A customer-centric distributed data-driven stochastic coordination method for residential PV and BESS
An aggregation scheme is an effective transactive manner of Distributed Energy Resources (DER) spreading across distribution networks. Distributed approach locally achieves cost minimization of an aggregator and customers. The uncertainties of wholesale market price and rooftop PV output will impact...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172718 |
| _version_ | 1826118407143030784 |
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| author | Liu, Huichuan Qiu, Jing Zhao, Junhua Tao, Yuechuan Dong, Zhao Yang |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Liu, Huichuan Qiu, Jing Zhao, Junhua Tao, Yuechuan Dong, Zhao Yang |
| author_sort | Liu, Huichuan |
| collection | NTU |
| description | An aggregation scheme is an effective transactive manner of Distributed Energy Resources (DER) spreading across distribution networks. Distributed approach locally achieves cost minimization of an aggregator and customers. The uncertainties of wholesale market price and rooftop PV output will impact on aggregator's scheduling decision and each customer's cost, while solar energy fluctuation can cause an overvoltage problem in distribution networks. However, the probability distributions of these uncertainties always have errors, even in emerging data-based methods. There is no stochastic method using real data with an out-of-sample guarantee suitable for this distributed approach so far to help an aggregator avoid price risk and manage customers' energy against solar energy fluctuation. To address these unsolved issues, we propose a data-driven Wasserstein distributionally robust formulation of the aggregator's agent and customer's agent respectively. The Wasserstein metric is employed to construct the Wasserstein ambiguity set. The mathematical models are then reformulated equivalently to convex programming respectively so that the operating model can be solved by the off-the-shelf solver. To improve the efficiency of the distributed solving framework, an alternating optimization procedure (AOP) process is proposed to overcome the issue caused by binary variables in the alternating direction method of multipliers (ADMM). The proposed operation framework is verified on the modified IEEE 33-bus distribution network and realistic single-feeder LV network. |
| first_indexed | 2024-10-01T04:43:13Z |
| format | Journal Article |
| id | ntu-10356/172718 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:43:13Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1727182023-12-18T02:47:35Z A customer-centric distributed data-driven stochastic coordination method for residential PV and BESS Liu, Huichuan Qiu, Jing Zhao, Junhua Tao, Yuechuan Dong, Zhao Yang School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Stochastic Optimization Residential PV And BESS An aggregation scheme is an effective transactive manner of Distributed Energy Resources (DER) spreading across distribution networks. Distributed approach locally achieves cost minimization of an aggregator and customers. The uncertainties of wholesale market price and rooftop PV output will impact on aggregator's scheduling decision and each customer's cost, while solar energy fluctuation can cause an overvoltage problem in distribution networks. However, the probability distributions of these uncertainties always have errors, even in emerging data-based methods. There is no stochastic method using real data with an out-of-sample guarantee suitable for this distributed approach so far to help an aggregator avoid price risk and manage customers' energy against solar energy fluctuation. To address these unsolved issues, we propose a data-driven Wasserstein distributionally robust formulation of the aggregator's agent and customer's agent respectively. The Wasserstein metric is employed to construct the Wasserstein ambiguity set. The mathematical models are then reformulated equivalently to convex programming respectively so that the operating model can be solved by the off-the-shelf solver. To improve the efficiency of the distributed solving framework, an alternating optimization procedure (AOP) process is proposed to overcome the issue caused by binary variables in the alternating direction method of multipliers (ADMM). The proposed operation framework is verified on the modified IEEE 33-bus distribution network and realistic single-feeder LV network. This work was supported in part by the Australian Research Council Research Hub under Grant IH180100020, in part by the ARC Training Centre under Grant IC200100023, in part by the ARC Linkage Project under Grant LP200100056, in part by the ARC under Grant DP220103881, in part by the Shenzhen Institute of Artificial Intelligence and Robotics for Society, in part by the National Natural Science Foundation of China (Key Program), under Grants 71931003 and 72061147004, and in part by the National Natural Science Foundation of China under Grant 72171206. 2023-12-18T02:47:34Z 2023-12-18T02:47:34Z 2023 Journal Article Liu, H., Qiu, J., Zhao, J., Tao, Y. & Dong, Z. Y. (2023). A customer-centric distributed data-driven stochastic coordination method for residential PV and BESS. IEEE Transactions On Power Systems, 38(6), 5806-5819. https://dx.doi.org/10.1109/TPWRS.2022.3227178 0885-8950 https://hdl.handle.net/10356/172718 10.1109/TPWRS.2022.3227178 2-s2.0-85144754904 6 38 5806 5819 en IEEE Transactions on Power Systems © 2022 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Stochastic Optimization Residential PV And BESS Liu, Huichuan Qiu, Jing Zhao, Junhua Tao, Yuechuan Dong, Zhao Yang A customer-centric distributed data-driven stochastic coordination method for residential PV and BESS |
| title | A customer-centric distributed data-driven stochastic coordination method for residential PV and BESS |
| title_full | A customer-centric distributed data-driven stochastic coordination method for residential PV and BESS |
| title_fullStr | A customer-centric distributed data-driven stochastic coordination method for residential PV and BESS |
| title_full_unstemmed | A customer-centric distributed data-driven stochastic coordination method for residential PV and BESS |
| title_short | A customer-centric distributed data-driven stochastic coordination method for residential PV and BESS |
| title_sort | customer centric distributed data driven stochastic coordination method for residential pv and bess |
| topic | Engineering::Electrical and electronic engineering Stochastic Optimization Residential PV And BESS |
| url | https://hdl.handle.net/10356/172718 |
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