Driven by the global transition toward carbon neutrality, power systems with high penetration of variable renewable energy are confronted with dual challenges: hourly power imbalance induced by short-term renewable fluctuations, and seasonal energy mismatch caused by inter-month and inter-annual climatic variations. This paper systematically reviews the state-of-the-art multi-timescale risk-aware planning methods, and this review systematically identifies three critical research gaps in current studies. On this basis, it develops an integrated analytical framework embedded with Conditional Value at Risk (CVaR) for quantitative comparative verification. This work aims to elucidate the risk hedging effect of dual-timescale coordination, and provide methodological reference for flexible resource allocation in high-renewable power systems. Existing studies have made substantial advances in single-timescale risk measurement and stochastic planning, and the hybrid-resolution modeling scheme that couples hourly operational simulation with multi-scenario monthly energy balance has been widely acknowledged as an effective approach to strike a balance between simulation fidelity and computational tractability. As a coherent risk measure, CVaR has been extensively applied in short-term dispatch and long-term planning respectively, but systematic research on its nested deployment across dual timescales within a unified optimization framework remains absent. This paper embeds CVaR into both hourly operational constraints and monthly energy balance constraints, realizing the coordinated quantification of short-term load-shedding risk and long-term energy shortage risk. Comparative case studies on the Garver-6 and Modified 14-bus TEP benchmark system validate that the proposed dual-timescale risk planning scheme can effectively optimize the trade-off between system economy and tail risk, significantly improve renewable energy consumption rate, and enhance power supply reliability, with a slight and reasonable increase in levelized cost of electricity.
Electricity systems are experiencing increased penetrations of renewable generation. Because such resources have low, zero, or (e.g., due to policy choices) negative marginal costs, wholesale markets that operate such systems yield an increasing number of time periods with low or negative prices. Contemporaneously, wholesale energy prices tend to remain high during time periods with limited renewable-generation availability. These price dynamics have the potential to embiggen the economic value of energy storage. This paper examines the potential value proposition of energy-storage technologies that can exploit these changing price patterns and dynamics. We focus on the value of thermal energy storage, which is a commercially viable and scalable energy-storage technology, that sees less deployment compared to electro-chemical energy storage. We find that thermal energy storage and electro-chemical energy storage are economically viable. Thermal energy storage has some technical characteristics, including sizing flexibility and synchronous inertia, which may make it preferable under some scenarios. Changing price patterns and dynamics are likely to improve the economic rationale of energy-storage technologies. This includes technologies that are receiving attention, such as electro-chemical energy storage, and other technologies, such as thermal energy storage.
Residential energy analytics has advanced with increasing availability of energy data, e.g., from smart meters, to provide new insights into consumption behaviors and enhance energy efficiency and management. This review synthesizes public datasets, machine learning methods, and applications across two major analytical approaches: behavioral analysis through clustering and load disaggregation using Non-Intrusive Load Monitoring (NILM). We examine how characteristics of residential energy data are used in these two analytical approaches and analyze how data properties, including temporal resolution, population scale, and measurement granularity, relate to reported applications and performance. A structured analysis of 24 major residential energy datasets reveals distinct data utilization patterns across reported applications. Studies demonstrating consumption behavioral analysis through clustering typically utilize population scale datasets with medium temporal resolution for customer segmentation and demand reduction. In contrast, studies achieving effective load disaggregation through NILM often rely on high-frequency measurements for appliance-level disaggregation. These patterns reflect prevailing data collection and usage practices, while both analytical approaches continue to expand through integration of hybrid models and privacy-preserving training. This review integrates methodological development, energy applications, and dataset utilization, providing a comprehensive understanding of residential energy analytics and future research directions. Continued progress is needed to address challenges related to data scarcity, methodological adaptability, and deployment constraints, including advances in privacy-preserving frameworks, such that residential energy efficiency and management can be further enhanced across diverse residential contexts.
In a world increasingly transitioning toward renewable energy this review presents an overview of the solar energy landscape in South Africa highlighting recent developments, emerging trends, and opportunities as well as existing barriers. The central question of this paper is whether solar energy can meaningfully reshape South Africa’s energy mix and play a transformative role in shaping the country’s energy landscape driving the transition from fossil fuels. South Africa’s reliance on coal is unsustainable and presents a huge risk to a sustainable, resilient, and stable energy sector. Renewable energy has the potential to diversify the energy sector in South Africa which remains reliant on fossil fuels. There is urgent need for energy transformation in South Africa as the sector continues to face mounting pressure. South Africa’s energy sector is in an increasingly challenging landscape stemming from chronic energy shortages, load shedding, aging infrastructure, and rising energy demand. Solar energy presents a significant opportunity in South Africa as the country grapples with energy challenges. There has been noteworthy progress to adopt solar energy, particularly in the affluent communities. Solar energy in South Africa has shown increasing visibility, especially in the urban areas, for households and businesses looking to address the energy crisis, which has impacted the availability of energy. Recent loadshedding challenges have spurred the growth of the solar energy sector but there is need to cascade this growth into less affluent areas for more impact and sustainable energy transformation. The barriers that impede the growth of solar energy need to be addressed and while there has been evident policy development in the renewable energy sector in South Africa a greater focus on solar energy specific policies and effective implementation strategies would be beneficial. A sustainable approach to catalysing the adoption of solar energy is crucial to address the energy challenges facing South Africa. Future research should engage more clearly with the factors impeding the adoption of solar energy in communities and assess the awareness, acceptance and understanding of the solar energy sector in lower income communities.
This review summarizes recent studies on large-scale electric vehicle (EV)-charging infrastructure-grid interaction, focusing on charging and discharging load modeling, prosumer incentive mechanisms, and coordinated scheduling for vehicle-to-grid (V2G) interaction. Existing studies have progressed from statistical charging-demand prediction to analyses considering user preferences, bounded rationality, and transportation-power network coupling. Incentive mechanisms have expanded from time-of-use pricing and critical peak pricing to dynamic pricing, market bidding, demand response, and ancillary service participation. Scheduling methods have also developed from local load shifting to hierarchical coordination through aggregators, charging operators, and virtual power plants. Current research has provided an initial framework for coordinated EV-charging infrastructure-grid interaction. However, load modeling remains limited in scenarios, vehicle types, and time scales; incentive mechanisms insufficiently address short-term system changes, grid and charging-station capacity, battery degradation, multi-market coordination, and station-level carbon signals; and hierarchical scheduling still lacks unified cross-level flexibility boundaries. Future research should improve load modeling, incentive design, and coordinated scheduling for large-scale V2G interaction.
This paper examines nuclear policy reversals across the EU-27 since 2022, asking what drives shifts in national positions and how Small Modular Reactors factor into energy strategies. Recent research identifies the Russian invasion of Ukraine as a key catalyst for policy change. East Central European countries are moving most decisively toward nuclear expansion, driven by coal phaseout and energy security. Western and Southern Europe show more mixed trajectories, with some reconsidering phaseouts or exploring SMRs, while others remain opposed. Across regions, rising electricity demand, price pressures, political realignments, and advances in reactor and waste technologies are shaping policy shifts. The EU is not converging on a single nuclear pathway, with East Central Europe advancing deployment. Nuclear energy is increasingly framed as a pragmatic tool for energy security and decarbonization. Future research should assess technology standardization, financing models, and public acceptance as catalysts for long-term deployment.
The development of the Common European Energy Data Space (CEEDS) emerges as a pillar to enable secure, interoperable, trusted, and resilient data exchange among stakeholders of the European energy system. We study the potentials for the CEEDS from an energy infrastructure perspective. We focus on the improvement of operational processes, reducing barriers to entry and to data exchanges, utilising AI-driven analytics, new cross-sectoral processes, and flexibility. Recent innovative projects and solutions implemented, show promising and relevant potential for the implementation of the CEEDS in the energy sector. However, there are still not enough empirical analyses of the impact of real-life energy data space initiatives, which are essential for implementing these large-scale solutions.
The UK parliament recently introduced a cap-and-floor mechanism for net revenues of Long-Duration Energy Storage in Great Britain (GB). The article summarizes learnings from the UK proceedings around four questions: (a) What drives the need for LDES? (b) What are the barriers to LDES deployment in GB? (c) Which options could mitigate these barriers? (d) What are the key design choices for the cap-and-floor mechanism? GB evidence indicates that (1) energy shifting over long timeframes and security-of-supply benefits drive LDES needs; (2) revenue uncertainty is the primary barrier; and (3) a cap-and-floor mechanism could mitigate investment risk. Evidence on LDES needs is extensive, whereas analysis of barriers, their impact on investment, and the effects of alternative policies and cap-and-floor designs remains limited. Further research could address this gap and contribute insights into the interplay of long-term contracts and short-term markets in hybrid electricity markets for deeply decarbonized power systems.
The rapid adoption of Electric Vehicles (EVs) is expected to increase the load on existing power grids due to rising charging demands, but bidirectional energy flow in vehicle-to-grid (V2G) systems allows EVs to function as mobile energy storage units within the energy market. Grid resilience depends on effective EV charging and discharging coordination through price incentives, demand forecasting, load balancing, decentralized control, and adaptive peer-to-peer energy trading – primarily enabled by artificial intelligence. This paper aims to systematically summarize AI approaches for energy management in V2G systems, review their advantages and challenges, analyze existing case studies, and propose strategic recommendations for overcoming identified barriers. Deep learning-based load forecasting and fault detection improve accuracy, scalability, and responsiveness by accounting for weather, social events, and user behavior. Predictive algorithms enable dynamic pricing and coordinated EV charging, optimizing timing, balancing grid load, and preventing overload. Bidirectional energy flow allows peer-to-peer trading via bidirectional chargers, with AI optimizing schedules and pricing for efficient prosumer-consumer matching. AI and blockchain-based federated learning, combined with encryption, privacy systems, and performance monitoring, create a secure feedback loop for continuous improvement. AI also enhances battery longevity and EV performance through state-of-charge prediction and energy optimization, while game theory and multi-agent systems enable decentralized scheduling for smart grid–V2G integration. AI plays a vital role in V2G systems through demand forecasting, load balancing, decentralized EV charging control, and cybersecurity, using techniques like load prediction and fault detection for effective load management. AI-driven dynamic pricing and smart charge/discharge scheduling enable real-time V2G optimization, while multi-agent frameworks support decentralized control, adaptive peer-to-peer energy trading, and SoC estimation. Blockchain and federated learning further strengthen cybersecurity, encouraging greater EV participation in energy management services.
Amidst global efforts to achieve carbon neutrality, the large-scale integration of renewable energy into power grids has highlighted the critical challenge of long-duration power imbalances, stemming from the inherent variability of renewable sources over extended periods and posing significant threats to grid stability. This paper provides a comprehensive review of current research on addressing long-duration balance in renewable-dominated power systems, identifying key challenges in the stochastic long-term fluctuations of renewable energy generation and strategies to mitigate these imbalances. To address these challenges, we propose a comprehensive research framework encompassing four dimensions: modeling and analysis of long-duration uncertainties in renewable energy; development of a unified methodology for modeling Long-Duration Energy Storage (LDES) in power system planning; simulation of power system operations considering long-duration balance; and strategic integration of long-duration flexibility resources into optimization and planning processes. Each of these domains is examined in depth, providing detailed insights into their critical research components. By addressing the key challenges and proposing actionable solutions, this study provides a systematic foundation for advancing long-duration balance in renewable-dominated power systems, contributing to the sustainable and reliable operation of future energy infrastructures.
Contract for Difference (CfD) have been widely adopted across various countries and regions. CfD not only enable market participants to hedge against risks in the electricity spot market but also be used by government to support the development of renewable energy and new technologies, as well as to alleviate many regulatory and external issues such as market power. This review focuses on the mechanism and practice of CfD and the analysis of key issues. Currently, the excess profit recovery mechanism embedded in government authorized CfD and the capital repatriation it generates has played a crucial role in alleviating the surging electricity costs during the electricity price crisis caused by the Russia-Ukraine conflict. Moreover, in the final draft of the European Union’s electricity market reform, government authorized CfD have become one of the mandatory measures to support the development of renewable energy. But CfD exhibit certain drawbacks. Such as traditional CfD often lead to a “produce-and-forget” effect, as it suppresses spot market price signals. Consequently, power plants may prioritize maximizing production rather than responding to market price signals, leading to misaligned incentive. Furthermore, CfD can induce strategic bidding behavior of power generators in the intraday or balancing markets, distorting the intraday and real-time market. This paper introduces the fundamental concepts, applications, key parameter design, and auction processes of CfD. Furthermore, it analyzes the critical issues associated with traditional CfD, aiming to provide valuable references for the future development and construction of CfD.
Dividing the electric grid into the functions of generation, transmission, anddistribution enabled the drawing of jurisdictional lines and the application of the U.S.Constitution’s federalist system to energy regulation. Energy storage technologies,which can be placed throughout the grid to increase flexibility, can provide serviceacross all three of those functions. But the jurisdictional boundaries that have beendrawn around those functions have created barriers that restrict energy storagetechnologies from achieving their full potential. This review analyzes regulatory changes made to reduce barriers to storage deployment and their broader impacts on energy regulation in the U.S. Major energy regulations promulgated at the state and federal levels have generally focused on liberalizing the U.S. electric system through deregulation and increased competition. Paradoxically, however, these efforts have erected strict regulatory barriers that prevent energy storage technologies from providing service across multiple functions. A new wave of regulations in recent years has endeavored to reduce and remove those barriers. Energy regulations adopted in recent years to reduce barriers to energy storage functionality in recent years have had deep and far-reaching impacts on U.S. electric regulation. These impacts go beyond storage and affect all energy technologies. This paper traces the development of energy regulation in U.S., the functional barriers that they created that impede energy storage functionality, recent efforts to remove those barriers, and the broader effects of those efforts. It concludes with a brief discussion of remaining barriers that prevent energy storage from reaching their full potential on the U.S. electric grid.
The Bonneville Power Administration (BPA) provides a unique window into how long-standing public agencies adapt to rapidly evolving governance challenges in the energy transition. This paper traces BPA’s institutional evolution from its New Deal origins to its current role in climate mitigation, market integration, and ecological stewardship, with attention to how policy feedback and institutional layering shape its adaptability. BPA’s historical trajectory demonstrates how agencies can persist through crises not by abandoning their core mandates but by reinterpreting them under new conditions. The Northwest Power Act of 1980 codified this adaptive process, embedding environmental and participatory obligations alongside economic efficiency. More recently, BPA has faced bottlenecks around transmission interconnection, permitting under legacy environmental statutes, and the need to integrate with regional electricity markets. These challenges highlight the institutional contradictions of pursuing decarbonization within frameworks designed for ecological protection and democratic accountability. The paper concludes that BPA shows how established public agencies can evolve under constraint, adapting their practices to meet diverse and competing demands. Its experience underscores the importance of procedural innovation and coalition–building for maintaining legitimacy in governing shared infrastruscture under climate imperatives.
This paper addresses the integration of reserve scarcity pricing into distribution locational marginal prices (DLMPs) by proposing a computationally tractable formulation of reserve deliverability. The primary goal is to understand how network congestion and generation scarcity affect DLMPs and to evaluate the effectiveness of different market design approaches in providing accurate investment signals in distributed energy systems. Recent research has introduced flexibility platforms and models that attempt to integrate distributed energy resources within market operations. This work builds upon the Caramanis model, and introduces an inscribed-boxes formulation that allows for scalable application to meshed networks. The proposed model is shown to be equivalent to existing approaches on radial networks and offers computational tractability. Furthermore, it enables detailed analysis of DLMP pricing patterns under congestion and various energy and reserve flow scenarios. The analysis reveals that accounting for reserve deliverability significantly impacts DLMPs and investment incentives. The findings emphasize that without incorporating network constraints and scarcity pricing, investment signals may be distorted, potentially leading to suboptimal infrastructure placement.
This review examines the emerging Book Claim mechanism as a potential market and accounting framework to accelerate decarbonization in the cement and concrete industries. The paper explores how this approach, when adapted from renewable energy markets, can enable verified emissions reductions from low-carbon concrete production to be transacted independently of physical material delivery. Key questions addressed include: How can Book Claim overcome geographic and economic barriers to low-carbon concrete adoption? What verification, governance, and policy frameworks are needed to ensure its credibility and alignment with international carbon accounting standards? Recent advances in low carbon technologies have demonstrated measurable CO₂ reductions in cement-based materials. Parallel developments such as digital registries, environmental attribute certificates, and government-supported demonstration projects highlight growing policy interest in market-based decarbonization tools. However, the mechanism remains nascent, requiring acceptance from standard setters like ISO, GHGP, and SBTi, as well as harmonization with existing carbon markets and procurement standards. The review finds that Book Claim could complement existing decarbonization pathways by creating verifiable, tradable environmental attributes for low-carbon materials. Its broader success will depend on robust measurement, reporting, and verification (MRV) protocols, transparent registries, and policy recognition across jurisdictions. The mechanism offers a scalable model for connecting innovation in cement manufacturing with global emissions-reduction goals, but further empirical validation and coordinated policy support are essential for its long-term credibility and market uptake.
Integrated Transmission and Distribution (ITD) systems for electric power are complex systems encompassing strongly coupled physical, economic, and legal processes. This review identifies and assesses design strategies for ITD systems that permit this complexity to be systematically addressed. A nine-tiered Design Readiness Level (DRL) classification is used to sort an illustrative collection of recent ITD system design studies into readiness tiers ranging from conceptualization to real-world deployment. Computational platforms are seen to provide key support for traversing the “Valley of Death” tiers separating typical university design research from typical design research carried out at industrial and government facilities. Agent-based co-simulation platforms, enhanced by developments in data-assisted generative artificial intelligence, permit reduced computational complexity, flexible tailoring of model simplifications to purposes at hand, matching of modeled agents to empirical referents, and systematic testing of design aspects that involve coupled physical, economic, and legal processes.
As carbon dioxide removal (CDR) becomes an increasingly important component of net-zero strategies, understanding how scientific collaboration is structured globally is essential. This review applies bibliometric methods to map and analyze co-authorship networks, institutional communities, and thematic structures in CDR-related research from 2015 to 2024, using Web of Science data. The analysis of 9,280 publications from 151 countries shows a fragmented research landscape dominated by national and regional clusters. The EU27, the United States, and China account for most of the global output, yet their bilateral collaborations are often thematically specialized rather than broadly interdisciplinary. Only a small proportion of institutions act as connectors across clusters, limiting cross-regional knowledge exchange. These patterns indicate that, while international collaborations exist, structural fragmentation may constrain the development of shared methodologies and coordinated policy support for CDR. Strengthening bilateral and multilateral partnerships through co-funded initiatives, harmonized monitoring standards, and better alignment between scientific capacity and policy priorities could help bridge gaps.
Carbon Dioxide Removal (CDR) is deemed critical to achieve the climate targets set by the Paris Agreement, underscoring the need for robust and scalable CDR markets. This review explores how CDR has been, and could increasingly be, integrated into existing and emerging carbon markets. Despite growing policy attention, the deployment of CDR, especially permanent forms, remains marginal and challenging to scale within existing carbon pricing and crediting frameworks. Although certification schemes are becoming more tailored to the unique characteristics of different CDR approaches, cost disparities between permanent removal credits and cheaper emissions avoidance credits continue to hinder uptake. In response, several jurisdictions are exploring hybrid policy models that combine market-based mechanisms with public financial support. Additionally, increasing interlinkages between carbon market mechanisms are expected to facilitate more effective and widespread integration of CDR over time. The report provides an overview of the role of CDR across three types of carbon market mechanisms: voluntary, quasi-compliance, and compliance-based systems. It highlights the defining features and interconnections among these markets, and assesses how each framework contributes to the deployment of removals, in particular novel CDR. While voluntary markets serve as innovation hubs for CDR, their limited scalability and voluntary nature highlight the importance of compliance markets and targeted policy support for permanent CDR. Those quasi-compliance and compliance markets, including mechanisms under Article 6 of the Paris Agreement and Emissions Trading Systems (ETS) like UK and EU ETS, are beginning to incorporate CDR more explicitly. This signals a shift toward broader carbon market integration and geographical coverage, requiring robust accounting and certification procedures for both emission reduction and removal projects.
Utilities and regulators must weigh the benefits of electricity resilience projects against their costs, which would be passed on to customers. Cost-benefit analysis (CBA) is an appropriate method for assessing this tradeoff. In this paper we review the literature on CBA of electricity resilience projects and analyze gaps in the available methods and tools. The costs of resilience projects are typically straightforward to estimate but their benefits — particularly the avoided costs of power interruptions — are complex to quantify and monetize. A common perception among practitioners is that current CBA tools are not sufficiently mature to be adopted into real-world practice. We propose an electricity resilience CBA framework consisting of several elements: risks, physical impacts, power interruptions, economic impacts, and resilience projects. While methods for some elements are well-developed, there is a need for novel approaches to value avoided power interruptions, integrate multiple risks and benefit streams, and incorporate uncertainty.
This review aims to systematically synthesize recent developments in artificial intelligence (AI)-driven power system planning under the low-carbon transition. It focuses on how AI methodologies address increasing system complexity and uncertainty by improving scenario generation, model optimization, and decision support transparency. Recent advances demonstrate that deep learning, reinforcement learning, and generative models significantly enhance input accuracy and optimization efficiency for large-scale, non-convex planning problems. Explainable AI techniques, such as SHAP and LIME, have been increasingly integrated to improve the interpretability and credibility of planning outcomes. However, challenges persist in ensuring physical consistency in scenario generation, modelling realistic multi-agent interactions, and developing trustworthy AI frameworks. AI technologies are reshaping power system planning by offering intelligent, robust, and sustainable solutions aligned with high renewable penetration and evolving market dynamics. Future research must address key gaps in physical feasibility, behavioural realism, and explainability to fully leverage AI’s potential for supporting low-carbon power system transitions.
