Design and Optimization Strategy of a Net-Zero City Based on a Small Modular Reactor and Renewable Energy

Abstract

This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy storage systems, SMRs provide a reliable and flexible baseload power source. Sector coupling systems—such as hydrogen production and heat generation—enhance grid stability by absorbing surplus energy and supporting the decarbonization of non-electric sectors. The core contribution of this study lies in its real-time data emulation framework, which overcomes a critical limitation in the current energy landscape: the absence of operational data for future technologies such as SMRs and their coupled hydrogen production systems. As these technologies are still in the pre-commercial stage, direct physical integration and validation are not yet feasible. To address this, the researchers leveraged real-time data from an existing commercial microgrid, specifically focusing on the import of grid electricity during energy shortfalls and export during solar surpluses. These patterns were repurposed to simulate the real-time operational behavior of future SMRs (ProxySMR) and sector coupling loads. This physically grounded simulation approach enables high-fidelity approximation of unavailable technologies and introduces a novel methodology to characterize their dynamic response within operational contexts. A key element of the SSNC control logic is a day–night strategy: maximum SMR output and minimal hydrogen production at night, and minimal SMR output with maximum hydrogen production during the day—balancing supply and demand while maintaining high SMR utilization for economic efficiency. The SSNC testbed was validated through a seven-day continuous operation in Busan, demonstrating stable performance and approximately 75% SMR utilization, thereby supporting the feasibility of this proxy-based method. Importantly, to the best of our knowledge, this study represents the first publicly reported attempt to emulate the real-time dynamics of a net-zero city concept based on not-yet-commercial SMRs and sector coupling systems using live operational data. This simulation-based framework offers a forward-looking, data-driven pathway to inform the development and control of next-generation carbon-neutral energy systems.

1. Introduction

Achieving net-zero carbon emissions in urban areas has become one of the most pressing global challenges, as cities [1] account for approximately 70% of global carbon emissions and represent a significant portion of the world’s energy consumption [2,3,4,5,6,7]. According to the International Energy Agency (IEA) report [3], urban areas currently account for approximately 75% of global energy consumption and 70% of global greenhouse gas emissions. Despite growing efforts to develop large-scale net-zero cities, significant technical and economic barriers remain [8]. Major projects, such as Google’s Sidewalk Toronto project [9,10,11] and Saudi Arabia’s NEOM City project [12,13,14], have demonstrated the inherent difficulties associated with large-scale net-zero city development, including the intermittency of renewable energy and the limitations of large-scale energy storage systems (ESSs). Existing research has primarily focused on small-scale, community-based microgrid models, which provide valuable insights but face scalability issues when applied to larger urban settings due to differences in infrastructure, demand complexity, and grid stability requirements [15,16,17]. The study by Soshinskaya et al. [15] provides a detailed discussion of the challenges of achieving net-zero operation in microgrids, particularly emphasizing the issue of renewable energy intermittency. Similarly, the work by Akinyele et al. [17] also addresses the implications of renewable energy intermittency for microgrid operation. It presents the intermittency problem as a major obstacle and discusses potential strategies to mitigate its effects. Additionally, renewable-based net-zero city models require prohibitively large ESS capacities to store surplus energy and ensure a stable power supply, making them economically and technically infeasible in large-scale urban settings [16,18,19].

To address these limitations, research has increasingly focused on integrating renewable-based microgrid models with stable baseload power sources. However, renewable energy sources such as solar and wind are inherently intermittent, creating challenges in balancing supply and demand within large-scale urban microgrids [20,21,22,23,24]. The limited capacity of ESSs further exacerbates this issue, as ESSs alone cannot fully address large-scale energy imbalances. This has led to growing interest in Small Modular Reactors (SMRs) as a stable and scalable baseload power source for complementing renewable energy systems and reducing dependence on external grids [25,26,27]. The World Nuclear Association [28] notes that SMRs, typically defined as reactors producing up to 300 MWe, are designed with modular technology for factory fabrication. This approach aims for economies of series production and shorter construction times, making SMRs a practical option for providing stable, low-carbon baseload power in urban settings. SMRs offer reliable and consistent power generation, enhancing grid stability and operational efficiency [27,29,30,31,32]. According to recent studies by the U.S. Department of Energy (DOE), integrating SMRs with renewable energy systems can reduce the cost of achieving carbon neutrality by up to 40% compared to a renewables-only approach [33]. However, SMRs are not yet commercially available—the first commercial SMR is expected to become operational around 2027 [34,35,36]. This lack of real-world operational data on SMR integration with renewable energy and urban demand creates a significant gap in existing research. Most studies rely on simulation-based data rather than real-world data, limiting the accuracy and applicability of their findings. The Idaho National Laboratory (INL) report presents a comprehensive model for integrating small reactors (SRs), including Small Modular Reactors (SMRs) and microreactors, into microgrid systems. To accurately simulate the integration of SMRs into microgrids, the researchers employed the Xendee platform, a specialized tool for modeling and analyzing clean energy microgrids. Within this platform, they developed an external module tailored to represent the unique characteristics of an SMR with a Gas Plant [37,38,39,40]. These studies collectively contribute to understanding how SMRs can be effectively integrated and controlled within microgrid systems through advanced simulation techniques.

To address this gap, this study developed a ProxySMR model that simulates SMR behavior based on real-time grid data. The ProxySMR model assumes that SMRs replace external grid input during supply shortages and reduce output during periods of surplus generation. Additionally, sector coupling demand was modeled to absorb surplus renewable generation and repurpose it for hydrogen production and heat pumps, thereby enhancing grid stability and reducing emissions. Several studies closely relate to the objectives and methodology of this research, particularly in the modeling and control of energy systems using advanced optimization techniques such as model predictive control (MPC) and reinforcement learning (RL) [41,42,43,44]. Zhan et al. [41] compared MPC and RL for optimizing building–PV–battery systems, showing that both methods can effectively manage real-time operations under uncertainty. This aligns with the control strategy in the SSNC framework, where RL and MPC are combined to optimize SMR and sector coupling behavior using real-time data. Bordon et al. [42] applied RL and MPC in electric microgrids to optimize energy dispatch and load balancing, reinforcing the applicability of such methods in dynamic, distributed energy environments similar to the ProxySMR control system. Zhang et al. [43] demonstrated the benefits of MPC for the optimal operation of residential microgrids, especially under renewable generation uncertainty and storage constraints. This supports the approach of minimizing ESS use and improving grid stability in this study. Mignacca and Locatelli [44] conducted a systematic review of SMR economics and deployment challenges. Their findings support the feasibility of integrating SMRs as clean, flexible baseload sources in urban decarbonization strategies. Together, these references validate the proposed ProxySMR approach by demonstrating the effectiveness of MPC and RL in managing renewable volatility, optimizing storage, and enabling cost-effective microgrid operation.

This study introduces the SSNC framework, which integrates SMRs and sector coupling systems within a microgrid architecture to achieve cost-effective and scalable net-zero cities [45,46]. A key novelty of this work is the establishment of a real-time operational testbed that leverages ProxySMR behavior and sector coupling controls using live microgrid data, addressing the current gap in operational datasets for emerging technologies. By combining model predictive control (MPC) with reinforcement learning (RL), the proposed system dynamically adjusts supply and demand in real time, reducing ESS reliance and enhancing decarbonization pathways for complex urban systems.

2. SSNC Microgrid Design for Net-Zero City

2.1. Energy Block Modeling of Net-Zero City

The net-zero city concept is realized through energy block modeling, which provides a systematic and visual framework for analyzing the city’s energy dynamics, as shown in Figure 1. The prototype model, referred to as the SSNC, was developed by Korea Hydro & Nuclear Power (KHNP). It consists of a generation block row and a demand block row. The generation block includes SMRs, solar power, wind power, fuel cells, and energy storage systems (ESSs). The demand block incorporates residential, commercial, industrial, and agricultural loads, along with sector coupling systems such as heat pumps, desalination, and hydrogen production.

The capacity of each energy block is determined during the design phase to ensure a real-time supply–demand balance. For a city with a population of 100,000, the estimated power demand is approximately 910 MW. SMRs are designed to meet 37% of this demand, equating to 340 MW, which corresponds to two units of KHNP’s standard iSMR [47,48]. This modular approach facilitates efficient energy distribution and supports the development of sustainable and resilient urban energy systems.

2.2. SSNC Operation Platform of Net-Zero City

An operational platform for the SSNC is structured based on the energy block design, as depicted in Figure 2. Each energy block is equipped with an edge server module that interfaces with a centralized cloud server. This platform integrates edge servers into a cloud-based operational framework, enabling real-time monitoring and control of energy facilities within the net-zero city.

Edge servers function as gateways, managing data exchange between energy blocks and the cloud server. They perform metering, monitoring, and control functions, while the cloud server oversees centralized processing and management. The platform includes an Integrated UI server for real-time visualization at the Network Operation Center and an optimization server for enhancing ESS performance. This architecture ensures streamlined communication, efficient operation, and optimized energy management across the net-zero city.

2.3. SSNC Dynamic Modeling

The SMR Smart Net-Zero City (SSNC) is designed as an integrated and dynamic energy system that balances various energy sources and demands in real time. The dynamic modeling of the SSNC aims to analyze the operational characteristics of its key energy components and integrate them into a unified framework to simulate and optimize the daily, weekly, and annual operational dynamics of the net-zero city.

2.3.1. Consumer Load Modeling

Consumer load in SSNC consists of two main types: residential load and commercial and industrial (C&I) load.

Residential Load Modeling

Residential load is highly variable depending on time of day, weather, and user behavior. Real-time modeling based on Advanced Metering Infrastructure (AMI) data is employed to capture dynamic patterns. The residential load at time t can be expressed as

$$P_{res}\left(t\right)=\sum _{i=1}^N\left(P_{base,i}+P_{var,i}\left(t\right)\right)$$

where the following definitions hold:

• $P_{res}\left(t\right)=\mathrm{total} \mathrm{residential} \mathrm{load} \mathrm{at} \mathrm{time} t$;
• $P_{base,i}=\mathrm{base} \mathrm{load} \mathrm{for} \mathrm{household} i$;
• $P_{var,i}\left(t\right)=\mathrm{time-dependent} \mathrm{variation} \mathrm{of} \mathrm{load} \mathrm{for} \mathrm{household} i$;
• $N=\mathrm{number} \mathrm{of} \mathrm{residential} \mathrm{consumers}$.

A machine-learning-based clustering method is applied to identify load patterns:

$$P_{var,i}\left(t\right)=w_1{X}_{1,i}\left(t\right)+w_2{X}_{2,i}\left(t\right)+\dots +w_n{X}_{n,i}\left(t\right)$$

where the following definitions hold:

• $w_k=\mathrm{weight} \mathrm{coefficients} \mathrm{for} \mathrm{input} \mathrm{variable} X_k$;
• $X_k=\mathrm{external} \mathrm{factors} \mathrm{such} \mathrm{as} \mathrm{temperature}, \mathrm{day} \mathrm{of} \mathrm{the} \mathrm{week}, \mathrm{and} \mathrm{time} \mathrm{of} \mathrm{day}$.

Machine learning models such as Random Forest and LSTM (Long Short-Term Memory) are used to improve prediction accuracy [49,50,51,52].

Commercial and Industrial (C&I) Load

Commercial and industrial load consists of relatively stable baseline demand with periodic peaks during working hours. It is modeled as

$$P_{CI}\left(t\right)=P_{base}+P_{peak}\left(t\right)$$

where the following definitions hold:

• $P_{base}=\mathrm{baseline} \mathrm{load} \mathrm{for} \mathrm{C}\&\mathrm{I} \mathrm{consumers}$;
• $P_{peak}\left(t\right)=\mathrm{peak} \mathrm{load} \mathrm{variation} \mathrm{based} \mathrm{on} \mathrm{production} \mathrm{and} \mathrm{business} \mathrm{hours}$.

2.3.2. Sector Coupling Demand Modeling

Sector coupling demand represents the use of surplus renewable energy to produce valuable carbon-free resources such as hydrogen and heat [53,54,55,56].

Hydrogen Production Through Electrolysis

Hydrogen production is modeled based on the electrolysis process, where water is split into hydrogen and oxygen using electricity. The hydrogen production rate is given by

$$P_{H_2}\left(t\right)={\displaystyle \frac{{\mathrm{\eta }}_{elec}\cdot P_{input}\left(t\right)}{E_{H_2}}}$$

where the following definitions hold:

• $P_{H_2}\left(t\right)=\mathrm{hydrogen} \mathrm{production} \mathrm{rate} (\mathrm{kg}/\mathrm{h})$;
• ${\mathrm{\eta }}_{elec}=\mathrm{electrolyzer} \mathrm{efficiency}$;
• $P_{input}\left(t\right)=\mathrm{power} \mathrm{input} \mathrm{to} \mathrm{electrolyzer}$;
• $E_{H_2}=\mathrm{energy} \mathrm{required} \mathrm{to} \mathrm{produce} 1 \mathrm{kg} \mathrm{of} \mathrm{hydrogen}$.

Heat Generation Through Heat Pumps

Heat generation from heat pumps is modeled using the coefficient of performance (COP):

$$Q_{heat}\left(t\right)=COP\cdot P_{input}\left(t\right)$$

where the following definitions hold:

• $Q_{heat}\left(t\right)=\mathrm{heat} \mathrm{output} \left(\mathrm{kWh}\right)$;
• $COP=\mathrm{coefficient} \mathrm{of} \mathrm{performance} \mathrm{of} \mathrm{the} \mathrm{heat} \mathrm{pump}$;
• $P_{input}\left(t\right)=\mathrm{power} \mathrm{input} \mathrm{to} \mathrm{heat} \mathrm{pump}$.

2.3.3. Solar Power Generation Modeling

Solar power generation is modeled using the following equation [57,58]:

$$P_{pv}\left(t\right)=GHI\left(t\right)\cdot {\mathrm{\eta }}_{panel}\cdot A_{panel}\cdot \cos \mathrm{\theta }\left(t\right)$$

where the following definitions hold:

• $P_{pv}\left(t\right)=\mathrm{solar} \mathrm{power} \mathrm{generation} \mathrm{at} \mathrm{time} t (\mathrm{kW})$;
• $GHI\left(t\right)=\mathrm{global} \mathrm{horizontal} \mathrm{irradiance} \mathrm{at} \mathrm{time} t (\mathrm{W}/{\mathrm{m}}^2)$;
• $A_{panel}=\mathrm{surface} \mathrm{area} \mathrm{of} \mathrm{the} \mathrm{solar} \mathrm{panel} (\mathrm{W}/{\mathrm{m}}^2)$;
• $\mathrm{\theta }\left(t\right)=\mathrm{incidence} \mathrm{angle} \mathrm{of} \mathrm{sunlight} \mathrm{at} \mathrm{time} t \left(\mathrm{degrees}\right)$.

2.3.4. SMR Power Generation Modeling

Small Modular Reactors (SMRs) are modeled using neutron kinetics equations for Pressurized Water Reactors (PWRs) [59]:

$${\displaystyle \frac{d{P}_{SMR}\left(t\right)}{dt}}={\displaystyle \frac{\mathrm{\rho }\left(t\right)-\beta }{\mathrm{\Lambda }}}{P}_{SMR}\left(t\right)+\sum _{i=1}^6{\mathrm{\lambda }}_i{C}_i\left(t\right)$$

where the following definitions hold:

• $P_{SMR}\left(t\right)=\mathrm{SMR} \mathrm{output} \mathrm{power} \mathrm{at} \mathrm{time} t \left(\mathrm{MW}\right);$
• $\mathrm{\rho }\left(t\right)=\mathrm{reactivity} \mathrm{at} \mathrm{time} t$;
• $\beta =\mathrm{delayed} \mathrm{neutron} \mathrm{fraction}$;
• $\mathrm{\Lambda }=\mathrm{neutron} \mathrm{generation} \mathrm{time} \left(\mathrm{s}\right)$.

2.3.5. Governing Equation of SSNC

The SSNC governing equation integrates all major system components into a single balance equation:

$$P_{ESS}\left(t\right)=C_{res}\left(t\right)+C_{CI}\left(t\right)+P_{SC}\left(t\right)-S\left(t\right)-P_{SMR}\left(t\right)$$

where

$$P_{ESS}\left(t\right)=\mathrm{Power} \mathrm{charged} \mathrm{to} \mathrm{or} \mathrm{discharged} \mathrm{from} \mathrm{ESS} \mathrm{at} \mathrm{time} t \left(\mathrm{MW}\right)$$

3. SSNC Microgrid Framework for Development and Demonstration

The foundational modeling of the SSNC extends the operational framework of a conventional commercial microgrid, as depicted in Figure 3. A typical commercial microgrid (left) comprises three primary components: consumer demand, solar power generation, and an ESS (energy storage system) for charging and discharging. However, maintaining a supply–demand balance solely with ESSs is challenging due to significant discrepancies between generation and consumption. The practical limitations of ESS capacity make it infeasible to fully address these imbalances. Consequently, most commercial microgrids rely on external grid interconnections, importing power when demand exceeds generation and exporting surplus power when generation exceeds demand. Smaller fluctuations are managed by ESS charging and discharging.

The SSNC design (right) scales this framework to a city-level system by expanding the traditional three-node microgrid into a five-node architecture. This enhanced model introduces two critical components: SMR power and sector coupling demand. These additions enable the SSNC to effectively balance supply and demand at a city scale while significantly reducing dependence on external grid connections.

3.1. Pattern Generation from Dynamic Characteristics

Operational data from the commercial microgrid were analyzed to identify dynamic characteristics, including energy generation, consumption trends, and responses to demand fluctuations. These characteristics formed the basis for generating synthetic patterns that replicate the microgrid’s operational behavior under scaled-up conditions.

To meet the capacity requirements of the SSNC microgrid design, these patterns were scaled up by a factor of 1000. The scaling process was meticulously performed to preserve the temporal and behavioral integrity of the original data, ensuring that the scaled patterns accurately represented the dynamic operations of the commercial microgrid at a city-scale level.

3.2. State Variables: Consumer Demand and Solar Power Generation

In a microgrid, consumer demand serves as a key load node, with demand predictions based on both historical and real-time data. An edge server manages consumer demand by collecting real-time data from smart meters and accurately estimating demand levels. This approach enables real-time adaptation to consumption variations, ensuring efficient system management. The consumer demand data gathered by the edge server are transmitted to the cloud server, where they are utilized for continuous monitoring and control of the microgrid, maintaining optimal operation and system reliability.

In a microgrid, solar power generation is modeled as a key generation node, with predictions of output informed by both historical and real-time data. An edge server manages solar generation by collecting real-time data from inverters and estimating generation output with high accuracy. This approach allows the real-time reflection of performance variations and ensures efficient system management. Solar power data collected by the edge server are transmitted to the cloud server, where they are used for continuous monitoring and control of the microgrid. Consumer demand and solar power generation data patterns are presented in Figure 4.

3.3. Control Variables: SMR Generation and Sector Coupling Demand (Hydrogen Production)

In conventional commercial microgrids, nighttime power deficits—when solar power is unavailable—are typically resolved through interconnection with the external power grid. Conversely, during the daytime, surplus electricity generated by solar PV systems is commonly exported to the grid. This power exchange pattern with the external grid is illustrated in Figure 5.

In this study, the SSNC aims to maintain power supply and demand stability by internally substituting the grid’s imported and exported power with SMR generation and sector coupling demand (hydrogen production), respectively. Assuming that the electricity currently imported from the external grid originates from a nearby nuclear power plant, it can be considered a proxy for future SMR deployment. Hence, we define this as ProxySMR, which emulates the real-time operational characteristics that would be expected from an actual SMR.

Additionally, instead of exporting surplus electricity to the external grid, the excess power is consumed internally by hydrogen production facilities within SSNC. This internally used power is defined as sector coupling demand. Accordingly, the current power export patterns observed in the demonstration site’s grid interface can serve as a proxy for deriving the real-time operational pattern of future sector coupling hydrogen loads

Therefore, this study defines ProxySMR Generation (SMR power) and sector coupling demand (hydrogen production load) as the two main control variables. The key challenge in this study lies in the fact that real-time operational data for these variables are currently unavailable. Since neither SMR units nor hydrogen production systems are yet in operation, integrated real-time testing using live consumer and solar generation data at a demonstration scale is not feasible.

To overcome this limitation, this study extracts the dynamic characteristics of the target control variables from real-time power supply and demand data of the external grid. The extracted patterns are then used to estimate the initial values of the control variables, as shown in Figure 6. Based on these inferred patterns, an optimal control algorithm was developed and successfully applied to operate the SSNC over a continuous 7-day period.

As a result, both the state variables and control variables within the SSNC framework are interpreted as integrated operational outcomes derived from real-time data patterns observed in the testbed. The next section will propose a control strategy designed to minimize ESS usage while maximizing carbon emission reduction.

4. SSNC Testbed and Optimal Control Algorithm

4.1. Overview of SSNC Testbed

To simulate the operation of an SSNC, a testbed was developed based on a commercial microgrid in Busan. This testbed was scaled up by a factor of 1000 to evaluate its performance in a realistic large-scale system environment. The expanded system includes the concept of a ProxySMR and virtual sector coupling demand (hydrogen production), providing an environment for realistically evaluating the performance of the SSNC framework under practical conditions.

4.2. Operation of the State-Variable-Based SSNC Testbed

The testbed was operated using real-time data collected from an actual microgrid, including consumer demand and solar generation data. These data were used to dynamically update key operational parameters (state variables), allowing for an accurate capture of interactions between demand and generation.

The configuration and state variable integration structure of the testbed are shown in Figure 4. This approach provides a solid foundation for thoroughly evaluating the performance of the SSNC system and gaining valuable insights into the operational stability and efficiency of the system.

4.3. SSNC Optimal Control Algorithm

The following setup was applied to the microgrid control problem:

State Variables

• $C\left(t\right)=\mathrm{Consumer} \mathrm{demand} \mathrm{at} \mathrm{time} t \left(\mathrm{MW}\right)$;
• $S\left(t\right)=\mathrm{Solar} \mathrm{generation} \mathrm{at} \mathrm{time} t \left(\mathrm{MW}\right)$.

Control Variables

• $P_{SMR}\left(t\right)$= SMR generation at time $t$ (MW)
• $P_{SC}\left(t\right)=\mathrm{Sec}\mathrm{tor} \mathrm{coupling} \mathrm{d}\mathrm{emand} \mathrm{at} \mathrm{time} t \left(\mathrm{MW}\right)$.

Constraints

• $100\le P_{SMR}\left(t\right)\le 280$ (maximum change rate ± 50 MW/h);
• $50\le P_{SC}\left(t\right)\le 250$ (maximum change rate ± 50 MW/h);
• $P_{ESS}\left(t\right)=$ ESS charging/discharging rate $\mathrm{at} \mathrm{time} t$ (MW)

: $-100\le P_{ESS}\left(t\right)\le 100$;

• $SoC\left(t\right)=$ State of Charge at $\mathrm{time} t$ (MWh)

: $-100\le SoC\left(t\right)\le 100$.

Control Equation:

The core power balance equation is

$$P_{ESS}\left(t\right)=C\left(t\right)+P_{SC}\left(t\right)-S\left(t\right)-P_{SMR}\left(t\right)$$

Objective Function:

Minimize the sum of the absolute values of $SoC\left(t\right)$ over 168 h:

$$J=\sum _{t=1}^{168}\left|SoC\left(t\right)\right|$$

where $SoC\left(t\right)$ is defined as

$$SoC\left(t\right)=SoC\left(t-1\right)+P_{ESS}\left(t\right)$$

Operation Targets:

• Optimal Control: minimize objective function;
• Robust Control: maintain $-5\le SoC\left(t\right) in \left[\mathrm{\%}\right]\le 95,$ where $SoC\left(t\right) in \left[\mathrm{MWh}\right]=SoC\left(t\right) in \left[\mathrm{\%}\right]\times 2-100$

Optimization

Lagrangian Equation: Apply Sequential Quadratic Programming (SQP):

$$\begin{gathered} \mathcal{L}=\sum _{t=1}^{168}\left|SoC\left(t\right)\right|+{\mathrm{\lambda }}_1\left(t\right)\left[P_{ESS}\left(t\right)-C\left(t\right)-P_{SC}\left(t\right)+S\left(t\right)+P_{SMR}\left(t\right)\right] \\ \hspace{1em}\hspace{1em}+{\mathrm{\lambda }}_2\left(t\right)\left[SoC\left(t\right)-SoC\left(t-1\right)-P_{ESS}\left(t\right)\right] \end{gathered}$$

Karush–Kuhn–Tucker (KKT) Conditions: first-order optimality conditions

$${\displaystyle \frac{\mathsf{\partial }\mathcal{L}}{\mathsf{\partial }{P}_{SMR}\left(t\right)}}=0$$

$${\displaystyle \frac{\mathsf{\partial }\mathcal{L}}{\mathsf{\partial }{P}_{SC}\left(t\right)}}=0$$

4.4. Results of the Optimal Control Algorithm

The optimal control algorithm was applied continuously over a seven-day period to balance supply and demand while optimizing ESS performance. Adjustments to control variables, including sector coupling demand and SMR output, were dynamically managed, as shown in Figure 7.

While the advanced SMR and the eletrolyzer for sector coupling allow for operation at very low load factors, we recognize that this would be economically unfavorable for commercial applications. As shown in Figure 6 and Figure 7, the system is operated to maintain a weekly average load factor of almost 60~80%, even though the instantaneous minimum load may drop to 20~35%.

The optimal control algorithm was continuously applied over 7 days (168 h), during which the supply–demand balance was successfully maintained. Adjustments to sector coupling demand and SMR output were performed dynamically. The operational results, shown in Figure 8, demonstrate that the algorithm effectively maintained system stability.

The ESS charging and discharging were conducted smoothly within the 100 MW capacity limit. Specifically, +100 MW represents full charging, while −100 MW indicates full discharging. This demonstrates that the demo site was operated under a robust control strategy that maintained ESS activity strictly within its rated capacity.

In addition to robustness, another key control objective was optimality, namely minimizing unnecessary ESS activity. This is achieved by maintaining the State of Charge (SoC) as close as possible to 50%, which corresponds to 0 MWh on the energy axis. In this representation, 0% SoC equals −100 MWh, and 100% SoC equals +100 MWh.

The effectiveness of this optimal control strategy is clearly demonstrated in Figure 9, where the SoC remains stably centered around 0 MWh, reflecting balanced system dynamics with minimal ESS excursions. The proposed MPC- and RL-based optimization algorithm demonstrated excellent adaptability and efficiency in real-time operation, achieving stable operation and economic performance of the SSNC framework. Through this study, the SSNC has demonstrated its potential as a sustainable and efficient energy management model for net-zero cities.

5. Conclusions

This study proposes a novel simulation-based framework to explore the real-time operation of SMR-based net-zero cities under current infrastructure limitations. The central innovation lies in the use of a live operational microgrid as a proxy environment to emulate the future behavior of SMR and sector coupling systems that are not yet commercially available. By mapping grid import data to ProxySMR generation during energy shortages and grid export data to sector coupling demand during surplus, the framework provides a physically grounded characterization of unavailable system dynamics. The SSNC framework demonstrated effective real-time operation over seven consecutive days, achieving approximately 75% SMR utilization and maintaining grid stability near a 50% State of Charge for ESSs. It dynamically coordinated residential and commercial loads, renewable generation, SMR output, and sector coupling demand within a unified architecture. To the best of our knowledge, this is the first publicly reported attempt to emulate real-time operations of a net-zero city architecture based on emerging nuclear–renewable integration.

This contribution offers an actionable, data-informed platform that may inform future decision-making in urban energy transitions.