Real Energy Prices Research Articles

Declining real interest rates: The role of energy prices in energy importers

This study investigates whether the upward trend in real energy prices since the 1990s can account for the decreasing trend in real interest rates in energy-importing countries. By estimating dynamic panel data models, I first provide empirical evidence that a rise in real energy prices leads to falls in real interest rates and aggregate consumption in energy-importing OECD member countries. Using a life-cycle model, I show that the increasing trend in real energy prices decreased the equilibrium real interest rate by about 1.4 percentage points between 1990 and 2018. The mechanism for this effect is as follows: As a result of the increases in the real energy price, energy consumption falls, dampening the consumption of manufactured goods because of the complementarity in consumption between energy and manufactured goods. Accordingly, aggregate consumption declines, putting downward pressure on the real interest rate. Moreover, the increased real energy price decreases energy input in production, raising the capital/energy ratio and inducing a lower real interest rate via a fall in the marginal product of capital. Through a simulation, I also demonstrate that increases in the real energy price had a greater influence on the declining real interest rate in energy-importing countries during 1990–2018, compared to population aging.

Energy price shocks and current account balances: Evidence from emerging market and developing economies

This paper investigates the effects of real energy price shocks on current account balances of 45 emerging market and developing economies. The investigation is based on country-specific structural vector autoregression models, where alternative specifications and identification schemes are considered for robustness purposes. The empirical results suggest that 1 % of a positive real oil price shock results in up to 0.11 (0.08) percentage points of a cumulative improvement (deterioration) in current account balances of oil exporters (importers) after five years, whereas 1 % of a positive real natural gas price shock results in up to 0.06 (0.04) percentage points of a cumulative improvement (deterioration) in current account balances of natural gas exporters (importers) after five years. Real coal price shocks result in higher current account balances of oil exporters and natural gas exporters, suggesting substitution of coal with oil and natural gas in such cases. When contributions of alternative real energy prices to the variance of current account balances are compared, real oil price shocks dominate those of real natural gas and real coal prices. The empirical results investigating the effects of oil demand versus oil supply shocks on current account balances suggest that oil demand shocks (rather than oil supply shocks) result in similar reactions of current account balances to real oil price shocks, supporting the view that the effects of oil demand shocks are different from those of oil supply shocks. The results are robust to the consideration of country-specific changes in real GDP and real effective exchange rates.

Quantifying the financial value of building decarbonization technology under uncertainty: Integrating energy modeling and investment analysis

The built environment accounts for approximately 40% of global emissions. The evaluation of returns linked to investments in building decarbonization is impeded when confronted with uncertainties surrounding future energy prices, building emission regulations, and real estate market conditions. This paper develops an integrated framework that combines energy modeling with investment analysis to support the adoption of decarbonizing technologies under a context of uncertainty. The integrated framework includes projections of costs and benefits associated with the adoption of technology (i.e., changes in rental cashflows, vacancy rates, maintenance and energy costs) under a wide range of potential pathways of the energy markets and regulatory environment, and local real estate market demand for green buildings using Monte Carlo simulations. Additionally, this paper develops the approach to investigate the potential value of flexibility in designing for building electrification (i.e., the ability to fully electrify gas heating systems at a later year). We illustrate the use of this approach in a case study of a new office building in New York City, faced with design choices between natural gas and electrified heating systems. We consider 10,000 scenarios changing future greenhouse emission penalties, investments in the local electric grid, local real estate market conditions and energy prices. In approximately 60 percent of simulations, the most profitable decision for the building owner is to adopt a natural gas-powered heating system. However, adopting a building design that provides a building the flexibility to fully electrify at a later date is more profitable than a natural gas-heating building in 96 percent of scenarios. A sensitivity analysis shows that the most influential parameter in the valuation of a design option is the size of the green premiums associated with improved energy efficiency.

Real-Time Pricing-Enabled Demand Response Using Long Short-Time Memory Deep Learning

Sustainable energy development requires environment-friendly energy-generating methods. Pricing system constraints influence the efficient use of energy resources. Real-Time Pricing (RTP) is theoretically superior to previous pricing systems for allowing demand response (DR) activities. The DR approach has been useful for correcting supply–demand imbalances as technology has evolved. There are several systems for determining and controlling the DR. However, most of these solutions are unable to control rising demand or forecast prices for future time slots. This research provides a Real-Time Pricing DR model for energy management based on deep learning, where the learning framework is trained on demand response and real-time pricing. The study data in this article were taken from the Australian Energy Market Operator (AEMO), and the learning framework was trained over 17 years of data to forecast the real future energy price and demand. To investigate the suggested deep learning-based dynamic pricing strategy, two prediction instances are addressed: actual–predicted demand and actual–predicted price. We estimated pricing and demand outcomes using long short-term memory (LSTM), which were then greatly improved by architectural changes in the model. The findings showed that the suggested model is suitable for energy management in terms of demand and price prediction.

Multiobjective home energy management systems in nearly-zero energy buildings under uncertainties considering vehicle-to-home: A novel lexicographic-based stochastic-information gap decision theory approach

• A novel hybrid stochastic-IGDT model is developed for HEM problems in nZEBs. • Real energy price and vehicle-to-home capability are properly modelled. • Unpredictable behavior of the electric vehicle is formulated via IGDT. • A bi-objective structure is presented to jointly optimize the energy consumption and cost using lexicographic optimization. • The problem is kept linear being solvable by standard solvers. Residential sector is being promoted to evolve towards nearly-Zero Energy Buildings (nZEBs), which draw a yearly net energy consumption near zero. This target can be attained through on-site renewable generation, achieving high efficiency in consumption. In this context, Home Energy Management (HEM) systems become an indispensable tool for obtaining optimally coordinating smart appliances, renewable generation, and on-site storage facilities. Due to the high unpredictability of renewable generation and some emerging appliances like electric vehicles, these tools must be able to deal with different uncertainties properly. At the same time, a variety of objectives are jointly considered. The existing approaches commonly fail to deal jointly with these two premises. This paper aims to fill this gap by developing a novel solution for HEM systems in nZEBs. The proposed procedure uses lexicographic optimization to find a compromise solution among objectives. At the same time, the variety of uncertainties caused by unpredictable weather, demand, energy pricing, and electric vehicle behavior is adequately modeled using a hybrid stochastic-Information Gap Decision Theory (IGDT) approach. The mathematical modeling is sufficiently comprehensive (comprising various energy sources and vehicle-to-home capability) and tractable due to its integer-linear structure. A case study on a benchmark nearly zero energy home is considered to validate the developed approach, which its results reveal its effectiveness in terms of minimizing various objective functions while the degree of robustness is preserved and the whole procedure is efficient yet.

Does structural change boost the energy demand in a fossil fuel-driven economy? New evidence from Iran

Based on data availability, this study investigates the impact of structural change on energy consumption (EC) in Iran from 1976 to 2017. Therefore, the economic complexity index (ECI) was incorporated into the energy demand besides real GDP (RGDP) and real energy price index (REPI), which is constructed based on Iran's leading energy carriers. Multiple econometrics techniques are applied to deliver a more reliable and accurate analysis, such as the autoregressive distributed lag model, error correction model, Bayer and Hanck cointegration test, and three cointegration regressions: fully-modified ordinary least square (OLS), dynamic OLS, and canonical cointegrating regression, and finally Breitung-Candelon Spectral Granger. In the short and long-run, RGDP and ECI increase (rebound effect) EC while REPI reduces it. Furthermore, the causality results show that RGDP, ECI, and REPI are linked to EC in the short, medium, and long run. The study's results have implications for policymakers to manage Iran's energy consumption. They highlighted the importance of raising public environmental awareness, diversifying the energy mix, and continuing to implement the law on targeted subsidy.

Investigating the effects of energy price index on technical environmental efficiency in Iran

This study investigates the effects of increasing real energy prices on technical environmental efficiency in Iran using a directional distance function. To achieve this purpose, six variables are used, namely labor, capital stock, energy consumption, real energy price, industrial value added, and environmental cost index, for the period 2009–2015. Moreover, two indexes for industrial environmental cost and real energy price are introduced. The environmental cost index is derived from the pollution cost of six types of emissions (NOx, SO2, CO2, CO, CH4, SPM) produced by 23 industries. The results show that the real energy price and distance function have an inverse relationship. Specifically, higher real energy prices cause industries to move toward using more energy-efficient technologies and more technical environmental efficiency. This study is one of the few studies that have evaluated the environmental effects of targeted subsidy law or energy price liberalization in Iran and demonstrates that an increase in real energy prices would lead to improved environmental technical efficiency.

Aggregate Energy Demand in Korea: An Empirical Model with Total Factor Productivity

This study examines the relationship among energy demand, GDP, real energy price, and total factor productivity in Korea. Total factor productivity is introduced into the model as a proxy variable for technical change, which helps to identify correctly the long-run relationship. This study employs various cointegration tests and estimation methods, yielding robust evidence of a cointegrating relationship and reliable estimates of the parameters. The estimated error correction model forecasts that the growth of energy demand in Korea will slow down as economic growth depends more on total factor productivity than in the past.

Declining Real Interest Rates: The Role of Energy Prices in Energy Importers

This paper studies whether the upward trend of real energy prices since the 1990s can account for the decreasing trend of real interest rates in energy importers. Using VARs, I first provide empirical evidence that a rise in real energy prices leads to a fall in real interest rate in energy-importing G-7 countries. I then show, using a life-cycle model, that the increasing trend of the real energy price decreases the equilibrium real interest rate by about 1.5 percentage points between 1990 and 2018. Due to increases in the real energy price, energy consumption falls, which dampens consumption of manufactures because of the complementarity between energy and manufactures in consumption. Accordingly, aggregate consumption goes down, which puts downward pressure on the real interest rate. Moreover, the increased real energy price decreases energy inputs in production, and hence capital/energy ratio rises, inducing lower real interest rate via falls in the marginal product of capital. By simulating the model, I also find that increases in the real energy price have more influences on the declining real interest rate in energy importers during 1990-2018 than population aging.

The impact of economic complexity on energy demand in OECD countries

This paper investigates the role of economic complexity on energy demand using the panel dataset of 25 Organization for Economic Co-operation and Development (OECD) countries from 1978 to 2016. Both real per capita income level and economy-wide real energy price index are critical determinants in energy demand modeling. The battery of the cross-sectional dependency test proposed by Pesaran (2004 and 2007) is used, signaling the presence of cross-sectional dependency in the dataset. Thus, the Westerlund (2007) cointegration test is also used, revealing the long-run relationship between the series. Moreover, the results from using the Augmented Mean Group (AMG) estimations illustrate that real per capita income level positively affects energy demand while real energy price and economic complexity negatively influence on it. From a policy perspective, we suggest increasing technological innovation (i.e., higher economic complexity) will reduce the energy demand. The reduction of massive energy usage may be beneficial for the natural environment's health in the OECD countries.

Development of Home Energy Management Scheme for a Smart Grid Community

The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.

Economic performance assessment of three renovated multi-family buildings with different HVAC systems

The EU has adopted several policies to improve energy efficiency. One of these policies aims to achieve energy efficient renovations in at least 3% annually of buildings in EU. The aim of this study was to provide an accurate economic comparison between three similar multi-family buildings that have undergone the same energy efficiency measures, with essential differences regarding the installed ventilation systems. The selected ventilation systems were: 1) balanced mechanical ventilation with heat recovery; 2) exhaust ventilation with air pressure control; and 3) exhaust ventilation with an exhaust air heat pump. In the latter two cases, radiators pre-heat supply air. Life cycle cost analysis were conducted using real investment and operational costs for the three buildings. Sensitivity analysis was also made for different discount rates and energy price escalation patterns. It was found that the building with exhaust ventilation has the lowest life cycle cost. At 2% inflation rate, 3% real discount rate and 1% real energy price escalation, the building with exhaust air heat pump and the building with mechanical ventilation with heat recovery has 13% and 29% higher life cycle cost than the building with exhaust ventilation, respectively. The sensitivity analysis further showed that a lower discount rate gives higher future costs and gives more profitability of systems with heat recovery with lower future costs. Energy price assumptions have a crucial impact on the results and change the profitability of studied renovation packages.

Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation

Abstract Forecasting energy market volatility by artificial neural network has long been a focus of economic research. Based on the discriminatory attitude to the historical price information, a novel hybrid forecasting model of gated recurrent unit with stochastic time effective weights (SW-GRU) is proposed and applied to global energy prices forecasting with empirical mode decomposition (EMD). Since crude oil and gasoline count much in global energy markets, the futures and spots prices of them are adopted in the present research. After real energy price series is decomposed into intrinsic mode functions (IMFs) and a residual, the forecasting IMFs and residual in the test set can be performed by SW-GRU and utilized to calculate the prediction prices. With several error criteria and double-scale complexity invariant distance, the forecasting errors of proposed model SW-GRU with EMD and other models are evaluated and compared. According to the empirical study in energy markets, the forecasting model of SW-GRU with EMD is distinguished from other models by its best performances.

Estimated cost of electricity with time horizon for micro grids based on the policy response of demand for real price of energy

The intelligent microgrids are an efficient alternative, which allows to supply the demand decreasing the losses of the electrical system and at the same time; the environment and the consumers are the main beneficiaries. This article develops a heuristic based on an energy management model based on the real price of electricity, which will allow end users to encourage the implementation of a policy of response to demand, in order to optimize their consumption, for which a micro smart grid is analyzed, with conventional and non-conventional renewable generation, In addition, a mechanism of "real energy price" will be implemented as a policy of response to demand, with the aim of optimizing the costs of energy that will be transferred to users depending on the stratum to which it belongs, these costs will have a short-term horizon with hourly intervals, achieving a reduction in the purchase of energy from the system

Fixed and Capture Level Reduction operating modes for carbon dioxide removal in a Natural Gas Combined Cycle power plant

The characteristics (flowrate, composition, CO2 partial pressure) of the flue gas exiting a Natural Gas Combined Cycle (NGCC) power plant are different from the ones of coal-fired units on which analyses in the literature are usually focusing, therefore for application of Carbon Capture and Storage (CCS) to the removal of CO2 from this type of stream a detailed study is needed. This work applies the method of flexible operation aimed at reducing the economic disadvantages of a CCS plant, and considers the Capture Level Reduction (CLR) mode for the carbon dioxide removal section of a NGCC power plant located in Italy, of which type the number of units is constantly increasing. The CLR mode consists in emitting part of the carbon dioxide at given times during the day by bypassing part of the rich solvent and thus reducing the energy requirements for regeneration of the solvent and compression of the removed CO2. For the study a proprietary code developed by the GASP group of Politecnico di Milano, based on real energy prices and demand and taking into account different values of possible carbon taxes, has been employed. The results show that the best load to the absorption section strongly depends on the value of the considered carbon tax, in addition to the time-dependent request and price of electricity. A carbon tax of 5 €/tonCO2 generally does not favor the operation of the CCS system, while higher values exert a more significant influence on the revenues. Carbon taxes of 115 €/tonCO2 for the winter period and of 145 €/tonCO2 for the summer period make the operation of the CCS section more economically advantageous than emitting carbon dioxide to the atmosphere. In any case, also when applied to CO2 removal in power plants fed by natural gas, the CLR mode results more advantageous than fixed operation, thus making power plants companies increasing their efforts in reducing greenhouse gas emissions.

Coordination of Electric Vehicle Charging Through Multiagent Reinforcement Learning

The number of Electric Vehicle (EV) owners is expected to significantly increase in the near future, since EVs are regarded as valuable assets both for transportation and energy storage purposes. However, recharging a large fleet of EVs during peak hours may overload transformers in the distribution grid. Although several methods have been proposed to flatten peak-hour loads and recharge EVs as fairly as possible in the available time, these typically focus either on a single type of tariff or on making strong assumptions regarding the distribution grid. In this article, we propose the MultiAgent Selfish-COllaborative architecture (MASCO), a Multiagent Multiobjective Reinforcement Learning architecture that aims at simultaneously minimizing energy costs and avoiding transformer overloads, while allowing EV recharging. MASCO makes minimal assumptions regarding the distribution grid, works under any type of tariff, and can be configured to follow consumer preferences. We perform experiments with real energy prices, and empirically show that MASCO succeeds in balancing energy costs and transformer load.

Energy intensity determinants in an energy-exporting developing economy: Case of Iran

While the empirical literature on energy intensity is focused mainly on reduced energy intensity, this study examines a case where energy intensity is increasing. The paper aims at identifying price and non-price determinants of Iran's energy intensity and analyzing their importance in dynamics of it. In order to answer the research question, LMDI decomposition technique as well as time series econometrics have been used to data analysis. Based on the results, both structural changes in economic activities and inefficiency of energy consumption have led to the increase in Iran's energy intensity, although the latter dominated the former. The elasticity of energy intensity with respect to total factor productivity, real energy prices, and industrial development is negative in the short-run and long-run. Additionally, per capita income has a positive linear relationship with energy intensity that implies a high income elasticity of energy demand. Furthermore, changes in TFP and energy prices contribute more than two-thirds of forecast error variance decomposition of energy intensity, which shows the importance of these factors in energy intensity dynamics. Hence, in order to change the increasing trend of energy intensity, it is important to consider the necessities of improving technology and productivity while realizing energy prices.

A comprehensive analysis of smart home energy management system optimization techniques

Development of smart grid technology provides an opportunity to various consumers in context for scheduling their energy utilization pattern by themselves. The main aim of this whole exercise is to minimize energy utilization and reduce the peak to average ratio (PAR) of power. The two way flow of information between electric utilities and consumers in smart grid opened new areas of applications. The main component is this management system is energy management controller (EMC), which collects demand response (DR) i.e. real time energy price from various appliances through the home gateway (HG). An optimum energy scheduling pattern is achieved by EMC through the utilization of DR information. This optimum energy schedule is provided to various appliances via HG. The rooftop photovoltaic system used as local generation micro grid in the home and can be integrated to the national grid. Under such energy management scheme, whenever solar generation is more than the home appliances energy demand, extra power is supplied back to the grid. Consequently, different appliances in consumer premises run in the most efficient way in terms of money. Therefore this work provides the comprehensive review of different smart home appliances optimization techniques, which are based on mathematical and heuristic one.

Steering the adoption of battery storage through electricity tariff design

The economic viability of electricity storage using batteries, under different tariff structures and system configurations, is investigated. The economic outcomes of the different combinations of tariff design and system configuration are evaluated. Based on a discussion of the relevant literature, the following tariff designs are used in the study: (i) fixed energy prices, (ii) real-time energy pricing, (iii) fixed rate capacity tariffs, and (iv) capacity dependent capacity tariffs. Next, the different simulated system configurations are outlined: (i) no battery storage, (ii) battery storage only, and (iii) battery storage and decentralized renewable energy production with PV. Our study provides insights for policy makers, showing that capacity block pricing only incentivises storage as part of an (existing) PV installation, while the combination of real time energy pricing and capacity block pricing promotes a wider adoption of battery storage.

Techno-economic sizing of a battery energy storage coupled to a wind farm: an Italian case study

The study focuses on the technical and economic issues which arise when a battery energy storage is coupled to a wind farm to improve its profitability. The electric energy storage technologies may fulfil the dual role of promoting the renewable energy sources development and of allowing an economic optimization of the energy output. In this study, the optimal economic and technical sizing of a lithium-ion battery, is analyzed by focusing on the day-ahead market profit maximization. This is done by scheduling the aleatory wind production using a 24-hours rolling horizon MILP optimization algorithm. The annual utilization profile and daily charge level are analyzed, and the related battery degradation is assessed. Finally, the battery cost is compared to the additional revenues, to assess the battery cost-optimal size in presence of battery degradation. The analysis is based on real Italian wind data and energy price time series.