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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.

Prices of primary energy commodities display marked fluctuations over time. Market-based climate policy instruments (e.g., emissions pricing) create incentives to reduce energy consumption by increasing the user cost of fossil energy. This raises the question of whether climate policy should respond to fluctuations in fossil energy prices? We study this question within an environmental dynamic stochastic general equilibrium (E-DSGE) model calibrated on the German economy. Our results indicate that the welfare implications of dynamic emissions pricing crucially depend on how the revenues are used. When revenues are fully absorbed, a reduction in emissions prices stabilizes the economy in response to energy price shocks. However, when revenues are at least partially recycled, a stable emissions price improves overall welfare. This result is robust to different modeling assumptions., CEPA Discussion Papers; 51

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.

Analysing causality among oil prices and, in general, among financial and economic variables is of central relevance in applied economics studies. The recent contribution of Lu et al. (2014) proposes a novel test for causality- the DCC-MGARCH Hong test. We show that the critical values of the test statistic must be evaluated through simulations, thereby challenging the evidence in papers adopting the DCC-MGARCH Hong test. We also note that rolling Hong tests represent a more viable solution in the presence of short-lived causality periods.

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.

We construct a GFAVAR model with newly released global data from the Federal Reserve Bank of Dallas to investigate the drivers of global official/policy interest rate. We find that 66% of movement in global official/policy interest rates is attributed to changes in global monetary aggregates (23%), oil prices (19%), global output (16%) and global prices (8%). Global official/policy interest rates respond significantly to increases in global output, inflation and oil prices. Increases in global policy interest rates are associated with reductions in global prices and global output. The response in official/policy interest rate for the emerging countries is more to global inflation, for the advanced countries (excluding the U.S.) is more to global output, and for the U.S. is to both global output and inflation.

Integrated energy-economic modelling is needed to support the development of energy and climate policies. This study asserts that it is important to consider a system dynamics modelling approach that includes dynamics, endogenous treatment of uncertainty and risks, and both aggregate economic and disaggregate technical or engineering levels of analysis. The study examined the economic growth and the factors of production, elasticities, macro- and technical substitutability; energy cost shares, heat engine efficiencies and energy services efficiencies. Emphasis was laid on the support of the future development of integrated energy-economic models covering (a) the key factors or components; (b) the relationships among these components; (c) a quantification of parameters; and (d) the implications for the development of an integrated energy-economic system dynamics model. The study suggested the following: a non-linear relationship in production and consumption; large variations among price and income elasticity values across time frames, across countries and regions, and across energy goods; a far from perfect substitution among factors of production and among energy goods on a macro-level; technical/engineering limits to substitution on a micro-level; and engineering and behavioural limits on what can be achieved with increased efficiencies. The study argues that integrated energy-economic modelling intensifies the accounting for the factors, relationships, quantifications, and implications, and that this practice allows for such models to describe a complex, emergent energy-economic reality that informs better energy policy.

Predictions of oil prices reaching $100 per barrel during the winter of 2021/22 have raised fears of persistently high inflation and rising inflation expectations for years to come. We show that these concerns have been overstated. A $100 oil scenario of the type discussed by many observers, would only briefly raise monthly headline inflation, before fading rather quickly. However, the short-run effects on headline inflation would be sizable. For example, on a yearover- year basis, headline PCE inflation would increase by 1.8 percentage points at the end of 2021 under this scenario, and by 0.4 percentage points at the end of 2022. In contrast, the impact on measures of core inflation such as trimmed mean PCE inflation is only 0.4 and 0.3 percentage points in 2021 and 2022, respectively. These estimates already account for any increases in inflation expectations under the scenario. The peak response of the 1-year household inflation expectation would be 1.2 percentage points, while that of the 5-year expectation would be 0.2 percentage points.

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.

Several recent studies have expressed concern that the Haar prior typically imposed in estimating sign-identified VAR models may be unintentionally informative about the implied prior for the structural impulse responses. This question is indeed important, but we show that the tools that have been used in the literature to illustrate this potential problem are invalid. Specifically, we show that it does not make sense from a Bayesian point of view to characterize the impulse response prior based on the distribution of the impulse responses conditional on the maximum likelihood estimator of the reduced-form parameters, since the the prior does not, in general, depend on the data. We illustrate that this approach tends to produce highly misleading estimates of the impulse response priors. We formally derive the correct impulse response prior distribution and show that there is no evidence that typical sign-identified VAR models estimated using conventional priors tend to imply unintentionally informative priors for the impulse response vector or that the corresponding posterior is dominated by the prior. Our evidence suggests that concerns about the Haar prior for the rotation matrix have been greatly overstated and that alternative estimation methods are not required in typical applications. Finally, we demonstrate that the alternative Bayesian approach to estimating sign-identified VAR models proposed by Baumeister and Hamilton (2015) suffers from exactly the same conceptual shortcoming as the conventional approach. We illustrate that this alternative approach may imply highly economically implausible impulse response priors.

We study the optimal oil extraction strategy and the value of an oil field using a multiple real option approach. The numerical method is flexible enough to solve a model with several state variables, to discuss the effect of risk aversion, and to take into account uncertainty in the size of reserves. Optimal extraction in the baseline model is found to be volatile. If the oil producer is risk averse, production is more stable, but spare capacity is much higher than what is typically observed. We show that decisions are very sensitive to expectations on the equilibrium oil price using a mean reverting model of the oil price where the equilibrium price is also a random variable. Oil production was cut during the 2008–2009 crisis, and we find that the cut in production was larger for OPEC, for countries facing a lower discount rate, as predicted by the model, and for countries whose governments’ finances are less dependent on oil revenues. However, the net present value of a country’s oil reserves would be increased significantly (by 100 percent, in the most extreme case) if production was cut completely when prices fall below the country's threshold price. If several producers were to adopt such strategies, world oil prices would be higher but more stable.

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.

The conventional wisdom that inflation expectations respond to the level of the price of oil (or the price of gasoline) is based on testing the null hypothesis of a zero slope coefficient in a static single-equation regression model fit to aggregate data. Given that the regressor in this model is not stationary, the null distribution of the t-test statistic is nonstandard, invalidating the use of the normal approximation. Once the critical values are adjusted, these regressions provide no support for the conventional wisdom. Using a new structural vector regression model, however, we demonstrate that gasoline price shocks may indeed drive one-year household inflation expectations. The model shows that there have been several such episodes since 1990. In particular, the rise in household inflation expectations between 2009 and 2013 is almost entirely explained by a large increase in gasoline prices. However, on average, gasoline price shocks account for only 39% of the variation in household inflation expectations since 1981.

We study the efficacy of releases from the U.S. Strategic Petroleum Reserve (SPR) within the context of fully specified models of the global oil market that explicitly allow for storage demand as well as unanticipated changes in the SPR. Using novel identifying strategies and evaluation methods, we examine seven questions. First, how much have exogenous shocks to the SPR contributed to the variability in the real price of oil? Second, how much would a one-time exogenous reduction in the SPR lower the real price of oil? Third, are exogenous SPR releases partially or fully offset by increases in private sector oil inventories and how does this response affect the transmission of SPR policy shocks? Fourth, how effective were actual SPR policy interventions, consisting of sequences of exogenous changes in the SPR, at lowering the real price of oil? Fifth, are there differences in the effectiveness of SPR emergency drawdowns and SPR exchanges? Sixth, how much did the creation and expansion of the SPR contribute to higher real oil prices? Finally, how much would selling half of the oil in the SPR, as recently proposed by the White House, lower the global price of oil (and hence the U.S. price of motor gasoline) and how much fiscal revenue would it generate.

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.

This paper tests for a carbon Kuznets curve (CKC) by examining the carbon emissions per capita–GDP per capita relationship individually, for 21 OECD countries over 1870–2010 using a reduced-form, linear model that allows for multiple endogenously determined breaks. This approach addresses several important econometric and modeling issues, e.g., (1) it is highly flexible and can approximate complicated nonlinear relationships without presuming a priori any particular relationship; (2) it avoids the nonlinear transformations of potentially nonstationary income. For 10 of 14 countries that were ultimately estimated, the uncovered emission–income relationship was either (1) decoupling—where income no longer affected emissions in a statistically significant way, (2) saturation—where the emissions elasticity of income is declining, less than proportional, but still positive, or (3) no transition—where the emissions elasticity of income is (or very near) unity. For only four countries did the emissions–income relationship become negative—i.e., a CKC. In concert with previous work, we conclude that the finding of a CKC is country-specific and that the shared timing among countries is important in income-environment transitions.

We analyze if the transmission of oil price shocks on the U.S. economy has changed with the shale oil boom. To do so, we put forward a framework that allows for spillovers between industries and learning by doing (LBD) over time. We identify these spillovers using a time-varying parameter factor-augmented vector autoregressive (VAR) model with both state level and country level data. In contrast to previous results, we find considerable changes in the way oil price shocks are transmitted to the U.S economy: there are now positive spillovers to non-oil investment, employment and production from an increase in the oil price - effcts that were not present before the shale oil boom.

There has been much interest in the relationship between the price of crude oil, the value of the U.S. dollar, and the U.S. interest rate since the 1980s. For example, the sustained surge in the real price of oil in the 2000s is often attributed to the declining real value of the U.S. dollar as well as low U.S. real interest rates, along with a surge in global real economic activity. Quantifying these effects one at a time is difficult not only because of the close relationship between the interest rate and the exchange rate, but also because demand and supply shocks in the oil market in turn may affect the real value of the dollar and real interest rates. We propose a novel identification strategy for disentangling the causal effects of traditional oil demand and oil supply shocks from the effects of exogenous variation in the U.S. real interest rate and in the real value of the U.S. dollar. Our approach exploits a combination of sign and zero restrictions and narrative restrictions motivated by economic theory and extraneous evidence. We empirically evaluate popular views about the role of exogenous real exchange rate shocks in driving the real price of oil, and we examine the extent to which shocks in the global oil market drive the U.S. real exchange rate and U.S. real interest rates. Our evidence for the first time provides direct empirical support for theoretical models of the link between these variables.

In contrast to previous studies, this paper uses a multi-sector setting to assess aggregate and sectoral impacts of reducing carbon dioxide emissions in the presence of stochastic productivity shocks. We develop a multi-sector dynamic stochastic general equilibrium model, calibrated to the U.S. economy, to compare the economic implications of reducing carbon emissions with an emissions cap and with an emission tax. As in previous studies, we find that an emission cap predicts lower volatility of aggregate variables than an emission tax. Still, our results point to the importance of going beyond a single-sector analysis in evaluating the relative merits of the cap and the tax policies. The ranking of the welfare costs under the two regimes depends on the sources of productivity shocks. While there is no difference in the welfare costs of the two regimes for productivity shocks originating from non-energy sectors, we find that an emissions cap policy is more costly than an emission tax policy for shocks that originate from the energy sectors. Moreover, we find that non-energy shocks have distinct sectoral impacts under the two regimes even though there are no significant differences between the two regimes for the aggregate variables.

Economic stability is dependent on the effective functioning and resilience of energy systems. Resilience is a term used across all research disciplines and in everyday discourse. As a concept it purports to serve as a useful indicator of sustainability and robustness, but it has proved difficult to measure. Ecological resilience, psychological resilience, risk management and energy security are all fields of research in which measures of the ability to respond to the unexpected are sought. The goal is to build adaptive capacity but quite different methods have been developed to achieve this end. Research on energy security, in particular, has focused on the security of oil supplies, not resilience or the adaptive capacity of the energy system or the role that renewable energy plays in building such capacity. This paper discusses how different disciplines seek to measure and build resilience and explores its connection with the state or quality of a system’s adaptive capacity. When the parameters of redundancy and diversity are present, resilience is enhanced. For this reason, in energy systems we must understand the size and scope of the key parameters required to facilitate the development of adaptive capacity and to build resilience that can enhance economic stability.

This paper examines the trade and trade-induced welfare effects of oil prices. Using a gravity model of trade, the paper finds that the distance elasticity of trade significantly increases with the oil price. This suggests that high oil prices make trade less global, as they affect longer shipping routes more. The paper estimates that an increase in the oil price from $100 to $200 (in 2014 US$) would have similar trade effects as an import tariff around 17 percent for two countries 10,000 km away. This is akin to a 55 percent increase in shipping distance. This trade reduction would lower welfare by 0.03 percent in the average non-oil-exporting country.

Some observers have conjectured that oil supply shocks in the United States and in other countries are behind the plunge in the price of oil since June 2014. Others have suggested that a major shock to oil price expectations occurred when in late November 2014 OPEC announced that it would maintain current production levels despite the steady increase in non-OPEC oil production. Both conjectures are perfectly reasonable ex ante, yet we provide quantitative evidence that neither explanation appears supported by the data. We show that more than half of the decline in the price of oil was predictable in real time as of June 2014 and therefore must have reflected the cumulative effects of earlier oil demand and supply shocks. Among the shocks that occurred after June 2014, the most influential shock resembles a negative shock to the demand for oil associated with a weakening economy in December 2014. In contrast, there is no evidence of any large positive oil supply shocks between June and December. We conclude that the difference in the evolution of the price of oil, which declined by 44% over this period, compared with other commodity prices, which on average only declined by about 5%-15%, reflects oil-market specific developments that took place prior to June 2014.

This paper investigates the relationship between oil prices and exchange rates in three African countries using a Vector AutoRegressive (VAR) model. We use daily dataset on nominal exchange rates, oil prices and short term interbank interest rates from 01/12/2003 to 02/07/2014. The results suggest that the exchange rate of three selected countries displayed differing in the event of an oil price shock, not only before and after the oil peak of July of 2008, but also between each other, implying that no general rule can be made for net oil importing sub-Saharan countries, such as Botswana, Kenya and Tanzania. From our analysis we conclude that after an oil price peak, the Botswanan pula clearly appreciates against the US dollar, the Kenyan and Tanzanian shilling.

This paper examines the influence of monetary aggregates shocks in the U.S., China and the Euro area on Japan. China's monetary expansion has significant effects on Japan's economy that are quite different from those of the U.S. and Euro area. In line with the implications of the Mundell–Fleming model when there are capital controls in place, Chinese monetary expansion is found to primarily affect Japan through trade. The income absorption effect of China's monetary expansion is substantial for Japan. China's monetary expansion results in significant increases in Japan's industrial production, exports and inflation, and decreases in the trade-weighted yen. After 24 months, monetary shocks in China forecast 20% of the variation in Japan's real trade balance. In contrast, U.S. monetary expansion results in contraction in Japan's industrial production, exports and trade balance (expenditure-switching). Monetary expansion in the Euro area does not significantly affect Japan. Structural vector error correction models and a factor-augmented model are estimated to establish robustness of results.

Since recently Mozambique is actively developing its large reserves of coal, natural gas and hydropower. Against this background, we present in this paper the first integrated long-run scenario model of the Mozambican energy sector. Our model makes use of the LEAP framework and is calibrated on the basis of recently developed local energy statistics, demographic and urbanization trends as well as cross-country based GDP elasticities for biomass consumption, sector structure and vehicle ownership. We develop four scenarios to evaluate the impact of the anticipated surge in natural resources exploration on aggregate trends in energy supply and demand, the energy infrastructure and economic growth in Mozambique. Our analysis shows that until 2030, primary energy production is likely to increase at least six-fold, and probably much more. This is roughly 10 times the expected increase in energy demand; most of the increase in energy production is destined for export. As a result, Mozambique is rapidly developing into an important player at international energy markets. Therefore, a major challenge for energy policy in Mozambique is to strike a balance in meeting domestic and international demand for energy, such that energy production benefits the entire Mozambican population.

This paper uses panel cointegration techniques to examine the causal relationship between output, renewable and non-renewable energy consumption, and international trade for a sample of 69 countries during the period 1980-2007. In the short-run, Granger causality tests show that there is evidence of a bidirectional causality between output and trade (exports or imports), a one way causality running from renewable energy consumption to trade, and a bidirectional feedback causality between non-renewable energy consumption and trade. Interestingly, there is no direct short-run causality between the three variables renewable energy, non-renewable energy and output. However, there is an indirect short-run causality running from renewable energy to both non-renewable energy and output, which occurs through trade. Also, there is an indirect and bidirectional short-run causality between non-renewable energy and output through trade. In the long-run, OLS, FMOLS and DOLS estimates suggest that renewable and non-renewable energy consumption and trade have a positive and statistically significant impact on economic growth. Our policy recommendations are to encourage trade openness because this may reduce the share of non-renewable energy consumption in total energy consumption. Indeed, through technology transfer and economies of scale, more trade openness could be a good policy for combating global warming by encouraging the use of renewable energies, while increasing output.

Given the geographic concentration of oil production in Canada, oil price shocks are primary drivers of regional variation in income. It is widely believed that global oil price fluctuations only matter for the economy of oil-rich regions in Canada but can effectively be ignored in other regions. Based on detailed Canadian city-level and province-level data, our analysis shows that this view is mistaken. Unexpected increases in oil prices stimulate the Canadian economy in general and raise housing demand and real house prices in all regions across Canada. We develop a theoretical model of how real oil price shocks spread across regions. The model differentiates between oil-producing and non-oil-producing regions and incorporates multiple sectors, trade between provinces, government redistribution and consumer spending on fuel. The model establishes that oil price shocks spread to housing markets in non-oil-producing regions as the government effectively redistributes oil revenue and as the volume of interprovincial trade increases. Our finding that oil price shocks affect housing demand not only in oil-rich but also in oil-poor regions will ease the concern that a common interest rate policy may not equally suit all regions. It also suggests that regulators of mortgage markets in oil-poor regions must consider the consequences of fluctuating oil prices on their regions. Finally, the positive effect we find of oil price shocks on household debt and bank lending has implications for monitoring the risks in credit markets., Étant donné la concentration géographique de la production pétrolière au Canada, les chocs des prix du pétrole comptent parmi les principales causes de disparités de revenu entre les régions. L’idée est répandue que les fluctuations des prix mondiaux du pétrole ne perturbent que les régions du Canada riches en pétrole et que les autres peuvent, de fait, ne pas s’en soucier. À partir de données canadiennes détaillées par ville et par province, notre analyse montre qu’il s’agit d’une perception erronée. Des hausses inattendues des prix du pétrole stimulent l’ensemble de l’économie canadienne et font augmenter la demande de logements et les prix réels des logements dans toutes les régions du pays. Nous avons élaboré un modèle théorique de la propagation des chocs des prix réels du pétrole d’une région à l’autre. Ce modèle, qui fait la distinction entre les régions productrices et non productrices, tient compte de nombreux secteurs, des échanges commerciaux entre les provinces, de la redistribution des revenus par l’État et des dépenses de consommation en carburant. Il permet d’établir que les chocs de prix du pétrole se propagent aux marchés du logement des régions non productrices puisque l’État redistribue, de fait, les revenus du pétrole et que les échanges commerciaux interprovinciaux augmentent. Notre principale conclusion — à savoir que les chocs pétroliers se répercutent sur la demande de logements non seulement dans les régions riches en pétrole, mais aussi dans celles qui en sont dépourvues — contribuera à atténuer les craintes entourant la capacité d’une politique de taux d’intérêt commune de convenir à toutes les régions. Il en découle également que les autorités de réglementation des marchés hypothécaires dans les régions non productrices de pétrole doivent s’intéresser aux conséquences des fluctuations des prix pétroliers pour leur région respective. Enfin, les effets positifs des chocs des prix pétroliers que nous avons relevés sur l’endettement des ménages et les prêts bancaires ont des implications pour la surveillance des risques sur les marchés du crédit.

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.

The objective of the study is to examine the causal relationship between i) nuclear energy consumption and economic determinants (i.e. labor force, gross fixed capital formation and GDP per capita), and ii) nuclear consumption and energy security issues (i.e., technology infrastructure, energy sources, conditions of land, concern of energy security and political stability) in the context of South Asia. The study brings an annual aggregate data for South Asia from the period of 1960-2012. The autoregressive distributed lag (ARDL) bounds testing approach employed for investigating the short- and long-run relationship between the variables, while modified version of the Granger causality used for determining the causal relationship among nuclear energy consumption, economic factors and energy security issues. The results show that there is a long-run relationship between nuclear energy consumption, economic indicators and energy security issue; however, the ‘a priori’ expectations of the variables differ with the nuclear energy consumption. The results of causality test validate the neutrality hypothesis between i) nuclear energy consumption & labor force; ii) nuclear energy consumption & conditions of land and iii) nuclear energy & GDP per capita. Further, the causality test validates the feedback hypothesis between i) nuclear energy & technology infrastructure; and ii) nuclear energy & energy sources in the region. The results indicate the unidirectional causality running from nuclear energy to gross capital formation but not vice versa. Similarly, energy security Granger cause nuclear energy which indicates that energy security has a vital role to increase nuclear energy in the region.

Using autoregressive distributed lag bound test framework, the dynamics of financial development, economic growth, energy prices and energy consumption was investigated in Nigeria for the period of 1972Q1-2011Q4. The finding signifies that variables were cointegrated as null hypothesis was rejected at 1% level of significance. In the short-run financial development has significant negative impact on fossil fuel consumption, economic growth also shows the same relationship. However, energy prices have positive and significant influence on the consumption of fossil fuel. In the long-run however, financial development has insignificant negative impact on energy consumption, and economic growth has negative but significant impact on energy consumption, while energy prices has positive and significant impact on fossil fuel consumption. The policy recommendation remains that Nigerian authority should try to explore other alternative sources of energy in order to curb the adverse effect of fossil fuel consumption on the financial market and overall economic growth. Exploring the potentials of green energy is paramount in Nigeria which is more eco-friendly and contain less carbon emissions.

This article contribution is two-fold. It presents new results based on quarterly frequency disaggregated data for the Czech Republic. It also goes through extensive identification of methodological deficiencies present in the literature and proposes a methodology to determine the direction and also the sign of the causality in the energy-economy nexus. The proposed improvements are illustrated on the case of Czech Republic during the period 1996-2011 in quarterly frequency. The data are taken in disaggregated form; the model estimates cover aggregate and sectoral levels of final energy consumption, and also fuel-specific consumption and sectoral electricity consumption. The evidence for the conservation hypothesis with a positive sign of the causality is found for, (i) The consumption of the solid fuels, (ii) the electricity consumption in the transportation sector, (iii) the final energy consumption of the industrial sector, and (iv) the electricity consumption of the industrial sector. Given the fact the solid fuels are: (a) Used heavily for the electricity generation in the Czech Republic, (b) the main differentiating fuel between the Czech and European Union industrial energy consumption, and (c) could contribute to the other environmental targets, it seems reasonable to favor the reduction of the solid fuels consumption in the Czech energy conservation plans.

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.

The objective of this paper is to analyze the rebound effect from increased efficiency in industrial use of energy in Sweden. Energy efficiency improvements can have sig-nificant micro- and macroeconomic effects that hampers the positive effect on real energy savings. To assess the size of the overall rebound effect in the Swedish econo-my we apply a computable general equilibrium model. The results show that the economy-wide rebound effect in Sweden depends on a number of factors, e.g. the extent of the energy efficiency improvement, how the labour market is modeled as well as if the increase in energy efficiency is combined with a cost or not. We find that the rebound effect following a 5 percent increase of energy efficiency in the Swedish industry lies in the range of 40-70 percent. When energy efficiency only is improved in energy-intensive production, the rebound effect becomes even higher. These findings are in line with the results in the literature.

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.