Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management

• Published in: Energy conversion and management 2015-12, Vol.106, p.308-321
• Main Authors: Aghajani, G.R., Shayanfar, H.A., Shayeghi, H.
• Format: Article
• Language: English
• Subjects: Consumer pricing; Demand response; Demand response provider; Energy management; Micro-grid; Photovoltaic power; Wind power
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2015.08.059

•Using DRPs to cover the uncertainties resulted from power generation by WT and PV.•Proposing the use of price-offer packages and amount of DR for implement DRPs.•Considering a multi-objective scheduling model and use of MOPSO algorithm. In this paper, a multi-objective energy management system is proposed in order to optimize micro-grid (MG) performance in a short-term in the presence of Renewable Energy Sources (RESs) for wind and solar energy generation with a randomized natural behavior. Considering the existence of different types of customers including residential, commercial, and industrial consumers can participate in demand response programs. As with declare their interruptible/curtailable demand rate or select from among different proposed prices so as to assist the central micro-grid control in terms of optimizing micro-grid operation and covering energy generation uncertainty from the renewable sources. In this paper, to implement Demand Response (DR) schedules, incentive-based payment in the form of offered packages of price and DR quantity collected by Demand Response Providers (DRPs) is used. In the typical micro-grid, different technologies including Wind Turbine (WT), PhotoVoltaic (PV) cell, Micro-Turbine (MT), Full Cell (FC), battery hybrid power source and responsive loads are used. The simulation results are considered in six different cases in order to optimize operation cost and emission with/without DR. Considering the complexity and non-linearity of the proposed problem, Multi-Objective Particle Swarm Optimization (MOPSO) is utilized. Also, fuzzy-based mechanism and non-linear sorting system are applied to determine the best compromise considering the set of solutions from Pareto-front space. The numerical results represented the effect of the proposed Demand Side Management (DSM) scheduling model on reducing the effect of uncertainty obtained from generation power and predicted by WT and PV in a MG.