A hydropower plant scheduling model considering the stochastic correlation between runoff and electricity prices
Evaluating and addressing the risks posed by runoff and electricity prices in hydropower plants involved in medium-to long-term scheduling is a pressing issue. To address this， a scheduling model is proposed that aims to maximize expected net revenue while minimizing medium- to long-term operational...
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| Format: | Article |
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zhejiang electric power
2025-01-01
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| Series: | Zhejiang dianli |
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| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=19c57fa0-220f-4ea3-8dba-b15e0582714c |
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| _version_ | 1823857079957323776 |
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| author | WU Ying WANG Juefei LI Junjie WANG Kun SHEN Yan WU Yingjun |
| author_facet | WU Ying WANG Juefei LI Junjie WANG Kun SHEN Yan WU Yingjun |
| author_sort | WU Ying |
| collection | DOAJ |
| description | Evaluating and addressing the risks posed by runoff and electricity prices in hydropower plants involved in medium-to long-term scheduling is a pressing issue. To address this， a scheduling model is proposed that aims to maximize expected net revenue while minimizing medium- to long-term operational risks. First， using the conditional value at risk （CVaR） theory， marginal distribution functions are utilized to characterize the risks of runoff uncertainty and electricity price volatility （EPV）， enabling accurate risk assessment for market-operated hydropower plants. Second， the least-squares cross validation （LSCV） is applied to determine the optimal bandwidth parameter in kernel density estimation （KDE）， ensuring a good fit for discrete runoff and electricity price data. Next， a Copula-Monte Carlo simulation method is used to model the joint risks of runoff and electricity prices， with Latin hypercube sampling （LHS） employed to enhance computational precision. Finally， case simulation and analysis are conducted to validate the effectiveness of the proposed model. |
| format | Article |
| id | doaj-art-ad3c0fedc97f4b8ea23fd1b5d19081bf |
| institution | Kabale University |
| issn | 1007-1881 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | zhejiang electric power |
| record_format | Article |
| series | Zhejiang dianli |
| spelling | doaj-art-ad3c0fedc97f4b8ea23fd1b5d19081bf2025-02-12T00:54:58Zzhozhejiang electric powerZhejiang dianli1007-18812025-01-01441243310.19585/j.zjdl.2025010031007-1881（2025）01-0024-10A hydropower plant scheduling model considering the stochastic correlation between runoff and electricity pricesWU Ying0WANG Juefei1LI Junjie2WANG Kun3SHEN Yan4WU Yingjun5Economic Research Institute of State Grid Zhejiang Electric Power Co.， Ltd.， Hangzhou 310020， ChinaCollege of Electrical and Power Engineering， Hohai University， Nanjing 211100， ChinaEconomic Research Institute of State Grid Zhejiang Electric Power Co.， Ltd.， Hangzhou 310020， ChinaEconomic Research Institute of State Grid Zhejiang Electric Power Co.， Ltd.， Hangzhou 310020， ChinaEconomic Research Institute of State Grid Zhejiang Electric Power Co.， Ltd.， Hangzhou 310020， ChinaCollege of Electrical and Power Engineering， Hohai University， Nanjing 211100， ChinaEvaluating and addressing the risks posed by runoff and electricity prices in hydropower plants involved in medium-to long-term scheduling is a pressing issue. To address this， a scheduling model is proposed that aims to maximize expected net revenue while minimizing medium- to long-term operational risks. First， using the conditional value at risk （CVaR） theory， marginal distribution functions are utilized to characterize the risks of runoff uncertainty and electricity price volatility （EPV）， enabling accurate risk assessment for market-operated hydropower plants. Second， the least-squares cross validation （LSCV） is applied to determine the optimal bandwidth parameter in kernel density estimation （KDE）， ensuring a good fit for discrete runoff and electricity price data. Next， a Copula-Monte Carlo simulation method is used to model the joint risks of runoff and electricity prices， with Latin hypercube sampling （LHS） employed to enhance computational precision. Finally， case simulation and analysis are conducted to validate the effectiveness of the proposed model.https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=19c57fa0-220f-4ea3-8dba-b15e0582714chydropower plantoptimal schedulingrunoffcvarkde |
| spellingShingle | WU Ying WANG Juefei LI Junjie WANG Kun SHEN Yan WU Yingjun A hydropower plant scheduling model considering the stochastic correlation between runoff and electricity prices Zhejiang dianli hydropower plant optimal scheduling runoff cvar kde |
| title | A hydropower plant scheduling model considering the stochastic correlation between runoff and electricity prices |
| title_full | A hydropower plant scheduling model considering the stochastic correlation between runoff and electricity prices |
| title_fullStr | A hydropower plant scheduling model considering the stochastic correlation between runoff and electricity prices |
| title_full_unstemmed | A hydropower plant scheduling model considering the stochastic correlation between runoff and electricity prices |
| title_short | A hydropower plant scheduling model considering the stochastic correlation between runoff and electricity prices |
| title_sort | hydropower plant scheduling model considering the stochastic correlation between runoff and electricity prices |
| topic | hydropower plant optimal scheduling runoff cvar kde |
| url | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=19c57fa0-220f-4ea3-8dba-b15e0582714c |
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