Open AccessPower Supply Company purchase’ portfolio optimisation considering electric vehicle charging load forecasting
Author(s) -
Li Yahong,
Ren Hui
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0783
Subject(s) - cvar , portfolio , expected shortfall , hedge , portfolio optimization , electricity , computer science , business , finance , engineering , electrical engineering , ecology , biology
In recent years, a large number of electric vehicles (EVs) were used and the deployment of EVs will lead to an increase in load and load uncertainty, which introduces volume risk in the bilateral contracts. In order to assess and hedge the risk, the EVs charging load model and the Power Supply Company purchase’ portfolio optimisation model are proposed. A linear programming problem for the Power Supply Company purchase’ portfolio optimisation with conditional value at risk (CVaR) have been formulated, and it contains load uncertainty caused by EVs load, price fluctuation, and the expected cost of errors. This study analyses optimal portfolio allocations to different markets, efficient frontier of CVaR, and the influence of different EVs market penetration levels on the portfolio strategy. The analysis results show that risk of the market increases as the EVs market penetration level increases. Power Supply Company may hedge risk through adjusting the optimal portfolio allocation to different electricity markets.