Future distribution network planning with demand response applications
The philosophy in distribution network planning is continuously evolving to ensure an efficient, reliable and cost-effective network design. This is particularly important with the increasing presence of Distributed Generation (DG) and Demand Response (DR) integration at the distribution network. Th...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2017
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| Online Access: | http://eprints.utem.edu.my/id/eprint/20461/1/Future%20Distribution%20Network%20Planning%20With%20Demand%20Response%20Applications.pdf http://eprints.utem.edu.my/id/eprint/20461/2/Future%20distribution%20network%20planning%20with%20demand%20response%20applications.pdf |
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| Summary: | The philosophy in distribution network planning is continuously evolving to ensure an efficient, reliable and cost-effective network design. This is particularly important with the increasing presence of Distributed Generation (DG) and Demand Response (DR) integration at the distribution network. Thus, there is a need to develop distribution network modelling tool so that the associated impacts and benefits of such integration can be properly assessed and quantified. In light of this, this thesis presents a fractal-based approach to generate a large number of consumer settlements for low voltage distribution networks. Subsequently, branching rate and minimum spanning tree concepts have been applied to connect the load points and create the network for low voltage and medium voltage, respectively. The Particle Swarm Optimization (PSO) technique was then utilized to determine the optimum rating and placement of transformers, DG and capacitors. The developed simulation tool allows the modelling and planning of distribution network to be carried out in a systematic way. In addition, a total of 10,000 network case studies have been performed to assess the network performance under the influence of demand response and solar PV penetration levels. Three different demand response strategies have been considered in this work, namely, consumer response to their own demand profile, consumer response to PV generation profile and the consumer optimized demand response facilitated by smart grid application. Methodology for generating optimum DR pattern for 2,000 individual consumers have also been proposed and implemented with the aim to improve network load factor. These comprehensive analysis of the benefits of DR would enable a more meaningful and robust conclusion to be made. The findings show that DR application at consumer level can greatly facilitate the integration of solar PV systems. The DR benefits include reduced network losses and increased network asset utilization levels. Last but not least, this research work has filed a patent for the invention of Internet-of-Things based remote demand response and energy monitoring system that could be used as an enabler for demand response application in the actual environment. |
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