Swarm Intelligence Based Multi-phase OPF For Peak Power Loss Reduction In A Smart Grid

Recently there has been increasing interest in improving smart grids efficiency using computational intelligence. A key challenge in future smart grid is designing Optimal Power Flow tool to solve important planning problems including optimal DG capacities. Although, a number of OPF tools exists for balanced networks there is a lack of research for unbalanced multi-phase distribution networks. In this paper, a new OPF technique has been proposed for the DG capacity planning of a smart grid. During the formulation of the proposed algorithm, multi-phase power distribution system is considered which has unbalanced loadings, voltage control and reactive power compensation devices. The proposed algorithm is built upon a co-simulation framework that optimizes the objective by adapting a constriction factor Particle Swarm optimization. The proposed multi-phase OPF technique is validated using IEEE 8500-node benchmark distribution system.Comment: IEEE PES GM 2014, Washington DC, US

Electric Power Loss Reduction Technique for Power Distribution System

Distribution system forms an important part of the electrical power system connecting high voltage transmission lines to low voltage consumers through feeders, distributors and service mains. Losses in the distribution system vary up to 70% of the total losses. Investigating techniques for the reduction of these losses will be the aim of this project. The project title is 'Electric Power Loss Reduction Techniques for Power Distribution System'. Continuous and on-going research work is being conducted, with an infinite number of literatures done on thepower distribution system. Various factors have beenidentified to be the causes of the losses in the specified system. Various techniques have been proposed to reduce these losses in the distribution entity. Among the common and most discussed technique for loss reduction in distribution system is the reactive power compensation by capacitor placement. Capacitor placement is implemented in two ways; shunt compensation (capacitors are placed in parallel with load) and series compensation (capacitors are placed in series with line). Series and shunt compensation helps to control and compensate thereactive power in the system, reducing the voltage regulation to make the voltage profile at the rated value, improving the power factor and enhancing the capacity of the power supplied to the load or customers. Byhaving all those as a result of capacitor placement implementation, the endconsumer as well as the powerutility companies will take the benefits in term of the reduced cost in supplying and consuming electrical power

A DNR Using Evolutionary PSO for Power Loss Reduction

The total power losses in distribution network system can be minimized by network configuration. In this area of research, most of the researchers have used multiple types of optimization technique to determine the optimal problem solving. In this paper, an efficient hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for feeder network reconfiguration. The main objective is to reduce the power losses in the distribution network system and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster to find the optimal solution. The proposed method is applied and its impact on the network reconfiguration for real power loss and voltage profiles is investigated. In network reconfiguration, the network topologies change through On/Off of the sectionalizing and tie switches in order to optimize network operation parameters. The aim is to find the best configuration which consists of switches that will contribute to a lower loss in the distribution network system. The method was tested on a IEEE 33-bus system to show the effectiveness of the EPSO method over the traditional PSO and EP method

Real Power Loss Reduction by Enhanced Imperialist Competitive Algorithm

In this paper, an Enhanced Imperialist Competitive (EIC) Algorithm is proposed for solving reactive power problem. Imperialist Competitive Algorithm (ICA) which was recently introduced has shown its decent performance in optimization problems. This innovative optimization algorithm is inspired by socio-political progression of imperialistic competition in the real world. In the proposed EIC algorithm, the chaotic maps are used to adapt the angle of colonies movement towards imperialist’s position to augment the evading capability from a local optima trap. The ICA is candidly stuck into a local optimum when solving numerical optimization problems. To overcome this insufficiency, we use four different chaotic maps combined into ICA to augment the search ability. Proposed Enhanced Imperialist Competitive (EIC) algorithm has been tested on standard IEEE 30 bus test system and simulation results show clearly the decent performance of the proposed algorithm in reducing the real power loss

Optimal allocation of sen transformer for active power loss reduction

Reduction of active power loss is one of the main functions that are carried in power system control centers. The main purpose of transmission systems is efficiently handling power from generation stations to load centers. Yet, a considerable amount of power is lost in transmission network components. As a corrective action, power flow redistribution in transmission lines using appropriate power flow control devices is useful to increase the transmission efficiency. However, the inserted power flow control devices themselves consume power. Thus, if not optimally sited, their own power loss may overcome the loss reduction which is obtained by their action, and as a consequence may increase the power loss further. Optimal allocation of these devices is critical to ensure fulfillment of the proposed goals. In this work, optimally placed Sen Transformer (ST) is suggested for power loss reduction considering security constraints. ST is modelled under MATLAB/SIMULINK, and optimally located using active power loss sensitivity index. The method is validated using IEEE-6 bus system and the results are encouraging

Optimal Allocation and Sizing of Distributed Generation for Power Loss Reduction using Modified PSO for Radial Distribution Systems

For the purpose of improving the voltage profile and power losses reduction, this paper proposes allocation and sizing of Distributed Generation (DG) in radial distribution system (69 IEEE bus test system.). A simple and effective approach for power loss reduction (PLR) value is employed for the allocation while the sizing was by using the results from the allocation as local optimum in a modified PSO called Ranked Evolutionary particle swarm optimization (REPSO) in order to obtain the global optimum. Load simulations in power flow yielded improvement not only in power loss reduction but also in voltage profile. The proposed algorithm was found to be faster and gives more accurate results than the EP and PSO algorithms. Keywords: Distributed Generation, Evolutionary programming,, Particle Swarm Optimization, Allocation and sizing, Power loss reduction

Power losses reduction of power transmission network using optimal location of low-level generation

Due to the growth of demand for electric power, electric power loss reduction takes great attention for the power utility. In this paper, a low-level generation or Distributed Generation (DG) has been used for transmission power losses reduction. Karbala city transmission network (which is the case study) has been represented by using MATLAB m-file to study the load flow and the power loss for it. The paper proposed the Particle Swarm Optimization (PSO) technique in order to find the optimal number and allocation of DG with the objective to decrease power losses as possible. The results show the effect of the optimal allocation of DG on power loss reduction

Power Loss Reduction in Radial Distribution System by Using Plant Growth Simulation Algorithm

The availability of an adequate amount of electricity and its utilization is essential for the growth and development of the country. The demand for electrical energy has outstripped the availability causing widespread shortages in different areas. The distribution network is a crucial network, which delivers electrical energy directly to the doorsteps of the consumer. In India the distribution networks are contributing to a loss of 15% against total system loss of 21%. Hence, optimal capacitor placement in electrical distribution networks has always been the concern of electric power utilities. As Distribution Systems are growing large and being stretched too far, leading to higher system losses and poor voltage regulation, the need for an efficient and effective distribution system has therefore become more urgent and important. In this regard, Capacitor banks are added on Radial Distribution system for Power Factor Correction, Loss Reduction and Voltage profile improvement. As Distribution Systems are growing large and being stretched too far, leading to higher system losses and poor voltage regulation, the need for an efficient and effective distribution system has therefore become more urgent and important. In this regard, Capacitor banks are added on Radial Distribution system for Power Factor Correction, Loss Reduction and Voltage profile improvement. Therefore it is important to find optimal location and sizes of capacitors required to minimize feeder losses. Reactive power compensation plays an important role in the planning of an electrical system. Reactive power compensation plays an important role in the planning of an electrical system. Capacitor placement & sizing are done by Loss Sensitivity Factors and Plant Growth Simulation Algorithm respectively. Loss Sensitivity Factors offer the important information about the sequence of potential nodes for capacitor placement. These factors are determined using single base case load flow study. Plant Growth Simulation Algorithm is well applied and found to be very effective in Radial Distribution Systems. The proposed method is tested on 33 and 34 bus distribution systems. The objective of reducing the losses and improvement in voltage profile has been successfully achieved. The main advantage of the proposed approach in relation to previously published random algorithms is that it does not require any external parameters such as barrier factors, crossover rate, mutation rate, etc. These parameters are hard to be effectively determined in advance and affect the searching performance of the algorithm new approach based on a plant growth simulation algorithm (PGSA) is presented for reactive power optimization. PGSA is a random search algorithm inspired by the growth process of plant phototropism. The objective function for optimization is to minimize the system active power loss. Keywords: Distribution systems, Loss Sensitivity Factors, Capacitor placement, Plant growth simulation algorithm