Search Results (114)

This research introduces a two-stage, low-carbon industrial heating process, leveraging advanced waste heat recovery (WHR) technologies and exploiting waste heat (WH) to drive decentralised hydrogen production. This study is supported by a data-driven analysis of individual technologies, followed by 0D modelling of the integrated system for technical and feasibility assessment. Within 10 years, the EU industry will be supported by two main strategies to transition to low-carbon energy: (a) shifting from grid-mix electricity towards fully renewable sources, and (b) expanding low-carbon hydrogen infrastructure within industrial clusters. On the demand side, process heating in the industrial sector accounts for 70% of total energy consumption in industry. Almost one-fifth of the energy consumed to fulfil the process heat demand is lost as waste. The proposed heating solution is tailored for process heat in industry and stands apart from the dual-mode residential heating system (i.e., heat pump and gas boiler), as it is based on integrated and simultaneous operation to meet industry-level reliability at higher temperatures, focusing on WHR and low-carbon hydrogen. The solution uses a cascaded heating approach. Low- and medium-temperature WH are exploited to drive high-temperature heat pumps (HTHPs), followed by hydrogen burners fuelled by hydrogen generated on-site by electrolysers, which are powered by advanced WHR technologies. The results revealed that the deployment of the solution at scale could fulfil ~14% of the process heat demand in EU/UK industries by 2035. Moreover, with further availability of renewable energy sources and clean hydrogen, it could have a higher contribution to the total process heat demand as a low-carbon solution. The economic analysis estimates that adopting the combined heating solution—benefiting from the full capacity of WHR for the HTHP and on-site hydrogen production—would result in a levelised cost of heat of ~EUR 84/MWh, which is lower than that of full electrification of industrial heating in 2035. Full article

The integration of renewable energy technologies into the building sector is critical for achieving climate and energy targets, particularly within the framework of the European Union’s decarbonization policies. Building-integrated photovoltaics (BIPV) offer a promising solution by enabling the dual function of building envelope components and on-site electricity generation. However, the widespread adoption of BIPV faces significant barriers, including high initial investment costs, design and integration complexity, fragmented standardisation and a shortage of skilled labour. This study systematically identifies, evaluates and prioritises the key factors influencing the implementation of BIPV technologies using a hybrid SWOT (strengths, weaknesses, opportunities, threats) and Analytic Hierarchy Process (AHP) methodology. A comprehensive literature review and a modified Delphi method involving expert input were employed to select and rank the most relevant factors in each SWOT category. The results indicate that external factors—particularly regulatory requirements for energy efficiency, renewable energy adoption and financial incentives—are the most significant drivers for BIPV deployment. Conversely, competition from building-attached photovoltaics (BAPV), high investment costs and the complexity of integration represent the main barriers and threats, compounded by internal weaknesses such as a lack of qualified workforce and fragmented standardisation. The findings underscore the importance of targeted regulatory and financial support, standardisation and workforce development to accelerate BIPV adoption. This research provides a structured decision-making framework for policymakers and stakeholders, supporting strategic planning for the integration of BIPV in the construction sector and contributing to the transition towards sustainable urban energy systems. Full article

This study presents preliminary findings from an experimental campaign conducted on a pilot-scale green hydrogen production plant powered by a photovoltaic (PV) system. The integrated setup, implemented at the University “Mediterranea” of Reggio Calabria, includes renewable energy generation, hydrogen production via electrolysis, on-site storage, and reconversion through fuel cells. The investigation assessed system performance under different configurations (on-grid and selective stand-alone modes), focusing on key operational phases such as inerting, purging, pressurization, hydrogen generation, and depressurization. Results indicate a strong linear correlation between the electrolyser’s power setpoint and the pressure rise rate, with a maximum gradient of 0.236 bar/min observed at 75% power input. The system demonstrated robust and stable operation, efficient control of shutdown sequences, and effective integration with PV input. These outcomes support the technical feasibility of small-scale hydrogen systems driven by renewables and offer valuable reference data for calibration models and future optimization strategies. Full article

As industries come under growing pressure to minimize carbon emissions without compromising the efficiency of operations, the integration of energy-aware production scheduling with emerging energy markets, renewable energy, and policy mechanisms is critical. This paper identifies critical shortcomings in current academic and industrial approaches—namely, an excessive reliance on deterministic assumptions, a limited focus on dynamic operational realities, and the underutilization of regulatory mechanisms such as carbon trading. We advocate for a paradigm shift to more robust, adaptable, and policy-compliant scheduling systems that provide space for on-site renewable generation, battery energy storage systems (BESSs), demand-response measures, and real-time electricity pricing schemes like time-of-use (TOU) and real-time pricing (RTP). By integrating recent advances and their critical analysis of limitations, we map out a future research agenda for the integration of uncertainty modeling, machine learning, and multi-level optimization with policy compliance. In this paper, we propose the need for joint efforts from researchers, industries, and policymakers to collectively develop industrial scheduling systems that are both technically efficient and adherent to sustainability and regulatory requirements. Full article

Global agriculture remains dependent on nitrogen fertilizers produced through fossil fuel-based processes, contributing to greenhouse gas emissions, energy use, and supply chain vulnerabilities. This review introduces plasma-activated water (PAW) as a novel, electricity-driven alternative for sustainable nitrogen delivery. Generated by non-thermal plasma, PAW infuses water with reactive oxygen and nitrogen species, offering a clean, decentralized substitute for conventional synthetic fertilizers derived from the Haber–Bosch and Ostwald processes. It can be produced on-site using renewable energy, reducing transportation costs and depending on fertilizers. Beyond its fertilizer properties, PAW enhances seed germination, plant growth, stress tolerance, and pest resistance, making it a multifunctional input for controlled environment agriculture. We also assess PAW’s techno-economic viability, including energy requirements, production costs, and potential scalability through renewable energy. These factors are crucial for determining its feasibility in both industrial systems and localized agricultural applications. Finally, the review examines PAW’s contribution to the ten United Nations Sustainable Development Goals, particularly in climate action, clean energy, and sustainable food production. By combining agronomic performance with circular production and emissions reduction, PAW presents a promising path toward more resilient, low-impact, and self-sufficient agricultural systems. Full article

The industrial sector’s increasing electricity demand (direct and indirect), driven by the electrification of processes and the production of green hydrogen, poses significant challenges for achieving decarbonisation goals. While switching to renewable electricity and offsetting emissions appears straightforward, the gap between current generation capacities and projected demand remains substantial. This article analyses survey data from the Energy Efficiency Index of German Industry (EEI), revealing that manufacturing companies aim to reduce 22.1% of their 2019 emissions by 2025 and 27.3% by 2030, primarily through on-site measures. However, given the slow pace of renewable capacity expansion and the increasing electrification across sectors, it becomes evident that the envisaged green electricity share of 80% by 2030 will require far more capacity than currently planned. To address this challenge, the article introduces a decarbonisability factor to better assess on-site versus off-site measures, highlighting the need for a strategic sequencing of efficiency and renewable generation. To support decision-makers, the article calls for improved data collection and periodic reassessment to account for changing geopolitical and economic conditions. Full article

The automotive industry faces urgent pressures to transition to carbon-neutral operations amid evolving policies, shifting consumer demands, and stringent environmental regulations. This study examines how implementing the EFQM Model 2020 can drive sustainability-oriented transformation in a leading European automotive plant. Over a two-year period (November 2021–December 2023), the company reduced CO₂ emissions by 17%, decreased water usage by 9.3%, and elevated recycling rates from 93.3% in FY19 to 98.1% in FY23. Although these improvements demonstrate the EFQM Model’s effectiveness in integrating economic, social, and environmental objectives, further progress toward net zero remains challenging due to diminishing returns on efficiency. Sustaining momentum will require continuous innovation such as passive building designs and on-site renewable energy generation supported by robust stakeholder engagement and compliance with evolving ESG reporting standards. These findings affirm the value of the holistic management framework for operational excellence and environmental stewardship, providing a replicable pathway toward carbon neutrality in resource-intensive industries. Full article

The reduction of harmful emissions is shaping trends across many industries, including architecture and building. With rising ecological awareness and the threat of climate change, architects, construction engineers, and developers are focusing on innovative solutions to minimize the construction sector’s environmental impact. This paper presents a technical and management approach system using renewable energy sources, based on an existing single-family house with known energy consumption. The aim is to achieve energy independence by relying solely on on-site electricity generation and storage, while remaining connected to water and sewage infrastructure. Utilizing renewable energy sources enhances self-sufficiency and investment profitability. The study evaluates the house’s energy consumption to optimally select electricity supply solutions, including a small wind farm and photovoltaic installation integrated with appropriate electricity storage. This is crucial due to the air heat pump used for heating and domestic hot water, which requires electricity. An hourly simulation of the system’s operation over a year verified the adequacy of the selected devices. Additionally, two different locations were analyzed to assess how varying climate and wind conditions influence the design and performance of off-grid energy systems. The analysis showed that solar and wind systems can meet annual energy demand, but limited storage capacity prevents full autonomy. Replacing the heat pump with a biomass boiler reduces electricity use by about 25% and battery needs by 40%, though seasonal energy surpluses remain a challenge. This concept aligns with the goal of achieving climate neutrality by 2050. Full article

Wastewater treatment plants (WWTPs) consume a considerable amount of energy. They also generate energy in combined heat and power (CHP) units, which utilise biogas from the anaerobic digestion of sewage sludge to produce renewable electricity. Different prices apply to electricity generated on site in CHP units, to the purchase of electricity from the grid, to the sale of surplus electricity to the grid and energy tariffs, which motivates the optimisation of energy costs. This paper presents a strategy for optimising electricity costs by adapting on-site electricity generation in CHP units to the demand of the WWTP. The approach is designed for a CHP system that generates electricity in multiple internal combustion gas engines. It is implemented as a two-level control system, where the lower control level dynamically adjusts the power of the individual gas engines, and the upper control level optimises the desired total power, taking into account the current energy consumption of the WWTP, biogas reserves and electricity tariffs. The proposed concept was implemented at the Domžale-Kamnik WWTP. A six-month evaluation showed that electricity purchased from the grid could be reduced from 8.7% to 3.3% of the WWTP’s electricity consumption. This reduction affects the system economically, as electricity purchased from the grid at low and high tariffs is 35% and 76% more expensive than electricity generated on site (excluding the grid fee). This approach can be extended to balance dispatchable electricity generation at the WWTP to respond to short-term grid demand. Full article

Water utilities use a significant amount of electrical energy due to the rising demand for wastewater treatment driven by environmental and economic reasons. The growing demand for energy, rising energy costs, and the drive toward achieving net-zero emissions require a sustainable energy future for the water industry. This can be achieved by integrating onsite renewable energy sources (RESs), energy storage, demand management, and participation in demand response (DR) programs. This paper analyzes the energy profile and load flexibility of water utilities using a data-driven approach to reduce energy costs by leveraging RESs for regional water utilities. It also assesses the potential for DR participation across different types of water utilities, considering peak-load shifting and battery storage installations. Given the increasing frequency of extreme weather events, such as bushfires, heatwaves, droughts, and prolonged cold and wet season floods, regional water industries in Australia serve as a relevant case study of sectors already impacted by these challenges. First, the data characteristics across the water and energy components of regional water industries are analyzed. Next, barriers and challenges in data acquisition and processing in water industries are identified and recommendations are made for improving data coordination (interoperability) to enable the use of a single platform for identifying DR opportunities. Finally, the energy profile and load flexibility of regional water industries are examined to evaluate onsite generation and battery storage options for participating in DR operations. Operational data from four regional sites across two regional Australian water utilities are used in this study. Full article

Energy hubs integrating onsite renewable generation and battery storage provide cost-efficient solutions for meeting building electricity requirements. This study presents methods for modeling uncertainties in load demand and solar generation, ranging from normal distribution assumptions to distributions sourced from CityLearn 2.3.0. We also implement kernel density estimation (KDE) to represent the non-parametric distribution characteristics of actual data. Through Monte Carlo simulation, we emphasize the value of robust, data-driven methodologies in optimizing energy hub operations under realistic uncertainty conditions and effectively conducting risk assessment. The CityLearn real-world data confirms that the non-Gaussian nature of building-level energy demand and solar PV electricity output is most accurately represented through KDE, leading to more precise cost projections for the considered energy hub. Full article

With the global commercialization of hydrogen fuel cell vehicles, the number of hydrogen refueling stations is steadily increasing. On-site hydrogen production stations are expected to play a key role in future power systems by absorbing renewable energy and supplying electricity during peak grid loads, aiding in peak shaving and load leveling. However, renewable energy sources like photovoltaic (PV) systems have highly fluctuating power generation curves, making it difficult to provide stable energy for hydrogen production. Traditional stations mainly use alkaline electrolyzers (AE), which are sensitive to power fluctuations, leading to operational instability. To address this, this paper proposes using capacitors and energy storage batteries to mitigate PV fluctuations and introduces a combined AE and Proton Exchange Membrane (PEM) electrolyzer hydrogen production method. Study cases demonstrate that capacitors and energy storage batteries reduce the variance of PV power output by approximately 0.02. Building on this, the hybrid approach leverages the low cost of AE and the rapid response of PEM electrolyzers to better adapt to PV fluctuations and maximize PV absorption. The model is mathematically formulated and the station’s equipment planning and operational strategy are optimized using CPLEX. The results show that, compared to pure AE and PEM hydrogen production, the combined AE and PEM hydrogen production method reduces the total annual cost of the hydrogen refueling station by 4.3% and 5.9%, respectively. Full article

It is urgent for the wastewater treatment sector to respond to global climate change. Although studies related to the water–energy–carbon (WEC) nexus have been widely conducted, the application of the coupling coordination indicator is still limited in the wastewater treatment sector. This study fills such a research gap by linking water footprint (WF), energy footprint (EF), and carbon footprint (CF) together and testing these indicators in 140 wastewater treatment plants (WWTPs) in Shandong province, China. Both the EF and CF of these WWTPs were calculated by conducting hybrid life cycle assessments, while WF was calculated by using a WF method. The results show that gray WF generated from 1 m³ of wastewater ranged from 9.58 to 12.90 m³, while EF generated from 1 m³ of wastewater ranged from 9.42 × 10⁻² to 0.22 kg oil eq and CF generated from 1 m³ of wastewater ranged from 0.58 to 1.27 kg CO₂ eq. Also, the total WF, EF, and CF of these WWTPs in Shandong were 4.26 × 10¹⁰ m³, 5.32 × 10⁸ kg oil, and 3.35 × 10⁹ CO₂ eq in 2021, respectively. Key factors contributing to the overall greenhouse gas (GHG) emissions were the on-site GHG emissions and off-site electricity-based GHG emissions. Meanwhile, total nitrogen was the dominant contributor to the gray WF. In addition, the coupling coordination indicators of WF, EF, and CF ranged from 0.7571 to 0.9293. Finally, this study proposed several policy recommendations to improve the overall sustainability of this wastewater treatment sector by considering local realities, including adopting multi-dimensional indicators, decarbonizing current electricity grids, promoting the utilization of renewable energy, and initiating various capacity building efforts. Full article

The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. Machine learning- and artificial intelligence-based methods are widely studied by researchers to tackle the demand forecasting problem. However, since the COVID-19 pandemic broke out, new challenges have surfaced for forecasting research. In such a short amount of time, significant shifts have emerged in electricity demand trends, making it apparent that the pandemic and the possibility of similar crises in the future have escalated the complexity of energy management problems. Motivated by the circumstances, this research presents an hour-ahead and day-ahead electricity demand forecasting benchmark using Deep Polynomial Neural Networks (DNN) and Gene Expression Programming (GEP) methods. The DNN and GEP algorithms utilize on-site electricity consumption data collected from a university hospital for over two years with a temporal granularity of 15-minute intervals. Quarter-hourly meteorological, calendar, and daily COVID-19 data, including new cases and cumulative cases divided by four restriction levels, were also considered. These datasets are used not only to predict the electricity demand but also to investigate the impact of the COVID-19 pandemic on the electricity consumption of the hospital. The hour-ahead and day-ahead nRMSE results show that the DNN outperforms the GEP by $8.27\%$ and $14.32\%$, respectively. For the computational times, the DNN appears to be much faster than the GEP by $82.83\%$ and $78.56\%$ in the hour-ahead and day-ahead forecasting, respectively. Full article

Energy consumption in buildings is a major contributor to greenhouse gas emissions, primarily due to the extensive burning of fossil fuels. This study focuses on an innovatively designed building named The Clover and utilises IES-VE software (2024) to create a digital twin for the building’s performance prediction. The goal is to achieve a zero-carbon-emission building through energy-efficient strategies, including the use of air-source heat pumps and renewable energy systems for sustainable heating, cooling, and electricity. Dynamic simulations conducted with the software analyse key performance metrics, including annual heating and cooling demands, electricity consumption, carbon emissions, and renewable energy supply. The results indicate that a 53% reduction in CO₂ emission is achieved when a heat pump system is applied instead of boiler and chiller systems. A total of 1243.96 MWh and 41.18 MWh of electricity can be generated by PV panels and wind energy systems. The net annual electricity generation from the energy system of the building is 191.64 MWh. Therefore, the results demonstrate that the building’s energy needs can be successfully met through on-site electricity generation using advanced perovskite–silicon tandem solar PV panels and wind turbines. This case study provides valuable insights for architects and building services engineers, offering a practical framework for designing green, energy-efficient, zero-carbon buildings and advancing the path to net zero. Full article