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The maturation of distributed energy resources (DER) has prompted the exploration of their deployment in commercial building applications due to their potential to supply energy at lower costs and emissions rates compared to centralized generation. While several software tools exist for evaluating the techno-economic potential of integrated renewable energy and combined heat and power (CHP) systems for distributed generation applications, many suffer from poor accuracy in capturing off-design (part load and changes in ambient air temperature and pressure) performance characteristics of microturbines, combustion turbines, or internal combustion engines. Thus, this paper presents a methodology for integrating these off-design characteristics in the mixed-integer linear program within REopt, a hybrid DER screening tool. The economic impact of the CHP off-design performance is observed through several application studies of various hybrid system configurations in different climates. Each study indicates how CHP off-design performance influences optimal sizing and dispatch decisions and therefore overall system economic value. We observe through case studies that modeling without the off-design effects, depending on the CHP prime mover and site, can result in Net Present Value predictions of hybrid systems that can be overoptimistic in frequently hot climates (up to 52 %), too conservative in frequently cold climates (up to 11 %), or unaffected (＋/−1 %) in temperate climates. Cases also highlight several advantages of hybrid systems relative to non-hybrid systems such as total economic value and the systems' ability to mitigate potentially negative consequences attributed to off-design performance. • A methodology for off-design performance as a function of load and ambient conditions is presented. • Scenarios where different application climates affect optimal sizing and dispatch are provided. • Not including off-design CHP modeling can result in overly optimistic or conservative economics. • Higher economic values for hybrid systems compared to either renewable energy or CHP-only systems. [ABSTRACT FROM AUTHOR]

The power sector significantly contributes to global greenhouse gas emissions, making its transition to low-carbon energy crucial for achieving carbon neutrality. Zhengzhou, recognized as one of the China's "National Central Cities", has a high energy demand. Its power sector, which is primarily reliant on coal, is responsible for half of the city's CO 2 emissions. The low emissions analysis platform (LEAP) model serves as an effective tool for exploring the power sector's transition to low-carbon sources. However, studies on the combined reduction in air pollutants and CO 2 at the city level are scarce. This study focuses on Zhengzhou's power sector, examining the power composition, coal usage, CO 2 and air pollutant emissions, social costs, and employment effects across five scenarios using LEAP. The five scenarios are the business-as-usual scenario, the reference scenario, the carbon neutrality scenario, and two optimization scenarios with different carbon peak and carbon neutrality time. CO 2 emissions will peak in 2030 at 34.6 Mt in the reference scenario, and in 2022 at 32.4 Mt in the carbon neutrality scenario. Emissions in the reference and carbon neutrality scenarios will decrease by 64.8% and 75.2%, respectively, from those in the business-as-usual scenario due to emission reduction strategies. In the carbon neutrality scenario, coal power reformation is the most significant measure, accounting for 45.7% of the total CO 2 reduction compared to that in the business-as-usual scenario, followed by the integration of external electricity (17.8%) and solar energy (13.6%). This scenario also demonstrated a strong synergistic effect on reducing air pollutants and CO 2 emissions. Although the carbon neutrality scenario incurs costs 60.4 billion yuan higher than those of the reference scenario, its CO 2 mitigation cost is 66.1 yuan/t lower. This indicated that more rapid CO 2 reduction, despite higher economic inputs, is justified by ambitious CO 2 reduction goals and environmental benefits. Enhancing the use of renewable energy technologies for research and development to lower costs could increase the economic feasibility of the carbon neutrality scenario. In the two optimization scenarios, complete elimination of CO 2 and air pollutant emissions is achieved by 2060, with all the electricity generated from renewable sources, although issues of uncertainty and stability need to be addressed. The study also highlights that renewable energy development will significantly boost employment, with an estimated 1.19 million jobs in 2060 in the carbon neutrality scenario. Finally, the paper concludes with a sensitivity analysis and offers policy recommendations for the power sector's low-carbon transition. [Display omitted] • Low-carbon transition paths of Zhengzhou power sector are investigated by scenario analysis. • Coal power reformation and external electricity are effective measures for CO 2 reduction. • Synergistic reduction of air pollutants and CO 2 is achieved in the CAN scenario. • The CAN scenario has a good cost-effectiveness of CO 2 mitigation. • Policy recommendations for Zhengzhou power sector are proposed. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of coordinating renewable and network investments in a context of unstable subsidies. It contributes to the literature by developing a three-stage analytical model to address the network operator investment strategies being either proactive or reactive and considering the sensitivity of investments to future renewable incentives, being uncertain and variable. A dynamic stochastic modeling is used to formalize actors' choices. The study's major results show that proactive operator may cause a loss of welfare compared to reactive operator. This depends on network investment costs and network investments delays, the intensity of support measures of renewable energy and the maturity of renewable technologies. While network operator needs to be proactive, especially if moderate new network investments are expected, our results demonstrate that there exists a threshold level of network investment beyond it being reactive is economically preferable. An assessment of some countries cases confirms our theoretical predictions. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) and hydrogen (H 2) present substantial feedstock potential for advancing sustainability in the chemical industry. In this paper, a new integrated system is proposed consisting of four subsystems, including an ICT-OEC six-step Cu–Cl cycle, a CO 2 capture using monoethanolamine (MEA), an ammonia production unit, and a methanation unit. The simulation of these subsystems was carried out using Aspen Plus software. Based on literature indicating a 3-step Cu–Cl cycle producing 125 TPD of hydrogen, comparative simulations of a similar-capacity six-step Cu–Cl cycle were conducted for the current analysis. All related values correspond to it. Converting CO 2 into methane and nitrogen into ammonia holds great promise in reaching the net-zero target and fostering sustainability. A comprehensive cradle-to-gate Life Cycle Assessment (LCA) investigates the environmental impacts of ammonia and methane production using green hydrogen and CO 2. Significantly, the utilization of renewable energy sources such as nuclear, wind, and hydropower in the CO 2 refinery process generates a net negative Global Warming Potential (GWP). It was noted that the GWP value of the electrical grid could increase significantly, reaching up to 370,000 kg CO 2 eq., particularly when producing 23,277.2 kg/h of ammonia and 2141.71 kg/h of methane at a hydrogen consumption rate of 5211 kg/h (125 metric tons of H 2 per day). The significant environmental impact of the CO 2 refinery process is notably influenced by the choice of electrical grid mix as the energy source. However, in the case of photovoltaics as an energy source, the value of 0.517 kg sb eq, surpasses that of the electrical grid mix. However, nuclear energy emerges as a particularly low-GWP impact, with values of −4190 and −4140 CO 2 kg eq. for the 3-step and 6-step Cu–Cl cycles, respectively. The findings suggest that the CO 2 refinery process generates a carbon credit in both scenarios. Based on the findings of this study, policymakers have a basis for developing effective strategies to address and alleviate environmental impacts. [Display omitted] [ABSTRACT FROM AUTHOR]

The present study focused on the ability to combine magnetic NMs to enable the use of their synergistic additives and new properties for the development of different sensing systems in various fields. A novel kind of integration of magnetic NPs in the sensing systems results in its significant improvement of sensitivity, limit of detection and stability. The applicability of NPs as colourimetric sensors, biosensors and sorbents is inevitable. Accordingly, the SPIONs used for drug delivery in vivo sized from 10 to 200 nm make them able to escape from attacks macrophages of the immune system part (>200 nm) and could avoid renal excretion (<10 nm). A novel Fe 3 O 4 @Pt NPs as a paper-based colourimetric sensor has determined LOD for glucose was 0.36 μM in a human sample. Moreover, PPY-based biosensor gave valid glucose evaluation in fruit juices and non-alcoholic drinks with LOD of 26.9 μM. Chemical approaches are the most used, even 90%. Characterization of nanostructures is done mainly by transmission electron microscopy, thermogravimetric analysis, X-ray diffraction, Fourier transform infrared spectroscopy and scanning electron microscopy. For the production and commercialization of the MNPs, controlled size, standardized, cost-effective and controlled synthesis methods are required. The focus on the green character of magnetic NMs, their synthesis and green properties would advance future studies, particularly for the development of non-toxic NMs for their safer further application. [Display omitted] • A comprehensive background on nanoparticles has been summarized. • Specific properties of nanomaterials enhance its effect. • Graphene/AuNPs have outstanding peroxidase-like potentiality at pH 3–5. • Biosensors could detect multiple analytes with the use of a singular magnetic NM. • Colourimetric sensors with MNPs enable long longevity for nanosensors. [ABSTRACT FROM AUTHOR]

Bangladesh has encountered several challenges, including issues like overpopulation, energy shortage, and global warming for the last few decades. Addressing the increasing energy demand has become a crucial concern because of rapid increase in population and lack of growth in the economic. The electricity production of a country heavily relies on fossil fuels, particularly natural gas, which makes up approximately 54% of the current installed capacity. Bangladesh is an agrarian nation and biomass stands out as a source of renewable energy having significant potential to mitigate the demand for heat and electricity. Biomass resources in the country mainly comprise animal dung, agricultural crop residuals, solid waste from municipalities and forest residues. The existing biomass resources contribute to producing a high amount of energy and content of around 1574.16 PJ of energy equivalent to 437.28 TWh of electricity in which agricultural residues, animal manure, municipal solid waste, and forest residues impart around 852.32 PJ, 399.04 PJ, 112.16 PJ, and 210.64 PJ of energy respectively. This paper explores the extent, possibilities, and technologies associated with biomass energy conversion. Additionally, the study delves into the several biomass projects that the government, as well as non-governmental groups, are working on for environmental sustainability along with plans, challenges, and methods that are being utilized to encourage biomass technologies across Bangladesh. [Display omitted] • The biomass potentiality in Bangladesh for heat and electricity generation is emphasized. • The energy scenario and necessity of alternative energy sources in Bangladesh are discussed. • The available energy conversion technologies from biomass in Bangladesh are presented. • The existing biomass resource content of around 1574.16 PJ (437.28 TWh of electricity) of energy. • The economic aspects and challenges of biomass conversion into energy are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a robust optimization siting and sizing strategy for the unified power quality conditioner (UPQC) in the active distribution network (ADN) integrated with renewable energy generators. The optimization objectives including investment cost, line loss, and voltage deviation are integrated into an overall cost for both the planning and operating stages. A novel UPQC equivalent power injection model is proposed based on the auxiliary branch method. A set of linear and second-order conic (SOC) constraints are employed to accurately describe the steady-state operation of UPQC. An improved branch current flow model is developed to incorporate radial distribution networks with UPQCs. The UPQC siting and sizing planning is formulated as a mixed integer second-order conic programming (MISOCP) problem. Accounting for the uncertainty in loads and distributed generators, a two-stage robust optimization model is established to address worst-case operational scenarios. A solving strategy is developed based on the strong duality theory and the column-and-constraint generation (C&CG) algorithm, and then commercial solvers can solve the problem accurately and efficiently. Case studies on an IEEE 33-node distribution network demonstrate this strategy's robustness in dealing with loads and renewable energy uncertainties, making it applicable across different UPQC topologies and operation modes. The proposed method has superior performance over the representative meta-heuristic approaches in terms of global optimality as well as an over 7.5 times faster computation speed. This work has significance in enhancing the quality and efficiency of modern power systems highly integrated with renewable energy. [ABSTRACT FROM AUTHOR]

As energy system design moves to more complex methods of optimisation including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilised to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilising both real and synthetic data is made using NREL's System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. It is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application. • Analysis of method to produce large weather data sets from short time histories. • Auto-Regressive Moving-Average models detailed for eight weather variables. • Data validated using PV, CSP and PV + Battery Storage Plant Models from SAM. [ABSTRACT FROM AUTHOR]

In this study, the optimization of a multisource hybrid photovoltaic (PV)/Wind/Diesel/Fuel cell (FC) system is performed to meet three realistic loads demand for heavy, medium and small activities observed at Figuil, Cameroon. This paper also examines and compares the techno-economic viability of an off-grid hybrid PV/Wind/Diesel/FC, PV/Diesel/FC and Wind/Diesel/FC systems in terms of net present cost (NPC), cost of energy (COE) and environmental emissions. The effectiveness of different evolutionary algorithms employed in the designing problem of the various hybrid systems is also investigated. The results revealed that, for maximum loss of power supply probability (LPSP) of 5% and minimum renewable energy fraction of 85%, the PV/Wind/Diesel/FC system is economically the best choice for supplying the three types of non-domestic loads demand with corresponding NPC, COE and LPSP values of 159319.4$, 1.087$/kWh and 0.0397 for heavy activity; 150185.8$, 1.753$/kWh and 0.0411 for medium activity; and 144588.0$, 2.384$/kWh and 0.0288 for small activity, respectively. The results also demonstrated that, in terms of pollution emissions minimization, the PV/Wind/Diesel/FC configuration is a preferable option, with an overall yearly CO 2 emissions of 2946.1 kg, 2043.6 kg and 1504.2 kg corresponding to an annual total fuel consumption of 1115.9L, 774.1L and 569.8L for heavy, medium and small activities, respectively. [ABSTRACT FROM AUTHOR]

To increase the share of electricity generation from renewable energies for both grid-connected and off-grid communities, storage systems are needed to compensate for their intermittent nature. Compressed air energy storage (CAES) processes are of increasing interest. They are now characterized as large-scale, long-lifetime and cost-effective energy storage systems. Compressed Carbon Dioxide Energy Storage (CCES) systems are based on the same technology but operate with CO 2 as working fluid. They allow liquid storage under non-extreme temperature conditions. A literature review of this new technology was conducted. The difference between the systems lies in the presence or absence of an external heat source, the thermodynamic state of the stored CO 2 , and the means of heat recovery and utilization. To better understand the wide variety of configurations, they have been classified according to the external heat use and the storage location (underground or aboveground). As there is no dynamic model for liquid storages, one is presented in this paper and bring new challenges that have to be considered in future researches. Also, experimental studies are lacking to validate the CCES behaviour and some components like turbomachines and thermal storages. • Providing an overview of current CCES studies and configurations. • Comprehensive comparison of different CCES categories. • A simplified dynamic model is presented. • CCES is a promising energy storage for renewable energies development. • Pointing out the issues and future needed investigations. [ABSTRACT FROM AUTHOR]

The transition from fossil fuels to renewable energy sources is critical to reduce future emissions and mitigate the consequences hereof. Yet, the expansion of renewable energy, especially the highly fluctuating production of wind energy, poses economic challenges to the existing energy system in Denmark. This paper investigates the economic feasibility of integrating a 250 kW, 500 kW, 750 kW and 1 MW water electrolysis system in the existing Danish energy market to exploit excessive off- and onshore wind energy for hydrogen production used as fuel for transportation purposes. In 2018, Danish wind turbines produced excess energy during 1238 h, which poses a capacity constraint as the electrolysis systems are limited to only produce hydrogen for 14% of the total available annual hours. This paper concludes that the net present value of each investment is negative as the fixed and variable production costs exceeds the generated revenues and it is therefore not economical feasible to invest in an electrolysis system with the purpose of only operating whenever excess off- and onshore wind energy is available. • Wind turbines produced excess electrical energy during 1238 h in 2018. • The target sale price of hydrogen from manufacturer is €5.7 kg/H 2. • The lowest achievable hydrogen sales price is estimated to €9.17 kg/H 2. • Hydrogen produced from excess electrical wind energy alone is not cost-competitive. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES) systems offer significant potential as large-scale physical energy storage technologies. Given the increasing global emphasis on carbon reduction strategies and the rapid growth of renewable energy sources, CAES has garnered considerable attention. However, the optimal design of CAES systems presents challenges due to the intricate coupling of multiple parameters, complex physical processes, and highly variable operating conditions. This paper comprehensively reviews three key aspects related to CAES optimal design. Firstly, it examines system analysis and optimization methodologies. Secondly, it investigates the off-design characteristics of CAES systems. Lastly, it explores system design methodologies specifically addressing off-design operations. Notably, the corresponding-point methodology (CPM) is proposed as a means to provide clearer guidance for system improvements. Future research should aim to combine CPM with traditional methods to establish more efficient and intuitive system optimization approaches. Furthermore, the exploration of active off-design regulation strategies, including the integration of various adjustment methods and multistage combined regulation for CAES systems experiencing wide load fluctuations, requires further investigation. Novel CAES configurations that enable efficient off-design conditions should also be explored, despite the inherent complexities associated with regulation and control. Finally, a coupled design methodology based on off-design operation data and probabilistic-load factor analysis is presented. This paper provides valuable insights and guidance for the optimal design and study of CAES systems. • CPM combined with traditional methods is needed to improve CAES round-trip efficiency • Not high temperature/pressure but "corresponding" is key factor to high round-trip efficiency • Multiple regulations and new system structure are needed for wide-load operation • Design methodology addressing complex off-design operations needs further improvement [ABSTRACT FROM AUTHOR]

[Display omitted] • SAMA, a new open-source microgrid optimization software, was introduced an validated. • Validation compared SAMA's results with industry-standard, Homer Pro proved accuracy. • Results for different locations and climates, confirming SAMA's reliability and accuracy. • SAMA introduces unique emission optimization and top-down pricing methodology for first time. • SAMA is open-source and free, which ensures accessibility and fosters innovation. • SAMA is broadly applicable for renewable, off-grid, grid-tied, and backup power systems. Although there are many software tools for simulations of hybrid energy systems, Homer Pro and Homer Grid are the only tools offering optimal sizing of hybrid energy systems. Originally developed as open-source software, they have been closed, which limits user-developers abilities to adapt and improve the software. In addition, all current proprietary offering suffer from the following limitations: i) high costs that limit accessibility to low-resource labs and individuals, ii) incapable of conducting multi-objective optimization, iii) constrained on any new innovations in hybrid energy system design and operation, iv) difficulties for users to specifically define prices and costs in inputs, v) inability to model different electric utility billing structures, vi) complex pricing methodology, and vii) the absence of machine learning-based predictive modeling. To overcome these limitations, this paper introduces and validates SAMA (Solar Alone Multi-objective Advisor), an open-source microgrid optimization software program designed to optimize hybrid energy system sizes economically using metaheuristic algorithms based on specific load profiles and meteorological data. Through rigorous validation exercises, SAMA's outcomes are compared against those generated by the Homer Pro software across distinct climatic conditions and geographical locations (i.e., Sacramento, California in the marine west coast and New Bern in humid continental climate zones of U.S., respectively). The results demonstrate congruence between SAMA and Homer Pro. SAMA can be used for a diverse array of applications from renewable energy systems to off-grid and grid-tied configurations, and from policy formulation to feasibility assessment. Unique features of SAMA include multi-objective optimizer, levelized emission optimization, different pre-defined utility billing structures, and a top-down pricing methodology. These features further enhance SAMA's adaptability, allowing users to tailor the software to their specific needs and system configurations. Finally, the paper anticipates future advancements that could refine component modeling, accelerate optimization processes, and incorporate advanced data analysis techniques such as machine learning to predict energy demand and production patterns. [ABSTRACT FROM AUTHOR]

To cope with the uncertainty of electric vehicles(EVs) and renewable energy in VPP, this paper proposes a two-stage robust optimal scheduling method for VPP considering the controllability of electric vehicle group (EVG). Highlights of the work in this paper are presented below: • The study found that the controlled capacity of EVG varies significantly, and the resulting operational benefits obtained by VPP vary significantly. It is recommended that operators should minimise the management of EVG in Category III status and increase the dispatch of EVs in Category I and II status in order to improve the controlled capacity of EVG and obtain more operational benefits. • Increasing the ESS capacity within a certain range can increase the revenue of the VPP, but the optimal capacity of the ESS should be configured is not the same when the controlled capacity of EVG varies. Further analysis reveals that when the total number of EVs and the nominal parameters of the system are constant, the VPP system shown in the paper can find the optimal ESS capacity for obtaining stable gains regardless of the number of EVs of each type. • When the forecast deviation coefficient is large, the day-ahead dispatching scheme for VPP developed by the two-stage robust optimization method has the highest total benefit and the strongest ability to resist fluctuations in WPP and PV output compared to other methods, with obvious advantages. To cope with the uncertainty of electric vehicles and renewable energy in virtual power plant(VPP), this paper proposes a two-stage robust optimal scheduling method for VPP considering the controllability of electric vehicle group (EVG). Firstly, the calculation method of the controlled capacity of EVG is given based on Minkowski's addition idea. Then a two-stage robust optimization model of min-max-min structure considering the controlled capability of EVG is developed, which involves the operational constraints and coordinated control of EVG, energy storage systems, convention gas turbine units, and distributed power sources. Finally, the model is solved based on the column and constraint generation(C&CG) algorithm. The results show that fully exploiting the controlled capabilities of EVG can help VPP optimize scheduling schemes and improve the economics of operation. [ABSTRACT FROM AUTHOR]

• Review paper on the application of energy tunnels to harness renewable energy. • The analytical, experimental and numerical studies are summarised. • Thermal and thermo-mechanical behaviour and influential factors are discussed. • Challenges and recommendations for future research are identified. The thermal activation of underground tunnels, also known as energy tunnels, has shown significant potential to harness geothermal and aerothermal energy as a low-carbon and economical solution for space heating and cooling, water heating and road de-icing. The heat and load transfer mechanisms in energy tunnels are, however, far less understood compared to other energy geostructures, due to their complex interaction with the ground and tunnel environment. This paper aims to provide a comprehensive overview of the current state of knowledge on the thermal and thermo-mechanical performance of energy tunnels based on recent analytical models, field tests, model-scale experiments, and numerical studies. Results suggest that a thermal yield ranging from 5 to 6 W/m2 is attainable for each degree Celsius difference between the fluid inlet temperature and the ground temperature in the presence of groundwater flow and tunnel airflow. In the reviewed studies, the thermally induced tunnel structural responses appear to be insignificant compared to those caused by tunnel excavation. However, the long-term thermally induced ground deformation in clay deposits can reach up to 15 mm, which may become a crucial issue and therefore needs to be examined in geotechnical design. As a new area of research, there is a lack of long-term experimental investigations to solidify the current knowledge of the thermal, structural and geotechnical performance of the system. Further research is also required to develop an integrated analysis and design method considering the techno-economic aspects and explore innovative business partnership models. This work can serve as a useful resource for researchers, engineers, and policymakers seeking to further develop and promote the adoption of this technology in infrastructure projects. [ABSTRACT FROM AUTHOR]

Ever since 2002, when the first Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES) was held in Dubrovnik, the SDEWES conferences series has been providing a forum for world-wide scientists and those interested in learning about the sustainability of development, to present research progress and to discuss the state of the art, the future directions and priorities in the various areas of sustainable development. One of the main issues of the coming decades is to improve efficiencies by integrating various energy systems, using excess from one, as resource in another in the correct moment. Integrating electricity, heating, cooling, transport, water, buildings, waste, wastewater, industry, forestry and agriculture systems will be pivotal towards sustainable development. SDEWES has maintained high publishing standard as 2408 SDEWES papers were published in special issues of leading scientific journals including "Energy - The International Journal". In 2022, the 17th SDEWES Conference was held during November 6–10 in Paphos, Cyprus, the 5th SEE Conference took place in May 22–26, 2022 in Vlorë, Albania, the 3rd Latin American Conference was held through a virtual platform during July 24–28, 2022. The Energy journal has continued its cooperation with SDEWES launching a special issue dedicated to the 2022 Conferences. The special issue includes papers which traditionally cover a range of energy issues - higher renewables penetration and various technologies and fuels assessments at energy supply side, as well as, energy efficiency in various sectors, buildings, district heating, electric vehicles and demand modelling at energy demand side. [ABSTRACT FROM AUTHOR]

Malaysia is one of the leading producers and exporters of oil palm, and the industry contributes significantly to the growth of the national economy. Nonetheless, the consequential oil palm biomass (OPB) with a potential yield of ∼97 million tonnes annually presents both challenges and opportunities, demanding innovative waste management solutions. This paper explores the feasibility of converting OPB into valuable commodities by employing concentrated solar-driven pyrolysis. A detailed overview of the biomass produced from oil palm plantations and mills is provided, along with a discussion of the industry stakeholders and associated biomass management challenges. The pyrolysis of OPB is explored, emphasizing benefits, potential barriers, and techniques. The focus then shifts to Malaysia's solar potential, which ranges from 1470 to 1900 kWh/m2 annually. The potentials of solar pyrolysis are explored, encompassing discussions on solar concentrator types, reactors, and the distribution of solar and OPB resources throughout Malaysia. The paper then recommends Tawau in Sabah, as an ideal location for OPB solar pyrolysis, due to its solar availability (up to 1873 kWh/m2), potential OPB resources from 19 mills and 241 plantations, and supportive infrastructure. The paper proposes solar-driven pyrolysis as a sustainable solution for Malaysia's OPB management, offers insights for policy initiatives and technological innovations in optimizing OPB and solar energy utilization. • Analyzes oil palm biomass potential for solar pyrolysis in Malaysia. • Explores solar resources in Malaysia for solar-driven pyrolysis. • Examines solar concentrators and reactors for pyrolysis. • Identifies potential city, as the optimal location for solar pyrolysis. • Discusses commercialization challenges of solar-driven pyrolysis. [ABSTRACT FROM AUTHOR]

This paper develops a framework for a financial, economic, and stakeholder analysis of a residential rooftop solar net-metering program. The empirical focus of the paper is the net-metering program in Ontario, Canada, but the methodology is applicable to evaluating other public programs. The results highlight that without the Federal Government's subsidy for the initial investment cost, net-metered solar systems are not financially viable for representative households. Moreover, the stakeholder analysis reveals that for each additional net-metered system installed in Ontario, non-net-metered households experience financial losses of six times the benefits to the net-metered households. The net losses to the Federal Government of Canada and the Canadian economy are five and nine times the benefit to the net-metered households, respectively. The only stakeholder who benefits marginally is the Government of Ontario. In terms of environmental benefits, our estimate of the cost of greenhouse gas abatement by residential net-metered solar is 325 CAD per ton of CO 2 , which is significantly higher than the current (65 CAD in 2023) and future (170 CAD by 2030) national carbon price set by the Government of Canada. [ABSTRACT FROM AUTHOR]

Renewable energies (RE) are rapidly reforming the power sector because they have become more economically competitive, thereby enabling nations to overcome high dependence on energy imports (i.e. reducing energy security concerns), as well as reducing their negative environmental footprints. Therefore, both the public and private sectors need to uncover the future technological trajectory of renewable energy. As part of an entity-linking approach, this paper examines REs developments from 2000 to 2021 and explores high-potential topics by analyzing hot topics during the period to identify evolutionary trends of relevant research interests (using published research papers). The results are analyzed and classified to determine how and when RE development issues "burst" into the public eye. The article tells the story of RE development from the past, the present, and the likely future hot topics for research. We analyze the results and cluster them into five categories: (1) energy sources, (2) sustainable development, (3) technological developments, (4) supply chains, and (5) energy management systems.se Findings reveal signals about the potential future energy market shifts and how REs evolve and contribute to the market. Signals are converted to policies and future market interpretations using an expert-based process. Analysis and methodology outcomes reveal that digital technologies have become an important factor in market changes. The findings of the study highlight how sustainable development actions and digital technologies will likely transform RE supply and management. [ABSTRACT FROM AUTHOR]

Power systems now face new issues due to incorporating electric vehicles (EVs) and renewable energy resources (RERs). This paper proposes a novel Enhanced Coati Optimization Algorithm (ECOA) for obtaining the optimal solution of the probabilistic optimal power flow (POPF) problems. The ECOA is a metaheuristic optimization algorithm that is robust and efficient for solving complex problems. It is used to tackle the OPF problem, which considers the stochastic characteristics of RERs. Moreover, EVs are included in the presented power systems in this paper. The novel approach is tested and verified on the IEEE-57 and IEEE-118 networks. The effectiveness of the proposed method is demonstrated by making a comparison with other metaheuristic-based methods. To obtain a practical study, real data of wind speed, solar irradiance, and electric vehicles profile are incorporated in the dynamic analyses. The simulation results show that the ECOA is robust and efficient for solving the OPF problem. It can also improve the performance of power systems with RESs and EVs. The findings of this research demonstrate that the suggested approach is promising for power system optimization problems, including RERs and EVs. • This paper presents a novel optimal energy management of smart grids. • The model contains renewables and electric vehicles uncertainties. • A practical study is presented including renewables and EVs real data. • A novel enhanced metaheuristic algorithm is proposed to solve the problem. • The effectiveness of the proposed method is compared with other methods [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]

Accurately predicting wind and photovoltaic power is one of the keys to improving the economy of wind-solar complementary power generation system, reducing scheduling costs and no-load losses, and ensuring grid stability. However, the natural properties of energy result in complex fluctuations in their corresponding power sequences, making accurate predictions difficult. Therefore, this paper proposes an intelligent prediction system that combines decomposition algorithms and deep learning for ultra-short-term prediction of wind and photovoltaic power. First, an improved decomposition algorithm is proposed, based on fuzzy entropy's property that its value increases with the increase of sequence uncertainty, particle swarm optimization (PSO) is used to search for the optimal parameter combinations of variational modal decomposition (VMD), so that it can automatically adjust the parameters for energy data with different characteristics to reduce the human error. Then, a convolutional neural network (CNN) architecture that balances operational efficiency and prediction performance is constructed, and the hyperparameters of the CNN are optimized using the wild horse optimization algorithm (WHO) to improve the stability and accuracy of the prediction model. In this paper, real data from wind power plants and photovoltaic power plants in China are used as experimental objects, and experiments are carried out in three aspects, namely, benchmark model selection, decomposition algorithm comparison and combined model comparison. The results show that selecting CNN as the benchmark model is a good choice; the improved VMD has better decomposition performance than other state-of-the-art decomposition algorithms. The system proposed in this paper is highly generalizable and adaptive, and its prediction performance and accuracy greatly outperform that of other comparative models, with prediction accuracies improved by 72% and 79%, respectively, compared to a single CNN model. • The hyperparameters of VMD are determined by using PSO based on fuzzy entropy. • Optimize convolutional neural network using the wild horse optimization algorithm. • The intelligent prediction system can accurately predict wind power and PV power. • Experiments based on power data from actual wind farms and PV plants. • A deep learning prediction method applied to wind and solar complementary systems. [ABSTRACT FROM AUTHOR]

With the increase in renewable energy penetration and the widespread use of electric vehicles, the uncertainty of controllable resources adversely affects the economic scheduling of smart buildings. Meanwhile, the smart building can take advantage of its flexibility through multiple markets mechanism. Therefore, this paper proposes an optimal scheduling model for the smart commercial building, including electric vehicle charging, which partakes in the day-ahead energy reserve markets. Firstly, the behavior of electric vehicles is obtained through Monte Carlo simulation. This paper categorizes electric vehicles according to their behavioral characteristics and proposes corresponding scheduling strategies. Then, the joint energy and reserve markets mechanism is introduced, and the robust adaptive optimization (ARO) method is employed to deal with uncertainties. The proposed model is a three-level and two-stage model, which can be solved by the column and constraints generation algorithm. Finally, the economy and feasibility of the proposed model are validated through a case study. Results show that the ARO method saves 3%–4% of expenses more than the pure robust optimization method, and the proposed joint markets mechanism can increase profits by 1%–4%. • A three-level optimal model of SCBEMS is proposed through ARO method to reduce its conservativeness. • The joint energy and reserve markets are introduced to coordinate smart commercial buildings' flexibility. • The travel behaviors of EVs are processed by Monte Carlo simulation and different scheduling strategies are applied. [ABSTRACT FROM AUTHOR]

Carbon emissions from the combustion of fossil fuels continue to be a significant barrier in the fight against global climate change. Carbon taxes are an energy policy tool for substantially carbon dioxide (CO 2) emissions reduction. This paper proposes a new strategy to promote the use of renewable energy by enterprises in industrial parks against the backdrop of a carbon tax. Using an evolutionary game model based on life cycle assessment (LCA), this study investigates the impact of dynamic carbon tax mechanisms, fines, and subsidies on the energy choices of industrial park enterprises. This paper's primary contribution is the incorporation of LCA into the evolutionary game model, which facilitates accurate calculation of the carbon tax parameter value. According to the study's findings, the carbon tax rate increases the propensity of enterprises to acquire a high percentage of renewable energy. While there is a negative relationship between fines and the propensity of enterprises to adopt a low proportion of renewable energy, an increase in subsidies decreases the proportion of enterprises that purchase a high proportion of renewable energy. This paper concludes by providing a theoretical foundation for policymakers to construct precise carbon tax schemes and promote renewable energy consumption among industrial park enterprises while simultaneously reducing carbon emissions. [Display omitted] • Carbon tax & energy decisions: life cycle assessment & evolutionary game theory. • Life cycle assessment for enterprise carbon taxes under multiple energy options. • Carbon tax mechanisms, fines, and subsidies influence enterprises energy decisions. • Excessive subsidies discourage renewable energy use, but fines can. [ABSTRACT FROM AUTHOR]

In the present situation, renewable energy has become a vital source to mitigate energy crises and environmental threats. In order to ensure that women's contributions, knowledge and opinions represent a significant portion of the expanding energy sector, it is crucial to approach the development of renewable energy from a gender perspective. Bibliometric analysis was performed to identify prior research on two primary terms, "Gender" and "Renewable Energy", from 1995 to 2022. Identifying the most influential countries, authors, publications, journals, and co-occurrence of keywords helps to understand the evaluation and trends of the research through network visualisations by bibliometric approach. This paper presents a comprehensive bibliometric review of the influence of gender on renewable energy. The result of this paper gives details picture of the gender impact on renewable energy during the period from 1995 to 2022. The paper centred on bibliometric and cluster analysis based on the two most important databases, Web of Science and Scopus. The study involved utilising various bibliometric tools, including R Studio, VOSviewer, and Biblioshiny. The study revealed an increase in publications and citations during the third period since 2017, after the United Nations set the target of the Sustainable Development Goal. Most influential countries and productive authors belong to China, the United States of America, and the United Kingdom. Energy Research and Social Science are predominated in publications on the research topic. In addition, the co-occurrence of keywords found major keywords related to this topic, such as renewable energy, gender, women, knowledge, sustainability, climate change and sustainable development. Finally, the study observed a huge research gap because developing nations are more affected by the lack of gender-influencing policies on the growth of renewable energy. Thus, the study provides a comprehensive overview of research in this field that can help researchers and stakeholders identify potential ways for future research in gender considerations on renewable energy access by formulating effective policies to change society and reach the Sustainable Development Goals. • Gender has become a focal point of interest within the renewable energy sector. • Using network visualisation, bibliometric analysis examines research trends by keyword occurrence and country collaborations. • Publication, citation, journal, and authorship contribution trends are to be analysed. • The research growth across different periods was discussed for potential future studies. [ABSTRACT FROM AUTHOR]

As a large number of renewable energy resources are connected to power systems, the operation, planning, and optimization of power systems have been becoming more and more complex. Power flow calculation, unit commitment, economic dispatch, energy pricing, and power system planning are essentially computation problems. A lot of computing resources are required for these problems, which are non-trivial, especially for large-scale power systems with the high penetration of renewable energy. Traditionally, the calculation and optimization of power systems are completed by classical computers based on the classical computing theory and the von Neumann architecture. However, with Moore's law getting closer and closer to the limit, the importance of quantum computing has become increasingly prominent. Quantum computing has been applied to some fields to a certain extent, yet the applications of quantum computing in power systems are rare. As the power industry is the foundation of the national economy, introducing quantum computing into the power system has far-reaching and crucial significance, such as improving the penetration of renewable energy, enhancing the computing efficiency, and helping in achieving the goal of net zero and climate neutrality by 2050. This paper first introduces the core concepts, essential ideas and theories of quantum computing, and then reviews the existing literature on the applications of quantum computing in power systems, and puts forward our critical thinking about the applications of quantum computing in power systems. In brief, this paper is dedicated to a tutorial on quantum computing targeting power system professionals and a review of its applications in power systems. The main contributions of this paper are: (1) introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor; (2) based on Dirac's notation, the related formulae are derived meticulously with sophisticated schematic diagrams; (3) elaborate and derive some critical quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems; (4) critically summarize and comment on the existing literature on the applications of quantum computing in power systems; (5) the future applications and challenges of quantum computing in power systems are prospected and remarked. • Introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor. • Based on Dirac's notation, the related formulae are derived meticulously with sophisticated schematic diagrams. • Elaborate and derive some important quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems. • Critically summarize and comment on the existing literature on the applications of quantum computing in power systems. • The future applications and challenges of quantum computing in power systems are prospected and remarked. [ABSTRACT FROM AUTHOR]

This paper proposes the first application of a split-range control technique on a concentrating solar collector to improve an absorption plant production. Solar absorption plants have solar power availability in phase with cooling demand under design conditions. Thus, it is a powerful cooling technology in the context of renewable energy and energy efficiency. These plants need control systems to cope with solar irradiance intermittency, reject irradiation disturbances, manage fossil fuels backup systems and dump closed-loop thermal-hydraulic oscillations. In this work, control techniques are proposed and simulated in an absorption plant in Spain. The plant consists of a concentrating Fresnel solar collector connected to an absorption chiller. The objectives are to operate with 100% renewable solar energy and avoid safety defocus events while reducing temperature oscillations and control actuators effort. Firstly, the current available plant controllers are defined, then two modifications are proposed. The first modification is a split-range controller capable of manipulating both flow and defocus of the Fresnel collector, the second modification is a PI controller to substitute the original chiller on-off controller. The results compare, through validated models, the different control systems and indicate that using both proposed controllers reduces 94% of the sum of actuators effort and 43% of the integral of absolute set-point tracking error compared to the plant's factory pre-set controllers. The suggested controllers increase 66% of energy production and 63% of exergy production. Besides, the split-range technique can be extended to any concentrating solar collector control. [Display omitted] [ABSTRACT FROM AUTHOR]

Local energy sharing (LES) is a concept that enables sharing between distribution system participants such as consumers, producers, and prosumers at the local level in a transparent and cooperative manner. It can improve local supply-demand balancing, reduce voltage deviations, and improve social welfare. However, the feasibility of such an approach is highly dependent on the regulatory framework and implementation requires investment in an adequate information and communication infrastructure. This paper examines components for the implementation of LES within energy communities in the EU, with a focus on price-forming methods that can be integrated into a net-billing system and adopted for different regulatory set-ups. Further, a method for the assessment of impacts on market participants is provided. The approach is applied for the assessment of the opportunities for LES in the city of Križevci, considering local generation, flexibility options, and real-life regulatory requirements. It is shown that LES under appropriate regulatory provisions can be an effective market-based mechanism for stimulating local generation and flexibility activation, and that way support decarbonization and local self-sufficiency. All members can benefit from participating in the energy community, but the distribution of the benefits notably depends on the applied LES price-forming method. On the other hand, subject to regulatory setup, the trade-offs are reflected on reduced revenues for market participants that generate income based on transmission fees, taxes, and/or surcharges. • Components for local energy sharing (LES) implementation within energy communities. • Framework for feasibility evaluation of LES under different regulatory set-ups. • MILP LES market-clearing model with energy sharing compensation price method. • LES price-forming methods integrated into a net-billing system. • Simulation of LES as a market with a local energy sharing coordinator. [ABSTRACT FROM AUTHOR]

The Philippines is exploring different alternative sources of energy to make the country less dependent on imported fossil fuels and to reduce significantly the country's CO 2 emissions. Given the abundance of renewable energy potential in the country, green hydrogen from renewables is a promising fuel because it can be utilized as an energy carrier and can provide a source of clean and sustainable energy with no emissions. This paper aims to review the prospects and challenges for the potential use of green hydrogen in several production and utilization pathways in the Philippines. The study identified green hydrogen production routes from available renewable energy sources in the country, including geothermal, hydropower, wind, solar, biomass, and ocean. Opportunities for several utilization pathways include transportation, industry, utility, and energy storage. From the analysis, this study proposes a roadmap for a green hydrogen economy in the country by 2050, divided into three phases: I–green hydrogen as industrial feedstock, II–green hydrogen as fuel cell technology, and III–commercialization of green hydrogen. On the other hand, the analysis identified several challenges, including technical, economic, and social aspects, as well as the corresponding policy implications for the realization of a green hydrogen economy that can be applied in the Philippines and other developing countries. • A green hydrogen economy could be feasible in the Philippines. • Renewable energy potential from geothermal, hydropower, wind, solar, biomass, and ocean for green hydrogen production. • Utilization pathways for transport, industry, utility, and energy storage. • The hydrogen economy faces challenges from technical, economic, and social perspectives. • Green hydrogen economy can be achieved by transforming the energy and other sectors to 100% renewables. [ABSTRACT FROM AUTHOR]

Hydrogen energy storage systems (HydESS) and their integration with renewable energy sources into the grid have the greatest potential for energy production and storage while controlling grid demand to enhance energy sustainability. This paper presents a bibliometric analysis based on a comprehensive review of the highly cited articles on HydESS to provide a detailed insight into future directions and applications. The study was carried out by using the Scopus database search engine to look for filtered keywords in the HydESS and related research. It can be demonstrated that the HydESS literature expanded rapidly from 2016 to 2021 compared to 2011 to 2015. It is found that 89.17% of published articles explained control and test systems-based methods, whereas 10.83% of publications discuss review assessments. Our analysis of highly cited articles on HydESS highlights several aspects, such as methods and systems, issues, difficulties, and challenges to establishing current constraints and research gaps. This evaluation can enhance operational performance, environmental friendliness, energy savings, uninterrupted power supply service, cost benefits, on-site generation, and adaptability. It would be beneficial for technology development and the growth of the hydESS industry. This study may act as a guideline not only for academics in determining the line of research and generating additional discoveries, but also for the government in formulating financial strategies. • Top-cited hydrogen energy storage system articles are reviewed under specific conditions. • Hydrogen storage integrated grids have the potential for energy sustainability. • A historical overview of hydrogen storage was analyzed using the Scopus database. • This survey has exhibited a developing hydrogen storage and renewable energy fields of research. • Bibliometric analysis was used to identify potential future research directions. [ABSTRACT FROM AUTHOR]

This paper uses established and recently introduced methods from the applied mathematics and statistics literature to study trends in the propagation and capacity of low-carbon hydrogen projects over the past two decades. First, we judiciously apply a regression model to estimate the association between various predictors and the capacity of global hydrogen projects. Next, we turn to the geographic propagation of low-carbon hydrogen projects, where we apply a recently introduced method to explore the geographic variance of hydrogen projects over time. Then, we demonstrate that most geographic regions display linear growth in cumulative plants and apply distance correlation to determine the nonlinear dependence between the two most prolific regions - North America and Europe. Finally, we study relationships between the propagation and capacity of green vs blue hydrogen over time, specifically, the time-varying regional consistency between the contribution of green vs blue plants to the total number and capacity of hydrogen plants. • We study a low-carbon hydrogen project database from the International Energy Agency. • An exponential increase in green hydrogen plant capacity is highlighted. • A dramatic closing between the capacity of green and non-green plants is revealed. • New insights revealing spatio-temporal trends in hydrogen propagation are shown. • Relationships between green and fossil-fuelled hydrogen plants are studied over time. [ABSTRACT FROM AUTHOR]

An important methodological point that has been over-looked by most of the literature is that renewable energy consumption (REC) is not only correlated in time but also in space. The omission of relevant spatial interaction terms in econometric analysis could be a methodological issue of major importance as it could lead to biased and inefficient estimates. This study accounts for spatial interaction effects to investigate the determinants of REC using a balanced panel of 41 countries in sub-Saharan Africa (SSA) over the period 2002 to 2015. The paper computes the Spatial Moran's I value to explore the spatial agglomeration phenomenon. Then, the Spatial Durbin Model (SDM) is applied to investigate the determinants of REC. The result from the Moran's I value indicates positive spatial correlation between REC and the spatial spill-over effect, representing spatial agglomeration. The decomposition of the SDM Model reveals that the direct effect of Gross Domestic Product per capita is negative while the direct effect of quality of governance level is positive. [ABSTRACT FROM AUTHOR]

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work. [ABSTRACT FROM AUTHOR]

Although renewable energy has been emphasized to mitigate carbon emissions, energy policies may require rearrangement. This paper aims to suggest the possibility of needs in energy policy rearrangement by revealing if the mitigation impact of the renewable energy supply has been diminished with a case study on Organization for Economic Cooperation and Development (OECD) countries. Adopting the idea of the environmental Kuznets curve hypothesis, the study investigates the nonlinear relationship between the renewable energy supply and carbon emissions. We collected the longest panel data possible for 34 OECD countries from 1971 to 2019. While the cointegrating equation results suggest that renewable energy has contributed to carbon mitigation, nonlinear estimation and quantile regression propose that the marginal mitigation impact from renewable energy might be weakening. 1% increase in renewable energy supply and the share of renewable in energy mix has mitigated carbon emissions by 0.134% and 1.260%, respectively. The empirical results insist that policymakers discover alternatives to decarbonize the energy sector, other than renewable energy expansion. Options to achieve carbon neutrality include heat expansion via unused heat, that is, national energy system innovation towards wasted energy utilization. [ABSTRACT FROM AUTHOR]

Renewable energy-based mini-grids have become increasingly popular over the last two decades. They have become crucial for increasing electricity access in developing countries, particularly in more remote locations. However, while access to such services can significantly contribute to socio-economic development, the functionality of mini-grid schemes is often poor. Many attributes of off-grid energy projects that contribute to sustainability have been considered within the literature. However, there has been minimal integration of these attributes across the different aspects of sustainability. Furthermore, the interaction between the sustainability dimensions, and the relative importance of these project attributes driving the sustainable performance of such mini-grids have been under-discussed. In this paper we use Partial Least Squares-Structural Equation Modelling (PLS-SEM) and analyse a quantitative dataset obtained from a survey of 175 micro-hydro projects. A theoretical framework of attributes that contribute to the sustainable performance of mini-grid projects is presented, and the empirical analysis provides evidence supporting 14 hypothesised relationships between 10 project attributes and five sustainability dimensions. The results provide insight into how remote mini-grid projects, with stability of power supply and demand, can achieve a self-reinforcing economic cycle in the village, and thus, maintain economically and environmentally sustainable outcomes. [ABSTRACT FROM AUTHOR]

• Shape interpolations between four common front shape extrema of the Energy-saving Chinese Solar Greenhouse (ECSG) were studied. • Two kinds of optimal ECSG shapes were identified in this study. • PCA analysis were conducted to determine the optimal greenhouse construction parameters. • The proposed model framework allows the investigation of the effects of different shape variations of solar greenhouses for any arbitrary latitude. Determining the optimal shape for greenhouses that is suitable for cold northern regions is essential for non-seasonal fruit production. In the present study, a structural model was developed for energy-saving Chinese solar greenhouses (ECSG), which combines a greenhouse energy balance model with a detailed shape analysis. All possible greenhouse shape interpolations within four common front shape extrema were systematically analysed to determine an optimal ECSG shape with a maximal energy performance for use during winter. The analysis revealed a direct relationship between the interception of solar radiation in the greenhouse and the height of the ridge. Our results indicated that the flatter the curve of the front cover, the more radiation is intercepted by the ground and north wall of the greenhouse. As a result, compared to the commonly used greenhouse type, two types of optimal ECSG shapes were identified, each attaining an increase of 2°C in the minimum night temperature. The general model framework developed in this study allows the investigation of the effects of different variations in the many small detailed interpolation shapes of ECSGs for any arbitrary latitude which can be used directly to provide guidance for the construction of a new generation of energy-efficient solar greenhouses. Also, with the interpolation method proposed in this paper, large-scale shape statistical analysis now can be performed to help qualified decision-making during the process of greenhouse construction. [ABSTRACT FROM AUTHOR]

The paper investigates the role of renewable energy and alternative and nuclear energy in mitigating CO 2 emissions. Trade openness is added to consider its effect on the environment, as it appears to be a crucial factor in interregional cooperation and development. We adopt a sample of nine signatories to the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) from 1971 to 2014. Various time-series econometric methods are utilized including two long-run estimators - fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) - and a Granger-causality test. Several noteworthy findings are achieved from this paper. First , the inverse U-shaped relationship is found in six countries: Australia, Canada, Chile, New Zealand, Peru and Vietnam. The U-shaped relationship is found in Japan and Malaysia whereas no evidence is observed in Mexico. Second , the adoption of either renewable energy or alternative and nuclear energy would mitigate CO 2 emissions and trade openness plays an important role in facilitating this effect. Third , the directions of Granger causality among the variables of interests including consumption of renewable energy; consumption of alternative and nuclear energy, trade openness and CO 2 emissions, do vary across countries and between the short term and the long term. • Inverse U-shaped relationship in Australia, Canada, Chile, New Zealand, Peru, Vietnam. • U-shaped relationship in Japan and Malaysia and no evidence in Mexico. • Consumption of renewable energy, alternative & nuclear energy reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

Short-term forecasting of electricity generation from variable renewable energy sources is not an end in itself but should provide some net benefit to its user. In the case of electricity trading, which is in the focus of this paper, the benefit can be quantified in terms of an improved economic outcome. Although some effort has been made to evaluate and to improve the profitability of electricity forecasts, the understanding of the underlying effects has remained incomplete so far. In this paper, we develop a more comprehensive theoretical framework of the connection between the statistical and the economic properties of day-ahead electricity forecasts. We find that, apart from the accuracy and the bias, which have already been extensively researched, the correlation between the forecast errors and the market price spread determines the economic implications - a phenomenon which we refer to as 'correlation effect'. Our analysis is completed by a case study on solar electricity forecasting in Germany which illustrates the relevance and the limits of both our theoretical framework and the correlation effect. • The economics of short-term electricity forecasts are studied analytically and empirically. • The value and the risk of forecast-based electricity trading are evaluated. • We find that the economics depend on the correlation of forecast errors with prices. • Hence, the most accurate forecast is not necessarily the most accurate. [ABSTRACT FROM AUTHOR]

• Solar photovoltaics use inverters to interface with the AC power system. • Inverters do not possess the rotational characteristics of synchronous generators. • High instantaneous inverter penetrations complicate traditional stability approaches. • Control techniques seen as the primary barrier to high inverter penetrations. • Research indicates no fundamental challenges to high inverter penetrations. As power system renewable energy penetrations increase, the ways in which key renewable technologies such as wind and solar photovoltaics (PV) differ from thermal generators become more apparent. Many studies have examined the variability and uncertainty of such generators and described how generation and load can be balanced for a wide variety of annual energy penetrations, at timescales from seconds to years. Another important characteristic of these resources is asynchronicity, the result of using inverters to interface the prime energy source with the power system as opposed to synchronous generators. Unlike synchronous generators, whose frequency of alternating current (AC) injection is physically coupled to the rotation of the machine itself, inverter based asynchronous generators do not share the same physical coupling with the generated frequency. These subtle differences impact the operations of power systems developed around the characteristics of synchronous generators. In this paper we review current knowledge and open research questions concerning the interplay between asynchronous inverter-based resources (IBRs) and cycle- to second-scale power system dynamics, with a focus on how stability and control may be impacted or need to be achieved differently when there are high instantaneous penetrations of IBRs across an interconnection. This work does not seek to provide a comprehensive review of the latest developments, but is instead intended to be accessible to any reader with an engineering background and an interest in power systems and renewable energy. As such, the paper includes basic material on power electronics, control schemes for IBRs, and power system stability; and uses this background material to describe potential impacts of IBRs on power system stability, operational challenges associated with large amounts of distributed IBR generation, and modern power system simulation trends driven by IBR characteristics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a complete modeling approach of hydraulic power plants and a control solution to improve the start-up performance of hydraulic turbines. This paper presents a novel simple speed controller that is capable of starting-up hydraulic turbines fast enough while limiting sharp pressure head oscillations. Its simplicity makes it easy to adopt by practitioners, and capable of running on existing power plants which utilize microcontrollers with limited computational abilities. The controller structure proposed in this paper has the potential of being tuned such that closed-loop stability is guaranteed and upper bounds on asymptotic tracking error are minimized. A modular mathematical model is also proposed where the method of characteristics is used to model water dynamics in multiple penstocks used in hydraulic power plants. The main problem that rises when modeling a system of multiple penstocks is systematically choosing the sampling period. Therefore, we propose a systematic approach to choose any sampling period. This leads to the development of a modular hydraulic simulator that takes into account power plants with multiple penstocks, the nonlinear dynamics of the hydraulic turbine, modeled by regression models generated using Hill charts data points and the guide vanes opening actuator's full nonlinear dynamics. • Recently faster hydraulic turbines start-up is gaining more interest. • Existing speed controllers are either too basic or too computationally expensive. • Novel industry-friendly speed controller is proposed. • A modular simulator based on method of characteristics is developed. [ABSTRACT FROM AUTHOR]

• Empirical studies of community acceptance of large-scale solar farms are lacking. • We present a case study of the UK's first 'nationally significant' solar farm. • We identify 28 determinants shaping community acceptance of large-scale solar farms. • Key issues are 'green-on-green'; scale; place attachment; policy; process; justice. • We also propose a novel 'relational' understanding of community acceptance. New renewable energy infrastructure is essential to deliver net zero policies in response to climate change, but a lack of community acceptance is a potential barrier. It is therefore important to understand what shapes community acceptance and identify policy responses. This paper presents a case study of community acceptance of a large-scale solar farm in the UK, the first to be classified as 'nationally significant' infrastructure. In doing so, it provides the first empirical study of community acceptance of a large-scale solar farm in a developed country context, building on existing studies which use hypothetical approaches such as choice experiments, or surveys which measure general attitudes rather than responses to specific developments. The paper uses mixed methods (quantitative content analysis of online comments on the planning proposal; qualitative semi-structured interviews with local residents and key stakeholders; and participant observation) to identify determinants shaping community acceptance of large-scale solar farms. We discover 28 determinants which we group into eight categories: aesthetic, environmental, economic, project details, temporal, social, construction and process. We argue that these findings help to reveal broader issues underlying community acceptance of solar farms and other renewable energy infrastructure: 'green-on-green' tensions; issues of scale and place attachment; policy, process and justice. We also contribute a novel understanding of community acceptance as 'relational', by which we mean it is informed by the deployment of other energy technologies and the wider energy policy landscape, not just the specific project. We conclude with recommendations for how policymakers can respond to the issues identified by this article. [ABSTRACT FROM AUTHOR]

The evolution of renewable energy especially wind energy over the past decade has surpassed all expectations. Short-term probabilistic wind power forecasting is a good option to increase the reliability of power system. But short-term forecasting of power generated by wind turbine has high error due to uncertainty parameter such as wind speed so, it is very important to find a way to increase the accuracy of forecasting. Therefore, in this paper, a novel forecasting method that has high reliability compared to other methods is presented. In this paper, in order to benefit the superiority of various prediction models, a new Improved Kernel Density Estimation (IKDE) method is exploited to estimate the wind energy possibility. The combination of various prediction models and the suggested method might develop the function of probabilistic prediction by supplying divergent types of compactness performance. KDE method is a powerful method to analyze background and foreground characteristic. In order to increase the efficiency of KDE method some of predicting model are combined and a detection algorithm based on an Improved Kernel Density Estimation (IKDE) model is defined. The appropriate bandwidths, comparative threshold, comparative background sample learning array, and an enhanced sample updating model for sample learning array are proposed as the basics of the IKDE model. Two levels of optimization are used to simplify the IKDE model parameters. Finally, in order to prove the superiority of the proposed method over other methods, this method and 4 other methods have been implemented on 10 wind farms. The simulation results show that the prediction accuracy of the proposed method is about 3.8% higher than other methods due to the improved structure. •Using of IKDE based on the combined of top features possibility prediction models. •Using of parametric and non-parametric membership models in IKDE assigning. •Considering of two levels function-base optimization with high rate of adaptively. •Use of a plenary possibility mixture model with considering explanation variables. •The accuracy of IKDE was higher than other methods. [ABSTRACT FROM AUTHOR]

With the end of economic production in sight, the oil and gas industry is preparing for the decommissioning of the offshore facilities. Beside of removing the infrastructure, there are numerous possibilities for its future use. Paper analyses possible use of offshore infrastructure for blue energy production from wind. The conversion of excess electricity into chemical energy, which can easily be stored, can be achieved using P2G (Power-to-Gas) technologies. Chemical conversion units performing a balancing for the power system and transformers needed for electricity transportation from offshore wind farms can be built on oil and gas platforms. The main contribution of the paper, based on a case study of the Northern Adriatic, includes hydrocarbon production prediction, electricity generation and hydrogen production based on wind potential calculation and synthetic natural gas production analysis. Energy production comparison of different technologies and conversion efficiencies have been given. Authors found that the most beneficial reuse option for offshore facilities is electricity generation from wind. Paper concludes that it is not likely that blue energy will match current hydrocarbon production, but it suggests postponing the decommissioning for the possibility of the blue energy production for which further detailed case by case economic analysis will be needed. Image 1 • Decline in natural gas production has been shown for the Northern Adriatic gas fields. • Possible reuse options for offshore oil and natural gas infrastructure have been discussed. • Analysis of blue energy production potential have been conducted. • Chemical energy storage utilization has been investigated. • Energy flows of various scenarios have been calculated and compared. [ABSTRACT FROM AUTHOR]

As the world's largest energy consuming country, it is important to study the mechanisms of improving renewable energy and energy efficiency to achieve green economic growth in China and globally. Therefore, this study investigates the impact of environmental regulations on green economic growth and renewable energy development in China, respectively. First, this study measures the level of green economic growth in Chinese cities by the Metafrontier-Global-SBM super-efficient data envelopment analysis (DEA) model from the perspective of energy efficiency. And the environmental regulation intensity was measured using an improved fuzzy comprehensive evaluation method. Second, a spatial econometric model based on longitudinal data was constructed to examine the non-linear effects of environmental regulations on green economic growth and its spatial spillover and feedback effects. The results indicated a U-shaped non-linear effect of energy efficiency-related environmental regulation on green economic growth as well as a spatial spillover effect and spatial feedback effect. Finally, the impact of environmental regulation on renewable energy development was investigated. The results show that renewable energy development, an important component of green economic growth, is equally dependent on the implementation of environmental regulation policies and that environmental regulation makes a significant contribution to renewable energy development. The findings of this paper provide a basis for the realization of green economic growth and the formulation of renewable energy policies in China. • Impact of environmental regulation on renewable energy development was investigated. • A spatial econometric model of 286 Chinese cities from 2003 to 2018 was constructed. • Environmental regulation has a significant U-shaped effect on green economic growth. • Non-linear effects include spatial spillover and spatial feedback effects. • Environmental regulation promotes green growth earlier in resource-based cities. [ABSTRACT FROM AUTHOR]

The increasing greenhouse gas (GHG) emission and environmental degradation (ED) is a serious concern for the various economies, and a similar issue is observed in Asia. This paper investigated the role of clean energy from renewable sources, urbanization, and economic growth in determining the level of GHG emissions from 1995 to 2018 for ten Asian States through a cross-sectional autoregressive distributed lagged (CS-ARDL) model. Meanwhile, the current research also examined the cross-sectional dependence, unit root properties, and co-integration between the study variables. The study findings confirmed that clean energy and GDP2 played their constructive role in reducing GHG emissions in the natural environment or targeted economies. In contrast, urbanization and economic growth caused more GHG emissions both in the long and short run. Furthermore, the robust check through augmented mean group (AMG) and common correlated effect means group (CCEMG) also confirmed that clean energy and GDP2 have a good sign for lowering ED compared to GDP and urbanization. The study findings could support policymakers, specifically in the field of energy economics and environmental sustainability. Therefore, it is highly recommended that some strong policy implications are needed to reduce environmental issues through controlling the negative impact of economic growth and urbanization. This study contributes in the literature of GHG emission with respect to economic growth, urbanization and clean energy and guided the regulators while formulating policies related to control the GHG emission. • The increasing GHG emission is a serious concern for the various economies. • We examined the clean energy, urbanization and economic growth impact on GHG emissions. • We examined cross-sectional dependence, unit root and cointegration among variables. • Clean energy plays constructive role in reducing GHG emissions in natural environment. • Urbanization and economic growth caused more GHG emissions in long and short run. [ABSTRACT FROM AUTHOR]

The transition to an energy system with a focus on electricity sector is becoming more and more crucial to increase the life expectancy of fossil fuels, but also to meet the current commitments regarding greenhouse gas emissions. This paper presents the most credible options to increase the share of renewable energy resource in the Algerian electricity power system by 2050. Screening curve method is used to assess the levelized cost of electricity (LCOE) of different technologies for electricity generation and EnergyPlan tool is used to estimate total annual cost, annual investment cost, renewable energy resource share and CO 2 emissions during the time horizon. The efficiency and the simplicity of multi-objective cuckoo search algorithm make it a powerful approach for solving energy strategy multi-optimization problem. Considering LCOE economic order and the availability of renewable energy resource, the energy transition strategy is established by minimizing the total annual cost and maximizing renewable energy share in 2035, 2040, 2045, and 2050 using multi-objective cuckoo search algorithm. The results revealed that achieving 75% is technically feasible but will require significant investment. [ABSTRACT FROM AUTHOR]

In order to alleviate the resource depletion as well as achieve decarbonization, developing renewable energy system is a feasible solution. This paper establishes a wind-photovoltaic-battery-thermal energy storage hybrid power system, and investigates its multi-objective planning-operation co-optimization. The hybrid system utilizes the cost-effectiveness of thermal energy storage and flexibility of battery to jointly tackle the intermittency of renewable energy. A novel coordinated operation strategy based on the operation threshold of power block is proposed, and the planning-operation co-optimization model considers the minimization of net present cost and loss of power supply probability to determine the optimal operation threshold and sizing decision variables. The co-optimization problem is solved by a proposed multi-objective evolutionary algorithm with decision-making (MOEA-DM), which introduces the preference information of decision-maker to guide the evolution towards preferred region. Furthermore, the uncertainties and losses of wind power are captured by a data-driven forecast model. Finally, the results of case study show that: (1) the data-driven model performs higher accuracy in wind power forecast compared to commonly-used physical models; (2) The proposed MOEA-DM has better convergence, diversity and robustness performance in decision-maker's preferred region compared to widely-used Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ); (3) Hybrid battery-thermal energy storage system achieves better economy and reliability through the optimal coordinated operation strategy compared to either single energy storage under different test conditions. [ABSTRACT FROM AUTHOR]

Despite the high commitments of the European Union (EU) member countries toward achieving the sustainable development goals (SDGs), on average, the region has reportedly under performed in the area of ensuring sustainable production and consumption. This paper uses the Generalized Method of Moments (GMM) estimation of panel vector autoregressive (PVAR) with impulse response functions (IMFs) to assess the effects of domestic material consumption, renewable energy, financial development, and greenhouse gas emissions on environmental quality in the EU-28 countries based on the panel data for the period 2000:Q1–2017:Q4. The empirical results reveal that the shocks to domestic material consumption, renewable energy, economic growth, financial development, and greenhouse gas emissions affect the drives towards a sustainable environment. Particularly, the shocks to renewable energy and financial development improve environmental quality, while the shocks to domestic material consumption and greenhouse gas emission deteriorate environment quality. The shock to economic growth improves environmental quality up to the 4th horizon after which it begins to deteriorate environment quality. Furthermore, the panel causality results indicate bidirectional causality between greenhouse gas emissions and the rest of the variables except renewable energy, which is unidirectional. The causality between economic growth and renewable energy, economic growth and financial development, and financial development and renewable energy has a feedback effect while a unidirectional causality flows from economic growth to domestic material consumption. These findings have implications for sustainable production and consumption. • Effects of domestic material consumption, renewable energy, and greenhouse gas emissions on the environment are examined • Improved GMM estimation of Panel VAR and impulse response functions are explored • Renewable energy and financial development improve environmental quality • Domestic material consumption and greenhouse gas emissions deteriorate environment quality • Economic growth only improves environmental quality up to the 4th horizon after which it deteriorates the environment. [ABSTRACT FROM AUTHOR]

Hydrogen production from water splitting is considered one of the most environmentally friendly processes for replacing fossil fuels. Among the various technologies to produce hydrogen from water splitting, thermochemical cycles using chemical reagents have the advantage of scale up compared to other specific facilities or geological conditions required. According to thermochemical processes using chemical redox reactions, 2-, 3-, 4-step thermochemical water splitting cycles can generate hydrogen more efficiently due to reducing temperatures. Increasing the number of cycles or steps of thermochemical hydrogen production could reduce the required maximum temperature of the facility. In addition, recently developed hybrid thermochemical processes combined with electricity or solar energy have been studied on a large scale because of the reduced cost of hydrogen production. Additionally, hybrid thermochemical water splitting combined with renewable energy can result in not only reducing the cost, but also increasing hydrogen production efficiency in terms of energy. As for a green energy, hydrogen production from water splitting using sustainable and renewable energy is significant to protect biological environment and human health. Additionally, hybrid thermochemical water splitting is conducive to large scale hydrogen production. This paper reviews the multi-step and highly developed hybrid thermochemical technologies to produce hydrogen from water splitting based on recently published literature to understand current research achievements. • Thermochemical hydrogen production from water splitting is viable on a large scale. • The multi-step thermochemical method can be an energy-saving process. • Hybrid methods are plausible in terms of cost and efficiency. • Renewable energy combined system is conducive to hydrogen production. [ABSTRACT FROM AUTHOR]

This study explores the dynamic effect of non-renewable energy, renewable energy, economic growth, and foreign direct investment on environmental degradation in twenty selected African countries over the period 2000–2015. We have adopted Environmental Kuznets Curve hypothesis and the Pollution Haven/Halo hypothesis simultaneously. In the first stage, this paper performs cross-independence test and found cross-sectional dependence in carbon emissions, non-renewable energy, and renewable energy. In the second stage, it applies first generation and second generation unit root tests for panel data and found that all concerning variables are I(1) except for economic growth which is found I(0). Therefore, we used the Panel Autoregressive Distributed Lag approach using Pooled Mean Group, Mean Group, and Dynamic Fixed Effect estimators. The results indicate that all independent variables are significant and positive to CO 2 emissions except for renewable energy found significant and negative to CO2 emissions, and foreign direct investment found significant and positive only in the long term. Moreover, the Environmental Kuznets Curve hypothesis did not hold in our sample, while we found strong evidence for the Pollution Haven hypothesis in selected African countries. • non-renewable energy, renewable energy, economic growth, and foreign direct investment-CO 2 emissions relationship is investigated. • Positive effect of non-renewable energy and negative effect of renewable energy are found. • The Environmental Kuznets Curve hypothesis does not hold. • The Pollution Haven hypothesis is verified. [ABSTRACT FROM AUTHOR]