• Energy Research

We consider the problem of data collection from a network of energy harvesting sensors, applied to tracking mobile assets in rural environments. Our application constraints favor a fair and energy-aware solution, with heavily duty-cycled sensor nodes communicating with powered base stations. We study a novel scheduling optimization problem for energy harvesting mobile sensor network, that maximizes the amount of collected data under the constraints of radio link quality and energy harvesting efficiency, while ensuring a fair data reception. We show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. Moreover, our algorithm is flexible in handling progressive energy harvesting events, such as with solar panels, or opportunistic and bursty events, such as with Wireless Power Transfer. We use empirical link quality data, solar energy, and WPT efficiency to evaluate the proposed algorithm in extensive simulations and compare its performance to state-of-theart. We show that our algorithm achieves high data reception rates, under different fairness and node lifetime constraints.

The increasing adoption of clean energy technologies, including solar and wind generation, demand response, energy efficiency, and energy storage (e.g. batteries and electric vehicles) have led to the evolution of the traditional electricity markets from centralised energy trading systems into Distributed Energy Trading (DET) systems. Consequently, savvy business executives are exploring how blockchain might impact their competitive advantage in the emerging DET markets. Due to its salient features of distributed ledger, consensus mechanisms, cryptography, and smart contracts, blockchain technology is being used to provide decentralised trust, immutability, security and privacy, and transparency in DET system. However, integrating blockchain in DET systems is facing technical, administrative, standardisation and economic barriers. Consequently, we seek to conduct a comprehensive market analysis to identify the specific challenges hindering the integration of blockchain in DET systems. Nonetheless, we noticed that there isn't any evaluation and review framework for conducting a systematic literature review on blockchain-based DET systems. Therefore, in this work we first proposed a conceptual evaluation and review framework for conducting a systematic literature review on blockchain-based DET systems. Then, using the proposed framework, we reviewed the current studies on blockchain-based DET systems to the identify specific challenges hindering the adoption of blockchain and their proposed solutions. Our review found that, although there has been tremendous progress in addressing the technical barriers, the administrative, standardisation and economic barriers have grossly been under reviewed.

Blockchain is increasingly being used as a distributed, anonymous, trustless framework for energy trading in smart grids. However, most of the existing solutions suffer from reliance on Trusted Third Parties (TTP), lack of privacy, and traffic and processing overheads. In our previous work, we have proposed a Secure Private Blockchain-based framework (SPB) for energy trading to address the aforementioned challenges. In this paper, we present a proof-on-concept implementation of SPB on the Ethereum private network to demonstrates SPB's applicability for energy trading. We benchmark SPB's performance against the relevant state-of-the-art. The implementation results demonstrate that SPB incurs lower overheads and monetary cost for end users to trade energy compared to existing solutions.

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.

The Internet of Things will encompass a rich variety of sensing systems including mobile phones, embedded sensor and actuator platforms, and even smart electricity meters. Through their collaborative operation, billions of such devices will realize the vision of smart homes, smart cities, and beyond. Smart electricity meters can, e.g., already help utilities monitor the stability of the power grid by periodically reporting each connected household's energy consumption. Even more sophisticated services can be realized when data is available at higher spatio-temporal resolutions, e.g., when distributed smart power meters (sometimes referred to as smart plugs) are deployed. In this paper, we present one such novel service for the smart home, namely making projections of an appliance's future energy demand based on previously observed power consumption data. In a first step, our system identifies and isolates unique characteristic signatures from collected power consumption traces. Subsequently, time series pattern matching is applied to detect these signatures in real-time data. Based on the occurrences of the extracted signatures in real-time data, the appliance's future power demand is predicted. We evaluate our approach with more than 2,500 appliance activity segments collected from 15 different appliance types, and show that accurate forecasts can be made in many cases.