Electricity demand,GDP and employment: evidence from Italy

This paper applies time series methodologies to examine the causal relationship among electricity demand, real per capita GDP and total labor force for Italy from 1970 to 2009. After a brief introduction, a survey of the economic literature on this issue is reported, before discussing the data and introducing the econometric techniques used. The results of estimation indicate that one cointegrating relationship exists among these variables. This equilibrium relation implies that, in the long-run, GDP and labor force are correlated negatively, as well as GDP and electricity. Moreover, there is a bi-directional Granger causality flow between real per capita GDP and electricity demand; while labor force does not Grangercause neither real per capita GDP nor electricity demand. This implies that electricity demand and economic growth are jointly determined at the same time for the Italian case. The forecast error variance decomposition shows that forecast errors in real per capita GDP are mainly caused by the uncertainty in GDP itself, while forecast errors in labor force are mainly resulted from the labor force itself, although aggregate income and electricity are important, too.     

Field demonstration of automated demand response for both winter and summer events in large buildings in the Pacific Northwest

There are growing strains on the electric grid as cooling peaks grow and equipment ages. Increased penetration of renewables on the grid is also straining electricity supply systems and the need for flexible demand is growing. This paper summarizes results of a series of field test of automated demand response systems in large buildings in the Pacific Northwest. The objective of the research was twofold. One objective was to evaluate the use demand response automation technologies. A second objective was to evaluate control strategies that could change the electric load shape in both winter and summer conditions. Winter conditions focused on cold winter mornings, a time when the electric grid is often stressed. The summer test evaluated DR strategies in the afternoon. We found that we could automate both winter and summer control strategies with the open automated demand response communication standard. The buildings were able to provide significant demand response in both winter and summer events.     

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

Demand response is receiving increasing interest as a new form of flexibility within low-carbon power systems. Energy models are an important tool to assess the potential capability of demand side contributions. This paper critically reviews the assumptions in current models and introduces a new conceptual framework to better facilitate such an assessment. We propose three dimensions along which change could occur, namely technology, activities and service expectations. Using this framework, the socio-technical assumptions underpinning ‘bottom-up’ activity-based energy demand models are identified and a number of shortcomings are discussed. First, links between appliance usage and activities are not evidence-based. We propose new data collection approaches to address this gap. Second, aside from thermal comfort, service expectations, which can be an important source of flexibility, are under-represented and their inclusion into demand models would improve their predicative power in this area. Finally, flexibility can be present over a range of time scales, from immediate responses, to longer term trends. Longitudinal time use data from participants in demand response schemes may be able to illuminate these. The recommendations of this paper seek to enhance the current state-of-the-art in activity-based models and to provide useful tools for the assessment of demand response.     

Review of barriers to the introduction of residential demand response: a case study in the Netherlands

Demand response, defined as the shifting of electricity demand, is generally believed to have value both for the grid and for the market: by matching demand more closely to supply, consumers could profit from lower prices, while in a smart grid environment, more renewable electricity can be used and less grid capacity may be needed. However, the introduction of residential demand response programmes to support the development of smart grids that includes renewable generation is hampered by a number of barriers. This paper reviews these barriers and categorises them for different demand programmes and market players. The case study for the Netherlands shows that barriers can be country specific. Two types of demand response programmes have been identified as being the most promising options for households in smart grids: price‐based demand response and direct load control, while they may not be beneficial for market players or distribution system operators. © 2016 The Authors. International Journal of Energy Research Published by John Wiley & Sons Ltd.     

Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka

This study investigates the short-run dynamics and long-run equilibrium relationship between residential electricity demand and factors influencing demand – per capita income, price of electricity, price of kerosene oil and price of liquefied petroleum gas – using annual data for Sri Lanka for the period, 1960–2007. The study uses unit root, cointegration and error-correction models. The long-run demand elasticities of income, own price and price of kerosene oil (substitute) were estimated to be 0.78, ? 0.62, and 0.14 respectively. The short-run elasticities for the same variables were estimated to be 0.32, ? 0.16 and 0.10 respectively. Liquefied petroleum (LP) gas is a substitute for electricity only in the short-run with an elasticity of 0.09. The main findings of the paper support the following (1) increasing the price of electricity is not the most effective tool to reduce electricity consumption (2) existing subsidies on electricity consumption can be removed without reducing government revenue (3) the long-run income elasticity of demand shows that any future increase in household incomes is likely to significantly increase the demand for electricity and (4) any power generation plans which consider only current per capita consumption and population growth should be revised taking into account the potential future income increases in order to avoid power shortages in the country.     

Fostering economic demand response in the Midwest ISO

The Midwest Independent System Operator (MISO) runs the power grid in 14 states and one Canadian province and has a peak demand of some 116,000 MW. Its operational area is richly supplied with reliability-triggered demand response programs such as direct load control of residential appliances and curtailable/interruptible rates for commercial and industrial customers. However, economic demand response programs are lacking. This paper discusses three different ways in which such programs can be introduced in the wholesale energy markets run by MISO. These include, first, an approach in which utilities and load serving entities move retail customers to dynamic pricing and other time-based pricing rates; second, an approach in which these same entities and possibly third-parties bid price responsive demand curves into the wholesale market; and third, an approach in which demand response is bid as a supply resource into the wholesale market.     

Determinants of import demand for non-renewable energy (petroleum) products: Empirical evidence from Nigeria

This study estimated determinants of import demand for refined petroleum products in Nigeria for the period 1984–2013. It employed the autoregressive distributed lag (ARDL) bounds test cointegration method and analysed both long-run and short-run determinants of import demand for total and specific petroleum products.In the long-run, aggregate and sectoral incomes are significant determinants of import of refined kerosene. Further, real effective exchange rate (REER), aggregate income (GDP), manufacturing sector's income, domestic energy production (DEP) and population growth rate (PGR) are drivers of import of refined motor spirit Moreover, REER, DEP and manufacturing sector's income are propellers of import of refined distillate fuel. Also, REER and total output of petroleum products are major drivers of total import of refined petroleum products.Short-run results show that previous period GDP, PGR and manufacturing and service sectors' incomes are determinants of import demand for refined kerosene. Moreover, REER, GDP, previous PGR and manufacturing sector's income exert significant effects on the import of refined motor spirit. Further, significant effects of REER, DEP, previous PGR, domestic output of the product and manufacturing and service sectors' incomes on the import demand for distillate fuel were found.Policy implications of the foregoing are articulated in the paper.     

Residential electricity demand in Spain: New empirical evidence using aggregate data

This paper presents an empirical analysis on residential demand for electricity. This analysis has been performed using aggregate panel data at the province level for 47 Spanish provinces for the period from 2000 to 2008. For this purpose, we estimated a log–log demand equation for electricity consumption using a dynamic partial adjustment approach. This dynamic demand function has been estimated using a two-step system GMM estimator proposed by Blundell and Bond (1998). The purpose of this empirical analysis is to highlight some of the characteristics of Spanish residential electricity demand. Particular attention has been paid to the influence of price, income, and weather conditions on electricity demand. The estimated short and long-run own price elasticities are negative, as expected, but lower than 1. Furthermore, weather variables have a significant impact on electricity demand.     

Integrating renewable energy using a smart distribution system: Potential of self-regulating demand response

A self-regulating distribution system simulation platform is presented for a smart-grid with Distributed Energy Resource (DER) wind power injection in which load flow fluctuations are controlled via self-regulating air-source heat pump (HP) cycling. The grid power injection fluctuations are mitigated by bus-level HP control, while ensuring compliance to both consumer comfort constraints and distribution grid load flow requirements.The effects of applying a number of different bus-level HP control algorithms are evaluated. The results show that using building thermal mass in conjunction with simple control strategies can effectively accommodate large fluctuations associated with high penetration of wind energy. The number of HPs in each distribution phase significantly affects the load flow characteristics and the ability of the bus-level control to smooth the distribution grid regulator power.The bus-level control improves power and voltage ramping rates, reduces wind power injection fluctuations, and also reduces the energy reserve requirements.     

The price and income elasticities of natural gas demand: International evidence

Natural gas contributes a growing share of the world's energy mix. In this paper we use national-level data for a sample of 44 countries to estimate the price and income elasticities of natural gas demand. We present both single-equation results and results instrumenting natural gas prices with proved natural gas reserves. Our instrument includes both domestic reserves and distance-weighted reserves in other countries. We obtain estimates of the average long-run price elasticity of natural gas demand of around −1.25 and of the average long-run income elasticity of natural gas demand of +1 and higher. We also present separate estimates for final natural gas demand by industry and households.     

Evaluating the efficiency of integrated systems in the process industry–Case: Steam cracker

A literature survey indicates that decisions about whether to reject or accept an investment are largely based on financial factors. The survey also indicates that a comparison of different efficiency improvement studies is difficult because their results are not readily commensurable. From the management perspective, more information on the physical aspects affecting efficiency is needed. From the engineering perspective, studies on integrated processes deal with a number of criteria that are often in conflict with each other, and for which information is difficult to produce.This paper discusses ways to analyse efficiency improvement studies in integrated industrial processes. The paper demonstrates how these analyses could be made more comprehensive. The paper presents a case study where test runs and simulations were carried out to improve process efficiency by adjusting process operating parameters. The case study was re-evaluated using a novel approach that has been developed to assess the efficiency of integrated systems. This includes considerations from three design perspectives: energy efficiency, material efficiency and operational efficiency, which, when analysed simultaneously, result in a more complete set of design and evaluation criteria.     

The optimization of demand response programs in smart grids

The potential to schedule portion of the electricity demand in smart energy systems is clear as a significant opportunity to enhance the efficiency of the grids. Demand response is one of the new developments in the field of electricity which is meant to engage consumers in improving the energy consumption pattern. We used Teaching & Learning based Optimization (TLBO) and Shuffled Frog Leaping (SFL) algorithms to propose an optimization model for consumption scheduling in smart grid when payment costs of different periods are reduced. This study conducted on four types residential consumers obtained in the summer for some residential houses located in the centre of Tehran city in Iran: first with time of use pricing, second with real-time pricing, third one with critical peak pricing, and the last consumer had no tariff for pricing. The results demonstrate that the adoption of demand response programs can reduce total payment costs and determine a more efficient use of optimization techniques.     

Stock-Watson dynamic OLS (DOLS) and error-correction modelling approaches to estimating long- and short-run elasticities in a demand function: new evidence and methodological implications from an application to the demand for coal in mainland China

In this paper robust elasticity estimates of coal demand for China are derived using annual data 1953–1992. In so doing, we illustrate the use of a powerful yet practically convenient and recently developed modelling procedure devised by Stock and Watson (known as Dynamic OLS (DOLS)), who provide evidence, based on Monte Carlo simulations, of this estimator being superior in small samples compared to a number of alternative estimators, as well as being able not only to accommodate higher orders of integration but also to account for possible simultaneity within regressors of a potential demand system. Furthermore, cointegration and error-correction methods are employed to derive short-run price and income elasticities. Estimated results are quite robust not only in terms of statistical prowess but also in terms of economic intuition and indicate that, over the long run, both price and income elasticities are close to unity. While short-run price and income elasticities are less (in absolute value) than their long-run counterparts, there seems to be some divergence in short-run parameters from a subsample analysis. Overall, results seem to imply that for China, coal consumption should remain relatively constant as future modernization strategies for economic development are pursued. In addition, the study has clear methodological implications for estimating the long- and short-run elasticities in a demand function in general, and in a wide variety of fields in future applied research.     

A bilevel model for electricity retailers' participation in a demand response market environment

Demand response programmes are seen as one of the contributing solutions to the challenges posed to power systems by the large-scale integration of renewable power sources, mostly due to their intermittent and stochastic nature. Among demand response programmes, real-time pricing schemes for small consumers are believed to have significant potential for peak-shaving and load-shifting, thus relieving the power system while reducing costs and risk for energy retailers. This paper proposes a game theoretical model accounting for the Stackelberg relationship between retailers (leaders) and consumers (followers) in a dynamic price environment. Both players in the game solve an economic optimisation problem subject to stochasticity in prices, weather-related variables and must-serve load. The model allows the determination of the dynamic price-signal delivering maximum retailer profit, and the optimal load pattern for consumers under this pricing. The bilevel programme is reformulated as a single-level MILP, which can be solved using commercial off-the-shelf optimisation software. In an illustrative example, we simulate and compare the dynamic pricing scheme with fixed and time-of-use pricing. We find that the dynamic pricing scheme is the most effective in achieving load-shifting, thus reducing retailer costs for energy procurement and regulation in the wholesale market. Additionally, the redistribution of the saved costs between retailers and consumers is investigated, showing that real-time pricing is less convenient than fixed and time-of-use price for consumers. This implies that careful design of the retail market is needed. Finally, we carry out a sensitivity analysis to analyse the effect of different levels of consumer flexibility.     

Area price and demand response in a market with 25% wind power

Denmark, east and west of the Great Belt are bidding areas with separate hourly area prices for the Nord Pool power exchange, covering four Nordic countries and parts of Germany. The share of wind power has now increased to 25% on an annual basis in western Denmark. This has a significant impact not only on the electricity wholesale prices, but also on the development of the market. Hourly market data are available from the website of Danish TSO from 1999. In this paper these data are analysed for the period 2004–2010. Electricity generators and customers may respond to hourly price variations, which can improve market efficiency, and a welfare gain is obtained. An important limitation for demand response is events of several consecutive hours with extreme values. The analysis in this paper is a summary and update of some of the issues covered by the EU RESPOND project. It shows that extreme events were few, and the current infrastructure and market organisation have been able to handle the amount of wind power installed so far. This recommends that geographical bidding area for the wholesale electricity market reflects external transmission constraints caused by wind power.     

Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands

This study assesses the electricity demand pattern in the relatively temperate climate of the Netherlands (latitude 52°30′N). Daily electricity demand and average temperature during the period from 1970 until 2007 are investigated for possible trends in the temperature dependence of electricity demand. We hypothesize that the increased use of cooling applications has shifted the temperature dependence of electricity demand upwards in summer months. Our results show significant increases in temperature dependence of electricity demand in May, June, September, October and during the summer holidays. During the period studied, temperature dependence in these months has shifted from negative to positive, meaning that a higher temperature now leads to an increased electricity demand in these months, rather than a decreased demand as observed historically. Although electricity demand in countries with moderate summer temperatures such as the Netherlands generally peaks in winter months and shows a minimum in summer months, this trend may signal the development of an additional peak in summer, especially given the expected climatic change. As power generating capacity may be negatively influenced by higher temperatures due to decreasing process cooling possibilities, an increasing electricity demand at higher temperatures may have important consequences for power generation capacity planning and maintenance scheduling.     

An economic welfare analysis of demand response in the PJM electricity market

We analyze the economic properties of the economic demand-response (DR) program in the PJM electricity market in the United States using DR market data. PJM's program provided subsidies to customers who reduced load in response to price signals. The program incorporated a “trigger point”, at a locational marginal price of $75/MWh, at or beyond which payments for load reduction included a subsidy payment. Particularly during peak hours, such a program saves money for the system, but the subsidies involved introduce distortions into the market. We simulate demand-side bidding into the PJM market, and compare the social welfare gains with the subsidies paid to price-responsive load using load and price data for year 2006. The largest economic effect is wealth transfers from generators to non price-responsive loads. Based on the incentive payment structure that was in effect through the end of 2007, we estimate that the social welfare gains exceed the distortions introduced by the subsidies. Lowering the trigger point increases the transfer from generators to consumers, but may result in the subsidy outweighing the social welfare gains due to load curtailment. We estimate that the socially optimal range for the incentive trigger point would be $66–77/MWh.     

Renewable generation and demand response integration in micro-grids: development of a new energy management and control system

The aim of this research resides in the development of an energy management and control system to control a micro-grid based on the use of renewable generation and demand resources to introduce the application of demand response concepts to the management of micro-grids in order to effectively integrate the demand side as an operation resource for the grid and improve energy efficiency of the elements. As an additional result, the evaluation of reductions in the total amount of CO₂ emitted into the atmosphere due to the improvement of the energy efficiency of the system is assessed.