Local energy markets in Clustering Power System Approach for smart prosumers

Morton, Danny, Holtschulte, D., Erlangga, A.S., Ortjohann, E., Kortenbruck, J., Leksawat, S., Schmelter, A., Premgamone, T. and Morton, D. (2017) Local energy markets in Clustering Power System Approach for smart prosumers. 2017 6th International Conference on Clean Electrical Power (ICCEP). IEEE, pp. 215-222. ISBN 978-1-5090-4682-9

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Abstract

Decentralized, renewable energy sources has grown fast as a sustainable and clean alternative energy to overcome the carbon emissions caused by conventional power plants. However, this change leads to several challenges related to grid control, resulting in a need of new smart grid concepts. Therefore, Clustering Power System Approach (CPSA) has been introduced as a suitable smart grid concept. Meanwhile, the impact of small prosumers in power supply operation increases continuously and they will emerge from being passive to become active participants in smart grid and smart market operation. In a previous paper genetic algorithms (GA) has been introduced as an adequate optimization technique tackling the issue of economic optimization of smart prosumers in a case study. In this paper a case study for a whole cluster network with smart prosumers/households operating under individual requirements is carried out. Additionally, a market model containing auction based local energy markets (LEM) suitable to be implemented in the CPSA is introduced. This is the next step to achieve the goal of smart grid and smart market under the foundation of the CPSA. The results show that the GA based optimization in combination with the involvement of LEM provides economic benefits for smart prosumers.

Item Type: Book Section
Additional Information: Paper given at and published in proceedings of 6th International Conference on Clean Electrical Power (ICCEP 2017), held 27-29 June 2017, Santa Margherita Ligure, Italy.© Copyright IEEE
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: School of Engineering > Engineering
Depositing User: Prof Danny Morton
Date Deposited: 27 Mar 2018 08:08
Last Modified: 27 Mar 2018 13:14
Identification Number: 10.1109/ICCEP.2017.8004818
URI: http://ubir.bolton.ac.uk/id/eprint/1599

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