Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid

Junyi Zhai, Sheng Wang, Lei Guo, Yuning Jiang, Zhongjian Kang and Colin N. Jones

Applied Energy, 2022, vol. 326, issue C, No S0306261922011965

Abstract: Multi-energy microgrid (MEMG) has the potential to improve the energy utilization efficiency. However, the uncertainty caused by distributed renewable energy resources brings an urgent need for multi-energy co-optimization to ensure secure operation. This paper focuses on the distributionally robust energy management problem for MEMG. Various flexible resources in different energy sectors are utilized for uncertainty mitigation, then, a data-driven Wasserstein distance-based distributionally robust joint chance-constrained (DRJCC) energy management model is proposed. To make the DRJCC model tractable, an optimized conditional value-at-risk (CVaR) approximation (OCA) formulation is proposed to transfer the joint chance-constrained model into a tractable form. Then, an iterative sequential convex optimization algorithm is tailored to reduce the solution conservatism by tuning OCA. Numerical result illustrates the effectiveness of the proposed model.

Keywords: Multi-energy microgrid (MEMG); Distributionally robust joint chance-constrained (DRJCC); Optimized conditional value-at-risk (CVaR) approximation (OCA); Sequential convex optimization (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922011965
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:326:y:2022:i:c:s0306261922011965

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.119939

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().