Advances in Electrical Engineering

Volume 2016, Article ID 9760538, 13 pages

http://dx.doi.org/10.1155/2016/9760538

## Fuzzy Logic Controller Based Distributed Generation Integration Strategy for Stochastic Performance Improvement

^{1}Mewar University, Chittorgarh, Rajasthan, India^{2}Malwa Institute of Technology, Indore, India

Received 15 July 2016; Accepted 12 October 2016

Academic Editor: Mamun B. Ibne Reaz

Copyright © 2016 Jagdish Prasad Sharma and H. Ravishankar Kamath. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

In the restructured environment, distributed generation (DG) is considered as a very promising option due to a high initial capital cost of conventional plants, environmental concerns, and power shortage. Apart from the above, distributed generation (DG) has also abilities to improve performance of feeder. Most of the distribution feeders have radial structure, which compel to observe the impact of distributed generations on feeder performance, having different characteristics and composition of time varying static ZIP load models. Two fuzzy-based expert system is proposed for selecting and ranking the most appropriated periods to an integration of distributed generations with a feeder. Madami type fuzzy logic controller was developed for sizing of distributed generation, whereas Sugeno type fuzzy logic controller was developed for the DG location. Input parameters for Madami fuzzy logic controller are substation reserve capacity, feeder power loss to load ratio, voltage unbalance, and apparent power imbalances. DG output, survivability index, and node distance from substation are chosen as input to Sugeno type fuzzy logic controller. The stochastic performance of proposed fuzzy expert systems was evaluated on a modified IEEE 37 node test feeder with 15 minutes characteristics time interval varying static ZIP load models.

#### 1. Introduction

In the restructured environment, feasibility study of distributed generation (DG) integration to existing grid is a key interesting area of research. Deployment of distributed generation (DG) in distribution system brings technical as well as financial benefits to utilities. The positive benefits are loss reduction, reliability enhancement, and power quality improvement and negative effects are increased fault level and false operation of the feeder. Most of the distribution feeders have radial structure and are designed to operate with a single source along the feeder. To compensate rapid growth of load demand and proliferation of electronic device, available options for utilities are network extension, substation capacity augmentation, and DG integration. Apart from the above, the majority of the existing radial distribution feeders are lengthy, over and nonuniform loading, which resulted in an excessive voltage drop, poor voltage profile, male tripping of protection devices, and power loss. DG integration to existing grid is well suited option to utilities due to lack of financial resources and long-term implementation of grid extension work. As DG installation greatly influences the performance of distribution feeder, the optimal location and sizing of distributed generation (DG) are an active research interest, which is needed to harness maximum benefits from the DG.

Several researchers have used different optimization methods such as analytical, numerical, and heuristic for the sake of power loss minimization, cost reduction, profit maximization, and environmental emission reduction. Barin et al. have used a fuzzy-based expert system for choosing and ranking of the most appropriated periods to integrate distribution generation with an existing distribution network [1]. A fuzzy-based power management technique is employed to schedule power dispatching for microgrid/utility grid. The proposed power management technique is subjected to a set of constraints, including weather conditions, load-shedding hours, and peak pricing hours [2]. In order to mitigate demand and avoid power outage in peak hour, two fuzzy logic controller are simulated for optimal location and sizing of distributed generation on IEEE 13 test feeder [3].

A fuzzy logic method is deployed for optimal DG placement subjected to minimize total power loss constraint, whereas a new analytical method is used for DG sizing. The effectiveness of DG on system voltage profile and branch power losses is carried out on IEEE 69 and IEEE 33 radial feeder [4]. Manjili and Rajaee proposed fuzzy controller for energy management and cost reduction microgrid. The amount of power exchange from storage unit is based on the load demand, renewable generation rate, and electricity price [5].

Metia and Ghosh. presented optimal location of DG units with a 33-bus system based on the available amount of DG using fuzzy logic [6]. A firefly based algorithm for optimal location and capacity of CHP technology DG or a photovoltaic DG is implemented on IEEE 37-node feeder with the objectives of profit maximization [7]. Harmony search algorithm is utilized to determine appropriate size of shunt capacitors with real power losses and installation cost of shunt capacitors, whereas the location of shunt capacitors is identified using voltage stability index [8]. Padma Lalitha et al. presented a fuzzy approach for finding optimal DG locations and a PSO algorithm for optimal DG sizes on IEEE 33 node feeder [9]. A probabilistic fuzzy solution is proposed to identify vulnerable nodes for the optimal reconfiguration problem [10]. Fuzzy expert system employed for optimal capacitor placement and sizing for 35 buses with multilevel of loads [11].

Arabali et al. carried out a stochastic framework to optimal sizing and reliability analysis for a hybrid power system having wind power, photovoltaic (PV), and energy storage system. The stochastic nature of wind, solar irradiation, and photovoltaic (PV) power and load are stochastically modelled using ARMA [12]. Soroudi and Ehsan investigated the impact of an uncertain power production of distributed generations (DGs) on active losses of distribution feeder. Uncertainty in wind speed and gas turbines is modelled by a Weibull probability distribution function (PDF) and fuzzy, respectively [13].

Sharma and Ravishankar Kamath have presented voltage assessment indices for modified IEEE 37 node test feeder having time varying composite voltage sensitive load [14]. Performance indices to assess feeder performance of modified IEEE 37 node test feeder were developed with the help of forward-backward sweep method and two port parameters representation of feeder components [15].

The objective of this paper is to integrate DG using two fuzzy logic controllers. One fuzzy logic controller is used to determine the sizing of DG on the basis of feeder performance parameter such as substation reserve capacity, feeder power loss to load ratio, voltage unbalance, and apparent power imbalance. Another fuzzy logic controller is used to choose the DG location node on the basis of DG output; survivability index and node distance from substation are chosen as input.

#### 2. Feeder Performance Indices

The quality of power supply for modified IEEE 37 node test feeder is evaluated to develop performance indices and these performance indices are substation reserve capacity, voltage unbalance factor, and feeder power loss to load ratio, branch loading, voltage deviation, and power factor [15]. It is observed that the said substation transformer of feeder is highly overloaded between 35 and 71 characteristics time interval, which could be relived with solar PV penetration at the feeder. Modified feeder has also accounted for stochastic characteristics and composition of voltage sensitive load models. These load models are categorized into residential, commercial, and industrial consumers. Each category consumers are appliances with constant power, constant current, and constant impedance in random propositions. The participation of real and reactive power load exponents for the different type categories of consumers is characterized by the following equations [15]:

Calculation of ZIP loads compositions is shown in Appendix. To meet smart grid implementation criterion: performance indices are evaluated for 15 minutes characteristics time interval for the whole day. In these days, distribution generation integration is a common practice to meet out continuously increased demand. The load and renewable DG generation probabilistic nature are considered in this study. It is observed from load flow solution that feeder is subjected to overloading during 8 AM to 6 PM. Therefore, the fuzzy expert system is developed in a way that DG operated between the above periods. The photovoltaic DG system under different power factor scenario is considered for investigation.

#### 3. Proposed Fuzzy Expert System

The proposed fuzzy logic controller for DG integration implies Mamdani and Sugeno fuzzy inference system. The DG sizing is determined using Mamdani type fuzzy logic controller on the basis of feeder performance parameter such as substation reserve capacity, feeder power loss to load ratio, voltage unbalance, and apparent power imbalance, whereas Sugeno type fuzzy logic controller is used to choose the DG location on the basis of DG output, survivability index, and node distance from the substation. The fuzzy-based expert system is tested using the MATLAB fuzzy logic tool box under multi-rules-based decision and multisets considerations as in Figure 1.