Bayesian Data Analysis (2nd ed.) (Book)

Adsorption of SO2 molecule on Ni-doped and Pd-doped graphene based on first-principle study

Given the ongoing transition towards a more decentralised and adaptive energy system, the potential of blockchain-enabled smart contracts for the energy sector is being increasingly recognised. Due to their self-executing, customisable and tamper-proof nature, they are seen as a key technology for enabling the transition to a more efficient, transparent and transactive energy market. The applications of smart contracts include coordination of smart electric vehicle charging, automated demand-side response, peer-to-peer energy trading and allocation of the control duties amongst the network operators. Nevertheless, their use in the energy sector is still in its early stages as there are many open challenges related to security, privacy, scalability and billing. In this paper, we systematically review 178 peer-reviewed publications and 13 innovation projects, providing a thorough analysis of the strengths and weaknesses of smart contracts used in the energy sector. This work offers a broad perspective on the opportunities and challenges that stakeholders using this technology face, in both current and emergent markets, such as peer-to-peer energy trading platforms. To provide a roadmap for researchers and practitioners interested in the technology, we propose a systematic model of the smart contracting process, by developing a novel 6-layer architecture, as well as presenting a sample energy contract in pseudocode form and as open-source code. Our analysis focuses on the two mainstream application areas we identify for smart contract use in this area: energy and flexibility trading, and distributed control. The paper concludes with a comprehensive, critical discussion of the advantages and challenges that must be addressed in the area of smart contracts and blockchains in energy, and a set of recommendations that researchers and developers should consider when applying smart contracts to energy system settings. 

https://doi.org/10.1016/j.rser.2021.112013

Smart contracts in energy systems: A systematic review of fundamental approaches and implementations

Review on recent optimization strategies for hybrid renewable energy system with hydrogen technologies: State of the art, trends and future directions

This study aims to find an appropriate system for microgeneration energy investments and identify optimal renewable energy alternatives for the effectiveness of these projects. In this context, a model is constructed by multi stepwise weight assessment ratio analysis (M-SWARA) and technique for order preference by similarity to ideal solution (TOPSIS) with q-rung orthopair fuzzy sets (q-ROFSs) and golden cut. At the first stage, five different systems are weighted for the effectiveness of the microgeneration energy investments. Secondly, four different renewable energy alternatives are ranked regarding the performance of these projects. In addition, a comparative analysis is also implemented with intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs). The findings are the same in all different fuzzy sets that demonstrates the reliability of the findings. It is determined that grid-connected with battery backup is the most important system choice. On the other hand, solar energy is the most appropriate alternative for microgeneration system investments. Grid-connected system should be implemented for the performance of the microgeneration projects. Hence, providing a sustainable access to the electricity can be possible. Sufficient amount of electricity may not be obtained from wind and solar energy because of the climate changes. In this process, grid-connected system can handle this problem effectively.

Golden Cut-Oriented Q-Rung Orthopair Fuzzy Decision-Making Approach to Evaluation of Renewable Energy Alternatives for Microgeneration System Investments

Medium-term load forecasting in isolated power systems based on ensemble machine learning models

This paper addresses the problems connected with fuzzy neural networks’ application in equipment technical state assessment problems at electrical substations. This paper discusses the main principles of fuzzy neural network formation and its construction algorithm. Also, the case study for the determination of fuzzy neural network synaptic weights for the unit “disconnectorˮ on the basis of technical diagnostic statistical data and tests is presented.

/pdf/fuzzy-neural-networks-application-for-substation-integral-1se450xi5x.pdf

Sergey Kokin

Papers

Medium-term load forecasting in isolated power systems based on ensemble machine learning models

Fuzzy neural networks' application for substation integral state assessment

Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change

Electrical Strength Analysis of SF6 Gas Circuit Breaker Element

Hydrogen energy storage systems to improve wind power plant efficiency considering electricity tariff dynamics