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We study the energy-efficient power allocation techniques for OFDM-based cognitive radio (CR) networks, where a CR transmitter is communicating with CR receivers on a channel borrowed from licensed primary users (PUs). Due to non-orthogonality of the transmitted signals in the adjacent bands, both the PU and the cognitive secondary user (SU) cause mutual-interference. We assume that the statistical channel state information between the cognitive transmitter and the primary receiver is known. The secondary transmitter maintains a specified statistical mutual-interference limits for all the PUs communicating in the adjacent channels. Our goal is to allocate subcarrier power for the SU so that the energy efficiency metric is optimized as well as the mutual-interference on all the active PU bands are below specified bounds. We show that the green power loading problem is a fractional programming problem. We use Charnes-Cooper transformation technique to obtain an equivalent concave optimization problem for what the solution can be readily obtained. We also propose iterative Dinkelbach method using parametric objective function for the fractional program. Numerical results are given to show the effect of different interference parameters, rate and power thresholds, and number of PUs.

The paper investigates the determinants of household energy spending and energy budget shares, with a focus on understanding their non-linear relationship with income, and the presence of economies of scale. The analysis is based on a unique, harmonized collection of official household surveys from 13 Latin American countries. This dataset allows distinguishing between expenditures on electricity, domestic gas, and fuel for private transportation, providing a comprehensive distributional view of the energy spending profile of the residential sector. The estimated empirical Engel curves behave similarly; however, the derived income elasticities show marked distinctions by fuel, and their actual values depend on the households’ relative position over the income distribution. For electricity, the elasticity tends to increase in income but stabilize at the wealthiest segments. For gas and transport fuel, it decreases under different income paths. In this dataset, the examination returns income elasticities on the (0,1) interval, suggesting that energy commodities are necessity goods. However, the distribution of aggregate energy expenditure needs to be considered. Specifically, there is a great concentration among the richer groups, particularly for transport fuels, where the top quintile gathers more than half of the aggregate spending. The results also indicate economies of scale ––for electricity and domestic gas–– with respect to family-age composition, and to a lesser extent with respect to dwelling size. In the case of electricity, these economies are more pronounced for richer households. These results join the previous literature in emphasizing the relevance of accounting for household demographic and socioeconomic trends for energy management.

The COVID-19 pandemic created the largest experiment in working from home. We study how persistent telework may change energy and transport consumption and costs in Germany to assess the distributional and environmental implications when working from home will stick. Based on data from the German Microcensus and available classifications of working-from-home feasibility for different occupations, we calculate the change in energy consumption and travel to work when 15% of employees work full time from home. Our findings suggest that telework translates into an annual increase in heating energy expenditure of 110 euros per worker and a decrease in transport expenditure of 840 euros per worker. All income groups would gain from telework but high-income workers gain twice as much as low-income workers. The value of time saving is between 1.3 and 6 times greater than the savings from reduced travel costs and almost 9 times higher for high-income workers than low-income workers. The direct effects on CO₂ emissions due to reduced car commuting amount to 4.5 millions tons of CO₂, representing around 3 percent of carbon emissions in the transport sector.

A partially linear model is often estimated in a two-stage procedure, which involves estimating the nonlinear component conditional on initially estimated linear coefficients. We propose a sampling procedure that aims to simultaneously estimate the linear coefficients and bandwidths involved in the Nadaraya-Watson estimator of the nonlinear component. The performance of this sampling procedure is demonstrated through Monte Carlo simulation studies. The proposed sampling algorithm is applied to partially linear models of gasoline consumption based on the US household survey data. In contrary to implausible price effect reported in the literature, we find negative price effect on household gasoline consumption.

Shale oil producers respond positively and significantly to favourable oil price signals. This finding is established using a novel proprietary data set consisting of more than 200,000 shale wells across ten U.S. states spanning almost two decades. We document large heterogeneity in the estimated responses across the various shale wells, suggesting that aggregation bias is an important issue for this kind of analysis. We find responses to be stronger for the largest oil producing firms, among wells that are spaced further apart and in regions where the density of shale wells is higher. The response also depend on the level of production. Our empirical results calls for new models that can account for a growing share of shale oil in the U.S., the inherent flexibility of shale extraction technology in production and the role of shale oil in transmitting oil price shocks to the U.S. economy.

We examine the impact of public policies that aim to decarbonate electricity production by replacing fossil fuel energy by intermittent renewable sources, namely wind and solar power. We consider a model of energy investment and production with two sources of energy: one is clean but intermittent (e.g. wind), whereas the other one is reliable but polluting (e.g. coal). A carbon tax decreases electricity production while simultaneously increasing investment in wind power. This tax may however increase total capacity because the retailing price of electricity does not depend on energy availability, which means that windmill capacity must be backed-up by thermal power plants. Feed-in tariffs and renewable portfolio standards enhance investment into intermittent sources of energy. However, both are likely to boost electricity production beyond the efficient level, in which case they must be complemented with a tax on electricity consumption. We also determine the social value of two technologies to accommodate intermittency: energy storage and smart meters. Lastly, we consider the case of a monopoly thermal power producer. The entry of a competitive fringe of wind power producers makes the thermal power producer reduce further its production capacity, which increases the electricity price.

Lowering the exergy content of heat required for heating purposes decreases the primary energy consumption. District heating systems are often an important link between facilities that generate heat with low exergy content and consumers. Exergetic efficiency of heat distribution is an important performance criterion in heat supply to consumers. It can serve as a criterion for optimization, towards a more sustainable distribution-network design and operation. This paper presents a methodology for an exergy-based distribution-network analysis in a district heating system. Criteria for performance evaluations are defined. They can be used to evaluate heat supply to different points in the network, or individual system components. A case study is performed on an existing district heating system. Energetic and exergetic efficiencies of supply lines are analyzed. Exergy destructions and exergy losses are studied. Large differences in efficiency of heat supply to different points in the network are discovered. Over-dimensioned parameters of the distribution network are investigated.

The ability to predict the expected internal behaviour of a given solid-propellant rocket motor under transient conditions is important. Research towards predicting and quantifying undesirable transient axial combustion instability symptoms typically necessitates a comprehensive numerical model for internal ballistic simulation under dynamic flow and combustion conditions. On the mitigation side, one in practice sees the use of inert or reactive particles for the suppression of pressure wave development in the motor chamber flow. With the focus of the present study placed on reactive particles, a numerical internal ballistic model incorporating relevant elements, such as a transient, frequency-dependent combustion response to axial pressure wave activity above the burning propellant surface, is applied to the investigation of using aluminum particles within the central internal flow (particles whose surfaces nominally regress with time, as a function of current particle size, as they move downstream) as a means of suppressing instability-related symptoms in a cylindrical-grain motor. The results of this investigation reveal that the loading percentage and starting size of the aluminum particles have a significant influence on reducing the resulting transient pressure wave magnitude.

A series of recent articles has called into question the validity of VAR models of the global market for crude oil. These studies seek to replace existing oil market models by structural VAR models of their own based on different data, different identifying assumptions, and a different econometric approach. Their main aim has been to revise the consensus in the literature that oil demand shocks are a more important determinant of oil price fluctuations than oil supply shocks. Substantial progress has been made in recent years in sorting out the pros and cons of the underlying econometric methodologies and data in this debate, and in separating claims that are supported by empirical evidence from claims that are not. The purpose of this paper is to take stock of the VAR literature on global oil markets and to synthesize what we have learned. Combining this evidence with new data and analysis, I make the case that the concerns regarding the existing VAR oil market literature have been overstated and that the results from these models are quite robust to changes in the model specification.

Do inflation expectations and the associated pass-through of oil price shocks depend on demand and supply conditions underlying the global market for crude oil? We answer this question with a novel structural vector autoregressive model of the global oil market that jointly identifies transmissions of oil demand and supply shocks through the real price of oil to both expected and realized inflation. Our main insight is that US households form their expectations of inflation differently when faced with long sustained increases in the price of oil, such as the early millennium oil price surge of 2003 to 2008, as compared to short and sharp price fluctuations that characterized much of the twentieth century. We also find that oil demand and supply shocks can explain a large proportion of expected and realized inflation dynamics during multiple periods of economic significance, and resolve disagreements around the role of oil prices in explaining the missing deflation puzzle of the Great Recession.

Using a novel dataset, we develop a structural model of the Very Large Crude Carrier (VLCC) market between the Arabian Gulf and the Far East. We study how fluctuations in oil tanker rates, oil exports, shipowner profits, and bunker fuel prices are determined by shocks to the supply and demand for oil tankers, to the utilization of tankers, and to the cost of operating tankers, including bunker fuel costs. Our analysis shows that time charter rates are largely unresponsive to tanker cost shocks. In response to higher costs, voyage profits decline, as cost shocks are only partially passed on to round-trip voyage rates. Oil exports from the Arabian Gulf also decline, reflecting lower demand for VLCCs. Positive utilization shocks are associated with higher profits, a slight increase in time charter rates and lower fuel prices and oil export volumes. Tanker supply and tanker demand shocks have persistent effects on time charter rates, round-trip voyage rates, the volume of oil exports, fuel prices, and profits with the expected sign. This paper examines the advantages and drawbacks of alternative methods of estimating oil supply and oil demand elasticities and of incorporating this information into structural VAR models. I not only summarize the state of the literature, but also draw attention to a number of econometric problems that have been overlooked in this literature. Once these problems are recognized, seemingly conflicting conclusions in the recent literature can be resolved. My analysis reaffirms the conclusion that the one-month oil supply elasticity is close to zero, which implies that oil demand shocks are the dominant driver of the real price of oil. The focus of this paper is not only on correcting some misunderstandings in the recent literature, but on the substantive and methodological insights generated by this exchange, which are of broader interest to applied researchers.

Empirical relationships between crude oil prices and exchange rates of oil exporting countries tend to vary over time. I use econometric models of the norwegian and canadian nominal exchange rates to investigate whether such time-variation could reflect shifts in the key oil price drivers over time. Results suggest that demand- and supply-driven oil price increases strengthen these currencies to different extents. In contrast, heightened geopolitical uncertainty and associated oil price increases go together with a weakening of oil exporters’ currencies. The latter result may explain coincidences of higher oil prices and a weakening of oil exporters’ currencies.

This article provides an overview of the application of behavioral public economics to energy efficiency. I document policy makers' arguments for “paternalistic” energy efficiency policies, formalize with a simple model of misoptimizing consumers, review and critique empirical evidence, and suggest future research directions. Although empirical results suggest that policies to address imperfect information and internalities may increase welfare in some cases, some existing policies may be mistargeted or miscalibrated.

Information provision is a key element of government energy-efficiency policy, but the information that is provided is often too coarse to allow consumers to make efficient decisions. An important example is the ubiquitous yellow "Energy Guide" label, which is required by law to be displayed on all major appliances sold in the United States. These labels report energy cost information based on average national usage and energy prices. We conduct an online randomized controlled trial to measure the potential benefits from providing more accurate information. We find that state-specific labels lead to significantly better choices. Consumers invest about the same amount overall in energy-efficiency, but the allocation is much better with more investment in high-usage high-price states and less investment in low-usage low-price states. The implied aggregate cost savings are larger than any reasonable estimate of the cost of implementing state-specific labels.

This paper analyzes the dynamic behavior of day-ahead spot prices in the German electricity spot market due to positive structural shocks in wind and solar power. It uses a dynamic structural vector autoregressive model to estimate the related structural impulse response functions. The estimates suggest that wind power shocks have a more prolonged negative effect on spot prices than solar power shocks. Significant autocorrelations of wind power for larger lags indicate these results. The total negative merit order effect of a solar power shock is larger when using a reasonable model specification. One reason might be that solar power shocks coincide with demand peaks. However, results regarding the relation of the effects of wind and solar should be dealt with carefully because these results do not hold for all specifications. Past static estimates show differences in the total average negative merit order effects. These estimates might be overstated if wind and solar power dynamics are transferred to the merit order effects.

We compare a number of GARCH and stochastic volatility (SV) models using nine series of oil, petroleum product and natural gas prices in a formal Bayesian model comparison exercise. The competing models include the standard models of GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, and t distributed and moving average innovations. We find that: (1) SV models generally compare favorably to their GARCH counterparts; (2) the jump component and t distributed innovations substantially improve the performance of the standard GARCH, but are unimportant for the SV model; (3) the volatility feedback channel seems to be superfluous; (4) the moving average component markedly improves the fit of both GARCH and SV models; and (5) the leverage effect is important for modeling crude oil prices—West Texas Intermediate and Brent—but not for other energy prices. Overall, the SV model with moving average innovations is the best model for all nine series.

Technological innovations in horizontal drilling and hydraulic fracturing have enabled tremendous amounts of natural gas to be extracted profitably from underground shale formations that were long thought to be uneconomical. In this paper, we provide the first estimates of broad-scale welfare and distributional implications of this supply boom. We provide new estimates of supply and demand elasticities, which we use to estimate the drop in natural gas prices that is attributable to the supply expansion. We calculate large, positive welfare impacts for four broad sectors of gas consumption (residential, commercial, industrial, and electric power), and a negative impact for producers, with variation across regions. We then examine the evidence for a gas-led "manufacturing renaissance" and for pass-through to prices of products such as retail natural gas, retail electricity, and commodity chemicals. We conclude with a discussion of environmental externalities from unconventional natural gas, including limitations of the current regulatory environment. Overall, we find that between 2007 and 2013 the shale gas revolution led to an increase in welfare for natural gas consumers and producers of $48 billion per year, but more data are needed on the extent and valuation of the environmental impacts of shale gas production.

What do we know about the size of the rebound effect? Should we believe claims that energy efficiency improvements lead to an increase in energy use? This paper clarifies what the rebound effect is, and provides a guide for economists and policymakers interested in its magnitude. We describe how some papers in the literature consider the rebound effect from a costless exogenous increase in energy efficiency, while others examine the effects of a particular energy efficiency policy — a distinction that leads to very different welfare and policy implications. We present the most reliable evidence available quantifying the energy efficiency rebound, and discuss areas where estimation is extraordinarily difficult. Along these lines, we offer a new way of thinking about the macroeconomic rebound effect. Overall, the existing research provides little support for the so-called “backfire” hypothesis. Still, much remains to be understood, particularly relating to induced innovation and productivity growth.

This note investigates the pass-through of global Brent oil notations to fuel prices across the oligopoly of retail majors in Germany. We assemble a high-frequency panel data set that encompasses millions of price observations and allows us to distinguish effects by brand. Upon establishing a cointegrating relationship between fuel and crude-oil prices using daily data, we estimate an error-correction model (ECM) and find that (1) the pass-through of oil prices critically depends on the number of time lags included in the ECM, (2) strict adherence to classical information criteria for determining lag length yields extremely long pass-through durations, and (3) the estimated impulse response functions are virtually identical across brands, irrespective of the lag count, suggesting a high degree of competition among brands.

The aim of this paper is to analyze the impact of the so-called “shale oil revolution” on oil prices and economic growth. We employ a general equilibrium model of the world oil market in which Saudi Arabia is the dominant firm, with the rest of the producers as a competitive fringe. Our results suggest that most of the expected increase in US oil supply due to the shale oil revolution has already been incorporated into prices and that it will produce an additional increase of 0.2% in the GDP of oil importers in the period 2010–2018. We also employ the model to analyze the collapse in oil prices in the second half of 2014 and conclude that it was mainly due to positive unanticipated supply shocks.

We study the impact of oil price shocks on U.S. stock market volatility. We derive three different structural oil shock variables (i.e. aggregate demand, oil-supply, and oil-demand shocks) and relate them to stock market volatility, using bivariate structural VAR models, one for each oil price shock. Identification is achieved by assuming that the price of crude oil reacts to stock market volatility only with delay. This implies that innovations to the price of crude oil are not strictly exogenous, but predetermined with respect to the stock market. We show that volatility responds significantly to oil price shocks caused by sudden changes in aggregate and oil-specific demand, while the impact of supply-side shocks is negligible.

International audience; Over the past decade, the rapid increase in shale gas and shale oil production in the United States has profoundly changed energy markets in North America, and has led to a significant decrease in American natural gas prices. The possible existence of large shale deposits in continental Europe, mainly in France, Denmark, the Netherlands and Germany, has fostered speculation on whether the U.S. shale revolution could be duplicated in Europe. However, a number of uncertainties, notably geological, technological, regulatory, and relating to public acceptance make this possibility unclear. We present a techno-economic model of shale gas production amenable to direct estimation on historical production data to analyze the main determinants of the profitability of shale wells and plays. We contribute an in-depth analysis of an extensive production dataset covering 40,000 wells and accounting for nearly 90% of shale gas production in the six main plays of the continental United States from 2004 to 2014. We combine this analysis with a discussion of the main differences between the American and European contexts to calibrate our model and conduct Monte-Carlo simulations. This enables us to estimate the distribution of breakeven prices for shale gas extraction in continental Europe. We find a median gross breakeven price before taxes and royalties of $10.1 per MMBtu. This would make extraction unprofitable in Europe in the current natural gas price environment, with < 47% of the well distribution reaching breakeven at the mean 2011–2016 price. Sensitivity analysis reveals that the breakeven price is most sensitive to initial production rate, drilling and completion costs, and decline rates. We also find that the economic outlook would be slightly better if the productivity of European shale gas plays was comparable to that of U.S. plays of similar depth, but not significantly so. We conclude that under assumptions calibrated on existing shale gas production data, it is unlikely that the U.S. shale revolution can be duplicated in continental Europe.

International audience; This paper studies the links between non-renewable and intermittent renewable energy sources in the production of electricity. Using U.S. state-level data from 1998 to 2015, we find that the relationship between the price of natural gas and investments in solar and wind capacity is non-linear and can be represented by an inverted U shape. Hence, for relatively low natural gas prices, the two modes of production are substitutes. After a price threshold is reached, the two are complementary. A theoretical explanation argues that this stylized fact is the result of a trade-off between two characteristics of these modes of production: the high degree of flexibility of electricity production using natural gas as an input and the low marginal cost of renewable energy sources.

In order to increase overall transparency on key operational information, power transmission system operators publish an increasing amount of fundamental data, including forecasts of electricity demand and available capacity. We employ a fundamental model for electricity prices which lends itself well to integrating such forecasts, while retaining ease of implementation and tractability to allow for analytic derivatives pricing formulae. In an extensive futures pricing study, the pricing performance of our model is shown to further improve based on the inclusion of electricity demand and capacity forecasts, thus confirming the general importance of forward-looking information for electricity derivatives pricing. However, we also find that the usefulness of integrating forecast data into the pricing approach is primarily limited to those periods during which electricity prices are highly sensitive to demand or available capacity, whereas the impact is less visible when fuel prices are the primary underlying driver to prices instead.

Zum Stromverbrauch der in privaten Haushalten verbreiteten Elektrogerate gibt es wenig empirische Evidenz. Um diese Lucke zu schliesen, wird im folgenden Beitrag ein okonometrisches Modell geschatzt, das auf Basis der Daten einer detaillierten Erhebung zum Stromverbrauch und zur Gerateausstattung unter 2 100 Haushalten aus dem Jahr 2014 den individuellen Stromverbrauch von Haushalten auf die jeweilige Gerateausstattung zuruckfuhrt. Im Ergebnis zeigt sich, dass die von uns geschatzten mittleren Verbrauchswerte fur Standardgerate wie Kuhl- und Gefrierschranke gut mit den Angaben von Verbraucherzentralen und Online-Informationsportalen ubereinstimmen. Der Vergleich unserer Schatzwerte mit offentlich zuganglichen Informationen zum Stromverbrauch von zumeist neuen Geraten offenbart zugleich, dass in privaten Haushalten ein nicht unerhebliches Stromsparpotential existiert. Insgesamt macht unsere Analyse deutlich, dass der Stromverbrauch privater Haushalte sehr heterogen ausfallt und es daher eine starke Vereinfachung darstellt, einem Haushalt einer bestimmten Grose (Personenzahl) einen typischen mittleren Stromverbrauch zuzuschreiben.

The countries of the Gulf Cooperation Council (GCC) have both large fossil fuel resources and vast renewable energy potentials. Here, we investigate in a literature meta-analysis and a survey, whether there is a need for renewables in the GCC, what barriers and risks presently deter investments, and what possible policy-solutions could be. We find that there is a long-term need for renewables, to diversify the economy and prepare for a post-fossil fuel era. In the short term, two main obstacles deter investments: inefficient bureaucracy, and the combination of fossil fuel/electricity subsidies and the absence of renewable energy support. Removing fossil fuel and consumption subsidies or introducing a support scheme could make investments in renewables profitable. Eliminating energy subsidies appears particularly beneficial to the economic outlook but this seems particularly difficult to implement, due to the political economy of rentier states. Increased bureaucratic transparency and efficiency is needed, so that potentially attractive investments can rapidly and predictably obtain the necessary permissions. Hence, the administrative and economic environment for renewable energy investments in the GCC is not right today, and no breakthrough is on the horizon, but there is a range of policy solutions to enable investments in the future., Energies, 8 (8), ISSN:1996-1073

This research is aligned with the engineering challenge of scaling-up individual battery cells into a complete energy storage system (ESS). Manufacturing tolerances, coupled with thermal gradients and the differential electrical loading of adjacent cells, can result in significant variations in the rate of cell degradation, energy distribution and ESS performance. The uncertain transition from cell to system often manifests itself in over-engineered, non-optimal ESS designs within both the transport and energy sectors. To alleviate these issues, the authors propose a novel model-based framework for cell-in-the-loop simulation (CILS) in which a physical cell may be integrated within a complete model of an ESS and exercised against realistic electrical and thermal loads in real-time. This paper focuses on the electrical integration of both real and simulated cells within the CILS test environment. Validation of the CILS approach using real-world electric vehicle data is presented for an 18650 cell. The cell is integrated within a real-time simulation model of a series string of similar cells in a 4sp1 configuration. Results are presented that highlight the impact of cell variability ( i.e. , capacity and impedance) on the energy available from the multi-cell system and the useable capacity of the physical cell.

The enzyme-assisted aqueous extraction of oil from wet microalgae was employed to avoid the energy consumption of a dewatering process. In this paper, oil-rich microalgae Scenedesmus sp. G4 was hydrolyzed by enzyme mixtures for oil extraction. The results showed that the algae concentration had the greatest influence on yield of extracted oil, and the temperature and the ratio of enzyme mixtures affected the results as follows: the maximum yield of oil extracted from Scenedesmus sp. G4 reached 86.1% under optimal conditions with an algae concentration at 2.5 g/L, temperature 30/50 °C, pH 3.5/4.5 and a cellulase:pectinase:hemicellulase ratio of 1:1:1 or 1:2:1 (w/w/w). The results also indicated that the enzyme mixtures had a significant impact on the integrity of microalgae cells and the crystallization index increased from 30.7% to around 36.0% after enzymatic hydrolysis treatment. The cell wall of Scenedesmus sp. G4 has a high content of cellulose Iα and low crystallization, which is beneficial to the oil extraction by the enzyme-assisted hydrolysis method.

This study was conducted to develop an artificial neural network (ANN)-based prediction model that can calculate the amount of cooling energy during the setback period of accommodation buildings. By comparing the amount of energy needed for diverse setback temperatures, the most energy-efficient optimal setback temperature could be found and applied in the thermal control logic. Three major processes that used the numerical simulation method were conducted for the development and optimization of an ANN model and for the testing of its prediction performance, respectively. First, the structure and learning method of the initial ANN model was determined to predict the amount of cooling energy consumption during the setback period. Then, the initial structure and learning methods of the ANN model were optimized using parametrical analysis to compare its prediction accuracy levels. Finally, the performance tests of the optimized model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results and the predicted results under generally accepted levels. In conclusion, the proposed ANN model proved its potential to be applied to the thermal control logic for setting up the most energy-efficient setback temperature.

Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC) method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long- and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.

In the context of sustainable buildings, this paper investigates power flow management for an isolated DC microgrid and focuses on efficiency and energy cost reduction by optimal scheduling. Aiming at high efficiency, the local produced power has to be used where, when, and how it is generated. Thus, based on photovoltaic sources, storage, and a biofuel generator, the proposed DC microgrid is coupled with the DC distribution network of the building. The DC bus distribution maximizes the efficiency of the overall production-consumption system by avoiding some energy conversion losses and absence of reactive power. The isolated DC microgrid aims to minimize the total energy cost and thus, based on forecasting data, a cost function is formulated. Using a mixed integer linear programming optimization, the optimal power flow scheduling is obtained which leads to an optimization-based strategy for real-time power balancing. Three experimental tests, operated under different meteorological conditions, validate the feasibility of the proposed control and demonstrate the problem formulation of minimizing total energy cost.

This paper considers the problem of distributed frequency regulation based on the consensus control protocol in smart grids. In this problem, each system component is coordinated to collectively provide active power for the provision of ancillary frequency regulation service. Firstly, an approximate model is proposed for the frequency dynamic process. A distributed control algorithm is investigated, while each agent exchanges information with neighboring agents and performs behaviors based on communication interactions. The objective of each agent is to converge to a common state considering different dynamic load characteristics, and distributed frequency control strategy is developed to enable the agents to provide active power support. Then, the distributed proportional integral controllers with the state feedback are designed considering the consensus protocol with topology . The theory of distributed consensus protocol isfurther developed to prove the stability of the proposed control algorithm. Whenproperly controlled, the controllers can provide grid support services in a distributed manner that turn out the grid balanced globally. Finally, simulations of the proposed distributed control algorithm are tested to validate the availability of the proposed approach and the performance in the electrical networks.

With the increasing popularity enjoyed by ultra-micro scale turbomachinery, designers are often faced with severe challenges due to the substantial phenomenological difference between the low-Reynolds fluid-dynamics in rotating or strongly curved flows and the established knowledge acquired through decades of theoretical and experimental studies on medium and large-scale machines. The problem is complicated by the absence of an extended and reliable database that might be used for preliminary design and provide indications for scale-up or scale-down. As a result, custom-designed experimental campaigns are necessary that make the development of any new machine exceedingly costly. The situation has seen some improvement in recent years, after the publication of a sufficient number of experimental results and numerical simulations that pave the way towards a development of semi-empirical correlations. The purpose of this work is to present and discuss a preliminary and simple method to extend the currently available design maps into the small scale range (Re &lt, 105) by introducing in the Balje charts an efficiency correction that depends on the specific speed ns. The method results in a Stodola-like formula which originates a lower-than-standard Cordier curve on the classical Balje charts. A validation with some experimental results is also presented and discussed. Though the agreement is more than satisfactory, it must be stressed that the method provides only approximate results, and thus it must be considered as an evolving temporary solution, that needs to be updated as long as larger series of (numerical or physical) experimental results become available.

In this paper, we present the design and evaluation of a software-based wireless power transfer platform that enables the development of a prototype involving various open- and closed-loop power control functions. Our platform is based on a loosely coupled planar wireless power transfer circuit that uses a class-E power amplifier. In conjunction with this circuit, we implement flexible control functions using a National Instruments Data Acquisition (NI DAQ) board and algorithms in the MATLAB/Simulink. To verify the effectiveness of our platform, we conduct two types of power-control experiments: a no-load or metal detection using open-loop power control, and an output voltage regulation for different receiver positions using closed-loop power control. The use of the MATLAB/Simulink software as a part of the planar wireless power transfer platform for power control experiments is shown to serve as a useful and inexpensive alternative to conventional hardware-based platforms.

Inverter voltage control is an important task in the operation of a DC/AC microgrid system. To improve the inverter voltage control dynamics, traditional approaches attempt to measure and feedforward the load current, which, however, needs remote measurement with communications in a microgrid system with distributed loads. In this paper, a load current observer (LCO) based control strategy, which does not need remote measurement, is proposed for sinusoidal signals tracking control of a three-phase inverter of the microgrid. With LCO, the load current is estimated precisely, acting as the feedforward of the dual-loop control, which can effectively enlarge the stability margin of the control system and improve the dynamic response to load disturbance. Furthermore, multiple PR regulators are applied in this strategy conducted in a stationary frame to suppress the transient fluctuations and the total harmonic distortion (THD) of the output voltage and achieve faster transient performance compared with traditional dual-loop control in a rotating dq0 frame under instantaneous change of various types of load (i.e., balanced load, unbalanced load, and nonlinear load). The parameters of multiple PR regulators are analyzed and selected through the root locus method and the stability of the whole control system is evaluated and analyzed. Finally, the validity of the proposed approach is verified through simulations and a three-phase prototype test system with a TMS320F28335 DSP.

A linear function submission-based double auction (LFS-DA) mechanism for a regional electricity network is proposed in this paper. Each agent in the network is equipped with a battery and a generator. Each agent simultaneously becomes a producer and consumer of electricity, i.e., a prosumer, and trades electricity in the regional market at a variable price. In the LFS-DA, each agent uses linear demand and supply functions when they submit bids and asks to an auctioneer in the regional market. The LFS-DA can achieve an exact balance between electricity demand and supply for each time slot throughout the learning phase and was shown capable of solving the primal problem of maximizing the social welfare of the network without any central price setter, e.g., a utility or a large electricity company, in contrast with conventional real-time pricing (RTP). This paper presents a clarification of the relationship between the RTP algorithm derived on the basis of a dual decomposition framework and LFS-DA. Specifically, we proved that the changes in the price profile of the LFS-DA mechanism are equal to those achieved by the RTP mechanism derived from the dual decomposition framework, except for a constant factor.

In this study we design and test a novel solar tracking generation system. Moreover, we show that this system could be successfully used as an advanced solar power source to generate power in greenhouses. The system was developed after taking into consideration the geography, climate, and other environmental factors of northeast China. The experimental design of this study included the following steps: (i) the novel solar tracking generation system was measured, and its performance was analyzed, (ii) the system configuration and operation principles were evaluated, (iii) the performance of this power generation system and the solar irradiance were measured according to local time and conditions, (iv) the main factors affecting system performance were analyzed, and (v) the amount of power generated by the solar tracking system was compared with the power generated by fixed solar panels. The experimental results indicated that compared to the power generated by fixed solar panels, the solar tracking system generated about 20% to 25% more power. In addition, the performance of this novel power generating system was found to be closely associated with solar irradiance. Therefore, the solar tracking system provides a new approach to power generation in greenhouses.

Biodiesel as a clean energy source could reduce environmental pollution compared to fossil fuel, so it is becoming increasingly important. In this study, we investigated the effects of different pilot injection timings from before top dead center (BTDC) and exhaust gas recirculation (EGR) on combustion, engine performance, and exhaust emission characteristics in a common rail diesel engine fueled with canola oil biodiesel-diesel (BD) blend. The pilot injection timing and EGR rate were changed at an engine speed of 2000 rpm fueled with BD20 (20 vol % canola oil and 80 vol % diesel fuel blend). As the injection timing advanced, the combustion pressure, brake specific fuel consumption (BSFC), and peak combustion pressure (P max ) changed slightly. Carbon monoxide (CO) and particulate matter (PM) emissions clearly decreased at BTDC 20° compared with BTDC 5°, but nitrogen oxide (NO x ) emissions increased slightly. With an increasing EGR rate, the combustion pressure and indicated mean effective pressure (IMEP) decreased slightly at BTDC 20° compared to other injection timings. However, the P max showed a remarkable decrease. The BSFC and PM emissions increased slightly, but the NO x emission decreased considerably.

Household energy consumption has been increasing in the last decades, the residential sector is responsible for about 40% of the total final energy use in Europe. Energy efficiency measures can both reduce energy needs of buildings and energy-related CO2 emissions. For this reason, in recent years, the European Union has been making efforts to enhance energy saving in buildings by introducing various policies and strategies, in this context, a common methodology was developed to assess and to certify energy performance of buildings. The positive effects obtained by energy efficiency measures need to be verified, but measuring and monitoring building energy performance is time consuming and financially demanding. Alternatively, energy efficiency can also be evaluated by specific indicators based on energy consumption. In this work, a methodology to investigate the level of energy efficiency reached in the Umbria Region (Central Italy) is described, based on data collected by energy certificates. In fact, energy certificates, which are the outcomes of simulation models, represent a useful and available tool to collect data related to the energy use of dwellings. A database of building energy performance was developed, in which about 6500 energy certificates of residential buildings supplied by Umbria region were inserted. On the basis of this data collection, average energy and CO2 indicators related to the building heritage in Umbria were estimated and compared to national and international indicators derived from official sources. Results showed that the adopted methodology in this work can be an alternative method for the evaluation of energy indicators, in fact, the ones calculated considering simulation data were similar to the ones reported in national and international sources. This allowed to validate the adopted methodology and the efficiency of European policies.

This paper presents a reinforcement learning (RL)–based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities are determined by extracting the transition probability matrix of the power request. Two RL-based algorithms, namely Q-learning and Dyna algorithms, are applied to generate optimal control solutions. The two algorithms are simulated on the same driving schedule, and the simulation results are compared to clarify the merits and demerits of these algorithms. Although the Q-learning algorithm is faster (3 h) than the Dyna algorithm (7 h), its fuel consumption is 1.7% higher than that of the Dyna algorithm. Furthermore, the Dyna algorithm registers approximately the same fuel consumption as the dynamic programming–based global optimal solution. The computational cost of the Dyna algorithm is substantially lower than that of the stochastic dynamic programming.

Fiber-optic daylighting systems have been shown to be a promising and effective way to transmit sunlight in the interior space whilst reducing electric lighting energy consumption. To increase efficiency in terms of providing uniform illumination in the interior, the current need is to illuminate optical fiber-bundle with uniform light flux. To this end, we propose a method for achieving collimated light, which illuminates the fiber-bundle uniformly. Light is collected through a parabolic concentrator and focused toward a collimating lens, which distributes the light over each optical fiber. An optics diffusing structure is utilized at the end side of the fiber bundle to spread light in the interior. The results clearly reveal that the efficiency in terms of uniform illumination, which also reduces the heat problem for optical fibers, is improved. Furthermore, a comparison study is conducted between current and previous approaches. As a result, the proposed daylighting system turns out convenient in terms of energy saving and reduction in greenhouse gas emissions.

Greenhouse gas emissions in China have been increasing in line with its energy consumption and economic growth. Major means for energy-related greenhouse gases mitigation in the foreseeable future are transition to less carbon intensive energy supplies and structural changes in energy consumption. In this paper, a bottom-up model is built to examine typical projected scenarios for energy supply and demand, with which trends of energy-related carbon dioxide emissions by 2050 can be analyzed. Results show that low-carbon technologies remain essential contributors to reducing emissions and altering emissions trends up to 2050. By pushing the limit of current practicality, emissions reduction can reach 20 to 28 percent and the advent of carbon peaking could shift from 2040 to 2030. In addition, the effect of electrification at end-use sectors is studied. Results show that electrifying transport could reduce emissions and bring the advent of carbon peaking forward, but the effect is less significant compared with low-carbon technologies. Moreover, it implies the importance of decarbonizing power supply before electrifying end-use sectors.

Densified Biomass Solid Fuel (DBSF) is a typical solid form of biomass, using agricultural and forestry residues as raw materials. DBSF utilization is considered to be an alternative to fossil energy, like coal in China, associated with a reduction of environmental pollution. China has abundant biomass resources and is suitable to develop DBSF. Until now, a number of policies aimed at fostering DBSF industry have been proliferated by policy makers in China. However, considering the seasonality and instability of biomass resources, these inefficiencies could trigger future scarcities of biomass feedstocks, baffling the resilience of biomass supply chains. Therefore, this review paper focuses on DBSF policies and strategies in China, based on the supply chain framework. We analyzed the current developing situation of DBSF industry in China and developed a framework for policy instruments based on the supply chain steps, which can be used to identify and assess the deficiencies of current DBSF industry policies, and we proposed some suggestions. These findings may inform policy development and identify synergies at different steps in the supply chain to enhance the development of DBSF industry.

The location of ground faults in railway electric lines in 2 × 5 kV railway power supply systems is a difficult task. In both 1 × 25 kV and transmission power systems it is common practice to use distance protection relays to clear ground faults and localize their positions. However, in the particular case of this 2 × 25 kV system, due to the widespread use of autotransformers, the relation between the distance and the impedance seen by the distance protection relays is not linear and therefore the location is not accurate enough. This paper presents a simple and economical method to identify the subsection between autotransformers and the conductor (catenary or feeder) where the ground fault is happening. This method is based on the comparison of the angle between the current and the voltage of the positive terminal in each autotransformer. Consequently, after the identification of the subsection and the conductor with the ground defect, only the subsection where the ground fault is present will be quickly removed from service, with the minimum effect on rail traffic. This method has been validated through computer simulations and laboratory tests with positive results.

The main objective of this study is to discuss the economical and the environmental analysis of a net zero energy (NZE) tourist village in Alexandria, Egypt, by maximizing the renewable energy fraction and minimizing the greenhouse gases (GHG) emissions. The hybrid photovoltaics (PV)/wind/diesel/battery system is found to be the optimum hybrid renewable energy system (HRES) for the proposed tourist village under the study. The optimum HRES consists of 1600 kW of PV panels (58.09% solar energy penetration), 1000 kW of wind turbines (41.34% wind energy penetration), 1000 kW of power converters, 200 kW diesel generator (only 0.57% diesel generator penetration) in addition to 2000 batteries with the capacity of 589 Ah each. The levelized cost of energy (COE) from the optimum HRES is $0.17/kWh and the total net present cost (NPC) of this system is $15,383,360. Additionally, the maximum renewable energy fraction is 99.1% and the amount of GHG emitted from the optimum HRES is only 31,289 kg/year, which is negligible in comparison with the other system configurations, therefore the optimum HRES can be considered as a green system. In addition to this, the achieved percentage of the capacity shortage and the unmet load in the optimal HRES is only 0% for both.

This paper presents a novel staggered-teeth cylindrical transverse-flux permanent-magnet linear machine (TFPMLM), which aims to improve the power factor and force density. Due to the compact structure and high performance requirement, thermal problems should be seriously considered. The three-dimensional (3-D) temperature field model is established. The determination of convection heat transfer coefficients is discussed. Equivalent thermal conductivities of stator core and winding are given to simplify the analysis. With the thermal effect of the adhesive coatings among permanent magnets (PMs) and mover yoke taken into account, the temperature field distribution and variation rules of the TFPMLM are obtained using the finite volume method (FVM). The influences of slot filling factor and air flow velocity on the temperature field distribution are analyzed. It is found that the hottest spot of the TFPMLM appears in the middle of the end winding, and there is no risk of demagnetization for PMs.

The paper discusses Home Area Networks (HAN) communication technologies for smart home and domestic application integration. The work is initiated by identifying the application areas that can benefit from this integration. A broad and inclusive home communication interface is analysed utilizing as a key piece a Gateway based on machine-to-machine (M2M) communications that interacts with the surrounding environment. Then, the main wireless networks are thoroughly assessed, and later, their suitability to the requirements of HAN considering the application area is analysed. Finally, a qualitative analysis is portrayed.

In recent years, the accumulated environmental problems resulting from excessive usage of fossil fuels have gradually loomed. Thus, an environmental tax, as an important policy tool, has been put on the agenda in China. In this paper, a dynamic recursive multi-sector Computable General Equilibrium (CGE) model is applied to explore the impacts of the environmental tax on China’s economy. To comprehensively analyze the impacts, we introduce four kinds of typical contaminants and set two kinds of scenarios: different tax rates and different tax refunds. The results show that the environmental tax is conducive to environmental improvement, but the economic variables are adversely affected, for example, the loss of GDP at the low level tax rate is 0.46%. In order to offset the negative impacts, we presume the government refunds the tax. The results show that this can indeed relieve the negative effects. When the government refunds the production tax and corporate income tax, 0.49% and 0.34% of GDP is relieved by respectively. This paper provides a comprehensive analysis of the implementation of the environmental tax in China and lays a solid foundation for the introduction of the environmental tax.

In order to solve environmental problems such as global warming and resource depletion in the construction industry, interest in new renewable energy (NRE) systems has increased. The ground source heat pump (GSHP) system is the most efficient system among NRE systems. However, since the initial investment cost of the GSHP is quite expensive, a feasibility study needs to be conducted from the life-cycle perspective. Meanwhile, the efficiency of GSHP depends most significantly on the entering water temperature (EWT) of the ground heat exchanger (GHE). Therefore, this study aims to assess the environmental and economic effects of the use of GHE for selecting the optimal GHE. This study was conducted in three steps: (i) establishing the basic information and selecting key factors affecting GHE performances, (ii) making possible alternatives of the GHE installation by considering EWT, and (iii) using life-cycle assessment and life-cycle cost, as well as comprehensive evaluation of the environmental and economic effects on the GHE. These techniques allow for easy and accurate determination of the optimal design of the GHE from the environmental and economic effects in the early design phase. In future research, a multi-objective decision support model for the GSHP will be developed.

Hydrothermal liquefaction (HTL) presents a viable route for converting a vast range of materials into liquid fuel, without the need for pre-drying. Currently, HTL studies produce bio-crude with properties that fall short of diesel or biodiesel standards. Upgrading bio-crude improves the physical and chemical properties to produce a fuel corresponding to diesel or biodiesel. Properties such as viscosity, density, heating value, oxygen, nitrogen and sulphur content, and chemical composition can be modified towards meeting fuel standards using strategies such as solvent extraction, distillation, hydrodeoxygenation and catalytic cracking. This article presents a review of the upgrading technologies available, and how they might be used to make HTL bio-crude into a transportation fuel that meets current fuel property standards.