虚拟电厂(virtual power plants,VPP)能有效聚合分散的多类型需求侧资源时,是需求侧资源参与电力市场的有效方式。但是VPP的市场行为无法直接采用传统的火电竞价模型进行描述,线路输电约束等出清过程中考虑的物理条件也会对虚拟电厂内...虚拟电厂(virtual power plants,VPP)能有效聚合分散的多类型需求侧资源时,是需求侧资源参与电力市场的有效方式。但是VPP的市场行为无法直接采用传统的火电竞价模型进行描述,线路输电约束等出清过程中考虑的物理条件也会对虚拟电厂内部资源管理策略产生影响,基于此,文章以风电、储能、燃气轮机及需求响应负荷组成虚拟电厂为研究对象,考虑VPP内部风电出力不确定性以及聚合对象的调节能力,建立市场环境下VPP多类型资源鲁棒竞标模型。该模型采用库恩塔克(Karush-Kuhn-Tucker,KKT)条件等值市场出清模型,并将其作为约束条件在VPP决策中进行考虑。算例表明该模型可以根据市场现状优化VPP内部多类型资源组合出力,提供经济可靠的竞标策略,有效提高VPP经济效益。展开更多
The competition among renewable power producers(RPPs)may cause the cleared power of RPPs to be less than the bidding power,while the impact of competition is neglected in the existing price-taker methods.To overcome t...The competition among renewable power producers(RPPs)may cause the cleared power of RPPs to be less than the bidding power,while the impact of competition is neglected in the existing price-taker methods.To overcome the above deficiency,this paper develops an optimal bidding strategy,considering the competition among RPPs.First,a bivariate stochastic optimization(BSO)model for a bidding strategy is proposed by considering the variable power output of RPPs and the competition among RPPs.Particularly,the cleared power estimated by the demand-supply ratio is a random variable in the proposed BSO model.Then,the Newton method and particle swarm optimization(PSO)are combined to solve the BSO model in which various probability distribution functions(PDFs)of renewable energy generation are considered.Finally,the effectiveness of the proposed method is verified based on the results of a case study,which shows that the proposed model performed better than the traditional chance-constrained programming(CCP)model in power market competition.展开更多
The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market.The impact of mutual bidding strategies on market prices,traded volumes,and revenues has been quantifi...The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market.The impact of mutual bidding strategies on market prices,traded volumes,and revenues has been quantified.The coordination assumes that hydro power generation is scheduled mainly to compensate the differences between actual and planned wind power outputs.The potential of this coordination in achieving and utilizing of market power is explored.The market equilibrium of asymmetric generation companies is analyzed using a game theory approach.The assumed market situation is imperfect competition and non-cooperative game.A nu-merical approximation of the asymmetric supply function equilibrium is used to model this game.An introduced novelty is the application of an asymmetric supply function equilibrium approximation for coordinated hydro-wind power generation.The model is tested using real input data from the Croatian power system.展开更多
The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in t...The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).展开更多
文摘虚拟电厂(virtual power plants,VPP)能有效聚合分散的多类型需求侧资源时,是需求侧资源参与电力市场的有效方式。但是VPP的市场行为无法直接采用传统的火电竞价模型进行描述,线路输电约束等出清过程中考虑的物理条件也会对虚拟电厂内部资源管理策略产生影响,基于此,文章以风电、储能、燃气轮机及需求响应负荷组成虚拟电厂为研究对象,考虑VPP内部风电出力不确定性以及聚合对象的调节能力,建立市场环境下VPP多类型资源鲁棒竞标模型。该模型采用库恩塔克(Karush-Kuhn-Tucker,KKT)条件等值市场出清模型,并将其作为约束条件在VPP决策中进行考虑。算例表明该模型可以根据市场现状优化VPP内部多类型资源组合出力,提供经济可靠的竞标策略,有效提高VPP经济效益。
基金supported by National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption,2018YFB0904200)Eponymous Complement S&T Program of State Grid Corporation of China(SGLNDKOOKJJS1800266)。
文摘The competition among renewable power producers(RPPs)may cause the cleared power of RPPs to be less than the bidding power,while the impact of competition is neglected in the existing price-taker methods.To overcome the above deficiency,this paper develops an optimal bidding strategy,considering the competition among RPPs.First,a bivariate stochastic optimization(BSO)model for a bidding strategy is proposed by considering the variable power output of RPPs and the competition among RPPs.Particularly,the cleared power estimated by the demand-supply ratio is a random variable in the proposed BSO model.Then,the Newton method and particle swarm optimization(PSO)are combined to solve the BSO model in which various probability distribution functions(PDFs)of renewable energy generation are considered.Finally,the effectiveness of the proposed method is verified based on the results of a case study,which shows that the proposed model performed better than the traditional chance-constrained programming(CCP)model in power market competition.
基金the H2020 project CROSSBOW-CROSS Border management of variable renewable energies and storage units enabling a transnational wholesale market(No.773430)this work was supported in part by the Croatian Science Foundation under the project IMPACT-Implementation of Peer-to-Pecr Advanced Concept for Electricity Trading(No.UIP-2017-05-4068).
文摘The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market.The impact of mutual bidding strategies on market prices,traded volumes,and revenues has been quantified.The coordination assumes that hydro power generation is scheduled mainly to compensate the differences between actual and planned wind power outputs.The potential of this coordination in achieving and utilizing of market power is explored.The market equilibrium of asymmetric generation companies is analyzed using a game theory approach.The assumed market situation is imperfect competition and non-cooperative game.A nu-merical approximation of the asymmetric supply function equilibrium is used to model this game.An introduced novelty is the application of an asymmetric supply function equilibrium approximation for coordinated hydro-wind power generation.The model is tested using real input data from the Croatian power system.
文摘The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).
