电力市场中零售商的战略零售定价与需求竞价:数据驱动的机会约束规划

IF 13 Q1 ENERGY & FUELS
Dawei Qiu , Zihang Dong , Guangchun Ruan , Haiwang Zhong , Goran Strbac , Chongqing Kang
{"title":"电力市场中零售商的战略零售定价与需求竞价:数据驱动的机会约束规划","authors":"Dawei Qiu ,&nbsp;Zihang Dong ,&nbsp;Guangchun Ruan ,&nbsp;Haiwang Zhong ,&nbsp;Goran Strbac ,&nbsp;Chongqing Kang","doi":"10.1016/j.adapen.2022.100100","DOIUrl":null,"url":null,"abstract":"<div><p>This paper proposes a novel bi-level optimization model to study the strategic retail pricing and demand bidding problems of an electricity retailer that considers the interactions between demand response and market clearing process. In order to accurately forecast the day-ahead demand bids submitted by the retailer, a novel deep learning framework based on convolutional neural networks and long short-term memory is proposed that can capture both local trends and long-term dependency of the forecasting data. In addition, uncertainties about the retailer’s served demand, rivals’ demand bids, and wind power generation are incorporated using the data-driven uncertainty set constructed from data. We further propose chance-constrained programming that introduces a set of chance constraints to represent the operational risk associated with the market uncertainties. To solve this problem, we first reformulate chance-constrained programming as a tractable second-order conic programming and then convert it into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker conditions. The scope of the examined case studies is four-fold. First, they evaluate the benefits of the proposed forecasting framework in terms of higher accuracy and expected profit compared to the conventional forecasting methods. Second, they demonstrate how demand flexibility affects the retailer’s strategies and its business cases. Third, they highlight the added value of the proposed bi-level model capturing the market clearing process by comparing its outcomes against the state-of-the-art bi-level model with exogenous market prices. Finally, they analyze the retailer’s strategies and business cases at different confidence levels regarding the imposed chance constraints.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"7 ","pages":"Article 100100"},"PeriodicalIF":13.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266679242200018X/pdfft?md5=9094ba34e1f6fcc630a959da13cd6aaa&pid=1-s2.0-S266679242200018X-main.pdf","citationCount":"14","resultStr":"{\"title\":\"Strategic retail pricing and demand bidding of retailers in electricity market: A data-driven chance-constrained programming\",\"authors\":\"Dawei Qiu ,&nbsp;Zihang Dong ,&nbsp;Guangchun Ruan ,&nbsp;Haiwang Zhong ,&nbsp;Goran Strbac ,&nbsp;Chongqing Kang\",\"doi\":\"10.1016/j.adapen.2022.100100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper proposes a novel bi-level optimization model to study the strategic retail pricing and demand bidding problems of an electricity retailer that considers the interactions between demand response and market clearing process. In order to accurately forecast the day-ahead demand bids submitted by the retailer, a novel deep learning framework based on convolutional neural networks and long short-term memory is proposed that can capture both local trends and long-term dependency of the forecasting data. In addition, uncertainties about the retailer’s served demand, rivals’ demand bids, and wind power generation are incorporated using the data-driven uncertainty set constructed from data. We further propose chance-constrained programming that introduces a set of chance constraints to represent the operational risk associated with the market uncertainties. To solve this problem, we first reformulate chance-constrained programming as a tractable second-order conic programming and then convert it into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker conditions. The scope of the examined case studies is four-fold. First, they evaluate the benefits of the proposed forecasting framework in terms of higher accuracy and expected profit compared to the conventional forecasting methods. Second, they demonstrate how demand flexibility affects the retailer’s strategies and its business cases. Third, they highlight the added value of the proposed bi-level model capturing the market clearing process by comparing its outcomes against the state-of-the-art bi-level model with exogenous market prices. Finally, they analyze the retailer’s strategies and business cases at different confidence levels regarding the imposed chance constraints.</p></div>\",\"PeriodicalId\":34615,\"journal\":{\"name\":\"Advances in Applied Energy\",\"volume\":\"7 \",\"pages\":\"Article 100100\"},\"PeriodicalIF\":13.0000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S266679242200018X/pdfft?md5=9094ba34e1f6fcc630a959da13cd6aaa&pid=1-s2.0-S266679242200018X-main.pdf\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Applied Energy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S266679242200018X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Applied Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266679242200018X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 14

摘要

本文提出了一个考虑需求响应和市场出清过程相互作用的双层优化模型来研究电力零售商的战略零售定价和需求竞价问题。为了准确预测零售商日前的需求出价,提出了一种基于卷积神经网络和长短期记忆的深度学习框架,既能捕捉预测数据的局部趋势,又能捕捉预测数据的长期依赖性。此外,利用数据构建的数据驱动不确定性集,将零售商的服务需求、竞争对手的需求出价和风力发电的不确定性纳入其中。我们进一步提出机会约束规划,引入一组机会约束来表示与市场不确定性相关的操作风险。为了解决这一问题,我们首先将机会约束规划重新表述为可处理的二阶二次规划,然后利用其Karush - Kuhn - Tucker条件将其转化为具有均衡约束的单级数学规划。所审查的案例研究的范围是四倍。首先,他们评估了与传统预测方法相比,所提出的预测框架在更高的准确性和预期利润方面的好处。其次,他们展示了需求灵活性如何影响零售商的战略和商业案例。第三,他们通过将其结果与最先进的具有外生市场价格的双水平模型进行比较,强调了所提出的捕捉市场出清过程的双水平模型的附加价值。最后,他们分析零售商的战略和商业案例在不同的置信水平关于强加的机会约束。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strategic retail pricing and demand bidding of retailers in electricity market: A data-driven chance-constrained programming

This paper proposes a novel bi-level optimization model to study the strategic retail pricing and demand bidding problems of an electricity retailer that considers the interactions between demand response and market clearing process. In order to accurately forecast the day-ahead demand bids submitted by the retailer, a novel deep learning framework based on convolutional neural networks and long short-term memory is proposed that can capture both local trends and long-term dependency of the forecasting data. In addition, uncertainties about the retailer’s served demand, rivals’ demand bids, and wind power generation are incorporated using the data-driven uncertainty set constructed from data. We further propose chance-constrained programming that introduces a set of chance constraints to represent the operational risk associated with the market uncertainties. To solve this problem, we first reformulate chance-constrained programming as a tractable second-order conic programming and then convert it into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker conditions. The scope of the examined case studies is four-fold. First, they evaluate the benefits of the proposed forecasting framework in terms of higher accuracy and expected profit compared to the conventional forecasting methods. Second, they demonstrate how demand flexibility affects the retailer’s strategies and its business cases. Third, they highlight the added value of the proposed bi-level model capturing the market clearing process by comparing its outcomes against the state-of-the-art bi-level model with exogenous market prices. Finally, they analyze the retailer’s strategies and business cases at different confidence levels regarding the imposed chance constraints.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信