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There is a widely believed myth that replacing the use of fossil fuels largely by renewable forms of energy is, with a possible exception of nuclear power, critically dependent on the development of appropriate new technologies. Accordingly, it is held that decarbonizing straight away is particularly difficult and expensive. There was a time when this idea had an element of reality, but this is no longer the case. Unfortunately, belief in this myth is shared by those in positions of influence. This paper serves to document that this presumed reality no longer holds, although the misconception may have been based on fact in the past. Whilst the survey of the available technology offered concentrates on electricity supply, it also documents that manufacture of synthetic fuels via hydrogen obtained by electrolysis of water and CO2 integrates smoothly with electricity grid stabilization as well as reducing the CO2 content of the atmosphere. The likely price and cost development in the energy market is also reviewed. In addition the role of CCS, in practice mainly capture from the air and industrial processes other than power generation is reviewed against the background of the cost effective generation of electricity by harvesting renewable forms of energy.

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

AbstractVegetation growth is affected by past growth rates and climate variability. However, the impacts of vegetation growth carryover (VGC; biotic) and lagged climatic effects (LCE; abiotic) on tree stem radial growth may be decoupled from photosynthetic capacity, as higher photosynthesis does not always translate into greater growth. To assess the interaction of tree‐species level VGC and LCE with ecosystem‐scale photosynthetic processes, we utilized tree‐ring width (TRW) data for three tree species: Castanopsis eyrei (CE), Castanea henryi (CH, Chinese chinquapin), and Liquidambar formosana (LF, Chinese sweet gum), along with satellite‐based data on canopy greenness (EVI, enhanced vegetation index), leaf area index (LAI), and gross primary productivity (GPP). We used vector autoregressive models, impulse response functions, and forecast error variance decomposition to analyze the duration, intensity, and drivers of VGC and of LCE response to precipitation, temperature, and sunshine duration. The results showed that at the tree‐species level, VGC in TRW was strongest in the first year, with an average 77% reduction in response intensity by the fourth year. VGC and LCE exhibited species‐specific patterns; compared to CE and CH (diffuse‐porous species), LF (ring‐porous species) exhibited stronger VGC but weaker LCE. For photosynthetic capacity at the ecosystem scale (EVI, LAI, and GPP), VGC and LCE occurred within 96 days. Our study demonstrates that VGC effects play a dominant role in vegetation function and productivity, and that vegetation responses to previous growth states are decoupled from climatic variability. Additionally, we discovered the possibility for tree‐ring growth to be decoupled from canopy condition. Investigating VGC and LCE of multiple indicators of vegetation growth at multiple scales has the potential to improve the accuracy of terrestrial global change models.

Abstract Standardization of biogas technology is immensely important for the promotion of the biogas industry worldwide. China has built a complete biogas standard system, which is divided into common, household biogas, biogas engineering, biogas digester for domestic sewage treatment, output utilization, and service system standards. The problems and potential barriers for biogas standardization in China are analyzed and come down to sluggish standard, overlapped standard, government-dominated standard, and lagging international standard. Accordingly, all potential biogas standards should be evaluated and placed under the same department. China Biogas Society and China Association of Rural Energy Industry play leading roles in developing enterprise or group/association biogas standards and ISO biogas standards. The bio-natural gas standard system and experimental standardization should be developed as well to replenish biogas standard system. A paradigm shift in biogas standardization should be from government-dominated to market-oriented model. The lessons learned for other developing countries includes expanding standardization to multi-aspects to realize full lifecycle control and management, building rapid responding mechanism of standardization to adopt industry transformation, integrating outdated standards into new versions, and establishing market-based standard system.

In this paper, we investigate how to apply industrial unmanned aerial vehicles (UAVs) for autonomous power line inspection in smart grid from an energy efficiency perspective. Firstly, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization and the small-timescale optimization. Then, the NP-hard joint optimization problem is transformed to a two- stage optimization problem based on energy consumption magnitude and optimization timescale differences. Next, the first-stage and second-stage problems are solved by exploring dynamic programming (DP) and auction matching, respectively. Finally, the proposed algorithm is verified based on realistic power grid topology. Simulation results demonstrate that the proposed scheme achieves significant energy consumption reduction.

This paper shows how to exploit the degree of freedom represented by the common-mode voltage, which is inherent in three-phase Cascaded H-Bridge (CHB) converters, to minimize the total Root Mean Square (RMS) current value of the distributed dc sources. An optimal common-mode voltage injection law is derived, constituting a Maximum Power Per Ampere (MPPA) modulation strategy with respect to the currents of the dc sources. The potential benefits introduced by the proposed algorithm are analyzed in the context of battery-fed CHB converters and validated experimentally with a three-time-constant Randles model of a battery cell. The MPPA strategy is compared with traditional common-mode voltage injection methods, and battery loss reduction is demonstrated. The obtained battery energy saving is dependent on the converter modulation index and power factor. Experimental tests on a 36-cell full-bridge CHB converter validates the simulation and numerical derivation. To demonstrate the efficacy of the proposed method in practical applications, a 3 MW Energy Storage System (ESS) and a 110 kW Battery Electric Vehicle (BEV) undergoing standard drive cycles are presented as case studies. Compared to traditional modulation strategies, the MPPA strategy reduces the battery losses by up to 10.9% in the ESS and 27.5% in the BEV application.