Electricity price risk management based on insurance and weather derivatives
Abstract
is insufficient. Based on the Japanese JEPX market data from 2018 to 2023, the PSO-LSSVM model is proposed to combine insurance and
weather derivatives to form a comprehensive hedging strategy, which significantly improves the risk management effect compared with a single means. The insurance hedging effect is increased by 10%, and the weather derivatives strategy effect is increased by 25%. These findings
provide an efficient and comprehensive solution to the risk of electricity price fluctuation for power enterprises, and enrich the practical strategy of risk management in power market.
Keywords
Full Text:
PDFReferences
[1] Matsumoto T, Yamada Y. Simultaneous hedging strategy for price and volume risks in electricity businesses using energy and
weather derivatives[J]. Energy Economics,2021, 95: 105-101.
[2] Lai S, Qiu J, Tao Y, et al. Risk hedging strategies for electricity retailers using insurance and strangle weather derivatives[J]. International Journal of Electrical Power & Energy Systems,2022, 134: 107-372.
[3] Zhang Z, Jiang Y, Lin Z, et al. Optimal alliance strategies among retailers under energy deviation settlement mechanism in China’s
forward electricity market[J]. IEEE Transactions on Power Systems, 2019, 35(3): 2059-2071.
[4] Qiu J, Yang H, Dong Z Y, et al. A probabilistic transmission planning framework for reducing network vulnerability to extreme
events[J]. IEEE Transactions on Power Systems, 2015, 31(5): 3829-3839.
[5] Liu Chuang, Lu Yin-jun, Liu Hong-Yun, et al. Hot spot Temperature prediction of transformer winding based on improved PSOLSSVM model [J]. Inner Mongolia Electric Power Technology, 201, 39 (01) : 94-97.
[6] Fang Quan, Liu Chuang, Song Min, et al. Failure rate prediction of LSSVM overhead transmission line based on Fuzzy Evaluation
and PSO Optimization [J]. Water and Electricity Energy Science, 21, 39 (01) : 171-175.
[7] Hisamatsu R, Tabeta S, Kim S, et al. Storm surge risk assessment for the insurance system: A case study in Tokyo Bay, Japan[J].
Ocean & coastal management, 2020, 189: 105-147.
[8] Sanchez G A V, Alzate J M, Cadena A I, et al. Setting up standard power options to hedge price-quantity risk in a competitive electricity market: the colombian case[J]. IEEE Transactions on Power Systems, 2010, 26(3): 1493-1500.
[9] Kennedy J.Eberhart R. Particle swarm optimization(C)IEEE International Conference on Neural Network,Perth,Australia,1995,4:1942-1948.
DOI: http://dx.doi.org/10.18686/fm.v9i5.13372
Refbacks
- There are currently no refbacks.
