Denes Csala

The global shift towards cleaner energy and sustainable mobility, motivated by international climate commitments such as the Paris Agreement, has accelerated the adoption of electric vehicles (EVs) and renewable energy sources (RESs). In this chapter, a comprehensive review of the key challenges is presented, including enabling technologies and optimisation approaches involved in jointly deploying electric vehicle charging stations (EVCSs) and renewable generation within active distribution networks. Its novelty lies in presenting a framework that integrates technical, economic, environmental, and regulatory perspectives, while formulating a taxonomy that links energy management, optimisation, and policy aspects into a unified planning framework. The review identifies four main categories of barriers: technical, economic and infrastructural, environmental, and planning or regulatory. It also explores a variety of EV charging strategies, such as vehicle-to-grid, grid-to-vehicle (G2V), inductive wireless charging, battery swapping, and smart charging, to assess their influence on load profiles and grid stability. To enable reliable and flexible integration of EVCSs and RESs, advanced energy management frameworks are examined, including demand response strategies, aggregator-based distributed control, and hierarchical control architectures. Furthermore, the chapter highlights the importance of multi-objective optimisation in managing trade-offs among competing goals, such as cost efficiency, carbon reduction, voltage stability, and social acceptance. It reviews a range of decision-support tools for optimal siting, sizing, and operation of EVCS and RES infrastructure. Overall, this work provides a well-structured synthesis of current research and practical developments, offering valuable insights for policymakers, utilities, and researchers to inform future energy planning, investment decisions, and policy formulation.

Environmental concerns and the Paris agreements have prompted intensive efforts towards greener and more sustainable transportation. Persistent expansion of electric vehicles (EV) in the transportation sector requires electric vehicle charging stations (EVCSs) to accommodate the increased demand. Offsetting demand and alleviating the resultant electrical grid stress necessitates establishing grid-integrated renewable energy sources (RESs) where these sustainable strategies are accompanied by variable-weather-related obstacles, such as voltage fluctuations, grid instability, and increased energy losses. Strategic positioning of EVCSs and RES as distributed generation (DG) units is crucial for addressing technical issues. While technical constraints have received considerable attention, there is still a gap in the literature with respect to incorporating the additional complex optimization problems and decision-making processes associated with economic viability, social acceptance, and environmental impact. A possible solution is the incorporation of an appropriate multi-criteria decision analysis (MCDA) approach for feasible trade-off solutions. Such methods offer promising possibilities that can ease decision-making and facilitate sustainable solutions. In this context, this paper presents a review of published approaches for optimizing the allocation of renewable energy DG units and EVCSs in active distribution networks (ADNs). Promising published optimization approaches for the strategic allocation of multiple DG units and EVCSs in ADNs have been analyzed and compared.

Industries today are tightly interconnected, necessitating a systematic perspective in understanding the complexity of relations. Employing network science, the literature constructs dense production networks to address this challenge. However, handling this high density involves carefully choosing the level of pruning to retain as much information as possible. Yet, current research lacks comprehensive insight into the extent of distortion the data removal produces in the network structure. Our paper aims to examine how this widespread thresholding method changes the production network’s topology. We do this by studying the network topology and centrality metrics under various thresholds on inter-industry networks derived from the US input-output accounts. We find that altering even minor threshold values significantly reshapes the network’s structure. Core industries serving as hubs are also affected. Hence, research using the production network framework to explain the propagation of local shocks and disturbances should also take into account that even low-value monetary transactions contribute to the interrelatedness and complexity of production networks.

This Special Issue focuses on proposing and analyzing systemic interdisciplinary approaches to support collaborative strategies and agreed-upon global sustainability policies toward addressing the challenges that lie ahead for our planet’s future. The contributions target applications in system dynamics, systems thinking, discrete event simulation, agent-based modelling, and hybrid approaches and provide valuable qualitative and quantitative insights to guide the collaborative efforts of governments, institutions, organizations in general, and even the financial sector toward the next Conference of Parties (COP28).

Climate change necessitates a global effort to reduce greenhouse gas emissions while adapting to increased climate risks. This broader climate transition will involve large-scale global interventions including renewable energy deployment, coastal protection and retreat, and enhanced space cooling, all of which will result in CO ₂ emissions from energy and materials use. Yet, the magnitude of the emissions embedded in these interventions remains unconstrained, opening the potential for underaccounting of emissions and conflicts or synergies between mitigation and adaptation goals. Here, we use a suite of models to estimate the CO ₂ emissions embedded in the broader climate transition. For a gradual decarbonization pathway limiting warming to 2 °C, selected adaptation-related interventions will emit ∼1.3 GtCO ₂ through 2100, while emissions from energy used to deploy renewable capacity are much larger at ∼95 GtCO ₂ . Together, these emissions are equivalent to over 2 y of current global emissions and 8.3% of the remaining carbon budget for 2 °C. Total embedded transition emissions are reduced by ∼80% to 21.2 GtCO ₂ under a rapid pathway limiting warming to 1.5 °C. However, they roughly double to 185 GtCO ₂ under a delayed pathway consistent with current policies (2.7 °C warming by 2100), mainly because a slower transition relies more on fossil fuel energy. Our results provide a holistic assessment of carbon emissions from the transition itself and suggest that these emissions can be minimized through more ambitious energy decarbonization. We argue that the emissions from mitigation, but likely much less so from adaptation, are of sufficient magnitude to merit greater consideration in climate science and policy.

Time series modeling is an effective approach for studying and analyzing the future performance of the power sector based on historical data. This study proposes a forecasting framework that applies a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast the long-term performance of the electricity sector (electricity consumption, generation, peak load, and installed capacity). In this study, the model was used to forecast the aforementioned factors in Saudi Arabia for 30 years from 2021 to 2050. The historical data that were inputted into the model were collected from Saudi Arabia at quarterly intervals across a 40-year period (1980−2020). The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, which helps reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. The experimental findings indicated that the SARIMAX model has promising performance in terms of categorization and consideration, as it has significantly improved forecasting accuracy compared with the simpler autoregressive integrated moving average-based techniques. Furthermore, the model is capable of coping with different-sized sequential datasets. Finally, the model aims to help address the issue of a lack of future planning and analyses of power performance and intermittency, and it provides a reliable forecasting technique, which is a prerequisite for modern energy systems.

Climate change necessitates an immediate and sustained global effort to reduce greenhouse gas emissions while adapting to the increased climate risks caused by historical emissions. This broader climate transition will involve mass global interventions including renewable energy deployment, coastal protection and retreat, and enhanced space cooling, which will result in CO2 emissions from energy and materials use. Yet, the magnitude of these emissions remains largely unconstrained, leaving open the potential for under-accounting of emissions and conflicts or synergies between mitigation and adaptation goals. Here, we use a suite of models to estimate the CO2 emissions embedded in the broader climate transition. For a pathway limiting warming to 2°C, we estimate that selected adaptations will emit ~1.5GtCO2 through 2100. Emissions from energy used to deploy renewable capacity are much larger at ~95GtCO2, equivalent to over two years of current global emissions and ~8% of the remaining carbon budget for 2°C. These embedded transition emissions are reduced by 80% to 20GtCO2 under a rapid decarbonization scenario limiting warming to 1.5°C. However, they roughly double to 185GtCO2 under a low-ambition transition consistent with current policies (2.7°C warming by 2100), mainly because a slower transition relies more on fossil fuels. Under this status-quo, the emissions embedded in the transition total nearly half the remaining carbon budget for 1.5°C. Our results provide the first holistic assessment of the carbon emissions embedded in the transition itself, and suggest that these emissions can be largely minimized through rapid energy decarbonization, an underappreciated benefit of enhanced climate ambition.  

The rapid growth of wind and solar energy penetration has created critical issues, such as fluctuation, uncertainty, and intermittence, that influence the power system stability, grid operation, and the balance of the power supply. Improving the reliability and accuracy of wind and solar energy predictions can enhance the power system stability. This study aims to contribute to the issues of wind and solar energy fluctuation and intermittence by proposing a high-quality prediction model based on neural networks (NNs). The most efficient technology for analyzing the future performance of wind speed and solar irradiance is recurrent neural networks (RNNs). Bidirectional RNNs (BRNNs) have the advantages of manipulating the information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BRNN’s output layer concurrently receives information from both the backward layers and the forward layers. The bidirectional long short-term memory (BI-LSTM) prediction model was designed to predict wind speed, solar irradiance, and ambient temperature for the next 169 h. The solar irradiance data include global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). The historical data collected from Dumat al-Jandal City covers the period from 1 January 1985 to 26 June 2021, as hourly intervals. The findings demonstrate that the BI-LSTM model has promising performance in terms of evaluation, with considerable accuracy for all five types of historical data, particularly for wind speed and ambient temperature values. The model can handle different sizes of sequential data and generates low error metrics.

Wind energy generation fluctuations and intermittency issues create inefficiency and instability in power management. The recurrent neural networks (RNNs) prediction approaches are an essential technology that can improve wind power generation and assist in energy management and power systems’ performance. In this paper, a prediction model based on Gated Recurrent Unit (GRU) neural networks is proposed to predict wind speed and temperature values one week ahead in the future at hourly intervals. The GRU prediction model automatically learnt the features, used fewer training parameters, and required a shorter time to train compared to other types of RNNs. The GRU model was designed to predict 169 hours ahead as a short-term period of wind speed and temperature values based on 36 years of hourly historical data (1 January 1985 to 6 June 2021) collected from Dumat al-Jandal city. The findings notably indicate that the GRU model has promising performance with significant prediction accuracies in terms of overfitting, reliability, resolution, efficiency, and generalizable processes. The GRU model is characterized by its good performance and influential evaluation error metrics for wind speed and temperature values.