Abstract
Portfolio optimization in finance is the optimal allocation of financial assets in different stocks, mutual funds, bonds etc. to maximize the returns with risk tolerance. Sortino ratio is a measure for calculating risk adjusted return of investment portfolios. Here, it is adapted for power portfolio optimization in microgrid where total load demand (including losses) is optimally distributed to different microsources so that profit per unit risk of aggregator is maximized. The diminishment in profit (from energy and reserve markets) with reference to a target profit, for different levels of uncertainties in renewable energy and EVs is consolidated to find an estimate of risk. The profit relating to deterministic forecasted data of renewable energy and pre-dispatch information from the EV parking lots is considered as the risk free target profit. The reserve market is balanced using demand response, grid power purchase, EV discharging and other dispatchable energy sources to compensate possible discrepancy between scheduled and actual dispatch. Stochastic weight tradeoff particle swarm optimization (SWT-PSO) is used to maximize Sortino ratio subjected to constraints of a modified backward-forward sweep (BFS) power flow problem. The results are found to be better in terms of reduced financial risk and increased robustness to uncertainties.
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