Explore the vast range of titles available.

This study presents an innovative framework for leveraging the potential of AI in energy systems through a multidimensional approach. Despite the increasing importance of sustainable energy systems in addressing global climate change, comprehensive frameworks for effectively integrating artificial intelligence (AI) and machine learning (ML) techniques into these systems are lacking. The challenge is to develop an innovative, multidimensional approach that evaluates the feasibility of integrating AI and ML into the energy landscape, to identify the most promising AI and ML techniques for energy systems, and to provide actionable insights for performance enhancements while remaining accessible to a varied audience across disciplines. This study also covers the domains where AI can augment contemporary and future energy systems. It also offers a novel framework without echoing established literature by employing a flexible and multicriteria methodology to rank energy systems based on their AI integration prospects. The research also delineates AI integration processes and technique categorizations for energy systems. The findings provide insight into attainable performance enhancements through AI integration and underscore the most promising AI and ML techniques for energy systems via a pioneering framework. This interdisciplinary research connects AI applications in energy and addresses a varied audience through an accessible methodology.

Conventional energy production based on fossil fuels causes emissions that contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, which is an endeavor that requires a methodical modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions via learning curves. This is followed by a literature survey to uncover learning rates for relevant low-carbon technologies required to model future energy systems. The focus is on (i) learning effects in hydrogen production technologies and (ii) the application of endogenous learning in energy system models. Finally, we discuss methodological shortcomings of typical learning curves and possible remedies. One of our main results is an up-to-date overview of learning rates that can be applied in energy system models.

Fossil fuels in transportation are a significant source of local emissions in and around cities; thus, decarbonising transportation can reduce both greenhouse gases (GHGs) and air pollutants (APs). However, the degree of these reductions depends on what replaces fossil fuels. Today, GHG and AP mitigation strategies are typically ‘decoupled’ as they have different motivations and responsibilities. This study investigates the ancillary benefits on (a) APs if the transport sector is decarbonised, and (b) GHGs if APs are drastically cut and (c) the possible co-benefits from targeting APs and GHGs in parallel, using an energy-system optimisation model with a detailed and consistent representation of technology and fuel choices. While biofuels are the most cost-efficient option for meeting ambitious climate-change-mitigation targets, they have a very limited effect on reducing APs. Single-handed deep cuts in APs require a shift to zero-emission battery electric and hydrogen fuel cell vehicles (BEVs, HFCVs), which can result in significant upstream GHG emissions from electricity and hydrogen production. BEVs powered by ‘green’ electricity are identified as the most cost-efficient option for substantially cutting both GHGs and APs. A firm understanding of these empirical relationships is needed to support comprehensive mitigation strategies that tackle the range of sustainability challenges facing cities.

Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

The COVID-19 pandemic and Russia’s war on Ukraine have impacted the global economy, including the energy sector. The pandemic caused drastic fluctuations in energy demand, oil price shocks, disruptions in energy supply chains, and hampered energy investments, while the war left the world with energy price hikes and energy security challenges. The long-term impacts of these crises on low-carbon energy transitions and mitigation of climate change are still uncertain but are slowly emerging. This paper analyzes the impacts throughout the energy system, including upstream fuel supply, renewable energy investments, demand for energy services, and implications for energy equity, by reviewing recent studies and consulting experts in the field. We find that both crises initially appeared as opportunities for low-carbon energy transitions: the pandemic by showing the extent of lifestyle and behavioral change in a short period and the role of science-based policy advice, and the war by highlighting the need for greater energy diversification and reliance on local, renewable energy sources. However, the early evidence suggests that policymaking worldwide is focused on short-term, seemingly quicker solutions, such as supporting the incumbent energy industry in the post-pandemic era to save the economy and looking for new fossil fuel supply routes for enhancing energy security following the war. As such, the fossil fuel industry may emerge even stronger after these energy crises creating new lock-ins. This implies that the public sentiment against dependency on fossil fuels may end as a lost opportunity to translate into actions toward climate-friendly energy transitions, without ambitious plans for phasing out such fuels altogether. We propose policy recommendations to overcome these challenges toward achieving resilient and sustainable energy systems, mostly driven by energy services.

Solar panel installations have increased significantly in Japan in recent decades. Due to this, world trends, such as clean/renewable energy, are being implemented in power systems all across Japan—particularly installations of photovoltaic (PV) panels in general households. In this work, solar power was estimated using solar radiation data from geographic information system (GIS) technology. The solar power estimation was applied to the actual distribution system model of the Jono area in Kitakyushu city, Japan. In this work, real power consumption data was applied to a real world distribution system model. We studied the impact of high installation rates of solar panels in Japanese residential areas. Additionally, we considered the voltage fluctuations in the distribution system model by assessing the impact of cloud shadows using a novel cloud movement simulation algorithm that uses real world GIS data. The simulation results revealed that the shadow from the cloud movement process directly impacted the solar power generation in residential areas, which caused voltage fluctuations of the overall distribution system. Thus, we advocate distribution system planning with a large number of solar panels.

Large and reliable volumes of water are required to cool thermal power plants. Yet across the world growing demands from society, environmental regulation and climate change impacts are reducing the availability of reliable water supplies. This in turn constrains the capacity and locations of thermal power plants that can be developed. The authors present an integrated and spatially explicit energy systems model that explores optimal capacity expansion planning strategies, taking into account electricity and gas transmission infrastructure and cooling water constraints under climate change. In Great Britain, given the current availability of freshwater, it is estimated that around 32 GW of combined cycle gas turbine capacity can be sustainably and reliably supported by freshwater. However, to maintain the same reliability under a medium climate change scenario, this is halved to 16 GW. The authors also reveal that the current benefit of available freshwater to the power sector is ∼£50 billion between 2010 and 2050. Adapting to expected climate change impacts on the reduced reliability of freshwater resources could add an additional £18–19 billion in system costs to the low-carbon energy transition over the time horizon, as more expensive cooling technologies and locations are required.

This paper presents global scenarios of sulphur dioxide (SO2), nitrogen oxides (NOx), and particulate matter (PM) emissions from road transport through to 2050, taking into account the potential impacts of: (1) the timing of air pollutant emission regulation implementation in developing countries; (2) global CO2 mitigation policy implementation; and (3) vehicle cost assumptions, on study results. This is done by using a global energy system model treating the transport sector in detail. The major conclusions are the following. First, as long as non-developed countries adopt the same vehicle emission standards as in developed countries within a 30-year lag, global emissions of SO2, NOx, and PM from road vehicles decrease substantially over time. Second, light-duty vehicles and heavy-duty trucks make a large and increasing contribution to future global emissions of SO2, NOx, and PM from road vehicles. Third, the timing of air pollutant emission regulation implementation in developing countries has a large impact on future global emissions of SO2, NOx, and PM from road vehicles, whereas there is a possibility that global CO2 mitigation policy implementation has a comparatively small impact on them.

With an increasing expected energy demand and current dominance of coal electrification, India plays a major role in global carbon policies and the future low-carbon transformation. This paper explores three energy pathways for India until 2050 by applying the linear, cost-minimizing, global energy system model (GENeSYS-MOD). The benchmark scenario “limited emissions only” (LEO) is based on ambitious targets set out by the Paris Agreement. A more conservative “business as usual” (BAU) scenario is sketched out along the lines of the New Policies scenario from the International Energy Agency (IEA). On the more ambitious side, we explore the potential implications of supplying the Indian economy entirely with renewable energies with the “100% renewable energy sources” (100% RES) scenario. Overall, our results suggest that a transformation process towards a low-carbon energy system in the power, heat, and transportation sectors until 2050 is technically feasible. Solar power is likely to establish itself as the key energy source by 2050 in all scenarios, given the model’s underlying emission limits and technical parameters. The paper concludes with an analysis of potential social, economic and political barriers to be overcome for the needed Indian low-carbon transformation.

Many countries worldwide have adopted policies to support the expansion of renewable energy sources aimed at reducing greenhouse gas emissions, combating climate change, and, more generally, establishing a globally sustainable energy system. As a result, energy systems around the world are undergoing a process of fundamental change and transformation that goes far beyond the technological dimension. While energy system models have been developed and used for several decades to support decision makers in governments and companies, these models usually focus on the techno-economic dimension, whereas they fall short in addressing and considering behavioural and societal aspects of decisions related to technology acceptance, adoption, and use. In fact, it is often the societal dimension that comes with the greatest challenges and barriers when it comes to making such a socio-technical transformation happen in reality. This paper therefore provides an overview of state-of-the-art energy system models on the one hand and research studying behavioural aspects in the energy sector on the other hand. We find that these are two well-developed fields of research but that they have not yet been integrated sufficiently well to provide answers to the many questions arising in the context of complex socio-technical transformation processes of energy systems. While some promising approaches integrating these two fields can be identified, the total number is very limited. Based on our findings, research gaps and potentials for improvement of both energy system models and behavioural studies are derived. We conclude that a stronger collaboration across disciplines is required.