A cost-efficient and reliable energy management of a micro-grid using intelligent demand-response program. (November 2015)

• Record Type: Journal Article
• Authors: Safamehr, Hossein; Rahimi-Kian, Ashkan
• Is Part Of: Energy. Volume 91(2015)
• Page Start: 283
• Page End: 293
• Publication Date: 2015-11
• Subjects: Energy management -- Economic demand response modeling -- Energy optimization -- Load management; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.08.051
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Providing a cost-efficient and reliable energy is one of the main issues in human societies of the 21st century. In response to this demand, new features of micro grid technology have provided huge potentials, specifically by the capability of having an interactive coordination between energy suppliers and consumers. Accordingly, this paper offers an improved model for achieving an optimal Demand Response programing. To solve the proposed multi-objective optimization problem, Artificial Bee Colony algorithm and quasi-static technique are utilized. The considered objectives in this paper are minimizing the overall cost of energy consumption and also improving the technical parameters of micro grid over a time horizon. This optimization is subject to several constraints such as satisfying the energy balance and the operating constraints of each energy supply sources. Manageable load or load as source is another enabling feature existing in smart energy networks, which is considered in this paper and its effect on cost reduction and reliability improvement is studied. Trying to examine the performance of the proposed Demand Response Programing in real conditions, the uncertainties are also analyzed by stochastic methods. The results show significant improvements which are obtained by applying just intelligent programming and management. Highlights: This paper presents a cost-efficient and reliable energy management of a micro-grid. New models of battery and manageable loads are formulated. Artificial Bee Colony algorithm is used to solve the optimization problem. Quasi-static technique is used to simplify the solving procedure. The uncertainties are also analyzed by stochastic methods.