Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.3390/su15129769
This paper develops a two-stage robust optimization (TSRO) model for prosumers considering multiple uncertainties from the sustainable energy of wind power generation and load demand and extends the existing nested column-and-constraint generation (C&CG) algorithm to solve the corresponding optimization problem. First, considering the impact of these uncertainties on market trading strategies of prosumers, a box uncertainty set is introduced to characterize the multiple uncertainties; a TSRO model for prosumers considering multiple uncertainties is then constructed. Second, the existing nested C&CG algorithm is extended to solve the corresponding optimization problem of which the second-stage optimization is a bi-level one and the inner level is a non-convex optimization problem containing 0–1 decision variables. Finally, a case study is solved. The optimized final overall operating cost of prosumers under the proposed model is CNY 3201.03; the extended algorithm requires only four iterations to converge to the final solution. If a convergence accuracy of 10−6 is used, the final solution time of the extended algorithm is only 9.75 s. The case study result shows that prosumers dispatch the ESS to store surplus wind power generated during the nighttime period and release the stored electricity when the wind power generation is insufficient during the daytime period. It can contribute to promoting the local accommodation of renewable energy and improving the efficiency of renewable energy utilization. The market trading strategy and scheduling results of the energy storage system (ESS) are affected by multiple uncertainties. Moreover, the extended nested C&CG algorithm has a high convergence accuracy and a fast convergence speed.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su15129769
- https://www.mdpi.com/2071-1050/15/12/9769/pdf?version=1687169621
- OA Status
- gold
- Cited By
- 11
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381435248
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381435248Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/su15129769Digital Object Identifier
- Title
-
Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-19Full publication date if available
- Authors
-
Qiang Zhou, Jianmei Zhang, Pengfei Gao, Ruixiao Zhang, Lijuan Liu, Sheng Wang, Cheng Lin, Wei Wang, Shiyou YangList of authors in order
- Landing page
-
https://doi.org/10.3390/su15129769Publisher landing page
- PDF URL
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https://www.mdpi.com/2071-1050/15/12/9769/pdf?version=1687169621Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2071-1050/15/12/9769/pdf?version=1687169621Direct OA link when available
- Concepts
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Mathematical optimization, Renewable energy, Robust optimization, Demand response, Computer science, Wind power, Optimization problem, Electricity generation, Electricity market, Electricity, Power (physics), Mathematics, Engineering, Physics, Electrical engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 3, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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