| Abstract |
With the global transition towards net-zero carbon buildings and carbon neutrality, peer-to-peer energy trading plays a significant role in mitigating spatio-temporal mismatches between on-site renewable energy generation and energy consumption. However, dynamic peer-to-peer trading prices and prosumer preference satisfaction with the dynamic peer-to-peer trading prices have not been fully considered to incentivize prosumers' participation in the sharing scheme. This research aims to provide frontier guidelines on peer-to-peer energy trading mechanisms and synergistic peer-to-peer sharing strategies to achieve the 'win-win-win' collaboration mode, so as to promote low-carbon and sustainable transformations. In this study, a Stackelberg game theory-based integrated community energy system is proposed, comprising hybrid solar-wind renewables, energy storage system, grid-connected prosumers, and energy sharing providers. Dynamic peer-to-peer trading price is set based on the fair cost-benefit allocation among prosumers through Nash bargaining. Results indicate that peer-to-peer trading reduces operational carbon emissions by over 24 %, shifting from 865 kg CO2,e reduction to 1073 kg CO2,e reduction. Load-shifting within peer-to-peer trading improves self-consumed renewable energy from 46 % to 70 %, covering electricity demand with renewables from 50 % to 74 %. Identified peer-to-peer trading prices for hotel, office, and residential buildings are 0.59 CNY/kWh, 0.15 CNY/kWh, and 0.46 CNY/kWh, respectively. Compared to traditional prosumer-to-grid trading, peer-to-peer trading reduces costs from 2394 CNY to 448 CNY, stimulating the proactivity of multi-stakeholders. Overall, peer-to-peer energy trading brings a further utilization of the renewable energy, and largely decreases the reliance on grid electricity, leading to great benefits from the techno-environmental-economic aspects. However, peer-to-peer energy trading should be continuously investigated under uncertainty prediction and sustainable business models to ensure its robust implementation and long-term feasibility. |