• Energy Research
• 2021-2025close
• AU close
• University of Sydney close

Abstract Recently, blockchain adoption in prosumer-side energy trading has been actively studied. However, most of the conventional frameworks permanently store all transactions which increases blockchain management cost and reduces the user privacy. Additionally, most of the existing solutions focus on facilitating energy trading and negotiation, while ignoring two critical issues: data acquisition and contract execution. The former refers to the process of collecting power generation/consumption information from on-site energy resources which is required to scale. The latter refers to the process of adjusting controllable loads’ operation in real-time. In this paper, we propose a removable blockchain architecture that introduces a Temporary Chain (TC) where transactions can be stored for a particular period of time. The architecture enables an energy manager node to effectively collect data for facilitating real-time load control. TC reduces the volume of transactions stored in blockchain which increases scalability, throughput, and privacy of the users and reduces latency. We present two approaches to implement TC which are: i) blackboard where a central authority stores temporary transactions, and ii) removable ledger. We introduce a lightweight mode to transfer data. The implementation results show that the proposed framework reduces blockchain storage size and delay and increases throughput.

Abstract Recently, blockchain adoption in prosumer-side energy trading has been actively studied. However, most of the conventional frameworks permanently store all transactions which increases blockchain management cost and reduces the user privacy. Additionally, most of the existing solutions focus on facilitating energy trading and negotiation, while ignoring two critical issues: data acquisition and contract execution. The former refers to the process of collecting power generation/consumption information from on-site energy resources which is required to scale. The latter refers to the process of adjusting controllable loads’ operation in real-time. In this paper, we propose a removable blockchain architecture that introduces a Temporary Chain (TC) where transactions can be stored for a particular period of time. The architecture enables an energy manager node to effectively collect data for facilitating real-time load control. TC reduces the volume of transactions stored in blockchain which increases scalability, throughput, and privacy of the users and reduces latency. We present two approaches to implement TC which are: i) blackboard where a central authority stores temporary transactions, and ii) removable ledger. We introduce a lightweight mode to transfer data. The implementation results show that the proposed framework reduces blockchain storage size and delay and increases throughput.

Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.

Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.

The global drive towards sustainability has ushered in a new era of transportation, prominently featuring the rise of Battery Electric Vehicles (BEVs). The rapid rise of BEVs has been widely hailed as a crucial milestone in promoting sustainable transportation and combating climate change. The existing empirical evidence provides undeniable support for the essential role of BEVs to support net zero targets. However, like any disruptive technology, BEVs are not without their hidden effects. This paper seeks to explore and analyse these lesser-known repercussions (i.e., externalities) of BEV adoption. In doing so, it sheds light on the environmental, infrastructure, socio-economic and safety externalities of BEVs, aiming to foster a holistic understanding of their impact and facilitate informed decision-making. Furthermore, it highlights the critical role of public awareness and user education in maximising the benefits of BEVs and the importance of maintaining a balance of information in developing such campaigns. Empowering individuals, communities, and policymakers with accurate information, dispelling misconceptions, and fostering responsible BEV practices are essential for realising maximal environmental impacts of BEVs. The discussion emphasises the need for moving beyond climate-change targets that are merely based on tailpipe emissions, towards life-cycle-based approaches.