Partha Kayal

A simple and fast approach for allocation and size evaluation of distributed generation

Penetration of distributed generation (DG) units in distribution network has increased rapidly stimulated by reduced network power loss, improved bus voltage profile, and better power quality. Appropriate size and allocation of DG units play a significant role to get beneficial effects. The objective of this study is to demonstrate a simple and fast technique to determine appropriate location and size of DG units. A voltage stability indicator (VSI) is derived which can quantify the voltage stability conditions of buses in distribution network. According to VSI, vulnerable buses of the network are arranged rank-wise to form a priority list for allocation of DG units. To determine the size of DG units, a feed forward artificial neural network is prepared in MATLAB environment (The MathWorks, Inc., Massachusetts, USA). The effectiveness of the proposed methodology has been tested on a 52-bus radial distribution network. After appropriate allocation of DG units, voltage profiles of most of the buses are increased significantly. The results also indicated that the total loss of the distribution network has reduced by nearly 76.39%, and voltage stability conditions of buses are improved considerably. Voltage stability conditions of bus-13, bus-36, and bus-44 are raised by 23.16%, 29.23%, and 37.64% respectively.

An ANN based network reconfiguration approach for voltage stability improvement of distribution network

Recent trend in power sector is to automate distribution system to improve their reliability, efficiency and service quality. To facilitate automation of distribution system, an Artificial Neural Network (ANN) based novel methodology for enhancement of voltage stability by network reconfiguration is presented in this paper. Network reconfiguration is a process which alters the feeder topological structure by changing the open/close status of the sectionalizing (normally closed) and ties switches (normally open) in the system. A new voltage stability index is developed for voltage stability assessment of whole distribution network. In this work, a two stage search of switching option i.e. local search and global search is implemented to achieve desired network configuration. A multilayer ANN model with Error Back Propagation Learning (EBPL) algorithm is simulated for global search to obtained optimal set of candidate switching. The proposed scheme is tested on an 11 kV practical radial distribution system consisting of 52 buses. The experimental results are promising and encouraging. After reconfiguration, better voltage stable condition of the system is attained. Other objectives which are also satisfied are minimization of active and reactive power losses and improvement of voltage profile of most of the buses.

Optimal sizing of multiple Distributed Generation units connected with distribution system using PSO technique

Planned application of Distributed Generation (DG) in distribution system can provide voltage support at feeder and help to avoid branch overloading, network power loss, high investment cost etc. A Particle Swarm Optimization based formulation of constrained optimization problem to facilitate determination of sizes of multiple DGs has been proposed in this paper. Both the minimum and maximum sizes of DG units are evaluated for a certain range of voltage level of buses. Proposed methodology can ensure an appropriate level of voltage enhancement of network. A novel voltage stability factor of equivalent two bus system of the whole network has been proposed which can measure the proximity of operating state of the system to the point of voltage collapse. The effectiveness of the proposed methodology has been tested on 15 bus and 33 bus radial distribution systems.

Simultaneous placement and sizing of renewable DGs and capacitor banks in distribution network

Competitive electricity market and ever increasing load demand create new challenges for the utilities to supply reliable and quality power to the customers. Suitable reinforcement of distributed generation (DG) and capacitor banks in distribution network can achieve the goal. This paper proposes a novel methodology for simultaneous placement and sizing of multiple renewable DG units and capacitor banks in distribution network considering reliability, voltage stability and economic criteria. The proposed method utilizes constrained discrete particle swarm optimization technique to select optimal locations and sizes of PV array and wind turbines along with capacitor banks. The validation of the methodology is demonstrated on an Indian rural distribution network. Simulation results show that proposed method is more effective than existing technique for planning of renewable DGs and capacitors in distribution network.

Optimal location, type and size selection technique of Distributed Generation based on economic index

The influence of Distributed Generation (DG) in distribution network is emphasized because of its promises to supply reliable and better quality of electric power to consumers. Moreover, changing economical and regulatory environment are encouraging electrical utilities to concentrate on DG. A Particle Swarm Optimization (PSO) based formulation of optimization problem to facilitate determination of location, type and size of DG has been proposed in this paper. Simulation of proposed technique has been done in MATLAB environment on view point of maximization of Economic Index of DG (EIDG) satisfying some technical constraints. Conventional and renewable DG technologies, namely photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), gas turbine (GT), and reciprocal engine (RE) has been considered in our study. Effectiveness of the proposed technique has been shown on a typical 15 bus radial distribution system. Results show that proper selection of location, type and size of DG in distribution network would increase profit margin. Proposed approach is also helpful for enhancement of voltage profile at distribution buses.

A multi-objective approach for selection of CHP based DG in radial distribution network

Distributed generation (DG) technology has become very noteworthy in modern power sector to meet the power demand in a sustainable mode. DG based on combined heat and power (CHP) technology offers concurrent generation of both electricity and heat from the same fuel for useful purposes. An encouraging method for optimal allocation of different types of CHP in distribution network via fuel cost reduction, mitigation of emission and maximization of heat generation is presented in this paper. Voltage stability indicator has been utilized to identify the prospective location of CHP units. Non-dominated sorting genetic algorithm II (NSGA-II) has been exploited to evaluate the trade off solutions to determine size of CHP units. Three types of diesel generator (Dg) and two types of microturbine (Mt) are incorporated in the analysis to highlight the effectiveness of the methodology in mixed DG scenario. To verify the efficacy of the proposed approach it has been tested on 33-node and 15-node distribution network. The result for both the systems confirms that the objectives are fulfilled suitably.

Selection of Distributed Generation for Distribution Network: A Study in Multi-criteria Framework

The worsening misalignment between energy demand and supply with major consequences on energy prices, threat of disruptive climate change and erosion of energy security have given momentum towards utilization of distributed generation (DG) technologies. However, proper choice of DG technology and its location and size in the distribution network have become a very challenging issue. This paper analyzes the scope of six different DG technologies namely solar photovoltaic, wind turbine, biomass, fuel cell, diesel engine and gas turbine for a distribution network in the district of South 24 Parganas, West Bengal, India. Analytical hierarchy process has been used to choose proper type of DG for the region considering technical, economical and environmental aspect. A weighted aggregation based multi-objective particle swarm optimization technique is employed to determine location and size of the DG in the distribution network. The optimization technique considers power and voltage constraints and generates potential solution to minimize power losses and maximize voltage stability level of the network.

Allocation and Size Evaluation of Distributed Generation

Recently technological innovation, environmental concerns and market liberalization help to penetrate Distributed Generation (DG) of significant number and capacity into power distribution system. This paper represents technique to minimize power losses in a distribution feeder by optimizing DG model in terms of size and location. A novel Voltage Stability Indicator (VSI) is proposed that can identify critical buses in distribution system. Based on VSI, the suitable location of distributed generator is identified. Desired size of DG is evaluated by simulating ANN model with proper training. The proposed methodology is tested by checking through 15 bus radial distribution and 52 bus practical distribution systems. There are considerable improvements of voltage profiles of the buses after appropriate allocation of DG. Appreciable reductions of distribution power losses for both the systems are also obtained.

Congestion management in transmission network on viewpoint of voltage stability enhancement

The objective of congestion management is to take actions or control measures in order to avoid line overloading of transmission networks. A restructuring based congestion management technique has been proposed in this paper. Proposed methodology can ensure an appropriate level of voltage stability enhancement of network. Minimization of system power losses is also obtained. A novel voltage stability index is derived in this paper which can quantify the condition of whole system on the viewpoint of voltage collapse. The effectiveness of the proposed method is illustrated on IEEE 14-bus reliability test system.