The Effect of Different Non-linear Demand Response Models Considering Incentive and Penalty on Transmission Expansion Planning

Amir Sadegh Zakeri,Oveis Asgari Gashteroodkhani,Iman Niazazari,Hossein Askarian-Abyaneh

doi:10.24018/ejece.2019.3.1.57

Abstract

The Transmission Expansion Planning (TEP) problem involves adding new lines to the existing electrical transmission network in order to meet the electrical demand requirements. Demand Response (DR) plays an important role in solving the TEP problem due to the delay in the investment costs. Researchers usually focus on the linear model of DR, while the focus on nonlinear models including power, exponential and logarithmic of DR is small. In this paper and in order to understand which model gives the realistic results, the linear model of DR is studied simultaneously with nonlinear models including power, exponential and logarithmic of DR. Moreover, the effect of incentive and penalty which has been neglected in the studies, is investigated. The study is investigated based on the viewpoint of different participants of the market including Independent System Operator (ISO), Customers and Utilities. In order to prioritize and select the most effective DR program, five characteristics including Peak Reduction, Energy Consumption, Load Factor, Peak to Valley and Customer’s Total Cost are extracted from the load curve. Then, using the weighting coefficients obtained by Entropy technique and implementing the TOPSIS and AHP technique, different DR programs are prioritized.

Highlights

The Transmission Expansion Planning (TEP) problem involves adding new lines to the existing electrical transmission network in order to meet the electrical demand requirements, taking into account technical and financial constraints during the predefined planning horizon [1].TEP is a large-scale mixed-integer nonlinear optimization problem which includes many equality and inequality constraints
To find the effect of linear and nonlinear models of DR and the effect of incentive and penalty, the DR is applied on a typical load considering the following scenarios: 1. Linear Demand Response (LDR); III
Nonlinear Demand Response (NLDR) based on Exponential modelling considering linear incentive and penalty; 8

Summary

Introduction

TEP is a large-scale mixed-integer nonlinear optimization problem which includes many equality and inequality constraints. Sensitivity analysis was used in another studies of evolutionary technique [6,7,8] to solve TEP problem. In these studies, it is used sensitivity index to determine the added lines. It is used sensitivity index to determine the added lines Different algorithms such as load feeding [6], lowest criteria [7] and the optimal load flow [8] was used to make sensitivity index

Objectives

Results

Conclusion