Electricity markets with growing shares of variable renewable energy face an increasingly acute challenge in balancing supply and demand. While demand-side flexibility has traditionally been framed in terms of demand reduction or shifting away from peak periods, these approaches need to be revisited to address periods of surplus renewable generation. This perspective paper conceptualises demand turn-up (i.e. mechanisms designed to raise electricity demand at specific moments or locations) as an economically grounded form of flexibility. It situates demand turn-up within the broader economics literature on flexibility, adjustment costs, and uncertainty, showing that flexibility has historically been treated as a supply-side attribute, with demand assumed to respond passively to prices. It then draws on evidence from other sectors, including capital-intensive manufacturing, waste and recycling markets, and shipping, where suppliers pay consumers to absorb surplus output. These cases share common structural features: inflexible supply, high shutdown or disposal costs relative to marginal operating costs, limited storage, and strong network effects. The paper argues that demand turn-up can be framed as a form of recurring economic response to surplus under structural rigidity. Applying this logic to electricity systems with high variable renewable electricity penetration, the paper suggests that explicitly incorporating demand turn-up into market design can improve the marginal utilisation of renewable assets.

This study investigates the role of artificial intelligence (AI) tokens in dynamic interactions, diversification, and hedging capabilities, in relation to non-fungible tokens (NFTs), decentralised finance (DeFi) tokens, and renewable energy assets. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, we examine return, volatility, and higher-order spillovers across both time and frequency domains. The results show that NFTs serve as persistent channels for the transmission of return and volatility shocks, driven by their speculative nature. AI and renewable tokens primarily absorb systemic risk due to their lower liquidity and niche adoption. DeFi tokens play flexible roles, shifting between transmitters and receivers across market regimes. The results demonstrate asset-specific idiosyncrasies and that volatility spillovers are generally stronger than return spillovers. Frequency-domain analysis highlights that digital tokens dominate short-term spillovers, while renewable assets absorb shocks across horizons. However, higher-order moment results reveal that extreme risk linkages shift transmission channels. Our results also confirm that oil market (OVX) shocks drive short-term return connectedness, CBOE volatility (VIX) volatility, and policy uncertainty (EPU) significantly impact return linkages. The results of our portfolio analysis show that AI tokens form the core of diversification, NFTs provide short-term speculative hedging, and renewable assets, particularly solar-linked tokens, act as low-cost stabilisers, underscoring the need for active rebalancing under different market regimes. These findings provide meaningful implications for policymakers, regulators, and portfolio managers for strengthening systemic risk oversight and considering asset-specific idiosyncrasies in investment strategies.

Plastics have emerged as a significant pollutant, posing a serious threat to the sustainability of the soil ecosystem and food security because of their long-term persistence, resilience, and robustness under different environmental conditions. The present investigation explored the impact of different doses of polypropylene (PP) on lentil plants and attenuation of the adverse impacts of PP by the application of pineapple fruit peel biochar (PBC). Lentil (Lens culinaris) plants exposed to PP treatment reduced morphological traits and relative water contents, reflecting photosynthetic injuries, a rise in lipid peroxidation, and electrolyte leakage. Utilization of PBC derived from waste biomass enhanced the growth attributes of lentils and alleviated PP-incited oxidative stress impacts. Polypropylene stress enhanced oxidative stress and increased enzymatic and non-enzymatic antioxidant variables in lentil plants. Antioxidant enzymes superoxide dismutase, catalase, ascorbate peroxidase, glutathione reductase, and glyoxalase enzymes were markedly upregulated in lentil after PBC amendment in PP3-treated soil. There was a significant reduction in methylglyoxal content by the activities of glyoxylase enzymes, minimizing the negative impacts of PP. Therefore, soil amendment with PBC protected lentil plants from PP-instigated oxidative disruption by modulating activities of antioxidant defense and glyoxalase system. Production of PBC from biomass wastes results in a safe, cost-effective, and ecofriendly material that can be used at the industrial level for the cultivation of crops in PP-contaminated soil. The novelty of the present research lies in promoting soil management practices and fostering our understanding of waste materials reutilization as renewable assets to combat the ecological implications of plastic pollution, and it emphasizes the treatment of plastic wastes with other waste materials and their practical applications to overcome plastic pollution.

Abstract In the industrial sector, optimizing energy consumption is a critical priority, yet the deployment of effective monitoring systems is often hindered by the need for extensive, last-mile customization. This paper addresses this challenge by presenting a flexible and modular architectural blueprint for an energy submetering system based on IoT devices. The proposed architecture is built on three core principles: the use of non-invasive and plug-and-play sensing technologies, a lightweight and adaptable data collection agent, and a high-performance time-series data store. This modular design allows for incremental deployment, starting small and scaling rapidly without significant operational disruption. We validate the effectiveness of this architecture through a real-world case study in a manufacturing facility, where we monitored five production departments for three months. The analysis of the collected data, visualized as 24-hour heatmaps, immediately revealed key operational patterns, including shift schedules, breaks, and overnight anomalies. Furthermore, we outline several high-impact use cases enabled by this granular data, including energy-aware job scheduling, data-driven sizing of renewable assets, and predictive maintenance. This paper provides a practical guide for Energy Managers, Plant Operators, and industrial decision-makers seeking to implement scalable and cost-effective energy management solutions.

Predictive maintenance (PdM), supported by artificial intelligence (AI) and digital twin methods, is gaining attention as a practical and cost-efficient way to manage power generation assets. In the renewable energy sector, where performance, stability, and cost control are central concerns, PdM enables operators to anticipate equipment faults, schedule interventions more effectively, and reduce unplanned downtime. This paper reviews how such approaches are being applied in four different national contexts: China, Germany, Norway, and the Netherlands, and considers their contribution to cleaner and more reliable energy systems. The discussion highlights several patterns that emerge across these countries. In China, the rapid expansion of wind and solar capacity has driven the use of PdM to improve fault detection and optimize turbine and panel performance. Germany demonstrates how PdM can be integrated into broader energy transition policies, using digital twins and AI to balance fluctuating renewable output with grid demands. Norway shows the value of predictive tools in extending the life and efficiency of hydropower equipment, while the Netherlands illustrates the benefits of PdM in offshore wind projects, where remote monitoring and early fault recognition are critical. Evidence from these cases points to three consistent outcomes: improved uptime of renewable assets, measurable reductions in maintenance costs, and smoother integration of intermittent power sources through more advanced grid management. Taken together, these findings suggest that PdM is not only a set of technical tools but also a strategic component in building sustainable, resilient, and economically viable energy systems. Its wider adoption may help accelerate the transition toward low-carbon power on a global scale.

The continuously degrading state of the environment due to increasing fossil fuel consumption demands a quick overturn in the resource consumption patterns i.e., shift from non-renewable assets like fossil fuels to renewable assets. The present review focuses on this theme and evaluates the trends of fossil fuel consumption, carbon dioxide emission, temperature fluctuations, renewable energy transitions, and efforts to reduce climate change with a special emphasis on India. Data-driven trend analysis provides useful insights into the consumption patterns, environmental trends, and energy transition analysis, which can help in strategic planning and development of policies for sustainable growth. The Industrial Revolution has significantly contributed to climate change, primarily through the combustion of fossil fuels such as coal, oil, and natural gas for energy production. As a result, global temperatures have risen. If the current trajectory continues, average global temperatures may exceed pre-industrial levels by more than 1.5°C within the next two decades according to IPCC. To counter this, transitioning from fossil fuels to renewable energy is critical, particularly in the power sector, which is among the largest contributors to CO₂ emissions worldwide. India is one of the leading emitters of CO2 worldwide contributing 8.2% of the global emissions. To reduce its carbon footprint, India has developed around 179.6 GW of capacity from renewable energy sector. As per 2021 Nationally Determined Contributions (NDC), India has committed to fulfill nearly 50% of its electrical needs from non-fossil fuel-based energy resources by 2030. The Indian government is actively promoting renewable energy through various policies, incentives, and subsidies aimed at scaling up clean energy technologies. This review highlights these pressing concerns and emphasizes the importance of coordinated action by policymakers, industries, and civil society to mitigate climate change and secure a healthier environment for future generations. GRAPHICAL ABSTRACT

Opportunities and problems are presented by the increasing use of renewable energy in the UK power grid. Renewable energy sources such as wind, solar and others are important to achieve the net-zero target of the UK, but their unpredictability causes issues with grid stability, storage management and intermittency. An increasing number of people believe that Artificial Intelligence (AI) can help solve these problems by improving energy storage systems, increasing renewable production and adapting grid operations. Through a secondary investigation into scholars' functions, official documents and studies of professional cases, the study seriously examines how Artificial Intelligence is changing the renewable power market in the United Kingdom. Results show how an AI system can increase dependence and efficiency, especially in areas of advanced forecasting, smart grid optimisation, future maintenance and storage management. The analysis indicates that AI applications directly tackle core renewable energy integration problems, yielding significant optimization: 90 – 95% increase in forecasting accuracy for wind and solar power generation; driving maintenance planning and lowering operating costs by up to 30%, while increasing accessibility to equipment by 20%; thus enhancing the profitability of renewable assets, grid resilience and efficiency. However, there are still issues with data security, regulator cohesion and openness. According to the findings of the study, Artificial Intelligence (AI) makes a great promise to speed up the UK's switch to sustainable power, but more empirical research and better rules are required.

Despite recent improvements to electricity access in lower-income countries, reliability remains low for many. Local renewable electricity infrastructure supplementing the national grid offers a promising route to improved reliability for rural communities. However, improvements in the reliability of national grids create risks for investors including the possibility of "stranded" renewable assets. We use energy-system modelling to explore ways in which solar photovoltaic (PV)-based mini-grids could be interconnected with national grids. We explore the impact of reduced electricity demand to quantify the investment risks of losing customers. Our results indicate that national grid-connected mini-grids can reduce the unit electricity costs for communities whilst also increasing reliability and reducing the carbon intensity of electricity in line with Sustainable Development Goal (SDG) 7. Reductions in demand have a minimal impact at lower levels but at moderate levels are likely to undermine economic viability. Finally, we discuss policy interventions to facilitate and protect investing in national grid-connected mini-grids.

Today, gas fired assets are an ideal companion for our renewable assets, being dispatched in a flexible way to ensure the grid stability. The EU came with taxonomy categorising power plants based on their CO 2 emissions, and thus their risk of harming reaching the Paris agreement goals. Within the ENGIE fleet, the GT26 units could achieve, with the latest engine upgrades and hydrogen cofiring, the threshold value of 270 g CO 2 /kWh, where these are considered not doing any harm to reach the goals. Based on an existing power plant configuration, ENGIE has conducted a feasibility study to explore the technical possibilities and on-site modifications to reach 45 vol% hydrogen cofiring on the GT26 gas turbine. Several challenges were identified, such as the risk of flashback, higher NOX emissions, and fuel gas compressor selection for the hydrogen preparation.

The accelerating growth of renewable infrastructure investments has intensified the need for transparent, standardized, and dynamic reporting systems that can foster investor confidence and accountability. Traditional static reporting models often fail to capture the evolving performance dynamics of renewable assets, creating uncertainty for both internal managers and external stakeholders. This paper explores the development of a dynamic reporting framework that integrates standardized key performance indicators (KPIs) with interactive dashboard technologies to enhance visibility across operational, financial, and environmental dimensions. Drawing from contemporary practices in sustainable finance and digital analytics, the discussion highlights how harmonized KPIs enable comparability, improve decision quality, and strengthen trust in portfolio performance disclosures. The study also examines how digital visualization tools, real-time data feeds, and predictive analytics can transform investor engagement by translating complex data into actionable insights. Finally, the paper underscores the strategic role of regulatory alignment, interoperability, and emerging technologies such as AI and blockchain in shaping future transparency frameworks within the renewable energy investment landscape.

This study analyzes the post-pandemic dynamics and investment potential of diverse clean energy equities, including solar, wind, nuclear, and other renewable assets, highlighting nuanced differences and investment opportunities within this critical sector. The analysis reveals that nuclear energy portfolios (NLR) exhibit notable resilience, sustaining growth amidst significant market volatility. Within the mean-variance portfolio optimization (MVO) framework, this study identifies strategic investments that balance risk and return, underscoring NLR’s role as a stabilizing force and return enhancer, as evidenced by its predominant allocation in both Minimum Variance and Tangency Portfolios. Employing advanced stochastic modeling and simulation techniques, the research uses a uniform distribution to generate random portfolio weights, ensuring comprehensive and unbiased exploration of the feasible solution space, thereby enhancing the robustness of the portfolio optimization process. The findings also illustrate the diversification merits of integrating clean energy equities into broader portfolios comprising traditional stocks and bonds, with nuclear-focused equity significantly enhancing the efficient frontier. Results underscore the superiority of the nuclear energy exchange-traded fund (ETF) both as a standalone investment and as a crucial component of diversified portfolios, highlighting its contribution to investment performance and risk management. This approach offers insights for investors and policymakers navigating the intersection of finance, sustainability, and economic growth post-pandemic.

Unraveling the mechanisms of biochar and steel slag in alleviating lithium stress in tomato (Solanum lycopersicum L.) plants via modulation of antioxidant defense and methylglyoxal detoxification pathways

A statistical study for the impact of REMS and nuclear energy on carbon dioxide emissions reductions in G20 countries

Presented on Tuesday 21 May: Session 2 Methane pyrolysis and water electrolysis offer alternative hydrogen pathways to methane reforming that utilise renewable power and avoid generating carbon dioxide (CO2). On a scope 1 and 2 basis, both technologies have the potential to generate carbon neutral emethanol fuel when combined with biogenic CO2. However, pyrolysis requires significantly less energy and has a lower capital expenditure (CAPEX). Being a more nascent technology, it also has upside potential for cost reductions and valorisation of the solid carbon by-product can yield lower hydrogen costs than the incumbent technology, however, limited demand from existing markets is a potential constraint. Industry decarbonisation is constrained due to a lack of available renewables and hydrogen infrastructure. Pyrolysis offers a potential cost-effective use of the available renewable assets in the early stages while the carbon by-product is not a constraint and until sufficient renewable infrastructure is in place to support water electrolysis at a cost-effective scale. To access the Oral Presentation click the link on the right. To read the full paper click here

Abstract Changing the way electricity is produced, as part of the energy transition process, brings new challenges to the energy industry and in particular to the power generation practices. The switch from energy systems based on fossil fuels to generation technologies based on renewable sources is proving to be much more complex than expected. Multiple recent studies have shown that the intermittency in renewable production processes is putting pressure on the system and increasing balancing and maintenance costs of the grid, calling for an efficient management of the electricity produced. Therefore, a complete phase-out of conventional power generation units (e.g. coal, gas and nuclear) is still difficult to imagine in practice, mainly because of their role in balancing the system during the hours when renewables are not available (e.g. no wind, no sun or reduced hydraulicity). From the perspective of technical and commercial management aspects, the operation of conventional assets involves the consideration of multiple constraints (such as a longer operating time before they are shut down, or a longer preparation time before they can be restarted) in order to avoid financial loss and further damages. Therefore, the aim of this is to: 1. study the impact brought by the large installed capacity of renewable assets on the energy system; 2. analyse the options for gradual phase-out of conventional assets, from the perspective of system stability; 3. draw conclusions on how the commercial optimization of the production assets should develop as the energy transition advances. The novelty of this paper lies in the fact that it makes a business study of this transition process, which does not really exist yet, and case and simulation studies, along with statistical and time series analysis were used as a methodological approach for the research.

Methane pyrolysis and water electrolysis offer alternative hydrogen pathways to methane reforming that utilise renewable power and avoid generating carbon dioxide (CO2). On a scope 1 and 2 basis, both technologies have the potential to generate carbon neutral emethanol fuel when combined with biogenic CO2. However, pyrolysis requires significantly less energy and has a lower capital expenditure (CAPEX). Being a more nascent technology, it also has upside potential for cost reductions and valorisation of the solid carbon by-product can yield lower hydrogen costs than the incumbent technology, however, limited demand from existing markets is a potential constraint. Industry decarbonisation is constrained due to a lack of available renewables and hydrogen infrastructure. Pyrolysis offers a potential cost-effective use of the available renewable assets in the early stages while the carbon by-product is not a constraint and until sufficient renewable infrastructure is in place to support water electrolysis at a cost-effective scale.

Renewable energy infrastructure such as solar farms, wind parks, hydropower assets, and battery-storage facilities has emerged as a critical investment class in global energy transitions. However, despite its long-term strategic value, the sector often lacks standardized asset management frameworks comparable to those used in commercial real estate, where investors routinely apply structured valuation metrics and risk-adjusted portfolio optimization models. This review examines how established real estate based methodologies including net present value (NPV) techniques, capitalization rate analysis, risk-return mapping, diversification modeling, and asset lifecycle forecasting can be adapted to enhance the financial and operational decision-making processes for renewable energy portfolios. By comparing asset characteristics across the two sectors, the study highlights synergies in valuation modeling, cash-flow stabilization strategies, and hedging approaches while accounting for the unique uncertainties in renewable assets such as policy volatility, intermittency, and technology degradation. The review further evaluates how portfolio optimization models, including Modern Portfolio Theory (MPT), real options analysis, sensitivity modeling, and discounted cash-flow (DCF) forecasting, can be recalibrated to improve investment resilience in renewable infrastructure. Ultimately, this paper proposes a cross-sector asset management framework that integrates real estate portfolio logic with renewable energy performance metrics to support investors, policymakers, and asset managers in achieving long-term sustainability, profitability, and risk mitigation in the evolving clean energy economy.

Energy communities (ECs) are increasingly put forward as one of the solutions for making the European energy system greener and more resilient, through the local production and sharing of renewable energy. In order to design and operate an efficient EC, data on energy consumption and production is needed. This allows to determine the optimal market model and the necessary investments needed for renewable assets. Unfortunately, the acquisition of energy data is often the biggest barrier for EC initiators. This paper brings together the various hurdles in data acquisition that were identified through interviews with 10 EC initiators throughout Europe. Contrary to the existing literature we found that consumers seem less hesitant to share their data than suggested, as many show little concern for data protection. The major hurdles identified are related to the physical assets, such as data gathering and smart meter installation procedures taking more effort and time than planned, as well as to the low quality of existing data. Our research shows that many EC initiators face similar but rarely mentioned hurdles, that could be mitigated and overcome easier when considered beforehand.

The growing concern about environmental damage and the inability to meet the demand for more versatile, environmentally friendly materials has sparked increasing interest in polymer composites derived from renewable and biodegradable plant-based materials, mainly from forests. These composites are mostly referred to as "green" and they can be widely employed in many industrial applications. Green composites are less harmful to the environment and could be potential substitutes for petroleum-based polymeric materials. It is helpful to limit usage of fossil oil assets by developing biopolymer matrices such as cellulose-reinforced biocomposites using renewable assets such as plant oils, carbohydrates, and proteins. This paper focuses on green composites processing utilizing a variety of naturally available resources, sustainable materials which are not detrimental to the environment, new scientific signs of progress in achieving green sustainable development, as well as nanotechnology and its environmental consequences. Additionally, the environmental impacts of different composite materials are examined in this paper, along with their production from eco-friendly materials. Moreover, the manufacturing aspects of green composites and some concerns related to their production are also discussed. The merits of green composite materials and valid reasons why they are a valuable substitute for the traditionally used composite materials are also covered.

ABSTRACT This manuscript deals with the energy storage technology with renewable energy design can directly reduce the overall constraint of electrical micro grid in upcoming power grids. The original energy monitoring arrangement can lower the typical price of micro grids and increase the utilization of renewable assets by targeting the entire site for essential energy storage systems to predefined functions. The limited progress of solar PV and wind turbines with nickel-cadmium battery storage integrates with the sum of increasing renewable systems, for this concept the hybrid integrated micro grids can be fundamentally modeled and calculated. The proposed hybrid micro grid system gives a photovoltaic output power of 37 kW, the developed output voltage is 80 V and the power of the wind module is 900 watts, the output power of the PV panel is 420 watts. The developed output voltage is 80 V. The convex coding system is used for schema planning and optimization route. The nominal time-optimal controller and receiver locations are simply well known by this planned convex programming method. An optimization algorithm was functional to define the optimal sizing of the PV/wind/battery hybrid system for power continuity in rural areas for continuity of electricity consumption.