Dual-stage day-ahead optimized performance of renewable-based microgrids

In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost-effective and efficient network service. Various meta-heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta-heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large-scale 295-bus system (composed of IEEE 141-bus, IEEE 85-bus, IEEE 69-bus subsystems) results illustrate the AOA and the ChOA outperform the other optimizers in terms of satisfying the objective functions, convergence, and execution time. The voltage profile is substantially improved at all buses with the penetration of the WTG with satisfactory power losses through the transmission lines. Day-ahead is considered generic and efficient in terms of total costs. The AOA records costs of 16.575M$/year with a reduction of 31% compared to particle swarm optimization. © 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Blockchain Based Trading Mechanism in Microgrid using CHIO Algorithm

Blockchain based microgrid mechanisms can be designed efficiently to provide uninterrupted power supply and to balance load demands dynamically. In this present work, a conceptual design of a microgrid system is proposed in power system modeling. A blockchain based trading mechanism has been implemented on this system. Various optimization algorithms have been used to maximize economic profit. Finally, the Coronavirus Herd Immunity Optimizer (CHIO) algorithm is described to accommodate the impression that arises for the optimal power flow (OPF) and energy capacity. A case study has been provided to authenticate the performance of this method. The result expresses that the present scheme can largely improve the power dispatch and trading system. © 2022 IEEE.

Dynamic spillovers between clean energy and non-ferrous metals markets in China: A network-based analysis during the COVID-19 pandemic

Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from "hydropower–gold” to "smart grid–zinc”, and the systematically influential markets correspondingly become smart grid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits. © 2023 Elsevier Ltd

Exploring the Acceptance of Smart Grid Technology in India: Role of Government Policies and Regulatory Framework

In the last few decades, technological advancements in the power sector have accelerated the evolution of the smart grid to make the grid more efficient, reliable, and secure. Being a consumer-centric technology, a lack of knowledge and awareness in consumers may lead to consumer opposition, which could imperil the grid modification process. This research aims to identify and prioritize the factors that can be considered barriers to technology acceptance for smart grid development in India. This study follows an integrated approach of literature review, AHP, and FERA. In the present work, 17 barriers have been identified and ranked on the basis of the social, technical, and economic paradigm. This study finds the impact of government policies and stakeholders' involvement in consumers' acceptance of smart grid technology and its importance towards improving the quality of life of Indians. The government should play as the main proponent. The present work will contribute to developing and upgrading the basic framework for the smart grid in a developing country like India. Copyright © 2022, IGI Global.

Forecasting energy consumption demand of customers in smart grid using Temporal Fusion Transformer (TFT)

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers' requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers' future demand. Using the proposed study, production companies can accurately have energy per their customers' needs by forecasting future energy consumption demands. Scientists and researchers are trying to minimize energy consumption by applying different optimization and prediction techniques;hence this study proposed a daily, weekly, and monthly energy consumption prediction model using Temporal Fusion Transformer (TFT). This study relies on a TFT model for energy forecasting, which considers both primary and valuable data sources and batch training techniques. The model's performance has been related to the Long Short-Term Memory (LSTM), LSTM interpretable, and Temporal Convolutional Network (TCN) models. The model's performance has remained better than the other algorithms, with mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) of 4.09, 2.02, and 1.50. Further, the overall symmetric mean absolute percentage error (sMAPE) of LSTM, LSTM interpretable, TCN, and proposed TFT remained at 29.78%, 31.10%, 36.42%, and 26.46%, respectively. The sMAPE of the TFT has proved that the model has performed better than the other deep learning models. © 2023 The Author(s)

Artificial Intelligence Assisted Smart Photovoltaics

The paper discusses research efforts in combining recent progress in Artificial Intelligence with automated management of solar energy generated in grid-connected photovoltaic (PV) systems along with their operation- and-maintenance (O&M) and their smart on-grid integration control. The outlined research aligns with the strategy of the European Union joining Digital and Green agendas as two major pillars for the COVID-19 economic recovery in the EU and is a part of the EU funded standardization action under the H2020 StandICT programme coordinated by the author and hosted by the Smart Energy Standards Group of the European Information Technologies Certification Institute (EITCI SESG) in cooperation with the European Solar Network. It also contributes to one of the four primary objectives of the European Green Deal, i.e. to achieve a fully integrated, interconnected and digitalized EU energy market by increasing research oriented towards technical reference standardization aimed at consolidation of the expert community and the technology uptake. © 2021. The Authors. Published by International Solar Energy Society Selection and/or peer review under responsibility of Scientific Committee.

Microclimate Monitoring System Design for the Smart Grid Analysis and Constructive Parameters Estimation

This paper describes a microclimate monitoring system consisting of a LoRaWAN network of wireless climate sensors, a data collector and analytical software. The system is a part of the ICS RAS SmartGrid Centre project for predicting building energy consumption. During the design phase, the authors considered the concept of comfort, which is involved in setting control objectives for HVAC plants. It was necessary to overcome some characteristics of the LoRaWAN protocol, such as floating data transmission period and limited intensity of sensor communication. These have been overcome by post-processing the data with Python software, using libraries numpy and scipy. The collected data was passed through an interpolation filter for synchronization, and the resulting data is freely available in dataset format on our website for all interested researchers. Additionally, weather data was collected using a local meteostation to be considered as external disturbances in analysis problems. This paper also considers an approach to passive identification of the thermal protection parameters of a building. The coronavirus lockdown period was chosen to assume the impact of visitors negligible. The parameters are supposed to be estimated by correlation analysis. The estimates obtained should be compared with the values calculated according to ISO and Russian construction standards for diagnostic reasons. © 2022 Elsevier B.V.. All rights reserved.

Regional PV Energy Forecasting using Distributed Data and Deep Neural Networks

Despite the COVID-19 pandemic, the global Photovoltaic market once more grew significantly in 2020, mainly on Grid-Connected Systems. The exponential increase of these systems raises new challenges for Smart Grid operators trying to predict load demand. That occurs because of the panels' output uncertainty and the leak of regional models for solar energy prediction. In this paper, we propose a distributed data approach to predict solar energy generated by Photovoltaic Systems. As input, we combine data from a community of solar panel owners and a historical weather website to build our dataset. This paper evaluates two scenarios: predicting regional next-day generation by using weather forecasting and the impact of a new system in that region. We sort the results seasonally and achieved 7% of MAE weighted percentage for summer predictions. © 2022 IEEE.

The Development and Implementation of a Microgrid SCADA System Simulator

Shifting the paradigm to decarbonized, distributed renewable future implies changes to conventional principles of power systems operation and requires the implementation of smart grid concepts. Microgrids have been widely recognized as a decentralized approach to successfully integrating renewable energy sources and end consumer empowerment. However, their implementation requires significant improvements and transformation of the distribution system in terms of increased observability and controllability, especially in the context of (near) real-time operation. Supervisory, Control, and Data Acquisition Systems (SCADA) enable system and infrastructure automated monitoring and control and serve as a foundation for advanced management and application of optimization-driven operation. Moreover, the development and testing of the functions mentioned above is a complex task, and today there is still a lack of holistic simulation tools, even though well-established power system simulators exist. The main objective of this paper is to introduce a novel simulation tool developed to simulate the SCADA system used in the Smart Grid Laboratory of the Faculty of Electrical Engineering and Computing for control, integration, and interactions between a microgrid's components. This paper includes simulator system architecture design, implemented functionalities, and future directions. Simulator testing shows successful communication, measurement generation, and meaningful response to commands and reference signals, proving correct functionality. Besides significant value in testing SCADA functionality, designing such a simulator has been of great benefit during restricted access to real-world devices in the Smart Grid Laboratory during the COVID-19 pandemic lockdown. © 2022 Croatian Society MIPRO.

Power grid data intelligent management and application under the background of big data

With the popularization of new technologies such as big data, cloud computing and the Internet of things in the construction of smart grid, power data is showing explosive growth. Traditional power enterprises rely on manpower to collect data and recover arrears. During the period of COVID-19, the closed management of government departments, the arrears of electricity customers and no power outages made the recovery work more difficult. In this paper, through the mining and analysis of customers' historical payment data, historical electricity and other data, using the deep learning algorithm in the field of artificial intelligence, this paper constructs a mathematical model and formulates the prevention and control strategy of electricity charge recovery risk of business travel alienation. The application results show that the new strategy can effectively reduce the amount of customer arrears and comprehensively improve the lean, digital and intelligent management level of power supply enterprises. © 2022 IEEE.

The Significance of Dissemination Activities at Mediterranean University in the frame of project 'Electrical Energy Markets and Engineering Education (ELEMEND)'

Engineering and science curricula are revamped to address sustainability as a cross cutting horizontal issue in order to have an impact on how students appreciate, understand and think critically about complex environmental, social and economic problems. Key to maximizing impact is the dissemination of a novel practice as well as its outcomes and outputs. The target of the ERASMUS+ project 'Electrical Energy Markets and Engineering Education - ELEMEND' is the capacity building of academic and teaching staff, students, electro engineering staff, employers as well as the general public. ELEMEND also aspires to create a favorable environment for energy related business and to modify the electricity user's behavior in the Western Balkan Countries in line with technological developments in smart grids. In order to ensure the sustainability of the ELEMEND results, a Dissemination and Exploitation Plan was elaborated by each of partner university taking into account all relevant stakeholders and key actors. In particular, we examine how the dissemination plan of Mediterranean University (MU), a partner university of the ELEMEND project, sets the targets to be achieved through the dissemination activities which make the project's objective and results visible and usable to all potential stakeholders. We focus on the dissemination activities of the Mediterranean University, we analyze applied methods and forms, we provide examples of how Mediterranean University pursued dissemination targets under Covid-19 conditions. © 2021 IEEE.

Data Communication Interfaces in Smart Grid Real-time Simulations: Challenges and Solutions

This paper presents the challenges and also suggests solutions associated with developing data communication interfaces between real-time digital simulator (RTDS) and hardware or software devices under study. While RTDS supports a wide range of standard and well-established communication protocols, employing such communication protocols generally increases the cost of the educational project as these standard communication protocols require licenses as well as third-party hardware and software devices to act as gateways. The need for these licenses and third-party hardware and software devices adds to the total cost of the project and also requires additional training. This paper provides two sets of cost-effective data interface solutions for local and remote networks based on the lessons learned from different projects that the authors were involved with. These practical solutions are especially useful for projects that involve multiple partners located remotely that are facing logistic challenges due to the Covid-19 pandemic. © 2021 IEEE.

Experimental and numerical assessment for HVAC management in an industrial building: A preliminary optimization

COVID-19 em ergency has ca used major changes in everyday life in the la st m onths, a nd it a lso affected the management of buildings. In particular, indoor a ir quality and ventilation ha ve been considered to play a key role in the spreading of the infection, causing national and interna tional subjects to draw up specific guidelines on ventilation and air recirculation rate in AHUs. The pa per deals with the “Loccioni Leaf Lab”, a n industria l building that hosts offices a nd workers operating on test benches. The building features high performance envelope, solar photovoltaic systems, groundwater heat pumps a nd a high -technology control a nd monitoring system and it is connected to a thermal and electric smart grid. A va lidated m odel of the building, im plem ented with the software DesignBuilder a nd EnergyPlus, wa s used to carry out numerical sim ula tions to optimize the m anagement of the HVAC through the Building Management System. Different working conditions have been sim ulated, a nd the numerical output has been used together with experimental data collected from the Company monitoring system. Ithas been possible to investigate how the extra ventilation required by the new guidelines would affect the tota l energy consumption a nd to compare, in term s of energy efficiency, the different HVAC m a nagement stra tegies tha t could be used to ensure occupants hea lth safety and indoor air qua lity. © 2021 Institute of Physics Publishing. All rights reserved.