Impact of Demand Response on Reliability Enhancement in Distribution Networks

Abstract

This paper presents an innovative instantaneous pricing scheme for optimal operation and improved reliability for distribution systems (DS). The purpose of the proposed program is to maximize the operator’s expected profit under various risk-taking conditions, such that the customers pay the minimum cost to supply energy. Using the previous information of the energy consumption for each customer, a customer baseline load (CBL) is defined; the energy price for consumption costs higher and lower than this level would be different. The proposed scheme calculates the difference between the baseline load and the consumption curve with the electricity market price instead of calculating the total consumption of the customers with the unstable price of the electricity market, which is uncertain. In the proposed tariff, the developed cost and load models are included in the distribution system operation problem, and the objective function is modeled as a mixed integer linear programming (MILP) problem. Also, the effect of demand response (DR) and elasticity on the load curve, the final profit of the distribution system operator, and payment risk and operation costs are examined. Since there are various uncertainties in the smart distribution grid, the calculations being time-consuming and volumetric is important in the evaluation of reliability indices. Thus, when computation volume can be decreased and computation speed can be increased, analytical reliability analysis methods can be used, as they were in the present work. Finally, the changes in the reliability indices were calculated for the ratio of the customers’ sensitivity to the price and the customers’ participation in the proposed tariff using an analytical method based on Monte Carlo simulation (MCS). The results showed the efficiency of the proposed method in increasing the operator profit, reducing the operation costs, and enhancing the reliability indices.

A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges

“…Bad data and measurement errors are also detected during this process. The estimated states are used to evaluate the system performance and evaluate its reliability [32]. SE techniques are widely developed and applied in transmission networks, but the application of these methods is limited in distribution grids because of some constraints.…”

A Spatial Analysis of Smart Meter Adoptions: Empirical Evidence from the U.S. Data

“…Smart meters, along with communication systems and meter data management systems, constitute advanced metering infrastructure (AMI) [10]. With the real-time information from AMIs, the utilities can utilize dynamic pricing, such as charging differently for different types of customers, different locations of the customer, different times of day, and different seasons, to balance the utility usage so that they can lower the risk of a power outage [11,12]. With the feedback information, customers have better knowledge about their spending patterns of using electric devices, and consequently are able to make behavioral adjustments to lower their electric bill and drive the reduction of electricity consumption [13][14][15].…”