IEICE Trans - Network Topology and Battery Size Exploration for Decentralized Energy Network with MIP Base Power Flow Optimization

Network Topology and Battery Size Exploration for Decentralized Energy Network with MIP Base Power Flow Optimization

Ittetsu TANIGUCHI
Kazutoshi SAKAKIBARA
Shinya KATO
Masahiro FUKUI

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E96-A No.7 pp.1617-1624
Publication Date: 2013/07/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E96.A.1617
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: General Fundamentals and Boundaries
Keyword:
design optimization, mixed integer programming (MIP), combinatorial optimization problem, smart grid,

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Summary:
Large-scale introduction of renewable energy such as photovoltaic energy and wind is a big motivation for renovating conventional grid systems. To be independent from existing power grids and to use renewable energy as much as possible, a decentralized energy network is proposed as a new grid system. The decentralized energy network is placed among houses to connect them with each other, and each house has a PV panel and a battery. A contribution of this paper is a network topology and battery size exploration for the decentralized energy network in order to make effective use of renewable energy. The proposed method for exploring the decentralized energy network design is inspired by the design methodology of VLSI systems, especially design space exploration in system-level design. The proposed method is based on mixed integer programming (MIP) base power flow optimization, and it was evaluated for all design instances. Experimental results show that the decentralized energy network has the following features. 1) The energy loss and energy purchased due to power shortage were not affected by each battery size but largely affected by the sum of all battery sizes in the network, and 2) the network topology did not largely affect the energy loss and the purchased energy. These results will become a useful guide to designing an optimal decentralized energy network for each region.