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The optimal capacity configuration of combined wind-storage systems (CWSSs) serves as a foundation and premise for building new electricity system. This paper proposes a novel capacity configuration model of CWSS. The model simultaneously considers economy, stability and low carbon by respectively minimizing Levelized cost of electricity (LCOE) and maximizing effective load carrying capacity (ELCC). The multi-objective optimization model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). And Multi-Attributive Border Approximation Area Comparison (MABAC) method is used to score the alternative solutions on the Pareto front and make decisions under different scenarios. The results of the case study show that: (1) The evaluation of the ELCC for CWSS can quantify its ability to replace the installed capacity of thermal power, thus greatly reducing the uncertainty of wind power output. (2) Under low-carbon scenario, economic scenario and standard scenario, the results of optimal capacity configuration for the CWSS show that LCOE and ELCC are negatively correlated. When the weights of ELCC and LCOE are equal, the three goals of economy, low carbon and safety and stability are achieved. (3) The results of a single sensitivity analysis on LCOE show that the sensitivity coefficient of electricity generation is the highest, exerting the greatest impact on the capacity configuration decision of the CWSS. A 20 % increase in power generation can reduce LCOE by 0.1203RMB/kWh. This paper provides energy planning recommendations for decision-makers in CWSSs, contributing to the development of a reliable new electricity system. • A multi-objective optimization of CWSSs considering ELCC and LCOE. • Multi-objective optimization model is established based on NSGA-II and MABAC. • The model of LCOE based on ELCC is proposed. • The model is presented to achieve the optimal capacity configuration of the CWSS. [ABSTRACT FROM AUTHOR]

In the process of vaporizing water within the biogas-driven CCHP system based on chemical recuperation, a significant temperature disparity in heat transfer exists, leading to increased irreversible loss, and the methane conversion rate is low because the gas turbine exhaust gas is difficult to drive efficient methane conversion. This paper proposed a novel biogas-driven CCHP system based on chemical reinjection. Through the reinjection of a portion of the exhaust gas into the reactor, a self-thermal reforming reaction process is engineered, leading to a substantial enhancement in methane conversion rate. The heat matching of the heat exchange unit is optimized by the reheat air, and the power generation and cooling capacity of the system are improved. Notably, the electricity and cooling generation of the new system increased by 55.52 kW (6.20 %) and 542.49 kW (101.49 %), respectively, while the exergy efficiency increased by 8.31 %. The study delves into the influence of key parameters on the system thermal performance. During the reforming process, the exhaust gas split ratio has a significant impact on carbon deposition and methane conversion. When the methane is nearly completely converted, there is no significant change in electricity generation of the system. Excessive turbine inlet temperature is found to be unfavorable for improving the exergy efficiency of the new system from a holistic system perspective. Finally, a comprehensive comparison of the thermal performance between the new system and the reference system is conducted under various ambient temperatures. This study offer a new design scheme for the efficient utilization of biogas. • A novel biogas-driven CCHP system based on chemical reinjection is proposed. • Partial exhaust gas reinjection for self-heating of biogas reforming reaction. • The thermal efficiency and exergy efficiency increased by 28.39 % and 8.31 % in the new system. • The system is subjected to parameter analysis in combination with carbon deposition analysis. [ABSTRACT FROM AUTHOR]

This paper focuses on the determination of the optimal operating scenario of an Externally-Fired Micro-Gas turbine utilizing syngas produced by biomass gasification. Biomass exploitation can contribute to the achievement of the net-zero emissions target in 2050. This can be boosted by the advancement of externally-fired technology. In this study, a dual-objective optimization process, including a validated thermodynamic model, is combined with preliminary cost insights to assist the decision-making process. The resulting performance is compared with the actual data-based performance of a reference natural-gas-fired Micro-Gas Turbine. The performance comparison is performed through an exergetic analysis, identifying the main losses contributors. The optimization results in an externally-fired performance of 77.9 kW el electrical power and 20.3% efficiency, improved by 31.5% compared to the average current externally-fired demonstrated efficiencies. Yet, there is still room for improvement to reach natural-gas-fired Micro-Gas Turbine efficiency. The study highlights an unavoidable efficiency decrease of at least 4.0 percentage points due to the use of syngas itself instead of natural gas, which conversely increases heat recovery potential. However, compared to the state-of-the-art demonstrations, the proposed analysis is a significant step towards efficiency improvement, which can be exploited for future biomass demonstrations, attractive for electricity generation as well. • Performance of an externally-fired microturbine using biomass syngas is optimized. • Design point is determined through an optimization with preliminary cost insights. • Performance is compared with the validated data from a natural gas microturbine. • Electrical efficiency improved by 31.5% compared to average current demonstrations. • Syngas use results in an unavoidable efficiency decrease of 4.0 percentage points. [ABSTRACT FROM AUTHOR]

Deck photovoltaic (PV) system has been widely investigated on ocean-going ships to reduce oil consumption. However, its adoption on smaller ship encounters obstacles such as limited deck space, reduced energy efficiency, and cumbersome support structures. This paper explores the application of vertical PV windows on small ships as an alternative solution. By retrofitting standard glass with PV windows, the electricity generation area is expanded without occupying valuable deck space or requiring heavy brackets. Unlike deck PV system that solely generate electricity, PV windows also have the added benefit of reducing cooling load inside cabin, enhancing the overall energy-saving effect. Solar energy received by vertical surface is related to heading angles during navigation, a factor that has received scant attention in previous studies. Therefore, hybrid energy saving performance of PV window system is investigated under sailing condition by experiment and simulation methods. The electricity performance of PV window exhibits periodic fluctuations in closed-route trails, with sailing resulting in better electricity performance than mooring. Annual simulation research indicates that PV window system can generate 300.0 kWh of electricity actively, decrease 374.4 kWh of air conditioning load passively and reduce 472.0 kg of CO 2 emission. The optimal transmittance of PV window is 0.4%. • Hybrid energy saving performances of ship PV window system are researched by experiment and simulation. • The periodical changes of received solar radiation and electricity performances are observed from experiment. • The electricity generation and air conditioning load reduction are 300.0 kWh and 374.4 kWh, respectively. • The recommended transmittance of PV window is 40%. • Sailing can promote the electricity efficiency and power generation. [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]

Solar-driven thermoelectric generator is an enticing avenue for sustainable global electricity generation. Nevertheless, its broad adoption is impeded by two significant challenges: low efficiency and reliability. To address these challenges, a novel hybrid system that integrates angle-independent evacuated U-tube solar collector (EUSC) with inhomogeneous thermoelectric generator (ITEG) is first proposed in this paper. The power density of the hybrid system is predicted to be 26.1 W m−2 with an efficiency of 2.61% under AM 1.5, signaling considerable potential for performance enhancement. Extensive parametric studies demonstrate that raising the inlet water temperature for EUSC and the spatial inhomogeneity coefficient of ITEG, reducing external environmental temperatures and fluid flow rate within the EUSC, and optimizing the number and geometric characteristics of ITEG can improve the output performance. By optimizing the number of thermocouples in ITEG, the power density could reach 39 W m−2 when the spatial inhomogeneity coefficient of ITEG is 0.83. Gradient-based local sensitivity analyses further identify that the geometrical characteristics of ITEG and inlet water temperature of EUSC are, respectively, the most and least sensitive variables. These findings offer valuable insights into the intricate integration and operational mechanisms pivotal for achieving efficiency and stability in solar-driven thermoelectric generator power systems. • A novel photo-thermal-electric hybrid system is conceptualized and formulated. • Power density and efficiency can reach 26.1 W m−2 and 2.61%, respectively. • Influential laws of key design parameters and operation conditions are revealed. • The most and least sensitive factors are identified by local sensitivity analyses. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach that addresses the intricate challenges associated with energy hubs, focusing on diverse issues in the transmission and production of energy, particularly within the realms of gas and electricity networks. A key emphasis is placed on the profitability of energy hubs, navigating through uncertainties heightened by the presence of varied energy carriers. The proposed model features an energy hub with electrical inputs and outputs encompassing natural gas, electricity, and thermal energy. Diverse sources contribute to electricity supply, including bilateral contracts, power basin purchases, and generation via a cogeneration unit. Thermal heat is provided by a heating furnace, alongside a simultaneous cooling, heating, and power production unit. Addressing market price uncertainties, the model incorporates probabilistic considerations for the next day's electricity prices. Notably, the model encompasses the intricacies of electric vehicle rechargeable modes, treating them as a bidirectional source for both energy production and consumption, contributing to effective demand-side management. To tackle the complexity inherent in these dynamics, a novel optimization method based on crow search is proposed, enhancing local and global search capabilities. The efficacy of the proposed method is demonstrated through a comprehensive application on a micro-energy grid, considering four distinct cases on a typical summer day. The analysis reveals that Case 2, incorporating PV and WT, significantly outperforms Case 1, with a 10.8 % surplus in electricity generation. Furthermore, the proposed Crow Search Optimization algorithm exhibits a 50 % improvement over alternative methods, showcasing its efficacy in achieving optimal solutions for microgrid performance. [Display omitted] • Model explores gas-electricity networks in an energy hub. • Addresses energy hub profitability amidst uncertainties. • Introduces crow search optimization for complex models. • Models EVs as two-way energy sources. • Applies proposed method to micro-energy grid scenarios. [ABSTRACT FROM AUTHOR]