为应对配电网络低碳化运行的挑战,并充分挖掘系统中分布式资源的灵活调控潜力,文中构建一种基于电碳综合边际定价的虚拟电厂(virtual power plant,VPP)双层点对点(peer-to-peer,P2P)交易模型。上层由配电网运营商(distribution system o...为应对配电网络低碳化运行的挑战,并充分挖掘系统中分布式资源的灵活调控潜力,文中构建一种基于电碳综合边际定价的虚拟电厂(virtual power plant,VPP)双层点对点(peer-to-peer,P2P)交易模型。上层由配电网运营商(distribution system operator,DSO)建立基于碳排放流(carbon emission flow,CEF)技术的碳感知最优潮流模型,在此基础上计算出电碳综合边际价格,DSO可利用该价格信号协调VPP低碳调度。下层组建多VPP合作联盟,可将电动汽车规模化整合并引入碳信号引导的电动汽车灵活调度机制,建立基于贡献度的非对称纳什议价交易模型,各VPP在价格信号的引导下平衡个体与联盟利益,制定生产与交易的最优策略。然后,采用自适应交替方向乘子法(adaptive-scaling alternating direction method of multipliers,AS-ADMM)对模型进行求解,解决变量耦合导致的收敛速度问题。最后,采用改进的IEEE 33节点配电系统进行仿真验证。案例分析结果表明,所提交易模型可以通过提高分布式能源利用效率并优化负荷分布,在降低VPP运营成本的同时减少配电网碳排放。展开更多
The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonl...The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.展开更多
This paper considers the use of the inherent structural characteristics of power system networks for improving the reactive power reserve margins for both topologically weak and strong networks. The inherent structura...This paper considers the use of the inherent structural characteristics of power system networks for improving the reactive power reserve margins for both topologically weak and strong networks. The inherent structural characteristics of the network are derived from the Schur complement of the partitioned Y-admittance matrix using circuit theory representations. Results show that topologically strong networks, operating close to the upper voltage limit could be made to increase their loadability margin by locating reactive power compensators close to generator sources, whereas topologically weak (ill conditioned) networks could be made to operate within the feasible operating limits by locating reactive power compensators on buses farther from generator sources.展开更多
This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhanc...This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhancing the performance of power system. GRADE Algorithm is a hybrid technique combining genetic and differential evolution algorithms. Control variables considered are Generator bus voltages, MVAR at capacitor banks, transformer tap settings and reactive power generation at generator buses. The optimal values of the control variables are obtained by solving the multi objective optimization problem using GRADE Algorithm programmed using M coding in MATLAB platform. With the optimal setting for the control variables, Newton Raphson based power flow is performed for two test systems, viz, IEEE 30 bus system and IEEE 57 bus system for three loading conditions. Minimization of Real power loss and improvement of voltage profile obtained are compared with the results obtained using firefly and particle swarm optimization (PSO) techniques. Improvement of Loadability margin is established through PV curve plotted using continuation power flow with the real power load at the most affected bus as the bifurcation parameter. The simulated output shows improved results when compared to that of firefly and PSO techniques, in term of convergence time, reduction of real power loss, improvement of voltage profile and enhancement of loadability margin.展开更多
大规模虚拟电厂(virtual power plant,VPP)逐步具备与传统发电资源对等的地位,其优化运行策略将显著影响电力市场的均衡状态。高效表征虚拟电厂在关键端口下的外特性将促进虚拟电厂与现有市场模式的有效兼容,对于其深度参与电力市场具...大规模虚拟电厂(virtual power plant,VPP)逐步具备与传统发电资源对等的地位,其优化运行策略将显著影响电力市场的均衡状态。高效表征虚拟电厂在关键端口下的外特性将促进虚拟电厂与现有市场模式的有效兼容,对于其深度参与电力市场具有十分重要的现实意义。基于改进多参数线性规划(multi-parametric linear programming,MPLP)理论提出虚拟电厂边际成本函数解析表征方法,通过虚拟电厂与主网在公共连接点(point of common coupling,PCC)处的交易电量这一低维参数,反映其整体灵活性、交易可行域及边际成本。基于成本最小化将初始参数空间优化分割为若干临界域(critical region,CR),随后,揭示优化分割的经济学特性,并利用该特性刻画参数空间与虚拟电厂成本的分段映射关系。最后,基于改进的IEEE 33及IEEE 123节点系统验证所提算法的有效性,为虚拟电厂以非迭代的方式参与市场出清提供理论基础。展开更多
文摘为应对配电网络低碳化运行的挑战,并充分挖掘系统中分布式资源的灵活调控潜力,文中构建一种基于电碳综合边际定价的虚拟电厂(virtual power plant,VPP)双层点对点(peer-to-peer,P2P)交易模型。上层由配电网运营商(distribution system operator,DSO)建立基于碳排放流(carbon emission flow,CEF)技术的碳感知最优潮流模型,在此基础上计算出电碳综合边际价格,DSO可利用该价格信号协调VPP低碳调度。下层组建多VPP合作联盟,可将电动汽车规模化整合并引入碳信号引导的电动汽车灵活调度机制,建立基于贡献度的非对称纳什议价交易模型,各VPP在价格信号的引导下平衡个体与联盟利益,制定生产与交易的最优策略。然后,采用自适应交替方向乘子法(adaptive-scaling alternating direction method of multipliers,AS-ADMM)对模型进行求解,解决变量耦合导致的收敛速度问题。最后,采用改进的IEEE 33节点配电系统进行仿真验证。案例分析结果表明,所提交易模型可以通过提高分布式能源利用效率并优化负荷分布,在降低VPP运营成本的同时减少配电网碳排放。
基金Sponsored by the Scientific and Technological Project of Heilongjiang Province(Grant No.GD07A304)
文摘The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.
文摘This paper considers the use of the inherent structural characteristics of power system networks for improving the reactive power reserve margins for both topologically weak and strong networks. The inherent structural characteristics of the network are derived from the Schur complement of the partitioned Y-admittance matrix using circuit theory representations. Results show that topologically strong networks, operating close to the upper voltage limit could be made to increase their loadability margin by locating reactive power compensators close to generator sources, whereas topologically weak (ill conditioned) networks could be made to operate within the feasible operating limits by locating reactive power compensators on buses farther from generator sources.
文摘This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhancing the performance of power system. GRADE Algorithm is a hybrid technique combining genetic and differential evolution algorithms. Control variables considered are Generator bus voltages, MVAR at capacitor banks, transformer tap settings and reactive power generation at generator buses. The optimal values of the control variables are obtained by solving the multi objective optimization problem using GRADE Algorithm programmed using M coding in MATLAB platform. With the optimal setting for the control variables, Newton Raphson based power flow is performed for two test systems, viz, IEEE 30 bus system and IEEE 57 bus system for three loading conditions. Minimization of Real power loss and improvement of voltage profile obtained are compared with the results obtained using firefly and particle swarm optimization (PSO) techniques. Improvement of Loadability margin is established through PV curve plotted using continuation power flow with the real power load at the most affected bus as the bifurcation parameter. The simulated output shows improved results when compared to that of firefly and PSO techniques, in term of convergence time, reduction of real power loss, improvement of voltage profile and enhancement of loadability margin.
文摘大规模虚拟电厂(virtual power plant,VPP)逐步具备与传统发电资源对等的地位,其优化运行策略将显著影响电力市场的均衡状态。高效表征虚拟电厂在关键端口下的外特性将促进虚拟电厂与现有市场模式的有效兼容,对于其深度参与电力市场具有十分重要的现实意义。基于改进多参数线性规划(multi-parametric linear programming,MPLP)理论提出虚拟电厂边际成本函数解析表征方法,通过虚拟电厂与主网在公共连接点(point of common coupling,PCC)处的交易电量这一低维参数,反映其整体灵活性、交易可行域及边际成本。基于成本最小化将初始参数空间优化分割为若干临界域(critical region,CR),随后,揭示优化分割的经济学特性,并利用该特性刻画参数空间与虚拟电厂成本的分段映射关系。最后,基于改进的IEEE 33及IEEE 123节点系统验证所提算法的有效性,为虚拟电厂以非迭代的方式参与市场出清提供理论基础。
