Optimal placement of multiple STATCOM for voltage stability margin enhancement using particle swarm optimization

This paper proposes an approach for optimal placement of STATic synchronous COMpensator (STATCOM) in power systems. The approach is based on the simultaneous application of particle swarm optimization (PSO) and continuation power flow (CPF) to improve voltage profile, minimizing power system total losses, and for maximizing system loadability with respect to the size of STATCOM. Simulation results show the suitability of the PSO technique in finding multiple optimal solutions to the problem with reasonable computational effort. The installation of the STATOCM on these buses can increase the system voltage stability margin. The proposed technique is examined on the IEEE57 bus test system.     

Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms

Management of reactive power resources is essential for secure and stable operation of power systems in the standpoint of voltage stability. In power systems, the purpose of optimal reactive power dispatch (ORPD) problem is to identify optimal values of control variables to minimize the objective function considering the constraints. The most popular objective functions in ORPD problem are the total transmission line loss and total voltage deviation (TVD). This paper proposes a hybrid approach based on imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) to find the solution of optimal reactive power dispatch (ORPD) of power systems. The proposed hybrid method is implemented on standard IEEE 57-bus and IEEE 118-bus test systems. The obtained results show that the proposed hybrid approach is more effective and has higher capability in finding better solutions in comparison to ICA and PSO methods.     

Optimal location of UPFC controller in transmission network using hybrid chemical reaction optimization algorithm

This paper presents a new hybrid chemical reaction optimization (HCRO) approach which is based on chemical reaction optimization (CRO) and differential evolution (DE) to find the optimal placement and parameter setting of unified power flow controller (UPFC) to achieve optimal performance of power system network. In the proposed algorithm, four elementary reactions, i.e., on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis, are developed. Moreover, mutation operation of DE is integrated with inter-molecular ineffective collision and crossover operation is introduced in the inter-molecular collision, synthesis, and decomposition process to accelerate the convergence speed and improve the solution quality of CRO algorithm. Here, three different single objectives namely, minimization of the overall cost, transmission loss, voltage deviation and one multi-objective which simultaneously minimizes the transmission loss and voltage deviation are used. To verify the effectiveness, the proposed HCRO approach is implemented on IEEE 14-bus and IEEE 30-bus power systems. Moreover, to establish the superiority, the simulation results of the proposed HCRO technique are compared to the CRO and other previously reported algorithms published in the literature such as genetic algorithm (GA), particle swarm optimization (PSO), immune GA (IGA), immune PSO (IPSO) and hybrid immune algorithm (HIA). It is found that the results obtained by the proposed HCRO technique are superior to those obtained by other discussed algorithms.     

Optimal location and parameter setting of UPFC for enhancing power system security based on Differential Evolution algorithm

This paper presents a new approach based on Differential Evolution (DE) technique to find out the optimal placement and parameter setting of Unified Power Flow Controller (UPFC) for enhancing power system security under single line contingencies. Firstly, we perform a contingency analysis and ranking process to determine the most severe line outage contingencies considering line overloads and bus voltage limit violations as a Performance Index. Secondly, we apply DE technique to find out the optimal location and parameter setting of UPFC under the determined contingency scenarios. To verify our proposed approach, we perform simulations on an IEEE 14-bus and an IEEE 30-bus power systems. The results we have obtained indicate that installing UPFC in the location optimized by DE can significantly enhance the security of power system by eliminating or minimizing the overloaded lines and the bus voltage limit violations.     

Optimal active and reactive nodal power requirements towards loss minimization under reverse power flow constraint defining DG type

In this paper a novel approach regarding the optimal penetration of Distributed Generation (DG) in Distribution Networks (DNs) towards loss minimization is proposed. More specific, a Local Particle Swarm Optimization (PSO) variant algorithm is developed in order to define the optimal active and reactive power generation and/or consumption requirements for the optimal number and location of nodes that yield loss minimization. Thus, the proposed approach provides the optimal number, siting and sizing of DGs altogether. In addition, based on the optimal power requirements of the resulted nodes, a combination of potential DG types to be installed is recommended. The proposed objective function in this paper is also innovative since it embeds the constraint of reverse power flow to the slack bus by the formation of a new penalty term. The proposed methodology is applied to 30 and 33 bus systems. The results indicate the optimal number, locations, and capacity of DG units, which were calculated simultaneously. Finally, the impact of the predefined amount of permissible reverse power flow to the optimal solution is also examined through two scenarios: the first considers zero reverse power flow and the second unlimited reverse power flow.     

Optimal allocation of phasor measurement unit for full observability of the connected power network

This paper presents binary particle swarm optimization (BPSO) technique for the optimal allocation of phasor measurement units (PMUs) for the entire observability of connected power network. Phasor measurement units are considered as one of the most important measuring devices in the prospect of connected power network. PMUs function may be incorporated to the wide-area connected power networks for monitoring and controlling purposes. The optimal PMU placement (OPP) problem provides reference to the assurance of the minimal number of PMUs and their analogous locations for observability of the entire connected power networks. Binary particle swarm optimization (BPSO) algorithm is developed for the solution of OPP problem. The efficacy and robustness of the proposed method has been tested on the IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, IEEE 118-bus and Northern Regional Power Grid (NRPG) 246-bus test system. The results obtained by proposed approach are compared with other standard methods and it is observed that this BPSO based placement of phasor measurement units is found to be the best among all other techniques discussed.     

Novel sensitivity factors for DG placement based on loss reduction and voltage improvement

This paper presents a method to select the load buses for the placement of Distributed generation (DG) based on loss reduction and voltage improvement sensitivity of the system. The strategic placement of DGs can help in reducing power losses and improving voltage profile. The proposed work discusses some new sensitivity factors that can be useful for selecting the locations. The method has the potential to be used for integrating the available DGs by identifying the best locations in a power system. The proposed method has been demonstrated on IEEE 24-bus Reliability Test System (RTS).     

考虑源荷不确定性的分布式电源选址定容

大规模的分布式电源入网给配电网的规划带来诸多不确定性因素。为使配网规划结果更加合理,考虑待规划地区的风速、光照强度和负荷的不确定性,构建了以年综合费用最少为目标函数的分布式电源选址定容规划模型。首先,对风、光和负荷进行了概率建模;其次,采用拉丁超立方采样方法生成初始场景,并采用改进的同步回代缩减法对场景进行削减;最后,鉴于粒子群算法具有收敛速度慢和容易早熟的缺点,将自适应惯性权重和混沌优化算法融入粒子群算法中,进而提出了一种改进型粒子群算法,并且在IEEE 33节点的标准算例系统上进行了仿真,结果表明所建模型和所提算法的合理性与有效性。     

Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine for optimal allocation of DG in radial distribution network

Optimal allocation of Distributed Generations (DGs) is one of the major problems of distribution utilities. Optimum locations and sizes of DG sources have profoundly created impact on system losses, voltage profile, and voltage stability of a distribution network. In this paper Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q) has been applied to solve a multi-objective function for optimal allocation and sizing of DGs in distribution systems. The objective is to minimize network power losses, achieve better voltage regulation and improve the voltage stability within the frame-work of the system operation and security constraints in radial distribution systems. The limitation of SIMBO-Q algorithm is that it takes large number of iterations to obtain optimum solution in large scale real systems. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning (QOBL) concept is introduced in basic SIMBO-Q algorithm. The proposed QOSIMBO-Q algorithm has been applied to 33-bus and 69-bus radial distribution systems and results are compared with other evolutionary techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Optimization (TLBO) and Quasi-Oppositional Teaching Learning Based Optimization (QOTLBO). Numerical studies represent the effectiveness and out-performance of the proposed QOSIMBO-Q algorithm.     

Swarm-Intelligence-Based Optimal Planning of Distributed Generators in Distribution Network for Minimizing Energy Loss

This research work proposes a heuristic approach for the planning of Distributed Generator (DG) to minimize annual system energy loss. The particle-swarm-optimization-based method has been used as an optimization tool for determining the optimal size and allocating the DGs to minimize energy loss. In this approach, time-varying characteristics of electrical load demand have been considered to mimic real load scenario in the electrical distribution system. The proposed approach is generic and simple. It can provide optimal solutions to the distribution utilities to select multiple DGs in stages under various constraints. The effectiveness of the proposed approach is validated on 16-, 33-, and 69-bus radial distribution networks. The results are compared with those of already existing methods as suggested in the literature.     

ILP-based multistage placement of PMUs with dynamic monitoring constraints

This paper addresses two aspects of the optimal Phasor Measurement Unit (PMU) placement problem. Firstly, an ILP (Integer Linear Programing) model for the optimal multistage placement of PMUs is proposed. The approach finds the number of PMUs and its placement in separate stages, while maximizing the system observability at each period of time. The model takes into account: the available budget per stage, the power system expansion along with the multistage PMU placement, redundancy in the PMU placement against the failure of a PMU or its communication links, user defined time constraints for PMU allocation, and the zero-injection effect. Secondly, it is proposed a methodology to identify buses to be observed for dynamic stability monitoring. Two criteria, which are inter-area observability and intra-area observability, have been considered. The methodology identifies coherent groups in large power systems by using a new technique based on graph theory. The technique requires neither full stability studies nor a predefined number of groups. Also, a centrality criterion is used to select a bus for monitoring each coherent area and supervise inter-area oscillations. Then, PMUs are located to ensure complete observability inside each area (intra-area monitoring). Methodology is applied on the 14-bus test system, the 57-bus test system with expansion plans, and the 16-machine 68 bus test system. Results indicate that the optimization model finds the optimal number of PMUs when the PMU placement by stages is required, while the observability at each stage is maximized. Additionally, it is shown that expansion plans and particular requirements of observability can be considered in the model without increasing the number of required PMUs, and the zero-injection effect, which reduces the number of PMUs, can be considered in the model.     

应用粒子群优化算法求解可用输电能力

在电力市场环境下,诸多问题(例如实时电价、网络阻塞等)都需要最优潮流作为理想的工具.本文以最优潮流为基础,应用一种简单有效、且收敛性很好的演化计算算法--粒子群优化算法(PSO)进行可用输电能力(ATC)问题的求解.根据约束条件的越限量大小,动态地调整罚函数,在保证全局搜索能力的基础上改进了收敛速度.应用此算法对IEEE-30节点系统进行了可用输电能力计算,并与传统的最优潮流算法进行了比较,结果表明该算法的有效性,具有实用意义.     

Differential search algorithm for solving multi-objective optimal power flow problem

In this paper, a simple and efficient nature inspired search method based on differential search algorithm (DSA) has been presented and used for optimal power flow (OPF) problem in power systems. By using the proposed DSA method, the power system parameters such as real power generations, bus voltages, load tap changer ratios and shunt capacitance values are optimized for the certain objective functions. Different types of single-objective and multi-objective functions on IEEE 9-bus, IEEE 30-bus and IEEE 57-bus power systems are used to test and verify the efficiency of the proposed DSA method. By comparing with several optimization methods, the results obtained by using the proposed DSA method are presented in detail. The results achieved in this work illustrate that the DSA method can successfully be used to solve the non-linear and non-convex problems related to power systems.     

Distributed generation placement for congestion management considering economic and financial issues

Congestion management is one of the most important functions of independent system operator (ISO) in the restructured power system. This paper presents two new methodologies for optimal sitting and sizing of distributed generations (DGs) in the restructured power systems for congestion management. The proposed methodologies are based upon locational marginal price (LMP) and congestion rent that forms a priority list of candidate buses to reduce the solution space. The proposed priority list facilitates the optimal placement as well as the level of output power of DGs. The proposed methods are implemented on the IEEE 14-bus and IEEE 57-bus test systems to illustrate their effectiveness. An economic consideration of DG placement and its operation is also studied. Simulation studies and results analysis show that the proposed methodologies are capable of finding the best location and optimal size for DGs, which can alleviate congestion in transmission systems.     

基于粒子群优化算法和动态调整罚函数的最优潮流计算

在电力市场环境下,诸多问题(例如实时电价,网络阻塞等)都需要最优潮流作为理想的工具.本文应用了一种简单有效、且收敛性很好的演化计算算法--粒子群优化算法(PSO)进行最优潮流问题的求解.在求解过程中,根据约束条件的越界量大小,动态的调节其罚函数,避免其收敛到局部最小点.应用此算法对IEEE 30 节点系统进行最优潮流计算,并且与线性规划和遗传算法进行了比较,结果表明该算法能够更好的获得全局最优解,具有实用意义.     

Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth

This paper presents a comparison of Novel Power Loss Sensitivity, Power Stability Index (PSI), and proposed voltage stability index (VSI) methods for optimal location and sizing of distributed generation (DG) in radial distribution network. The main contribution of the paper is: (i) optimal placement of DGs based on Novel Power Loss Sensitivity and PSI methods, (ii) proposed voltage stability index method for optimal DG placement, (iii) comparison of sensitivity methods for DG location and their size calculations, (iv) optimal placement of DG in the presence of load growth, (v) impact of DG placement at combined load power factor, (vii) impact of DG on voltage stability margin improvement. Voltage profile, the real and reactive powers intake by the grid, real and reactive power flow patterns, cost of energy losses, savings in cost of energy loss and cost of power obtained from DGs are determined. The results show the importance of installing the suitable size of DG at the suitable location. The results are obtained with all sensitivity based methods on the IEEE 12-bus, modified 12-bus, 69-bus and 85-bus test systems.     

Optimal VAR control for real power loss minimization using differential evolution algorithm

Differential evolution algorithm (DEA) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been proved to be a promising evolutionary algorithm for solving the ORPD problem and many engineering problems. However, the success of DEA in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies (mutation strategies) and their associated control parameter values. This paper presents a differential evolution technique with various trial vector generation strategies based on optimal reactive power dispatch for real power loss minimization in power system. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts compensator to be switched, for real power loss minimization in the transmission systems. The DE method has been examined and tested on the IEEE 14-bus, 30-bus and the equivalent Algerian electric 114-bus power system. The obtained results are compared with two other methods, namely, interior point method (IPM), Particle Swarm Optimization (PSO) and other methods in the literature. The comparison study demonstrates the potential of the proposed approach and shows its effectiveness and robustness to solve the ORPD problem.     

Novel Clustering-based Hidden Markov Model (CHMM) optimization approach for optimal PMU placement

A Phasor Measurement Unit (PMU) is an important device for monitoring the wide-area power distribution network. Placement of the PMUs across the network enables reliable monitoring of the network to identify the faults in the bus system. Due to the increase in the installation cost of the PMUs, optimal placement of the PMU is a significant task. But the existing techniques do not provide the optimal solution for the PMU placement. To overcome this issue, this paper proposes a novel Clustering-based Hidden Markov Model (CHMM) optimization approach to achieve optimal placement of PMUs in the power distribution network. Optimal PMU placement is achieved by applying cluster formation in the bus system to extract the data with neighboring buses. Best optimal position for placing the PMU is estimated by using Fuzzy logic-based rale formation to update the binary table of the bus system. The HMM approach is used for updating weight in the cluster formation. Our system is implemented in various bus systems like IEEE 28-bus system, 69-bus system and also in Karnataka 155-bus system. The proposed approach is implemented in the IEEE bus system and Karnataka Power Transmission Corporation Limited (KPTCL) bus system and compared with the existing approaches, based on the total number of PMU placement. The proposed approach achieves better performance in the optimal placement of PMU than the existing optimization algorithms.     

Improved meta-heuristic techniques for simultaneous capacitor and DG allocation in radial distribution networks

The active and reactive power flow in distribution networks can be effectively controlled by optimally placing Shunt Capacitors (SCs) and Distributed Generators (DGs). This paper presents improved versions of three evolutionary or swarm-based search algorithms, namely, Improved Genetic Algorithm (IGA), Improved Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO) to efficiently handle the problem of simultaneous allocation of SCs and DGs in radial distribution networks while considering variable load scenario. The benefit of network reconfiguration has also been taken into account after optimal allocation of these devices. Several algorithm specific modifications are suggested in the standard forms of GA, PSO and CSO to overcome their inherent drawbacks. In addition, an intelligent search approach is proposed to enhance overall performance of proposed algorithms. The proposed methods are investigated on IEEE 33-bus and 69-bus test distribution systems showing promising results when compared with other recently established methods. Application results also show a marked improvement in the performance of these algorithms while compared with their respective standard counterparts.