OPTIMAL PLANNING OF RADIAL DISTRIBUTION SYSTEM WITH DISTRIBUTED GENERATION

Abstract

Despite of excellent economies of scale, the justification for large centralized generation facilities is weakening nowadays due to rapid demand growth, increased transmission and distribution cost, escalating price and continuous depletion of fossil fuels, heightened environmental concerns, deregulation trends and technological advancements. Therefore, the utility generation paradigm is shifting from large scale generation to small scale generation. Such small-scale power generation, typically ranging from few kW to several MW, is referred to as Distributed Generation (DG). DGs are directly connected to the customers’ end. The current trend of placing DG within the distribution system is becoming a very promising option. The presence of DG in a distribution system is transforming it from a passive network to an active network. As a result, the branch power flow in such distribution systems is no longer unidirectional as in case of traditional distribution systems. Also, the integration of DG may impact the operation and performance of a distribution network in both beneficial and detrimental ways. Therefore, in this thesis work, attempts have been made to develop some methodologies for optimal planning of distribution networks with DG. The developed methodologies will be helpful to the utilities for integrating DGs into the existing electric power distribution systems to improve the system performance as well as to meet the growing load demand. Realizing the need to develop a comprehensive model for optimal allocation of dispatchable DG units in the distribution system considering technical, economical and environmental benefits of DG integration, a nonlinear programming problem based multi-objective formulation is presented. Micro-turbine, gas-turbine and fuel-cell based power generation units are considered as dispatchable DG units in this work. The hourly load variation is also considered in the problem formulation. An Analytic Hierarchy Process (AHP) technique is used for selection of suitable weights to different objectives. The developed formulation has been applied on 33-bus and 69-bus test distribution networks under different scenarios. Using a cost-benefit approach, the economic performance of both the test distribution systems has also been analyzed under different scenarios. The renewable energy resources based DGs are given high priority now-a-days. This is because the renewable energy resources have abundant availability and have potential to offset the fossil fuels and thus to reduce the emission of pollutants. Since the power generation from renewable energy resources based DGs (i.e., solar radiation, wind speed, etc.) are intermittent in nature, the power generation from such DGs are also ii intermittent. This makes such DGs non-dispatchable, i.e., it is difficult to follow a pre-defined operating strategy for such DG units. For optimum allocation of such non-dispatchable DG in the distribution system, Monte Carlo simulation (MCS) based method is considered as a benchmark method. However, MCS performs several time-consuming simulations to provide the solution. Further, simultaneous placement of dispatchable and non-dispatchable DG units in a distribution system is a challenging task. Therefore, exploiting probabilistic technique, a Mixed Integer Nonlinear Programming (MINLP) based formulation is developed for optimal allocation of dispatchable and non-dispatchable DG units in a distribution system. An objective function, comprising the cost associated with installation and operation of DGs, cost of energy from the upper grid, cost of energy losses and cost of emission, has been formulated. Gas-turbine and wind energy based power generation units are considered as dispatchable and non-dispatchable DG units, respectively. The uncertainties associated with load and wind speed are handled by employing suitable probabilistic techniques. The developed formulation has been applied on 33-bus and 69-bus test distribution networks under different scenarios. Further, the application of analytical probabilistic approach has been extended to develop a MINLP based multi-objective formulation for the optimal size and location of dispatchable and non-dispatchable DG units in a distribution system. An objective function, comprising technical, economical and environmental benefits of DG integration, has been formulated. In this work also, gas-turbine and wind energy based power generation units are considered as dispatchable and non-dispatchable DG units, respectively. The uncertainties associated with load and wind speed are taken in to consideration. An AHP technique is used for selection of suitable weights to different objectives. The developed formulation has been applied on 33-bus and 69-bus test distribution networks under different scenarios.