In smart grids, the large-scale integration of distributed renewable energy resources has enabled the provisioning of alternative sources of supply. Peer-to-peer (P2P) energy trading among local households is becoming an emerging technique that benefits both energy prosumers and operators. Since conventional energy supply is still needed to help fill the gap between local demand and supply when the local solar generation is not sufficient, demand–response management will keep playing an important role in the future P2P energy market. Blockchain and smart contract technology has gained increasing attention in P2P trading for its secure operation. The performance of blockchain-based P2P energy trading still remains to be improved, in terms of latency and cost of computation resources. This article studies the challenges of demand–response management in P2P energy trading and proposes a blockchain-empowered energy trading system for a community-based P2P market. The proposed demand–response mechanism is developed using two noncooperative games, in which dynamic pricing is applied for suppliers. The proposed energy trading system is prototyped on a cluster network, with a coordinator running as a smart contract in a Hyperledger blockchain. We implemented both on-chain and off-chain processing modes to study the system performance. The results from experiments with our prototype indicate that our proposed demand–response games have a great effect on reducing the net peak load, and at the same time, the off-chain processing mode provides lower latency and overhead compared to the on-chain mode while still keeping the same system integrity as the on-chain mode.

The development of automated home energy management (HEM) and peer-to-peer energy trading mechanisms encourages a greater number of energy consumers to switch roles and become providers. To sustain this trend and maintain their long-term commitment to energy platforms, we face the challenge of aligning the primary psychological motivators of prosumers with our developed energy services. Most existing approaches target maximizing prosumer utility based on extrinsic benefits, such as economic rewards. However, the intrinsic motivations which are inherently satisfying for prosumers, have not been thoroughly analyzed. This article explores both extrinsic and intrinsic motivations of prosumers from a psychological perspective and addresses these within the technological field. Self-determination theory is adopted as a psychological framework to analyze prosumer behavior in energy systems. The study quantifies prosumers’ motivations and proposes a quality-of-energy-service measure to reflect individual preferences. Additionally, a leader-follower-based optimization framework is introduced, enabling individual prosumers to make optimal decisions regarding their energy management and trading strategies in a P2P energy market. The proposed system features a deep reinforcement learning agent as the leader, targeting optimal HEM solutions, while the follower aims to find the optimal trading strategy for prosumers in an auction-based P2P trading environment. Numerical results demonstrate that our proposed model outperforms baseline models.

The decarbonisation of the energy system is accelerating the uptake of distributed and renewable energy resources. Nevertheless, this stresses already-constrained distribution networks, and it can lead to expensive grid infrastructure upgrades. Demand-side flexibility offers a non-network solution that can reduce power grid reinvestment and reinforcement. This paper investigates the potential of demand-side flexibility, where we explore community energy sharing and coordinated demand response through a data-driven case study for Waiheke Island, New Zealand. In particular, a demand response management framework is developed that coordinates household hot-water cylinders, EV charging, rooftop solar power, and home and community battery storage. The framework is first evaluated in a community-scale energy-sharing scenario that incorporates flexible load scheduling and market-level settlement. The analysis is then extended to the entire Waiheke Island under a realistic demand-response setting, supported by a digital representation of the island’s electricity network and energy system. The study integrates real household smart-meter data, feeder-level consumption data, and information obtained from multiple publicly available sources to assess the island-wide potential for demand-side flexibility. Detailed simulations show that rescheduling hot-water cylinder operation, EV charging, and household battery storage can substantially reduce the island’s evening peak demand, thereby delaying network reinforcement and generating significant economic benefits.