enabling the spinning reserve capacity of the virtualpower plant to meet the wind power fluctuation requirement and all components of the virtual power plant to operate in a safe range; and solving the model by using a particle swarm algorithm to obtaina next-day operation scheduling plan and earnings of the virtual power plant. The external thermoelectric coupling relation of the virtual power plant is reduced, the adjustable range of the power output of the virtual power plant is expanded, and the reserve capacity of the power grid is released.

The invention discloses a virtual power plant-based distributed power supply combination planning system and a virtual power plant-based distributed power supply combination planning method. According to characteristics of output power of the distributed power supplies and according to historical output power of the uncontrolled distributed power supplies, an interval set of the output power of the uncontrolled distributed power supplies is obtained; in a planning and scheduling cycle, with the minimum deviation between the predicted output power of a combined distributed power supply and the expected capacity of a virtual power plant as an aim, a virtual power plant-based distributed power supply combination optimizing model is established, and the optimizing model is solved to obtain an optimal combining scheme of the distributed power supplies. Through establishment of the virtual power plant, the distributed power supplies can be flexibly and efficiently integrated, so that the problem that the distributed power supplies are connected into a power grid on a large scale is solved; through reasonable selection of types and capacities of the distributed power supplies, the problems such as power supply reliability caused by uncertainty of the output power of the distributed power supplies can be solved, and the utilization ratio of the distributed power supplies can be improved.

The invention relates to a multi-target optimized energy management information processing method for a virtual power plant. The method comprises the following steps of: 1) acquiring power generation information of power supplies administered by a virtual power plant company and power utilization information of power utilization users, determining credits of users according to the acquired power generation and power utilization information and the historical information of the power supplies and the users, and determining the regulating ranges of power generation quantity and power utilization quantity according to the credits by using a fuzzy function; 2) constructing a multi-target united scheduling model; 3) substituting the power generation information and the power utilization information acquired in the step 1) into the multi-target united scheduling model with a priority in the step 2), and solving with an optimization algorithm; and 4) performing multi-target optimization and priority coordinating processing. Compared with the prior art, the method has the advantages of effective optimization and management of an assembly of a distributed generator and a controllable load and the like.