This manuscript is the preprint version of a conference paper that is accepted for presentation and publication, International Conference on Future Energy Solutions (FES2023) to be held in Vaasa, Finland, 12-14, June 2023.

For local energy markets to become a widely accepted, real-life phenomenon, the models must extend beyond the techno-economical aspects and energy justice within these market structures must be thoroughly addressed. To measure the perceived fairness of a local energy market, some studies have adapted fairness indicators originated from other fields. However, it is still unclear how these indicators can accurately reflect distributional fairness in various local energy trading scenarios. This study conducts a comprehensive review and qualitative assessment of the existing fairness indicators in the local energy market literature and their ability to accurately capture different situations in the market. The study results in a discussion on their strength and limitations, and an encouragement to alter the indicators to better quantify energy justice in a local energy market context.

Local energy communities and electricity markets have emerged as possibilities for interaction among prosumers. A substantial effort has been invested into creating efficient pricing mechanisms for various market arrangements, all of which take into consideration distinct characteristics of local electricity trading. However, since they are all evaluated in terms of various systems and market conditions, it is challenging to directly compare the mechanisms. In this research, three well-established pricing mechanisms from the literature are systematically compared and evaluated under identical settings on their influence on welfare distribution across various market participant groups, privacy protection, transparency and complexity level. According to the findings, the supply-demand ratio pricing system leads to the lowest costs for consumers and is also the most privacy compliant and transparent. Furthermore, prosumers obtain the highest cost-savings through the consensus alternating direction method of multipliers pricing mechanism, whereas the equilibrium pricing mechanism performs best regarding economic fairness. The aim of this article is to provide insight into the performance of different pricing mechanisms to energy regulators and local electricity market facilitators. The comparative analysis should aid in making informed decisions on the implementation of local electricity markets.

The power system and markets have become increasingly complex along with an effort to digitalize the energy sector. Accessing flexibility services, in particular, through digital energy platforms, has enabled communication between multiple entities within the energy system and streamlined flexibility market operations. However, digitalizing these vast and complex systems introduces new cybersecurity and privacy concerns, which must be properly addressed during the design of the digital energy platform ecosystems. In this study, the potential and operation of digital flexibility platforms are reviewed, and a generic architecture with/without a combined distributed ledger technology framework is proposed. Finally, concerns about cybersecurity and privacy on digital energy platforms are discussed.

The Digital Green Transition of the energy industry is accelerating as decarbonization and digitalization of the power market and system are gaining momentum. Unlocking the full and real potential of local energy markets is among the opportunities to be demystified in the coming years, with blockchain technology being a prominent enabler. Important subcomponents such as functional and fair local energy pricing mechanisms and management algorithms will be required for next-generation digital local energy trading and management platforms. Even though rapid digitalization of the energy sector offers considerable advantages, it also increases the cyber-physical risk levels of energy systems in general. The proposed framework of this paper accommodates considerable undiscovered aspects of democratization of the energy systems and future digital shared economy conventions. Nevertheless, privacy concerns must be addressed in tandem with the cyber-physical security of local energy markets and their ecosystem. This work proposes a joint privacy and cyber-physical security framework designed for local energy markets to improve cyber-physical resilience. All related parts, such as blockchain technology and self-sovereign identity, are used in an organized way to create a possible solution.

Digitization in the power industry enables wide connectivity among multiple new entrants such as DERs, prosumers, and P2P counterparts within or outside the Distributed Ledger Technology (DLT). The use of DLT to improve resilience in the power grid has growing support, but new technology provides new opportunities for adversaries to cause harm. This work completed by the Cybersecurity focused task force of IEEE SA P2418.5 evaluates the potential risks by applying the MITRE ATT&CK ICS matrix to the DLT Engineering and Cybersecurity Stack designed for power systems applications.

Future wireless networks \textcolor{black}{(5G and beyond),} also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have been dramatically growth with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated in applications throughout all layers of the network. However, the security concerns on network functions of NextG using AI-based models, i.e., model poising, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cyber attacks, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of  deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB’s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks, while making models more robust against any attacks through mitigation methods. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack against the channel estimation model. The results indicated that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.

In recent years, there has been a growing trend in research on smart contract applications. Smart contracts potential in the energy landscape is visible in their major applications such as peer-to-peer energy trading, electric vehicle charging, energy market management, and many more. Many studies have been conducted that produced a lot of literature in this area and many startups and companies have surfaced that exhibit large scope of its application in the energy sector. However, in comparison to other domains, there is still more development required. The literature available focuses on the different technical aspects and use cases, but there’s no such scientific article providing gathered details of the smart contracts development process that invites the attention of researchers in the energy domain for development or provides basic knowledge of available tools. It is therefore necessary to contribute with academic articles that summarize this information, thus opening up paths of development in this field and strengthening the community. This paper is the first step towards the implementation of this idea and that is intended to be extended in the future.

Smart contracts (SCs) are a set of logical procedures that can be run by individual peers participating within a Distributed Ledger Technology (DLT) network. By design, smart contracts inherit many of the benefits of DLT, including its immutability, scalability and  security properties. Nevertheless, they may introduce additional attack vectors, which can lead to cybersecurity explorations that could jeopardize the end-application’s ability to operate as intended or result in data leaks, and privacy violations. In this work an exploration of known problems, and possible attack scenarios will be presented. This is followed by a set of proposed best practices and mitigation strategies that are intended to assist developers, researchers and other relevant stakeholders to develop secure SC implementations.

As the global industrial complex gears toward fulfilling the tenets of Industry 4.0 and beyond, technologies such as distributed ledger technologies, digital twins, and artificial intelligence become pivotal enablers. In the last decade, metaverse as a concept and technology found its place among crucial enablers for technology and digital advancement across several engineering domains. Metaverse has the potential to combine the elements from distributed computing platforms, the digital evolution of physical systems, and advanced learning systems to unearth a fully digitized world of comparative properties of the real world. We should ensure the privacy, integrity, and confidentiality of personal data. These requirements will lead to proper identity management in the metaverse. Given the complex nature of the metaverse, traditional centralized systems may not offer a viable identity management solution. Therefore, this study explores a decentralized identity management system called the Self-sovereign Identity (SSI) as a logical alternative to traditional centralized identity management systems. The proposed holistic framework aims to ignite new ideas and discussions related to the combined deployment of DLT, SSI, and metaverse to inspire new implementation areas within the Industry 4.0 environment. The paper also discusses various opportunities, enablers, technical \& privacy aspects, legislation requirements, and other barriers related to SSI implementation.

This work presents a formal review of smart contracts, including definitions, technical requirements, and potential power and energy-related use cases. This includes in-depth discussions covering cybersecurity, legality and interoperability goals that must be taken into consideration by potential end-users. The paper presents a first attempt towards the standardization of smart contracts (SCs) within the field of power and energy as a work in progress activity under the IEEE Standards Association (IEEE SA) P2418.5 Working Group. This work also proposes a holistic, language-agnostic reference model that is intended to accelerate the adoption of Distributed Ledger Technology (DLT) by industry stakeholders by providing standardized processes. Finally, the paper discusses key takeaways that must continue to be developed to increase SC usage within the energy industry.