CVaR-based Stochastic Energy Management of a Smart Home

Considering recent developments in photovoltaic (PV) systems, storage, and electrical vehicles, not unexpected that one day smart homes will also take part in energy markets directly. In this regard, the presented paper proposes a stochastic programming approach to manage the consumption of a smart home according to intermittent PV system production and uncertain energy prices to make the smart home available for taking part in the local day-Ahead (DA) energy market. A battery storage system is integrated to make flexibility against price fluctuations. Furthermore, modeling of plug-in electric vehicles (PEV) is also provided, where the traveling pattern is modeled through scenarios. The goal is to maximize the daily profit of the smart home while the welfare of the inhabitants is satisfied by considering comfort constraints. In addition, the conditional value at risk (CVaR) risk index is considered to manage associated risk with gained profit. The obtained results show the effectiveness of the optimization framework, in which the expected daily profit of the homeowner can reach 1.72 per day in the risk-neutral condition

Optimal Battery Storage Arbitrage Considering Degradation Cost in Energy Markets

Energy arbitrage have monetary benefits for privately owned battery energy storage systems, such as the battery of an electric vehicle or residential batteries. However, the life cycle and degradation cost of the battery storage should be taken into consideration and can decrease obtained income in the long-term. This paper proposes an optimization framework to derive optimal bidding and offering curves for lead-acid battery storage participate in a stepwise energy market. The objective is to maximize the profit comes from participating in energy arbitrage action, while the life cycle of the battery is considered by objective function and constraints. Due to the small capacity of the considered storage unit, it can be assumed that this unit is a price-taker participant, which its actions cannot influence the market prices. Hence, the energy prices are modeled as uncertain parameters using stochastic programming approach. The second order stochastic dominance constraints are as risk management method.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed