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The energy sector prioritizes innovative clean energy production. This research explores a versatile hybrid solar-biomass trigeneration system, integrating organic Rankine cycle (ORC), a heat pump, and a proton exchange membrane electrolyser. Sustainable and adaptable, it produces electricity, heating, and hydrogen for Indian paper and pulp industries. The study assesses the system's thermodynamic, economic, and environmental feasibility. Initially, parametric analysis and first layer multi-objective optimization (MOO) are performed to select the efficient working fluid and operating temperatures. Benzene outperforms other fluids, achieving an energy utilization ratio (EUR) of 0.27 and exergy efficiency of 75 %. A typical heat requirement of 250 kW for processing 150 kg of paper necessitates collector areas of 3500 m2 and 4500 m2 in Aurangabad and Tiruchirappalli, respectively, and biomass waste of 0.05 kg/s. The combined system performance during day time and night time operation was studied by altering the critical solar radiation, ambient temperature and biomass waste flow rate. The second layer MOO revealed an optimum EUR of 0.25 and exergy efficiency of 9 % at the lowest cost of 33 $/h for biomass waste operation. In comparison to the conventional electricity-powered system, the proposed system would reduce CO 2 emissions by 90 kg/h to 270 kg/h. [Display omitted] • Solar and biomass waste powered systems proposed for paper and pulp applications. • ORC integrated heat pump system for heating, electricity, and green H 2 generation. • Collector area and biomass flow rate are designed for daily paper output of 300 kg. • Benzene is an efficient working fluid, produced 815 kW of heat and 3 kg/h of H 2. • The recommended system reduced CO 2 emissions by 90 kg/h to 270 kg/h. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges isolated islands face in transitioning from fossil fuel-based electricity generation to renewable energy sources. The Canary Islands serve as a case study, where photovoltaic and wind power are the primary renewables, but their variability requires a deep techno-economic analysis. The island's energy demand is predicted to rise by 100% due to economic growth, electrification and electric vehicles. However, implementing renewable systems encounters obstacles, such as limited suitable sites and protected areas. The study uses Wilks' methodology and Monte Carlo sampling to explore 59 combinations of randomly selected inputs of the uncertain variables, aiming for a 95/95% coverage and confidence level in the results. In most cases, they experience energy shortages, failing to meet electric demand. Even though a new generation mix appears to cover demand under all circumstances, the uncertainty unveils a different reality, leading to an approximate 25% increase in system costs. Surpluses in energy generation, while seemingly positive, can pose challenges. The new system's Levelized Cost of Energy increases from around 14 to 17c€/kWh. These cost increases are contingent upon future performance and the variability of uncertain parameters, leading to excesses ranging from slightly below 25% to over 40%. • Demand coverage scenario designed to meet electric demand through renewable sources. • Uncertainty analysis of a scenario for the complete electrification of the economy. • Best Estimate Plus Uncertainty analysis implementing the Wilks' methodology. • Resizing the system to cover the demand with 95% coverage and confidence levels. • Differences in using deterministic and stochastic methods for electric demand coverage. [ABSTRACT FROM AUTHOR]

The drive for improved energy efficiency and flexibility has birthed an innovative solution: an energy hub. This hub operates in sync with natural gas and electricity grids, integrating batteries and thermal storage to optimize renewable energy use, reduce costs, and bolster network stability. In addressing these objectives, a mathematical optimization model for energy hub operation is developed in this paper. This model captures the intricate interplays between electricity and natural gas networks, accounting for the intricacies posed by uncertainties and variations in renewable energy sources and demand profiles. Also, this paper uniquely delves into the integration of load response programs, an approach geared towards amplifying the energy hub system's flexibility and reliability. Through these programs, subscribers gain the agency to calibrate their energy consumption based on real-time pricing signals and incentives dispensed by the energy hub. The simulation findings serve as a compelling testament to the efficacy of the proposed system. Operating costs witness a substantial reduction, the adoption of renewable energy sources is markedly heightened, and the stability and reliability of the upstream energy networks are tangibly ameliorated. The integration of load response programs emerges as a particularly effective strategy in mitigating peak demand and augmenting the system's overall energy efficiency. Underpinning the entirety of this innovative solution is the transformation of the challenges into an optimization problem. This catalyzes the employment of a newly developed particle community algorithm, custom-tailored to surmount the challenges of local optima. This algorithm inherently bolsters the system's search capabilities, minimizing the risk of entrapment within localized solutions. Ultimately, the proposed energy hub system encapsulates a promising trajectory toward the realization of a sustainable and adaptable energy landscape. By seamlessly integrating diverse energy sources and storage systems, augmenting energy flexibility and efficiency, and aligning with broader energy transition and decarbonization goals, this solution presents a formidable contender. The simulation outcomes underscore its potential by showcasing a substantial 4.57 % reduction in the cost of procuring energy from the upstream electrical network. • Holistic energy hub engages in upstream electricity and heat markets, optimizing profit. • Empowered energy hub actively influences markets with optimized strategies. • Robust optimization ensures stable operations in uncertain renewable sources. • Advanced load management optimizes profiles, aligning consumption with pricing. • Particle swarm optimization algorithm enhances search for optimal solutions. [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]

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50% of the flexible loads, which is possible due to the use of the stationary energy storage. • Real-time load coordination strategy for optimizing electrical loads in smart homes. • Inclusion of renewable energy sources and stationary batteries in the coordination. • Definition of generalizable metrics for the evaluation of load shifting strategy. • Improvements in improved energy efficiency and reduced electricity costs. • Comprehensive analysis that considers diverse home characteristics such as location. [ABSTRACT FROM AUTHOR]

This paper examines renewable and technological prices' asymmetric multifractality and efficiency in international and Chinese marketplaces. The asymmetric multifractal detrended fluctuation analytics (A-MFDFA), multifractal detrended fluctuation analytics (MFDFA), and fractal dimension techniques are employed for multifractality and herding behavior. In addition, market deficiency measures (MDM) and hurst exponents are used to construct the inefficiency index (MLM- Magnitude of long-memory) during and before Covid-19. The empirical outcomes supported the existence of asymmetric multifractality across all renewable and technological marketplaces. This multifractality has been observed in up-and-down trends. Moreover, during the COVID-19 outbreak, inefficiency in CELS and SPGlobal's green energy prices increased, which is more apparent in the descending trends. Fractal dimension outcomes suggest a herding behavior in these markets during pandemic. The SPTSX green energy pricing statistics demonstrate that its upward multifractality is larger than the downward in both phases, signifying the strong efficiency position in the market. The SPIC-SH green energy pricing displayed considerable asymmetric multifractality, higher levels of efficiency, and even low levels of market uncertainty during COVID-19. The findings imply that all financial market players should prioritize various green energy investments depending on its asymmetric efficiency and predictability. It will help in their decision-making and reduces herding behavior in the market. • Asymmetric multifractal behavior is tested in global renewable and technological prices. • A-MFDFA and MFDFA and fractal dimension are used to analyze efficiency, scaling and herding behavior during and before COVID. • CELS and SPGlobal's green energy prices showed herding behavior before and inefficacy during COVID. • Green Technology and Chinese green energy prices were stable during COVID. • All green and technological prices were more stable before COVID-19. [ABSTRACT FROM AUTHOR]

The role of energy transition amidst the energy crisis and how policymakers can drive down emissions while focusing on energy security are critical. Given the geo-political situation, energy crisis volatility, energy shortage and climate change all affect the green transition and the short-term priorities for energy companies and policymakers. Energy security is not an isolated issue but has widespread implications as various sectors depend on energy supply to function properly. Governments around the world are faced with this trilemma, how to balance energy security with energy sustainability while also considering energy affordability. Sustainability has been in focus for about a decade. However, energy security is suddenly becoming one of the most important priorities that policymakers need to consider. Unfortunately, the renewable energy infrastructure is not yet ready to replace the growing volume of energy demand from hydrocarbon, which the world has been dependent on. This means, for now, a surge in energy generation through hydrocarbon to meet the existing energy demand deficit. However, it is important not to lose focus on the challenge of energy sustainability and climate change adaption and mitigation. Where trends like carbon capture and storage; solar, wind, hydro, green hydrogen, etc.; renewable energy infrastructure and integrations, with supply chain and engineering services consideration [in aspect for the growing market in this space] need better attention with regards to investment and full-scale implementation. This paper aims to analyze this 1st energy crisis of green transition with a priori on energy poverty with consideration of major influences and associated impacts. Furthermore, it proposes a specific framework for inclusive investigations, which considers the entire energy ecosystem with consideration of major influences, to enable the policymakers to better drive the green transition. This involves formulating energy policies that are not entirely conservative towards renewable energy sources but instead promote investments in both green and relatively more environmentally benign energy sources compared to high emission hydrocarbons. In this regard, this paper renders exhaustive prospects and recommendations. • The nexus between energy transition, energy poverty and their impacts are investigated. • Influence of climate change, COVID-19, geo-politics, and energy infrastructure on energy transition. • Exhaustive prospects and recommendations are rendered. • Requirement for inclusive investigations and recognition of energy poverty. [ABSTRACT FROM AUTHOR]

The global shift to renewable energy is conducive to carbon reduction, but its impact on the economy is still uncertain. Unlike existing research focusing on the effect of domestic renewable energy (RE), this paper considers the global RE connection among countries and examines the impact of global RE development on economic growth (EG) using China as a sample. In addition, we apply a novel wavelet-based quantile-on-quantile method, which can estimate the local influence of the α-quantile of global RE on the β-quantile of the EG in different periods. The finding shows that the upper quantiles of RE promote EG , and this effect is greater at the upper quantiles of EG in the medium- and long-term. This is mainly because China plays a leading role in RE development and has an advantage in exporting RE -related products with the development of global RE. The finding proves the growth effect, indicating that RE is a blessing for China's EG. However, in the short term, a higher level of global RE development impedes China's EG due to high upfront costs of RE production, fierce competition from other countries, and insufficient energy supply when significant investment shifts from non-renewable energy to RE. The empirical results provide important policy implications to balance RE 's short-term inhibitory effect and long-term promoting effect on EG in the process of the global energy transition. • The global RE connection and the impact of global RE development on economic EG. • Applying wavelet-based quantile-on-quantile method can estimate the local influence of global RE on EG in different periods. • Upper quantiles of RE promote EG is greater at the upper quantiles of EG in the medium- and long-term. • The growth effect indicating that RE is a blessing for China's EG. • Balance RE's short-term inhibitory effect and long-term promoting effect on EG in the process of energy transition. [ABSTRACT FROM AUTHOR]

This paper discusses the article " Assessing temporal complementarity between three variable energy sources through correlation and compromise programming." The discussed paper proposes a novel method of assessing complementarity between three energy sources using correlation, compromise programming, and normalization. The method is then used to calculate a complementarity index which is applied to a case study in Poland. However, upon inspection, the normalization of the index overestimates the complementarity potential. This issue is discussed in detail in this paper, and an alternative way of calculating the index is proposed, eliminating the issue of overestimating complementarity. [ABSTRACT FROM AUTHOR]

Industry poses one of the biggest challenges in the renewable energy transition. In this paper, fossil fuels in the European industrial sector are replaced by renewable energy using a novel tool, IndustryPLAN, a planning tool for the assessment of national industrial sectors. In a bottom-up approach, each industry sub-sector is addressed with energy efficiency and fossil fuel replacement measures based on best available and innovative technologies, and in a top-down approach, the fuel and electricity consumption per country is analysed and decarbonised. The results indicate that: 1. Known technologies can decarbonise most of the industrial sector; 2. Costs and efficiencies are improved by energy savings and electrification; 3. Limiting bioenergy consumption is a critical challenge, emphasising the key role of energy savings and electrification, and the alternative of using hydrogen or hydrogen-based electrofuels will make the transition more expensive and induce energy losses. A full transition to renewable energy and a decarbonised industry sector may be possible before 2050, however, this requires that all investments are sustainable from 2030 onwards and that grid electricity is fully decarbonised. This paper presents several pathways toward 100% renewable energy supply in the European industrial sector and discusses the implications of the outlined scenarios. • Four tangible 100% renewable energy scenarios for industry in EU27 + UK are established. • 100% renewable energy in industry is feasible before 2050. • Energy efficiency improvements are essential to limit biomass consumption. • Industry energy transition needs to be prioritised in short- and long-term planning. [ABSTRACT FROM AUTHOR]

The Norwegian Agrarian Association has the ambition to reduce greenhouse gas (GHG) emissions by 4–6 million tons of carbon dioxide equivalents (CO 2 -eq) by 2030 in the agricultural sector. As a part of this, 10–25% is expected to come from substituting fossil fuels with renewable energy sources. This paper focuses on the effects of phasing-out fossil fuel consumption in on-field tractor operations with electrification by introducing battery-electric and/or fuel cell tractors and on-site renewable energy generation and storage. The results show that electrification of on-field tractor operations can be a techno-economically feasible pathway to reduce tractor energy-use-related CO 2 emissions in the agricultural sector. Annual CO 2 emissions are observed to reduce by 69% in 2030 and 97% in 2050. However, the CO 2 reduction potential can vary significantly based on the farm type. In this regard, the analysis revealed high sensitivity to manufacturing costs of zero-emission tractors (ZETs), which in combination with a low utilization rate can render the investment to ZETs unprofitable. Moreover, electrification increases electricity consumption, especially peak electricity demand in the agricultural sector. This effect can be reduced with on-site renewable energy generation and energy storage systems. • TIMES-models with differing spatial and temporal resolutions are soft-linked. • The long-term effects of electrification in the agricultural sector in Norway are analysed. • Electrification of on-field tractor operations can reduce energy-use related CO 2 emissions. • Adverse effects of electrification can be mitigated with distributed energy generation. [ABSTRACT FROM AUTHOR]

Floating photovoltaic systems (FPV) can be a more sustainable alternative for the energy transition than ground-mounted photovoltaic systems, as they avoid occupying useable land and the power generation is more distributed. This paper presents the first study that calculates the FPV technical potential at the province/municipality level, focusing on water irrigation ponds, which it is a novelty in the literature that usually focuses on large water infrastructures in a national approach. In the province of Jaén (Spain), more than 3000 ponds dedicated to agricultural irrigation have been identified and their surface area and location was obtained. The results, calculated for each pond, reveal that, in a conservative scenario, in which only 25% of their surface area is covered, a minimum of 490 MWp can be installed, which can provide 251% of the province agricultural electricity consumption and 27% of the total electricity needs. This analysis has also been performed at the municipal level, where all possible FPV plants have been aggregated and compared with consumption that would be covered at this scale. Furthermore, this technology brings additional benefits, as it avoids the occupation of 12 km2 of useable land, 8.8·106 m3/year of water evaporated, while creating more than 7000 jobs. • A Technical floating PV potential assessment on water irrigation ponds is done • A Technical floating PV potential assessment at a municipal level is done • A calculation of Multi-benefits of floating PV installation is done • An energy consumption coverage assessment at municipal level with floating PV is done [ABSTRACT FROM AUTHOR]

Potential energy regeneration is an important hydraulic energy-saving technology in construction machinery. However, the existing hydraulic regenerative potential energy system (HRPES) is still limited by its large size, high cost, circuit interference, and so on. To solve the above problems, this paper intends to study novel HRPES by optimizing the hydraulic circuits and hydraulic components. First, we design four new HRPESs according to the working characteristics of energy recovery and energy reuse of construction equipment. Based on the load data of the 1.7-ton experimental excavator and the HRPES simulation model, the working characteristics of HRPES in the process of multicycle energy regeneration are studied. Finally, in addition to the conventional energy-saving efficiency, this paper proposes the recovery efficiency, reuse efficiency and regeneration efficiency to comprehensively analyse the energy efficiency characteristics of the four HRPESs. The results show that the energy-saving efficiencies of the four HRPESs are 16.02%, 22.84%, 21.95%, and 27.15% compared with traditional No-HRPES, and the regeneration efficiencies are 54.43%, 57.76%, 53.55%, and 57.97%, respectively. These results have an important reference for optimizing the structure of existing HRPES. Potential energy regeneration is an important hydraulic energy-saving technology in construction machinery. However, the existing hydraulic regenerative potential energy system (HRPES) is still limited by its large size, high cost, circuit interference, and so on. To solve the above problems, this paper intends to study novel HPRES by optimizing the hydraulic circuits and hydraulic components. [Display omitted] • Novel hydraulic regenerative potential energy systems (HRPESs) are discussed. • The hydraulic schematic of HRPESs is designed, and the energy flow is analysed. • Simulation models of HRPESs based on an experimental excavator are established. • An energy analysis of HRPESs is conducted for energy efficiency and energy loss. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of renewable energy consumption on material consumption, considering the relationship between Material Footrprint and Gross Domestic Product (GDP), and testing the assumptions of the Environmental Kuznets Curve. A STIRPAT variation is used to specify a model relating the Material Footprint to renewable energy consumption and GDP. The effect is tested for the Material Footprint of fossil fuels and for the Material Footprint of the other categories. The analysis is applied to the seven European countries with the highest proportion of renewable energy consumption. The model estimation shows that the relationship between GDP and Material Footprint follows an inverted N-shaped form, and that the renewable energy favours the reduction of the material consumption of fossil fuels. However, there is a positive effect between the renewable energy consumption and the Material Footprint of the other categories beyond fossil fuels. These results must be interpreted considering the context, as the development of renewable energy coincides with the effects of the 2008 crisis, which may distort the relation between the variables. To pose dematerialization scenarios, it seems necessary to consider reducing energy consumption even if it comes from renewable sources. • The hypotheses of the Environmental Kuzntes Curve are tested. • Renewable energy does not seem to contribute to dematerialization. • Renewable energy contributes to reducing the consumption of Fossil Fuels. • An inverted N-shaped relationship between the Material Footprint and GDP is verified. [ABSTRACT FROM AUTHOR]

Wave energy technologies have the potential to play a significant role in the supply of renewable energy worldwide. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this paper, we explore the optimisation of WEC arrays consisting of three-tether buoys. Such arrays can be optimised for total energy output by adjusting the relative positions of buoys in a wave farm. As there are complex hydrodynamic interactions among WECs, the evaluation of each parameter setting is computationally expensive and thus limits the feasible number of full model evaluations that can be made. Furthermore, these WEC interactions make up a non-convex, multi-modal (with multiple local-optima), continuous and constrained optimisation problem. This problem is challenging to solve using optimisation methods. To tackle the challenge of optimising the positions of WECs in a wave farm, we propose a novel multi-swarm cooperative co-evolution algorithm which consists of three meta-heuristics: the multi verse optimiser (MVO) algorithm, the equilibrium optimisation (EO) method, and the moth flame optimisation (MFO) approach with a backtracking strategy, we introduce a fast, effective new surrogate model to speed up the process of optimisation. To assess the effectiveness of our proposed approach, 11 state-of-the-art bio-inspired algorithms and three recent hybrid heuristic techniques were compared in six real wave situations located on the coasts of Australia, with two wave farm sizes (four and nine WECs). The experimental study presented in this paper shows that our hybrid cooperative framework exhibited the best performance in terms of the quality of obtained solutions, computational efficiency, and convergence speed compared with other 14 state-of-the-art meta-heuristics. Furthermore, we found that the power output of the best-found 9-buoy arrangements were higher than that of perpendicular layouts at at 4.15 % , 3.29 % , 3.62 % , 9.2 % , 5.74 % , and 2.43 % for the Perth, Adelaide, Sydney, Tasmania, Brisbane, and Darwin wave sites, respectively. Our investigations reveal that the best-found arrangement at the Tasmania wave site was able to absorb the highest level of wave power relative to the other locations. • A new Multi-swarm Cooperative optimisation framework is proposed to optimise wave farms performance. • A new fast surrogate model is developed to speed up expensive optimisation process of wave farm. • To improve the placement of converters, a symmetrical backtracking search algorithm is proposed. • To handle the infeasible wave converters layout during the optimisation, a new and effective repair function is developed. • The proposed optimiser enhances significantly the total power output at six wave farms compared with previous methods. [ABSTRACT FROM AUTHOR]

Hydrogen is seen as the future energy that will help decarbonise the emissions of global energy use. Hydrogen-related technologies have recently attracted considerable attention due to their relatively low emissions and high energy yield. Even then, little attention was given to hydrogen's use in energy distribution networks. A renewable-based multi-energy system (RMES) considers power, cooling, heating, and hydrogen energy as utility systems for integrated urban and industrial areas to achieve urban-industrial symbiosis. This paper formulates the RMES as a multi-period mixed-integer nonlinear programming (MINLP) model to optimise the RMES, which minimises the financial implications and environmental impacts. Renewable solar energy is provided to the system using the photovoltaic solar system for electrical generation and the solar thermal collector for heat generation. Thermal, battery and hydrogen energy storages are integrated into the RMES to mitigate the energy supply and demand fluctuations and intermittency. A comparative analysis is conducted to individually identify the performance of different energy storage systems for economic and environmental objective functions. The comparison findings indicate that ESS performs better with 45% usage increases under objective environmental functions. The multi-objective optimisation using the ϵ-constraint method obtains the Pareto optimal solutions to the proposed multi-objective problem, which the 4th solution (ATC: 782,500 USD/y; ACE: 2,777.03 kg CO 2 -eq/y) appears to be the most viable. The solution maintains a high-profit level without sacrificing many opportunities for carbon emissions reduction while satisfying both objective functions simultaneously to a degree of satisfaction of 0.75. Overall, the proposed RMES is proven economical and environmentally friendly for implementation; however, the model is needed to optimise the system based on the specific situation. This study provides the optimisation model for energy recovery and technology optimisation in the multi-energy system for urban-industrial symbiosis, which minimises carbon emission and energy cost. This could lead the energy sector to achieve the Sustainable Development Goals, considering the economically and environmentally viable. • Renewable-based multi-energy system for urban-industrial symbiosis. • Heating, electricity, cooling and hydrogen energy demands are considered. • Multi-period optimisation based on economic and environmental factors. • Thermochemical, battery and hydrogen storages are compared. • Multi-objective optimisation for obtaining the non-dominating Pareto optimal. [ABSTRACT FROM AUTHOR]

The multi energy system (MES) is promising in the process of carbon neutrality, such that multi energy resources are utilized comprehensively to reduce the operation cost. Another way is to promote carbon neutrality by increasing the penetration of renewable energy. Hence, in this paper, we study the energy management of a typical MES under the challenges of stochastic renewable supplies and energy demands. To address the challenges, a stochastic optimization problem is established as a Markov decision process (MDP). An improved deep reinforcement learning (DRL) method is then developed to achieve the dynamic optimal energy dispatch. In particular, the comfort experience of users and complex coupling are both considered in the MES. In this framework, we propose an improved soft actor critic (SAC) algorithm based on maximum entropy to improve exploration ability, together with a long short-term memory (LSTM) network to extract temporal features efficiently. Meanwhile, we add the prioritized experience replay (PER) to increase the training efficiency to speed up the convergence of the algorithm. Finally, the case study demonstrates that the proposed algorithm can converge rapidly and greatly reduce the operation cost. In addition, the effectiveness and robustness of the improved method are verified. • A stochastic optimization model is mapped to a Markov decision process. • Uncertainties of renewable supplies and energy demands are considered. • An improved deep reinforcement learning algorithm based on PLSAC is applied. • The effectiveness of the optimal dispatch in the multi energy system is validated. [ABSTRACT FROM AUTHOR]

In this paper, the synthetic inertia need of the small island of Pantelleria in the Mediterranean Sea is assessed. Firstly, the optimal renewable energy mix able to minimize the Levelized Cost of Energy for the generation system of the island is evaluated, considering the yearly load demand and the characteristics of the local natural resources. The optimal energy mix introduces in the island a quota of power generation with static power converters interfaced to the grid that could jeopardize the security of the system if not suitably controlled. Therefore, the inherent inertial response of the diesel generators of the system is calculated hour by hour for each month of one year, considering the new schedule of the generators for various cases characterized by different percentages of production from the optimum renewable energy mix. Finally, the rated power and capacity of a battery energy storage system is calculated in order to compensate the reduction of the inertial response in the power system thanks to a suitable synthetic inertia control. The calculation is done by considering the maximum allowed frequency nadir in case of a disturbance. For each scenario, the maximum disturbance is also assessed with and without synthetic inertia. The results are compared with those obtained in a previous study on the same island where batteries were used for different purposes. • Sizing of a renewable energy mix to cover a share of energy demand in small island. • The best energy mix is given by the minimization of the Levelized Cost of Energy. • Three daily contributions from unprogrammable renewable source are assessed. • The corresponding inertia of the power system is calculated in all scenarios. • A controlled battery energy storage system providing synthetic inertia is needed. [ABSTRACT FROM AUTHOR]

The smart control of the biogas-hydrogen engine is needed to improve the overall energy efficiency of the hybrid renewable energy system. The paper presents some simulation results of the optimal control parameters of the engine aiming to achieve the compromise between performance and pollutant emissions of the biogas-hydrogen engine. In neat biogas fueling mode, the optimal equivalence ratio changes from 1.05 to 1.01 as the CH 4 composition in biogas increases from 60% to 80%. By adding 20% hydrogen into biogas, the optimal equivalence ratio practically reaches the stoichiometric value, despite the variation of CH 4 concentration. At the same operating condition and hydrogen content, an increase of 10% CH 4 in biogas leads to a decrease of 2°CA in the optimal advanced ignition angle. However, at a given engine speed and biogas composition, the optimal advanced ignition angle decreased by 3°CA when adding 10% hydrogen into biogas. The optimal ignition angle is independent of the load regime. Under optimal operating conditions, the addition of 20% hydrogen content into biogas is found to improve the indicated engine cycle work by 6%, to reduce CO and HC emissions by 5–10 times; however, it increases NO x emission by 10–15% compared to neat biogas fueling mode. [Display omitted] • A blend of 20% hydrogen and 80% biogas is an optimal ratio. • Optimal equivalence ratio of poor biogas and biogas/hydrogen blend are 1.05 and 1. • Advanced ignition angle decreased by 2°CA and 3°CA with adding 10% CH 4 and 10% H 2. • Indicative cycle work increased by 6% as adding 20% H 2 to biogas in optimal condition. • HC and CO reduced by 5–10 times, but NO x increased by 10–15% in optimal condition. [ABSTRACT FROM AUTHOR]

The development of smart energy systems is a crucial enabler of the transition towards a decarbonized renewable energy (RE)-based society with universal energy access as it provides flexibility to integrate variable RE sources. This paper analyzes the development of new RE-based smart energy systems while introducing energy technologies and effective methods for energy systems development, recent progress of energy systems by using RE, practical government measures in pioneer countries for energy systems, development with quantitative data for these countries, and PEST (political, economic, socio-cultural and technological) analysis. Our findings show that the development of smart energy systems requires appropriate policies and that thorough collaboration between all national executive agents like national and local governments and citizens in this regard is crucial. Smart energy system development needs to be tailored to local conditions, and due consideration must be given new technologies and methods for development, correct policies, investment, collaboration national/local and their citizens, societal support, and citizens acceptance. • Smart Energy Systems reduces CO 2 emissions while increases energy efficiency. • Development of the Smart Sustainable Energy using PEST analysis are more effective. • Development of Smart Energy Systems needs to governments investment. • Combining RE with CHP Systems increases electricity production. [ABSTRACT FROM AUTHOR]

Rising penetrations of renewable distributed generations (RDG) units at the demand side in residential power markets imply a reduction in the revenue of a power plant. In addition, the intermittent nature of RDG units requires the power plant to maintain the generation capacity to meet the demand during the intermittent periods. This paper derived two resilient three-part tariff (TPT) pricing policies based on either the time-of-use (ToU) pricing or the increasing-block pricing (IBP) schemes. The proposed pricing policies aim to maximize the power plant profit and the social welfare of users (consumers and prosumers) and recover the losses related to the uncertainties in energy supply and demand. To handle hybrid uncertainties in demand and capacity, a novel mixed type-2 fuzzy stochastic approach based on the integration of a type-2 fuzzy set and scenario-based stochastic programming is proposed. The application of the proposed model was examined using an empirical case study. Our results indicated that the resilient TPT with ToU could help power plants improve their profits by approximately 41.14% compared to those earned through the traditional two-part tariff. Furthermore, a decision matrix is proposed to allow the power plant to visualize an appropriate tariff plan amongst various options accurately. • We derived two resilient three-part tariff (TPT) pricing policies. • The pricing policies aim at maximizing the power plant profit and the social welfare of users. • These schemes can recover the losses related to the uncertainties in energy supply and demand. • The application of the proposed model was examined using an empirical case study. [ABSTRACT FROM AUTHOR]

As the energy demand keeps growing with the world's increasing population, concerns have arisen about the carbon emissions that contribute to critical environmental issues. These environmental concerns drive many nations to commit themselves in pursuing sustainable energy resources. Different process integration techniques have been developed to achieve a viable energy planning sector. In reference to the literature review, the use of time-sliced based models in optimization problems is still sparse. Hence, this paper aims to develop a time-sliced based optimization model that can be integrated into energy integration models. With that, a multi-period pinch-based methodology (targeting, scheduling, and optimization) for electric power systems with the hybridization of renewables and fossil-based energy is proposed. The prospective framework is designed to meet the emissions limit, fulfil energy demand, and minimize the electricity bill. An illustrative case study in Malaysia incorporated with an actual billing system is used to demonstrate the effectiveness of the proposed framework. The results show that it is capable of reducing the total electricity bill by 35% and achieving a 71% reduction of emissions by using solar PV as the renewables. • Three-step multi-period pinch-based methodology for energy planning is presented. • The model incorporates an actual billing system. • Time-sliced based optimization model is developed in the proposed framework. • Optimal emission reduction ratio and storage size are identified as 0.71 and 236 kWh. • The total electricity bill can be further reduced by 35%. [ABSTRACT FROM AUTHOR]

This study explored the relationship between renewable energy consumption and economic growth from new risk-based perspectives, including political risks, financial risks, economic risks and composite risks. The study uses a panel threshold model to empirically analyze panel data for the Organization for Economic Cooperation and Development (OECD) countries from 1997 to 2015. The results show when composite risks and political risks are used as threshold variables, there is a single threshold between renewable energy consumption and economic growth. When that threshold is exceeded, the positive effect of renewable energy on economic development increases. When economic risks and financial risks are used as threshold variables, there is a double threshold between renewable energy consumption and economic growth. When the first threshold value is exceeded, but not the second, renewable energy positively impacts economic development. However, when economic risk and financial risk do not lie between the two threshold values, there is an insignificant negative correlation between renewable energy consumption and economic growth. [Display omitted] • This paper explores the impact of country risks on renewable energy consumption and economic growth. • At different risk levels, the impact of renewable energy consumption on economic development is different. • When the composite risk and political risk crosses the threshold, the positive effect is significantly enhanced. • Effect is positive only if economic risks and financial risks crosses the first threshold but not crosses the second. [ABSTRACT FROM AUTHOR]

As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy. In this paper, the model-free deep reinforcement learning algorithm Rainbow Deep Q-Networks is used to control a battery in a microgrid to perform energy arbitrage and more efficiently utilise solar and wind energy sources. The grid operates with its own demand and renewable generation, as well as dynamic energy pricing from a real wholesale energy market. Four scenarios are tested including using demand and price forecasting produced with local weather data. The algorithm and its subcomponents are evaluated against an actor-critic method and a linear programming model with Rainbow able to outperform all other methods. This research shows the importance of using the distributional approach for reinforcement learning for complex environments, as well as how it can visualise and contextualise the agents' behaviour for real-world applications. • Novel use of Rainbow Deep Q-Networks for demand response. • Proposed algorithm outperforms continuous actor-critic method and linear model. • Visualise benefit of using distributional approach for complex problems. • Nonlinear efficiency profiles considered in hybrid AC/DC microgrid. [ABSTRACT FROM AUTHOR]

The increasing depletion of natural resources, combined with a wider set of pressures on the environment, has, in recent years, highlighted the need for a more efficient use of energy and a development process that involves alternative energy sources. Energy efficiency has received much attention as a solution, implying both monetary and emissions savings. However, the latter may be partially offset by the income and demand effects of the former, both in more efficient sectors and in spreading to the wider economy. This is the problem of rebound effects. Taking Spain as a case study, and introducing an energy-related CGE model that develops the inclusion of renewables, this paper evaluates a combination of efficiency initiatives to deliver both reduced energy use by households and a more sustainable supply of energy. Our findings suggest that a package aimed at improving efficiency in household electricity and petroleum use, combined with a more competitive supply of energy from renewable sources, may be the only way to get reductions in all energy use, and thus benefit the economy. Specifically, we consider how this package may lead to positive economic impacts and associated rebound effects, where the latter are focused on a greener energy supply. • A package of improvements in electricity and petroleum household use are evaluated. • Rebounds are studied, distinguishing between renewable and non-renewable energies. • The success of renewables depends on competitive production and efficiency. [ABSTRACT FROM AUTHOR]

In order to achieve the targets of the EU climate protection strategy, the household sector with its overall high energy consumption offers great potential for efficiency improvements. Better insulated buildings and efficient heating and ventilation technologies can reduce the demand for energy regarding space heating and cooling. Efficiency improvements in domestic appliances can only be achieved with great effort. This paper presents an innovative system concept in which domestic appliances are thermally connected to the heating and ventilation system via the energiBUS, using a heat pump as the central heating and cooling device. The system benefits from the replacement of internal heating and cooling devices of the respective domestic appliances and the simultaneous utilization of both energy flows – warm and cold – of the heat pump. The system is analyzed by means of a detailed dynamic simulation model using MATLAB/Simulink®. The component models included are experimentally validated and have deviations of less than 5%. The results show that a reduction of 35 kWh/a in total energy consumption is achieved and the COP of the heat pump can be improved from 3.3 to 3.5. In this context, the integrated design of the system control still has potential for optimization. • Description of an integrated system concept for low-energy buildings. • Connecting domestic appliances with the heat pump and ventilating system. • Analysis of the system by means of a detailed dynamic simulation model. • The energiBUS system saves approximately 35 kWh/a. • The coefficient of performance increases from 3.3 to 3.5. [ABSTRACT FROM AUTHOR]