Day-Ahead Optimal Operation for Multi-Energy Residential Systems with Renewables
Abstract
The intermittency and stochasticity nature of distributed renewable energy sources has introduced great challenges to the efficiency and security of energy distribution system operations. To address the negative impacts of intermittent renewable energy sources, this paper proposes here a day-ahead optimal operation strategy utilizing distributed energy resources based on the framework of the interconnected multi-energy system. First, a framework and mathematical models of multi-energy residential systems (MERS) are proposed. Based on the characteristics of residential energy distribution networks, the complex MERS models are reformulated to relieve the computational burden. Furthermore, the uncertainty factors such as renewable energy generation fluctuations and demand variations are handled by a reformulated chance con-strained programming technique. The feasibility and effectiveness of the proposed method are validated through a combined electric power and natural gas test system. Compared to similar models and methods in the existing literature, the proposed method performs better in terms of solution time, model scalability, and robustness in handling uncertainties.
- Authors:
-
- Univ. of Saskatchewan, Saskatoon, SK (Canada). Dept. of Electrical and Computer Engineering
- Univ. of Saskatchewan, Saskatoon, SK (Canada). Dept. of Electrical and Computer Engineering; Brookhaven National Lab. (BNL), Upton, NY (United States). Sustainable Energy Technologies Dept.
- Zhejiang Univ., Hangzhou (China). School of Electrical Engineering
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States); Univ. of Saskatchewan, Saskatoon, SK (Canada)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1501583
- Report Number(s):
- BNL-211393-2019-JAAM
Journal ID: ISSN 1949-3029
- Grant/Contract Number:
- SC0012704
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Sustainable Energy
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 4; Journal ID: ISSN 1949-3029
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 24 POWER TRANSMISSION AND DISTRIBUTION; distributed energy resources; energy storage; multi-energy system; renewable energy
Citation Formats
Liu, Weijia, Zhan, Junpeng, Chung, C. Y., and Li, Yang. Day-Ahead Optimal Operation for Multi-Energy Residential Systems with Renewables. United States: N. p., 2018.
Web. doi:10.1109/TSTE.2018.2876387.
Liu, Weijia, Zhan, Junpeng, Chung, C. Y., & Li, Yang. Day-Ahead Optimal Operation for Multi-Energy Residential Systems with Renewables. United States. https://doi.org/10.1109/TSTE.2018.2876387
Liu, Weijia, Zhan, Junpeng, Chung, C. Y., and Li, Yang. Thu .
"Day-Ahead Optimal Operation for Multi-Energy Residential Systems with Renewables". United States. https://doi.org/10.1109/TSTE.2018.2876387. https://www.osti.gov/servlets/purl/1501583.
@article{osti_1501583,
title = {Day-Ahead Optimal Operation for Multi-Energy Residential Systems with Renewables},
author = {Liu, Weijia and Zhan, Junpeng and Chung, C. Y. and Li, Yang},
abstractNote = {The intermittency and stochasticity nature of distributed renewable energy sources has introduced great challenges to the efficiency and security of energy distribution system operations. To address the negative impacts of intermittent renewable energy sources, this paper proposes here a day-ahead optimal operation strategy utilizing distributed energy resources based on the framework of the interconnected multi-energy system. First, a framework and mathematical models of multi-energy residential systems (MERS) are proposed. Based on the characteristics of residential energy distribution networks, the complex MERS models are reformulated to relieve the computational burden. Furthermore, the uncertainty factors such as renewable energy generation fluctuations and demand variations are handled by a reformulated chance con-strained programming technique. The feasibility and effectiveness of the proposed method are validated through a combined electric power and natural gas test system. Compared to similar models and methods in the existing literature, the proposed method performs better in terms of solution time, model scalability, and robustness in handling uncertainties.},
doi = {10.1109/TSTE.2018.2876387},
journal = {IEEE Transactions on Sustainable Energy},
number = 4,
volume = 10,
place = {United States},
year = {Thu Oct 18 00:00:00 EDT 2018},
month = {Thu Oct 18 00:00:00 EDT 2018}
}
Web of Science