This study proposes a multi-agent-based framework for Peer-to-Peer (P2P) power trading in a locality electricity market (LEM) for self-interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win–win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day-ahead market. LETS computes the internal market prices employing any one of the market-clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation-demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27–68% under different operating conditions.

Smart city adoption and deployment has taken the centre stage worldwide with its realisation clearly hinged on energy efficiency, but its planning is threatened by the vulnerability of smart grids (SGs). Adversaries launch attacks with various motives, but the rampaging electricity theft menace is causing major concerns to SGs deployments and consequently, energy efficiency. Smart electricity meters deployments via the advanced metering infrastructure present promising solutions and even greater potential as it provides adequate data for analytical inferences for achieving proactive measures against various cyberattacks. This study suggests the sources of threats as the first step of such proactive measures of curbing electricity thefts. It provides a framework for monitoring, identifying and curbing the threats based on factors indicative of electricity thefts in a smart utility network. The proposed framework basically focuses on these symptoms of the identified threats indicative of possible electricity theft occurrence to decide on preventing thefts. This study gives a useful background to smart city planners in realising a more reliable, robust and secured energy management scheme required for a sustainable city.

Efficiency and reliability have been essential requirements for energy generation in smart cities. This study presents the design and development of dependable control schemes for microgrid management, which can be seamlessly integrated into the management system of smart buildings. Here, to recover from failures in the solar energy system of a building microgrid, dependable controllers are proposed along with their hardware implementation. The system features the use of Internet of Things (IoT) as its core to coordinate the operation of multiple subsystems in a scalable manner. The control scheme uses a number of controllers cooperatively functioning via a token-based mechanism within the network to provide redundancy and thus reliability in solar tracking. The system exploits data from not only local in-situ sensors but also online sources via IoT networks for fault-tolerant control. Experiments conducted in a 12-storey building indicate that the harvested solar energy meets the design requirement while the control reliability is maintained in face of communication or hardware disruptions. The results confirmed the validity of the proposed approach and its applicability to energy management in smart buildings.

Air-conditioner (AC) accounts for a significant share of residential energy consumption. Considering the widespread rise in electric vehicle (EV) usage, its charging would also contribute a considerable percentage of consumer's total energy consumption. Consequently, the concurrent operation of AC and EV charging would result in peaky load curves. Hence, this study proposes a system of agents for AC and EV charging applications, which incorporates load-management strategies to flatten the load curve. Thereby, the presented system includes two agents, namely: a smart load node for a thermostatically controlled load (SLN-TCL) and a smart battery charge controller. Subsequently, a subagent, namely micro-node, has been introduced to support SLN-TCL and to implement the concept of distributed temperature sensing (DTS). The implementation of DTS subdues the conventional temperature sensing mechanism of AC and ensures a more flexible operation. This study includes the design, development, and features of agents and subagents for AC and EV applications. Furthermore, this study also demonstrates the agent-based control actions for peak-shaving under real conditions to showcase the performance of this system.