Intelligent Automation System for Smart Grid Renewable Energy Generation on Climatic Changes

In the context of increasing sustainability through renewable energy utilization in Saudi Arabia, this study was motivated by the need to understand the impact of COVID-19 on gym-goers’ attitudes and behaviors towards renewable energy generating using their human kinetic energy (HKE). A comparative analytical study was conducted using a pre-COVID-19 survey (n = 96) and a post-COVID-19 survey (n = 385) of gym-goers in Saudi Arabia. The surveys consisted of closed-ended Likert-type questions that measured participants’ attitudes, behaviors, and willingness related to renewable energy and HKE generation. The results of the Chi-square test of independence showed that there was no significant difference in the participants’ attitudes towards behaviors and awareness regarding renewable energy and HKE generation between the pre-COVID-19 and post-COVID-19 surveys. However, the study revealed an overall promising level of support for sustainable energy and HKE generation among gym-goers. This led to the conclusion that there is potential for the installation of sustainable HKE generation systems in gyms. The main implication of this finding is the need for greater education and awareness raising campaigns as well as incentives to support the implementation of renewable energy solutions. The research contributes to sustainable development and Saudi Vision 2030 by exploring potential opportunities for increasing the share of renewable energy in the overall energy mix and promoting sustainable development.

The global energy landscape has always been dominated by fossil fuels, but with the intensification of climate change and environmental issues, people realize that the traditional energy system must be transformed. In order to integrate renewable energy and improve the sustainability and resilience of the energy system, smart grids have emerged. Smart grid is a modern grid system based on advanced communication, control and information technology, which can integrate renewable energy more effectively. As one of the renewable energy sources, solar energy plays an important role in the smart grid. It has the advantages of wide distribution, environmental protection, and renewable, and with technological progress and cost reduction, solar power generation is widely used in smart grids. Similarly, wind energy, another important renewable energy source, also plays an important role in the smart grid. The continuous development of wind turbine technology has made wind power generation more efficient and reliable, and the integration with smart grid infrastructure has further improved the efficiency of wind energy utilization. In addition, hydrogen energy has great potential as a clean energy carrier, but the storage and distribution of hydrogen energy in smart grid systems still face challenges.

This chapter presents novel approaches for the efficient generation, storage, and usage of energy in the smart home environment and their link to the smart grid. It also demonstrates innovative concepts and their feasibility for these purposes. The generation of renewable energy on the level of individual homes and housing estates is achieved by aerodynamically and aeroacoustically optimized small wind turbines as well as combined heat and power (CHP) micro plants using organic Rankine cycles (ORCs) to complement solar energy. The chapter further discusses the intelligent distribution of electric energy between the smart home and the smart grid. In order to tackle the transition of the electric power supply toward a renewable‐based generation plant system, it is necessary to fully exploit locally available energy sources and to generate production surpluses in rural areas for the supply of urban agglomerations and industrial centers.

SUMMARYIn the future, renewable energy (RE) generation systems will be constructed and interconnected to the power system by nonutility entities such as generation companies and residential customers. Therefore, generation expansion planning (GEP) should consider the effects of RE generation. The purpose of this paper is to discuss the best GEP from the economic, supply reliability, and environmental aspects considering the penetration of RE generation. For this purpose, this paper proposes a model of a utility's GEP process considering RE generation. The proposed method is based on stochastic dynamic programming (SDP) in which long‐term uncertainties are modeled by geometric Brownian motion (GBM) and the binomial lattice process. The variation of RE generation output due to the weather conditions (short‐term uncertainty) is also considered in the proposed method by means of the net load curve.

Since the European Union aims to generate by renewable energy sources up to 80% of the total electric demand by 2050, the ideas towards integration traditional and distributed sources have been developed. Smart grid is one of such idea. In fact it helps to provide the concept of an interactive methodology implemented in the design IT tool for planning, observing and operating energy power infrastructure within rural areas. The Rural Intelligent Grid (“Rigrid”) Network in the framework of the initiative ERA-Net Smart Grids Plus leads to the emergence of demonstration installations for modern renewable and distributed energy in Podlaskie voivodship in Poland. The aim of the project Rigrid is to design and demonstrate an innovative tool for the planning and the operation of energy power infrastructures in rural areas. The final result of the project shall be an energy & measurement & IT system, verified in a laboratory in real conditions. The system shall have both options: to operate in parallel with the power grid or in island operating conditions, dedicated for the optimal planning and efficient operation of integrated renewable energy equipment in rural areas, ensuring continuous and secure supply of energy. The repetitive methodologies developed during the project will enable the efficient introduction and implementation of smart grid solutions that maximize RES and low carbon energy, while providing secure electricity supplies.

Unveiling the dynamic impact of energy generation on economic sustainability in Canada: A roadmap towards sustainable development

The energy domain worldwide is experiencing high transformative pressure due to the imperative of climate change and new opportunities brought by various rapidly evolving digital technologies. Particularly, this change is affecting smart grids (SGs), which are increasingly shifting toward utilizing renewable and distributed energy sources. The natural intermittency of these energy sources increases the complexity of SG operations, such as ensuring continuity of energy supply and demand response balancing. In mitigation of these challenges, the tools and complex approaches that digitalization can provide have shown themselves particularly advantageous. There is a solid body of work showing how technologies like the Internet of Things (IoT), distributed ledgers, edge and cloud computing, machine learning (ML), etc., can be applied to address a variety of technical and economic problems in the energy sector, emphasizing SGs. This paper presents the most comprehensive literature review to date on digitalization in renewable energy source-based SGs, synthesizing over 200 studies across data analytics, artificial intelligence and ML, digital twins, edge–fog–cloud computing, the IoT, advanced metering infrastructure, and distributed ledger technologies. Unlike previous reviews, which are often limited to a single technology or narrow application, this work provides a cross-technology synthesis linking technical and financial aspects, identifies consolidated research gaps, and proposes a unified research agenda. The review further highlights future trends, including large language models, 6G communications, and distributed autonomous organizations, and discusses their implications for both industry practice and academic research.

Climate change can strongly affect renewable energy production. The state of the art in projecting future renewable energy generation has focused on using regional climate prediction. However, regional climate prediction is characterized by inherent uncertainty due to the complexity of climate models. This work provides a comprehensive study to quantify the impact of climate uncertainties in projecting future renewable energy potential over five climate zones of Europe. Thirteen future climate scenarios, including five global climate models (GCMs) and three representative concentration pathways (RCPs), are downscaled by the RCA4 regional climate model (RCM) over 90 years (2010–2099), divided into three 30-year periods. Solar and wind energy production is projected considering short-/long-term climate variations and uncertainties in seven representative cities (Narvik, Gothenburg, Munich, Antwerp, Salzburg, Valencia, and Athens). The results showed that the uncertainty caused by GCMs has the most substantial impact on projecting renewable energy generation. The variations due to GCM selection can become even larger than long-term climate change variations over time. Climate change uncertainties lead to over 23% and 45% projection differences for solar PV and wind energy potential, respectively. While the signal of climate change in solar radiation is weak between scenarios and over time, wind energy generation is affected by 25%.

Introduction: To date, a variety of studies have been published on the topic of long term energy system transition. Most studies on future energy systems, however, have a shorter time frame or adopt a supranational focus (e.g. the Energy Roadmap, 2011 or the World Energy Outlook, 2015). It then constitutes a sincere challenge to perform a national energy system transition study with as time horizon 2050 and covering a far-reaching transformation of the energy system. VITO, together with the FPB (Federal Planning Bureau) and ICEDD, performed a study to scrutinise the transition of the Belgian national energy system towards a future mix entirely based on renewable energy sources. The focus on renewable energy sources and on building a national energy system completely running on renewable energy can be traced back to three main concerns: – Climate change: Renewable energy sources (RES) are a major instrument in the fight against climate change as RES do not release (net) greenhouse gas emissions. – Security...

Cities host over 50% of the world’s population and account for nearly 75% of the world’s energy consumption and 80% of the global greenhouse gas emissions. Consequently, ensuring a smart way to organize cities is paramount for the quality of life and efficiency of resource use, with emphasis on the use and management of energy, under the context of the energy trilemma, where the objectives of sustainability, security, and affordability need to be balanced. Electrification associated with the use of renewable energy generation is increasingly seen as the most efficient way to reduce the impact of energy use on GHG emissions and natural resource depletion. Electrification poses significant challenges to the development and management of the electrical infrastructure, requiring the deployment of Smart Grids, which emerge as a key development of Smart Cities. Our review targets the intersection between Smart Cities and Smart Grids. Several key components of a Smart City in the context of Smart Grids are reviewed, including elements such as metering, IoT, renewable energy sources and other distributed energy resources, grid monitoring, artificial intelligence, electric vehicles, or buildings. Case studies and pilots are reviewed, and metrics concerning existing deployments are identified. A portfolio of 16 solutions that may contribute to bringing Smart Grid solutions to the level of the city or urban settings is identified, as well as 11 gaps existing for effective and efficient deployment. We place these solutions in the context of the energy trilemma and of the Smart Grid Architecture Model. We posit that depending on the characteristics of the urban setting, including size, location, geography, a mix of economic activities, or topology, the most appropriate set of solutions can be identified, and an indicative roadmap can be built.

With the increasing penetration of renewable energy generation (such as wind power) in the future power systems, the requirement for peak regulation capacity is becoming an important issue for the utility operators. Energy storage is one of the most effective solutions to address this issue. Under this background, this paper proposes a novel multi-objective optimization model to determine the optimal allocation capacity of energy storage in a thermal power plant for provision of peak regulation service in smart grid. To achieve this, we limit our study to a context based on the compensation mechanism performed in the auxiliary peak regulation market of northeast China. On this basis, an optimal energy storage allocation model in a thermal power plant is proposed, which aims to maximize the total economic profits obtained from peak regulation and renewable energy utilization in the system simultaneously, while considering the operational constraints of energy storage and generation units. A classical weighted summation technique has been used to deal with the bi-objective structure of the problem and a compound solution algorithm based on Generic Algorithm is employed to resolve the developed model. The effectiveness of the proposed methodology is examined based on a real-world regional power system in northeast China and the obtained results verify the effectiveness of our approach.

The smart grid (SG) idea was created to give the power lattice the elements and abilities it requirements to effectively integrate environmentally renewable energy sources (RES) and achieve versatility. Since energy sources like sun oriented and wind power are innately unstable and unusual, controlling in shrewd organizations can be troublesome. This study proposes an original answer for this issue by combining the upsides of particle swarm optimisation (PSO) and extreme learning machine (ELM) approaches. The proposed approach models and conjectures sustainable power age utilizing ELM, taking into account more exact preparation and determining. PSO guarantees maximized execution and effectiveness by improving the ELM calculation's boundaries in the meantime. Using a dataset of sun based energy yield, this study surveyed the proposed procedure and stood out its outcomes from those of other improvement techniques. The findings demonstrate that our ELM-PSO method lowers energy costs in smart grids and greatly increases the accuracy of predictions for renewable energy. Because the proposed approach can be employed to a range of RS sources, including hydroelectric power plants, wind turbines and solar panels, this investigation has wide-ranging implications. It can build a more robust and sustainable energy future by increasing the effectiveness and dependability of using renewable energy.

The integration of non-fossil fuel-based vehicles with non-conventional renewable energy, is necessary in the field of sustainable energy usage. This review paper explains how non-conventional energy sources are related to the Electric Vehicles. This paper also reviews advantages, problems and their solutions and prominent technologies. There are two terms: Non-conventional Energy is a clean energy however in reducing emission of greenhouse gases, EVs are the perfect solutions. By combining these two, a new system with less dependency on petroleum-based fuels and stable grids can be developed. In this paper we reviewed, different techniques of integrating EVs with non-conventional energy, V2G technology, smart grid, and energy storage system along with new trends, problems, solutions and future scopes. As demand of EV technology increases, it requires a fast fuel-efficient charging which can carry demands of high power and integration of non-conventional energy with extremely-fast EV charging systems by the use of MPCs, which can manage and distribute energy between locations.

Cellular operators are increasingly turning towards renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE generation and mobile traffic at each base station (BS), a surplus or shortfall of energy may occur at any given time. To increase energy self-reliance and minimize the network's energy cost, the operator needs to efficiently exploit the RE generated across all BSs. In this paper, a hybrid energy sharing framework for cellular network is proposed, where a combination of physical power lines and energy trading with other BSs using smart grid is used. Algorithms for physical power lines deployment between BSs, based on average and complete statistics of the net RE available, are developed. Afterwards, an energy management framework is formulated to optimally determine the quantities of electricity and RE to be procured and exchanged among BSs, respectively, while considering battery capacities and real-time energy pricing. Three cases are investigated where RE generation is unknown, perfectly known, and partially known ahead of time. Results investigate the time varying energy management of BSs and demonstrate considerable reduction in average energy cost thanks to the hybrid energy sharing scheme.

There are advantages and disadvantages to integrating renewable energy sources into smart networks, especially when it comes to guaranteeing sustainability, efficiency, and dependability. This chapter examines how artificial intelligence (AI) and cutting-edge semiconductor technology can be used to optimize smart grids and renewable energy systems. By facilitating precise demand forecasting, real-time energy distribution, and predictive maintenance, AI systems improve grid management. At the same time, advancements in semiconductor devices and materials, such wide bandgap semiconductors, help to decrease energy losses and increase power conversion efficiency. This chapter illustrates how AI and cutting-edge semiconductor technologies can work together to increase grid stability, lower operating costs, and better integrate intermittent renewable energy sources like wind and solar. The findings demonstrates how these technologies could accelerate the shift to an energy infrastructure that is more intelligent, resilient, and carbon-neutral.