Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism

Adamu Sani Yahaya, Nadeem Javaid, Fahad A. Alzahrani, Amjad Rehman, Ibrar Ullah, Affaf Shahid and Muhammad Shafiq
Additional contact information
Adamu Sani Yahaya: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Nadeem Javaid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Fahad A. Alzahrani: Computer Engineering Department, Umm AlQura University, Mecca 24381, Saudi Arabia
Amjad Rehman: Artificial Intelligence & Data Analytics Lab (AIDA), CCIS Prince Sultan University, Riyadh 11586, Saudia Arabia
Ibrar Ullah: Department of Electrical Engineering, University of Engineering and Technology Peshawar, Bannu 28100, Pakistan
Affaf Shahid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Muhammad Shafiq: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea

Sustainability, 2020, vol. 12, issue 8, 1-28

Abstract: With the increase in local energy generation from Renewable Energy Sources (RESs), the concept of decentralized peer-to-peer Local Energy Market (LEM) is becoming popular. In this paper, a blockchain-based LEM is investigated, where consumers and prosumers in a small community trade energy without the need for a third party. In the proposed model, a Home Energy Management (HEM) system and demurrage mechanism are introduced, which allow both the prosumers and consumers to optimize their energy consumption and to minimize electricity costs. This method also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. The proposed solution shows how energy consumption and electricity cost are optimized using HEM and demurrage mechanism. It also provides economic benefits at both the community and end-user levels and provides sufficient energy to the LEM. The simulation results show that electricity cost is reduced up to 44.73% and 28.55% when the scheduling algorithm is applied using the Critical Peak Price (CPP) and Real-Time Price (RTP) schemes, respectively. Similarly, 65.15% and 35.09% of costs are reduced when CPP and RTP are applied with demurrage mechanism. Moreover, 51.80% and 44.37% electricity costs reduction is observed when CPP and RTP are used with both demurrage and scheduling algorithm. We also carried out security vulnerability analysis to ensure that our energy trading smart contract is secure and bug-free against the common vulnerabilities and attacks.

Keywords: blockchain; HEM; optimization; local energy market; decentralization; peer to peer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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