Social Factors in P2P Energy Trading Using Hedonic Games

Lately, the energy communities have gained a lot of attention as they have the potential to significantly contribute to the resilience and flexibility of the energy system, facilitating widespread integration of intermittent renewable energy sources. Within these communities the prosumers can engage in peer-to-peer trading, fostering local collaborations and increasing awareness about energy usage and flexible consumption. However, even under these favorable conditions, prosumer engagement levels remain low, requiring trading mechanisms that are aligned with their social values and expectations. In this paper, we introduce an innovative hedonic game coordination and cooperation model for P2P energy trading among prosumers which considers the social relationships within an energy community to create energy coalitions and facilitate energy transactions among them. We defined a heuristic that optimizes the prosumers coalitions, considering their social and energy price preferences and balancing the energy demand and supply within the community. We integrated the proposed hedonic game model into a state-of-the-art blockchain-based P2P energy flexibility market and evaluated its performance within an energy community of prosumers. The evaluation results on a blockchain-based P2P energy flexibility market show the effectiveness in considering social factors when creating coalitions, increasing the total amount of energy transacted in a market session by 5% compared with other game theory-based solutions. Finally, it shows the importance of the social dimensions of P2P energy transactions, the positive social dynamics in the energy community increasing the amount of energy transacted by more than 10% while contributing to a more balanced energy demand and supply within the community.Comment: to be submitted to journa

Multi P2P Energy Trading Market, Integrating Energy Storage Systems and Used for Optimal Scheduling

The increasing use of renewable energy and storage systems by end users has changed the paradigm of electricity markets, with consumers changing their role from passive to active players, the so-called prosumers. Different countries have encouraged the aggregation of these prosumers in energy communities. In these communities, it is essential to create a market to manage energy exchanges between neighbors, who can sell surpluses or buy energy to reduce their bills. This paper presents the framework definition of a multi-peer-to-peer market. As contributions, it defines how storage systems can participate in the market and multiple exchanges between prosumers are possible. This market can be integrated in an optimization process to perform optimal scheduling in the community by setting an objective. All this has been tested in a community with 5 prosumers with generation and storage, where the effect of multiple exchanges and valuation of assets is observed, achieving as a result higher bill reductions

State-of-the-art analysis and perspectives for peer-to-peer energy trading

As a promising solution to address the “energy trilemma” confronting human society, peer-to-peer (P2P) energy trading has emerged and rapidly developed in recent years. When carrying out P2P energy trading, customers with distributed energy resources (DERs) are able to directly trade and share energy with each other. This paper summarizes and analyzes the global development of P2P energy trading based on a comprehensive review of related academic papers, research projects, and industrial practice. Key aspects in P2P energy trading are identified and discussed, including market design, trading platforms, physical infrastructure and information and communication technology (ICT) infrastructure, social science perspectives, and policy. For each key aspect, existing research and practice are critically reviewed and insights for future development are presented. Comprehensive concluding remarks are provided at the end, summarizing the major findings and perspectives of this paper. P2P energy trading is a growing field with great potential and opportunities for both academia and industry across the world

Federating smart cluster energy grids for peer-to-peer energy sharing and trading

With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project

Reinforcement Learning Based Cooperative P2P Energy Trading between DC Nanogrid Clusters with Wind and PV Energy Resources

In order to replace fossil fuels with the use of renewable energy resources, unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To resolve this problem, a reinforcement learning (RL) technique is introduced in this paper. For RL, graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters based on cooperative game theory. The flexible and reliable DC nanogrid is suitable to integrate renewable energy for distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid clusters, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of electric vehicle (EV) is performed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as deep Q-learning network (DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit utilizing the time of use (ToU) tariff-based electricity cost and system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on the distribution test feeder in real-time and proposed GCN-PPO technique reduces the electricity cost of nanogrid clusters by 36.7%.Comment: 22 pages, 8 figures, to be submitted to Applied Energy of Elsevie