Fuzzy optimization for peer-to-peer (P2P)multi-period renewable energy trading planning. (25th September 2022) - Kong, Karen Gah Hie; Lim, Juin Yau; Leong, Wei Dong; Ng, Wendy Pei Qin; Teng, Sin Yong; Sunarso, Jaka; How, Bing Shen
Abstract: The deficiency of natural resources and serious climate change have driven the global community's efforts to optimize energy planning using various process integration approaches. The inter-entities energy planning that allows internal trading of resources presents a great potential to enhance energy planning. It is believed that the effective management of such relationships offers lower environmental impacts on top of the economic benefits. The developed inter-collaborative energy trading model gives a handy lens to evaluate the effectiveness of the suggested inter-entities collaboration and how it provides economic benefits for the involved "peers". To demonstrate the economic viability of inter-entity energy planning, a multi-period Peer-to-peer (P2P) energy trading model – a horizontal cooperation among entities that allows internal energy trading, is developed in this work. An illustrative case study in Malaysia that involved three entities using the actual billing system adapted from Tenaga Nasional Berhad (TNB) is used to demonstrate the proposed methodology. First, two different single-objective optimization scenarios are considered: (i) minimization of electricity bills, and (ii) minimization of carbon emissions. A fuzzy optimization approach is then adopted in the case study to ensure the conflicting objectives are optimized simultaneously without over-prioritizing any of the objectives using the fuzzy sets theory. In the single-objective optimization scenarios, the entities managed to mitigate their total bills by USD 20, 185.99/month and have a 82.55% carbon emissions reduction, respectively. Based on the results obtained from the multi-period P2P energy trading model using the fuzzy optimization approach, the involved entities can reduce their total electricity bill by USD 20, 185.99/month (without over-prioritization of any of the entities involved) and achieve a total of 61% carbon emissions reduction. The optimal traded renewables unit cost of USD 0.089/kWh is determined via the sensitivity analysis conducted at the end of this work. Highlights: Peer-to-peer (P2P) energy trading cost reflectivity is demonstrated. Minimization of carbon emissions and electricity bills are considered. Fuzzy approach is adopted to solve the optimization problem. The total electricity bills can be further reduced by USD 20, 185.99/month. 61% of carbon emissions reduction is attained in P2P energy trading.