## 2 Literature Survey
They investigate three primary methods: pricing techniques such as as bill sharing, mid-market rate pricing, and auction-based pricing also come under the second category of pricing strategies. The goal of the study is to build a more robust and resource-optimal power system by utilizing local production and consumption, which means that there is less focus on possibly high losses of energy, which are usually present when energy is transported through longer distances. Another innovative approach is presented in [21], which is the Alternating-Direction Method of Multipliers (ADMM), a distributed optimization approach. The paper's methodology involves modeling the utility functions of prosumers and consumers, establishing a P2P market operator (P2 PMO) to balance supply and demand, and solving the pricing problem in a decentralized manner. The ADMM approach is used to handle the optimization problem, ensuring that each prosumer or consumer optimizes its objectives independently, leading to an equilibrium state in the market. Another ADMM approach is proposed in [22]. A decentralized framework for P2P energy trading accounts for distribution network constraints while preserving the independence of participants. The authors apply the ADMM to address the coordination between agents, ensuring minimal information exchange while maintaining network security. By using a sensitivity-based approach, the study incorporates voltage and current constraints into the agents' local optimization problems, which effectively maintains network integrity during P2P transactions. The framework reduces the role of the Distribution System Operator (DSO) while empowering prosumers to participate autonomously, ensuring secure and independent energy exchanges. The results demonstrate the model's efficiency in handling network voltage and current constraints in practical applications, offering a solution for integrating decentralized renewable energy resources into existing grids. The framework is particularly notable for its computational efficiency, allowing fast convergence even under complex network constraints. The authors of [23] introduce a decentralized energy management system for P2P community microgrids, focusing on maximizing social welfare while ensuring privacy and communication efficiency. The system integrates demand response (DR) programs and P2P energy trading, where prosumers and consumers exchange surplus energy in a decentralized framework. By employing a novel Segmentation-Based Distributed Alternating-Direction Method of Multipliers (SBD-ADMM), the authors optimize energy scheduling and reduce the overall electricity costs for consumers and prosumers. The decentralized nature of the system preserves participants' privacy and reduces the number of communications required for optimal energy trading, offering a scalable solution for modern community microgrids. Through simulations, the approach proves its efficiency in achieving lower electricity bills, better integration of renewable energy, and enhanced community-wide energy sustainability. As mentioned above, there are a number of research papers in which the authors employ one or more of the above-discussed methodologies for managing the former problem and exploiting the latter opportunity associated with P2P energy trading. Thus, this work seeks to achieve the above noble goals by developing an appropriate framework geared towards the enforcement of P2P energy trading in South Korea. Unlike other studies, this study introduces a novel multi-objective optimization framework for P2P energy trading within a regulated, tiered pricing system, addressing key deficiencies in previous studies. While most P2P frameworks optimize for cost savings or prosumer benefits individually, they often overlook the complex trade-offs required in a tiered pricing structure. By employing the MOEA/D algorithm, this study achieves a comprehensive optimization of multiple objectives: cost minimization, revenue maximization, and energy utilization, demonstrating adaptability for regulated markets.