CVaR-based Stochastic Energy Management of a Smart Home

Alireza Akbari-Dibavar, K. Zare, B. Mohammadi-ivatloo, A. Anvari‐Moghaddam, Z. Abdul-Malek
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引用次数: 1

Abstract

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.
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基于cvar的智能家居随机能量管理
考虑到光伏(PV)系统、存储和电动汽车的最新发展,总有一天智能家居也会直接参与能源市场,这并不令人意外。为此,本文提出了一种随机规划方法,根据光伏系统的间歇性生产和不确定的能源价格对智能家居的消费进行管理,使智能家居能够参与当地的日前能源市场。集成了电池存储系统,以灵活应对价格波动。此外,还对插电式电动汽车(PEV)进行了建模,通过场景对其行驶模式进行建模。目标是在考虑舒适约束的同时,实现智能家居的日常利润最大化,同时满足居民的福利。此外,考虑了条件风险值(CVaR)风险指数来管理与获得利润相关的风险。所得结果表明了优化框架的有效性,在风险中性条件下,房主的日预期利润可达1.72美元/天。
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