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This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system. Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency. This study was conducted along two main axes. The first axis focuses on designing a digital twin for a residential community microgrid platform. This phase involves data collection, cleaning, exploration, and interpretation. Moreover, it includes replicating the functionality of the real platform and validating the results. The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid, while maintaining the concept of peer-to-peer energy trading. Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid. Meanwhile, a single prosumer with a storage system actively engages in energy exchange to maximize the community's profit. This is achieved by optimizing battery usage using a cost optimization solution. The proposed solution is validated using the developed digital twin. [ABSTRACT FROM AUTHOR]

The integration of a large number of photovoltaic systems into the electricity grid presents significant technical challenges. To address the issue of excess power injection into the grid, various standards recommend curtailment of PV systems, which results in penalizing renewable energy. This paper proposes a solution to the problem without resorting to curtailment. The key innovation is to optimize the energy injected into the grid, achieving a delicate balance between benefits for prosumers and the grid operator, while also avoiding PV curtailment. The results obtained underscore substantial advantages for prosumers when contrasted with commercial solutions that resort to curtailing PV power, while ensuring the alignment of interests with network providers. The proposed energy management system is based on a multi-objective linear programming algorithm and aims to increase energy injection into the grid during peak regional demand while simultaneously considering the electricity price profile. The study utilizes digital twin technology to establish a dependable framework for validating proposed algorithms and comparing simulated results with experimental ones. By doing so, this process guarantees the accuracy of simulation results and allows for informed decision-making regarding the system under investigation. [ABSTRACT FROM AUTHOR]