Agent-Based Model of a Blockchain Enabled Peer-to-Peer Energy Market: Application for a Neighborhood Trial in Perth, Australia

Abstract

The transfer of market power in electric generation from utilities to end-users spurred by the diffusion of distributed energy resources necessitates a new system of settlement in the electricity business that can better manage generation assets at the grid-edge. A new concept in facilitating distributed generation is peer-to-peer energy trading, where households exchange excess power with neighbors at a price they set themselves. However, little is known about the effects of peer-to-peer energy trading on the sociotechnical dynamics of electric power systems. Further, given the novelty of the concept, there are knowledge gaps regarding the impact of alternative electricity market structures and individual decision strategies on neighborhood exchanges and market outcomes. This study develops an empirical agent-based modeling (ABM) framework to simulate peer-to-peer electricity trades in a decentralized residential energy market. The framework is applied for a case study in Perth, Western Australia, where a blockchain-enabled energy trading platform was trialed among 18 households, which acted as prosumers or consumers. The ABM is applied for a set of alternative electricity market structures. Results assess the impact of solar generation forecasting approaches, battery energy storage, and ratio of prosumers to consumers on the dynamics of peer-to-peer energy trading systems. Designing an efficient, equitable, and sustainable future energy system hinges on the recognition of trade-offs on and across, social, technological, economic, and environmental levels. Results demonstrate that the ABM can be applied to manage emerging uncertainties by facilitating the testing and development of management strategies.

A Smart Water Grid for Micro-Trading Rainwater: Hydraulic Feasibility Analysis

“…In this research, we explore the impact of the depth of rainfall and the distribution of consumers and prosumers on the performance metrics that are used to evaluate the hydraulic feasibility of the smart water grid. In related research, an agent-based modeling framework was developed to explore how the numbers of prosumers and consumers affect performance of a peer-to-peer household-level energy trading market, and results demonstrated that the presence of too many prosumers in the market led to market inefficiencies [8]. In the smart water grid, however, the volume of water that is required by consumers for irrigation is much higher than the volume of water that is produced by prosumers.…”

“…Data structures are both immutable and cryptographically verifiable, promising security, accuracy, authentication and traceability of transactions [53]. Blockchain can also reduce transaction costs associated with third-party brokers, though some fees may be necessary to maintain a critical centralized infrastructure [8]. Smart contracts can be used with blockchain, where smart contracts work as simple scripts encoded on the blockchain that contain predefined directions for automating workflows on recorded data and finalizing the settlement of financial transactions between buyers and sellers [54].…”

“…Other agent-based models capture the decisions of polluters to bid and sell permits in an emissions market, and these models are applied to assess the effect of trading on air quality [16,17]. More recently, an agent-based modeling was applied to simulate peer-to-peer markets by modeling households as agents that buy and sell energy in a residential smart energy grid [8]. In the research presented in this manuscript, agent-based modeling is loosely coupled with hydraulic simulation modeling to assess network performance metrics that are affected by agent behaviors to trade water.…”

“…In the energy sector, micro-trading has been demonstrated as a viable market for decentralized resource production, in which households can generate energy through solar photovoltaic cells, store energy in batteries, and sell and transmit excess energy to neighbors through existing power distribution infrastructure [7,8]. Micro-trading water has a number of nuanced constraints that may limit its adoption by utilities and community members.…”

“…A few studies couple agent-based models with hydraulic simulation to explore how changes in household demands affect system-level performance, such as pressure and flow directions [12][13][14]. Further, agent-based modeling has been applied to simulate markets for trading natural resources [11,[15][16][17], and recent research applied agent-based modeling to simulate how households trade solar generated energy in a peer-to-peer energy market [8].…”