Contributions toward net-zero carbon in the water sector: application to a case study

Microgrids (MGs) are emerging as pivotal solutions for addressing the growing global demand for reliable, decentralized energy systems, particularly given the variability of renewable energy sources (RESs) and integration challenges. This study critically reviews advancements in MG modeling, control dynamics, and optimization strategies, with a specific focus on dual-mode operations encompassing grid-connected and islanded systems. The primary objective is to identify key trends, challenges, and research gaps while proposing pathways to enhance MG performance and scalability. This research employs a systematic review methodology, analyzing 140 peer-reviewed studies to extract 81 insights on MG system stability, renewable integration, and emerging technologies such as artificial intelligence (AI) and blockchain. These studies were examined through comparative and contrastive evaluations, categorizing them based on MG control techniques, modeling tools, and technological innovations. The analysis focused on key performance metrics, stability solutions, and emerging trends, synthesizing findings to highlight advancements and research gaps. Key results indicate that 75% of reviewed studies emphasize control techniques for RES stability, while 90% utilize tools like MATLAB Simulink for MG modeling. Dual-mode systems demonstrate significant potential for enhanced energy reliability through advanced control strategies and hybrid RES configurations. Furthermore, the study underscores the need for standardization, scalability, and real-world validation in MG research while highlighting the implications of AI-based optimization, advanced energy storage, and smart grid integration. By addressing gaps in dual-mode operations and economic feasibility, this study lays a foundation for future research to develop robust, efficient, and adaptable MG systems, driving efforts toward resilient, decentralized energy infrastructure.

The integration of Variable Renewable Energy (VRE) sources in power systems is increased for a sustainable environment. However, due to the intermittent nature of VRE sources, formulating efficient economic dispatching strategies becomes challenging. This systematic review aims to elucidate the economic value creation of Artificial Intelligence (AI) in supporting the integration of VRE sources into power systems by reviewing the role of AI in mitigating costs related to balancing, profile, and grid with a focus on its applications for generation and demand forecasting, market design, demand response, storage solutions, power quality enhancement, and predictive maintenance. The proposed study evaluates the AI potential in economic efficiency and operational reliability improvement by analyzing the use cases with various Renewable Energy Resources (RERs), including wind, solar, geothermal, hydro, ocean, bioenergy, hydrogen, and hybrid systems. Furthermore, the study also highlights the development and limitations of AI-driven approaches in renewable energy sector. The findings of this review aim to highlight AI’s critical role in optimizing VRE integration, ultimately informing policymakers, researchers, and industry stakeholders about the potential of AI for an economically sustainable and resilient energy infrastructure.

The integration of Large Language Models (LLMs) within renewable energy systems presents an innovative approach to optimizing energy efficiency, enhancing sustainability, and improving operational performance (Bai, J., Wang, Y., Chen, Y., et. al. 2021). Despite their potential, a clear methodology for evaluating the success of LLM implementations remains underdeveloped. This paper introduces a structured framework for evaluating Key Performance Indicators (KPIs) tailored to LLM applications in the renewable energy sector. The framework systematically addresses the assessment of LLM-driven improvements in energy forecasting accuracy, grid management, predictive maintenance, and resource optimization (Dasgupta, I., Lampinen, A. K., et. al. 2022). Critical KPIs include reductions in energy consumption during LLM training and inference, the accuracy of energy demand predictions, the optimization of renewable energy resource utilization, and the minimization of carbon footprints (Piantadosi, S. 2023). By establishing this framework, the paper provides a robust tool for measuring the impact of LLM technologies on both operational efficiency and sustainability outcomes. The study’s findings offer valuable insights for policymakers, researchers, and industry stakeholders to guide the responsible and effective integration of AI-driven solutions in renewable energy infrastructures.

ABSTRACTThis research proposes a novel spatio‐temporal approach that integrates convolutional long short‐term memory (ConvLSTM) networks and graph neural networks (GNNs) to model and predict wind power generation and its impact on power flow. The methodology uniquely combines ConvLSTM for capturing wind generation dynamics with GNN‐based power flow analysis, offering a unified framework for renewable energy grid integration. Testing on IEEE standard systems (14‐300 bus) demonstrates the approach's scalability and computational efficiency, achieving up to 11x faster computation compared to traditional Newton–Raphson methods. Applied to wind generation scenarios in the Norwegian grid, the ConvLSTM model achieves an R value of 0.977 in forecasting wind generation dynamics, while the GNN model demonstrates robust power flow prediction capabilities with an R of 0.948. This scenario‐based framework bridges wind prediction and power flow analysis, enabling efficient grid performance assessment under varying wind conditions, while offering improved computational efficiency for real‐time renewable energy integration and grid management.

In light of current energy policies responding to rapid climate change, much attention has been directed to developing feasible approaches for transitioning energy production from fossil-based resources to renewable energy. Although existing studies analyze regional dispatch of renewable energy sources and capacity planning, they do not fully explore the impacts of the energy storage system technology's technical and economic characteristics on renewable energy integration and energy transition, and the importance of energy storage systems to the energy transition is currently ignored. To fill this gap, we propose an integrated optimal power flow and multiple criteria decision-making model to minimize system cost under operational constraints and evaluate the operational performance of renewable energy technologies with multidimensional criteria. The proposed method can identify the most critical features of energy storage system technologies to enhance renewable energy integration and achieve New York State's climate goals from 2025 to 2040. We discover that lead-acid battery requires an additional 38.66 GW capacity of renewable energy sources than lithium-ion battery to achieve the zero carbon dioxide emissions condition. Based on the cross-sensitivity analysis in the multidimensional evaluation, the vanadium redox flow battery performs the best, and the nickel-cadmium battery performs the worst when reaching the zero carbon dioxide emissions target in 2040. The results of the proposed model can also be conveniently generalized to select ESS technology based on the criteria preferences from RE integration and energy transition studies and serve as a reference for ESS configurations in future energy and power system planning.

A flexible coupling of power and heat sectors can contribute to both renewable energy integration and decarbonization. We present a literature review of model-based analyses in this field, focusing on residential heating. We compare geographical and temporal research scopes and identify state-of-the-art analytical model formulations, particularly considering heat pumps and thermal storage. While numerical findings are idiosyncratic to specific assumptions, a synthesis of results indicates that power-to-heat technologies can cost-effectively contribute to fossil fuel substitution, renewable integration, and decarbonization. Heat pumps and passive thermal storage emerge as particularly favorable options.

Little time left to reverse emissions—Growing hope despite disappointing CO₂ trend

This meta-analysis presents a comprehensive evaluation of the implementation, performance outcomes, and sectoral maturity of digital twin (DT) technology across two critical utility infrastructure domains: electricity and water. Drawing on 122 peer-reviewed empirical studies published between 2010 and 2024—with a cumulative citation count exceeding 10,000—the study assesses the efficacy of DTs in enhancing operational performance, reducing system downtime, improving cost efficiency, and bolstering infrastructure resilience. The findings indicate that the electricity sector demonstrates the highest degree of DT maturity, characterized by widespread adoption in smart grid optimization, real-time asset monitoring, fault prediction, and renewable energy integration. In contrast, the water sector, while moderately advanced, has achieved significant progress through the deployment of digital twins in hydraulic simulation, leak detection, stormwater forecasting, and wastewater treatment automation. Quantitative evidence reveals average downtime reductions ranging from 15% to 45%, alongside cost savings of up to 30% through predictive maintenance and optimized energy and resource use. Regional benchmarking highlights Europe and Asia as leaders in digital twin innovation, supported by robust regulatory frameworks, significant investment in smart infrastructure, and advanced ICT ecosystems. In contrast, utilities in emerging economies continue to face constraints related to legacy infrastructure, limited digital readiness, and fragmented policy environments. The study also identifies emerging opportunities shaping the next generation of DT deployment, including the integration of artificial intelligence for autonomous operational control, cloud-based Digital Twin-as-a-Service (DTaaS) platforms for scalable adoption, and strategic alignment with sustainability initiatives such as climate resilience, net-zero emissions, and smart city governance. Despite these advancements, persistent challenges remain in achieving data interoperability, ensuring cybersecurity resilience, and facilitating equitable access to DT solutions across regions. This meta-analysis not only consolidates the empirical knowledge base but also provides a forward-looking roadmap for enhancing the role of digital twins as a transformative technology in sustainable utility infrastructure management.

This study investigates resource adequacy and renewable energy integration in the United States, European Union, and Pakistan amid global energy market liberalization and greenhouse gas reduction efforts. It explores how these regions are adapting to the surge in renewable sources like wind and solar, which, despite their financial and environmental benefits, challenge resource adequacy and the economic viability of traditional energy sources. In the US and EU, significant improvements have been introduced in wholesale electricity markets and capacity accreditation mechanisms, which enhanced the large-scale deployment of renewables. This shift has prompted a reevaluation of resource adequacy, leading to the increased deployment of battery storage and demand response. Presently, gas-based generation is largely upholding resource adequacy; however, future trends indicate a move towards greater consumer participation, energy efficiency, and utility-scale storage, with a decline in fossil fuel use. Pakistan aims to adopt a liberalized market structure by balancing competitive markets with legacy contracts. Public pressure is driving a shift from costly fossil-based generation to renewables. Similarly, a trend in the rise of behind-the-meter solar generation can be witnessed. In the future, Pakistan may also experience resource adequacy challenges. It will likely need to implement battery storage, demand response, and modern capacity accreditation tools, by drawing lessons from developed markets.

With the fuel prices going up and many states mandating use of more renewable energy, a number of utilities are forced to convert some of their base loaded units to cycling operation. This change in operation requires a departure from the standard maintenance practices established for a given unit. This includes changes to Preventative Maintenance (PM), Predictive Maintenance (PdM), Planning and Scheduling and Key Performance Indicators (KPIs). When a unit is cycled — either minimum load to maximum load or two shift operation — it goes through stress cycles and its expected life decreases relative to the severity of cycling. When a decision is made to cycle a base loaded unit, the impact of the cycling has to be analyzed and the PM and PdM procedures need to be modified in order to maintain the expected life of the components. Cycling affects different components to different degrees and appropriate inspection and maintenance schedules need to be developed. The Key Performance Indicators (KPIs) have to be modified to monitor the effectiveness of the inspections and maintenance performed on the equipment. For example, Maintenance Basis Violation (MBV) is an important KPI for a cycling unit. Similarly, more attention has to be paid to the Planning and Scheduling activities as there are many uncertainties in the availability of the unit for preventative maintenance. The paradigm of performing PMs on a time basis should be changed to a throughput or hours of operation basis. This paper reviews the impact and severity of different cycling modes on a unit, vulnerability of common components adapting to the new mode, and discusses — in general terms — the required changes which need to be made in the inspection and maintenance practices. Also the paper reviews various KPIs that can be put in place for monitoring the impact of these procedures.

Insular grids of non-interconnected islands are heavily dependent on oil-fired generators which have high fuel costs and greenhouse gas emissions. Hence, share of renewables, like solar, are being increased in insular grids, as a cheaper and cleaner alternative. In this work, a unit commitment model has been proposed for island grids with a growing renewable share. Firstly, reserve constraints for dealing with uncertainty produced by renewables, like solar, are proposed. Secondly, a road map for analyzing the situation arising from increasing renewable penetration is proposed. The same is implemented on the insular grid of Andaman and Nicobar Islands, in order to understand the complexity associated with the inclusion of dynamic renewable sources alongside conventional generators of the island. Results demonstrate that at this current stage of technology, integration of renewables with insular grids is going to be a far more challenging affair wherein the inclusion of solar after a certain point, would result in system reliability aspect outweighing the financial and environmental benefits therein.KeywordsInsular gridOptimal generation schedulingRenewablesDiesel generatorsSolar power plant

Meet the Editors

Electric power has become an essential part of daily life: we plug our electronic devices in, switch our lights on, and expect to have power. As the availability of power is usually taken for granted in modern societies, we mostly feel annoyed at its absence and perceive the importance of power during outages which have severe effects on the public order. Blackouts have had disastrous consequences for many countries and they continue to occur frequently. Such examples demonstrate the necessity for careful analysis and planning of power grids, to ultimately increase the reliability of power grids. The power grids have evolved due to economic, environmental and human-caused factors. In addition to the contingency analysis, nowadays, the operation and planning of power grids are facing many other challenges (such as demand growth, targeted attacks, cascading failures, and renewable energy integration). Thus, many questions arise, including: which buses (nodes) to connect with a new line (link)? What are the impacts of malicious attacks on power grids? How may an initial failure result in a cascade of failures? How to prepare for the integration of renewable energy? Answering such questions requires developing new concepts and tools for analysing and planning of power grids. Power grids are one of the largest and the most complex man-made systems on earth. The complex nature of power grids and its underlying structure make it possible to analyse power grids relying on network science. The applications of network science on power grids have shown the promising potential to capture the interdependencies between components and to understand the collective emergent behaviour of complex power grids. This thesis is motivated by the increasing need of reliable power grids and the merits of network science on the investigation of power grids. In this context, relying on network science, we model and analyse the power grid and its near-future challenges in terms of line removals/additions, malicious attacks, cascading failures, and renewable integration.

The research focuses on the multiple ways in which technological innovation in the management, governance, and security of renewable energies contributes to making smart cities capable of integrating renewable energy systems that lead to more sustainable, efficient governance, and security resilience for the cities. A literature review of Scopus has been done with the aim of asserting at the juncture of smart cities, technological innovation, and renewable energy integration. Co-occurring and co-authorship analyses have been performed by VOSviewer for mapping the key research themes, collaborative networks, and research gaps. Core research themes include smart cities, IoT, renewable sources of energy, urban planning, AI, big data - lots of them, actually. Key areas for concern comprise cybersecurity, social equity, human-centered design, and adaptation to climate change. International collaborations identified through co-authorship analysis mirrored strong contributions from Pandolfi Alessandra and Galiulo Valentina. There are major knowledge gaps with respect to addressing, for example, how combining the elements of energy efficiency and cybersecurity under one umbrella could be defined as a smart city. Contribute uniquely in this area, and analyze the effects that the aggregate technological innovations and integration of renewable energy have on enhancing urban sustainability, governance, and security. These advances indicate ways in which urban systems can be optimized in order to enhance quality of life and lend to more sustainable and resilient urban environments. This study synthesizes interdisciplinary research in a novel manner in pointing out new areas that smart city development has barely touched upon.

Pursuing a net-zero carbon future for all: Challenges for commercial real estate