Abstract

Global economic growth, demographic explosion, digitization, increased mobility, and greater demand for heating and cooling due to climate change in different world areas are the main drivers for the surge in energy demand. The increase in energy demand is the basis of economic challenges for power companies alongside several socio-economic problems in communities, such as energy poverty, defined as the insufficient coverage of energy needs, especially in the residential sector. Two main strategies are considered to meet this increased demand. The first strategy focuses on new sustainable and eco-friendly modes of power generation, such as renewable energy resources and distributed energy resources. The second strategy is demand-side oriented rather than the supply side. Demand-side management, demand response (DR), and energy efficiency (EE) programs fall under this category. On the other hand, the decentralization and digitization of the energy sector convoyed by the emersion of new technologies such as blockchain, Internet of Things (IoT), and Artificial Intelligence (AI), opened the door to new solutions for the energy demand dilemma. Among these technologies, blockchain has proved itself as a decentralized trading platform between untrusted peers without the involvement of a trusted third party. This newly introduced Peer-to-Peer (P2P) trading model can be used to create a new demand load control model. In this article, the concept of an energy cap and trade demand-side management (DSM) model is introduced and simulated. The introduced DSM model is based on the concept of capping consumers’ monthly energy consumption and rewarding consumers who do not exceed this cap with energy tradeable credits that can be traded using blockchain-based Peer-to-Peer (P2P) energy trading. A model based on 200 households is used to simulate the proposed DSM model and prove that this model can be beneficial to both energy companies and consumers.

Highlights

  • Electric utility companies worldwide deal with severe and recurring power crises caused by a combination of supply-side problems, including fuel supply challenges, maintenance requirements and unplanned outages, and a continuous increase in energy demand, driven by global demographic growth, electrification of the transportation sector, and greater need for heating and cooling

  • The simulation is conducted over four different months of the year to account for the seasonal effect on the energy demand

  • The obtained results show that even when incorporating the selsumers as energy sellers and competitors to the utility company in terms of selling electricity at a lower rate to penalized pursumers, the monthly turnover of the utility is either comparable to the baseline, for the soft and moderate scenario, or exceeding the baseline for the aggressive scenario

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Summary

Introduction

Electric utility companies worldwide deal with severe and recurring power crises caused by a combination of supply-side problems, including fuel supply challenges, maintenance requirements and unplanned outages, and a continuous increase in energy demand, driven by global demographic growth, electrification of the transportation sector, and greater need for heating and cooling. The digitalization of the energy sector is the cornerstone of the broad integration of distributed energy resources (DERs) in any electric grid, which aims to increase load flexibility and diversity in the power generation systems. This digital transformation unlocks new potentials for users to manage their energy consumption and supply while offering them the possibility to become active stakeholders in electricity grid management. Blockchain technology promises a secure, near real-time, and low-cost method for conducting digital assets transactions [10] It increases process automation while managing more significant volumes of data with limited human intervention at lower cost and risk.

Related Works
Blockchain Technology
What Is a Smart Contract?
Concept
System Architecture and Functionality
Case Study
Selected Model
Energy Cap and Trade Formulation
Simulation Results
Sensitivity Analysis
Other Application
Conclusions
Full Text
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