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Abstract
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The inherent volatility and unpredictable nature of renewable generations and load demand pose
considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges,
this paper proposes a new risk-based multi-objective energy exchange optimization for networked
MGs from economic and reliability standpoints under load consumption and renewable power
generation uncertainties. In so doing, three various risk-based strategies are distinguished by using
conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective
function. The first function minimizes the operation and maintenance costs, cost of power transaction
between upstream network and MGs as well as power loss cost, whereas the second function minimizes
the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated
into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction
method has been implemented to reduce the computational burden. Finally, non-dominated sorting
genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best
solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the
performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and
the obtained results show that the presented approach can be considered as an efficient tool for optimal
energy exchange optimization of MGs.
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