State-aware stochastic optimal power flow
The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/153086 |
| _version_ | 1811683396895637504 |
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| author | Pareek, Parikshit Nguyen, Hung D. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Pareek, Parikshit Nguyen, Hung D. |
| author_sort | Pareek, Parikshit |
| collection | NTU |
| description | The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the generation cost and expectation of deviations in generation and node voltage set-points during real-time operation. We formulate SA-SOPF for a given affine policy and employ Gaussian process learning to obtain a distributionally robust (DR) affine policy for generation and voltage set-point change in real-time. In simulations, the GP-based affine policy has shown distributional robustness over three different uncertainty distributions for IEEE 14-bus system. The results also depict that the proposed SA-OPF formulation can reduce the expectation in voltage and generation deviation more than 60% in real-time operation with an additional day-ahead scheduling cost of 4.68% only for 14-bus system. For, in a 30-bus system, the reduction in generation and voltage deviation, the expectation is achieved to be greater than 90% for 1.195% extra generation cost. These results are strong indicators of possibility of achieving the day-ahead solution which lead to lower real-time deviation with minimal cost increase. |
| first_indexed | 2024-10-01T04:12:05Z |
| format | Journal Article |
| id | ntu-10356/153086 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:12:05Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1530862021-11-05T01:35:49Z State-aware stochastic optimal power flow Pareek, Parikshit Nguyen, Hung D. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution Stochastic Optimal Power Flow Machine Learning for Energy Systems The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the generation cost and expectation of deviations in generation and node voltage set-points during real-time operation. We formulate SA-SOPF for a given affine policy and employ Gaussian process learning to obtain a distributionally robust (DR) affine policy for generation and voltage set-point change in real-time. In simulations, the GP-based affine policy has shown distributional robustness over three different uncertainty distributions for IEEE 14-bus system. The results also depict that the proposed SA-OPF formulation can reduce the expectation in voltage and generation deviation more than 60% in real-time operation with an additional day-ahead scheduling cost of 4.68% only for 14-bus system. For, in a 30-bus system, the reduction in generation and voltage deviation, the expectation is achieved to be greater than 90% for 1.195% extra generation cost. These results are strong indicators of possibility of achieving the day-ahead solution which lead to lower real-time deviation with minimal cost increase. Energy Market Authority (EMA) Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) Published version This research is supported by NTU SUG, MOE AcRF TIER 1- 2019-T1-001-119 (RG 79/19), EMA & NRF EMA-EP004-EKJGC-0003, and NRF DERMS/ADMS 002899-00004 WP2. 2021-11-05T01:34:05Z 2021-11-05T01:34:05Z 2021 Journal Article Pareek, P. & Nguyen, H. D. (2021). State-aware stochastic optimal power flow. Sustainability, 13(14), 7577-. https://dx.doi.org/10.3390/su13147577 1937-0695 https://hdl.handle.net/10356/153086 10.3390/su13147577 2-s2.0-85110621014 14 13 7577 en 2019-T1-001-119 (RG 79/19) EMA-EP004-EKJGC-0003 002899-00004 WP2 Sustainability © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution Stochastic Optimal Power Flow Machine Learning for Energy Systems Pareek, Parikshit Nguyen, Hung D. State-aware stochastic optimal power flow |
| title | State-aware stochastic optimal power flow |
| title_full | State-aware stochastic optimal power flow |
| title_fullStr | State-aware stochastic optimal power flow |
| title_full_unstemmed | State-aware stochastic optimal power flow |
| title_short | State-aware stochastic optimal power flow |
| title_sort | state aware stochastic optimal power flow |
| topic | Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution Stochastic Optimal Power Flow Machine Learning for Energy Systems |
| url | https://hdl.handle.net/10356/153086 |
| work_keys_str_mv | AT pareekparikshit stateawarestochasticoptimalpowerflow AT nguyenhungd stateawarestochasticoptimalpowerflow |