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This paper examines the potential of solar and hydrogen (H 2) energy-based hybrid energy systems for residential applications. The growing need for energy and the demand for sustainable energy sources have led to the development of integrated energy systems that combine renewable energy resources to meet energy needs. This paper overviews recent studies on hybrid energy systems for on-grid and off-grid residential utilizations. It discusses the system configuration and components of hybrid energy systems, including solar panels, electrolyzers, fuel cells (FC), and batteries. It also covers the technical optimization of integrated energy systems, including sizing, control strategies, and economic analysis. The key findings of this review paper indicate that hybrid energy systems can offer dependable and sustainable energy for residential applications. Through numerical analyses, the optimal dimensioning of the system elements and control strategies can significantly enhance the system's performance and lower the cost of energy. This study also highlights the challenges and opportunities for integrating hybrid energy systems within residential applications. Overall, it provides in-depth perspectives on the possibilities inherent in solar and hydrogen energy-based hybrid energy systems for residential applications. The findings can guide future research and the advancement of hybrid energy systems for sustainable energy solutions. • Comprehensive review on solar and hydrogen-based residential applications. • Holistic approach to system configuration, optimization, and economics. • Detailed analysis of interconnected components enhancing system efficiency. • Unique focus on real-world residential applications and scenarios. • Broad applicability across diverse geographical regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel method to estimate total technical potential for improving energy efficiency. • Bottom-up sector energy model used to characterize energy demands and opportunities. • Technology-explicit analysis framework used to estimate integrated energy savings. • Application to Canada's pulp & paper sector indicates major savings are possible. • Technical potential to reduce natural gas use by 95% and net electricity by 54% Industrial energy efficiency has long been recognized as a critical tool for energy management and emissions reduction and is increasingly considered to be an energy resource in its own right. However, the complexity of most industrial sectors creates barriers for modellers, companies, and policymakers in understanding and achieving the overall potential for efficiency-driven energy savings. To address this gap, we have developed a novel method that combines disaggregated bottom-up sector energy modelling with analysis of a comprehensive set of technology-explicit energy efficiency measures. This approach overcomes the deficiencies of top-down analyses and better represents the complexities of multi-measure efficiency investments compared to piecewise studies of individual measures. In this study, we demonstrated application of our method to the case of the Canadian pulp and paper sector. Our analysis shows that the sector can reduce its natural gas and electricity use by 95% and 41%, respectively, by adopting economical efficiency measures. This equates to 71 PJ of natural gas and 44 PJ of electricity saved annually and would result in cost savings of $81 per tonne of output sector-wide. Notably, we find that efficiency and fuel switching have the technical potential to reduce natural gas consumption by 98% across the sector. Taken together, the results provide quantitative evidence that energy efficiency is an underestimated resource for cost-effective energy savings in the Canadian pulp and paper sector. Our analysis framework can be applied to any industrial sector in any region to provide insights regarding energy conservation strategies and policy design. [ABSTRACT FROM AUTHOR]

• Novel damped conformable fractional accumulation method. • Prediction of energy related series with the uncertainties. • Design of damped conformable fractional grey bernoulli model. • Improved prediction accuracy, robustness and applicability. • Prediction of world coal consumption and production in 2025–2026. Energy resources, such as oil, coal are important to people's life and the development of the economy. Predicting the energy consumption and production with uncertainties can help the government and policymakers make the reasonable energy strategy and give constructive guidance. In this paper, a novel damped conformable fractional accumulation operator with two parameters are proposed to have control of both accumulation generation and new information involvement. The new accumulation can be adjusted from linear approximation to high order accumulation. A structure adaptive grey Bernoulli model based on proposed accumulation method is established, with the parameters optimized by particle swarm optimization algorithm. For testifying the effectiveness and the robustness of the model, two validation cases and two application cases are used to build the prediction model of diverse series with unexpected trends or fluctuations in energy fields. Compared with other existing fractional grey models and non-grey models, the proposed model has best prediction accuracy, better model stability and greatly improved applicability. The global coal consumption by industry is predicted as 30.85×106 TJ in 2025 and 30.81×106 TJ in 2026. The world coal production could be 182.93–187.73 exajoules in 2025 and 180.85–182.16 exajoules in 2026. [ABSTRACT FROM AUTHOR]

This paper presents a novel off-grid hybrid renewable energy system integrated with hydrogen production and retired electric vehicle (EV) batteries for combined power and heat supply to a small rural community. The system consists of photovoltaic (PV) panels, evacuated tube solar collectors, a proton exchange membrane fuel cell (PEMFC), an alkaline electrolyzer, compressed hydrogen storage tanks, and reused lithium-ion batteries from EVs. A new battery model is developed in the Transient System Simulation Program (TRNSYS) to capture the capacity degradation of retired batteries. A two-stage energy management strategy is proposed to regulate power flow and maximize solar energy utilization. The system is optimized for minimum power supply disruption, hot water supply disruption, energy waste, and annualized costs over a 20-year lifetime using a multi-objective NSGA-II algorithm coupling MATLAB and TRNSYS. Optimization results reveal tradeoffs between the competing objectives. Dynamic simulations show the water tank temperature fluctuates between 20 and 100 °C with mean values dropping slightly in later years due to battery degradation. Energy waste from components rises in later years for the same reason. The replacement interval for a retired EV battery module is around 2.5 years. The integrated system proves feasible for off-grid combined power and heat supply using solar energy and retired EV batteries as storage, though battery degradation impacts long-term performance. • Integrates solar, hydrogen, retired EV battery for off-grid power and heat supply with optimized storage and backup. • Utilizes multi-objective optimization to balance system reliability, waste minimization, and costs through Pareto solutions. • Implements long-term performance over twenty years to provide insights into component lifetimes and replacement needs. [ABSTRACT FROM AUTHOR]

• Energy consumption based Battery Energy Storage and rooftop Solar PV sizing. • Typical high-end units consumes 22% more than the medium-cost units and 56% more than low-cost units. • Community BESS and rooftop Solar PV has to be sized at maximum or 125% of maximum to supply for VPP. • More n R is needed if sizing is based on max E C while lesser n R if size is based on 125% of the max E C. Virtual Power Plants (VPP) have been seen as one of the techniques to integrate more decentralized and distributed variable renewable energy systems into the grid. They will thus facilitate the greater democratization of the energy systems. VPP is an aggregation of renewable energy technologies, such as solar PV. It is usually operated with battery energy storage system (BESS) facilities to solve the variability issue of solar PV. However, BESS used as part of VPP has to be appropriately sized to work in the electrical network. While VPP operated with BESS may provide significant technical and financial advantage, most of the BESS sized to be used as VPP in the literature are large scale, operated with large scale VRE's in the grid. Some of the literatures also provide the use of BESS sizing with Solar PV but for shared residential community use only. The context of BESS sizing for VPP from a residential community is not prevalent in the literature. In this paper, the context of a sizing BESS for VPP from a residential neighbourhood is studied. It is aimed that by optimally sizing BESS for a residential community with rooftop solar PV can supply power to the community and any excess energy may be used for VPP application for the grid. This paper provides a practical process for evaluating the proper size of a centralized BESS in a community with rooftop Solar PV by considering the energy consumption and peak demand profiles of the residential units in three types of communities with rooftop Solar PV, namely low-cost, medium-cost, and high-end community, by considering the mean and maximum energy consumption and peak demand profiles of each residential unit. Such values are used to allocate for the Solar PV system per residential unit and the centralized BESS at mean, 75% of maximum, maximum, and 125% of maximum energy consumption, to be used for the community and for any excess energy to support the electrical networks reliability improvement. The result shows that using the maximum and 125% of maximum energy consumption can provide more than enough energy capacity for VPP application, particularly electrical networks reliability improvement by supplying for the Energy not Supplied (EnS), E N. The result provides that for a High-end community, the stored energy in the BESS can reach up to 24.578 MW h using 125% of maximum sizing at either mean or maximum energy consumption. While for Medium-cost and Low-cost communities, the stored energy can reach up to 17.549 MW h and 14.527 MW h using 125% of maximum sizing at either mean or maximum energy consumption, respectively. Moreover, the result also shows that to alleviate Energy not Supplied (EnS) and contribute to reliability improvement, more residential units are needed if sizing is based on maximum energy consumption, while a lesser number of residential units if the size is based on 125% of the maximum energy consumption. [ABSTRACT FROM AUTHOR]

• Energy and environmental performances of strawberry supply chain. • Field measurement and interviews of professional stakeholders. • Non-plastic packaging showed significantly better environmental performance. • Most impactful processes are packaging production and refrigerated transport. Berries are highly perishable fruits and require both low storage temperature and suitable packaging throughout the supply chain to preserve their organoleptic qualities. However, the energy consumption of refrigerated equipment and the use of packaging materials, plastic in particular, might generate important environmental impacts. Besides, there is a strong commitment to reduce the use of plastic in the food industry. The aims of the current work are first to assess the energy consumption of refrigerated equipment and second to analyze the environmental performance of the strawberry supply chain. Various stages of the supply chain from transport from growers to retail storage were modeled using data from field measurement and interviews with professional stakeholders. Life Cycle Assessment (LCA) was performed for the strawberry supply chain. Different packaging materials, plastic (PET, RPET) and alternatives (molded pulp, recycled paper, cardboard), were used. The processes that generated the most important environmental burden were the packaging production and the long-distance refrigerated transport. To limit the impact related to packaging production, it is necessary to consider not only the type of packaging material but also the processes and energy consumption used in their manufacturing. [ABSTRACT FROM AUTHOR]

In this paper, wind resource availability was determined by analyzing the meteorological wind energy potential at the hub height of wind turbines. The wind resource potential was identified in specific geographic locations. Technical factors, such as siting requirements and resource availability, were taken into account in the investigation. The economic potential was calculated for each area of Algeria, considering overall losses. Subsequently, the potential for reducing fossil fuel usage was determined through wind to hydrogen estimation. The main components utilized in this work were a Vestas-90 wind turbine (V90–2.0 MW) and an alkaline electrolyzer. The analysis employed a Geographical Information System (GIS) tool. The results demonstrate Algeria's significant potential for wind to hydrogen production, estimated at 1.093 Gt/year without constraints, and reduced to 1.066 Gt/year when considering constraints. The levelized cost of wind to hydrogen production in Algeria varies between 1.51 and 15.37 US $/kg, depending on the chosen wind turbine. The wind to hydrogen production potential identified has the capacity to replace the entire current fossil fuel consumption in the transportation sector, which amounts to 5.28 Mt H 2 /year. The utilization of wind to hydrogen in the transport sector results in a significant reduction of CO 2 emissions, with a calculated avoidance of 45.69 Mt compared to the use of gasoline and diesel. Hydrogen produced from wind energy resources also could be used to support other sectors, including residential, agricultural, and industry in Algeria. • GIS-Based approach is used to evaluate the potential for wind to hydrogen production in Algeria. • The total wind to hydrogen potential in Algeria, when considering constraints, is estimated at 1.066 Gt/year, covering an area of 730,719 km2. • The levelized cost of wind to hydrogen production in Algeria ranges between 1.51 and 15.37 US $/kg, depending on the selected wind turbine (V90–2.0 MW). • The CO 2 emissions avoided in the transport sector are 45.69 Mt compared if the same amount of gasoline and diesel used. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

Electrical Distribution Systems (EDS) are extensively penetrated with Distributed Energy Resources (DERs) to cater the energy demands with the general perception that it enhances the system's resilience. However, integration of DERs may adversely affect the grid operation and affect the system resilience due to various factors like their intermittent availability, dynamics of weather conditions, non-linearity, complexity, number of malicious threats, and improved reliability requirements of consumers. This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events and determines the withstand capability of the electrical network. The proposed framework is developed by effectively employing the complex network theory. Correlated networks for undesirable configurations are developed from the time-series data of active power monitored at nodes of the electrical network. For these correlated networks, compute the network parameters such as clustering coefficient, assortative coefficient, average degree, and power law exponent for the anticipation; and percolation threshold for the determination of the network's withstand capability under extreme conditions. The proposed methodology is also suitable for identifying the hosting capacity of solar panels in the system while maintaining resilience under different unfavorable conditions and identifying the most critical nodes of the system that could drive the system into non-resilience. This framework is demonstrated on IEEE 123 node test feeder by generating active power time-series data for a variety of electrical conditions using the simulation software, GridLAB-D. The percolation threshold resulted as an effective metric for the determination of the planning and operational resilience of the power distribution system. [ABSTRACT FROM AUTHOR]

• Introducing an energy management in renewable based MGs with DRP considering tidal and solar units in real-time digital twin case. • Developing a GWOA to find the optimal economic operating point in a nonlinear constraint multi-objective framework. • Proposing an intelligent deep GAN based model to predict the output power of tidal and solar units. • Critical and technical analysis of different algorithms in the microgrid operation and management. In this research study, a novel demand response program (DRP) has been proposed for renewable-based microgrids (MGs) which takes into account the high penetration of tidal units and solar energy as important, prevalent renewable resources in the power systems. To this end, multi-objective problem (MOP) structure is a promising solution to reduce the whole operating costs of the scheduling problem as well as decrease the high risk of failure in electrical power transmission because of component increasing failure rates and extended repair time. The complexities and nonlinearity of the problem necessitate an innovative heuristic solution which is derived from the Grey Wolf optimization algorithm to help for resolving the problem without making any assumptions or compromising precision. This paper also proposes the dynamic 3-phase correction (DPC) formulation to boost the layout convergence ability by increasing the global search features. Through such a modification, the diversity of the members in the algorithm population increases which would result in low computational burden and low possibility of trapping in local optima. Moreover, it is necessary to have a clear and accurate estimation of the output power of renewable sources in the system. Considering that solar irradiance is hard to anticipate, this paper develops a deep learning layout that uses generative adversarial networks (GAN) to forecast the hourly power generation of the tidal and solar agents. GAN model consists of two competing networks which help to enhance the training process by increasing the accuracy of distinguishing real data from fake data. At the end, IEEE standard test system is used to evaluate the efficiency and effectiveness of the suggested multi-layer problem. The simulation results display that the suggested deep model outperforms other well-known algorithms in smart microgrids. [ABSTRACT FROM AUTHOR]

Challenges and future perspectives of desalination integration are presented. [Display omitted] • Different optimized models for desalination technologies integration are summarized. • The control strategies of RES-desalination for reducing environmental are discussed. • Impact of desalination process innovation on energy saving and emission reduction. In the present framework, desalination, as an energy-intensive industry, has garnered increased attention due to the imperative need for reducing energy consumption. In this paper, various challenges in the integration and optimal design of desalination processes have been presented. The energy consumption, energy supply method, water production method and improvement measures of various desalination are studied in detail, of which energy consumption is a major aspect. This article summarizes the integration modes of different desalination technologies. Low integration density, inadequate energy utilization and unsystematic theory guidance have been the focus of research. Operational strategies and optimal design methods for the integration of renewable energy and desalination technologies are studied in detail. The integration of renewable energy emerges as a pivotal trend, contributing significantly to the reduction of carbon footprints and costs. Additionally, the significance of recently developed desalination processes, such as pre-treatment process, equipment enhancements and advanced materials have been discussed. Furthermore, various unresolved issues in de integration desalination technologies system have been investigated in the paper along with the possible solutions to address the reported open issues. By exploring the integration of desalination technologies, the paper aims to provide valuable insights for enhancing the efficiency of commercial desalination plants. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

A 50 kW p photovoltaic (PV) power plant installed in a hospital had its efficiencies analyzed in this work. Two emergency services of the hospital have been chosen to be fed with produced energy. In the present paper, easy sizing up means were used. The PV system is synchronized to the electrical grid to ensure the solar energy production and the batteries charging in priority. In case of electric grid perturbation, the batteries provide with the stored energy. For the studied system, the month of May is the better energy production month among the eight studied months with almost perfect efficiency in terms of meeting the needs of the two connected services. However, this month is one of the heat wave months in the sahelian strip. The batteries energy is very requested to ensure the energy supply in the two emergency services of the hospital. The best operation month is May. However to find the optimum operation, it well be better to install off-grid system. The temperature and local dust are also degradation factors to take into account. This paper shows that the PV off-grid can be used to improve the resilient of the population in areas with high security challenges. The failure of the PV plant is estimated at 0%. Indeed, once the SONABEL grid is desynchronized, the grid inverters use the energy in batteries to continue the injection. The environmental constraints such as low illumination, high temperature, dust, etc. must be added in the perturbation factors of the solar energy production. • New way to improve the population resilience in areas with high security challenges. • In the Sahel the optimum operation of PV power plant is found in the month of May. • The PV plant failure is brought near 0%. • The environmental constraints are the degradation factors to take into account. [ABSTRACT FROM AUTHOR]

With the increasing adoption of solar photovoltaics (PVs) in the power grid, the grid authorities are faced with significant challenges in managing PV intermittency, variability and uncertainty. The inherent ramping behaviour of the PVs results in short-term PV power fluctuations which in-turn affects the grid voltage regulation. This has resulted in implementation of new grid codes to limit the ramp-rate (RR) behaviour of PVs allowing the grid resources to curb the power fluctuations. Energy storage system (ESS) is one such fast acting resource that helps in limiting and smoothing PV power fluctuations when coordinated by RR control algorithms. This paper proposes an ESS-based RR control strategy to smoothen and limit PV power fluctuations in the power grid. The proposed RR control strategy has been implemented for two types of smoothing namely, aggregated smoothing that considers the overall PV output, and individual node-level smoothing that considers the individual PV output. The technical feasibility of this RR control strategy to limit the PV power fluctuations is analysed using a standard IEEE test network with both centralized and distributed ESS placements at various PV penetration levels. The effectiveness of the proposed RR control strategy in reducing voltage fluctuations in the power network is also studied. Moreover, cost optimization is carried out with various PV penetration levels to ensure optimal ESS operation at the lowest possible cost while adhering to the optimization and network constraints. The paper further provides detailed techno-economic analysis for various case scenarios. • Ramp-rate control of solar PV is implemented using energy storage system. • Different types of smoothing techniques are used in ramp-rate control strategy. • Impacts of both centralized and distributed energy storage systems are analysed. • Techno-economic analysis is conducted for optimal operation of energy storage. [ABSTRACT FROM AUTHOR]

• A novel technique to trace Sun location upon the lunar sky is proposed. • LOLA databases are used to recreate the lunar topography. • Harvesting solar energy from multi-sites on the Moon south pole is proposed. • The multi-site strategy removes the need of energy storage system. • Algorithm to reproduce time series illumination profiles of the Moon. Recently, space organizations have considered the Moon to host lunar bases. Such bases require power and energy to function. However, the efficient and safe use of the energy resources on the Moon is a huge challenge. Space photovoltaic (PV) power systems are appealing technologies due to their maturity and high solar energy availability at some locations on the Moon. The effectiveness of these PV systems depends on their selenographic location, which might necessitate the deployment of energy storage technologies to cover the base's energy demand. Some analysts have proposed the installation of PV modules on kilometers-tall towers near the lunar poles to harvest more solar energy and limit the need for energy storage systems (ESSs). Alternatively, this paper proposes to harvest the energy from multiple sites in the lunar South Pole region using a novel technique to compute the Sun illumination profile and the LOLA topographic databases to compute the terrain elevations. The proposed algorithm seeks the most optimal configuration of sites and tower heights to minimize the longest night period and total distance between the sites. This study assesses groups of 1 to 6 sites assuming the use of towers having heights of 10, 100, and 500 [m]. The time horizon for the analysis is one Axial Precession Cycle, which is approximately 18.6 years. According to the results, a system of two sites with a separation of 42.05 [km] and towers of 500 [m] height has a maximum darkness period of only 3 [h] while another solution proposes a system of three sites with towers of 10 [m] that removes the need of EES (solar eclipse periods by the Earth are not considered). The proposed technique is suitable for engineering applications, such as base planning and operation management. [ABSTRACT FROM AUTHOR]

This paper thoroughly analyses energy, economic and environmental (3E) performance of using different battery (BAT) energy storage system like lead acid battery (LAB), lithium-ion battery (LIB), vanadium redox flow (VRF) battery and mechanical energy storage (MES) like flywheel and pumped hydro storage (PHS) using three different dispatch strategies (DS) such as cycle charging (CC), load following (LF) and generator order (GO), respectively. Andaman and Nicobar(A&N) Island of India have no main grid. Hybrid Optimization Model for Electric Renewable (HOMER) Pro software is used to study feasibility of power generation using various sources. Study incorporating tidal plant with wind turbine and Bio-DG in micro grid is considered first time for an Indian scenario as per authors' knowledge with application of various DS and energy storage technologies. The superiority of LIB over LAB and MES is proved in this study. Wind/Bio-DG /tidal/LIB have relatively least net present cost(NPC), cost of energy (COE) of $1.30M and $0.263, respectively among five energy storage devices using CC dispatch strategy compared to other two strategies. It is investigated that LIB uses only 250 units of batteries, which is 46% less than total utilized LAB units for catering same load. In terms of expected lifespan, energy losses, energy production and state of charge (SOC), performance of LIB are superior to LAB. SWOT and sensitivity analysis have been performed considering fuel price and wind speed. Different techno economic indicators like cost summary, cash flow, fuel summary, economics, electrical parameters, renewable fraction (RF), return of investment (ROI), lowest simple payback period, internal rate of return (IRR), pollutant emission are considered and compared with each other for LAB and LIB combinations. [ABSTRACT FROM AUTHOR]

Economic, technological, and environmental causes are moving energy toward smart distribution networks. The change from fossil fuels to renewable energy sources is extremely environmentally friendly in both the building and transport sectors. Microgrids could be efficiently used in remote areas utilizing renewable energy resources for supplying different energy demands namely power, heat and cold. This paper presents a mixed-integer linear programming (MILP) model for optimizing planning and sizing decisions in microgrids connected to main grid. Planning decisions the amount of generation of each distributed generation (DG) technology and the amount of power transmission to other nodes. Also, sizing of different DGs including photovoltaic and combined heat and power facilities are performed by the proposed model. According to the results, the model is able to optimize sizing and planning decisions in grid-connected microgrids. [ABSTRACT FROM AUTHOR]

This paper presents and compares three approaches to deal with the uncertain nature of energy supply and demand in the problem of optimal sizing of a stand-alone, reliability-constrained hybrid renewable energy system. The system is composed of wind turbines, photovoltaic solar panels, a battery bank, and a diesel generator. The proposed approaches are: (a) adaptive robust optimization with unmet demand as the (un)-reliability metric, (b) scenario-based robust satisficing, and (c) stochastic-free robust satisficing. A column-and-constraint generation algorithm is developed to solve the adaptive robust optimization problem. Furthermore, the theoretical connection between the proposed approaches is drawn, and their numerical performances are compared, in terms of optimal solution cost and reliability, through computational experimentation on a realistic case study. Numerical results show that both the computational times and the out-of-sample performances of the optimal solutions obtained from the two proposed robust satisficing approaches outperform that of the adaptive robust optimization approach, providing a strong justification for their application • Robust optimal design of a stand-alone hybrid renewable energy system. • Adaptive robust optimization versus robust satisficing optimization. • Optimization under the uncertainty of demand and supply. [ABSTRACT FROM AUTHOR]

This paper investigates the performance and fuel-saving effect of a velocity control algorithm on modern 50 cc scooters (Euro 5). The European Parliament has adopted major CO 2 emission reductions by 2030. But modern combustion-powered scooters are inefficiently restricted and emit unnecessary amounts of CO 2. Replacing the original restriction method with the system presented in this paper, the engine's operating point is being improved significantly. A Throttle-by-Wire-System senses the rider's throttle command and manipulates the throttle valve. A redundant wheel speed sensor measures the precise vehicle velocity using the Hall effect. The entire system is managed by a central ECU, executing the actual velocity control, fail-safe functions, power supply and handling inputs/outputs. For velocity control, an adaptive PI-controller has been simulated, virtually tuned and implemented, limiting the max. velocity regulated by legal constraints (45 km/h). In this way, the environmentally harmful restrictors used today can be bypassed. By implementing a human–machine interface, including a virtual dashboard, the system is capable of interfacing with the rider. For evaluation purposes a measurement box has been developed, logging vehicle orientation, system/control variables and engine parameters. A Peugeot Kisbee 50 4T (Euro 5) is serving as test vehicle. Finally, the system has been evaluated regarding performance and fuel efficiency both through simulation and road testing. Fuel savings of 13.6% in real-world test scenarios were achieved while maintaining vehicle performance. • Efficiency is increased and the throttle valve opening is reduced by 50%. • The controlled air supply minimizes the fuel injected by up to 25%. • Significant improvements of fuel consumption and CO 2 emissions by 14%. • Consistent performance is demonstrated by using energy more effectively. [ABSTRACT FROM AUTHOR]

• A new control method for DER is suggested. • The proposed control method balances the load between feeders. • DER connects to feeder through SST. • DER-controlled circuit shared the load between two feeders like ring connection. • Voltage profiles are improved. The multi-purpose use of distributed energy resources (DER) can bring a lot of income to its owners. For example, the load imbalance of the radial feeders in the distribution network is an old problem that occurs due to the simple structure of the radial topology and the unidirectional power flow in traditional networks. With emerging DERs and the development of power electronic technology, previous concepts are changing continuously. This paper aims to balance the load between two feeders whilst, it yields better voltage profiles and more appropriate utilization of distribution feeders. This paper proposes a new control method for DERs (including PV panels plus battery storage) that makes the radial system pretends a ring topology properties. It is done with the help of a solid-state transformer (SST). The relationships of the DER current controller are formulated based on current passes through the ring connection. Case studies are carried out on improved IEEE 33-node and 13-node systems with DERs and SSTs. The results show that the suggested control method can simulate the real ring topology and operating objective namely, load balancing is reached. [ABSTRACT FROM AUTHOR]

Nowadays, climate change is a major global societal challenge that significantly increases environmental stress. Most international organizations and policies have promoted initiatives to minimize emissions, reduce fossil fuel dependence and increase renewable energy resource integration into different sectors. An energy transformation towards more renewable systems is thus a priority. Under this scenario, the present paper describes and evaluates an alternative energy conversion matrix-based model to combine sector electrification, power generation units from renewables, and new clean technologies. The proposed green matrix-based model allows analyzing future scenarios, including electricity participation in end-use consumption and electric power generated by renewables – potentially integrated into different sectors –. The proposed model is evaluated in the state of Maine (United States). This case study is focused on decarbonizing both residential heating and transport sector through the integration of large offshore wind power plant. Results and discussion is also included in the paper, providing expected energy demand reductions and decreasing emissions through the integration of renewables. This energy transition integration case study is proposed in three road-maps with different penetration rates and time scales. The proposed green matrix-based model can be also applied to other areas and energy resources, as an alternative way to analyze and estimate renewable integration into different sectors. • Green Electrical Matrix to link electricity supply and end-use for energy transition. • Assesses renewable potential and electrification tech for end uses, e.g., transport. • Applies model to Maine, USA to show reductions in fossil fuels and emissions. • GIS and MCDM for optimal offshore wind location, maximizing electricity generation. • Short, medium, and long-term projections of renewable integration into energy system. [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well. [Display omitted] [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well. [Display omitted] [ABSTRACT FROM AUTHOR]

• This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. • The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. • An interval security region model of integrated energy system is constructed, according to the N-1 safety constraints of equipment and pipeline. • An engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. • A dimension reduction observation method based on observation variable optimization is proposed, which can enlarge the volume unaffected by renewable energy uncertainty. This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. Reason: Integrated energy system is conducive to achieving low-carbon transition and efficient utilization of energy. As the penetration of renewable energy increases, the security of integrated energy system faces severe challenges. It holds significance for comprehensively studying N-1 security test method in the operation scenario of integrated energy system, the impact of renewable energy on the IES security region model, and the optimal selection for observation variables. Methods: Firstly, a model for interval security region of integrated energy system is constructed, according to the N-1 safety constraints of energy hub key equipment and pipeline. The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. Then, an engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. On the basis, a dimension reduction observation method based on observation variable optimization is proposed, the selection of observation variables help to expand the volume of area unaffected by renewable energy uncertainty. Results: To the existed problem in current research, an example is presented to verify the effectiveness of proposed method, which brings forward N-1 security test method in the operation scenario of integrated energy system, analyzes the impact of renewable energy on the security region model, and realizes the optimal selection for observation variables of simplified security region. Conclusions: Interval security region of integrated energy system can intuitively estimate the security status of operating points and observe the security boundaries. The simplified model can satisfy the needs of calculating precision. Optimal selection of observation variables is necessary, and the volume of area unaffected by renewable energy uncertainty can be effectively increased. [ABSTRACT FROM AUTHOR]

• A new DPV-5G BS-ES communication and power mutual coupling system is developed. • A BSSCP model is proposed to minimize the number of 5G base station deployments. • The ISOM model is used to optimize the microgrid connection lines in 3D space. • Economically optimal simulation experiments are conducted for different PV systems. The emergence of ultra-dense 5G networks and a large number of connected devices will bring with them significant increases in energy consumption, operating costs, and CO 2 emissions. At the same time, the deployment of distributed photovoltaic (DPV) in megacities plays an important role in promoting the integration of "building-photovoltaic", adjusting the city's clean energy supply system, and building a green, and low-carbon smart city. The development of a new "DPV-5G Base Station-Energy Storage (DPV-5G BS-ES)" coupled DC microgrid system and its pre-deployment investment costs are fundamental factors to be considered when the problem of large-scale DPV and BS deployment in cities has to be addressed. There are almost no published papers on this issue in the literature, which only consider the problem of maximizing the deployment of 5G BS service coverage or only perform optimization analyses for the performance of microgrid systems without considering the economic optimality and low-carbon green issues in the construction of urban utilities. In order to solve the problem of 5G BS deployment and economic optimization of topology in the "DPV-5G BS-ES" coupled DC microgrid system, a novel deployment strategy is proposed in this paper. The strategy is divided into: (a) The Geographic Information System (GIS) for extracting and analyzing 3D data from all buildings in the area. (b) A GIS-based formulation of a mixed integer quadratic constrained planning model (BSSCP) under discrete fixed radius coverage constraints is used to delineate building clusters within the area and determine the optimal deployment location of 5G BS under each sub-cluster. (c) An Improved Self-Organizing Mapping (ISOM) model combines GIS and microgrid performance parameters to address the economically optimal connection of grid lines for DPV in 3D space. (d) Simulation experiments were conducted for the power output and profitability of eight different PV panels and tracking systems to calculate the economically optimal type of DPV generation hardware. The experimental results show that the BSSCP model proposed in this study reduced the 5G BS deployment cost input by up to 35.8974 % compared to that of the control experimental model. Meanwhile, the ISOM model obtained an average reduction of 52.3741 % in the cost of connecting cables for the optimized 25 DPV planning and DC microgrid topology. [ABSTRACT FROM AUTHOR]

• The Belgium nuclear phase-out plan is analyzed. • A multivariate model through time-series analyses is implemented over the 1974–2019 period. • We develop and apply an innovative partial differential equations (PDE) algorithm. • Time-series and machine learning findings demonstrate suggests that the nuclear phase-out might neither adversely impact the economy's growth nor threaten the emission reduction target in the long-run. By 2025, Belgium will phase-out nuclear power. Unassessed so far, this policy reform may modify the economic and environmental channels through which energy and society interfere in this country. In this paper, we investigate whether this structural energy change may adversely impact the growth of the Belgian economy (i) and its ability to meet its long-term greenhouse gas emission targets (ii). A multivariate model comprising production factors (labor, capital, and exports), nuclear and renewable energy uses, total primary energy supply, economic growth, and CO 2 emissions from the power and heating sector is combined with real time-series data spanning the 1974–2019 period. The analysis consists in sequentially assessing two distinct nexuses (energy-economy and energy-economy-environment) over reduced- and augmented frameworks (excluding and including nuclear energy), and through a two-stage empirical strategy: time-series econometric estimations (Toda-Yamamoto causality test, Impulse Response Functions (IRFs), and the Auto-Regressive Distributed Lags (ARDL) and Machine Learning (ML) experiments with a Partial Differential Equations (PDEs) algorithm. For robustness purposes, we conduct two seminal tests which relate to dynamic predictive processes (T-Mat and Verticality tests). Besides confirming the time-series findings, our ML results highlight the necessity to timely manage the process of nuclear phase-out, along with a progressive deployment of installed renewable energy capacity. This should avoid additional economic costs, energy security threats, and undermining of climate targets. In doing so, this study combines macro-level nexus investigations with the politics and institutional determinants of nuclear energy reliance and seeks to bring inclusive knowledge on this topic. [ABSTRACT FROM AUTHOR]

In modern power systems, the reliability of energy supplies is a real challenge for the operators. The emergence of renewable energy resources, along with multi-career users, requires multi-career systems. In this regard, the energy hub (EH) as an integrated system can be used to increase the reliability of the system. The power-to-gas (P2G) and P2G storage are two practical technologies to achieve high efficiency in energy systems. In this paper, the contribution is optimal scheduling of stochastic problem in EH system amalgamated with CHP unit, P2G storage, thermal storage, boiler, wind power, and electrical storage to supply the heat, gas, and power loads by regarding demand response program (DRP). For the electrical loads, the load shifting strategy is considered to minimize the operational cost of the EH system. In order to manage related uncertainties about electricity price, wind power, and electrical loads, the downside risk constraint (DRC) method is applied to investigate the EH system function. According to the obtained results, by increasing approximately 2.8% of the operational cost, the risk level can be reduced remarkably. And also, almost 10% of the energy shifted from peak hours to the off-peak time after DRP is applied. • Optimization of power, heat and gas energy consumption in an energy hub. • Energy hub system is integrated with power-to-gas storage. • All uncertainty parameter are modeled via scenario based stochastic programming. • Downside risk constrained approach is proposed for risk modeling in energy hub. • Risk-averse and risk-neutral strategies are compared with each other. [ABSTRACT FROM AUTHOR]

This paper presents an overview of current electricity generation and consumption patterns in the Arctic. Based on published data and new data collection this paper provides an overview of the installed capacity per energy source. A more detailed view of electricity systems is also presented, showing how different types of resources can be used and combined within Arctic communities. The paper briefly discusses which aspects of transmission systems are of special interest under the harsh climatic conditions of the Arctic. A description is given of the various electricity generation technologies that have been proven to be effective for use in Arctic environment, and those that are currently in use, including diesel, hydropower, photovoltaic, and wind power, in addition to discussion of different back-up options. Furthermore, for a more holistic view concerning such energy systems the socio-economic dimension is addressed with a focus on energy security, through an investigation of energy availability and affordability in the Arctic. The availability of energy in the harsh Arctic environments can be crucial for survival and affordability, and can have a huge impact among low-income communities. • Overview of current electricity generation and consumption patterns in the Arctic. • Potential use of different energy resources in the Arctic. • Arctic Energy security. • Socio-economic dimension of energy systems in the Arctic. [ABSTRACT FROM AUTHOR]

Transistor threshold voltage (V t) scaling causes higher power consumption by increasing the subthreshold leakage and short-circuit currents in CMOS circuits. Leakage currents are significant contributors to the overall power consumption of digital systems-on-chip as threshold voltage, channel length, and gate oxide thickness are reduced with CMOS technology scaling. A new dual-pullup/dual-pulldown (DPU/DPD) repeater is proposed in this paper for higher energy efficiency in low-voltage and low-frequency applications. The standby mode leakage power consumption is reduced by 59.11% with the proposed clock tree as compared to the conventional 3 level H-tree operating with a power supply voltage of 1.0V in a 45 nm CMOS technology. The short-circuit currents are suppressed by selectively employing high-V t transistors in the repeaters. The clock network with the proposed buffer lowers the active mode energy consumption by up to 24.91% as compared to a conventional clock tree under equal silicon area constraint. Post layout results reveal that the statistical spread of clock skew in the DPU/DPD H-tree is also 20.60% lower than the conventional H-tree network. • A novel dual-pullup/dual-pulldown (DPU/DPD) repeater is presented for low leakage and high frequency clock distribution. • The minimum operating voltage of a clock tree is determined for achieving the lowest total energy consumption while satisfying the clock frequency and signal transition time (10% of clock cycle time) requirements at the leaves. • The influences of leakage currents on the total energy consumption of the clock trees designed with three different repeaters (conventional CMOS inverter, previously published SPLIT-IO, and the new DPU/DPD) are benchmarked at various supply voltages and frequencies. • The leakage power reduction provided with the DPU/DPD buffers is evaluated. • The impact of process parameter fluctuations on the clock skew of the novel clock distribution network is also characterized in this paper. [ABSTRACT FROM AUTHOR]

Microalgae originating from wastewater has been exhibiting particularly promising results in terms of biohydrogen production and wastewater treatment. This paper aims to review the factors affecting production, pretreatment techniques to improve synthesis, advanced technologies utilized for enhancing biohydrogen production, and techno-economic feasibility evaluation of the processes at a commercial scale. Microalgae possess metabolic components to synthesize biohydrogen using photobiological and fermentative processes but must undergo pretreatment for efficient biohydrogen production. The efficiency of these processes is influenced by factors such as the microalgae species, light intensity, cell density, pH, temperature, substrates, and the type of bioreactors. Moreover, many limitations, such as oxygen sensitivity, altered thylakoid constitution, low photon conversion efficiency, light capture disruption, and the evolution of harmful by-products hinder the sustainability of biohydrogen production processes. High operational and maintenance costs serve as the major bottleneck in the scaling up of the process as an industrial technology. Therefore, future research needs to be directed towards increasing optimization of the processes by reducing energy and resource demand, recycling metabolic wastes and process components, genetically engineered microalgae to adopt more efficient routes, and conducting pilot studies for commercialization. • Wastewater-based microalgae is a potential source of biohydrogen. • Factors affecting the biohydrogen production from microalgal biomass are discussed. • Technologies related to biohydrogen production from microalgae are reviewed. • Key challenges of biohydrogen production process from microalgae are highlighted. [ABSTRACT FROM AUTHOR]

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing. For example, determining when it is best to relocate VMs from an overloaded host is one aspect of dynamic VM consolidation that directly influences the resource utilization and Quality of Service (QoS) offered by the system. This paper presents a new proposal with an Interval-valued Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. The results obtained in a simulated cloud computing environment showed improvements for energy efficiency of 2.3% with Inter Quartile Range (IQR). With regard to Service Level Agreement (SLA), results of 10.1% were achieved with Threshold Policy (THR), and finally, IQR obtained 7.9% better results considering the reduction in the number of VM migrations. [ABSTRACT FROM AUTHOR]

The exploitation and utilisation of solar energy is challenging because of both diurnal and seasonal variation. Seasonal thermal energy storage is a prominent solution to solve the problem of seasonal variation of solar production. This paper investigates both the optimal design and energy management of a renewable energy plant with seasonal thermal energy storage. As a case study, the thermal, cooling and electrical energy demands of a university campus during one year are considered. Three scenarios, characterized by a different available area (20,555 m2, area covered at present; 50,000 m2, potentially available area by exploiting the entire rooftop and parking area; unlimited area) for solar thermal collectors and photovoltaic panels are investigated. In the first two scenarios, the exploitation of solar energy allows a primary energy saving of 31% and 57%, respectively compared to the use of a conventional plant. The seasonal storage reaches an overall efficiency (thus considering both charging and discharging) up to 84%. Finally, the analysis of the third scenario shows that the feasibility of meeting the energy demands by only exploiting solar energy is technically challenging because a large area equal to about 100,293 m2 is needed for solar thermal collectors and photovoltaic panels. [ABSTRACT FROM AUTHOR]

Because of the requirement of the utilization of energy resources in a way that is both effective and efficient, solid oxide fuel cells have become a notable preference due to their advantages such as high efficiency and use with different fuels. In addition, the integration of these systems in the production of alternative fuels such as hydrogen and ammonia are important for a sustainable future to combat environmental problems. For this reason, the main intention of this paper is to introduce a new plant combining the different systems that use the solid oxide fuel cell for a cleaner and sustainable future. In the modeled work, a solid oxide fuel cell, a gas turbine, an organic Rankine cycle, a Kalina cycle with ejector, a hydrogen generation and storage process, a wood steaming plant, and an ammonia production system is integrated, to generate useful products. Detailed thermodynamic modeling is performed through energy and exergy methods, to determine the performance of the advised system and subsystem. Moreover, energy efficiency, exergy efficiency, and exergy destruction analyses methods are applied to each sub-plant and the whole system separately. In addition, parametric research is carried out to examine the effects of modifying key parameters on the plant's and subsystems' performance. Looking at the analysis results, the amount of the hydrogen and ammonia generation capacities of this work are 0.0085 kgs−1and 0.2023 kgs−1, respectively. In addition, the modeled power plant produces a power rate of about 20,180 kW. As a result, this proposed study is calculated to have 61.04% energy efficiency, and 57.13% exergy efficiency. • A novel combined plant with SOFC is developed for hydrogen and ammonia generation. • Modeled system is investigated thermodynamically with energy and exergy efficiencies. • Parametric analysis is conducted to determine the how the effect of each parameter on the system's performance. • Overall energy and exergy efficiency of this modeled plant is 61.04% and 57.13%, respectively. [ABSTRACT FROM AUTHOR]

This paper deals with the emission reduction in synthesis-gas production by better integration and increasing the energy efficiency of a high-temperature co-electrolysis unit combined with the Fischer-Tropsch process. The investigated process utilises the by-product of Fischer-Tropsch, as an energy source and carbon dioxide as a feedstock for synthesis gas production. The proposed approach is based on adjusting process streams temperatures with the further synthesis of a new heat exchangers network and optimisation of the utility system. The potential of secondary energy resources was determined using plus/minus principles and simulation of a high-temperature co-electrolysis unit. The proposed technique maximises the economic and environmental benefits of inter-unit integration. Two scenarios were considered for sharing the high-temperature co-electrolysis and the Fischer-Tropsch process. In the first scenario, by-products from the Fischer-Tropsch process were used as fuel for a high-temperature co-electrolysis. Optimisation of secondary energy sources and the synthesis of a new heat exchanger network reduce fuel consumption by 47% and electricity by 11%. An additional environmental benefit is reflected in emission reduction by 25,145 tCO 2 /y. The second scenario uses fossil fuel as a primary energy source. The new exchanger network for the high-temperature co-electrolysis was built for different energy sources. The use of natural gas resulted in total annual costs of the heat exchanger network to 1,388,034 USD/y, which is 1%, 14%, 116% less than for coal, fuel oil and LPG, respectively. The use of natural gas as a fuel has the lowest carbon footprint of 7288 tCO 2 /y. On the other hand, coal as an energy source has commensurable economic indicators that produce 2 times more CO 2 , which can be used as a feedstock for a high-temperature co-electrolysis. This work shows how in-depth preliminary analysis can optimise the use of primary and secondary energy resources during inter-plant integration. [Display omitted] • Transition to sustainable energy and share of high hydrogen-containing fuels. • Inter-plant integration via by-products recycling and better heat recovery was proposed. • Methodology combined process integration and process modification principles. • Unified process flow diagram for different fuel types was developed. • In-depth integrated syngas production consumes 47% less energy and saves 25,145 tCO 2 /y. [ABSTRACT FROM AUTHOR]

This paper explores opportunities for effective policy intervention for residential energy savings in southern China, by analyzing achievable potential of residential energy savings in the city of Xiamen as a case study. In contrast to static technical and economic potential analyses, the achievable potential analysis is based on dynamic energy consumption projections, which consider two real-world factors: (1) gradual ramping-up adoption process of advanced technical measures; and (2) consumers’ adoption of these measures. A bottom-up type Residential Energy Consumption (REC) projection model specifically tailored for southern China was developed in this paper, based on the general logic and calculation principles utilized in the U.S. EIA’s “National Energy Modeling System (NEMS),” and the adoption theory of advanced technical measures proposed by Kastovich. This REC projection model was then used as a policy analysis tool to quantitatively evaluate the impact of various policies on residential energy savings in the case study city of Xiamen. The analysis of the Xiamen case shows that although there is a significant technical potential for residential energy savings in the city (about 20.9–24.9%), the maximum achievable potential (MAP) in 2020 is only about 8.3–8.4% of that year’s business-as-usual baseline consumption. Moreover, with existing policies only about one-fourth to half of the calculated MAP is likely to be achieved by 2020. These findings indicate that in order to realize a larger share of achievable potential for residential energy savings in China, additional and more effective polices are needed. [ABSTRACT FROM AUTHOR]

Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ -constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus. • A bi-level model for water allocation in energy-water nexus system is proposed. • The hierarchical decision sequence between the river basin and subareas is solved. • The tradeoffs between social equality and ecological damage are made. • Optimal water allocation for multiple subareas and energy resources are quantified. • Uncertainties in decision-making environment are handled by fuzzy set theory. [ABSTRACT FROM AUTHOR]

China's rural solar photovoltaic projects (SPVPs), as an innovative initiative to improve livelihoods through clean energy, have played a positive role in poverty reduction. However, whether this measure can enhance energy services for rural families remains unconfirmed. This paper employs a quasi-experimental method from the perspectives of energy service accessibility and affordability to quantify the net contribution of China's rural SPVPs to enhancing family energy services. The results indicate: (1) SPVPs can significantly improve family energy services, with a net effect of 14.7 % on the energy service accessibility and a net effect of 44.5 % on the energy service affordability. (2) SPVPs can increase the energy consumption capacity, energy supply capacity and energy equipment utilization rate by 2.6 %, 7.1 % and 5 %, respectively. Furthermore, these initiatives can effectively slash the energy costs of rural families by 4.3 %, bolster the energy access affordability by 3.9 %, and catapult energy investments by an impressive 44.9 %. (3) These projects also perform well in the family energy consumption cleanliness. (4) The effect of SPVPs in providing energy services presents a regional difference, with the best performance observed in 'Type III' solar resource areas. Meanwhile, the implementation of China's rural SPVPs does not involve "elite capture." • We analyze the changes of energy accessibility and affordability in rural China. • We quantify the energy services effect of solar PV projects (SPVPs). • SPVPs perform well in improving family energy consumption cleanliness. • The energy services effect of solar PV projects has spatial heterogeneity. • The implementation of China's rural SPVPs does not involve "elite capture.". [ABSTRACT FROM AUTHOR]

Vision-based displacement measurement has become increasingly popular in bridge health assessment. However, this technology faces challenge that a complicated power-consuming supply system must be installed in the darkness, making it unable to meet the needs of low energy consumption and low cost. This paper proposes a bridge displacement measurement method using power-free target and thermography to address it. First, a thermography measurement system for bridge displacement in the darkness is developed. The power-free target with a hollowed-out circular feature is adopted to form the contrast to the background, and graphene is coated on the target surface to enhance the infrared emissivity and contrast further. Second, a sub-pixel elliptic center positioning algorithm based on thermography enhancement and data optimization is proposed to improve the accuracy and the ability to resist occlusion. The feasibility of the method is verified by laboratory tests and bridge field tests. When the measurement distance is within 15 m, the results meet the engineering error requirements of 5%, with the prospect of popularization in small and medium-sized bridges displacement measurement. • A bridge displacement measurement method in the darkness using thermography is proposed for the first time to address the power supply problem in bridge measurement. • A thermography measurement system for bridge displacement is developed and power-free target with graphene coated is adopted to form the contrast to the background. • A two-step parameter optimization strategy for elliptic center positioning is proposed. [ABSTRACT FROM AUTHOR]

Urban transportation is a significant contributor to energy consumption and environmental degradation, while solar electric bikes (SE-bikes) represent a viable solution for alleviating both problems. In this paper, the charging and discharging properties of the SE-bike are investigated. An SE-bike is constructed for short and long term experiments. Experimental results indicate that the SE-bike can increase endurance by 20.5 km on a sunny day, and can meet daily cycling needs of 10 km without charging in both summer and autumn in Hefei. To assess the annual performance of the SE-bike, a theoretical model is developed, which includes the effects of environmental factors, vehicle shapes, daily electricity usage, etc. Simulation results reveal that the SE-bike only needs to be charged 5 times per year to meet daily demands in Hefei, which is a relative reduction of 93.0 % compared to 71 times per year for the regular bike without solar power supply. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]

As the number of transistors on a chip surface increases, power consumption becomes more and more a serious concern. A promising solution to bridge the gap between resource-constrained gadgets and computation-intensive applications could be the approximate computing paradigm. This paper presents four efficient approximate full adder cells based on dynamic logic and carbon nanotube field-effect transistors (CNFETs). To the best of our knowledge, dynamic logic has never been deployed in the design of approximate full adders before. Comprehensive simulations and analyses are conducted to study the efficacy of the new circuits. Simulation results indicate remarkable improvements compared to state-of-the-art circuits. For instance, at 0.9 V power supply, our final proposed design improves the power-delay-area product (PDAP) metric by at least 63% compared to its peers. Moreover, the applicability of the proposed adders in the image sharpening application is examined by measuring peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) using the MATLAB tool. The proposed designs have also a reasonable performance in this regard. • Deploying dynamic logic (DL) in the design of approximate full adders for the first time. • Designing ultra-low area and energy consumption full adder structures. • Applying the proposed adders in the image processing application. [ABSTRACT FROM AUTHOR]

The transfer of energy-intensive projects to areas abundant in clean energy resources holds promise for optimizing the energy structure. After obtaining specific information on transferred projects of listed energy-intensive firms through search or phone contact, this paper innovatively constructs a three-dimensional panel containing 1,859,220 observations to empirically examine the attraction of clean energy to energy-intensive projects under the carbon intensity constraint policy (CICP). Our findings reveal that: (1) the CICP promotes the transfer of gray energy-intensive projects, leading to the transfer structure trending towards graying. (2) Furthermore, we observe a positive correlation between the proportion of thermal power in transfer-out areas and the outflow of gray energy-intensive projects. Conversely, areas with higher wind power and photovoltaics attract a greater inflow of gray energy-intensive projects. (3) Notably, clean energy predominantly attracts the inflow of gray projects rather than green projects. Our findings provide micro evidence and valuable insights into the transfer of energy-intensive industries, informing the design and implementation of future environmental policies for policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong's (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively. [Display omitted] • Priority-ordered incentive-based DR program for the EV and non-EV loads is proposed. • Fast dynamic reconfiguration of the off-grid microgrid is presented. • Multi-objective energy scheduling problem for the off-grid microgrid is modeled. • Utopia-tracking approach is used to solve the conflicting multi-objective problem. [ABSTRACT FROM AUTHOR]

Given the stochastic nature of solar energy, integrating PV arrays with energy storage systems (ESSs) is crucial for a consistent power supply. Effective maximum power point tracking (MPPT) control is of great importance in this context. The significance lies not just in achieving high energy efficiency but also in ensuring overall system safety and reliability, as rapid and stable power tracking control alleviates power stress on ESSs. Challenging factors like parameter uncertainty from the PV array linearization process, dynamic DC bus variations induced by pulsed power loads, and sudden irradiance variation under cloudy weather, make the maximum output power of PV arrays largely dependent on the effectiveness of the control laws. Therefore, this paper presents a comparative study for MPPT control using PV-interfaced DC/DC converters. The aim is to demonstrate the advantages of employing average large-signal model-based sliding mode-type controllers in PV system integration with health-conscious objectives. A comprehensive system-level control-oriented modeling process is presented, followed by the theoretical background of three types of controllers (classical proportional–integral (PI)-type controller, μ -synthesis-based robust controller, and sliding mode controller) for practical applications. Extensive tests with real experimental data reveal sliding mode-type controllers' superiority in various integration scenarios. For example, with random solar irradiance variation over 0.5 s, they achieve a 54.75% reduction in energy loss, outperforming the commonly used PI-type counterparts. [ABSTRACT FROM AUTHOR]

Understanding the connection between water supply and energy consumption in a circular economy is crucial for managing the demands of these resources. Sankey diagrams have the potential to enhance the visualisation of resource flows. However, there is a knowledge gap regarding the effective use of Sankey diagrams to capture the water-energy nexus and circularity of water. This study focused on effectively utilizing Sankey diagrams to model the nexus of water and energy flows and to assess the circularity of water. A comprehensive literature review was conducted on major studies on water material flows and modelling. This was supported by a review of approaches for modelling circularity, which have evolved from the material circularity index. The newly developed framework in this paper models water resource flows at all stages of the water supply chain, embedding the nexus as an interaction and considering resource intensity and material circularity index at each node. A city-level case study in South Africa was then used to apply the recommendations, model circularity, and the water-energy nexus using Sankey diagrams. In terms of circularity, the results indicate that water loss in the study area was very high, accounting for up to 40% of the total water supply and the water recycle rate was still very low, ranging from 3% to 7%. In terms of the nexus, the residential sector was found to have the highest energy intensity for water supply, primarily due to energy demand for water heating. Exploring the use of renewable or solar energy for water heating will be of interest and value. The new approach presented in this study allows for the simultaneous consideration of objectives, enabling a more robust analysis of energy-effective water use and water circularity along the water supply chain. [Display omitted] • A new framework for modelling the water energy nexus enriched by circularity options, energy intensity factors and confidence modelling. • Options for embedded circularity through the water supply chain. • The urgent challenge to address massive water loses in water supply systems for water scarce regions. • New Sankey diagrams showcasing how to model nexus and circularity. [ABSTRACT FROM AUTHOR]

The 2023 revision of the Energy Performance of Building Directive underlines the need to consider the whole life cycle emissions for buildings. The decarbonization targets could not be achieved if reduction of energy demand in the operational phase is accompanied by a high energy resource use in the whole life. This paper proposes a novel investigation of an energy renovation project, involving both building envelope components and space heating system. The adopted method is based on the life cycle assessment, in the variant cradle-to-gate with options, to analyze and compare the design data achieved through an analysis of product (embodied) and use (operational) stages. Different solutions for energy improvement of the building performance are identified, and the scenario with the lowest embodied energy was found to be more energy efficient globally. The most effective scenario, with energy and CO 2 savings of 39% and 40% is characterized by a weight of embodied energy and emissions of 6.5% and 6.9%, respectively. Given the rise of the weight of the embodied energy in the whole life cycle of future buildings, the life cycle assessment will become crucial for sustainable design and in the transition to an energy-efficiency built environment. [Display omitted] • The impact of life cycle analysis in the process of energy efficiency is investigated. • The "cradle-to-gate" with options approach is implemented. • The efficiency of walls and windows involves the greater use of energy resources. • The most energy-efficient scenarios from an operational and non-operational point of view are different. • The scenario identified in the analysis of A1-A4 phases allows a higher total energy saving. [ABSTRACT FROM AUTHOR]

Energy is one of the essential components for the social and economic growth of urban and rural communities worldwide. However, the lack of energy supply is one of the most significant challenges facing remote or local communities in Nigeria. Distributed generation systems based on renewable energy, conventional sources, or hybrid resources are possible energy production solutions for these communities. This paper, therefore, focused on providing an energy generation system based on photovoltaics (PV) and diesel generators (DG) for stand-alone applications. A comparative analysis was carried out to assess the impact of control strategies, namely load following (LF), cycle charging (CC), and combined dispatch (CD). The designed energy systems: 60 kW PV, 78.69 kW PV, PV-DG-LF, PV-DG-CC, and PV-DG-CD were simulated using HOMER Pro while technical, economic, and environmental indices were used as performance metrics. Simulation results showed that 60 kW PV generated 84,927 and 75,859 kWh, without and with temperature effect, respectively, with the corresponding unmet electric load (UEL) of 13.8 and 14.2%. The result further demonstrated that PV-DG-LF is the optimal design with the highest renewable penetration of 80.7% and the least annual total fuel consumption (TFC) of 6,594 l. The economic results further confirm the suitability of PV-DG-LF with the lowest fuel cost (FC) and moderate cost of energy (COE) of $219,934 and 0.302 $/kWh, respectively. The PV-DG-LF is found to be more environmentally friendly, with the least annual CO 2 emission of 16,629.652 kg. The study considers veritable strategies to achieve affordable, clean, and reliable energy in line with research efforts on realizing the sustainable development goal (SDG) 7. [ABSTRACT FROM AUTHOR]

With the growing share of the climate-sensitive renewable energy market supply in China, estimating future energy drought risk (ED) is essential. In this paper, based on CMIP6, energy demand and power generation are estimated from 2023 to 2100 under SSP2-4.5 and SSP5-8.5 climate scenarios, and ED is defined from high demand (HD), low wind-solar generation (LWS), and residual load (RL). The results show that the ED exacerbation is relatively high under the SSP5-8.5. Between 2023 and 2100, the duration and severity of HD and RL notably increase, while LWS changes are relatively weak. We find that HD and RL exhibit high summer risks in the south and high winter risk in the north. The return periods vary significantly and extends over time in most regions. Moreover, there are few ED when the installed power generation capacity (IPGC) is increased. When the IPGC increases by 2%–5%, the LWS will decrease at least 20%–72%. With a 10% increase, the LWS will be mitigated by at least 80%, whereas the mitigation for RL is limited, approximately 3–17% at different rates. This work performs the first evaluation of future ED and provides the useful references for risk mitigation in China based on CMIP6. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze through simulation how the energy efficiency of a single electrolysis stack at various loads is affected by shunt currents and to determine the energy-optimal load distribution between lines in a multiline electrolysis system under various magnitudes of shunt current losses. A dynamic energy and mass balance model of an industrial 3 MW , 16 bar alkaline water electrolyzer (AWE) process was developed using MATLAB. The optimization goal is to determine the power supply for each AWE line so that it can meet any hydrogen demand while minimizing the global specific energy consumption (SEC). The Particle Swarm Optimization (PSO) algorithm is used to minimize the objective function. According to the results of the single stack investigation, shunt current reduction could significantly improve the energy efficiency of partial-load operation. In addition, the optimization study revealed that whenever two or more lines are required to run in order to satisfy the hydrogen demand, the global SEC is minimized when the lines operate at equal loads. [ABSTRACT FROM AUTHOR]

With the rapid development of global informatization, data center computing tasks are increasing, and the resulting energy consumption problems are particularly prominent in the context of carbon emission reduction, and affect the safe and green operation of data centers. Aiming at the problem of high and large fluctuation energy load of data center servers, this paper presents a method of optimizing data center servers load considering spatial-temporal load distribution of computing resources. Co-optimized allocation of computing resources and load is studied with examples from the property that computing resources is adjustable in time and space dimensions. The results show that the proposed method is able to reduce the peak electric and cooling loads of the data center room by 10.7 %, reduce the total annual electric consumption by 2.66 % and the total annual cooling consumption by 2.46 %, and reduce the economic and environmental costs of energy supply for the data center in the case study, which comprehensively shows that the load optimization of data center servers has been achieved through the optimal allocation of computing resources. [ABSTRACT FROM AUTHOR]