**1. Introduction**

Driven by ever-increasing climate change, a worldwide consensus has been reached on the urgency of transitioning global energy towards a green future [1,2]. Common agreements such as the Kyoto Protocol and the Copenhagen Accord have been ratified by hundreds of governments, forcing the replacement of coal-fired electricity generation with cleaner and more reliable energy resources [3,4]. In this regard, being the cleanest fossil fuel with a relatively high efficiency, natural gas has become an indispensable option for supplying electricity demand [5]. The International Energy Agency (IEA) predicted in its 2017 report that, by 2040, natural gas would surpass oil as the second-largest fuel in the global fuel mix, accounting for one quarter of the world's energy demand [6,7]. Despite the fact that reliance on gas for electricity has kept rising over the years, unanticipated risks may occur and disrupt the gas-to-electricity progression. For instance, since natural gas is distributed unevenly across the globe, certain countries rely heavily on the international supply to satisfy their gas consumption needs [6]. However, due to human attacks, economic disputes or geopolitical issues, the supply is subject to significant uncertainties and fluctuations [8]. Examples include Russia's suspension of natural gas shipments to Ukraine in 2018 and the rupture of the Nord Stream pipelines in 2022, which resulted in a severe shortage of gas in European countries [9]. In the most recent quarterly report of the gas market [10], the IEA warned of an impending supply crisis and revised its 2022

**Citation:** Wang, L.; Xing, Y. Risk Assessment of a Coupled Natural Gas and Electricity Market Considering Dual Interactions: A System Dynamics Model. *Energies* **2023**, *16*, 223. https://doi.org/ 10.3390/en16010223

Academic Editors: Junpeng Zhu and Xinlong Xu

Received: 31 October 2022 Revised: 19 December 2022 Accepted: 21 December 2022 Published: 25 December 2022

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

gas demand forecast downward. Additionally, the electricity market may be vulnerable to a variety of hazards, such as natural disasters and technical issues, which can hinder the functioning of energy markets [11]. Hence, to ensure energy security, a comprehensive risk assessment approach is essential.

Alongside the diverse risks stemming from distinct sectors, complex interactions render risk assessments of the coupled two markets more challenging than those of individual energy markets. On the one hand, interactions may exist between multiple risks, e.g., excessively cold weather may cause a surge in electricity demand while enhancing the likelihood of a gas pipeline failure [12]. On the other hand, interactions between the two energy sectors enable the transfer of risks from one market to the other, e.g., the surge in electricity demand may drive an increase in natural gas demand, thus further widening the demand–supply imbalance [3]. Markets may adapt to the disruptions and dynamically evolve, e.g., a reduction in gas supply may result in a subsequent rise in gas prices and discourage the use of natural gas, which may exacerbate the insufficiency of electricity. The dynamic behaviors of numerous variables constitute feedback loops with various time delays, and these are too complex for decision-makers to grasp [3,13]. In light of these conditions, this study aimed to explore the impact of potential risks on the overall gas–electricity market from a holistic perspective, taking the diverse interactions into account.

In the current literature on energy security, many scholars have analyzed the risks encountered in the natural gas and electricity markets. Regarding the natural gas market, Chen et al. [6] established a worldwide gas trading network and examined the structural risks using data from the gas import trade in 2015. Considering the risk of supply shortages, Ding et al. [8] evaluated the resilience capabilities of China's natural gas system by integrating a system dynamics model and a resilience curve. Dong and Kong [14] investigated the impact of risks affecting gas imports on the Chinese economy by analyzing three categories of risk, i.e., exporting countries, transportation and foreign dependency. Egging and Holz [15] focused on three scenarios in a stochastic natural gas model and investigated the infrastructure investments under various risks based on the data from Europe, North America and China. Some research has highlighted the inherent vulnerabilities of the market. Using a natural gas pipeline in Zhuhai, China, Liu et al. [16] developed a simulation model for assessing the risks to gas pipelines by considering the probability of failure, the consequences of an accident and individual risks. Chen et al. [17] investigated the supply security of a gas pipeline network with stochastic demand. Zarei et al. [18] used FMEA to study the dynamic safety of a gas station and revealed that human error was the leading cause of system failure. Regarding the electricity market, Ahmad et al. [13] reviewed the studies that applied system dynamics in electricity sector modelling and highlighted the microworld models facilitating the trade and risk analysis in electricity markets. Salman and Li [19] proposed a framework for assessing multihazard risks in electric power systems exposed to seismic and hurricane threats, which could be used for disaster preparedness, mitigation and response planning. Based on the core elements of risk identification, measurement, assessment, evaluation, control and monitoring, Tummala and Mak [20] developed a risk management framework to improve the operations and maintenance of electricity transmission systems. Chiaradonna et al. [21] applied the stochastic activity network to construct a framework for quantitatively analyzing interactions between electricity generation and transmission infrastructures, so as to mitigate the losses induced by risks. Considering the context of the new economic normal, He et al. [22] applied system dynamics to a power consumption scenario for Tianjin to derive long-term energy demand predictions. Taking a systematic overview of the electricity market, the natural gas market and other energy markets, Burger et al. [23] investigated multiple categories of risks involved, as well as stochastic models for electricity and gas.

A recent emphasis has also been placed on the coupling between and interactions of the gas and electricity markets. Hibbard and Schatzki [24] reviewed multiple risk factors rising from the interdependence between electricity and natural gas markets and provided prominent strategies for mitigating the most significant risks. Different levels of interaction between gas and electricity systems were investigated in [25], and a two-stage stochastic programming approach was utilized to develop an integrated operational model for these systems with an unreliable power supply. Considering hourly real-time pricing in the gas and electricity markets, Tian et al. [26] explored the influence of gas market reform on the development of natural gas-fired units through a dynamic game-theoretic model. By applying a graph-theory-based technique, Beyza et al. [27] assessed structural robustness and the vulnerability of coupled gas and electricity systems by considering their interactions. Bao et al. [28] developed an integrated model to evaluate bidirectional cascade failures in an electricity–natural gas system by including coupling components such as gas-fired generators and electricity-driven gas compressors. Portante et al. [29] integrated two validated energy models (i.e., EPfast for electric power and NGfast for natural gas) to assess the propagation impact of risks and disruptions through interdependencies between the natural gas and electric power systems. Poljanšek et al. [30] constructed a probabilistic reliability model of the European gas and electricity transmission networks from a topological perspective, and the increased vulnerability resulting from market interdependencies could be observed from the results. Nazari-Heris et al. [31] exhaustively analyzed the interactions among electricity, gas and water systems, and improved the operation, economics and pollutant emissions of the integrated systems. Some studies considered both the dynamism and interactions involved in the coupled markets. Xiao et al. [1] analyzed the development pattern and constraints of China's natural gas power production, forecasting the natural gas prices of generation by using the market netback pricing approach. Esmaeili et al. [3] simulated the long-term impact of renewable energy resources' penetration on the natural gas–electricity market. Eusgeld et al. [32] constructed an integrated model to incorporate interdependencies between critical infrastructures and demonstrated the cascading effects of vulnerabilities and failures. Zhang et al. [33] coordinated the operations of power-to-gas units and generators in order to smooth the load curve of an integrated electricity and natural gas system.

These earlier studies established significant theoretical and methodological foundations for identifying the risk factors affecting the natural gas market or the electricity market, as well as the interactions between the two markets. However, the majority of them either addressed various risks in an individual market or concentrated on the impact of one specific risk event on interconnected markets, while the interactive behaviors of multiple risks and multiple markets still call for a comprehensive analysis. With the aim of observing the long-term behaviors of coupled natural gas and electricity markets under various interrelated risks, this study contributes to the research field by extending the risk assessment scenario to a more complex and dynamic setting, identifying prominent risks affecting the markets and constructing a quantitative model incorporating dual interactions between both risks and markets. System dynamics (SD) was introduced to support the assessment because of its advantages in integrating nonlinear interactions and modeling dynamic social systems [34].

The remaining sections of this article are structured as follows. The theoretical framework is presented in Section 2, depicting the dynamism and complexity of the coupled natural gas–electricity market. Section 3 proposes a system dynamics model with detailed descriptions of each component. The simulation experiments and results are presented in Section 4, followed by the conclusions.
