Electric power grids, which form an essential part of the critical infrastructure, are evolving into highly distributed, dynamic networks in order to address the climate change. This fundamental transition relies on extensive automation solutions based on communications and information technologies. Thus, it also gives rise to new attack points for malicious actors and consequently, increases the vulnerability of the electric energy system. This study presents a qualitative assessment of power grid cybersecurity through expert interviews across countries in Europe and the U.S. to gain understanding of the latest developments and trends in the cybersecurity of future electric energy systems. The horizon of the assessment is 10 years spanning until the early 2030s. Thereafter, the study identifies how and to which extent the risks identified to be most significant are understood and addressed in the latest research and industry publications aiming at identifying areas deserving specific further attention. The most significant threats based on the assessment are False Data Injection (FDI), Denial of Service (DoS) supply chain, and ransomware and malware attacks.

The electric energy system is undergoing a major change due to the increasing requirements of dynamic performance. In distribution grids, this evolution will necessitate expanded automation, which in turn will require enhanced connectivity solutions. Strongly evolving communications technologies and architectures, particularly mobile communications as well as cloud and edge computing, will provide new opportunities and alternatives for connectivity solutions. This paper contributes by identifying potential technical and industry architectures for the connectivity solutions required to manage distribution grids in the early 2030s. The study utilizes a senior expert panel and a Delphi survey. Industry architectures are modelled as value networks. The paper uses the Finnish distribution grids as a case example. Regarding technical architectures, the results reveal skepticism concerning those emerging 5G mobile network features that target industrial applications and about the need for extensive distributed computing in the proximity of consumers and prosumers. The most probable industry architectures are found to be those that enable the Distribution System Operators (DSOs) to maintain direct control of critical technical components, or that enable Communications Service Providers (CSPs) to handle the operations of both communications solutions and distributed computing. CSPs are seen as well positioned for this task due to their existing networking and computing infrastructure. However, this may also involve business risks for both DSOs and CSPs.

The electricity sector has a central role in the efforts to meet climate targets. Consequently, efforts are taking place to electrify industry, heating, and transportation. The Finnish government has set the target to halve carbon dioxide traffic emissions by 2030 and achieve carbon neutrality by 2045. Due to this target, the currently small proportion of electric vehicles (EVs) in Finland could expand in a manner that is difficult to forecast but could be exponential. Amid already strained investment budgets, anticipating the alternative scenarios and impacts of such a transport electrification evolution is of high importance to distribution system operators in order to optimize network planning and enhancements during the coming 15–25 years. The novelty and contribution of this paper is in utilizing a formal scenario planning process to envision what the alternative scenarios are (i.e., possible futures) for the evolution of the electric car fleet in Finland until 2040 and how these alternative scenarios could impact distribution grids. The impact analysis is performed in terms of additional energy and additional power in order to gain an understanding of the high-level impacts and investment needs. The analysis utilizes a real distribution grid in southern Finland as a case example that enables quantification. The results indicate the electric vehicles will, depending the scenario, pose an essential additional load in terms of both energy and power and that the required investment levels and investment types will be heavily dependent on the scenario.

This paper examines the evolution of the Finnish electric energy system up to 2035, focusing on the likelihood of different development paths. The primary contribution of this paper is the development of an extensive Bayesian Network, designed to model and analyze the evolution of power generation capacity mix, assess the likelihood of different grid management scenarios, and understand the causal relationships underlying these scenarios. A target optimization was carried out using the constructed Bayesian Network to explore possibilities to minimize grid management complexity. The results of the optimization reveal that the authorities and stakeholders should prioritize increasing demand response, gas power, and battery storage capacities. These mature technologies are well-suited to guarantee energy adequacy during peak consumption periods, which in Finland typically occur during consecutive cold, dark and windless winter weeks. Although this study focuses on the evolution of the Finnish power grid, the constructed Bayesian Network approach is broadly applicable and can be utilized to explore causal relationships in other countries by employing the designed questionnaire and engaging a panel of experts specific to the country’s energy infrastructure.

This paper examines the evolution of the Finnish electric energy system up to 2035, focusing on the likelihood of different development paths. The primary contribution of this paper is the development of an extensive Bayesian Network, designed to model and analyse the evolution of power generation capacity mix, assess the likelihood of different grid management scenarios, and understand the causal relationships underlying these scenarios. A target optimisation was carried out using the constructed Bayesian Network to explore possibilities to minimise grid management complexity. The results of the optimisation reveal that the authorities and stakeholders should prioritise increasing demand response, gas power, and battery storage capacities. These mature technologies are well-suited to guarantee energy adequacy during peak consumption periods, which in Finland typically occur during consecutive cold, dark and windless winter weeks. Although this study focuses on the evolution of the Finnish power grid, the constructed Bayesian Network approach is broadly applicable and can be utilised to explore causal relationships in other countries by employing the designed questionnaire and engaging a panel of experts specific to the country's energy infrastructure.