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ispartof: APPLIED ENERGY vol:329 status: published

The performance of lithium-ion battery packs are often extrapolated from single cell performance however uneven currents in parallel strings due to cell-to-cell variations, thermal gradients and/or cell interconnects can reduce the overall performance of a large scale lithium-ion battery pack. In this work, we investigate the performance implications caused by these factors by simulating six parallel connected batteries based on a thermally coupled single particle model with the solid electrolyte interphase growth degradation mechanism modelled. Experimentally validated simulations show that cells closest to the load points of a pack experience higher currents than cells further away due to uneven overpotentials caused by the interconnects. When a cell with a four times greater internal impedance was placed in the location with the higher currents this actually helped to equalise the cell-to-cell current distribution, however if this was placed at a location furthest from the load point this would cause a ∼6% reduction in accessible energy at 1.5 C. The influence of thermal gradients can further affect this current heterogeneity leading to accelerated aging. Simulations show that in all cases, cells degrade at different rates in a pack due to the uneven currents, with this being amplified by thermal gradients. In the presented work a 5.2% increase in degradation rate, from −7.71 mWh/cycle (isothermal) to −8.11 mWh/cycle (non-isothermal) can be observed. Therefore, the insights from this paper highlight the highly coupled nature of battery pack performance and can inform designs for higher performance and longer lasting battery packs.

Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

Building integrated photovoltaic (BIPV) systems provide an opportunity for renewable energy generation in the built environment. In order to quantify the BIPV potential, numerical models of varying levels of complexity have been developed. This paper investigates how the complexity of BIPV models affects their predictions. The study starts with a detailed multi-physics BIPV model that combines a high-resolution one-diode model with physics-based thermal and airflow models. Next, simplifications are introduced into the model. The model predictions are compared to experimental data from a BIPV curtain wall installed in a test building in Leuven, Belgium. The results show that the detailed BIPV model is capable of estimating the BIPV daily energy yield with an average difference of 6.2 % (2.0 % for clear sky days) and the back-of-module temperature with an average difference of 1.74 °C. The use of a linear power model instead of a high-resolution one-diode model affects the average differences, but not significantly: 8.7 % for daily energy yield predictions (4.5 % for clear sky days) and 1.71 °C for temperature predictions. The use of two different empirical temperature correlations instead of a physics-based approach increases the average temperature difference to 3.5 and 4.4 °C. The average difference in daily energy yield increases to 10.2 and 10.4 %, respectively (5.9 and 5.5 % for clear sky days). These findings indicate that the detailed version of multi-physics BIPV model provides the best agreement with experimental data, but it is still possible to reduce the model complexity with acceptable accuracy.

The mass-scale integration of electric vehicles into the power system is a key pillar of the European energy transition agenda. Yet, the extent to which such integration would represent a burden for the power system of each member country is still an unanswered question. This is mainly due to a lack of accurate and context-specific representations of aggregate mobility and charging patterns for large electric vehicle fleets. Here, we develop and validate against empirical data an open-source model that simulates such patterns at high (1-min) temporal resolution, based on easy-to-gather, openly accessible data. We hence apply the model – which we name RAMP-mobility – to 28 European countries, showing for the first time the existence of marked differences in mobility and charging patterns across those, due to a combination of weather and socio-economic factors. We hence quantify the impact that fully-electric car fleets would have on the demand to be met by each country's power system: an uncontrolled deployment of electric vehicles would increase peak demand in the range 35–51%, whilst a plausible share of adoption of smart charging strategies could limit the increase to 30–41%. On the contrary, plausible technology (battery density) and infrastructure (charging power) developments would not provide substantial benefits. Efforts for electric vehicles integration should hence primarily focus on mechanisms to support smart vehicle-to-grid interaction. The approach is applicable generally beyond Europe and can provide policy makers with quantitatively reliable insights about electric vehicles impact on the power system.

© 2018 Elsevier Ltd Meteorological measurements are conducted in Antwerp, Belgium in July 2013, followed by CFD urban microclimate simulations considering the same city and time period. The simulations are found to be able to reproduce measured air temperatures inside central Antwerp with an average absolute difference of 0.88 °C. The simulation results supplemented with measurements are used to generate location-specific Microclimatic Conditions (MCs) in three locations: (1) a rural location outside Antwerp; (2) an urban location inside Antwerp, away from an urban park; and (3) another urban location, close to the same park. Building Energy Simulations (BES) are performed for 36 cases based on three different MCs, two building use types and six sets of construction characteristics, ranging from pre-1946 buildings to new, low-energy buildings. Monthly Cooling Demands (CDs) are extracted for each case and compared with each other. The results demonstrate that compared to the air temperatures in the rural area, on average, air temperatures at the urban sites away and close to the park are 3.3 °C and 2.4 °C higher, respectively. This leads to an additional monthly CD of up to 90%. CDs of buildings with better thermal insulation and lower infiltration rates can increase by 48% once moved from the rural location to an urban location, which may lead to the reconsideration of design guidelines of low-energy buildings exposed to an urban MC. Although the proximity of an urban park cannot fully compensate the increased CD by an urban MC, residential buildings close to the park are found to have on average 13.9% less CD during July 2013, compared with buildings away from the same park. The influence of the urban park on the CDs of buildings in its vicinity is strongly linked to the meteorological wind direction. Professionals focusing on energy-efficient buildings in cities are advised to conduct energy predictions with location-specific MC data, instead of only using city-averaged meteorological data. ispartof: APPLIED ENERGY vol:228 pages:852-872 status: published

Efficient and affordable electricity storage systems have a significant potential to support the growth and increasing penetration of intermittent renewable-energy generation into the grid from an energy system planning and management perspective, while differences in the demand and price of peak and off-peak electricity can make its storage of economic interest. Technical (e.g., roundtrip efficiency, energy and power capacity) as well as economic (e.g., capital, operating and maintenance costs) indicators are anticipated to have a significant combined impact on the competitiveness of any electricity storage technology or system under consideration and, ultimately, will crucially determine their uptake and implementation. In this paper, we present thermo-economic models of two recently proposed medium- to large-scale electricity storage systems, namely ‘Pumped-Thermal Electricity Storage’ (PTES) and ‘Liquid-Air Energy Storage’ (LAES), focusing on system efficiency and costs. The LAES thermodynamic model is validated against data from an operational pilot plant in the UK; no such equivalent PTES plant exists, although one is currently under construction. As common with most newly proposed technologies, the absence of cost data results to the economic analysis and comparison being a significant challenge. Therefore, a costing effort for the two electricity storage systems that includes multiple costing approaches based on the module costing technique is presented, with the overriding aim of conducting a preliminary economic feasibility assessment and comparison of the two systems. Based on the results, it appears that PTES has the potential to achieve higher roundtrip efficiencies, although this remains to be demonstrated. LAES performance is found to be significantly enhanced through the integration and utilisation of waste heat (and cold) streams. In terms of economics on the other hand, and at the system size intended for commercial application, LAES (12 MW, 50 MWh) is estimated in this work to have a lower capital cost and a lower levelised cost of storage than PTES (2 MW, 11.5 MWh), although it is noted that the prediction of the economic proposition of PTES technology is particularly uncertain if customised components are employed. However, when considering the required sell-to-buy price ratios, PTES appears (by a small margin) economically more competitive above an electricity buy price of ∼0.15 $/kWh, primarily due to its higher roundtrip efficiency. When considering the two systems at the same capacity, the costs are similar with a slight edge to PTES. Finally, it is of interest that the most expensive components in both systems are the compression and expansion devices, which suggests that there is a need to develop affordable high-performance devices for such systems.

The large penetration rate of renewable energy sources leads to challenges in planning and controlling the energy production, transmission, and distribution in power systems. A potential solution is found in a paradigm shift from traditional supply control to demand control. To address such changes, a first step lays in a formal and robust characterization of the energy flexibility on the demand side. The most common way to characterize the energy flexibility is by considering it as a static function at every time instant. The validity of this approach is questionable because energy-based systems are never at steady-state. Therefore, in this paper, a novel methodology to characterize the energy flexibility as a dynamic function is proposed, which is titled as the Flexibility Function. The Flexibility Function brings new possibilities for enabling the grid operators or other operators to control the demand through the use of penalty signals (e.g., price, CO2, etc.). For instance, CO2-based controllers can be used to accelerate the transition to a fossil-free society. Contrary to previous static approaches to quantify Energy Flexibility, the dynamic nature of the Flexibility Function enables a Flexibility Index, which describes to which extent a building is able to respond to the grid’s need for flexibility. In order to validate the proposed methodologies, a case study is presented, demonstrating how different Flexibility Functions enable the utilization of the flexibility in different types of buildings, which are integrated with renewable energies. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/)

Reducing the cost of redox flow batteries (RFBs) is critical to achieving broad commercial deployment of large-scale energy storage systems. This can be addressed in a variety of ways, such as reducing component costs or improving electrode design. The aim of this work is to better understand the relationship between electrode microstructure and performance. Four different commercially available carbon electrodes were examined – two cloths and two papers (from AvCarb® and Freudenberg Performance Materials) – and a comprehensive study of the different pore-scale and mass-transport processes is presented to elucidate their effect on the overall cell performance. Electrochemical measurements were carried out in a non-aqueous organic flow-through RFB with these different electrodes, using two supporting solvents (propylene carbonate and acetonitrile) and at a variety of flow rates. Electrode samples were scanned using X-ray computed tomography, and a customised segmentation technique was employed to extract several microstructural parameters. A pore network model was used to calculate the pressure drops and permeabilities, which were found to be within 1.26 × 10−11 and 1.65 × 10−11 m2 for the papers and between 8.61 × 10−11 and 10.6 × 10−11 m2 for the cloths. A one-dimensional model was developed and fit to polarisation measurements to obtain mass-transfer coefficients, km, which were found to be between 1.01 × 10−6 and 5.97 × 10−4 m s−1 with a subsequent discussion on Reynolds and Sherwood number correlations. This work suggests that, for these fibrous materials, permeability correlates best with electrochemical cell performance. Consequently, the carbon cloths with the highest permeability and highest mass-transfer coefficients, displayed better performances.

Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e.g. economic gain vs. environmental impact. Moreover, a large number of input variables and different variable types, e.g. continuous and categorical, are challenges commonly present in real-world applications. In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions. This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems with heterogeneous variable spaces for which underlying system dynamics are either too complex to model or unknown. In an extensive case study comprised of synthetic benchmarks and relevant energy applications we demonstrate the competitive performance and sampling efficiency of the proposed algorithm compared to other state-of-the-art tools, making it a useful all-in-one solution for real-world applications with limited evaluation budgets., Comment: 36 pages, 8 figures, 5 tables

Tidal range power plants represent an attractive approach for the large-scale generation of electricity from the marine environment. Even though the tides and by extension the available energy resource are predictable, they are also variable in time. This variability poses a challenge regarding the optimal transient control of power plants. Here we consider simulation methods which include the main modes of operation of tidal power plants, along with algorithms to regulate the timing of these.This paper proposes a framework where simplified power plant operation models are coupled with gradient-based optimisation techniques to determine the optimal control strategy over multiple tidal cycles. The optimisation results in turn inform coastal ocean simulations that include tidal power plants to gauge whether the benefits of an adaptive operation are preserved once their hydrodynamic impacts are also taken into consideration. The combined operation of two prospective tidal lagoon projects within the Bristol Channel and the Severn Estuary is used as an example to demonstrate the potential benefits of an energy maximisation optimisation approach.For the case studies considered, the inclusion of pumping and an adaptive operation is shown to deliver an overall increase in energy output of 20-40 % compared to a conventional two-way uniform operation. The findings also demonstrate that smaller schemes stand to gain more from operational optimisation compared to designs of a larger scale.

Natural gas plays an important role in the Southern cone energy system, and is expected to increase in primary supply in the future. This paper presents a new energy systems model for the Southern Cone region of Latin America, covering five regions (Argentina, Bolivia, South and Centre Chile, North Chile, and Brazil) with the aim to explore, up to 2030, the interplay between (i) the expected consumption of natural gas for electricity generation and end-use consumption (i.e. residential, commercial, transport and industry) in each country, (ii) the inter- and intra-country potential role as producer and consumer of natural gas, and (iii) the possible supply network of LNG and natural gas via pipeline and domestic production. It is found that, under a Constrained Investment Scenario, the gross domestic gas production of the Southern Cone from 2012 to 2030 could be 62 Tcf, whereas under an Unconstrained Scenario, it could rise to 75 Tcf. This highlights the economic potential of the unconventional gas resources of Argentina and projections of associated gas from the Campos and Santos basins in Brazil. However, accessing these resources poses financial challenges. Nonetheless, results clearly indicate significant potential for an increase in regional natural gas trade in the Southern Cone.

The complexity of unconventional rock systems is expressed both in the compositional variance of the microstructure and the extensive heterogeneity of the pore space. Visualizing and quantifying the microstructure of oil shale before and after pyrolysis permits a more accurate determination of petrophysical properties which are important in modeling hydrocarbon production potential. We characterize the microstructural heterogeneity of oil shale using X-ray micro-tomography (µCT), automated ultra-high resolution scanning electron microscopy (SEM), MAPS Mineralogy (Modular Automated Processing System) and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM). The organic-rich Eocene Green River (Mahogany zone) oil shale is characterized using a multi-scale multi-dimensional workflow both before and after pyrolysis. Observations in 2-D and 3-D and across nm-µm-mm length scales demonstrate both heterogeneity and anisotropy at every scale. Image acquisition and analysis using µCT and SEM reveal a microstructure of alternating kerogen-rich laminations interbedded with layers of fine-grained inorganic minerals. MAPS Mineralogy combined with ultrafast measurements reveal mineralogic textures dominated by dolomite, calcite, K-feldspar, quartz, pyrite and illitic clays along with their spatial distribution, augmenting conventional mineral analysis. From high resolution Backscattered electron (BSE) images, intra-organic, inter-organic-mineral, intra- and inter-mineral pores are observed with varying sizes and geometries. By using FIB milling and SEM imaging sequentially and repetitively, 3-D data sets were reconstructed. By setting 3-D gradient and marker-based watershed transforms, the organic matter, minerals and pore phases (including pore-back artifacts) were segmented and visualized and the pore-size distribution was computed. Following pyrolysis, fractures from the mm-to-µm scales were observed with preferential propagation along the kerogen-rich laminations and coalescence leading to an interconnected fracture network. The application of these techniques to worldwide oil shale deposits will allow significant insights into estimating mechanical and chemical proprieties of oil shale formations for modeling and designing oil shale pyrolysis processes.

Decarbonising electricity systems is essential for mitigating climate change. Future systems will likely incorporate higher penetrations of intermittent renewable and inflexible nuclear power. This will significantly impact on system operations, particularly the requirements for flexibility in terms of reserves and the cost of such services. This paper estimates the interrelated changes in wholesale electricity and reserve prices using two novel methods. Firstly, it simulates the short run marginal cost of generation using a unit commitment model with post-processing to achieve realistic prices. It also introduces a new reserve price model, which mimics actual operation by first calculating the day ahead schedules and then letting deviations from schedule drive reserve prices. The UK is used as a case study to compare these models with traditional methods from the literature. The model gives good agreement with and historic prices in 2015. In a 2035 scenario, increased renewables penetration reduces mean electricity prices, and leads to price spikes due to expensive plants being brought online briefly to cope with net load variations. Contrary to views previously held in literature, a renewable intensive scenario does not lead to a higher reserve price than a fossil fuel intensive scenario. Demand response technology is shown to offer sizeable economic benefits when maintaining system balance. More broadly, this framework can be used to evaluate the economics of providing reserve services by aggregating decentralised energy resources such as heat pumps, micro-CHP and electric vehicles.

As the tidal energy industry moves from demonstrator arrays comprising just a few turbines to large-scale arrays made up of potentially hundreds of turbines, there is a need to optimise both the number of turbines and their spatial distribution in order to minimise cost of energy. Optimising array design manually may be feasible for small arrays, but becomes an impractically large approach when the number of devices is high, especially if taking into account both the cost effectiveness of each turbine and also the coupled nature of the turbine locations and the local as well as far-field hydrodynamics. Previous work has largely focused on producing computational tools to automatically design the size and layout of large-scale tidal turbine arrays to optimise power. There has been some limited preliminary work to incorporate costs into these models, in order to improve the economic viability of tidal arrays. This paper provides the first in depth implementation and analysis of economic functionals, based upon metrics such as break even power and levelised cost of energy, used for design of explicit array sizing and spatial variation. The addition of these new economic functionals introduces complexity by increasing the number of inputs to the model, each of which are subject to their own uncertainty in value. For this reason, sensitivity analysis becomes both more important as well as more difficult to undertake. This paper presents a novel rapid methodology for deriving the optimal array design (number of turbines and their spatial distribution throughout the farm area) to minimise cost functionals, and its sensitivity to variations in the economic inputs. Importantly, the new aspects of this method introduced here do not rely on repeated model runs and iterative optimisation, two aspects that typically prove to be impractically expensive computationally. This more readily allows for the impact of changes in investor priorities to be investigated. It is also shown that, while the optimal solution varies greatly with uncertainty in the input parameters, this uncertainty is reduced significantly through Monte Carlo analysis.

The possibility of molten-salt freezing in pipe-flow systems is a key concern for the solar-energy industry and a safety issue in the new generation of molten-salt reactors, worthy of careful consideration. This paper tackles the problem of coolant solidification in complex pipe networks by developing a transient thermohydraulic model and applying it to the ‘Direct Reactor Auxiliary Cooling System’ (DRACS), the passive-safety system proposed for the Generation-IV molten-salt reactors. The results indicate that DRACS, as currently envisioned, is prone to failure due to freezing in the air/molten-salt heat exchanger, which can occur after approximately 20 minutes, leading to reactor temperatures above 900 °C within 4 hours. The occurrence of this scenario is related to an unstable behaviour mode of DRACS in which newly formed solid-salt deposit on the pipe walls acts to decrease the flow-rate in the secondary loop, facilitating additional solid-salt deposition. Conservative criteria are suggested to facilitate preliminary assessments of early-stage DRACS designs. The present study is, to the knowledge of the authors, the first of its kind in serving to illustrate possible safety concerns in molten-salt reactors, which are otherwise considered very safe in the literature. Furthermore, and from a broader prospective, the analytical tools developed in this study can also be applied to examine the freezing propensity of molten-salt flows in other complex piping systems where standard, finite element approaches are computationally too expensive.

A third-order numerical model based on one-dimensional computational fluid dynamics is developed for kinetic Stirling engines. Various loss mechanisms in Stirling engines, including gas spring hysteresis loss, shuttle loss, appendix displacer gap loss, gas leakage loss, finite speed loss, piston friction loss, pressure drop loss, heat conduction loss, mechanical loss and imperfect heat transfer, are considered and embedded into the basic control equations. The non-equilibrium thermal model is adopted for the regenerator to capture the oscillating features of the gas and solid temperatures. To improve the numerical stability and accuracy, the implicit second-order time difference scheme and the second-order upwind scheme are adopted for discretizing the time differential terms and convective terms, respectively. Experimental validations are then conducted on a beta-type Stirling engine with the extensive experimental data for diverse working conditions. The results show that the developed model has better accuracies than the previous second-order models. Good agreements are achieved for predicting various critical system parameters, including pressure-volume diagram, indicated power, brake power, indicated efficiency, brake efficiency and mechanical efficiency. In particular, both the experiments and simulations show that the Stirling engine charged with helium tends to have much lower optimal working frequencies and poorer performances compared to the hydrogen system. Based on the analyses of the losses, it reveals that the pressure drop in the flow channels plays a critical role in shaping the different behaviors. The pressure drop in the helium system is much larger and more sensitive to the frequency increase due to the much larger viscosity of gaseous helium. Hydrogen is a superior working gas for a Stirling engine. The transient characteristics of the oscillating flow and the associated thermal interactions between gas and solid in the regenerator are finally analyzed in order to have an insight of the complex thermodynamic process. The study provides a promising numerical approach in simulating Stirling engines for further understandings of their operating characteristics and the underling mechanisms.

Power-generation systems based on organic Rankine cycles (ORCs) are well suited and increasingly employed in the conversion of thermal energy from low temperature heat sources to power. These systems can be driven by waste heat, for example from various industrial processes, as well as solar or geothermal energy. A useful extension of such systems involves a combined ORC and ejector-refrigeration cycle (EORC) that is capable, at low cost and complexity, of producing useful power while having a simultaneous capacity for cooling that is highly desirable in many applications. A significant thermodynamic loss in such a combined energy system takes place in the ejector due to unavoidable losses caused by irreversible mixing in this component. This paper focuses on the flow and transport processes in an ejector, in order to understand and quantify the underlying reasons for these losses, as well as their sensitivity to important design parameters and operational variables. Specifically, the study considers, beyond variations to the geometric design of the ejector, also the role of changing the external conditions across this component and how these affect its performance; this is not only important in helping develop ejector designs in the first instance, but also in evaluating how the performance may shift (in fact, deteriorate) quantitatively when the device (and wider energy system within which it functions) are operated at part load, away from their design/operating points. An appreciation of the loss mechanisms and how these vary can be harnessed to propose new and improved designs leading to more efficient EROC systems, which would greatly enhance this technology’s economic and environmental potential. It is found that some operating conditions, such as a high pressure of the secondary and discharge fluid, lead to higher energy losses inside the ejector and limit the performance of the entire system. Based on the ejector model, an optimal design featuring a smoothed nozzle edge and an improved nozzle position is found to achieve an improved entrainment ratio, significantly better performance and reduced energy losses in the ejector.

In this paper we evaluate the impact of knowledge about environmental and energy issues on potential pro-environmental behaviour in households, specifically relating to behaviours, attitudes and habits towards energy use. Our results are based on an empirical survey and we find significant correlations which indicate that residents with positive environmental values and greater environmental knowledge are more likely to demonstrate energy behaviours, attitudes and habits which lead to energy saving activities in households. This is further supported through a Principal Component Analysis (PCA), which suggests that energy saving behaviour may also vary according to gender and employment status. Conversely, we find only limited evidence of statistical associations between environmental predisposition and knowledge, and ownership and frequency of use of household appliances. We argue that our results contribute to the significant body of literature supporting the role of knowledge in active engagement with energy issues. This study is timely following closely policy developments in active consumer engagement by the European Commission.

This paper presents an advanced stochastic analytical framework to quantify the benefits of smart electric vehicles (EVs) and heat pumps (HPs) on the carbon emission and the integration cost of renewable energy sources (RES) in the future UK electricity system. The typical operating patterns of EVs/HPs as well as the potential flexibility to perform demand shifting and frequency response are sourced from recent UK trials. A comprehensive range of case studies across several future UK scenarios suggest that smart EVs/HPs could deliver measurable carbon reductions by enabling a more efficient operation of the electricity system, while at the same time making the integration of electrified transport and heating demand significantly less carbon intensive. The second set of case studies establish that smart EVs/HPs have significant potential to support cost-efficient RES integration by reducing: (a) RES balancing cost, (b) cost of required back-up generation capacity, and (c) cost of additional low-carbon capacity required to offset lower fuel efficiency and curtailed RES output while achieving the same emission target. Frequency response provision from EVs/HPs could significantly enhance both the carbon benefit and the RES integration benefit of smart EVs/HPs.

Soft Open Points (SOPs) are power electronic devices installed in place of normally-open points in electrical power distribution networks. They are able to provide active power flow control, reactive power compensation and voltage regulation under normal network operating conditions, as well as fast fault isolation and supply restoration under abnormal conditions. A steady state analysis framework was developed to quantify the operational benefits of a distribution network with SOPs under normal network operating conditions. A generic power injection model was developed and used to determine the optimal SOP operation using an improved Powell’s Direct Set method. Physical limits and power losses of the SOP device (based on back to back voltage-source converters) were considered in the model. Distribution network reconfiguration algorithms, with and without SOPs, were developed and used to identify the benefits of using SOPs. Test results on a 33-bus distribution network compared the benefits of using SOPs, traditional network reconfiguration and the combination of both. The results showed that using only one SOP achieved a similar improvement in network operation compared to the case of using network reconfiguration with all branches equipped with remotely controlled switches. A combination of SOP control and network reconfiguration provided the optimal network operation.

The growth of China’s road transport sector has driven huge increases in China’s oil demand and CO2 emissions over the last two decades, and these trends are likely to continue in the absence of specific measures to reduce the average carbon intensity of road vehicles. This paper describes a model, provided in full online, to undertake scenario analysis on the cost and CO2 emissions impact of substituting current vehicle drivetrain types with alternatives during the period 2010–2050. A detailed decomposition of the additional costs and CO2 emissions savings of each low-carbon vehicle type into their component parts is undertaken to calculate the marginal abatement cost of each vehicle and drivetrain type in 2050. The results indicate that passenger cars and heavy-duty trucks constitute the majority of future CO2 emissions savings potential, but that, using the central cost assumptions, alternative vehicle drivetrains are significantly more cost-effective for trucks than passenger cars. The low-carbon scenario sees demand for oil products (gasoline and diesel) more than 40% below the business-as-usual scenario in 2050. The total mitigation cost in 2050 is (US2010)$64 billion per year, or 1.3% of the total annual expenditure on road transport in China in 2050, using a discount rate of 5% to annualise vehicle purchase costs, although this cost increases with higher discount rates. A sensitivity analysis demonstrates that measures in addition to those assumed in the low-carbon scenario could achieve further emissions reductions, in some cases at negative costs. The availability and transparency of the model allows testing and development of a range of further scenarios and sensitivities, to aid in planning an optimal decarbonisation strategy for this highly carbon-intensive sector.

Future vehicles for clean transport will require new powertrain technologies to further reduce CO 2 emissions. Mobile organic Rankine cycle systems target the recovery of waste heat in internal combustion engines, with the exhaust system identified as a prime source. This article presents a design methodology and working fluid selection for radial turbo expanders in a heavy-duty off-road diesel engine application. Siloxanes and Toluene are explored as the candidate working fluids, with the latter identified as the preferred option, before describing three radial turbine designs in detail. A small 15.5 kW turbine design leads to impractical blade geometry, but a medium 34.1 kW turbine, designed for minimum power, is predicted to achieve an isentropic efficiency of 51.5% at a rotational speed of 91.7 k min - 1 . A similar 45.6 kW turbine designed for maximum efficiency yields 56.1% at 71.5 k min - 1 . This emphasizes the main design trade-off – efficiency decreases and rotational speed increases as the power requirement falls – but shows reasonable radial turbine efficiencies and thus practical turbo expanders for mobile organic Rankine cycle applications are realizable, even considering the compromised flow geometry and high speeds imposed at such small scales.

This paper presents the novel MILP formulation of the Biomass Value Chain Model (BVCM), a comprehensive and flexible optimisation toolkit that models a large number of bioenergy system pathways. The model accounts for the economic and environmental impacts associated with the end-to-end elements of a pathway: crop production, conversion technologies, transport, storage, local purchase, import (from abroad), sale and disposal of resources, as well as CO2 sequestration by CCS technologies and forestry. It supports decision-making around optimal use of land, biomass resources and technologies with respect to different objectives, scenarios and constraints. Objectives include minimising cost, maximising profit, minimising GHG emissions, maximising energy/exergy production or any combination of these. These objectives are combined with a number of scenarios (such as including different CO2 prices, different technology and climate scenarios, import scenarios, waste cost scenarios), different credits (e.g. by-product and end-product, CCS and forestry carbon sequestration) and a number of constraints such as minimum levels of energy production and maximum environmental impacts. The toolkit includes an extensive database of different biomass technologies including pretreatment, densification, liquid and gaseous fuel production, heat and power generation (separately or combined, biodedicated or co-fired), waste-to-energy conversion and carbon capture and sequestration. A large number of resources are considered including a variety of bio-resources (e.g. energy crops such as Miscanthus and SRC willow, arable crops such as winter wheat, sugar beet and oilseed rape and short and long rotation forestry), intermediates, products, by-products and wastes. The BVCM is a spatio-temporal model: currently it is configured for the UK using 157 square cells of length 50 km and the planning horizon is from the 2010s to the 2050s, with seasonal variations considered. The framework is data-driven so the model can be easily extended: for example adding new resources, technologies, transport modes, etc. or changing the time horizon and the location to another country is only a matter of changing the data. Results of example UK case studies are presented to demonstrate the functionality of the model.

The aim of this paper is to present a methodology for optimizing the operation of compressors in parallel in process industries. Compressors in parallel can be found in many applications for example in compressor stations conveying gas through long pipelines and in chemical plants in which compressors supply raw or processed materials to downstream processes. The current work presents an optimization framework for compressor stations which describe integration of a short term and a long term optimization approach. The short-term part of the framework suggests the best distribution of the load of the compressors (where the time scale is minutes) and the long-term optimization provides the scheduling of the compressors for large time periods (where the time scale is days). The paper focuses on the short-term optimization and presents a Real Time Optimization (RTO) framework which exploits process data in steady-state operation to develop regression models of compressors. An optimization model employs the updated steady-state models to estimate the best distribution of the load of the compressors to reduce power consumption and therefore operational costs. The paper demonstrates the application of the RTO to a network of parallel industrial multi-stage centrifugal compressors, part of a chemical process in BASF SE, Germany. The results from the RTO application showed a reduction in power consumption compared to operation with equal load split strategy.

During CO2 storage in depleted oil fields, under immiscible conditions, CO2 can be trapped in the pore space by capillary forces, providing safe storage over geological times - a phenomenon named capillary trapping. Synchrotron X-ray imaging was used to obtain dynamic three-dimensional images of the flow of the three phases involved in this process - brine, oil and gas (nitrogen) - at high pressure and temperature, inside the pore space of Ketton limestone. First, using continuous imaging of the porous medium during gas injection, performed after waterflooding, we observed chains of multiple displacements between the three phases, caused by the connectivity of the pore space. Then, brine was re-injected and double capillary trapping - gas trapping by oil and oil trapping by brine - was the dominant double displacement event. We computed pore occupancy, saturations, interfacial area, mean curvature and Euler characteristic to elucidate these double capillary trapping phenomena, which lead to a high residual gas saturation. Pore occupancy and saturation results show an enhancement of gas trapping in the presence of both oil and brine, which potentially makes CO2 storage in depleted oil reservoirs attractive, combining safe storage with enhanced oil recovery through immiscible gas injection. Mean curvature measurements were used to assess the capillary pressures between fluid pairs during double displacements and these confirmed the stability of the spreading oil layers observed, which facilitated double capillary trapping. Interfacial area and Euler characteristic increased, indicating lower oil and gas connectivity, due to the capillary trapping events.

The proposed supervisory control system (SCS) uses a control map to maximize the powertrain efficiency of a hybrid electric vehicle (HEV) in real-time. The paper presents the methodology and structure of the control, including a novel, comprehensive and unified expression for the overall powertrain efficiency that considers the engine-generator set and the battery in depth as well as the power electronics. A control map is then produced with instructions for the optimal power share between the engine branch and battery branch of the vehicle such that the powertrain efficiency is maximized. This map is computed off-line and can thereafter be operated in real-time at very low computational cost. A charge sustaining factor is also developed and introduced to ensure the SCS operates the vehicle within desired SOC bounds. This SCS is then tested and benchmarked against two conventional control strategies in a high-fidelity vehicle model, representing a series HEV. Extensive simulation results are presented for repeated cycles of a diverse range of standard driving cycles, showing significant improvements in fuel economy (up to 20%) and less aggressive use of the battery.

© 2019 In the present energy scenario, buildings are playing more and more as energy prosumers. They can use and produce energy and also actively manage their energy demand. The energy flexibility quantifies their potential to adjust the energy demand on the basis of external requests. The objective of this paper is to propose a method for buildings energy flexibility labelling at design conditions in the same fashion as the energy performance label. The flexibility quantification is based on the calculation of four flexibility parameters, which contribute to the definition of the Flexibility Performance Indicator. In order to assess the Flexibility Performance Indicator, buildings dynamic simulations are necessary and the boundary conditions (i.e. demand response event, representative day, comfort constraints)to be considered during the evaluation are provided as part of the proposed methodology. The method was applied to different Italian buildings, which differ for geographic location and design specifications and, in particular, the effects of building structure, heating/cooling systems and energy storage systems were compared. Results show that the climatic conditions affect the flexibility performance, while the building feature more relevant is the thermal mass of the building envelope, more than that provided by the distribution system. A sensitivity analysis to evaluate how the results are influenced by the proposed boundary conditions was also performed. Their choice confirms to have a relevant impact on flexibility quantification, then their unique definition has a paramount importance within this methodology. ispartof: APPLIED ENERGY vol:251 status: published

A traveling-wave thermoacoustic electric generator, which is composed of a traveling wave thermoacoustic engine and linear alternators, is promising in solar power generation and energy recovery due to its high efficiency, high reliability, and capability of utilizing low-grade heat. An equivalent acoustic circuit of a linear alternator is first built and analyzed using electro–mechano-acoustical analogy. It is found that the acoustic coupling of the linear alternators to the traveling-wave thermoacoustic engine is crucial to the performance of the system. A traveling-wave thermoacoustic electric generator with a variable electric R-C load is then constructed and experimentally studied. Both the theoretical analysis and the experimental results show the importance of mechanical and electrical resonances to the overall performance of the system. Furthermore, the thermal-to-electric efficiency and the electric power are found to be proportional to the pressure amplitude and the square of it in front of the piston of the linear alternator, respectively. By optimizing the load impedance, the traveling-wave thermoacoustic electric generator has achieved a maximum electric power of 345.3 W with a thermal-to-electric efficiency of 9.34% and a maximum efficiency of 12.33% with an electric power of 321.8 W at around 65 Hz when helium of 3.0 MPa is used as the working gas.

Soft Open Points (SOPs) are power electronic devices installed in place of normally-open points in electrical power distribution networks. They are able to provide active power flow control, reactive power compensation and voltage regulation under normal network operating conditions, as well as fast fault isolation and supply restoration under abnormal conditions. Two control modes were developed for the operation of an SOP, using back-to-back voltage-source converters (VSCs). A power flow control mode with current control provides independent control of real and reactive power. A supply restoration mode with a voltage controller enables power supply to isolated loads due to network faults. The operating principle of the back-to-back VSCs based SOP was investigated under both normal and abnormal network operating conditions. Studies on a two-feeder medium-voltage distribution network showed the performance of the SOP under different network-operating conditions: normal, during a fault and post-fault supply restoration. During the change of network operating conditions, a mode switch method based on the phase locked loop controller was used to achieve the transitions between the two control modes. Hard transitions by a direct mode switching were noticed unfavourable, but seamless transitions were obtained by deploying a soft cold load pickup and voltage synchronization process.

Significant changes in the power generation mix are posing new challenges for the balancing systems of the grid. Many of these challenges are in the secondary electricity grid regulation services and could be met through demand response (DR) services. We explore the opportunities for a water distribution system (WDS) to provide balancing services with demand response through pump scheduling and evaluate the associated benefits. Using a benchmark network and demand response mechanisms available in the UK, these benefits are assessed in terms of reduced green house gas (GHG) emissions from the grid due to the displacement of more polluting power sources and additional revenues for water utilities. The optimal pump scheduling problem is formulated as a mixed-integer optimisation problem and solved using a branch and bound algorithm. This new formulation finds the optimal level of power capacity to commit to the provision of demand response for a range of reserve energy provision and frequency response schemes offered in the UK. For the first time we show that DR from WDS can offer financial benefits to WDS operators while providing response energy to the grid with less greenhouse gas emissions than competing reserve energy technologies. Using a Monte Carlo simulation based on data from 2014, we demonstrate that the cost of providing the storage energy is less than the financial compensation available for the equivalent energy supply. The GHG emissions from the demand response provision from a WDS are also shown to be smaller than those of contemporary competing technologies such as open cycle gas turbines. The demand response services considered vary in their response time and duration as well as commitment requirements. The financial viability of a demand response service committed continuously is shown to be strongly dependent on the utilisation of the pumps and the electricity tariffs used by water utilities. Through the analysis of range of water demand scenarios and financial incentives using real market data, we demonstrate how a WDS can participate in a demand response scheme and generate financial gains and environmental benefits.