## 1 Introduction
### Related Works and Contributions
In [32], the authors mention that a model must consider both the fair sharing of energy and flexible commercial pricing, as these two aspects are essential for consumers and therefore have the most significant influence on their choice. In [33], the authors state that market exchange procedures are essential to peer-to-peer energy exchange. According to them, today's P2P market techniques mainly focus on centralized, decentralized, and distributed markets (community, integer, and hybrid P2P markets). In [34], the authors argue that the centralized market can maximize the total value accruing for the entire energy trading community by using a centralized management technique. In [35], the authors mention that in a centralized market, a market coordinator can control the operation of the whole microgrid from a macroeconomic point of view. This allows the microgrid to operate more efficiently. The coordinator determines the objective function and maximizes the overall benefits of the group. In [36], the authors show that the decentralized system has no market organizer. Consequently, it allows users to participate in direct transactions and agreements between themselves. This prevents users from exposing their energy statistics to a third party. In [37], the authors consider dispersed marketplaces, which influence consumer behavior in a roundabout way through the price mechanism, another technique that has received much attention and study. This is comparable, but different, to decentralized and centralized marketplaces. When using the distributed system, users also benefit from greater confidentiality. In [38], the authors use the k-means technique to evaluate the historical data of prosumers and achieve a balance between supply and demand in the microgrid. In [39], the authors proposed an improved energy management model for prosumers to help them develop appropriate consumption plans. In [40], the authors proposed the concept of energy collectives, which allows the community negotiation process to be defined and modeled based on distributed optimization concepts. In [41], the authors proposed an optimization model to optimize the financial benefits of households equipped with PV and batteries. The idea is to set a fixed price for users to share energy via peer-to-peer (P2P) trading. In [42], the authors advocate for using local energy reserves for blockchain-based P2P energy trading in the industrial Internet of Things (IoT). The objective is to reduce blockchain length and energy transfer losses. In [43], the authors propose an \"intelligent energy exchange platform\", a centralized P2P energy exchange market. As a constraint in this system, the price of P2P transactions is fixed in advance. In [44], the authors describe a market mechanism for operating multiple hubs based on P2P transactions occurring within energy hubs. In [45], the authors propose an event-driven local market in which energy retailers choose tariffs in a temporary market through an event-driven bilateral auction mechanism. A peer-to-peer (P2P) energy-sharing approach for microgrids based on price demand response has been described [46]. In this paradigm, the relationship between supply and demand defines the dynamic internal pricing mechanism. In [47], the authors describe a viable network cost allocation strategy to compensate for peer-to-peer energy trading. By modeling P2P energy trading with auction payments, this strategy can improve the performance of P2P trading. In [48], the authors propose a trading strategy based on a Nash-type non-cooperative game model. Examining blockchain technology in the context of peer-to-peer (P2P) energy trading, the authors propose an Ethereum-based blockchain test bed that illustrates several blockchain principles [49]. In [50], the authors suggest that decentralized marketplaces' continuous bilateral auction procedure could reduce some users' production or load.