## 2 Literature Survey
In terms of both breadth and depth, the study provides an adequate picture of prospective trading mechanisms, which provides the necessary information to distinguish profitable and worthwhile trading strategies for players within the energy trading market. Reference [11] reveals the integration of distributed photovoltaic (PV) systems and battery storage to optimize P2P energy trading, demonstrating significant cost savings and enhancing market participation through the effective use of locally generated energy. The model is beneficial for making households spend less on electricity and earn money for prosumers. In the social context, it recognizes the de-centralized nature of energy delivery, the possibility of a reduction in state finances in power infrastructure, and the promotion of distributed renewable energy. The authors of [12] utilize a multi-objective optimization approach to determine the optimal sizing of a hybrid microgrid that includes PV systems, wind turbines, diesel generators, and batteries. The study incorporates the probability of power supply loss, electricity cost, and the renewable energy factors as the primary objective functions for the optimization process. In [13], the authors proposed Genetic Algorithm (GA) and Wind-Driven Optimization (WDO) methods, comparing their outcomes. The results indicated a reduction of 29% in bill cost and 36.2% in peak-to-average ratio. Reference [14] develops a modified MOEA/D to address the multi-objective optimal power flow (OPF) problem. It aims to minimize multiple conflicting objectives, including fuel costs, emissions, power losses, and voltage magnitude deviations. Studies by [15] used the MOEA/D algorithm to address objectives such as minimizing completion time, machine load, energy consumption, and the number of tools required. Moreover, to optimize multiple conflicting objectives like energy consumption, cost, and user comfort, a multi-agent-assisted MOEA/D framework is developed to address multi-objective optimization problems [16]. Further contributions to the literature have applied advanced computational techniques, such as game theory and machine learning, to P2P energy trading [17]. The researchers of [18] discuss solutions, built on game theory, including how to guarantee the equal shares of gains for all prosumers, how to minimize the data exchange and enhance convergence through the decentralized platforms, and how to raise the profitability of self-organized markets with the help of deep Q-Learning (DQL). Peer-to-grid (P2G) regimes are integrated in a coordinated fashion with P2P protocols to deal with them, and intelligent battery participation and demand response proposals stand out for the value they create. Studies also analyze the key issues associated with external retailer-based energy trading and ancillary services and have increased the community revenue as well as operation efficiency demonstrative. These techniques are used in agent-based methods to optimize energy sharing and dispatch control, and this substantially minimizes costs and enhances dependability. Moreover, ref. [19] discusses two main types of pricing mechanisms: energy pricing and network service pricing (NSP). This reveals that although great effort has been made in the research with regard to energy pricing, NSP receives little attention. Inasmuch as there is a growing body of research on these two forms of pricing, there is a lack of research that compares both the financial conflicts and the financial relationship between these two pricing types. The authors [19] suggest that integrating both pricing mechanisms can provide valuable insights for the development of a comprehensive P2P energy market, ultimately contributing to a more sustainable and economically viable energy sector. Consequently, for P2P energy trading, the authors [20] seek to enhance energy efficiency and cost savings by enabling direct energy transactions between households owning PV systems and those without them.