Different Types Of Distributed Generation Research Articles

The Environmental Benefits-Considered Cost Characteristics of Distributed Generation Analysis

The increasing environmental stress and energy crisis promote the distributed generation (DG) to integrate in the distribution network, thus cost characteristics of DGs are important to decision-makers for distribution network expansion and reinforcement. Firstly, this paper analyzed the fixed cost and variable cost of different types of DGs (such as micro-turbine, internal combustion engine, wind power, PV and fuel cell. and then drew the yearly cost characteristic curves. Secondly, we respectively calculated the relevant costs of thermal power generation and distributed generation. These costs include pollutant-emission cost and governance cost considering three types of pollutants, i.e. SO2, NOx and TSP. By doing this, we could better know the environmental benefits of DG compared with conventional power generation. Thirdly, we added the environmental benefits of distributed generation to the variable cost and regarded the final result as comprehensive variable cost. If reflects the environmental advantage of DG. Finally, we drew the cost characteristic curve with environmental benefit of DG. The research would provide reference when choosing the type and capacity of distributed generation in distribution network planning.

Capacity Evaluation and Identification of Grid Integration Points of Distributed Generation in a Distribution System

A novel iterative method for identification of grid integration points and size of distributed generators (DGs) with simultaneous capacity evaluation of a distribution system for DG integration is proposed in this paper. In order to achieve this, various performance indices governing the optimal operation of a distribution system with DG units are strategically combined to form a multi-objective index. Identification of suitable nodes and size of each DG unit for integration in the distribution system is performed by minimising the multi-objective index such that on placement of every additional DG unit, reduction in the values of performance indices is resulted without any violation in the system operating constraints. The effects of different types of DGs and load models in this DG placement problem are also analysed. The proposed technique is tested on 33- and 25-bus systems, and is found to be very effective in identifying the suitable number of locations, size of DG units and evaluating the capacity of distribution system for DG integration.

Grid Integration of Distributed Generation—Consequences on Node Voltage under Grid Perturbations

Wind generators are very popular in most parts of the world as distributed generations (DG) owing to their technological advancement for grid integration, minimal environmental impact and free abundant availability. These wind generators usually employ induction machine for their electric power conversion due to its control simplicity and robustness in construction. The power output of such induction generator based wind turbine generator system (WTGS), depends on the node voltage and frequency of the connected radial distribution system (RDS). The node voltages of RDS are however subject to change due to variations in loading conditions. Therefore, a requirement arises to analyze the resulting mutual effect on the node voltages of RDS embedded with DG during such grid perturbations. Hence, the consequences of DG on the node voltages of RDS under anticipated small grid perturbations such as slow variation in grid frequency and connected loads are presented in this paper. The analysis is carried out on a practical Indian 25 bus radial distribution system embedded with different types of DG. From the results presented, it is observed that RDS embedded with fixed speed stall regulated WTGS are largely prone to voltage deviations at all nodes.

Optimal multiple distributed generation placement in microgrid system by improved reinitialized social structures particle swarm optimization

This paper proposes an improved reinitialized social structures particle swarm optimization (IRS-PSO) for solving optimal multiple distributed generations (DG) placement in a microgrid (MG) system. The movement of each particle in IRS-PSO is pulled by an inertia term, a cognitive term (personal best), and three social learning terms including global best, local best, and near neighbor best. The objective is to minimize the real power loss within real and reactive power generation limits and voltage limits. Five types in a MG system are considered including MG with DG supplying real power only, MG with DG supplying reactive power only, MG with DG supplying real power and consuming reactive power, MG with DG supplying real power and reactive power, and MG with four different types of DG regulating the bus voltage. For a given number of DG units in each type, IRS-PSO can find better sizes and locations of multiple DGs than repetitive load flow, basic particle swarm optimization (BPSO), adaptive weight particle swarm optimization (APSO), and global best, local and near neighbor best particle swarm optimization (GLN-PSO) on the 69-bus radial MG distribution system. Copyright © 2010 John Wiley & Sons, Ltd.