Considering Different Network Topologies in Optimal Overcurrent Relay Coordination Using a Hybrid GA

The directional overcurrent relays (DOCRs) coordination problem is usually studied based on a fixed network topology in an interconnected power system, and is formulated as an optimization problem. In practice, the system may be operated in different topologies due to outage of the transmission lines, transformers, and generating units. There are some situations for which the changes in the network topology of a system could cause the protective system to operate without selectivity. The aim of this paper is to study DOCRs coordination considering the effects of the different network topologies in the optimization problem. Corresponding to each network topology, a large number of coordination constraints should be taken into account in the problem formulation. In this situation, in addition to nonlinearity and nonconvexity, the optimization problem experiences many coordination constraints. The genetic algorithm (GA) is selected as a powerful tool in solving this complex and nonconvex optimization problem. In this paper, in order to improve the convergence of the GA, a new hybrid method is introduced. The results show a robust and optimal solution can be efficiently obtained by implementing the proposed hybrid GA method.     

基于改进粒子群优化算法的电力市场下的无功优化

在厂网分开、竞价上网的市场模式下综合考虑电力系统安全约束,建立了以有功网损和无功费用最小为目标函数并包含各种运行约束条件的电力系统无功优化数学模型。应用改进粒子群优化算法求解该无功优化模型,并结合动态调整罚函数法将无功优化问题转化成无约束求极值问题,从而有效地提高了改进粒子群优化算法的全局收敛能力和计算精度,使电网公司取得了最大经济效益。以IEEE30节点系统为例进行了仿真计算,结果表明了本文采用的无功优化模型和算法的正确性、适用性和较好的经济性。     

混合粒子群优化算法在电网规划中的应用

在含被动聚集因子的粒子群优化(particle swarm optimization with passive congregation,PSOPC)算法和和谐搜索(harmony search,HS)的基础上,构建了一种新的混合粒子群优化(heuristic particle swarm optimization,HPSO)算法。该算法根据电网规划的特点,采用“飞回机制”处理变量的约束条件,利用和谐搜索处理规划问题的约束条件,使粒子群在迭代过程中始终保持在可行域内,同时该算法中引入了被动聚集因子,有效改善了粒子的进化机制,提高了粒子的自由搜索能力。18节点算例验证了该算法应用于电网规划的正确性和有效性,HPSO算法、粒子群优化算法和PSOPC算法的比较结果表明该HPSO算法具有较好的收敛性能。     

Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network

This paper proposes an improved priority list (IPL) and augmented Hopfield Lagrange neural network (ALH) for solving ramp rate constrained unit commitment (RUC) problem. The proposed IPL-ALH minimizes the total production cost subject to the power balance, 15 min spinning reserve response time constraint, generation ramp limit constraints, and minimum up and down time constraints. The IPL is a priority list enhanced by a heuristic search algorithm based on the average production cost of units, and the ALH is a continuous Hopfield network whose energy function is based on augmented Lagrangian relaxation. The IPL is used to solve unit scheduling problem satisfying spinning reserve, minimum up and down time constraints, and the ALH is used to solve ramp rate constrained economic dispatch (RED) problem by minimizing the operation cost subject to the power balance and new generator operating frame limits. For hours with insufficient power due to ramp rate or 15 min spinning reserve response time constraints, repairing strategy based on heuristic search is used to satisfy the constraints. The proposed IPL-ALH is tested on the 26-unit IEEE reliability test system, 38-unit and 45-unit practical systems and compared to combined artificial neural network with heuristics and dynamic programming (ANN-DP), improved adaptive Lagrangian relaxation (ILR), constraint logic programming (CLP), fuzzy optimization (FO), matrix real coded genetic algorithm (MRCGA), absolutely stochastic simulated annealing (ASSA), and hybrid parallel repair genetic algorithm (HPRGA). The test results indicate that the IPL-ALH obtain less total costs and faster computational times than some other methods.     

Impact of the Transformer Tap-Ratio Control on the Electricity Market Equilibrium

In this paper, the primal-dual nonlinear interior point method has been used as a basis for the derivation of a novel supply function equilibrium (SFE) algorithm for modeling the strategic interactions in the electricity market, using the ac network model, which incorporates modeling of the transformer tap-ratio control. The algorithm is used to investigate the impact of transformer tap-ratio control on the electricity market equilibrium and the effect of the interactions between network constraints and transformer tap-ratio control. The interior point social welfare optimization problem is combined with the optimization problem for maximizing the profit of each strategic generating firm in the market. The final combined single-level SFE problem is solved iteratively based on solution techniques of the interior point method. Numerical examples illustrate the effect of network constraints and especially the impact of transformer tap-ratio control on market equilibrium.     

基于混合整数二阶锥规划的主动配电网最优潮流研究

最优潮流是一个非线性优化问题,其具有复杂繁琐、维数高、约束多以及变量多的特性。将其应用于主动配电网也是当下研究热点。文章建立了使主动配电网有功损耗最小的最优潮流模型;考虑配电网可控单元和开关状态,采用改进后的辐射状约束与潮流约束进行变换,合理松弛后变为二阶锥约束,建立混合整数二阶锥规划模型。采用Yalmip工具包进行建模,调用Gurobi商用算法包对其进行求解计算;通过算例对采用粒子群算法与采用混合整数二阶锥规划方法的计算结果进行对比分析,结果证明混合整数二阶锥方法更适用于主动配电网,验证了该方法的高效性和稳定性。     

基于改进粒子群优化算法的负荷分配方法研究

针对多目标粒子群优化算法求解负荷优化分配问题时所出现的最优解分布不均,局部最优等问题,引入了精英交叉算子并基于拥挤度对非劣解集进行排序,给出了精确计及网损时的机组出力等式不等式约束处理方法。最后在有无网损两种情况下针对3机组系统进行负荷优化分配。仿真结果表明改进后的粒子群优化算法寻优能力得到提升,同样利用模糊隶属度函数筛选Pareto解集得到的结果明显优于常规粒子群优化算法,有效降低了发电成本及污染物排放,且求解结果严格满足约束条件。     

考虑用户出行成本预算的电力-交通耦合网络充电站定价策略

随着电动汽车的规模化发展,研究如何有效考虑用户的出行行为机理并制定合理的充电站充电价格,对电力-交通网络的协同优化调度具有重大意义。针对此问题,提出了考虑用户出行成本预算的电力-交通耦合网络充电站定价策略。首先,建立考虑出行成本预算的交通用户均衡模型,将均衡状态通过变分不等式进行等效描述,从而对电动汽车出行需求和充电行为进行刻画。其次,构建考虑功率削减的配电网二阶锥优化模型,将充电站定价问题转化为含有变分不等式约束的优化问题,并根据问题设计交替迭代算法和外梯度算法进行求解。最后,通过算例对所提模型和方法的有效性进行验证,结果表明了考虑出行成本预算对耦合网络充电定价的必要性。     

基于节点深度编码技术的配电网故障恢复

应用智能优化算法求解配电网故障恢复问题时,不仅需要在网络结构发生改变时频繁进行网络拓扑分析,而且往往需要在寻优过程中增加辐射校验环节以保证不违背配电网辐射运行约束,导致消耗了大量时间,降低了算法的寻优性能.将节点深度编码(NDE)技术引入多目标优化算法--改进的非支配遗传算法(NSGA-Ⅱ)中,采用NDE技术中的保留初始节点(PAO)和改变初始节点(CAO)操作取代传统算法中的交叉和变异操作,并针对配电网故障恢复问题的特点,提炼了PAO和CAO操作中的选点规则,保证了算法在寻优过程中始终满足配电网辐射运行约束;同时,通过应用NDE技术,可快速得到新的网络拓扑结构,无需重复进行网络拓扑分析,大大减少了算法的寻优时间.算例计算结果表明,基于NDE技术的NSGA-Ⅱ比普通NSGA-Ⅱ具有更好的收敛性、分布性,以及更快的计算速度.     

Optimal reconfiguration of distribution systems with representation of uncertainties through interval analysis

The present work presents an approach for optimal reconfiguration of electrical distribution systems (EDS) to minimize energy losses considering uncertainties in the load demand and in the wind based distributed generation (DG). The optimization algorithm applied to solve the reconfiguration problem is based on the bio-inspired metaheuristic Artificial Immune Systems (AIS). An interval power flow model is used to obtain an interval energy loss from the representation of the uncertainties. The interval loss is used to guide the AIS algorithm through the search space. Network and operational constraints as the radiality and connectivity of the network as well as different load levels are considered. Well-known test systems are used to assess the impact of the uncertainties representation in the reconfiguration problem.     

Optimal power flow for distribution networks using gravitational search algorithm

This paper presents a gravitational search algorithm (GSA)-based approach to solve the optimal power flow (OPF) problem in a distribution network with distributed generation (DG) units. The OPF problem is formulated as a nonlinear optimization problem with equality and inequality constraints, where optimal control settings in case of fuel cost minimization of DG units, power loss minimization in the distribution network, and finally simultaneous minimization of the fuel cost and power loss are obtained. The proposed approach is tested on an 11-node test system and on a modified IEEE 34-node test system. Simulation results obtained from the proposed GSA approach are compared with that obtained using a genetic algorithm approach. The results show the effectiveness and robustness of the proposed GSA approach.     

The application of artificial intelligent tools to the transmission expansion problem

The purpose of transmission expansion problem (TEP) is to determine the timing and type of new transmission facilities. The TEP has been formulated as an optimization problem. The objective was to minimize the transmission investment costs that handle the increased load and the additional generation requirements in terms of line additions and power losses. Several constraints were considered including the power flow on the network lines, the right-of-way's validity and its maximum line addition. The TEP was then solved using artificial intelligence (AI) tools such as the genetic algorithm, Tabu search and artificial neural networks (ANNs) with linear and quadratic programming models. The effectiveness of the AI methods in dealing with small and large-scale systems was tested through the applications of a six-bus system, the IEEE-24 bus network and a Saudi Arabian network. The hybridization of GA, TS and ANN has several features. Its results confirm that it is superior in dealing with a large-scale problem in which the size of the search spaces increases exponentially with the dimension of the network.     

基于改进TLBO算法的输电网规划

输电网规划问题维数高、变量多以及约束条件复杂,导致问题难于求解。本文采用新型的智能算法教与学算法(TLBO)对问题进行求解。教与学算法具有收敛速度快、设置参数少的优点,但在求解时容易陷入局部最优解。本文通过加入自主学习环节和反思环节以及自适应扰动策略,提高算法寻找全局最优解的能力,使其适应大规模输电网规划问题的求解。采用目标函数为线路投资费用、网损费用、过负荷费用之和的输电网规划模型,通过在Garver-6节点系统和IEEE-18节点系统中的计算,验证了该算法可以正确有效地解决输电网规划问题。     

基于改进粒子群算法的含DG配电网无功优化

含分布式电源(DG)配电网的无功优化是一个复杂的非线性优化问题,文中采用改进的粒子群算法(PSO)对配电网进行无功优化计算,建立以系统网损和电压平均偏离最小为目标函数,节点电压和电容器投切容量为约束条件的优化模型。在PSO中引入位置方差防止PSO陷入局部最优解,根据种群中粒子的适应度值对粒子进行变异处理,在保证算法收敛速度的基础上,改善算法性能。以含分布式电源的IEEE14节点配电系统为例进行无功优化分析,结果表明DG能增强电网运行的稳定性,所提算法具有较好的优化性能。     

PMU最优配置问题的混合优化算法

为使得电力系统在完全可观测的条件下,PMU安装数目最少,提出了一种混合优化算法以解决相量测量单元PMU的最优配置问题.混合优化算法以粒子群优化算法为主体,引入交叉、变异操作,并结合模拟退火机制控制粒子的更新.在处理解的约束问题时,采用了一种基于概率的启发式修补策略,避免修复后的解特征单一.将混合算法与其他算法在多个IEEE标准系统上进行了比较分析,结果表明在较大规模系统上,混合优化算法收敛率比标准粒子群算法提高数倍,计算量比模拟退火算法减少了数十倍,表明了较好的可行性和较高的效率.     

基于DPSO算法以负荷恢复为目标的网络重构

研究了大停电事故后输电系统的重构优化问题,提出了一种求解最优目标网的离散粒子群优化（DPSO）算法。将网络重构问题表示为以重要负荷恢复量占已恢复负荷总量的比例最高为目标的非线性优化问题,在求解目标网时考虑了负荷重要性、网络连通性、电网所需满足的各种安全和运行约束等问题。该算法在求解输电网重构问题时,编码容易且能方便地处理网络连通性问题,求解效率高、速度快。在IEEE 57节点系统和IEEE 118节点系统中的应用结果验证了文中方法的有效性。     

机器人关节空间B样条轨迹设计的混沌优化

为了研究机器人关节空间轨迹时间最短的优化计算问题,依据混沌优化理论,采用改进的基于混沌变量优化算法,以时间最短为性能指标对轨迹进行优化求解.研究了均匀非周期四阶B样条曲线的优化,每一段B样条曲线的运行时间作为优化参数,优化问题模型包括关节角速度、角加速度、角加加速度及力矩4种约束.给出了PUMA560前三铰B样条轨迹优化算例,优化结果明显优于采用复合形法或有约束随机搜索方法的优化结果;该算法简单,易于实现,求解速度快,任给一组初值得到优化结果的可靠性达90%以上,逼近约束条件的误差几乎为零,进一步减少了运行时间,从而有效地实现了机器人在关节空间的轨迹优化.     

基于连锁环网与改进离散粒子群算法的多目标配电网重构

配电网重构本质上是一个复杂的高维数非线性组合优化问题。为避免其不可行解的影响,同时实现快速寻优,提出了一种通过连锁环网矩阵快速判断粒子是否满足配电网拓扑约束的方法。采用基于Pareto准则的离散二进制粒子群算法(Binary Particle Swarm Optimization,BPSO)以求解配电网重构多目标优化问题。从三方面对BPSO算法进行改进:改进粒子更新策略以提升新代粒子的可行概率;改进sigmoid函数同时提出邻域搜索机制以强化算法后期的收敛能力;提出基于次优解保留策略的小生境共享机制以改进群体最优粒子更新方式,进而强化算法的全局搜索能力。对IEEE33系统算例进行仿真,结果表明改进BPSO算法在求解含分布式电源(Distributed Generation,DG)的配电网重构多目标优化问题时,能够更加精确高效地收敛至Pareto最优前沿。     

基于树木生长算法的含UPFC的最优潮流计算

最优潮流(OPF)计算是一个非凸优化问题,统一潮流控制器(UPFC)的引入增加了OPF问题的非凸程度,使得基于内点法的传统优化算法难以获取全局最优解。文中提出基于树木生长算法(TGA)的计及UPFC的最优潮流计算方法,将发电成本与有功网损、电压偏移加权作为目标函数,并考虑网络与UPFC设备的安全运行约束,优化了OPF模型。最后基于IEEE 30节点系统以及南京西环网116节点实际系统进行算例测试,对比TGA、粒子群与内点法的结果,并使用蒙特卡洛方法对不同的启发式算法分别进行50次计算,验证了TGA具有更好的求解精度与鲁棒性。     

改进自适应遗传算法在网络重构中的应用

研究了大停电事故后输电系统的重构优化问题,提出一种求解最优目标网的改进自适应遗传算法。将网络重构问题表示为以恢复负荷总量最高为目的的非线性优化问题,在求解目标网时考虑了负荷的重要性、网络连通性、电网所需满足的各种安全和运行约束等问题。采用改进的自适应遗传算法对问题进行求解,通过适应值函数的计算得到了系统允许条件下的最大允许恢复负荷量。该算法在求解电网重构问题时,编码容易且能方便地处理网络连通问题,求解效率高,用IEEE30节点算例验证了本文方法的有效性。