Abstract
This paper presents a risk-constrained scheduling optimization model for a grid-connected hybrid microgrid including demand response (DR), electric vehicles (EVs), variable wind power generation and dispatchable generation units. The proposed model determines optimal scheduling of dispatchable units, interactions with the main grid as well as adjustable responsive loads and EVs demand to maximize the expected microgrid operator’s profit under different scenarios. The uncertainties of day-ahead (DA) market prices, wind power production and demands of customers and EVs are considered in this study. To address these uncertainties, conditional value-at-risk (CVaR) as a risk measurement tool is added to the optimization model to evaluate the risk of profit loss and to indicate decision attitudes in different conditions. The proposed method is finally applied to a typical hybrid microgrid with flexible demand-side resources and its applicability and effectives are verified over different working conditions with uncertainties.
Highlights
The penetration level of renewable energy sources (RESs) such as wind and photovoltaic generation has increased at a rapid rate in smart distribution networks
To make microgrids more flexible, they should be evolved into smart active networks by implementing innovative concepts such as demand response (DR) actions [4,5] and e-mobility based on the usage of battery powered electric vehicles (EVs) [6,7]
This study proposes a stochastic framework for optimal scheduling of a grid-connected hybrid microgrid which consists of several responsive loads, EVs, wind turbines and dispatchable generation units
Summary
The penetration level of renewable energy sources (RESs) such as wind and photovoltaic generation has increased at a rapid rate in smart distribution networks. To make microgrids more flexible, they should be evolved into smart active networks by implementing innovative concepts such as demand response (DR) actions [4,5] and e-mobility based on the usage of battery powered electric vehicles (EVs) [6,7]. These flexible sources could bring significant advantages to future microgrids by managing the demand side, they might negatively affect the system performance through their stochastic behaviors. Due to high uncertainties in both supply and demand sides of microgrids, proper mechanisms are required to manage the uncertainties
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