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Electronics 2019, 8(1), 48; https://doi.org/10.3390/electronics8010048

Enhanced Time-of-Use Electricity Price Rate Using Game Theory

1
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2
Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan
3
College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
4
College of Computer and Information Systems, Al Yamamah University, Riyadh 11512, Saudi Arabia
*
Author to whom correspondence should be addressed.
Received: 25 November 2018 / Revised: 27 December 2018 / Accepted: 28 December 2018 / Published: 2 January 2019
(This article belongs to the Section Systems & Control Engineering)
Full-Text   |   PDF [1226 KB, uploaded 2 January 2019]   |  

Abstract

The emergence of the Demand Response (DR) program optimizes the energy consumption pattern of customers and improves the efficacy of energy supply. The pricing infra-structure of the DR program is dynamic (time-based). It has rather complex features including marginal costs, demand and seasonal parameters. There is variation in DR price rate. Sometime prices go high (peak load) if the demand of electricity is more than the generation capacity. The main objective of DR is to encourage the consumer to shift the peak load and gets incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this work, Game Theory (GT)-based Time-of-Use (ToU) pricing model is presented to define the rates for on-peak and shoulder-peak hours. The price is defined for each user according to the utilize load. At first, the proposed model is examined using the ToU pricing scheme. Afterward, it is evaluated using existing day-ahead real-time pricing scheme. Moreover, shifting load from on-peak hours to off-peak hours may cause rebound peak in off-peak hours. To avert this issue, we analysis the impact of Salp Swam Algorithm (SSA) and Rainfall Algorithm (RFA) on user electricity bill and PAR after scheduling. The experimental results show the effectiveness of the proposed GT-based ToU pricing scheme. Furthermore, the RFA outperformed SSA. View Full-Text
Keywords: pricing scheme; game theory; meta-heuristic; optimization; scheduling; salp swarm algorithm; rain fall optimization algorithm pricing scheme; game theory; meta-heuristic; optimization; scheduling; salp swarm algorithm; rain fall optimization algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Khalid, A.; Javaid, N.; Mateen, A.; Ilahi, M.; Saba, T.; Rehman, A. Enhanced Time-of-Use Electricity Price Rate Using Game Theory. Electronics 2019, 8, 48.

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