Effects of emissions caps on the costs and feasibility of low-carbon hydrogen in the European ammonia industry

The European ammonia industry emits 36 million tons of carbon dioxide annually, primarily from steam methane reforming (SMR) hydrogen production. These emissions can be mitigated by producing hydrogen via water electrolysis using dedicated renewables with grid backup. This study investigates the impact of decarbonization targets for hydrogen synthesis on the economic viability and technical feasibility of retrofitting existing European ammonia plants for on-site, semi-islanded electrolytic hydrogen production. Results show that electrolytic hydrogen cuts emissions, on average, by 85% (36%-100% based on grid price and carbon intensity), even without enforcing emission limits. However, an optimal lifespan average well-to-gate emission cap of 1 kg carbon dioxide equivalent (CO2e)/kg H2 leads to a 95% reduction (92%-100%) while maintaining cost-competitiveness with SMR in renewable-rich regions (mean levelized cost of hydrogen (LCOH) of 4.1 euro/kg H2). Conversely, a 100% emissions reduction target dramatically increases costs (mean LCOH: 6.3 euro/kg H2) and land area for renewables installations, likely hindering the transition to electrolytic hydrogen in regions with poor renewables and limited land. Increasing plant flexibility effectively reduces costs, particularly in off-grid plants (mean reduction: 32%). This work guides policymakers in defining cost-effective decarbonization targets and identifying region-based strategies to support an electrolytic hydrogen-fed ammonia industry.

Resilient design in nuclear energy: Critical lessons from a cross-disciplinary analysis of the Fukushima Dai-ichi nuclear accident

This paper presents a multidisciplinary analysis of the Fukushima Dai-ichi Nuclear Power Plant accident. Along with the latest observations and simulation studies, we synthesize the time-series and event progressions during the accident across multiple disciplines, including in-plant physics and engineering systems, operators' actions, emergency responses, meteorology, radionuclide release and transport, land contamination, and health impacts. We identify three key factors that exacerbated the consequences of the accident: (1) the failure of Unit 2 containment venting, (2) the insufficient integration of radiation measurements and meteorology data in the evacuation strategy, and (3) the limited risk assessment and emergency preparedness. We conclude with new research and development directions to improve the resilience of nuclear energy systems and communities, including (1) meteorology-informed proactive venting, (2) machine learning-enabled adaptive evacuation zones, and (3) comprehensive risk-informed emergency planning while leveraging the experience from responses to other disasters.

Gas Flow Models and Computationally Efficient Methods for Energy Network Optimization

The equations governing gas flow dynamics are computationally challenging for energy network optimization. This paper proposes an efficient solution procedure to enable tractability for an hourly resolved yearly decision horizon. The solution procedure deploys linear and second-order cone gas flow models alternatively based on the length-diameter ratio of pipes, achieving maximum efficiency within accuracy limits. Moreover, it addresses the computational complexity of bidirectional pipe flows by fixing the associated integer variables according to a preceding optimization with a static flow approximation. The procedure also precisely aggregates parallel and serial pipes for increased efficiency. Mathematical derivations and single-pipe analyses substantiate the model selection criterion. Network optimizations validate the accuracy, success rate, and scalability of the procedure, achieving up to 3.1% cost savings compared to static models, enhancing the success rate by a minimum of 96%, and boosting computational efficiency up to 3 orders of magnitude over full dynamic models.

Resilience analysis of cyber-physical systems: A review of models and methods

Cyber-physical systems (CPSs) are monitored and controlled by a computing and communicating core. This cyber layer enables better management of the controlled subsystem, but it also introduces threats to the security and protection of CPSs, as demonstrated by recent cyberattacks. The resulting governance and policy emphasis on cybersecurity is reflected in the academia by a vast body of literature. In this article, we systematize existing knowledge on CPS analysis. Specifically, we focus on the quantitative assessment of CPSs before and after the occurrence of a disruption. Through the systematic analysis of the models and methods adopted in the literature, we develop a CPS resilience assessment framework consisting of three steps, namely, (1) CPS description, (2) disruption scenario identification, and (3) resilience strategy selection. For each step of the framework, we suggest established methods for CPS analysis and suggest four criteria for method selection. The framework proposes a standardized workflow to assess the resilience of CPSs before and after the occurrence of a disruption. The application of the proposed framework is exemplified with reference to a power substation and associated communication network.The case study shows that the proposed framework supports resilience decision making by quantifying the effects of the implementation of resilience strategies.

Collaborative and selfish mitigation strategies to tackle energy scarcity: The case of the European gas crisis

Following the disruption of Russian natural gas flows to Europe, we investigate the impact of collaborative and selfish behavior of European countries to tackle energy scarcity and supply electricity, heat, and industrial gas to end users. We study how the operation of the European energy system will need to adapt to the disruption and identify optimal strategies to overcome the unavailability of Russian gas. Those strategies include diversifying gas imports, shifting energy generation to non-gas-based technologies, and reducing energy demands. Findings suggest that: (1) selfish behavior of Central European countries exacerbates the energy scarcity for many Southeastern European countries; (2) proactive collaborative energy savings, together with a mild winter, can fully relieve the stress of the gas shortage; (3) diversification of gas imports leads to bottlenecks in the gas network, especially in Southeastern Europe; and (4) electricity generation is mostly shifted to coal-based power plants, causing higher carbon emissions.

Storage power purchase agreements to enable the deployment of energy storage in Europe

We propose a contractual setup, the proxy storage power purchase agreement (PPA), to foster the deployment of energy storage technologies. We define a threshold price below which the PPA becomes financially attractive for PPA buyers. We compute the threshold price for several storage technologies and configurations, in seven European countries. Such threshold prices overlap with the best-case forecast of the battery levelized cost of storage in 2030, indicating that proxy storage PPAs can play a role in enabling battery storage installations within the next ten years in Europe (generating about €180 million per year). Moreover, we identify UK and Germany as the most attractive countries for storage PPAs in Europe due to the high projected threshold prices and planned storage capacities. We show that revenues are maximized when coupling storage with wind energy generation rather than solar. This points to the design of policies that efficiently subsidize storage installations.

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Novel contractual setup for power purchase agreements (PPAs) with energy storage

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Calculation of PPA threshold price defining profitable cases for buyers in Europe

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The UK and Germany are the most promising European markets for storage PPAs

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For high-price scenarios, storage PPAs can generate 180 MEUR/year in 2030 in Europe

An Optimization-Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi-level attacker-defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability-based analyses and the over conservatism of the pure attacker-defender interdiction models. Mathematically, the proposed model configures a bi-level max-min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one-level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more-informed prehazard preparation decisions.

Agent-Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent-based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community-built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent-based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS-community system is quantified using direct, EPSS-related, societal, and community-related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS-community seismic resilience. The use of the agent-based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent-based EPSS-community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.

Effects of stressor characteristics on early warning signs of critical transitions and "critical coupling" in complex dynamical systems

Complex dynamical systems face abrupt transitions into unstable and catastrophic regimes. These critical transitions are triggered by gradual modifications in stressors, which push the dynamical system towards unstable regimes. Bifurcation analysis can characterize such critical thresholds, beyond which systems become unstable. Moreover, the stochasticity of the external stressors causes small-scale fluctuations in the system response. In some systems, the decomposition of these signal fluctuations into precursor signals can reveal early warning signs prior to the critical transition. Here, we present a dynamical analysis of a power system subjected to an increasing load level and small-scale stochastic load perturbations. We show that the auto- and cross-correlations of bus voltage magnitudes increase, leading up to a Hopf bifurcation point, and further grow until the system collapses. This evidences a gradual transition into a state of "critical coupling," which is complementary to the established concept of "critical slowing down." Furthermore, we analyze the effects of the type of load perturbation and load characteristics on early warning signs and find that gradient changes in the autocorrelation provide early warning signs of the imminent critical transition under white-noise but not for auto-correlated load perturbations. Furthermore, the cross-correlation between all voltage magnitude pairs generally increases prior to and beyond the Hopf bifurcation point, indicating "critical coupling," but cannot provide early warning indications. Finally, we show that the established early warning indicators are oblivious to limit-induced bifurcations and, in the case of the power system model considered here, only react to an approaching Hopf bifurcation.

A General Framework for the Assessment of Power System Vulnerability to Malicious Attacks

The protection and safe operations of power systems heavily rely on the identification of the causes of damage and service disruption. This article presents a general framework for the assessment of power system vulnerability to malicious attacks. The concept of susceptibility to an attack is employed to quantitatively evaluate the degree of exposure of the system and its components to intentional offensive actions. A scenario with two agents having opposing objectives is proposed, i.e., a defender having multiple alternatives of protection strategies for system elements, and an attacker having multiple alternatives of attack strategies against different combinations of system elements. The defender aims to minimize the system susceptibility to the attack, subject to budget constraints; on the other hand, the attacker aims to maximize the susceptibility. The problem is defined as a zero-sum game between the defender and the attacker. The assumption that the interests of the attacker and the defender are opposite makes it irrelevant whether or not the defender shows the strategy he/she will use. Thus, the approaches "leader-follower game" or "simultaneous game" do not provide differences as far as the results are concerned. The results show an example of such a situation, and the von Neumann theorem is applied to find the (mixed) equilibrium strategies of the attacker and of the defender.

Component criticality in failure cascade processes of network systems

In this work, specific indicators are used to characterize the criticality of components in a network system with respect to their contribution to failure cascade processes. A realistic-size network is considered as reference case study. Three different models of cascading failures are analyzed, differing both on the failure load distribution logic and on the cascade triggering event. The criticality indicators are compared to classical measures of topological centrality to identify the one most characteristic of the cascade processes considered.