• Energy Research

Governments' net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advanced information and control infrastructures of smart grids which allow the interoperability among various stakeholders. Under this circumstance, increasing number of consumers produce, store, and consume energy, giving them a new role of prosumers. The integration of prosumers and accommodation of incurred bidirectional flows of energy and information rely on two key factors: flexible structures of energy markets and intelligent operations of power systems. The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems. This paper attempts to address how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets. To achieve this objective, first, this paper reviews how policy designs price carbon emissions caused by the fossil-fuel based generation so as to facilitate the integration of prosumers with renewable energy sources. Second, the potential structures of energy markets with the support of the blockchain technologies are discussed. Last, how to apply the AI for enhancing the state monitoring and decision making during the operations of power systems is introduced. Accepted by Renewable & Sustainable Energy Reviews on 21 Feb 2022

Demand response is one of the most promising tools for smart grids to integrate more renewable energy sources. One critical challenge to overcome is how to establish pricing and control strategies for integrating more electric vehicles (EVs) and renewable energy sources. This paper proposes a dynamic optimal operation of a solar-powered EV charging station where onsite solar generation, number of EVs in the system, historical EV response to price, EV technical specifications and EV driving behaviour vary. A bi-level optimisation approach is proposed, where pricing tariffs ensure an economic and price responsive operation, then EV charging schedules are computed for energy bidding capacity to provide balancing services. Simulations are conduced to evaluate the performance of unidirectional and bidirectional EV charging at different charging speeds and demand elasticity. Results demonstrate the potential of extra revenue streams coming from the participation in energy markets compared to that of EV charging alone. Additionally, limitations of energy bidding with battery size, trip requirements and charging ratings are discussed to show insights into the operation of charging stations.

Smart grids are the next generation of power distribution network, using information and communications technologies to increase overall energy efficiency and service quality of the power grid. A significant challenge in smart grid development is the rapidly rising number of smart devices and how to meet the associated load on the backbone communication infrastructure. This paper designs an Internetof-Things smart grid testbed simulator to provide crucial insight into communication network optimization. Simulation for a large number of smart devices under various heterogeneous network topologies is used to analyze the maximum number of clients supportable for a given demand-response latency requirement. This latency includes all protocol overheads, retransmissions and traffic congestion, and simulator processing time is successfully eliminated from the final delay calculation via data post-processing. For a specific three-tier topology, given a round-trip latency requirement, the effect of number of smart devices per local hub and overall number of local hubs on network performance is analyzed, and crucial design insights are drawn relevant to cost-efficiency optimization of network deployment.

Prosumers are active participants in future energy systems who produce and consume energy. However, the emerging role of prosumers brings challenges of tracing carbon emissions behaviours and formulating pricing scheme targeting on individual prosumption behaviours. This paper proposes a novel blockchain-based peer-to-peer trading framework to trade energy and carbon allowance. The bidding/selling prices of prosumers can directly incentivise the reshaping of prosumption behaviours to achieve regional energy balance and carbon emissions mitigation. A decentralised low carbon incentive mechanism is formulated targeting on specific prosumption behaviours. Case studies using the modified IEEE 37-bus test feeder show that the proposed trading framework can export 0.99 kWh of daily energy and save 1465.90 g daily carbon emissions, outperforming the existing centralised trading and aggregator-based trading.

Within smart homes, consumers could generate a vast amount of data that, if analyzed effectively, can improve the convenience of consumers and reduce energy consumption. In this paper, we propose to organize household appliance data into a knowledge graph by using the consumers’ usage habits, the periods of usage, and the location information for graph modeling. A framework, ‘DARK’ (Device Action Recommendation with Knowledge graphs), is proposed that includes three parts for enabling demand response. Firstly, a household device action recommendation algorithm is proposed that improves the knowledge graph attention algorithm to make accurate household appliance recommendations. Secondly, graph interpretable characteristics are developed in the DARK using trained graph embeddings. Finally, with the recommendation expectation, the consumers’ comfort level and appliances’ average power load are modeled as a multi-objective optimization problem in the DARK to participate in demand response. The results demonstrate that the proposed system can generate appliances’ action recommendations with an average of 93.4% accuracy and reduce power load by up to 20% while providing reasonable interpretations for the device action recommendation results on the customized UK-DALE dataset.

Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables consumer-centric decarbonization by automating iterative negotiations between policy makers and consumers or generators to reduce carbon emissions. This iterative negotiation is modeled as a Stackelberg game-theoretic problem, and securely facilitated by Blockchain technologies. The policy maker formulates carbon prices and monetary compensation rates to dynamically incentivize the carbon reduction, whereas consumers and generators schedule their power profiles to minimize bills and maximize profits of generation, respectively. The negotiating agreement is yielded by reaching a Stackelberg equilibrium. The exchanged information and controlling functions are realized by using smart contracts of Blockchain technologies. Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources. As a consumer-centric decarbonization framework, it can at least reduce carbon emissions by 40%.