Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design |
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| Affiliation: | 1. University of Alaska Anchorage, Anchorage, AK, USA;2. Mechanical Engineering Dept., Ohio University, Athens, OH, USA;3. Civil Engineering Dept., University of Alaska Anchorage, Anchorage, AK, USA;4. Electrical Engineering Dept., University of Alaska Anchorage, Anchorage, AK, USA;5. Mechanical Engineering Dept., University of Alaska Anchorage, Anchorage, AK, USA;1. Agricultural & Biological Engineering, Purdue University, West Lafayette, IN 47907, USA;2. Biobased Products and Energy Center, Department of Biosystems & Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078, USA;1. Institut UTINAM UMR CNRS 6213, Université de Franche-Comté, UFR Sciences et Techniques, 16 Route de Gray, 25030 Besançon Cédex, France;2. Solaronix SA, 129, rue de l''Ouriette, 1170 Aubonne, Switzerland |
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| Abstract: | This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found. |
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| Keywords: | Distributed energy resources Cost Emission Uncertainty Multiobjective optimization MINLP |
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