Hybrid Small Modular Reactor—Renewable Systems for Smart Cities: A Simulation-Based Assessment for Clean and Resilient Urban Energy Transitions

Abstract

The global transition to clean energy necessitates integrated solutions that ensure both environmental sustainability and energy security. This paper proposes a scenario-based modeling framework for urban hybrid energy systems combining small modular reactors (SMRs), photovoltaic (PV) generation, and battery storage within a smart grid architecture. SMRs offer compact, low-carbon, and reliable baseload power suitable for urban environments, while PV and storage enhance system flexibility and renewable integration. Six energy mix scenarios are evaluated using a lifecycle-based cost model that incorporates both capital expenditures (CAPEX) and cumulative carbon costs over a 25-year horizon. The modeling results demonstrate that hybrid SMR–renewable systems—particularly those with high nuclear shares—can reduce lifecycle CO₂ emissions by over 90%, while maintaining long-term economic viability under carbon pricing assumptions. Scenario C, which combines 50% SMR, 40% PV, and 10% battery, emerges as a balanced configuration offering deep decarbonization with moderate investment levels. The proposed framework highlights key trade-offs between emissions and capital cost and seeking resilient and scalable pathways to support the global clean energy transition and net-zero commitments.

1. Introduction

The rapid urbanization observed in the 21st century has created an unprecedented demand for clean, secure, and adaptive energy systems in cities. Smart cities—defined by their integration of digital technologies into urban infrastructure—are emerging as a transformative response to challenges such as climate change, energy security, and quality of life. These cities rely on real-time data, interconnected systems, and intelligent control to optimize energy use across sectors including residential buildings, industry, transportation, and public services [1].

In this context, the global shift toward net-zero emissions by mid-century has become a central objective of international climate and energy policy. According to the International Energy Agency (IEA), achieving net zero by 2050 requires a complete transformation of the global energy system, including a rapid scaling of low-carbon power generation technologies [2]. The Intergovernmental Panel on Climate Change (IPCC) also emphasizes the vital role of both renewable energy and nuclear power in deep mitigation scenarios compatible with the 1.5 °C goal [3]. At the European level, the REPowerEU plan outlines an ambitious framework for reducing dependence on fossil fuels by accelerating renewable deployment and supporting innovative technologies such as small modular nuclear reactors (SMRs) [4]. These initiatives collectively underscore the need for integrated, flexible, and urban-compatible energy solutions that can combine the stability of nuclear baseload with the variability of renewables, thereby enabling resilient and climate-aligned smart city infrastructures.

One of the central pillars of a smart city is the smart energy system: a decentralized, flexible, and data-driven grid capable of integrating a high share of renewable energy sources (RES), managing load fluctuations, and empowering consumers through real-time feedback. However, the practical realization of smart cities introduces multiple challenges: high initial investment in digital infrastructure, interoperability between legacy and new systems, energy storage constraints, data privacy and cybersecurity, and ensuring reliable baseload generation to support dynamic energy flows [5].

Smart cities must support increased electrification of transport, heating, and industry, resulting in significantly higher electricity consumption and more complex demand profiles. Peak load conditions, seasonal variations, and intermittent RES production require resilient and flexible energy strategies [6].

In the context of smart cities, the implementation of smart electrical grids plays a key role in ensuring sustainable, efficient, and adaptive energy resource management. With increasing urbanization and growing electricity demand, traditional energy infrastructures are proving insufficient to meet the needs of modern urban systems. Smart grids, through the integration of digital technologies, sensors, automation, and two-way communication between providers and consumers, enable better responsiveness to changes in consumption, load balancing, and the integration of renewable energy sources. This not only enhances energy efficiency and security but also contributes to reducing carbon emissions—an essential aspect in building sustainable and environmentally oriented smart cities [7,8].

To address these issues, small modular reactors (SMRs) are gaining global attention as a viable addition to the smart urban energy mix. SMRs offer clean, continuous baseload power with compact footprints, passive safety systems, and modular deployment. Unlike traditional nuclear reactors, they are designed for urban proximity, remote siting, or industrial integration, offering opportunities for combined heat and power (CHP), hydrogen production, or district heating—key features in smart city ecosystems [9,10].

By integrating SMRs with advanced smart grid infrastructures, cities can benefit from both stability and flexibility, ensuring that low-carbon energy systems remain resilient to load volatility and external disruptions. Furthermore, such integration supports dynamic energy pricing, peak shaving, and demand-side management—core functionalities of intelligent grid systems [11,12].

The aim of this study is to evaluate the long-term economic and environmental performance of hybrid SMR–renewable urban energy systems under realistic operational conditions and to identify cost-effective decarbonization pathways using lifecycle-based scenario modeling.

The paper is structured as follows: Section 1 provides an introduction to the context of the global clean energy transition, the challenges of smart urban electrification, and the potential role of hybrid SMR–renewable systems in supporting resilient and low-carbon infrastructures. Section 2 provides a comprehensive review of the latest advancements in nuclear energy, with a particular focus on small modular reactors (SMRs). Section 3 presents the proposed hybrid energy architecture that integrates SMRs with solar power, battery storage, and intelligent digital control mechanisms. Section 4 evaluates the system-level performance, including emissions reduction, cost estimation, and grid flexibility. Section 5 concludes the paper and discusses the practical implications of the proposed system. Additional sections focus on scenario-based emissions modeling, capital investment calculations, and their relevance for urban energy decision-makers and planners.

2. Overview of Current SMR Research and Development

SMRs are an emerging category of nuclear technologies that offer a number of advantages, including smaller size, modularity, integrated safety, and flexible installation options. They are seen as a key part of future energy systems, especially in the context of smart cities, where decentralized and sustainable energy production is critical. In recent years, numerous SMR concepts have been proposed worldwide, some of which are still in the conceptual phase, while others have already passed the engineering design stage and are now undergoing pre-legitimization activities. In this sense, several projects have already started the construction of pilot or demonstration reactors. On this basis, the following classification and systematization is described:

2.1. Conceptual Phase

In the conceptual phase, many SMR projects are still in the early stages of development, focusing mainly on theoretical models and feasibility studies. The main objective of these projects is to explore new concepts for nuclear reactors that can be integrated into decentralized energy systems and provide safe and efficient solutions for future clean energy requirements.

Examples of conceptual SMR technologies [13,14]:

-

Terrestrial Energy IMSR (Integrated Small Salt Reactor): A concept that uses molten salt as a fuel and coolant. This reactor is designed to be much more efficient than traditional nuclear technologies, focusing on high operating temperatures and low pressures, making it suitable for a variety of applications, including hydrogen production.

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Seaborg CMSR (Modular Salt Reactor): A technology that also uses molten salt and aims to offer a safe and cost-effective way to produce nuclear energy. The CMSR is characterized by high efficiency and low pressure, making it suitable for use in coastal and remote areas [15,16].

2.2. Engineering Design and Licensing Preparation

SMR designs that have passed the engineering design phase are beginning to prepare for licensing and potential construction. These technologies are often more advanced and are undergoing the more stringent safety and durability requirements required to begin construction [17,18,19].

Examples of SMRs in the engineering design phase:

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NuScale VOYGR (USA): NuScale is among the first companies to receive license approval from the US Nuclear Regulatory Commission (NRC) for its SMR design. The design proposes a 77 MW reactor per module that can be integrated into larger power plants. It is the first SMR to receive such approval and is already scheduled for construction.

-

GE Hitachi BWRX-300 (USA/Canada): Based on boiling water reactor (BWR) technology, this 300 MW SMR is in the engineering design phase. GE Hitachi plans to complete this project as part of a global effort to deploy safe, compact nuclear technologies.

2.3. Construction Phase of SMR Deployment

Several SMR projects have already moved into the construction phase, with some in the early stages of pilot or demonstration plants. Although this phase is still early, it shows the promising potential of SMR for commercial and industrial deployment.

Examples of SMRs in construction [19,20]:

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Rolls-Royce SMR (UK): In recent years, Rolls-Royce has launched its small modular Reactor project in the UK. The project is in the early stages of construction and is expected to provide 470 MW of capacity. The technology aims to reduce carbon emissions and provide stable energy production in line with the UK’s climate goals.

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Brest-OD-300 (Russia): A leader in the development of ultra-high temperature SMRs that use liquid lead as a coolant. Russia has started construction of its first SMR under the Brest-OD-300 project, with the reactor expected to be fully operational by the end of 2025.

2.4. Classification of SMR Technologies

SMR technologies can be classified based on their coolant, pressure, and fuel type, each offering unique characteristics [21,22,23].

Pressurized Water Reactor (PWR): This is the most common SMR technology, such as the NuScale VOYGR. It has a proven safety and stability of operation.

Boiling Water Reactor (BWR): This simpler and cheaper technology, such as the GE Hitachi BWRX-300, generates steam directly in the reactor and powers the turbine.

High-temperature Gas-cooled Reactor (HTGR): This system used helium cooling and offers high thermal efficiency, suitable for hydrogen production and process heating.

Molten Salt Reactor (MSR): Molten salt reactors offer unique safety characteristics and efficiency at high temperatures; examples include the Seaborg CMSR and Terrestrial IMSR.

Lead-cooled Fast Reactor (LFR): This system uses liquid lead for cooling, allowing it to operate at high temperatures and reducing radiation waste; BREST-OD-300 is an example.

2.5. Global Efforts and Innovations

As SMR technologies develop, many countries are beginning to adopt nuclear energy innovation strategies, with a significant number launching national programs to develop SMR solutions. SMR projects not only provide a solution to future energy needs, but also represent an important aspect of global carbon reduction policies [23,24].

USA: SMR initiatives in the USA are based on projects such as NuScale, which has already completed licensing processes and is planning to build commercial reactors.

Canada: The Canadian company Terrestrial Energy is working on IMSR as part of efforts to develop new nuclear infrastructure.

UK: The UK is leading the way in SMR development with the Rolls-Royce SMR project, with the aim of achieving energy autonomy and reducing carbon emissions.

Denmark: Seaborg Technologies also has leading developments in SMR technologies, such as CMSR, which will be tested in the future.

Small modular reactors (SMRs) represent a transformative class of advanced nuclear power technologies. With an electrical capacity typically up to 300 MW per module, they are designed to be factory-fabricated, easily transported, and rapidly deployed, offering scalable and safer nuclear solutions for modern energy systems. Unlike conventional gigawatt-scale nuclear plants, SMRs are optimized for flexibility, modularity, and integration into distributed energy infrastructures [13,14].

Table 1. Comparative summary of SMR technologies [17,18,19,20].

Reactor Type	Coolant	Key Benefits	Challenges	Example(s)
PWR	Pressurized Water	Mature technology; strong safety track record	High pressure systems; complex containment	NuScale VOYGR, Rolls-Royce SMR
BWR	Boiling Water	Simple design; lower capital cost	Radioactive steam; limited isolation	GE Hitachi BWRX-300
HTGR	Helium Gas	High efficiency; suitable for process heat	Large heat exchangers; low heat transfer gas	X-Energy Xe-100
MSR	Molten Salt	Low pressure; high heat transfer	Material corrosion; immature licensing	Seaborg CMSR, IMSR
LFR	Liquid Lead/Lead-Bismuth	High temperature operation; waste burning	Heavy coolant; slow heat transfer	BREST-OD-300

systematizes the main technologies, advantages and disadvantages of small modular reactors.

The development and implementation of SMRs are accompanied by a complex balance of technological, economic, and social considerations. On one hand, SMRs offer several compelling advantages such as enhanced safety, modularity, and suitability for integration with renewable energy sources. On the other hand, they also face notable barriers including high capital costs, regulatory uncertainties, and public acceptance issues. Economic advantages of SMR [23,24,25]:

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Reduced initial investment: One of the main economic aspects of SMRs is that they require significantly lower initial investment compared to traditional large nuclear reactors. This is a result of their modular design, which allows for the production and construction of factory-made parts, reducing costs and construction time. This means that the design and construction of SMRs can be significantly faster and cheaper compared to traditional nuclear power plants.

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Scalability and flexibility: SMRs offer the possibility of adding additional modules if there is a need to increase capacity in the future. This flexibility allows operators to start with smaller investments and increase production depending on the growing demand for energy, without having to build large and expensive plants.

-

Improved safety and reduced operating costs: These features are due to the use of passive safety systems. SMRs have passive safety systems that reduce the need for active interventions in the event of an accident. This reduces the risk of accidents and the associated economic losses. These systems also reduce the safety costs traditionally associated with large nuclear reactors. Furthermore, the small scale of SMRs allows for more efficient management and optimization of operating processes, which can lead to reduced operating costs in the long term. This is particularly important when it comes to operating costs, which can traditionally be high in the nuclear industry.

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Competitiveness against renewables and gas technologies: SMRs have a stable and continuous production process energy. Although renewable sources such as solar and wind power are becoming increasingly competitive, they cannot provide a stable baseload due to their intermittency. SMR, as a nuclear source, can provide a stable, continuous and reliable baseload, which is critical for energy security and grid stability. Despite the high initial investment, nuclear power and SMR in particular offer significantly lower carbon emissions compared to coal and natural gas plants. This is essential for achieving the climate goals of many countries and for minimizing the negative economic consequences of climate change.

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Improved public acceptance and integration opportunities in urban areas. Traditional nuclear reactors are often located in remote areas, which can raise questions about safety and public perception. SMRs, thanks to their smaller size and the possibility of modular expansion, can be integrated into more densely populated urban areas, which reduces the need to build new facilities in remote areas.

The conclusion is that the economic advantages of SMRs consist not only in the reduced initial investment, but also in their flexibility, safety, and lower operating costs compared to traditional nuclear technologies. These advantages, combined with the ability to provide a stable baseload, make SMRs economically competitive in the context of modern energy needs and decarbonization efforts.

Table 2. The most relevant benefits and barriers to SMR deployment [12,13,14,15,16,17,18,19,20,21,22,23,24,25].

Advantages	Challenges
Passive safety and low meltdown risk	High upfront capital costs per kW
Modular, fast construction	Lack of established licensing paths
Low operational CO2 emissions	Nuclear waste and long-term liability
Urban/remote deployment suitability	Public acceptance concerns
Supports load-following with renewables	Supply chain readiness and industrial scale-up

summarizes the most relevant benefits and challenges associated with the deployment of SMRs across different technological platforms.

Over the past decade, the global interest in SMRs has led to a growing portfolio of designs under various stages of development, licensing, and deployment. These projects reflect the diversity of SMR technologies and national strategies aimed at achieving clean, reliable, and decentralized energy generation.

Table 3. Overview of current SMR initiatives worldwide [26,27,28,29,30].

Developer	Reactor	Power	Status	Country
NuScale (Corvallis, OR, USA)	VOYGR	77 MW	NRC Licensed	USA
GE Hitachi (Wilmington, NC, USA; Peterborough, ON, Canada)	BWRX-300	300 MW	Design phase	USA/Canada
Rolls-Royce (Derby, UK)	UK SMR	470 MW	Planning stage	UK
Terrestrial Energy (Oakville, ON, Canada)	IMSR	192 MW	Conceptual	Canada
Seaborg (Copenhagen, Denmark)	CMSR	~100 MW	Prototype	Denmark

provides an overview of selected SMR initiatives worldwide, highlighting their developer, technological type, power output, current development status, and country of origin. The table emphasizes the international nature of SMR innovation and the variety of approaches pursued to meet future energy needs.

Each SMR technology presents unique characteristics. While PWR and BWR types are closest to commercial deployment due to regulatory familiarity, advanced designs like MSR and HTGR offer higher efficiency and new capabilities for future smart city energy systems.

A nuclear generator works by converting the thermal energy produced by nuclear reactions into electrical energy. Nuclear fission processes occur in the core of the reactor, which generate a significant amount of heat. This heat is transferred to a coolant—usually water under high pressure—which circulates in a closed primary circuit, preventing it from boiling [18,19,20].

The heated coolant transfers the thermal energy to a steam generator, in which a secondary circuit of non-radioactive water is heated and converted into steam. The resulting steam drives a turbine, which transforms the thermal energy into mechanical energy. The turbine, in turn, powers an electric generator, which produces electrical energy.

After passing through the turbine, the steam is taken to a condenser, where it is cooled and condensed back into liquid water. This water is returned to the cycle, re-entering the steam generator. The entire process is managed by a centralized control room, which provides monitoring and regulation of all key systems, while ensuring the safety of operators by physically separating them from the reactor area.

The system is protected by multiple levels of shielding and containment structures that prevent radiation leakage and provide resilience against internal damage and external threats. This integrated and compact architecture is typical of small modular reactors (SMRs), which are characterized by a high degree of safety, factory assembly, and the ability to be flexibly deployed in various energy environments [29,30].

SMRs offer a strategic path toward carbon-neutral, resilient energy systems—especially in the context of smart cities and decentralized grids. While technological, regulatory, and societal challenges remain, the growing portfolio of pilot projects and supportive policy landscapes signal strong momentum for their adoption.

3. Justification and Structure of a Combined SMR–Renewable Urban Power System

3.1. System Configuration

The implementation of a hybrid energy system that integrates a small modular reactor (SMR), photovoltaic (PV) solar generation, and energy storage technologies is strategically justified by the evolving energy requirements of modern smart cities. Although SMRs offer a stable and continuous baseload power supply, they alone are insufficient to address the dynamic demands of contemporary urban energy systems, which require not only reliability but also operational flexibility [31,32].

The key arguments in support of this combined configuration are as follows:

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Operational Flexibility and Peak Load Management:

SMRs are optimized for continuous, steady-state operation and are not designed to ramp output rapidly in response to fluctuations in demand. During periods of peak load, this lack of agility can lead to system stress or the need for supplementary power sources. In contrast, solar power—when available—and energy storage systems can be rapidly dispatched to meet sudden increases in consumption. This enhances the overall flexibility and responsiveness of the energy system.

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Mitigation of Renewable Intermittency:

While solar energy contributes significantly to decarbonization goals, its intermittent nature limits its ability to provide consistent power, especially during nighttime or unfavorable weather conditions. However, during periods of high solar irradiance, PV systems can reduce the burden on the grid and decrease reliance on nuclear output or external sources. Furthermore, coupling solar generation with energy storage allows excess energy to be captured and deployed during low-generation periods, thus stabilizing supply.

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Economic and Environmental Benefits:

The declining cost of solar photovoltaic technologies has made them increasingly economically viable. When integrated with SMRs, solar generation can contribute to a reduction in total system capital expenditures, while simultaneously improving the environmental profile of the system. Energy storage plays a pivotal role by enabling time-shifting of solar output, ensuring that renewable energy contributes not only during peak production periods but also during times of increased demand or reduced generation.

In summary, the synergistic combination of a SMR, solar power, and energy storage addresses the core challenges of modern energy systems—ensuring stability, flexibility, and sustainability. This integrated approach is particularly well-suited for deployment in smart urban environments, where energy systems must dynamically adapt to variable loads and contribute to long-term decarbonization objectives.

Although SMR provides a stable and safe baseload, the cost of building such a reactor and its operation is significantly higher compared to renewable energy sources. Solar power and energy storage can provide the necessary flexibility of the system and reduce the overall cost of building and operating the energy infrastructure. By combining SMR with solar panels and batteries, it is possible to reduce operating costs by using solar energy during sunny weather and also to improve the resilience of the system by providing additional sources of energy during supply interruptions or peak loads. In addition, it is possible to ensure better compliance with climate goals by reducing carbon emissions from the energy mix [33,34].

The actual operational time of solar energy depends on the geographical location and atmospheric conditions; however, for temperate regions such as Central Europe, solar energy is usually available for about 4–6 h a day during sunny days, which represents about 30–40% of the daytime period. For the rest of the time, when the sun is not shining or the conditions are not favorable, solar energy will not be able to provide enough power, making the combination with energy storage and baseload from SMRs necessary.

The decision to include solar energy and energy storage in the system configuration is motivated by the need to ensure not only a stable baseload, but also flexibility during peak loads and intermittency of renewable sources. Combining SMRs with renewable sources such as solar panels and the use of energy storage technologies allows for a better economic and environmental result, while ensuring flexibility and resilience of the energy system.

The proposed smart urban energy system consists of the following components:

• A SMR acting as a baseload, low-carbon generator;
• Solar photovoltaic (PV) array, representing variable renewable energy;
• Lithium-ion battery storage unit for peak shaving and load balancing;
• Smart grid control system with demand-response logic and time-of-use pricing;
• Dynamic load model for residential, industrial, and electric vehicle (EV) charging profiles.

The system is designed to serve a daily urban load of approximately 100 MWh, representative of a mid-size smart city district. The structure of the proposed system is shown in Figure 1. In this study, all power values for reactors, PV generators, and batteries refer to electrical power (MWe), unless explicitly stated otherwise.

Figure 1 illustrates the architecture of a smart hybrid energy system tailored for urban environments. It integrates a variety of distributed energy resources with intelligent control and bidirectional communication to enable flexible, low-emission, and resilient electricity supply in the context of smart cities.

On the generation side, the system combines a SMR, responsible for providing continuous baseload power, with solar photovoltaic (PV) units that contribute renewable electricity under favorable irradiance conditions. A battery energy storage system, positioned between the generators and the loads, stores excess energy from the PV array and discharges it during peak demand or supply deficits, thereby enhancing system flexibility and reliability.

At the core of the system lies the smart grid controller, which manages the energy flows in real time based on demand forecasts, price signals, and operating constraints. The controller also interacts with a demand-response logic module, allowing the system to dynamically modulate consumption in industrial, residential, and electric vehicle (EV) charging domains according to grid conditions.

On the consumption side, the system delivers energy to several types of urban end-users. EV charging infrastructure is managed adaptively to avoid grid stress during peak hours. Industrial loads benefit from the stable supply provided by the SMR, while residential loads are controlled using smart meters and home energy management systems. All energy flows and control commands are coordinated through a two-way digital communication infrastructure, ensuring responsiveness, transparency, and optimization. This configuration reflects a realistic and forward-looking model for urban energy systems, in which nuclear, renewable, and storage technologies are harmonized under the principles of digitalization and sustainability.

3.2. Emissions and Cost Estimation

To evaluate the sustainability of the proposed hybrid energy system, two parallel assessments were conducted: (1) carbon emissions estimations and (2) capital expenditure (CAPEX) calculations. Both analyses were applied to multiple operational scenarios defined by varying shares of nuclear, renewable, and storage sources.

3.2.1. Emissions Model

Carbon emissions were computed using standardized emission factors (EF) expressed in grams of CO₂ per kilowatt-hour (gCO₂/kWh) generated. For each scenario, the total daily electricity generated from each source was multiplied by its respective EF, and the sum was normalized by the total energy demand (100 MWh/day) to obtain the average emissions in gCO₂/kWh. This normalization is performed using the formula:

$$Average\hspace{0.33em}Emissions={\displaystyle \frac{{\displaystyle \sum _{i=1}^n\left(E{F}_i\times E_i\right)}}{D}},$$

(1)

where

EF_i is the energy produced by source i (in kWh),

E_i is the emission factor of source i (in gCO₂/kWh),

D is the total daily energy demand (100,000 kWh).

This approach ensures that the emissions result is a comparable metric across scenarios, reflecting how much CO₂ is emitted per kilowatt-hour of electricity delivered to the city. These values, based on the literature [31,32] and international reports, represent average life-cycle or operational emissions for each energy source and are systematized in

Table 4. Emission factors used for each energy source in scenario-based modeling [31,32,33,34,35].

Source	Emission Factor (gCO2/kWh)	Source Type
SMR (nuclear)	12	Life cycle (IEA 2023) [32]
Solar PV	45	Life cycle
Wind	15	Life cycle
Battery Storage	30	Charged from RES/SMR
Natural Gas	450	Operational
Coal	950	Operational

.

For nuclear power (SMR), we now apply a life cycle emission factor of 12 gCO₂/kWh, based on data from the International Energy Agency (IEA, 2023). This value includes emissions from uranium mining, enrichment, reactor construction, operations, and decommissioning. Battery storage is assigned a life-cycle emission factor of 30 gCO₂/kWh, reflecting upstream impacts from material extraction, manufacturing, and end-of-life processing. Although charged from low-carbon sources in the model, storage introduces indirect emissions through its embedded energy footprint.

3.2.2. CAPEX Model

Investment costs were estimated based on typical installation costs per capacity unit, derived from contemporary vendor and institutional data. The following values were applied, summarized in

Table 5. Capital cost assumptions for each technology used in scenario calculations [36,37,38,39,40,41].

Technology	CAPEX Estimate	Reference
SMR	5000 EUR/kW	IAEA, NuScale, RR-UK
Solar PV	800 EUR/kW	IRENA (2023) [39]
Wind	1200 EUR/kW	IRENA (2022) [40]
Battery Storage	400 EUR/kWh	Bloomberg NEF (2022) [41]
Natural Gas Plant	1000 EUR/kW	US DOE (2021) [38]
Coal Plant	1500 EUR/kW	US DOE (2021) [37]

.

Capital expenditure (CAPEX) values are expressed per unit of installed capacity (EUR/kW or EUR/kWh). The cost model enables comparative evaluation of required investments across mixed energy portfolios designed for smart urban applications.

For each scenario, the technology mix was applied proportionally to 100 MW of installed capacity. The CAPEX for each technology was computed using the formula:

$$CAPE{X}_{scenario}={\displaystyle \sum _{i=1}^n\left(\frac{P_i}{100}\times C_i\right)}\times 100000 ,(inEUR)$$

(2)

where Pi is the share of technology i in percent, Ci is the cost in EUR/kW (or EUR/kWh for battery), and 100,000 is the base capacity in kW.

This enabled a comparative assessment of trade-offs between emissions performance and required capital investments for smart urban deployment.

The current CAPEX-based cost model does not explicitly include long-term cost components such as nuclear insurance, decommissioning, or radioactive waste management for SMRs. These costs are known to vary significantly across jurisdictions, often being internalized through national regulatory frameworks or dedicated state-managed funds. While their exclusion simplifies cross-scenario comparison, it represents a modeling limitation. Future iterations of the model will aim to integrate these externalities to provide a more comprehensive lifecycle economic assessment.

3.3. Scenario Design Rationale

The definition of the six energy supply scenarios (A–F) was driven by the need to establish a robust analytical framework that explores a wide spectrum of real-world energy transition pathways, particularly applicable to smart urban environments. Each scenario was constructed to reflect varying degrees of technological maturity, emissions performance, investment requirements, and operational feasibility. The goal is to enable comparative analysis not only of the technical configurations, but of their policy, economic, and environmental implications.

A. Principles of Scenario Construction.

Three guiding principles shaped the scenario selection process:

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Technological Representativeness: The scenarios cover a full set of currently viable and emerging technologies, including fossil fuels (natural gas and coal), renewable sources (solar PV and wind), advanced nuclear (SMR), and battery energy storage. This ensures that the model reflects plausible near- and long-term energy mixes, grounded in both deployment trends and strategic planning documents from the EU, IEA, and national frameworks;

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Emissions Performance Spectrum: The configurations were explicitly chosen to span the full spectrum of carbon intensities—from highly carbon-intensive baselines (Scenarios A and F), through partially decarbonized hybrid systems (Scenario B) to highly decarbonized or near-zero emission configurations (Scenarios C, D, and E). This stratification supports a nuanced understanding of how technology portfolios affect net emissions at the city scale;

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Policy and Investment Realism: Each scenario is not hypothetical but reflects either a current status quo (fossil-dominant scenario), a transitional phase (hybrid with renewables), or a long-term strategic target (SMR-centered scenario with resilience enhancements). The chosen mixes are intended to match plausible deployment stages and investment profiles found in current EU energy transition policies, including REPowerEU, Net Zero Industry Act, and National Energy and Climate Plans (NECPs).

B. Justification for Percentage Allocations

The internal distribution of technologies within each scenario was selected based on three key parameters: (1) operational complementarity; (2) cost-emissions trade-offs; and (3) empirical alignment with modeled urban loads.

Scenario A (100% Natural Gas): This scenario serves as a reference baseline for moderate emissions and low CAPEX and represents the status quo in many mid-income urban environments.

Scenario B (70% Gas, 30% Solar): This scenario reflects early-stage transition strategies where renewables are integrated without large structural overhaul. Here, 30% PV is a realistic penetration level for cities with moderate irradiance and limited storage.

Scenario C (50% SMR, 40% Solar, 10% Battery): This scenario represents a balanced and feasible hybrid system. Here, 50% SMR ensures continuous baseload supply, 40% PV utilizes daytime solar potential, and10% battery provides limited but cost-effective flexibility. The 10% storage was chosen to match the battery capacity needed for peak shaving under typical irradiance patterns in Central Europe.

Scenario D (70% SMR, 20% Wind, 10% Battery): This scenario prioritizes system resilience. Here, 70% SMR allows maximum decarbonization while incorporating 20% wind to address nocturnal supply gaps and 10% battery to enhance operational flexibility.

Scenario E (80% SMR, 10% Solar, 10% Battery): This scenario represents an ultra-low emission strategy designed for high-reliability districts such as medical or industrial zones. This configuration assumes significant capital availability and long-term policy commitment.

Scenario F (100% Coal): This scenario is used as a worst-case reference, highlighting the maximum emissions and outdated planning trajectory still applicable in legacy systems globally.

C. Analytical Purpose

This spectrum of scenarios allows for the following:

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Quantifying trade-offs between emissions and investment;

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Assessing how incremental integration of advanced technologies (e.g., SMRs) affects carbon intensity and capital needs;

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Supporting decision-makers in identifying cost-effective pathways under carbon taxation or emissions trading schemes;

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Stress-testing the impact of variable renewable energy penetration under solar irradiance conditions typical of Central Europe.

D. Validation and Comparability

The scenario structure is consistent with practices in multi-criteria energy systems modeling, including those used in EU JRC studies, the IEA Energy Technology Perspectives, and IPCC pathway analysis. The shares are aligned with documented technical limits and policy targets (e.g., the expected SMR share in EU 2035 forecasts ranges between 20% and 70% depending on scenario).

By constructing scenarios that are both analytically diverse and policy-relevant, the study provides a stable foundation for comparative simulation and optimization of future-ready urban energy systems.

3.4. Performance, Emissions, and Investment Assessment of Hybrid Urban Energy Systems

The performance of the proposed smart hybrid energy system was evaluated using detailed simulations and comparative scenario analysis. The objective was to assess how varying combinations of SMRs, renewable energy sources, and battery storage influence energy reliability, carbon emissions, and investment costs in a smart urban context. This section presents the outcomes of the simulation models, analyzes the emission and economic performance of six strategic energy mixes, and interprets their implications for future urban energy planning.

3.4.1. Performance of the Hybrid Energy System

The simulation results demonstrate that the integration of a SMR into a smart grid architecture provides significant improvements in energy stability, flexibility, and carbon performance. The SMR delivers continuous baseload power, while solar PV contributes intermittent generation during daylight hours. The battery storage unit efficiently balances short-term fluctuations, reducing reliance on fossil backup. Figure 2 illustrates the energy contribution of each source over a 24-h cycle, confirming that the system maintains stable power output under varying demand.

The smart grid controller successfully manages energy dispatch based on real-time load and pricing signals. Demand peaks during the early morning and evening hours are efficiently covered through a combination of SMR generation and battery discharge. Solar input reduces mid-day grid stress and charges the battery during surplus periods.

3.4.2. Scenario-Based Emission and Investment Analysis

To quantify the trade-offs between environmental impact and required investment, six energy supply scenarios were evaluated. Each scenario assumes a total urban energy demand of 100 MWh/day, supplied through different combinations of SMR, solar PV, wind, battery storage, and conventional generation. The results are summarized in

Table 6. Summary of emissions and capital expenditure estimates for each energy supply scenario.

Scenario	Emissions	Estimated CAPEX	Comment
Scenario A—100% Natural Gas	450 gCO2/kWh	EUR 100 million	Highest emissions, lowest cost
Scenario B—70% Natural Gas, 30% Solar	346 gCO2/kWh	EUR 91 million	Moderate emissions reduction
Scenario C—50% SMR, 40% Solar, 10% Battery	27 gCO2/kWh	EUR 378 million	Balanced sustainability and cost
Scenario D—70% SMR, 20% Wind, 10% Battery	21 gCO2/kWh	EUR 434 million	Low emissions, high resilience
Scenario E—80% SMR, 10% Solar, 10% Battery	18 gCO2/kWh	EUR 446 million	Lowest carbon footprint, highest cost
Scenario F—100% Coal	950 gCO2/kWh	EUR 150 million	Maximum emissions, outdated strategy

.

The selection of scenarios was motivated by the need to explore a broad spectrum of technological pathways for meeting the energy demand of a smart urban environment. The six scenarios were designed to capture representative energy mixes that range from fossil-dominated (Scenarios A and F) to hybrid (Scenarios B, C, D) and nuclear-intensive configurations (Scenario E). These scenarios were not only chosen for their technical feasibility but also for their relevance to real-world policy and investment considerations.

Scenario A (100% natural gas) and Scenario F (100% coal) serve as baseline references, representing widely deployed but carbon-intensive legacy systems. Scenario B introduces partial decarbonization by supplementing fossil generation with solar PV. Scenarios C and D illustrate balanced hybrid models with varying contributions from SMR, renewables, and battery storage, offering both emissions reductions and investment manageability. Scenario E was included as a future-oriented configuration, prioritizing deep decarbonization through a dominant SMR share, suitable for high-resilience and climate-neutral urban planning.

This scenario set enables a comprehensive assessment of technological trade-offs, informing decision-makers on the cost-effectiveness and sustainability potential of different smart energy strategies.

In the context of urban energy planning, it is essential to evaluate not only the upfront investment in infrastructure but also the long-term economic and environmental implications of different energy configurations. With increasing regulatory pressure through mechanisms such as carbon pricing, scenarios that appear financially viable in the short term may become less attractive when cumulative emissions costs are considered.

To this end, we introduce a lifecycle-based cost model that combines initial capital expenditure (CAPEX) with the cumulative cost of carbon dioxide emissions over a standard operational lifetime. This approach allows for a more accurate comparison between traditional fossil fuel-based systems and emerging low-carbon technologies such as small modular reactors (SMRs) and renewable energy sources.

The total cumulative carbon cost over the 25-year period is calculated using the following equation.

Cumulative carbon cost computed as follows:

$$C{O}_{2}COS{T}_{Total}={\displaystyle \frac{g_{CO2}}{kW/h}}\times E_{day}\times Day\times T\times C_{CO2} ,(inEuro)$$

(3)

where

g_CO2 is the average carbon intensity of the scenario (grams CO₂ per kWh);

E_day is daily electricity consumption (in kWh) = 100,000;

Day = 365 is the number of days per year;

T = 25 is the operational lifetime in years;

C_CO2 = 80 is the carbon price (EUR/ton CO₂),

The stacked bar chart below (Figure 2) illustrates the distribution of total cost over 25 years for each scenario, combining the initial capital investment (CAPEX) and cumulative carbon cost. The chart clearly shows how high-emission scenarios accumulate significant environmental penalties, while SMR-based hybrid systems maintain low total costs over time despite higher upfront investments.

The extended cost analysis demonstrates that short-term capital cost considerations alone are insufficient for informed energy planning. When cumulative carbon costs are accounted for over a standard 25-year operational horizon, high-emission scenarios (A and F) become economically unattractive, while hybrid systems with strong SMRs and renewable integration (C, D, and E) show superior long-term performance. Notably, Scenario C emerges as a balanced option with significantly reduced emissions and acceptable total cost. These findings emphasize the strategic importance of lifecycle-based assessment frameworks for sustainable urban energy infrastructure design.

3.4.3. Interpretation

The results indicate that hybrid energy architectures combining SMRs with renewables and storage offer significant advantages in reducing emissions and enhancing grid reliability. Although scenarios with higher SMR shares require greater upfront investment, they enable long-term decarbonization, support 24/7 energy availability, and simplify grid management.

Scenario C emerges as a particularly promising configuration, balancing ecological impact and investment feasibility, making it ideal for cities in transition. On the other hand, Scenario E represents a future-proof approach for fully climate-neutral smart cities with robust infrastructure budgets.

4. Simulating Different Power Distribution Profiles

To evaluate the operational dynamics of the proposed hybrid energy system for smart urban environments, a system-level modeling approach was developed. This section describes the mathematical representation, control strategies, and simulation setup that underpin the performance assessment.

4.1. Mathematical Model Structure

To realize the general power balance model, the hybrid energy system’s individual elements, including SMRs, solar panels, and battery storage, are described by the following component-specific equations, capturing their dynamic behavior over time.

-

For SMR power generation, the following equation is used:

P_SMR(t) = C_SMR × E_SMR × T(t),

(4)

where P_SMR(t) is the power generated by the SMR at time t, C_SMR is the SMR efficiency coefficient, E_SMR is its rated capacity, and T(t) represents the time-dependent demand/load factor.

-

For photovoltaic solar panels, the following equation is used:

P_PV(t) = η_pv × G(t) × A_solar × [1 − β × (T_cell(t) − T_ref)],

(5)

where P_PV(t) is the power produced by the solar array; η_PV is nominal efficiency of the PV module under standard test conditions (STC), dimensionless (e.g., 0.2); G(t) global solar irradiance incident on the panel surface at time t [W/m²]; A_solar is the total panel surface area [m²]; T_cell(t) is cell temperature at time t [°C], and T_ref is the reference temperature [°C], typically 25 °C.

-

For battery charge/discharge dynamics, the following equation is used:

E_batt(t) = η_charge × [P_PV(t) + P_SMR(t) − P_load(t)],

(6)

where E_batt(t) represents the battery energy level at time t, η_charge is the charge/discharge efficiency, and P_load(t) is the total energy demand.

The hybrid system consists of three primary energy sources: a SMR, a PV solar array, and a lithium-ion battery energy storage system (BESS). These are integrated using a smart grid controller that performs real-time optimization of power flows based on load demand, resource availability, and economic signals.

The energy balance equation for the system is defined as follows:

P_gen(t) = P_SMR(t) + P_PV(t) + P_batt(t) = P_load(t) + P_grid(t),

(7)

where

-

output power of the SMR—P_SMR(t) is assumed constant or semi-variable for advanced SMRs with load-following capability;

-

power generated by the PV array—P_PV(t) is modeled as a function of solar irradiance Ir(t) and panel characteristics;

-

P_batt(t) is net power from the battery system (positive when discharging, negative when charging);

-

P_load(t) is aggregated load profile of residential, industrial, and EV consumption;

-

P_grid(t) is import/export power exchanged with the external grid (positive for import);

In the model, power exchange with the external grid, represented by P_grid(t), is governed by system-level balance constraints. Grid import (positive P_grid(t)) is triggered only when local resources—SMR output, solar PV, and battery discharge—are insufficient to meet instantaneous load. Grid export (negative P_grid(t)) is allowed only when there is surplus generation and the grid has the technical capacity to accept power.

The system is modeled with bounded grid exchange capacity (±20 MW), reflecting realistic infrastructure constraints for remote or semi-islanded smart city contexts. Furthermore, the optimization incorporates economic penalties for importing electricity (e.g., high marginal grid cost) and low or capped compensation for export, which aligns with real-world market limitations and promotes local energy autonomy.

Battery dynamics are governed by the state-of-charge (SoC) as follows:

SoC(t + 1) = SoC(t) + [η_ch × P_ch(t) − P_dis(t)/η_dis] × Δt/C_batt

(8)

where η_ch and η_dis are the charge and discharge efficiencies; P_ch and P_dis are the charging and discharging powers, respectively; and C_batt is the total battery capacity.

A hierarchical control strategy is adopted:

-

Primary control ensures baseload is met by the SMR.

-

Secondary control dispatches PV generation based on real-time irradiance.

-

Tertiary control schedules battery operation to minimize grid interaction, prioritize self-consumption, and reduce peak loads.

The smart controller utilizes a rule-based energy management system (EMS) supplemented with price signals, weather forecasts, and demand predictions to coordinate resources.

Hourly load profiles were generated using synthetic data models calibrated to match the energy usage of a mid-sized urban district (100 MWh/day). These include household, commercial, and EV charging patterns. Solar generation profiles were derived from irradiance datasets typical for Central European conditions.

Three solar scenarios were considered:

-

Low irradiance (overcast)

-

Moderate irradiance (partly cloudy)

-

High irradiance (clear skies)

Each scenario was simulated over a 24-h period with 1-h time steps. Simulations were conducted using a Python-based model [42] the performance of the proposed hybrid urban energy system. The SMR was modeled as a constant-output generator supplying 50 MW of baseload power. The solar photovoltaic (PV) array followed a typical daily irradiance curve representative of Central Europe. Battery energy storage was managed using a rule-based energy management system (EMS), which prioritized renewable surplus for charging and dispatched stored energy during high-demand periods.

Hourly load profiles were generated using synthetic demand models for residential, industrial, and electric vehicle (EV) consumers. These profiles reflect real-world variability and peak load clustering throughout the day. The simulation timeframe covered 24 h with 1-h resolution.

4.2. Power Balance Profiles Under Scenario C

Several tables summarize the model parameters used to simulate the results for Scenario C at three solar radiation levels: bad, medium, and good. The model represents a hybrid energy system serving a medium-sized smart city.

Table 7. General model parameters.

Parameter	Value	Description
Total Energy Demand	100 MWh/day	Representative of a mid-sized urban area
Time Interval	1 h	Simulation covers 24 h
SMR Power Output	50 MW (constant)	Baseload energy supply
Battery Capacity	10 MWh	Maximum energy storage capacity
Battery Power Limit	±10 MW	Charge/discharge limit
Battery Efficiency	95%	Charge/discharge efficiency
PV Area and Efficiency	100,000 m2 @ 20%	Effective area and panel efficiency
Battery Initial State	10 MWh	Fully charged at 00:00
Demand Response	Disabled	No dynamic load reduction

provides general model parameters used in the simulation.

Table 8. Solar irradiation used to implement the studied scenario C.

Hour Range	Poor Sunlight (kW/m2)	Average Sunlight (kW/m2)	Good Sunlight (kW/m2)
5–7	0.05	0.10	0.15
8–10	0.10	0.30	0.45
11–14	0.20	0.50	0.70
15–17	0.10	0.30	0.45
18–19	0.05	0.10	0.15
Other	0.00	0.00	0.00

presents the hourly solar irradiance values applied in the simulation of Scenario C under three different weather conditions: poor, average, and good sunlight. These profiles were used to model the variable photovoltaic (PV) generation throughout the day. The values reflect typical seasonal patterns observed in Central European regions and serve as input to assess system behavior under fluctuating renewable availability.

The battery charges during midday hours when solar generation is available and discharges during peak demand periods in the morning and evening. In the poor sunlight scenario, battery usage is limited due to insufficient solar input, increasing dependence on the grid. In contrast, the good sunlight scenario results in full battery charging and significant reduction of grid import. The SMR operates continuously in all scenarios, ensuring a stable base power supply.

The following figures illustrate the hourly power balance of the proposed smart hybrid energy system under Scenario C, which combines 50% SMR, 40% solar PV, and 10% battery storage. Each figure corresponds to a different solar irradiance condition: bad, average, and good sunlight. The system is designed to meet a 24-h urban load profile. When SMR and PV generation are insufficient, the battery compensates up to its capacity (10 MW/h), and any remaining demand is supplied by grid imports. Figure 3 presents simulation results on a bad sunny day. In this case, the photovoltaic contribution is minimal, requiring significant battery discharge and significant grid input.

Figure 4 shows simulation results for an average sunny day. In this case, PV generation reduces battery and grid reliance during midday.

Figure 5 shows the simulation results on a very sunny day. In this case, photovoltaic generation significantly supports the load, minimizing battery usage and eliminating the need for grid imports, for most of the day.

4.3. Conclusions on Grid Dependency and Mitigation Strategies

The simulation results confirm that even well-configured hybrid systems composed of baseload nuclear, batteries and solar photovoltaic generators, when applying optimization based on economic and emission indicators, have a high degree of grid dependence, caused by the variability of a part of the generated energy. The degree of this dependence is strongly influenced by the availability of solar energy:

-

Under poor sunlight conditions, the system relies heavily on grid imports due to insufficient photovoltaic production and depleted battery reserves. Despite the constant power of the SMR, demand exceeds the available local supply during the morning and evening peaks.

-

During average sunlight, the photovoltaic systems contribute significantly around noon, partially charging the battery and slightly reducing the dependence on the grid. However, the battery is not able to fully cover both peaks, leading to moderate grid use.

-

In a scenario with good sunlight, the PV system reaches its optimal contribution, allowing the battery to be fully charged and discharged during peak demand periods. Grid input is minimal and limited to very early and late hours.

These results highlight the need for strategic solutions to improve grid independence, especially in a variable weather environment. Based on this, the following mitigation strategies are recommended [41,43]:

-

Increasing battery storage capacity

Expanding the installed capacity beyond the current 10 MWh (≈10% of daily load) allows for better use of excess solar energy and extends coverage during cloudy or winter days.

-

Implementing predictive energy management

Integrating weather forecasts and data-driven demand forecasting can optimize battery usage and SMR scheduling, minimizing reactive grid drawdowns.

-

Diversify the renewable energy portfolio

Adding wind power complements photovoltaic systems by generating power during nighttime hours or when solar radiation is low, thereby stabilizing the overall renewable energy production.

-

Implementing flexible demand response

Enabling flexible loads, especially in the industrial and commercial sectors, can help shift demand away from critical off-peak hours, relieving pressure on local generation and storage.

-

Enabling load-following SMR modes

Where technologically feasible, configuring SMRs to adjust their production partially based on residual demand can smooth grid interactions and reduce dependence on external imports.

-

Introducing seasonal thermal or hydrogen energy storage

These technologies can store excess energy from photovoltaics or small marine systems (SMRs) during periods of low demand and feed it back during prolonged deficits, improving seasonal energy autonomy.

-

Using vehicle-to-grid (V2G) integration

Using electric vehicle batteries for two-way energy exchange allows distributed and mobile storage assets to contribute during peak loads or solar power shortages, effectively acting as additional flexibility without additional stationary capacity.

Through a combination of these strategies, a smart hybrid energy system can further reduce its dependence on centralized electricity supply, increase its operational flexibility and significantly contribute to decarbonization goals while maintaining energy security [44,45,46].

4.4. Minimum Configuration for Full Grid Autonomy

To evaluate the minimum required configuration for achieving complete grid independence, simulations were performed by varying SMR output, battery capacity, and battery charge/discharge power. The goal was to find the lowest combination of these parameters that results in zero grid import under each PV condition. The results are summarized below in

Table 9. The lowest combination of these parameters that leads to zero import from the grid under different photovoltaic conditions.

PV Scenario	SMR Power (MW)	Battery Capacity (MWh)	Battery Power (MW)	Grid Import
Good Day	64	20	20	0 MWh
Average Day	64	35	25	0 MWh
Poor Day	86	60	40	0 MWh

.

These data illustrate that grid independence is technically feasible across all weather conditions, provided that appropriate scaling of SMR output and battery infrastructure is implemented:

-

On good sunlight days, a moderately sized SMR (64 MW) combined with 20 MWh of battery capacity is sufficient to fully meet demand, thanks to strong PV contribution during the day and effective energy shifting by the battery.

-

Under average sunlight conditions, PV generation is less pronounced, necessitating an increase in battery capacity (35 MWh) and discharge rate (25 MW) to maintain full autonomy, while SMR output remains unchanged.

-

Under poor sunlight conditions, where PV contribution is minimal, the system must compensate by significantly increasing SMR output (86 MW) and battery storage (60 MWh at 40 MW) to fully cover demand fluctuations and avoid grid import.

These findings confirm that solar intermittency can be mitigated entirely through flexible and adaptive system design. Instead of depending on the external grid, the system leverages higher local generation (via SMR) and expanded storage to ensure uninterrupted supply. Notably, the grid is never used, even in the worst-case irradiance scenario.

This supports the article’s core proposition that hybrid nuclear–renewable systems, when properly dimensioned, can serve as resilient, autonomous, and decarbonized energy solutions for smart urban environments.

5. Discussion

The results of the simulation and scenario analysis highlight several critical insights regarding the design of hybrid energy systems for smart urban environments. The integration of SMRs, renewable sources, and intelligent control mechanisms offers not only environmental benefits but also operational flexibility and long-term energy security. However, the adoption of such architectures raises complex trade-offs between capital investment, technological maturity, and regulatory acceptance.

5.1. Trade-Offs Between Emissions and Cost

The scenario comparison reveals a clear inverse relationship between carbon emissions and investment costs. Scenarios dominated by fossil fuels (A and F) are economically attractive in the short term but incompatible with decarbonization goals. In contrast, scenarios with high SMR penetration (D and E) achieve dramatic reductions in CO₂ emissions (10 g/kWh) at the cost of significantly higher upfront capital expenditure. Scenario C presents a middle ground, offering a 97% reduction in emissions compared to the natural gas baseline while maintaining financial feasibility.

This trade-off underscores the importance of policy instruments such as carbon pricing, green public financing, and investment tax credits, which can shift the cost-benefit balance in favor of cleaner solutions.

5.2. Role of SMRs in Urban Energy Planning

The results confirm that SMRs can play a vital role in future urban energy systems by supplying stable baseload power, reducing dependency on volatile renewables, and enabling grid stability. Their compact size and modular construction make them suitable for urban deployment, district heating, and even hydrogen production. Moreover, SMRs can enhance the resilience of critical infrastructure during extreme weather events or geopolitical disruptions, a growing concern in energy policy [44,45].

However, the deployment of SMRs must be accompanied by transparent regulatory frameworks, community engagement, and lifecycle waste management strategies to gain public trust and ensure sustainable operation.

5.3. Grid Flexibility and Demand Response

The implementation of smart grid controls and demand-response logic within the simulation framework demonstrated measurable benefits in balancing load, shifting peak demand, and improving energy use efficiency. These digital tools are essential to maximizing the value of intermittent renewables and minimizing battery discharge cycles. In urban contexts with high EV adoption and diverse consumer profiles, demand-side flexibility will become a strategic asset for system operators.

The findings support the integration of real-time pricing models, smart metering, and predictive analytics into urban energy platforms, which can further reduce costs and enhance user engagement [46,47].

5.4. Vehicle-to-Grid and Long-Term Storage Strategies

The integration of vehicle-to-grid (V2G) technology in the proposed architecture represents a transformative approach to distributed energy balancing. In scenarios with high EV penetration, coordinated control of bidirectional charging allows urban fleets to support the grid during critical load periods, particularly in the evenings when solar production drops. Unlike fixed battery storage, V2G assets are mobile, distributed, and scalable through user participation. However, successful implementation depends on the availability of standardized charging infrastructure, communication protocols, and incentives for vehicle owners.

In parallel, long-duration storage technologies such as thermal storage (linked to district heating systems) and hydrogen production (via electrolysis) can act as energy buffers on seasonal time scales. These solutions increase resilience and reduce long-term dependency on the main grid, especially under extended periods of low renewable input.

5.5. Policy and Market Integration

As demonstrated by the system-level simulations presented in Section 4, the successful deployment of hybrid SMR–renewable energy systems at scale requires not only technological maturity and cost competitiveness, but also supportive policy frameworks and well-designed market mechanisms. As cities seek to achieve carbon neutrality while maintaining energy security and affordability, public policy will play a central role in enabling the integration of advanced nuclear technologies with intermittent renewables.

One of the most effective instruments to incentivize low-emission systems is the implementation of carbon pricing schemes, either through emissions trading systems (ETS) or carbon taxes. When lifecycle carbon costs are internalized—such as in the modeling presented in this study—SMR-based configurations become significantly more attractive compared to fossil-fueled baselines. Scenario C, for instance, demonstrates favorable cost-emissions trade-offs under a carbon price of EUR 80/ton CO₂, suggesting that regulatory alignment with decarbonization goals can directly affect investment decisions.

Green finance mechanisms, such as tax credits, investment guarantees, and public–private partnerships, are also critical to offset the high upfront capital costs typically associated with SMR deployment. In the United States, for example, the Inflation Reduction Act (IRA) provides nuclear-specific clean energy tax credits, while the European Union is actively exploring regulatory inclusion of SMRs within the sustainable finance taxonomy. Such measures can de-risk capital-intensive infrastructure and accelerate the scale-up of hybrid nuclear–renewable systems in urban areas.

Furthermore, regulatory innovation is needed to address the licensing challenges of SMRs, particularly in urban or distributed environments. Current nuclear regulations in most countries are oriented toward large-scale reactors and centralized siting. Fast-tracked, risk-informed licensing processes—already being piloted in Canada, the UK, and the US—could be adapted for modular urban deployment, provided that safety, environmental, and community concerns are transparently addressed.

On the market side, capacity remuneration mechanisms and flexibility services markets can incentivize SMRs to operate synergistically with renewables and storage, rather than competing against them. Through dynamic pricing, ancillary services compensation, and demand-response programs, operators can optimize multi-source portfolios and enhance overall system efficiency.

Lastly, strong community engagement and public acceptance will be essential for urban SMR projects to succeed. Public perception of nuclear energy remains cautious, and transparent communication, participatory planning, and long-term waste management strategies must be integrated into all phases of deployment.

To ensure the practical implementation of the proposed policy instruments, we outline several actionable pathways. First, regulatory innovation such as the introduction of licensing sandboxes can enable pilot deployment of SMRs under flexible oversight, expediting time-to-market while maintaining safety standards. Second, targeted fiscal incentives, including investment tax credits or accelerated depreciation schemes for hybrid systems, can reduce upfront capital costs and de-risk private investment. Third, capacity-building programs aimed at municipal energy planners and regulators can accelerate institutional readiness for advanced energy system integration. These measures should be embedded within national climate and energy strategies to ensure alignment with long-term decarbonization goals and urban resilience planning.

In sum, a coherent policy and market ecosystem is essential to unlock the full potential of SMR–renewable systems in the global clean energy transition. Integrating these technologies into national climate strategies, municipal energy plans, and green investment frameworks can catalyze resilient, low-carbon urban energy infrastructures at scale.

5.6. Limitations and Future Research

While the model provides valuable insights, it is based on simplified representations of generation, demand, and storage dynamics. Real-world deployment would require more granular time resolution, inclusion of seasonal variations, market dynamics, and reliability constraints. Additionally, social, legal, and environmental dimensions such as land use, supply chain resilience, and nuclear waste disposal need to be considered in future studies.

The results obtained in this study are consistent with findings from other scientific groups working on hybrid energy systems that incorporate Small Modular Reactors (SMRs). In [18] conducted real-time simulation studies of SMR-integrated microgrids and demonstrated similar improvements in system stability and emissions reduction when SMRs are combined with renewable energy and storage technologies. In [19,20] provided a comprehensive review of nuclear–renewable synergies and highlighted the role of SMRs as key enablers of flexible, low-carbon energy systems—an outcome that aligns with the performance of Scenario C in this work. Furthermore, ref. [48] offered a detailed overview of modeling and control strategies for SMR-based applications, supporting the relevance of simplified simulation frameworks such as the one used here. Although differences exist in terms of modeling depth, control strategies, and geographic assumptions, the general conclusion is consistent: SMRs significantly enhance the adaptability, reliability, and decarbonization potential of future smart urban energy infrastructures.

While the proposed hybrid system demonstrates strong potential for decarbonization and grid resilience, the introduction of SMRs into urban or distributed energy systems also raises important long-term considerations. One critical issue is the management of spent nuclear fuel and radioactive waste. Although SMRs are designed with advanced safety and fuel efficiency features, they still produce high-level nuclear waste that requires secure and sustainable treatment and storage solutions. Future research should explore the life-cycle implications of SMR deployment, including fuel recycling, long-term repository strategies, and the development of compact on-site waste management systems. Relevant studies such as those by [49,50,51] provide valuable frameworks for assessing the environmental and regulatory challenges associated with SMR-generated waste. Incorporating such aspects into integrated planning models will be essential to ensure the long-term sustainability and public acceptance of SMR-based energy solutions.

While the proposed hybrid SMR–renewable system offers promising performance in terms of reliability and emissions, several limitations of the current model must be acknowledged.

First, the simulation framework uses deterministic inputs with fixed solar irradiance profiles and predefined urban load patterns. In reality, both solar availability and electricity demand are inherently stochastic and time-variant. The absence of probabilistic modeling limits the ability to assess the system’s performance under uncertainty or to quantify risks related to underperformance or overinvestment.

Second, the time resolution of one hour is adequate for observing daily trends but may not capture fast dynamics, especially during transient demand spikes or sudden drops in renewable input. The model also assumes idealized operation of the SMR as a constant output source and does not simulate thermal inertia, reactivity feedback, or ramping limitations, which are important for system-level dispatch coordination.

Third, the absence of statistical or sensitivity analysis prevents deeper insights into how variation in input parameters (e.g., solar area, battery size, SMR capacity) influences system performance. This restricts the robustness of the conclusions drawn from Scenario C or the minimum autonomy configuration. Additionally, the cost model does not include uncertainty bounds, market fluctuations, or long-term degradation factors for storage and PV systems.

Finally, the environmental and regulatory implications of SMR deployment—such as nuclear waste management, decommissioning costs, and social acceptance—are only briefly mentioned and not quantitatively integrated into the evaluation [52].

Table 10. Policy instruments supporting hybrid SMR–renewable systems. (Note: Arrows indicate the expected direction of impact—“↑” denotes increase or positive effect, and “↓” denotes decrease or reduction).

Policy Instrument	Target Effect	Example Initiative
Carbon Pricing	↓ Lifecycle emissions, ↑ Competitiveness of low-carbon tech	EU ETS, Canada Carbon Tax
Green Finance and Tax Incentives	↓ Initial CAPEX, ↑ Investment Attractiveness	IRA (USA), EU Innovation Fund
Regulatory Acceleration for SMRs	↓ Time-to-market, ↑ Deployment Feasibility	CNSC (Canada), UK ONR Generic Design Process
Capacity Remuneration Schemes	↑ Flexibility, ↑ Reliability during peak demand	UK Capacity Market, PJM (USA)
Sustainable Taxonomy Inclusion	↑ Investor confidence, ↓ Cost of capital	EU Sustainable Finance Taxonomy (under review)
Public Engagement Strategies	↑ Acceptance, ↓ Project opposition	SMR Community Engagement Framework (OECD NEA)

summarizes key policy and market instruments that can support the deployment of SMR–renewable hybrid systems, along with their intended impacts and real-world implementation examples. These mechanisms, when aligned with national decarbonization goals, provide a robust foundation for enabling the clean energy transition at the urban level.

Future work will address these limitations by incorporating stochastic simulations (e.g., Monte Carlo or Latin Hypercube sampling), scenario-based sensitivity analysis, and time-series variability in both generation and demand. Moreover, a dynamic thermal model of the SMR, combined with real-time control co-simulation, will be developed to assess operational flexibility under real-world constraints. The introduction of multi-objective optimization techniques will allow a more comprehensive exploration of trade-offs among emissions, cost, reliability, and autonomy, thereby advancing the model toward practical urban planning applications.

6. Conclusions

This study delivers a comprehensive simulation-based assessment of a hybrid urban energy system architecture that integrates small modular reactors (SMRs), photovoltaic (PV) solar generation, battery storage, and smart grid control. The primary objective was to evaluate the long-term economic and environmental performance of such systems using lifecycle-based modeling, with the aim of identifying viable decarbonization strategies that are aligned with global clean energy transition goals.

Simulation results across diverse solar irradiance conditions lead to several key insights with direct relevance for urban energy planning:

-

Full energy autonomy is technically feasible—even under low solar availability—by appropriately scaling SMR output and battery storage capacity;

-

Scenario C (50% SMR, 40% PV, 10% battery) emerges as a balanced configuration, offering a 97% reduction in lifecycle carbon emissions compared to fossil-dominated systems, while maintaining cost competitiveness;

-

Dynamic coordination of SMR and battery systems enables complete elimination of grid imports, supporting full decentralization and urban energy resilience;

-

Intelligent control strategies—including predictive energy management and demand-response—enhance system stability, optimize asset utilization, and reduce peak loads;

-

The integration of vehicle-to-grid (V2G) functionality and long-duration storage (thermal or hydrogen-based) adds further flexibility, enabling seasonal balancing and reducing reliance on centralized infrastructure.

These findings confirm that hybrid SMR–renewable systems, when combined with adaptive digital control and supported by favorable policy frameworks, can serve as technically viable, economically robust, and strategically scalable solutions for resilient low-carbon urban energy infrastructures. Their ability to provide stable baseload power complements renewable intermittency and strengthens the path toward net-zero and autonomy targets in smart cities.

Future research should advance this work by developing an integrated multi-objective optimization model that identifies optimal configurations of hybrid systems under real-world constraints. Such a framework should consider the following:

-

Realistic techno-economic parameters for SMRs, PV, battery storage, and potentially wind or hydrogen components;

-

Dynamic, sector-specific electricity demand, including residential, industrial, and EV loads;

-

Local resource availability (irradiance/wind), market prices, and regulatory policy environments.

By applying evolutionary or Pareto-based optimization techniques, the framework can generate a set of trade-off solutions that guide decision-makers in designing future-proof urban energy systems. This approach provides valuable support for municipalities, energy planners, and policymakers engaged in implementing cost-effective, scalable, and climate-aligned infrastructures within the broader context of the global clean energy transition.