IEICE TRANSACTIONS on Information
Re-Scheduling of Unit Commitment Based on Customers' Fuzzy Requirements for Power Reliability
Summary :
The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.7 pp.1378-1385
- Publication Date
- 2011/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1378
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Keyword
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Bo WANG, You LI, Junzo WATADA, "Re-Scheduling of Unit Commitment Based on Customers' Fuzzy Requirements for Power Reliability" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 7, pp. 1378-1385, July 2011, doi: 10.1587/transinf.E94.D.1378.
Abstract: The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1378/_p
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@ARTICLE{e94-d_7_1378,
author={Bo WANG, You LI, Junzo WATADA, },
journal={IEICE TRANSACTIONS on Information},
title={Re-Scheduling of Unit Commitment Based on Customers' Fuzzy Requirements for Power Reliability},
year={2011},
volume={E94-D},
number={7},
pages={1378-1385},
abstract={The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.},
keywords={},
doi={10.1587/transinf.E94.D.1378},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Re-Scheduling of Unit Commitment Based on Customers' Fuzzy Requirements for Power Reliability
T2 - IEICE TRANSACTIONS on Information
SP - 1378
EP - 1385
AU - Bo WANG
AU - You LI
AU - Junzo WATADA
PY - 2011
DO - 10.1587/transinf.E94.D.1378
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2011
AB - The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.
ER -
English
