Application of AI/IoT for Smart Renewable Energy Management in Smart Cities

2022 ◽  
pp. 115-138
Author(s):  
Pradeep Bedi ◽  
S. B. Goyal ◽  
Anand Singh Rajawat ◽  
Rabindra Nath Shaw ◽  
Ankush Ghosh
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Smart Cities

scholarly journals Digitalization in Buildings and Smart Cities on the Way to 6G

2021 ◽  
Author(s):  
Walter Konhäuser
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Communication Networks ◽  
Mobile Communications ◽  
Energy Production ◽  
Smart Cities ◽  
Local Energy ◽  
Management Concepts ◽  
Energy Turnaround ◽  
Digital Components

AbstractThe energy turnaround created a high volatility in the energy production based on renewable energy. To integrate renewable energy economically in buildings and smart cities an additional concept of energy storage and energy supply based on energy management concepts must be claimed. The political views have changed during the last years and energy efficiency in buildings is seen important because 35% of greenhouse gas is produced by the final energy consumption. The deployment of local energy production concepts is an important step to energy turnaround. To generate and distribute energy effectively in buildings, digital components such as sensors, actuators, meters, and energy management systems must be installed in the buildings and the digital components must be able to communicate via communication networks. The paper describes systems for local energy generation, necessary communication networks for buildings and smart cities and digitization applications in industrial buildings. As an example of energy management, the Oktett64 system is presented, which is based on Enterprise IT technology and has implemented AI and blockchain technology. Digitalization with platforms such as Oktett64 are based on technologies that are superior to today's often commercially available Programmable Logic Controllers. The article also shows how the future mobile communications standards 5G beyond and 6G can offer special solutions for the digitization of buildings in their edge clouds.

scholarly journals Energy Management of Microgrids for Smart Cities: A Review

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5976
Author(s):  
Muhammad Salman Sami ◽  
Muahmmad Abrar ◽  
Rizwan Akram ◽  
Muhmmad Majid Hussain ◽  
Mian Hammad Nazir ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Management System ◽  
Smart Cities ◽  
High Energy ◽  
Energy Resources ◽  
Energy Management System ◽  
Renewable Energy Resources ◽  
Comprehensive Overview ◽  
Energy Demands

Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO2 emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities.

A day-ahead joint energy management and battery sizing framework based on θ-modified krill herd algorithm for a renewable energy-integrated microgrid

2021 ◽  
Vol 282 ◽  
pp. 124435
Author(s):  
Nan Yin ◽  
Rabeh Abbassi ◽  
Houssem Jerbi ◽  
Alireza Rezvani ◽  
Martin Müller
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Krill Herd Algorithm ◽  
Krill Herd

scholarly journals The Energy System of an Ecovillage: Barriers and Enablers

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 682
Author(s):  
Zita Szabó ◽  
Viola Prohászka ◽  
Ágnes Sallay
Keyword(s):  
Renewable Energy ◽  
Spatial Structure ◽  
Energy Management ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Energy Sources ◽  
Energy Management System ◽  
Production And Consumption ◽  
Ecologically Sustainable

Nowadays, in the context of climate change, efficient energy management and increasing the share of renewable energy sources in the energy mix are helping to reduce greenhouse gases. In this research, we present the energy system and its management and the possibilities of its development through the example of an ecovillage. The basic goal of such a community is to be economically, socially, and ecologically sustainable, so the study of energy system of an ecovillage is especially justified. As the goal of this community is sustainability, potential technological and efficiency barriers to the use of renewable energy sources will also become visible. Our sample area is Visnyeszéplak ecovillage, where we examined the energy production and consumption habits and possibilities of the community with the help of interviews, literature, and map databases. By examining the spatial structure of the settlement, we examined the spatial structure of energy management. We formulated development proposals that can make the community’s energy management system more efficient.

scholarly journals Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2700
Author(s):  
Grace Muriithi ◽  
Sunetra Chowdhury
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Critical Role ◽  
Winter Season ◽  
Learning Approach ◽  
Solar Pv ◽  
Optimal Energy ◽  
Optimal Energy Management

In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season.

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