Lower partial moment and information entropy-based dynamic electricity purchasing strategy

Shang, Jincheng, Wang, Hui, Song, Qi, Wen, Fushuan, Ledwich, Gerard, Xue, Yusheng, & Huang, Jiansheng (2014) Lower partial moment and information entropy-based dynamic electricity purchasing strategy. Journal of Energy Engineering.

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In the electricity market environment, load-serving entities (LSEs) will inevitably face risks in purchasing electricity because there are a plethora of uncertainties involved. To maximize profits and minimize risks, LSEs need to develop an optimal strategy to reasonably allocate the purchased electricity amount in different electricity markets such as the spot market, bilateral contract market, and options market. Because risks originate from uncertainties, an approach is presented to address the risk evaluation problem by the combined use of the lower partial moment and information entropy (LPME). The lower partial moment is used to measure the amount and probability of the loss, whereas the information entropy is used to represent the uncertainty of the loss. Electricity purchasing is a repeated procedure; therefore, the model presented represents a dynamic strategy. Under the chance-constrained programming framework, the developed optimization model minimizes the risk of the electricity purchasing portfolio in different markets because the actual profit of the LSE concerned is not less than the specified target under a required confidence level. Then, the particle swarm optimization (PSO) algorithm is employed to solve the optimization model. Finally, a sample example is used to illustrate the basic features of the developed model and method.

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ID Code: 74427
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Electricity market, Dynamic electricity purchasing strategy, Risk management, Lower partial moment, Information entropy
DOI: 10.1061/(ASCE)EY.1943-7897.0000208
ISSN: 0733-9402
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 American Society of Civil Engineers
Deposited On: 27 Jul 2014 23:16
Last Modified: 20 May 2015 09:25

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