###### Abstract
Using this balanced approach, this study aimed to formulate policy implications that maximize efficiency gains while ensuring market stability throughout the transition process. This study focused on the South Korean electricity market as a case study. South Korea's electricity market, which currently employs uniform pricing based on SMP, is plagued by significant challenges in managing the geographical separation between load centers and generation sources, as well as congestion management owing to the absence of locational price signals. These limitations have become increasingly pronounced with the growing share of renewable energy, thereby rendering transitioning to zonal or nodal pricing an urgent task. However, a detailed analysis of this transition is lacking. Thus, this facilitates a suitable environment for analyzing the effects of transitioning to zonal and nodal pricing. Furthermore, South Korea's unique market structure, wherein a single transmission company (Transco) functions as a monopoly retailer, offers a distinctive opportunity to examine the effect of a transition in pricing schemes on potential conflicts of interest for the Transco. To achieve the research objectives, this study adopted a stepwise approach as follows:  
First, we utilized PLEXOS version 10.0 (R07), an advanced software tool for power system modeling and simulation [29], to model the power system at various levels of granularity, yielding a detailed 4,579-node representation. This comprehensive modeling approach facilitated a nuanced analysis of complex interactions within a power system. Second, we constructed a comprehensive set of zonal and nodal pricing schemes that reflected the changes in pricing mechanisms and market-clearing models. These schemes represented various potential pricing schemes that could be introduced into the Korean electricity market in the long-term. 1. Uniform pricing based on SMP (currently adopted pricing scheme)
2. Zonal pricing with two bidding zones based on SMP
3. Zonal pricing with two bidding zones based on ZMP
4. Zonal pricing using ATC with five bidding zones based on ZMP
5. Zonal pricing using FBMC with five bidding zones based on ZMP* 6. Nodal pricing based on LMP
* 7. Nodal pricing based on ELMP  
Third, we conducted quantitative assessments through simulations for each of the aforementioned pricing schemes, covering the entire year 2023. These assessments focused on evaluating important metrics such as market prices, power flow in the system, power purchase costs for the retailer, generation costs, revenue, and profits for power producers. This study made the following key contributions:
* We quantified the impact of various pricing schemes on the electricity market, incorporating simultaneous changes in both the pricing mechanisms and market-clearing models, an aspect not fully explored in previous studies. * We identified the potential conflicts of interest for the Transco in the case of the transition to zonal and nodal pricing within a unique market structure wherein the Transco functions as a monopoly retailer. * We evaluated quantifiable metrics, such as financial impacts on market participants and potential social risks associated with various pricing schemes. This can serve to guide policymakers towards an appropriate pricing transition pathway and policy implications, while offering crucial information for stakeholders to adapt to potential changes. * We contributed valuable insights applicable to electricity markets worldwide, considering the transition to zonal and nodal pricing, based on a comprehensive analysis of the Korean case study. The remainder of this paper is structured as follows. Section 2 describes the pricing schemes (uniform, zonal, and nodal), preceded by explanations of the market-clearing models and pricing mechanisms that form the basis for these pricing schemes. Section 3 introduces a comprehensive set of zonal and nodal pricing schemes for the simulations, provides a brief overview of the Korean electricity market, and details the data used in these simulations.