Enhanced merit order and augmented Lagrange Hopfield network for ramp rate constrained unit commitment

This paper proposes an enhanced merit order (EMO) and augmented Hopfield Lagrange neural network (ALH) for solving ramp rate constrained unit commitment (RUC) problem. The proposed EMO-ALH minimizes the total production cost subject to the power balance, 15 minute spinning reserve response time cons...

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Bibliographic Details
Published in:2006 IEEE Power Engineering Society General Meeting p. 7 pp.
Main Authors: Dieu, V.N., Ongsakul, W.
Format: Conference Proceeding
Language:English
Published: IEEE 2006
Subjects:
augmented Lagrange Hopfield neural network
Costs
Delay
Dynamic programming
Enhanced merit order
Lagrangian functions
Logic testing
Neural networks
Power generation
ramp rate constrained economic dispatch
ramp rate constrained unit commitment
Spinning
System testing
Time factors
ISBN:1424404932, 9781424404933
ISSN:1932-5517
Online Access:Get full text
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Summary:This paper proposes an enhanced merit order (EMO) and augmented Hopfield Lagrange neural network (ALH) for solving ramp rate constrained unit commitment (RUC) problem. The proposed EMO-ALH minimizes the total production cost subject to the power balance, 15 minute spinning reserve response time constraint, generation ramp limit constraints, and minimum up and down time constraints. The EMO is a merit order enhanced by a heuristic search algorithm based on average production cost of units, and the ALH is a continuous Hopfield network whose energy function is based on augmented Lagrangian relaxation. The EMO is used to solve unit scheduling problem satisfying spinning reserve, minimum up and down time constraints, and the ALH is used to solve ramp rate constrained economic dispatch (RED) problem by minimizing the operation cost subject to the power balance and new generator operating frame limits. For the hours with insufficient power due to ramp rate or 15 minute spinning reserve response time constraints, repairing strategy based on heuristic search is used to satisfy the constraints. The proposed EMO-ALH is tested on 26-unit IEEE reliability test system, 38-unit and 45-unit practical systems and compared to combined artificial neural network with heuristics and dynamic programming (ANN-DP), improved adaptive Lagrangian relaxation (ILR), dynamic programming (DP), Lagrangian relaxation (LR), simulated annealing (SA), constraint logic programming (CLP), fuzzy optimization (FO), and parallel repair genetic algorithm (PRGA). Test results indicate that the proposed method is superior to the others, leading to substantial cost savings and faster computing time
ISBN:1424404932
9781424404933
ISSN:1932-5517
DOI:10.1109/PES.2006.1709185