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Increasing amounts of fluctuating renewable energy lead to decreasing electricity prices and impair security of electricity supply. Consequently, sustainable and economically feasible solutions need to be found to ensure both ongoing renewable energy expansion and stable electricity supply. We examine the impact of batteries on security of the electricity supply and achieving renewable energy expansion. For this purpose we develop an electricity market model that enables the simulation of batteries both as an economic-driven investment option and as a government subsidized option. We present six policy scenarios in which batteries are utilized as an option that is subsidized by the government to secure electricity supply and engender renewable energy expansion. Our simulations, based on empirical data, indicate that, in a free market, battery investments are not profitable for private investors. On the other hand, these six policy scenarios show that by subsidizing investments in batteries governments could ensure a secure electricity supply as well as ongoing renewable energy expansion. A comparison to similar policy scenarios that do not adopt batteries indicates that the total sum of government subsidies and external costs is up to 36% lower when utilizing batteries.

The use of solar photovoltaic has strongly increased in the last decade. A significant part of this growth comes from home owners installing rooftop photovoltaic. Despite this key role, most long-term model-based scenarios do not consider decentralized supply of rooftop photovoltaic but concentrate on utility-scale photovoltaic instead. In this paper, we implement rooftop photovoltaic in the Integrated Assessment Model IMAGE to study its possible role in energy and climate scenarios. We first calculated the global technical and economic potential to derive regional cost-supply curves for rooftop photovoltaic. Next, we have added a new decision in the IMAGE model allowing household investment in rooftop photovoltaic based on the comparison of the whole-sale electricity price with the price of rooftop photovoltaic. The global suitable roof surface area was assessed at 36 billion m2, or 4.7 m2 capita−1, leading to a potential for rooftop photovoltaic of 8.3 PWh y−1, roughly 1.5 times the 2015 global residential electricity demand. In the baseline scenario, adding rooftop photovoltaic could lead to a 80–280% increased share of photovoltaic electricity production in 2050 (i.e. from 6% to 17% in total power production). This increase depends on regional characteristics that are essential to the deployment of rooftop photovoltaic: differences in social-economic and policy factors (capital costs, household income, and electricity prices) are considerably more important than physical factors, such as solar irradiance.

This article focuses on maximizing the thermal energy collected by parabolic-trough solar collector fields to increase the production of the plant. To this end, we propose a market-based clustering model predictive control strategy in which controllers of collector loops may offer and demand heat transfer fluid in a market. When a transaction is made between loop controllers, a coalition is formed, and the corresponding agents act as a single entity. The proposed hierarchical algorithm fosters the formation of coalitions dynamically to improve the overall control objective, increasing the thermal energy delivered by the field. Finally, the proposed controller is assessed via simulation with other control methods in two solar parabolic-trough fields. The results show that the energy efficiency with the clustering strategy outperforms by % that of traditional controllers, and the method is implementable in real-time to control large-scale solar collector fields, where significant gains in thermal collected energy can be obtained, due to its scalability.

The design of efficient energy systems, through the development of new technologies and the improvement of current ones, requires the use of rigorous process synthesis methods for generating and analysing design alternatives. We introduce a digital twin of process and energy system design that interactively translates needs and preferences of decision makers into an optimization-based model and generates meaningful solutions. The Interactive Digital Twin (InDiT) assists decision makers in steering the exploration of the solution space and guiding them towards relevant system design decisions, taking into account multiple aspects such as the impact of uncertainties and multi-criteria analysis. InDiT enhances step-by-step communication with the decision maker, relying on visual aids to keep the communication during solution generation and exploration intuitive and flexible. In this way, decision makers are guided towards relevant solutions and improve their understanding of relations between the problem definition and system design decisions, while InDiT builds on the decision makers’ preferences and can, after training, suggest solutions that are best-suited to their interests. The novelty of this work lies in the holistic approach of addressing both (i) the systematic generation and exploration of solutions with the assistance of a digital consultant, which translates the decision maker’s needs into machine language and vice versa, and (ii) the interactive step-by-step technique on filtering and evaluating solutions intuitively. This guarantees that the decision maker does not only get solutions based on the design specifications made, but that personal preferences are taken into account during the solution synthesis step, and that the solution space can easily be explored under different criteria. The proposed methodology is demonstrated and applied to the design case of an integrated multi-product biorefinery.

Support schemes have been central to the expansion of renewable electricity globally and in the European Union. As technologies mature, individual member states may decide to phase out these policies. While previous research has shown that such policy changes affect investors’ decisions, we investigate how they affect pathways and electricity prices by simulating investment decisions in an agent-based model in two case countries. This paper contributes and applies an adapted investment decision algorithm that incorporates empirically observed technology and return preferences and is calibrated by return observations. The new algorithm yields more refined and stronger effects compared to its predecessor. Results show that the phase-out of auctions in Germany and the Netherlands slows down their deployment of renewable capacity by up to ∼60% and ∼35%, respectively. With the exception of photovoltaics and onshore wind projects in the Netherlands, the targeted capacities can only be reached by continuing support in both countries. Furthermore, ending support in a large country like Germany leads to higher electricity prices and fosters a market-driven but insufficient capacity expansion in smaller neighbours like the Netherlands. As the electricity grids in many countries are strongly interconnected, such cross-border effects are of international relevance. Our findings suggest that continued auctions may be necessary and that countries should coordinate policy changes to stay on track for meeting their renewables targets.

Ammonia is one of the most commonly used feedstock chemicals globally. Therefore, decarbonisation of ammonia production is of high relevance towards achieving a carbon neutral energy system. This study investigates the global potential of green ammonia production from semi-flexible ammonia plants utilising a cost-optimised configuration of hybrid PV-wind power plants, as well as conversion and balancing technologies. The global weather data used is on an hourly time scale and 0.45° × 0.45° spatial resolution. The results show that, by 2030, solar PV would be the dominating electricity generation technology in most parts of the world, and the role of batteries would be limited, while no significant role is found for hydrogen-fuelled gas turbines. Green ammonia could be generated at the best sites in the world for a cost range of 440–630, 345–420, 300–330 and 260–290 €/tNH3 in 2020, 2030, 2040 and 2050, respectively, for a weighted average capital cost of 7%. Comparing this to the decade-average fossil-based ammonia cost of 300–350 €/t, green ammonia could become cost-competitive in niche markets by 2030, and substitute fossil-based ammonia globally at current cost levels. A possible cost decline of natural gas and consequently fossil-based ammonia could be fully neutralised by greenhouse gas emissions cost of about 75 €/tCO2 by 2040. By 2040, green ammonia in China would be lower in cost than ammonia from new coal-based plants, even at the lowest coal prices and no greenhouse gas emissions cost. The difference in green ammonia production at the least-cost sites in the world's nine major regions is less than 50 €/tNH3 by 2040. Thus, ammonia shipping cost could limit intercontinental trading and favour local or regional production beyond 2040.

Thermochromic (TC) materials can switch solar absorptance (α) based on temperature stimuli. When coatings with TC properties are applied on building envelope surfaces, the amount of solar heat gains can be controlled to reduce the heating and cooling demand of buildings. To date, limited research has been conducted in investigating optimal TC coating properties for application on opaque building envelopes in various scenarios. In this research, a method to model TC coatings using building performance simulation (BPS) tools has been developed and coupled with python to optimize solar absorption states (α) and switching temperatures and reduce the annual heating and cooling demand. The simulation-based approach has been employed to perform early-stage exploration studies on multiple building types and climates to support material R&D in developing optimized coatings for target applications and assess the potential energy savings. The results indicate that the optimum TC properties are unique to climate and building types. TC coatings with high switching temperatures result in larger energy savings for scenarios with high heating demands, while TC coatings with low switching temperatures produce larger energy savings in scenarios with high cooling demands. Similarly, increasing the high solar absorption (αhigh) to 1 increases the heating savings, while reducing the low solar absorption (αlow) to 0 results in higher cooling savings. Furthermore, it was found that solar irradiance causes temperature spikes triggering the TC coatings to unnecessarily switch from high to low absorptance state in winters leading to heating penalties. Replacing optimal static with TC coatings on terraced houses in the Spanish climate with a 2:3 heating to cooling demand ratio results in 2 to 13% energy savings.

The power to gas concept is promising for the next generation of electrochemical energy storage and grid stabilization technologies. The fuel produced from electricity-driven fuel production can be an efficient energy carrier for excessive grid power. Here, a reversible solid oxide cell(s) system integrated with methane synthesis (ReSOC-MS) is proposed for the grid stabilization application at Mega Watts class. CH4 can be synthesized at grid surplus conditions and can be a transportation friendly energy carrier. A control strategy is proposed for this combined system, based on the grid state and H2 tank state of the system for the normal solid oxide fuel cell (SOFC) mode and solid oxide electrolysis cell (SOEC) mode. Simulation results of these two operational modes demonstrate that the ReSOC-MS can achieve 85.34% power to gas efficiency in SOEC mode and 46.95% gas to power efficiency in SOFC mode. Dynamic simulations of stepping grid state for 5000 s operation show that the power to gas efficiency can be higher than 70%, thereby successfully demonstrating the capability of grid-balancing and methane production.

This project study involves the techno-economic assessment of two new advanced configurations for amine-based carbon dioxide capture from a pulp mill. The newly proposed advanced configurations include advanced rich amine four split (ARA4S), and advanced rich amine three split with desorber inter-heating (ARA3S-DI) which is aimed at reducing carbon capture cost and carbon emissions. The rate-based model in ProMax® 4.0 was used for the simulation study while the flue gas was provided by a 365,000 air dry tonne of pulp (ADt Pulp) per annum pulp mill in British Columbia, Canada. Comparative analysis revealed that the Capture Cost (US$/tCO2 and US$/ADt Pulp) of the 5 kmol/m3 MEA system was lowest for the conventional configuration, while for the 2 kmol/m3 AMP-3 kmol/m3 MEA blend it was lowest for the advanced rich amine 3-split with desorber inter-heating configuration. In addition, the Capture Costs of the MEA solution with a conventional configuration system is 8.7% higher than the AMP-MEA blend with advanced rich amine three split with desorber inter-heating configuration. Sensitivity analysis revealed that the combined effect of a carbon tax (US$ 40/tCO2), CO2 sales price (US$ 40/tCO2) and advanced configuration only led to a slight increase (2.6% for MEA and 2.1% for AMP-MEA blend) in the price of the northern bleached softwood kraft pulp. With an even better-performing solvent system and process configuration, the price of NBSK pulp may not be affected given the indicated CO2 tax and CO2 price regime.

Membrane liquid desiccant dehumidification is deemed as an advanced alternative to air dehumidification, as its operation can be sustained using low-grade waste heat. Many of the existing studies focused on the development and investigation of the dehumidifiers with different flow patterns and geometries, however, the key factors that dominate the dehumidification process are not clear. Therefore, in this paper, an in-depth scaling and dimensional analysis is carried out to investigate the basic physical process in the membrane liquid desiccant dehumidifier. A cross-flow flat-plate membrane liquid desiccant dehumidifier has been developed and tested. Concurrently, a 2-D computational fluid dynamics (CFD) model has been proposed. The model was converted into a dimensionless form so that the governing dimensionless numbers/groups were identified. The relative importance of these dimensionless numbers was determined. A key finding emerged from this study revealed that the relevant dimensionless numbers that dictate performance in the dehumidifier are Re a , Re l , H L , δ H , D vm D va and β . In addition, the heat and mass transfer process is controlled by heat and mass convection, as well as transverse heat conduction and mass diffusion. Finally, a correlation has been judiciously formulated for the dehumidified air temperature and humidity which demonstrated good predictive accuracy within ±3.5% and can potentially evolve to become an invaluable tool to predict and optimize the dehumidifier’s performance.

In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling – which typically aims to minimize makespan for purely economic objectives – and consider how three common carbon reduction policies – namely, taxes on emissions, baselines on emissions, and emissions trading schemes – can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed-integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia’s carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies.

China has adopted an ambitious plan for wind power to achieve grid parity with the on-grid price of coal-fired power in 2020. Whether this target can be achieved is a great concern for policy makers as well as potential investors. To address this issue, we first estimate the future levelized cost of electricity (LCOE) of wind power using a learning curve method, and then determine whether grid parity can be achieved by comparing it with the on-grid price of coal-fired power. Specially, the effect of carbon pricing on the grid-parity is explored, and a sensitivity analysis on how the discount rates, learning rates, and curtailment rates affect grid parity is conducted. The learning rate of onshore wind power is estimated using a panel dataset consisting of information of 2059 onshore wind projects in China from 2006 to 2015. Based on this learning rate, the future LCOE of Chinese onshore wind power from 2016 to 2025 is calculated. The results show that the LCOE of onshore wind power decreases by 13.91% from 0.40 RMB/kWh in 2016 to 0.34 RMB/kWh in 2025. By comparing the LCOE with the on-grid price of coal-fired power, the grid parity of onshore wind power may be achieved in 2019. With the implementation of the carbon pricing policy, the grid parity will be achieved earlier. More specifically, with the carbon price reaching 10, 35, and 60 RMB/t CO2, the grid parity can be achieved in 2019, 2017, and 2016, respectively. The results of the sensitivity analysis show that in the case of high discount rates, low learning rates, high curtailment rates, high O&M cost and low capacity factor, the grid parity will be delayed, and a high carbon price will be required to achieve the grid parity.

The sulfur dioxide (SO2) and carbon dioxide (CO2) emissions from fuel combustion in a coal-fired power plant constitute a significant source of damage to the environment. Therefore, SO2 and CO2 should be captured before being released into the atmosphere. However, the competitiveness between SO2 and CO2 for calcium carbonate (CaCO3)/calcium oxide (CaO) solid sorbents is still unclear. In this study, unsteady state computational fluid dynamics simulation in a riser of an industrial scale circulating fluidized bed boiler integrated with heterogeneous combustion, carbonation, calcination, and desulfurization reactions using a mixed feeding of CaCO3/CaO solid sorbents was developed in a two-dimensional model to investigate the competition between SO2 and CO2 capture. Then, the effect of three operating variables, the mixed solid CaCO3/CaO sorbent particle size, feed position, and the proportion of inlet fuel velocity on the SO2 and CO2 capture were evaluated using a 23 factorial experimental design. The CaCO3/CaO solid sorbent particle size had a significant effect on the SO2 capture, while the interaction between CaCO3/CaO solid sorbent particle size and feed position had a significant effect on the CO2 capture. The reaction rate for CO2 capture was higher than that for SO2 capture. For SO2 capture, CaO reacted with SO2 faster than CaCO3 while, for CO2 capture, solid sorbents had higher carbonation rate than calcination rate. In addition, the overall level of SO2 and CO2 capture with a mixed CaCO3/CaO solid sorbent feed was higher than those with the conventional CaO solid sorbent.

Recently, dimpled surfaces have found great attention due to their abilities in energy management and heat transfer enhancement, low weight, small values of pressure drop penalty, simple fabrication, and small maintenance costs. Many experimental and numerical studies are accomplished to investigate the potentials of dimpled surface technology in energy management of various thermal energy systems. This paper presents a comprehensive review about the developments and current status of this technology employed in different thermal energy systems including heat exchangers, mini/micro energy systems, two-phase flow energy systems, jet impingement cooling systems, and solar thermal energy systems. Moreover, comprehensive discussions concerning the flow structures and heat transfer behaviours in dimpled surfaces and different geometries and arrangements of dimples are provided. Finally, based on current status of studies in this field, some suggestions are suggested for future researches. The results showed that the main reasons for the convective heat transfer improvement achieved by dimples are flow reattachment, flow impingement, and upwash flow at the downstream region of the dimples, while the heat transfer can be reduced due to flow separation and recirculation in the upstream region of the dimples.

This study utilised a previously developed support vector machine (SVM) (trained using empirical data from 56 dairy farms) for predicting and analysing annual dairy farm electricity consumption to help improve the sustainability of the projected expansion of milk production in Ireland. Firstly, the capability of the SVM to predict annual electricity consumption was investigated at both a farm and catchment-level (combined consumption). Electricity consumption data were attained from 16 pasture-based, Irish dairy farms between June 2016 and May 2017 in conjunction with farm data related to herd size, milk production, infrastructural equipment and managerial tendencies, required to generate predictions using the SVM. The SVM predicted annual electricity consumption of dairy farms to within 10.4% (relative prediction error). Concurrently, catchment-level electricity consumption was predicted with an error value less than 5.0%. Secondly, an investigation was carried out to assess the impact of increasing herd size and milk production on dairy farm related electricity consumption at a catchment-level across ten hypothetical infrastructural scenarios. The dairy expansion analysis showed electricity economies of scale across all ten infrastructural scenarios. The greatest reduction in electricity consumption per litre was observed when all farms employed ground water for pre-cooling milk with two additional parlour units, reducing by 4% in 2018, relative to a base scenario (no change to infrastructural equipment). The results presented in this article demonstrate the potential effectiveness of the SVM as a macro-level simulation forecast tool for dairy farm electricity consumption that may be used to quantify the impact of milk production on electricity resources, or to offer decision support to dairy farmers.

Distribution networks usually have a meshed structure but are operated radially to improve the operating efficiency and ensure the power supply under an emergency situation. It is of great significance to guarantee the observability of the entire system under the topology changes in operation. This paper proposes an optimal placement method of phasor measurement unit (PMU) for distribution networks. A generalized binary integer linear programming (ILP) model for PMU placement is proposed. The changes in topology are considered to guarantee the observability under any possible operation mode. The existence of zero injection nodes (ZINs), the existing measurements, and their relevance are also covered in the proposed method to reduce the installed PMU number. The objective of the ILP problem is weighted by the degree of the corresponding node to obtain a scheme with higher measurement redundancy. The correctness and effectiveness of the proposed method are verified via case studies on the IEEE 33-node test feeder, PG&E 69-node test feeder, and a medium voltage distribution network in Southern China.

Renewable electricity generations have been growing rapidly in recent years worldwide as a result of efforts to address the global energy issue. However, it has also imposed increasing challenges on the balance of power grids due to their intermittent nature. Building sector, as the largest consumer, is expected increasingly to play an important role in providing ancillary services (AS) to relieve stress and reduce the needs of investment for power balance of the smart grids. Grid-responsive building therefore becomes a worthwhile and essential feature of buildings today and in the future. This paper provides an overview of the classification, benefits, applications as well as methods/technologies for HVAC systems in non-residential buildings to provide ancillary services, as fast responses (i.e., in the magnitude of seconds or minutes) to the needs of smart grids. In addition, the challenges of applications and potential solutions are investigated and summarized. The potential capacities of HVAC systems in the entire non-residential building sector in Hong Kong for providing frequency regulation (FR) and spinning reserve are also quantified.

This work concerns a type of efficient and promising solar energy utilization systems namely concentrating photovoltaic/thermal (CPV/T) systems. In CPV/T systems, uneven irradiance and PV temperature introduced by concentrators cause a rise in ohmic loss, decrease in PV fill factor and electrical efficiency, and local hot spot. Thus, the objective of this work is to develop a two-dimensional optical-thermal-electrical coupled model to predict PV temperature distribution and investigate effects of non-uniform irradiance and temperature on performance of linear concentrating CPV/T systems. Firstly, rays tracing of two kinds of truncated compound parabolic concentrators (CPCs) with different concentration ratios was simulated by TracePro to obtain their irradiance profiles. These two kinds of CPCs were designed and defined as HEMR and LEMR respectively in our previous work. Then, rays tracing results namely non-uniform irradiance distributions functioned as the inputs of a two-dimensional electrical-thermal coupled model developed by FORTRAN to obtain temperature distribution of PV array and performances of CPV/T systems. Meanwhile, a CPV/T prototype was set up to validate the theoretical results. The degrees of illumination and PV temperature non-uniformity were weighed based on the concept of standard deviation. Ultimately, the effects of non-uniform irradiance and temperature on performances of the CPV/T system were investigated with different concentration ratios. Results revealed that with a rise in concentration ratio, irradiance and PV temperature non-uniformity in the case of LEMR concentration experience a rapider increase than those in the case of HEMR concentration. Correspondingly, with increasing concentration ratios, both the fill factor and the electrical efficiency in the case of LEMR concentration experience a rapider decrease than those in the case of HEMR concentration. When the concentration ratio was 8, compared to HEMR concentration case, non-uniformity of irradiance and PV temperature in the case of LEMR concentration is increased by 63.1% and 65.7% respectively; the fill factor and the electrical efficiency in the case of LEMR concentration are decreased by 2.6% and 2.1% respectively.

Direct air capture, the removal of carbon dioxide from air, requires special sorbents, with high capture capacity, fast kinetics and long lifetime. Beyond that they also need to be affordable. However, since air capture is still in an early development stage, costs are still uncertain. We present a techno-economic model to value a sorbent based on the CO2 market price and the most important sorbent characteristics: cycle time, loading capacity, and rate of degradation. The model gives a net present value equation for any air capture sorbent, whether it uses a moisture swing, pressure swing or thermal swing for regeneration and makes it possible to estimate the maximum allowable budget for any air capture sorbent, i.e., the sorbent value. The analysis aims to focus the rapidly growing field of air capture sorbent development onto the most important parameters. The value of a sorbent is dramatically affected by its longevity. To be economically viable, the typical sorbent must survive tens if not hundreds of thousands of loading and unloading cycles. The model can also be used to investigate the interactions between different sorbent parameters. For example, the correlation between loading capacity and cycle time makes it possible to determine the cycle time that optimizes the economics of an air capture device. Our analysis highlights the significance of some neglected parameters in air capture cost analysis such as cycle duration and stability of the sorbent. Finally, the analysis can also be adapted to post-combustion flue gas sorbents.

Clean energy deployment scenarios typically assume a diverse mix of low-emission generation. However, because of historically low natural gas prices and cost reductions for onshore wind and photovoltaic technologies, recent new capacity deployment, especially in the United States, has come largely from these three generation sources. Here, we quantify competitive cost targets for three less-common zero-emission generation technologies—nuclear, concentrating solar, and offshore wind—under a range of future conditions. We determine the cost targets based on the technologies’ system value, such that technology value equals or exceeds its cost. Using a capacity expansion model that develops system-wide, economically optimal generation scenarios of the United States power sector, we find that nuclear, concentrating solar, and offshore wind would need to attain levelized cost ranges of $53–84/MWh, $65–91/MWh, and $39–77/MWh, respectively, to achieve 1% additional grid penetration. We refer to this metric as the “required cost,” which is a technology’s levelized cost of energy needed to achieve a specific penetration level. Higher penetrations lead to lower required costs as lower-cost generation is displaced. We estimate that to reach 10% penetration, the required cost for nuclear declines to $45–76/MWh. Because of resource variability and increased transmission needs for concentrating solar power and offshore wind, even lower costs are needed to reach higher penetrations: required cost ranges to achieve 10% penetration are estimated to be $30–56/MWh and $18–40/MWh for these two technologies respectively. Our analysis informs research and development priorities and the design of policies supporting clean technologies.

This work investigated the influence of biomass hydrothermal treatment (HT) on co-gasification reactivity and synergy of anthracite coal and banana residue hydrochar (HTC) prepared at varying HT temperatures (180 °C, 200 °C and 220 °C). In addition, the effect of HTC char on chemical structure evolution of coal char in co-gasification process was studied to correlate the co-gasification synergy. The results revealed that the co-gasification reactivity of the blended chars was enhanced with decreasing HT temperature. Moreover, the differences in synergistic effect on co-gasification reactivity was greatly influenced by HT temperature, and a more pronounced synergistic effect was achieved at the lowest HT temperature (180 °C). This phenomenon could be well explained by the chemical structure variations of coal char during co-gasification. Active AAEM contained in HTC char showed positive effect on the chemical structure evolution of coal char, specifically on the decrease in the order degree of carbon structure as well as increase in the amount of amorphous carbon structure of coal char; furthermore, this positive effect was weakened as HT temperature increased, being more evident for HT temperature of 180 °C than 200 °C and 220 °C. The findings in this work would be useful in revealing the co-gasification synergy mechanism of biomass hydrochar and coal blends, and may provide valuable information on the relevance of HT to industrial-scale gasification systems.

This study examined the catalytic co-pyrolysis of yellow poplar wood and polyethylene terephthalate over basic calcium oxide and acid zeolites, such as HY (SiO2/Al2O3: 30), Hβ (25), HZSM-5 (23), to maximize the yields of aromatics using thermogravimetric analysis and tandem μ-reactor-gas chromatography/mass spectrometry. The maximum decomposition temperature of polyethylene terephthalate on the catalytic thermogravimetric analysis over HZSM-5 (452 °C) was reduced by co-feeding with yellow poplar wood to 444 °C because of its catalytic property and the effective interaction between the catalytic co-pyrolysis intermediates of yellow poplar wood and polyethylene terephthalate. Non-catalytic co-pyrolysis produced smaller amounts of large molecular polyethylene terephthalate pyrolyzates because of the more effective secondary cracking and deoxygenation. Calcium oxide was effective in the deacidification and acid zeolites were efficient in aromatics production during the catalytic co-pyrolysis of yellow poplar wood and polyethylene terephthalate. Among the acid zeolites, HZSM-5 showed the highest efficiency on benzene, toluene, ethylbenzene, and xylenes (BTEXs) production, followed by Hβ and HY because of its strong acidity and proper pore size. The experimental MS intensities of BTEXs obtained from the catalytic co-pyrolysis of yellow poplar wood and polyethylene terephthalate over HZSM-5 (1083 × 106) were larger than their theoretical value (998 × 106). Compared to the single stage catalytic co-pyrolysis of yellow poplar wood and polyethylene terephthalate over ex-situ HZSM-5, the two-stage catalytic co-pyrolysis over in-situ calcium oxide and ex-situ HZSM-5 produced the much larger amounts of BTEXs during seven times sequential experiments.

Network and load recovery (NLR) during power system restoration is a multi-step mixed-integer nonlinear programming (MINP) problem. The NLR is difficult to be solved as it is NP-hard. Thus, NLR is commonly solved step by step, which is short-sighted and will result in longer restoration time. To obtain a far-sighted NLR plan, this paper proposes an improved two-stage optimization method for NLR. The first stage adopts a mixed-integer linear programming (MILP) model to obtain optimal solutions of integer variables in NLR, namely the load pick-up schedules and transmission line charging schedules. Then in the second stage, a continuous non-linear optimization method based on AC power flow with frequency constraints and load model is established to minimize the restoration duration of the plan generated in the first stage step by step. Case studies are undertaken on a 10-machine 39-bus system and Southeast Hubei Provincial power system of China. Simulation results indicate that the restoration plan obtained from the improved two-stage optimization method is highly effective, while the computational efficient meets the intensive need for restoration scheduling after blackouts.

A heat pump with low power consumption and a simple mechanical configuration is required to save energy and reduce carbon dioxide emissions. We show the design and demonstration of a novel heat pump using a Knudsen compressor, referring this device as a Knudsen heat pump. Water is employed as a refrigerant. The motionless nature of the Knudsen heat pump is accomplished by using a Knudsen compressor to transport the refrigerant vapor from the evaporator to the condenser, and thermal energy alone supplies the needed power. We employ a simple single-stage Knudsen compressor powered by light from a halogen lamp as an energy source. Experiments confirmed heat transport from the evaporator to the condenser with an output power of 3.09 W. The output power tended to increase linearly as the temperature difference between the evaporator and condenser decreased. The pressure-enthalpy diagram of the proposed Knudsen heat pump cycle was shown, and the thermal efficiency of this heat pump was analyzed. The application of a multi-stage Knudsen compressor to improve the heat pump performance was also discussed. This heat pump is free from noise, vibration, and maintenance, and it can utilize low-quality energy, such as a solar energy, and it does not require any electrical input. These properties make the Knudsen heat pump a particularly significant technology in harsh environments such as space and desert.

Hydropower is the widely used source of clean energy which includes some hazardous emissions that affect human health, ecosystems, and resources. However, in spite of an enormous amount of hydropower generation in Europe, no research has been carried out in evaluating the hazardous emissions from the plants located in alpine and non-alpine areas. Therefore, this paper will analyse the comparative environmental impacts of hydropower plants in alpine and non-alpine areas of Europe by a systematic life-cycle assessment (LCA) approach. The impacts are estimated by the ReCiPe 2016, Impact 2002+, Eco-points 97 methods under a number of effect-assessing indicators such as global warming, ozone formation, eco-toxicity, water consumption, acidification, eutrophication, ionizing radiation, carcinogenic radiation, ozone depletion, and land use. Moreover, the fossil-fuel-based power consumptions and the greenhouse-gas emissions in the life-cycle of hydropower plants in both locations are estimated using the Cumulative Energy Demand (CED) and the Intergovernmental Panel on Climate Change (IPCC) methods, respectively. The outcomes reveal that hydropower plants of alpine regions offer a better environmental profile for the global-warming indicator ( 2.97 × 10 - 5 kg CO2-eq/MJ) than non-alpine plants ( 3.92 × 10 - 4 kg CO2-eq/MJ), but the effects are nearly identical for the other indicators. Overall, the hydropower plants of non-alpine regions are responsible for climate change with a rate 10 times as high as for alpine ones. The findings of this research will play a pivotal role in promoting sustainable production of hydropower, especially the full potentials of the alpine region, and thus leading towards environmentally friendly clean renewable electricity generation.

We propose a non-resonant finger-triggered electromagnetic energy harvester (EMEH) using multiple gear modules and an array of disc Halbach magnets, which can generate significant voltage and power under low input-frequency vibrations. The proposed EMEH converts the applied low frequencies into higher frequencies using a multiplying gear module and forwards the linear excitation to a rotational module by gear parts. The multiplying gear module was used for increasing the speed of magnet motion. In the energy generation part, an array of disc Halbach magnets were used for concentrating the magnetic flux toward the coils. The proposed energy harvester generated an open-circuit voltage of 1.39 V at an average power of 7.68 mW, with an optimal load of 36 Ω at an input frequency of 3 Hz. The prototype harvester offers a power density of 0.833 mWcm−3, which is much higher than those of recently reported EMEHs. We demonstrate wearable and portable electronics such as a stopwatch and pedometer-based on finger triggering. This study presents an important step toward low-frequency-vibration-powered devices for portable smart electronic applications and is expected to be widely applicable.

This paper presents the experimental results of thermal behavior in an air-alumina packed bed storage system using a new technique for charging/discharging processes. A normal packed bed system, 100 cm in length, is divided into layers via pipes inserted internally along the axial length of the bed. Alumina beads were used as solid storage material and air was used as the heat transfer fluid (HTF) with an inlet temperature of 150 °C. This study analyzes the thermal behavior for full charge/discharge processes by dividing the bed domain into layers, focusing on the thermal exergetic efficiency for different charging/discharging schemes. One, two, and three layer configurations are considered along with various schemes including duration and magnitude of mass flow to each layer. In the most efficient configuration, the thermal exergetic efficiency increases with the number of layers from 53.2% to 69.6% for 0.0048 m3/s and 55.4% to 73.4% for 0.0061 m3/s, from one layer to two layers. At these same flow rates, thermal exergetic efficiencies increase to 76.8% and 80.3% for three layers. To determine the contributions of axial thermal dispersion and heat losses, a numerical model was run for a full charge/discharge cycle in adiabatic and non-adiabatic cases. The model results show that the dispersive effect is reduced by 23.2% in the best two-layer case and 25.6% in the best three-layer case for 0.0048 m3/s and by 22.8% in the best two-layer case and 26.5% in the best three-layer case for 0.0061 m3/s, resulting in these gains in exergetic efficiency.

Hydrothermal co-liquefaction of biomass has attracted considerable research interest as it has potential to reduce logistics costs, increase biocrude yield and improve biocrude quality. This work summarizes hydrothermal co-liquefaction of various biomass, in which co-liquefaction effect (either synergistic, antagonistic or additive) on biocrude yield of was examined. Observed synergetic effects must be critically assessed due to inconsistent reaction/separation conditions, uncertain statistical significance of experimental data and varied indicator of co-liquefaction effect in the studies reported. The chemical interactions among biomass model components (cellulose, hemicellulose, lignin, lipid, and protein) were also thoroughly investigated to explore the origin of co-liquefaction effect. Recent progresses on modeling hydrothermal liquefaction was reviewed as well, including quantitative models for predicting product yield as a function of the biochemical composition of biomass feedstock and/or reaction parameters, and kinetics models of reaction network among individual component of biomass. These models are useful tools to investigate co-liquefaction effect, optimize feedstock mixing for co-liquefaction, and ultimately facilitate more efficient biomass conversion. The major challenges in the study of co-liquefaction are identified such as statistical significance of co-liquefaction effect, evaluation of co-liquefaction effect under identical liquefaction and separation conditions, and understanding of co-liquefaction effect at a molecular level.

The complex random characteristics of dynamic loads in smart grid lead to the metering errors exceed the limits of electricity meters used in real field, these errors are called dynamic errors. By analyzing the typical intrinsic characteristics of large power dynamic loads and the nonadaptive linear modulation of compressive sensing (CS), this paper firstly constructs an orthogonal pseudo random measurement (OPRM) matrix to modulate distorted steady-state test signal (DSTS) and then proposes an orthogonal pseudo random measurement distorted dynamic test signal (OPRM-DDTS) with the intrinsic characteristics. In addition, an indirect likelihood function testing method is also proposed to solve the problems of both electricity meter dynamic error testing and dynamic reference electrical energy traceability. At last, a dynamic error testing system is built for verifying the indirect testing method under orthogonal pseudo random distorted dynamic test signal conditions, the dynamic errors of different electricity meters are given. Experimental results show that the orthogonal pseudo random distorted dynamic test signal and the indirect likelihood function testing method are effective for dynamic error testing of electricity meters, meanwhile, the measurement uncertainty of the dynamic error testing system is better than 0.042% (coverage factor k = 2).

This work focuses on the effects of charged single-walled carbon nanotubes (SWCNT) on the heat capacity of composite carbonate salt (Li2CO3-K2CO3) using the Electric Double-Layer modeling (EDL) and the Molecular Dynamics (MD) simulation. The nanoparticle-enhanced molten salt ensemble is modeled considering the compressed ion layer surrounding the SWCNT, and the specific heat capacity (cp) enhancement of the nanoparticle-enhanced molten salt is analyzed. The results present the following issues. First, compressed ion layer is formed around the SWCNT surface. The density distributions of Li+, K+ and CO32– are strongly related to the SWCNT charge. The density distributions of the ions present characteristics of oscillatory, and the densities of the ions can be increased by rise of SWCNT charge. Second, the charge density distribution is analyzed. The local enrichment of positive and negative charges is found to occur inside the compressed ion layer. It is found that increasing the SWCNT charge can promote the local enrichment of positive and negative charges, which contributes to the increase of the internal energy of the nanoparticle-enhanced molten salt ensemble and results in cp enhancement. Finally, cp is found to be increased with increasing SWCNT charge. The cp enhancement of 19.2% is achieved when the SWCNT carries surface charge of −280e. The obtained results can provide guidance on the application of charged nanoparticles to enhance the specific capacity of molten salt.

A numerical model is developed to describe the hydrothermal decomposition of municipal solid waste (MSW) in the pilot scale facility. The modelling process involves the use of high-pressure steam to directly heat MSW to the temperature and pressure at which decomposition reactions occur. This process converts the organic matter into solid coal-like fuel having properties suitable for co-firing or co-gasification with coal. The model used in the present work uses the Combustion Mathematics and Energy Transport (COMMENT) code. In order to appropriately simulate the behaviour of the inherent batch process, a transient model is developed. The model facilitates the calculation of important process parameters such as the temperature, the pressure, the gas flow and the composition with respect to time. The model is also used to determine the possible operational scenarios of the proposed pilot scale test. Based on the calculated results, the numerical predictions of the model are compared with the experimental profiles obtained in the base case of the hydrothermal treatment, which shows a good agreement. Considering its advantages, the innovative hydrothermal treatment process can be considered as an alternative to MSW treatment and can be used to produce safe, usable and low-chlorine content solid fuels.

A successful gas production trial in the South China Sea in 2017 proves that low-permeability hydrate reservoirs have great gas production potential. To improve the efficiency of energy exploitation in the future commercial hydrate production, it is necessary to predict the gas/water production rate and the multiphysical responses of the low permeability reservoirs to the gas production induced by depressurization. In this study, a Mohr-Coulomb geomechanical model based on the test data of the low permeability silty hydrate deposits is embedded into a fully coupled thermo-hydro-mechanical model, which is applied to systematically investigate the responses of hydrate reservoir including the pore pressure, temperature, and hydrate saturation, as well as the mechanical behaviors of the hydrate reservoir during one year of gas production by depressurization using a horizontal wellbore at GMGS3-W19 site in the South China Sea. The simulation results show that the gas production rate increases at the beginning of production then rapidly decreases after no further reduction in the downhole pressure, finally remains at 100 m3/day. Due to the low permeability, the decreased pressure propagates to 100 m, which is not very far from the wellbore. One year of gas production by horizontal wellbore does not severely disturb the reservoir. However, the stress relaxation due to the hydrate dissociation may induce stress redistribution leading to the deposits displacement of 0.35 m around the wellbore, which potentially results in the deformation of production well, further affecting the safety and efficiency of the gas production.

Liquid metal battery (LMB) is a novel battery technology that shows great application potential in the electric energy storage system. For the utilization of battery systems, an accurate estimate of the state of charge (SOC) for LMBs is of great significance. However, there are still many challenges need to be addressed due to the relatively low voltage and flat open-circuit-voltage versus SOC curve of LMBs. In this work, a novel state and parameter co-estimator is developed to concurrently estimate the state and model parameters of a Thevenin model for LMBs. The adaptive unscented Kalman filter is employed for state estimation including the battery SOC, and the forgetting factor recursive least squares is applied for online parameter estimation, which increase the model fidelity and further enhance the accuracy and robustness of the SOC estimation. A comparison with other algorithms is made based on the experimental data from laboratory-made LMBs. The evaluation results show that the proposed co-estimator exhibits the smallest root mean square error of 0.21% and is robust to external disturbances.

The residential air conditionings (RACs) are widely considered as one of the most important demand response (DR) resources due to the thermal storage characteristics. However, due to the uncertainties of the outdoor environment and the customers’ behaviors, the RACs’ operation states and power consumption are difficult to predicate. Facing this issue, this paper proposes a dynamic control strategy for the RACs to participate in DR program considering these uncertainties. Firstly, a single dynamic RAC model considering the uncertain environment and customer behaviors is developed. On this basis, a dynamic aggregate model of RACs is established with different number of RACs. Then, the dynamic aggregate model is identified by actual operation data. A dynamic rolling control strategy-based temperature set-points for large-scale RACs to participate in DR program is formulated. Moreover, the DR provided by RACs is divided into three levels according to the power reduction, where the corresponding control strategies at each level are proposed. Finally, the proposed models and methods are verified by employing the actual data of the urban residential communities in Changzhou City, China. The simulation results show that the proposed control strategy is accurate and effective.

Cooling of the Li-ion battery module is a critical issue for electric vehicles in regard with the battery performance and lifetime, particularly at abused operating conditions. A water cooling system consisting of two novel cover plates with T-shape bifurcation structures and eight traditional cold plates was proposed and investigated for cooling of prismatic LiFePO4 battery modules. A computational fluid dynamics simulation model of the cooling system was established and validated by experimental data. The results show that a cold plate with four circular mini-channels flowing water can keep the maximum temperature of a single battery around 35 °C at 1 °C discharging rate and ambient temperature of 40 °C. Effect of the coolant flow rate is much less significant than the inlet coolant temperature on the cooling performance. Two sets of the proposed water cooling systems can significantly reduce the maximum temperature of a battery module consisting of 15 batteries. Moreover, great temperature distribution uniformity between different batteries can be achieved because the novel cover plate is able to distribute water evenly into each mini-channel of the cold plates. The cooling system can keep the maximum temperature of the module below 32.5 °C and the difference between the highest and lowest temperatures around 1.5 °C at abused operating conditions.

As the cooling energy demand in buildings is set to increase dramatically in the future, the exploitation of passive solutions like natural ventilation could prove vital in reducing the reliance on mechanical systems. Models that can predict air temperature accurately in naturally ventilated mode are key to understanding the potential of natural ventilation now and in the future. This article presents a simulation based case study of a retrofitted nearly zero energy test-bed university building, in naturally ventilated mode only. The study had three aims: (1) calibration and validation of a whole building energy model, (2) a comparative analysis of occupancy schedules and opening control strategies, and (3) a comparison of researcher and practitioner approaches. Results showed the detailed building model was capable of predicting room level air temperature with a low level of error (0.27 °C ≤ RMSE ≤ 1.50 °C) that was well within the limits of existing calibration standards (MBE ±10%, CVRMSE

Electric mobility is a trending topic around the world, and many countries are supporting electric vehicle technologies to reduce environmental impacts from transportation such as greenhouse gas emissions and air pollution in cities. While such environmental impacts are widely studied in the literature, there is not much emphasis on a comprehensive sustainability assessment of these vehicle technologies, encompassing the three pillars of sustainability as the environment, society, and economy. In this study, we presented a novel comprehensive life cycle sustainability assessment for four different support utility electric vehicle technologies, including hybrid, plug-in hybrid, and full battery electric vehicles. A hybrid multi-regional input-output based life cycle sustainability assessment model is developed to quantify fourteen sustainability indicators representing the three pillars of sustainability. As a case study, we studied the impacts for Qatar, a country where 100% of electricity generation is from natural gas and have a very unique supply-chain, mainly due to a wide range of exported products and services. The analysis results showed that all-electric vehicle types have significant potential to lower global warming potential, air pollution, and photochemical oxidant formation. A great majority (above 90%) of the emissions occurs within the region boundaries of Qatar. In the social indicators, internal combustion vehicles performed better than all other electric vehicles in terms of employment generation, compensation of employees, and taxes. The results highlighted that adoption of electric vehicle alternatives doesn't favor macro-economic indicators and they have slightly less for a life-cycle cost. The proposed assessment methodology can be useful for a comprehensive regionalized life cycle sustainability assessment of alternative vehicle technologies and developing regionalized sustainable transportation policies worldwide.

Denmark has the ambitious plan of being independent from fossil fuels by 2050 and to run the entire energy system based on renewable energy sources. One of the most likely scenarios is a bigger deployment of wind farms and a massive electrification of the industry and transportation sectors. In 2016, the industry sector accounted for 20% of the final energy use, which was by more than 50% covered directly with fossil fuels. Electrification is a promising way for decarbonizing this sector but it will require significant economic investments and changes of the infrastructures. In this work, several strategies for electrifying industrial processes, based on the integration of heat pumps and electric heaters are presented. They were compared using energy, exergy, economic and environmental performance indicators. The production of milk powder was taken as a case study, as current factories are energy-intensive and require high-temperature heat generated by natural gas combustion. The highest energy efficiency and lowest exergy destruction was found for a system using a central heat pump system, with energy savings of 65%. The implementation of decentralised heat pumps that exchange heat between process streams and electric heaters, results in smaller reductions of only 56%. These two systems are likely profitable based on the energy price forecasts from 2020, but the decentral system allows for a gradual implementation of the most cost-effective measures.

In the search for sustainable transport solutions, fuel production from renewable resources has received significant attention. Some proposed synthetic fuels have favorable combustion properties compared to existing fuels, e.g., significant reductions in pollutant formation. However, penetration of such fuels requires a favorable social acceptance, as demonstrated by the consumer boycott of the ethanol-blend fuel with 10% ethanol and 90% gasoline (E10) in Germany. Therefore, the consumer perspective and their preferences regarding alternative fuels should be considered in the fuel design. We use conjoint methodology to analyze the preferences of German car drivers for alternative fuels. This aims at understanding which criteria determine consumer preferences and usage decisions. Among the five considered fuel attributes (fuel availability, driving range, pollutant emissions, fuel costs, and usage requirements to enable the use of alternative fuels), fuel costs had the highest decision impact for alternative fuel preferences, followed by fuel availability and usage requirements. Pollutant emissions had the lowest impact on alternative fuel choices. A market simulation of conventional diesel and alternative fuels (dimethyl ether (DME) and a blend of diesel with oxymethylene dimethyl ethers (OME)) revealed that currently a large majority of car drivers would prefer conventional fossil fuel options, indicating a currently low consumer demand for alternative fuels. Thus, the findings demonstrate the importance of integrating social acceptance as an objective function in the design of novel fuels and production processes.

The peak-load regulation of a coal-fired power plant is critical to promote renewable energy power generation in grid systems. A low-temperature multi-effect distillation (LT-MED) was proposed to improve the dispatchability of a 600 MW coal-fired power generating unit; here, an unsteady thermal system model was established using the Ebsilon software to study peak shaving capacities. The results show that the increased extraction amount and pressure will reduce the power generation and increase the coal consumption. The peak shaving capacity curve was obtained based on the extraction amount. It is indicated that the peak shaving capacity can reach a maximum of 477 MW when the amount of steam extraction is less than 238 ton/h. When the amount of extraction is sufficiently large, the exhaust steam from the low-pressure turbine attains the minimum allowable value, the peak shaving capacity of the unit then sharply decreases. The dynamic response of an LT-MED system to the step variation of the output load of the power generating unit was also revealed. Moreover, the power generation and coal consumption are slightly influenced by the steam consumption in a single LT-MED system. The maximum extraction of the unit can afford a maximum of 19 LT-MED systems. The results of this study will provide a useful reference for the peak shaving of coal-fired power generating units.

Real-time prediction of electricity pricing signals is essential for scheduling load demand in price-directed grids. In a deregulated electricity market, this helps substantially increase the gains of utility companies and minimize the electricity cost to the consumers. This paper introduces a novel model for electricity locational marginal price forecasting primarily centered on relevance vector machine. Two different versions of relevance vector machine are used based on Gaussian radial basis function and polynomial kernels in the first stage. The performance of the model is boosted using Extreme Gradient Boosting to incorporate the stochastic changes in prices. In the second stage, the outputs of the three models are stacked using Elastic net regression and the final price is forecasted after bagging the computed values. The model is trained and tested on real-time data of New England electricity market. Specifically, data for two years from 2012 to 2013 have been collected with a resolution of one hour. The proposed model has proven to be highly accurate and computationally cheap at the same time. It has been compared with various models that have been previously proposed for electricity forecasting including relevance vector machine, multilayer perceptron, random forest regressor, support vector machine, recurrent neural network, and least absolute shrinkage and selection operator. The proposed model is found to outperform all the other mentioned models with a mean absolute error of 2.6 on the test set and is sufficiently cheap computationally with a training time of 88 s.

In order to reach the goals of the United Nations Framework Convention on Climate Change, a stepwise reduction of energy related greenhouse gas emissions as well as an increase in the share of renewable energies is necessary. For a successful realization of these changes in energy supply, an integrated view of multiple energy sectors is necessary. The coupling of different energy sectors is seen as an option to achieve the climate goals in a cost-effective way. In this paper, a methodical approach for multi-modal energy system planning and technology impact evaluation is presented. A key feature of the model is a coupled consideration of the sectors electricity, heat, fuel and mobility. The modeling framework enables system planners to optimally plan future investments in a detailed transition pathway of the energy system of a country, considering politically defined climate goals. Based on these calculations, in-depth analyses of energy markets as well as electrical transmission and distribution grids can be performed using the presented optimization models. Energy demands, conversion and storage technologies in households, the Commerce, Trade and Services (CTS) area and the industry are modeled employing a bottom-up modeling approach. The results for the optimal planning of the German energy system until 2050 show that the combination of an increased share of renewable energies and the direct electrification of heat and mobility sectors together with the use of synthetic fuels are the main drivers to achieve the climate goals in a cost-efficient way.

The efficiency improvement of organic Rankine cycle, which is an efficient way to recover the waste heat of internal combustion engines, is an eternal topic. The 3D construction method of thermodynamic cycle provides a possible way to construct efficient thermodynamic cycles based on zeotropic working fluid, whose core is to improve the thermodynamic performance by using different working fluids in different thermodynamic processes. Based on the 3D construction method of thermodynamic cycle, a composition adjustment organic Rankine cycle was proposed in this paper to recover the waste heat of internal combustion engines. The optimum working fluid used in trunk and branch processes were selected according to the requirements of different thermodynamic processes. The effects of key parameters were analyzed, and the optimized operation conditions and optimal performance were obtained. The results shown that the composition adjustment organic Rankine cycle performs best when using R123/toluene (0.9/0.1) in trunk process and R123/toluene (0.96/0.04), R123/toluene (0.66/0.34) in low-pressure and high-pressure branch processes, respectively. Under the optimal condition, the net output power, thermal efficiency and exergy efficiency are 50.07 kW, 10.96% and 45.79%, respectively. The irreversible losses in evaporator I and evaporator II account for 35.01% and 30.54% of the total irreversible losses in composition adjustment organic Rankine cycle respectively. This work provided an advanced power cycle for the waste heat recovery of internal combustion engines. What’s more, composition adjustment organic Rankine cycle proves the feasibility of the 3D construction method and opens up a novel way to construct efficient thermodynamic cycles.

In this paper, a novel parabolic trough solar receiver-reactor (PTSRR) system of a locally-installed Kenics static mixer (KSM) is proposed for efficient solar thermal hydrogen production. A three-dimensional comprehensive model was established for PTSRRs of the methanol-steam reforming reaction (MSRR) for hydrogen production, by combining the Finite Volume Method and the Monte Carlo ray-tracing method with a MSRR comprehensive kinetic model. The validated model was preliminarily applied to study the effects and mechanisms of the concentrated solar flux nonuniformity and the locally-installed KSM on PTSRR photo-thermal-chemical comprehensive characteristics and performance, taking the methanol flow rate and the catalyst sintering temperature limitation into account. With a preliminary optimization on the concentrated solar flux nonuniformity, the optical efficiency and the solar flux nonuniformity are improved by 6.58% and 30.42% respectively. It is further revealed that these PTSRRs of better concentrated solar flux density nonuniformity also have better thermal-chemical comprehensive characteristics and performance. Novel PTSRRs of the locally-installed KSM have better comprehensive characteristics and performance than corresponding original PTSRRs or even optimized PTSRRs, with a maximum increase in the methanol conversion rate of 6.92%. It thus will operate more safely and more efficiently, by the cost of a little more pump power to overcome corresponding larger flow resistance caused by the locally-installed KSM. From the mechanism, this kind of novel PTSRR of a locally-installed KSM provides a useful option of high potential for improving uniformities of a series of key field variables in the whole photo-thermal-chemical conversion process, and thus improves the comprehensive characteristics and performance of PTSRRs.

For the past two decades, extensive research has been conducted to evaluate the performance of urban form scenarios such as sky view factors, solar radiation, or energy performance. The relationships between urban design parameters and multiple performance should be stipulated to evaluate scenarios for supporting timely and reliable design decisions. However, there are three major challenges: (1) only a few design alternatives are devised, (2) the multiple performance has not been provided synthetically, and (3) the relationships between design parameters and performance are difficult to be determined because they are multivariate relationships. This research proposes a new methodology applying a generative design approach using a reinforcement learning algorithm, a parametric performance modeling, and a multivariate adaptive regression splines approach to identify relationships between design parameters and urban performance. This research aims to support decision-making of urban design to achieve an energy efficient and visually qualified urban environment. A data-driven urban design approach is proposed to generate possible design alternatives using reinforcement learning, and a design-driven analysis is conducted to evaluate multiple performance of urban buildings using parametric modeling. The multivariate analysis presented relationships between urban geometric forms and performance criteria by using 30 samples. The findings show that to maintain optimal solar potential, the building coverage ratio is recommended to be bigger than 0.17. To maintain the optimal energy balance, the threshold for sky view factor is recommended as 54.17%. These relationships contribute to deriving design strategies and guidelines for designing a sustainable campus.

This paper presents a holistic approach to promote bioenergy in India by designing a conceptual framework that combines resource, technology and market. The proposed concept is an attempt to integrate pelletization and gasification technology for bioenergy system development through an end-to-end approach. The potential of bioenergy resource (i.e. agro waste) was estimated based on survey. The study further assessed the economic feasibility of agro waste pelletization. The economic evaluation was made using indicators such as net present value (NPV), internal rate of return (IRR), discounted payback period (DPBP) etc. Pellet plant capacity of 0.5 ton h−1 showed acceptable economics and the NPV, IRR and DPBP were ₹9.35 million ($0.13 million), 41% and 2.8 years, respectively. Moreover, the larger capacity plants (>2 ton h−1) were subjected to more risk under low pellet prices (

This study examines the feasibility of capturing and storing the coolth from the winter and the heat from the summer in the ground by utilizing the groundwater’s ability to phase change as a storage media. A novel system that implements a bayonet tube heat exchanger is proposed in this study due to it’s simple design and ease of installation using a single drill hole. A lab-scale experiment of a thermally controlled ground simulator was conducted to provide a proof-of-concept of the energy storage mechanism. A conjugate, multiphase heat transfer model was developed taking into account conversation of mass, momentum and energy and validated using the experimental results. The model framework is then extended to study the energy storage potential at full scale for four Canadian cities. The first set uses an averaged sinusoidal temperature profile, while the other set uses hourly temperatures from weather monitoring stations. Results of the study showed the long-term aggregated energy extraction was similar between both sets, however, over the short-term the results are more chaotic due to the nature of the weather. The system is thus best suited as a pre-heating or pre-cooling stage, making use of low-grade heat/cooling to decrease the need to use high grade energy (electricity or natural gas) over which the operator has stronger control.

This paper analyses an energy recovery solution based on the Pump-as-Turbine (PaT) technology applied in an oil refinery. Since under-ground water might be contaminated by the chemical processes, an intervention named “Pump & Treat” was adopted in the studied oil refinery to reclaim the installation site and to safeguard the zones nearby. After the groundwater’s treatment is performed by a specific water treatment plant, part of the wastewater is discharged, by gravity, into the sea through a sewer. In this work, the installation of an Axial Flow Pump (AFP) is studied for a branch of the wastewater sewer and its use as turbine for energy recovery purposes is evaluated. Knowing both flow rate and available head inside the sewer, which were used as Best Efficiency Point (BEP) values, a proper axial PaT design was identified in the ANSYS® Workbench and Computational Fluid Dynamics (CFD) simulations of the axial PaT design were performed in both pump and turbine modes. The obtained results were used to scale down the hydraulic machine and the new scaled axial PaT was simulated and analysed in detail by means of steady CFD simulations. The characteristic curves as well as the efficiency ones of the PaT are reported and an insight of the internal fluid flow is carried out. Finally, the installation of the designed PaT was analysed and discussed from both technical and economic points of views, allowing to achieve an economic saving of about 1706 €/year that lead to a PayBack Period (PBP) of about 1 year and 10 months.

To enable the smart technologies on the freight railcars, such as the global positioning system (GPS), real-time train condition monitoring and positive train control, a cost-effective power source is required. This paper presents the design, modeling, in-lab and onboard field-tests of an electromagnetic energy harvester for freight railcars. The proposed harvester with a mechanical motion rectification (MMR) mechanism can scavenge the vibration energy that is usually dissipated or wasted. An analytical model considering the train-harvester interaction is established to analyze the dynamic characteristic and predict the performance of the harvesters on different tracks at various train speeds. An in-lab bench test is carried out to experimentally validate the harvester model and evaluate the characteristics of the proposed energy harvester. The experimental results show that an average power of 14.5 W and 9.2 W are achieved respectively for the harvester using 66:1 and 43:1 gearhead under typical suspension vibrations recorded on an operational railcar at 90 km/h. An onboard field test is also performed using the harvester with 43:1 gearhead on a test track, which yields a peak phase power of 73.2 W and an average power of 1.3 W at 30 km/h. Both the in-lab and onboard test results indicate that the proposed energy harvester could continuously generate an amount of power useful for the implementation of smart technologies to improve the operational safety on the freight cars.

Straight-bladed vertical axis wind turbines (SBVAWTs) with a fixed pitch are subject to continuous variation in wind attack angle, which leads to low power generation. Variable pitch techniques are thus required to tackle the problem, but a proper analytical tool for pitch optimization and performance assessment must first be determined. This study thus aims to find a proper analytical tool for high-solidity SBVAWTs of low operational speeds for real-world applications. The applicability of the double-disk multiple stream-tube (DMST) model, a mainstream tool for analyzing moderate-solidity SBVAWTs and optimal pitch angles, is first examined for high-solidity SBVAWTs using measurement data obtained from wind tunnel tests. The complex flow field around a high-solidity SBVAWT is found to make current DMST models’ predictions unsatisfactory. A hybrid DMST model, using the attack angle-dynamic aerodynamic force coefficient relationship, is then proposed. The proposed hybrid DMST model predicts the aerodynamic forces acting on the blades with greater accuracy than those from current DMST models. Based on the proposed hybrid DMST model, a method of determining optimal pitch angles is developed and applied to the high-solidity SBVAWT. The results show that the novel method has the potential to significantly enhance power generation in high-solidity SBVAWTs.