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[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]

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]

• 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]

Meeting global energy demands has been a major challenge due to the shortage of primary energy resources. Adding renewable energy resources to the grid has become increasingly popular. During peak hours of energy demand, the sun's irradiance is strong enough to generate a significant amount of energy. This paper will design a photovoltaic energy generating system that harnesses solar energy using the latest technological advancements available. A system like this one is an ideal solution to addressing rising energy demands and reducing carbon footprint. Renewable energy sources can minimize carbon emissions, operating costs, etc., but their intermittency and uncertainty make their reliability a serious concern. The objective of this paper is to improve the reliability of the power grid through the use of solar photovoltaic (PV), battery energy storage systems (BESS), and distributed generation systems (DG). [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]

This research paper presents a novel technique for improving load-shedding in DC microgrids by incorporating a binary number generator (BNG). Load shedding, a controlled strategy utilized to maintain equilibrium between power supply and demand within a microgrid when the aggregate demand surpasses generated power, is widely employed. Nevertheless, conventional load-shedding methods frequently encounter challenges, notably over-shedding and voltage deviations, particularly in scenarios involving numerous controllable loads. To overcome these challenges, an enhanced algorithm is proposed, which integrates the BNG technique. Simulation results conclusively demonstrate the effectiveness of the proposed algorithm in ensuring microgrid stability during periods of supply-demand imbalances. [ABSTRACT FROM AUTHOR]

Cloud computing technology provides access on demand to virtualized resources, services, and applications via a distributed network. In cloud data centers effective energy utilization is a critical concern in today's technology-driven world. Cloud data centers (CDC) are massive facilities that host and manage an enormous amount of data and computing resources. This article addresses the growing significance and energy-intensive nature of data centers. Due to the rapid growth of cloud computing, it offers on-demand access to resources globally and leads to substantial power consumption and carbon impact on the environment. They consume substantial amounts of energy, and optimizing their energy utilization is essential for reducing operational costs, minimizing environmental impact, and ensuring sustainable growth. To combat this, efficient energy-saving approaches using machine learning methods have been researched. ML methods hold great potential for enhancing energy efficiency in CDCs by analysing data, detecting patterns, and optimizing resource usage. The focus areas include CPU usage prediction, overload finding, underload estimation, selection, migration, and relocation of VMs to attain resources and improve energy utilization. The paper compares energy-saving results achieved by different machine learning techniques in data centers to minimize energy usage and meet service level agreements (SLA). The machine learning approaches reduce energy consumption from 1.6% to 88.5% compared to the benchmark approach mentioned, considering various settings and parameters. [ABSTRACT FROM AUTHOR]

A micro-grid (MG) is regarded as a localized small-scale power supply system designed for providing electrical power for a remote community, which may be connected to the grid. The paper considers six participants' sites in a remote community. The peak energy consumption patterns for these participants must be different to ensure coalition for mutual benefits and this would facilitate efficient utilization of energy resources. This community concerned may include rural communities having participants such as schools, housing estates, industrial sites, etc. The propose approach would ensure that many participants and their needs are captured and optimized profit of the participants would ensure mutual benefits. In islanded MG, most of the cost/profit distributions in energy trading problems are done based on the self-interest of the participants. Some consider a fair (acceptable or reasonable) cost/profit distribution using the cooperative game theory (CGT) based on the Nash bargaining solution (NBS). However, this method leads to a high degree of dissatisfaction. To improve the fairness and mutual benefits for all MG participants, a new flexible approach is developed. Specifically, CGT which uses a generalized NBS (GNBS) has been proposed, which relaxes certain axiom of the NBS to ensure that fairness of game theory is achieved. The approach allows the profit of the MG participants to be shared using a mechanism based on the negotiation power (i.e. weighted fairness) so that the MG would attain an improved economic outcome. Empirical evaluation of the approach indicates that overall profits using CGT is higher when compared with an independent approach. Moreover, different in negotiation power is achieved in which higher profit is allocated to any participant based on participant's mutual agreement in achieving fair profit distribution. A robust optimization algorithm called Teaching-Learning-Based-Optimization (TLBO) is developed to obtain optimal results. The heuristics algorithms are used in testing the effectiveness of TLBO, which confirm that the TLBO is indeed powerful and robust enough to give quality solutions in solving energy-trading problems. [ABSTRACT FROM AUTHOR]

Faced with the growing demand for energy worldwide, fossil fuels, like any other resource available in finite quantities, are in the process of disappearing since their production cannot increase indefinitely. On the other hand, Russia's war with Ukraine is driving up fossil fuel prices, which in turn is impacting price increases at several levels. Added to this is the need to limit global warming, which is largely due to CO2 emissions from the consumption of fossil fuels. Faced with all these constraints, it is essential that the world looks to other sources of energy and that Morocco, in particular, can limit its energy dependence on fossil fuel imports by ensuring an internal energy supply; the best solution appears to be the development of biofuel and in particular, biodiesel. Due to their high energy content and compatibility with gasoline supply infrastructure, biodiesel may be utilized in motor vehicles. To achieve the optimal utilization of biodiesel in internal combustion engines, it is first required to choose the best feedstock that will provide both increased engine performance and economic viability. In this perspective, this paper provides a summary of the climate and energy situation in the world and in Morocco in particular, and then addresses the interest of biodiesel and its production. [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]

Although energy poverty has obtained wide focus in recent years, there is no consensus on the measurement of energy poverty. Given that China is experiencing socio-economic transformation as well as energy transition, this paper employed a total of 28 indicators, which representing three categories (energy service availability, energy consumption and generation cleanliness, and residential energy affordability and efficiency) that capture China's features as well as consider policy factors, to develop a new multi-dimensional energy poverty index by using entropy weight method. The result reveals that the energy poverty level has been improved nationwide for the period 2014–2019; three sub-indices also show downward trends while household energy affordability and efficiency suggests the fastest improvement; however, significant differences exist in energy poverty not only between urban and rural areas but also across provinces in China. Policy recommendations are as follows: continue to promote rural power grid renovation and upgrading and the construction of natural gas pipelines, increase clean energy support; encourage diversified development of rural energy supply methods, and provide more innovative and diversified green energy and financial products. • An integrated energy poverty index system for China was established. • A total of 28 indicators used in this paper are applicable to China's features as well as considering policy factors in China. • The energy poverty level has been improved nationwide from 2014-2019, and all three sub-indices show downward trends, but varies greatly between urban and rural areas and across geographic regions [ABSTRACT FROM AUTHOR]

The exploitation of limited energy resources can generate socio-economic inequalities calling for a combination of justice with the socio-technical study of modern energy systems to advance its understanding and remediation. Within this context, this paper uses energy justice and econophysics as theoretical and methodological frameworks to discuss issues of energy poverty in Mexico. The results emerging from this research illustrate that, according to data from 2014, around 61 % of Mexican households suffered from energy poverty due to issues of accessibility or affordability of modern energy services and fuels (with 11.54 % of households facing both types of energy poverty). This paper provides a novel approach that combines advanced quantitative methods based on econophysics, with conceptual frameworks from social sciences like energy justice to discuss issues of energy poverty in Mexico. Furthermore, this research performs an approximation to energy consumption in Mexican households based on monetary expenditure, the heat value of fuels and unit prices during 2014. These methods contribute to the understanding and characterisation of household energy needs and energy consumption in Mexico, reducing the existing gap in the academic literature on the analysis and critical thinking of energy poverty in Global South contexts (specifically in Latin America). • Energy poverty in Mexico is analysed based on metrics of affordability and accessibility. • 38.84% of Mexican households suffered energy poverty by accessibility, 34% to affordability, and 11.54% suffered both. • Econophysics and energy justice are demonstrated as valuable methodological and conceptual tools to examine energy poverty. • This study demonstrates the need to consider transport as a core energy service for future energy poverty studies. • Although quantitative methods are crucial to understanding energy poverty, complementary qualitative studies are needed. [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]

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]

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]

In most business and residential organizations, Diesel Generators (DG) is a viable supplementary power source for ensuring an undisturbed power supply. The DG is a hybrid machine that generates electrical energy using a Diesel Engine (DE) and an Electric Generator (EG). By routinely monitoring crucial machine parameters, alternative power source efficiency can be improved. Furthermore, Condition Monitoring Systems (CMS) based on the Internet of Things (IoT) have supplanted the traditional equipment maintenance method. Predictive maintenance is also an important building block of Industry 4.0, whose entire process and performance can be fully understood by using IoT-enabled Remote Monitoring (RM) schemes. Firstly, this paper introduces a remote monitoring and data acquisition scheme to realize the concept of predictive maintenance. Secondly, this article discusses a strategy for real-time observation of DG parameters as well as a comprehensive analysis of various metrics. Thirdly, this research article includes a monitoring and analysis scheme of crucial factors in a DG, like the speed of an engine, voltage output, the current produced, power factor, coolant required, fuel consumption, and battery health. Different mathematical models are formulated by correlating experimental data and estimating the coefficients. Finally, to create suitable real-time warnings under critical circumstances, a fuzzy logic-based Decision Support System (DSS) and web-based integration elements are presented. [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]

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]

• 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]

Our paper analyzes the optimal capacity investment in solar panels system for consumers. During the daytime, solar panel system absorbs energy from the sun and supplies the energy generated to the building structure. Our model takes into consideration the variations in the daily consumption of electricity and the variations in the daily available sun energy. We model these variations in our paper as uncertainty in energy demand and uncertainty in energy supply. Excessive usage of electricity that is beyond the energy generation of the solar panels are bought at a higher price from the utility company than the payment received from the utility company for excess energy sold to the grid. We analytically solve for the optimal quantity of solar panels to purchase and install. Our paper fills the research gap in quantifying the impact changes in solar energy yield rate, consumption, and sell-back rate has on the consumer's optimal investment decision in solar panels. We find that the optimal quantity of solar panels increases with the cost of energy during the daytime and payment for excess energy sold to the grid. Interestingly, when solar yield increases, the optimal solution only increases initially, and then it will eventually decrease. Furthermore, our analytical models provide consumers with a decision support system to determine the optimal number of solar panels to purchase and install while taking into consideration the random yield of solar panels, variations in energy consumption, and potential for sell back of excess unused energy generated under complex billing structures from the utility company. Our mathematical models are adaptable to policy changes such as rebates and purchase and sell back rates for electricity. Our numerical study validates the mathematical models and further analyzes the impact changes in solar energy yield rate, energy consumption, and sell-back coefficient have on the consumer's capacity investment decision in solar panels. Our numerical studies find that the lower the reliability factor of solar yield, the more impact the daytime energy cost has on the optimal quantity. When the sell-back coefficient exceeds a certain threshold, the optimal quantity of solar panels increases exponentially. [Display omitted] • We model the optimal quantity of solar panels to purchase and install for consumers. • Optimal quantity increases with consumption, but the rate of increase is decreasing. • Optimal quantity increases with energy daytime cost and sell-back rate. • When solar yield is high, the impact of payment coefficient is significant. • Increased variability in consumption, optimal quantity increases. [ABSTRACT FROM AUTHOR]

Forecasting energy demand has become increasingly important due to technological advances, especially new power systems and population growth. Accurate predictions of energy demand and supply are crucial for academics and policymakers. Also, energy consumption is non-stationary and dynamic in time, requiring an adaptive forecasting algorithm. This paper presents a new adaptive hybrid approach for energy time series forecasting using statistical methods. Some of the most popular energy demand time series applications are used to demonstrate the superiority of the proposed algorithm, which covers a wide range of critical energy demand forecasting situations at various scales. The paper's objectives are presented in three phases. In Phase I, ARFIMA is used to forecast the entire original time series as a statistical inspector and later for comparison. Although ARFIMA's fractionality feature helps to capture long and short-term memory behavior in time series, to be more adaptive and become close to a nonlinear algorithm that can handle non-stationarity, and Gaussian and non-Gaussian time series closely, a novel dynamic statistical structural break detection framework is developed, and used in phase II to identify the time series' change points and associated time indexes. Since time series are partitioned based on structural change detections, they approach one in terms of the Hurst exponent and exhibit pure long memory, which is ideal for ARFIMA modeling. Phase III captures non-stationary statistical properties and memory characteristics by applying adaptive ARFIMA modeling to segmented time series. Finally, the predicted partitions are concatenated. Various performance assessment metrics, e.g., Mean Absolute Percentage Error (MAPE), tested on the final achieved results show that the proposed adaptive algorithm outperforms the forecasting capabilities of the existing algorithms on the same energy demand time series used in the current paper. MAPE of the proposed approach for the four utilized real-life cases are 1.72%, 1.35%, 0.22%, and 4.4%, respectively. Therefore, the proposed model is a satisfactory method for energy demand forecasting due to its high accuracy. • A novel adaptive hybrid algorithm is developed for energy demand forecasting. • The prediction system uses ARFIMA and a novel dynamic structural break detection framework. • The adaptive prediction method can forecast energy demand with high accuracy. • The structural break detection works dynamically, allowing ARFIMA to be adaptive. • The prediction method captures the energy time series' nonstationary statistical features. [ABSTRACT FROM AUTHOR]

The current single energy storage approach and market incentives do not fully exploit the potential of diverse energy storage methods and market incentives. Additionally, the traditional operation method often results in a poor alignment between energy supply and demand. Moreover, the conventional solution algorithm struggles to address complex problem-solving involving multiple variables. To address these issues, this paper proposes an improved carbon-neutral energy system (ICNES). Firstly, ICNES enhances residual energy utilization by implementing dual-layer residual power and heat treatment. Secondly, to enhance the effectiveness of market incentives, ICNES incorporates an improved approach that combines a dual-layer carbon capture drive, step carbon penalty, carbon reward, and green certificate trading. Thirdly, to improve the supply–demand matching, ICNES introduces a dynamic hybrid operation approach (DFHL). Finally, the paper proposes a honey badger-pattern search algorithm (HB-PSA) to optimize the configuration of ICNES for the DFHL multivariate scenario. Results indicate that compared to conventional carbon-neutral energy systems, ICNES reduces annual operating costs by 6.686%, CO 2 emissions by 9.538%, primary energy consumption by 12.826%, and energy waste rate by 17.465%. Furthermore, HB-PSA increases the optimization rate from 48.693% to 53.88% of the local optimum. • Dual-layer residual electric and residual heat treatment system is proposed. • Dual-layer carbon treatment for carbon neutrality and carbon profitability. • Carbon markets and green certificate are used in carbon-neutral energy system. • Dynamic hybrid operation to improve the matching of supply and demand. • A honey badger-pattern search algorithm is proposed for solving. [ABSTRACT FROM AUTHOR]

In the context of the increasing popularity of distributed energy, this paper proposes a home energy management system method considering the working state of flexible load to deal with the problem of mismatch between home energy supply and demand. Specifically, in this paper, the flexible load in the system is studied, and the model of flexible load and its working state are established. On this basis, combined with the real-time electricity price of the grid, the model of home energy management system is constructed to minimize the electricity purchase cost and improve the utilization rate of renewable energy, the genetic algorithm is used to solve it, and the influence of time step, flexible load ratio and whether to use energy storage equipment on the optimization degree of the model are analyzed. The research results show that, while meeting the requirements of human comfort and electricity demand, the daily electricity purchase cost of the system with flexible load scheduling is 22.5% lower than that of the system without flexible load scheduling, and the utilization rate of renewable energy is increased by 16.5%. In terms of model optimization, shortening the prediction time step, increasing the proportion of flexible load and using energy storage equipment can further reduce the electricity purchase cost. The case study confirms the effectiveness of the proposed home energy management system model, which can provide an efficient optimal scheduling scheme for household energy saving. • The optimal scheduling model for home energy management system of rural dwellings with flexible loads is proposed. • Influence of the proportion of flexible load and different time step sizes on the scheduling model are studied. • The operation cost by 26.62% of battery integration strategy based on load scheduling is further reduced. [ABSTRACT FROM AUTHOR]

Distributed integrated energy system (DIES) will become the main energy supply method for end-users, however, the volatility of renewable energy makes energy consumption more difficult. To address the problem, this paper proposed an optimization method of DIES energy transaction based on Stackelberg game. Firstly, the typical structure of DIES is studied and a energy transaction framework of electric-heat energy is proposed by introducing integrated energy operator(IEO); secondly, with IEO as the leader and DIES as the follower, considering the highest profit of IEO and the lowest cost and carbon emission of DIES as the objectives, and combining the operational constraints of different subjects, a one-master-multiple-slave Stackelberg game model is established; thirdly, the existence of Nash equilibrium of this game model is demonstrated, and a solution algorithm based on Particle swarm optimization nested Non-dominated Sorting Genetic Algorithm-II is proposed; Finally, to verify the validity of this model, two modes are designed and simulated, and the results show that the proposed method can improve the renewable energy consumption by 2.92%, reduce the DIES energy cost by 3.94%, and reduce the interactive power with the grid by 30.91%, while the sensitivity analysis shows that the method can better cope with energy price changes. • The main contributions of this paper can be summarized as follows: • Analyzed the typical architecture of DIES and proposed a new energy trading framework. • A Stackelberg game model with IEO as the leader and DIES as the follower is developed. • The solution algorithm of PSO nested NSGA-II is proposed. • This method can reduce the cost of energy use and increase the consumption of renewable energy. [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]

The structure of direct current (DC) appliances is simpler and more energy-efficient than that of alternating current (AC) appliances. With the proliferation of novel DC loads (e.g., e-bikes and electric vehicles), DC loads will dominate the loads in buildings. It is necessary to develop a DC power distribution method that better accommodates renewable energy, effectively improves the working efficiency of loads, and significantly enhances energy utilization efficiency. However, there is not yet a unified design for the multi-port converter of DC microgrids. The experience is severely lacking in the overall energy planning for buildings from the perspectives of economy and environmental friendliness. To solve the problems, this paper explores the intelligent decision optimization for energy control in DC power distribution system with multi-port access for intelligent buildings. Firstly, a feature analysis was carried out on the multi-port converter of the DC power distribution system for intelligent buildings. With economy and environmental friendliness as the goals, the authors designed constraints related to energy output power, power balance, and distribution node voltage, and established an intelligent decision optimization model for the energy control of the DC microgrid for intelligent buildings. The model was combined with the multi-attribute decision-making framework into a decision-making framework for the coordinated operation of distributed energy supply units, which is based on the interval evidential reasoning (IER). Finally, the authors proposed an evaluation method for the decision-making of the energy control of DC power distribution for intelligent buildings, based on the Maclaurin symmetric mean (MSM) operator, and the IER-based decision-making framework. The proposed method was proved effective through experiments. [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 proposes a deep reinforcement learning-based approach to optimally manage the different energy resources within a microgrid. The proposed methodology considers the stochastic behavior of the main elements, which include load profile, generation profile, and pricing signals. The energy management problem is formulated as a finite horizon Markov Decision Process (MDP) by defining the state, action, reward, and objective functions, without prior knowledge of the transition probabilities. Such formulation does not require explicit model of the microgrid, making use of the accumulated data and interaction with the microgrid to derive the optimal policy. An efficient reinforcement learning algorithm based on deep Q-networks is implemented to solve the developed formulation. To confirm the effectiveness of such methodology, a case study based on a real microgrid is implemented. The results of the proposed methodology demonstrate its capability to obtain online scheduling of various energy resources within a microgrid with optimal cost-effective actions under stochastic conditions. The achieved costs of operation are within 2% of those obtained in the optimal schedule. [ABSTRACT FROM AUTHOR]

With the development of electronics and communication techniques, the interest in realizing sensor networks with a large number of end nodes is growing. The main idea is to install devices in remote locations without direct supervision, which requires an uninterrupted power supply and secure communication to the rest of the network. In this paper, an experimental comparative analysis of popular practical LoraWAN end nodes (WisNode RAK811 and Seeeduino SX1301) regarding energy consumption in different security modes, spreading factors, energy consumption in sleep/idle mode as well as the security keys extraction from memory was performed. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) technology is seen as a solution to the limitation of computing capability of Internet of Things (IoT) devices. In this paper, we investigate an edge computing framework that supports energy savings for UAV in the case of coexistence of edge computing users and latency-sensitive communication users, who use uplink non-orthogonal multiple access (NOMA) technique to communicate with UAV. The users are grouped to satisfy the delay-sensitive requirements of communication users. Then, the system energy consumption is minimized by optimizing the transmission power and computational resource allocation of the computation users while satisfying the quality-of-service (QoS) of the communication users. Since the proposed problem is nonconvex, it is transformed into a convex problem using the successive convex approximation (SCA) algorithm in this paper. At last, we propose a joint global computational resource and communication power optimization algorithm (GCPA) to minimize the system energy consumption. The simulation results show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

Energy extraction from renewable sources like hydrokinetic energy from the flowing water of rivers and canals is one of the affordable and clean energy generation technique. The current work is carried out towards performance enhancement of the Darrieus hydrokinetic turbine for open water channel utilization application. The investigation has been done using the flat and concave shape of blocking plate. Both blocking plates have been analyzed for different sizes as well as location at the upstream side of the turbine. The obtained results are reported after considering appropriate velocity correction. The blocking plates with projected width of 76 mm, 89 mm, 101 mm, 127 mm, 152 mm, and 203 mm are selected for the investigation. It is observed that 101 mm (Width Ratio , WR =0.36) with concave blocking plate provides maximum C p of 0.245 at 0.99 TSR. Also, the optimum region is found between 0.143 and 0.286 Distance Ratio (DR) from the top corner of the acrylic plate. The concave blocking plate enhance the performance more compared to flat blocking plate. The conclusion is made that, the performance with flat blocking plate with optimized width and location improve the power coefficient by 10.92% and that of concave blocking plate by 18.45%. • Energy extraction using Darrieus hydrokinetic turbine is one of the affordable and clean energy generation techniques. • The performance of the Darrieus water turbine has been compared with a bare rotor and with using two different types of blocking plates. • The concave and flat blocking plates have been experimentally analyzed, and its effect has been explored in the paper. • The different sizes of concave and flat blocking plates were placed at the upstream side of the Darrieus turbine to enhance the performance. • The paper also includes the optimum region of the optimized shape concave and flat blocking plates placed at the upstream location of the rotor. [ABSTRACT FROM AUTHOR]

The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated. • Short-term solar power generation prediction model established. • An influential factors analysis is designed to obtain the optimal combination of the influential factors. • A novel decomposition-ensemble framework is proposed to mine the intrinsic modes of the solar power generation sequence. • An attention-based Bayesian Seq2Seq method is presented. [ABSTRACT FROM AUTHOR]

The utilization of wireless sensor networks (WSNs), which combine sensing, communication, and computation in an intelligent environment. The use of energy becomes the primary obstacle for sensor nodes to overcome. Clustering and routing algorithms are promising ways to fix the issue and prolong the network's effectiveness. The availability of restricted energy resources is the most significant challenge in WSNs. In this paper, we propose an energy-efficient clustering scheme integrated with a particle swarm optimization method called PSO-EECS for wireless sensor networks to raise energy efficiency while simultaneously increasing the lifetime of sensor nodes. The predominant concern being addressed in this paper is the selection of a Cluster Head (CH), which helps gather, aggregate and forward the data from the cluster-based routing paradigm. PSO-EECS algorithm is used to optimize the fitness parameters for CH selection, which include the energy ratio, distance considerations, node density, load balancing, and the Network's average energy, among other things. The simulation results reveal that the proposed technique offers a considerable improvement in network stability and operation time compared to the currently used approaches. Compared to the PSOGSA, GAPSO H, and ABE-DE protocol, simulation findings demonstrate that PSO-EECS enhances stability by 95.4%, 34.8%, and 29.2%, respectively. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IES) have become a popular direction in the field of energy research due to their economic, efficient and environmental friendly advantages. Among them, multi-region integrated energy systems (M-RIES) are more valuable than single-region integrated energy systems (S-RIES) due to their low energy loss, high economy and reliability. At present, there is a lack of an optimisation method that integrates station-network synergy, inter-station interaction, shared energy storage configuration, overall planning of equipment configuration and multi-timescale rolling for the multi-faceted performance enhancement of M-RIES. Therefore, this paper proposes an M-RIES with station-storage interaction and inter-station interaction under the consideration of station-network synergy, and conducts a study on the optimal configuration of M-RIES from the viewpoint of economy and environmental protection. The final analysis is based on a northern region. The results show that the system achieves an energy efficiency of 1.07, a 16.9% reduction in total station equipment configuration, a 3.87% reduction in cost and a 0.76% increase in new energy consumption rate compared to a stand-alone operating system. Taking one of the energy stations as an example, under the operating conditions of 10% intra-day and 5% real-time error, the multi-timescale rolling optimisation method significantly improves the energy supply rate compared to the day-ahead dispatching method, where the error rates for the three typical days of transition, winter and summer are only 0.43%, 2.56% and 0.06%, corresponding to a reduction of 5.02%, 2.49% and 5.06%, and a smaller cost error than the ideal solution The cost error rates for the three typical days are only 1.99%, 3.60% and 3.46%. In summary, the collaborative autonomous planning and operation method proposed in this paper has great advantages in terms of economy, reliability, energy efficiency and environmental protection. • A multi-area collaborative integrated energy system with shared energy storage is proposed. • Day-ahead collaborative, intra-day autonomous multi-timescale rolling optimisation method. • The system has advantages in terms of economy, energy efficiency and the rate of new energy consumption. • The multi-time-optimised rolling method has advantages in terms of both meeting demand and economy. [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]