Home energy management system considering effective demand response strategies and uncertainties

被引:29
|
作者
Tostado-Veliz, Marcos [1 ]
Arevalo, Paul [1 ]
Kamel, Salah [2 ]
Zawbaa, Hossam M. [3 ,4 ]
Jurado, Francisco [1 ]
机构
[1] Univ Jaen, Dept Elect Engn, Jaen 23700, Spain
[2] Aswan Univ, Fac Engn, Dept Elect Engn, Aswan 81542, Egypt
[3] Beni Suef Univ, Fac Comp & Artificial Intelligence, Bani Suwayf, Egypt
[4] Technol Univ Dublin, Dublin, Ireland
基金
欧盟地平线“2020”;
关键词
Demand response; Home energy management system; Uncertainty; ALGORITHM;
D O I
10.1016/j.egyr.2022.04.006
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Nowadays, load serving entities require more active participation from consumers. In this context, demand response programs and home energy management systems play a crucial role in achieving multiple goals such as peak clipping. However, the adoption of demand response initiatives typically has a negative impact on the monetary expenditures of the users. This way, a demand response program should be as effective as possible to make the different goals more easily achievable without compromising the financial requirements of the users. This paper develops a home energy management system that incorporates three novel effective demand response strategies. The effectiveness of the adopted demand response strategies is checked through extensive simulations in a benchmark prosumer environment. To this end, a novel scenario-based approach is developed in order to manage uncertainties. The introduced strategies are compared with other well-known demand response mechanisms. To that end, a novel comparative index, which serves to evaluate the compromise between demand response achievements and energy bills, is introduced. Results obtained demonstrate that the developed strategies are more effective than other approaches. In fact, through the use of the proposed mechanisms, different indicators can be improved until -70%, while the electricity bill is only scarcely increased (-0.11=C). Other relevant aspects like the influence of the storage capacity and computational performance of the introduced optimization framework are also analysed. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:5256 / 5271
页数:16
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