Optimal Dynamic Multi-source Multi-community Power Schedule and Trading

IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 110–115

Abstract

As peer-to-peer energy trading and local energy market are gaining momentum, a follow-up challenge is scaling up to include multi-community, multi-region power schedule and trading. This study introduces community-to-community power trading and schedules considering various generating units, including coal, gas, wind, and solar, as well as import and export prices from community transactions. These generating sources are used to fulfil the demand requirements of each community over a time horizon, as well as absorbing or trading the margin among the inter-connected communities, while fulfilling certain distribution network constraints. For a practical case, the uncertainties in wind power generations are considered. An optimality condition decomposition technique is used to decompose the overall problem into a community-based local problem. Thus, individual community solves their optimisation local problem in parallel for operational efficiency of real-time multi-commodity power schedule and trading. The initial results indicate that each community acts in its best interest to minimise its costs when there is a change in the variable. When emission costs are applied as a constraint to their generation, a reduction in power generation is observed augmented by an increase of up to 30.8% of power transferred in the inter-community transaction.

Authors 5

1. Olamide Jogunola

   Manchester Metropolitan University

   Affiliation as printed

   Manchester Metropolitan University,Department of Computing and Mathematics,Manchester,UK

   Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK

2. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Manchester Metropolitan University,Department of Engineering,Manchester,UK

   Department of Engineering, Manchester Metropolitan University, Manchester, UK

3. Haris Gacanin Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Germany

   RWTH Aachen University, Germany

4. Mohammad Ali A. Hammoudeh

   King Fahd University of Petroleum and Minerals

   Affiliation as printed

   College of Computing and Mathematics, King Fahd University of Petroleum & Minerals,Dhahran,KSA,Saudi Arabia,31261

5. Guan Gui

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   Nanjing University of Posts and Telecommunications,China

   Nanjing University of Posts and Telecommunications, China