An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation

► Proposes a stochastic model for optimal energy management. ► Consider uncertainties related to the forecasted values for load demand. ► Consider uncertainties of forecasted values of output power of wind and photovoltaic units. ► Consider uncertainties of forecasted values of market price. ► Prese...

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Bibliographic Details
Published in:Applied energy 2012-11, Vol.99, p.455-470
Main Authors: Niknam, Taher, Azizipanah-Abarghooee, Rasoul, Narimani, Mohammad Rasoul
Format: Article
Language:English
Subjects:
algorithms
Applied sciences
Energy
Exact sciences and technology
Improved teaching–learning-based algorithm
market prices
Micro grid
Multi-objective stochastic optimization
optimization methods
probability distribution
Renewable energy management
Self adaptive probabilistic modification strategy
solutions
stochastic models
stochastic processes
storing
system optimization
Uncertainty
wind power
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Summary:► Proposes a stochastic model for optimal energy management. ► Consider uncertainties related to the forecasted values for load demand. ► Consider uncertainties of forecasted values of output power of wind and photovoltaic units. ► Consider uncertainties of forecasted values of market price. ► Present an improved multi-objective teaching–learning-based optimization. This paper proposes a stochastic model for optimal energy management with the goal of cost and emission minimization. In this model, the uncertainties related to the forecasted values for load demand, available output power of wind and photovoltaic units and market price are modeled by a scenario-based stochastic programming. In the presented method, scenarios are generated by a roulette wheel mechanism based on probability distribution functions of the input random variables. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. An improved multi-objective teaching–learning-based optimization is implemented to yield the best expected Pareto optimal front. In the proposed stochastic optimization method, a novel self adaptive probabilistic modification strategy is offered to improve the performance of the presented algorithm. Also, a set of non-dominated solutions are stored in a repository during the simulation process. Meanwhile, the size of the repository is controlled by usage of a fuzzy-based clustering technique. The best expected compromise solution stored in the repository is selected via the niching mechanism in a way that solutions are encouraged to seek the lesser explored regions. The proposed framework is applied in a typical grid-connected micro grid in order to verify its efficiency and feasibility.
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2012.04.017