Abstract
The auxiliary problem principle is a powerful tool for solving multi-area economic dispatch problem. One of the main drawbacks of the auxiliary problem principle method is that the convergence performance depends on the selection of penalty parameter. In this paper, we propose a self-adaptive strategy to adjust penalty parameter based on the iterative information, the proposed approach is verified by two given test systems. The corresponding simulation results demonstrate that the proposed self-adaptive auxiliary problem principle iterative scheme is robust in terms of the selection of penalty parameter and has better convergence rate compared with the traditional auxiliary problem principle method.
Highlights
The aim of economic dispatch (ED) problem in power systems field is to determine the allocation of real power outputs for the generating units economically while satisfying corresponding physical and operational constraints [1]
Approximate Newton directions method is applied to address multi-area optimal power flow problem [3,4], the upside is that it allows the energy management system (EMS) in each sub-network to operate its system independently while obtaining an optimal coordinated but decentralized solution; the downside is that it is limited by strict condition which is a difficult requirement to meet in practice
We propose a self-adaptive Auxiliary problem principle (APP) for solving multi-area economic dispatch (MAED) problem, the key is that penalty parameter is allowed to vary per iteration according to the iterate information for better convergence performance
Summary
The aim of economic dispatch (ED) problem in power systems field is to determine the allocation of real power outputs for the generating units economically while satisfying corresponding physical and operational constraints [1]. Approximate Newton directions method is applied to address multi-area optimal power flow problem [3,4], the upside is that it allows the EMS in each sub-network to operate its system independently while obtaining an optimal coordinated but decentralized solution; the downside is that it is limited by strict condition which is a difficult requirement to meet in practice. Auxiliary problem principle (APP) has been originally introduced by Cohen in 1980 [5], this method has been extensive applied in power systems field, such as daily generation scheduling optimization [6], unit commitment [7] and distributed optimal power flow [8,9]. We propose a self-adaptive APP for solving multi-area economic dispatch (MAED) problem, the key is that penalty parameter is allowed to vary per iteration according to the iterate information for better convergence performance.
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