A practical approach for reliability-oriented multi-objective unit commitment problem

Hossein Narimani

doi:10.1016/j.asoc.2019.105786

2019-12-01

PRE

Mohammad Rasoul Narimani *

DOI：10.1016/j.asoc.2019.105786

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摘要

En 中文

Modern-day power systems have to operate not only to satisfy techno-economic constraints but also to retain a reasonable level of system's reliability accompanied by social welfare. This can be achieved by short-term planning of capacity reserve of power networks including hour-to-hour scheduling that unit commitment (UC) problem, one of the most important tools in power system operation, is designed for. In this connection, this paper develops an effective and straightforward hybrid structure to expeditiously solve reliability-oriented multi-objective UC problem integrated with spinning reserve as well as minimum up/down time constraints. Reliability and economic efficiency must be taken into account at the same time to make the UC problem a suitable tool for improving the stable operation of power systems in both economic and reliability standpoints. Considering total operation cost and total expected energy not supplied (TEENS) as objective functions, which almost always are in stark contrast with one another, makes the UC problem more complicated. To this end, the proposed hybrid framework is armed with a multi-objective Pareto-based solution methodology to solve the proposed problem. Performance of the proposed algorithm is scrutinized in detail on three test systems including 10-, 38-, and 54-unit test systems. Comparing the obtained results by the proposed approach with those available in literature corroborates the supremacy of the proposed algorithm respect to other alternatives in this realm of power engineering. The proposed approach tangibly reduces the operation cost for all three test systems i.e., 10-unit, 38-unit, and 54-unit power networks, by 11.8%, 0.38%, and 1.51% compare to the state-of-the-art algorithms in literature. A slight improvement in operation cost of realistic power networks saves millions of dollars annually because of the vastness of these networks. (C) 2019 Elsevier B.V. All rights reserved.

Keyword:

Hybrid Gray Wolf Optimizer-Particle

Swarm Optimization (HGWO-PSO)

Multi-Objective Unit Commitment (MOUC)

Pareto-optimal method

Total Expected Energy Not Supplied (TEENS)