Open AccessCost reduction in microgrid using demand response program of loads and uncertainty modeling with point estimation method
Author(s) -
Rajamand Sahbasadat
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12299
Subject(s) - microgrid , demand response , renewable energy , load management , incentive , computer science , reliability engineering , reduction (mathematics) , energy storage , mathematical optimization , economics , engineering , microeconomics , power (physics) , electrical engineering , electricity , mathematics , geometry , physics , quantum mechanics
Summary Uncertainty in microgrid causes degradation in load supporting and stability of the system. The origin of uncertainty may be from renewable distributed generators (DGs), market price, load demand, and other issues. To compensate for this problem, energy storage system (ESS) and demand response (DR) program are applied in microgrid energy management process. ESS is used as a reserve energy and DR is defined as cooperation of users on demand side of microgrid. DR is done in two forms: one is based on load reduction of users due to the price tariffs called price‐based DR and the second is DR based on incentive payment to customers for load reduction especially in peak times named as incentive‐based DR. In this article, price‐based DR and three types of load as industrial, commercial and house load are considered with three DR features to cooperate in energy management. Moreover, the uncertainty of renewable DGs, market price and load demand are modeled and estimated by point estimation method (PEM). Simulation results are performed in three scenarios including initial battery charge and the possibility of being off‐state of some DGs in stochastic medium with and without DR program and the results are compared in these scenarios. The effect of DR on microgrid cost is investigated using these scenarios where the influence of DR on the total cost reduction is observable in the studied microgrid.