Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid

• Published in: Renewable energy Vol. 89; pp. 730 - 742
• Main Authors: Coelho, Vitor N., Coelho, Igor M., Coelho, Bruno N., Cohen, Miri Weiss, Reis, Agnaldo J.R., Silva, Sidelmo M., Souza, Marcone J.F., Fleming, Peter J., Guimarães, Frederico G.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2016
• Subjects: batteries; Costs; Criteria; Distributed generation; Electric power generation; energy; Energy storage; Energy storage management; linear programming; Microgrids; photovoltaic cells; planning; Plug-in electric vehicle; Power dispatching; prediction; Probabilistic forecast; Renewable energy; Schedules; Sharpe ratio; uncertainty; vehicles (equipment); Volatility; wind power; Sharpe ratio; Plug-in electric vehicle; Probabilistic forecast; Energy storage management; Microgrids; Power dispatching
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2015.11.084

This paper describes a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units. We formulate the energy storage planning as a Mixed-Integer Linear Programming (MILP) problem, respecting PEV requirements, minimizing three different objectives and analyzing three different criteria. Two novel cost-to-variability indicators, based on Sharpe Ratio, are introduced for analyzing the volatility of the energy storage schedules. By adding these additional criteria, energy storage planning is optimized seeking to minimize the following: total Microgrid (MG) costs; PEVs batteries usage; maximum peak load; difference between extreme scenarios and two Sharpe Ratio indices. Different scenarios are considered, which are generated with the use of probabilistic forecasting, since prediction involves inherent uncertainty. Energy storage planning scenarios are scheduled according to information provided by lower and upper bounds extracted from probabilistic forecasts. A MicroGrid (MG) scenario composed of two renewable energy resources, a wind energy turbine and photovoltaic cells, a residential MG user and different PEVs is analyzed. Candidate non-dominated solutions are searched from the pool of feasible solutions obtained during different Branch and Bound optimizations. Pareto fronts are discussed and analyzed for different energy storage scenarios. Perhaps the most important conclusion from this study is that schedules that minimize the total system cost may increase maximum peak load and its volatility over different possible scenarios, therefore may be less robust. •Design of a novel multi-objective power dispatching using plug-in vehicles.•Consideration of PEVs located at SmartParks as storage units.•Analysis of energy storage scenarios considering a novel cost-to-variability indicator.•Different scenarios are generated from probabilistic forecasts.•Storage schedules that minimize system total cost are subject to high volatility.