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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 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]