Smart Grid as advanced technology enabler of demand response

Numerous papers and articles presented worldwide at different conferences and meetings have already covered the goals, objectives, architecture, and business plans of Smart Grid. The number of electric utilities worldwide has followed up with demonstration and deployment efforts. Our initial assumptions and expectations of Smart Grid functionality have been confirmed. We have indicated that Smart Grid will fulfill the following goals: enhance customer service, improve operational efficiency, enhance demand response and load control, transform customer energy use behavior, and support new utility business models. For the purpose of this paper, we shall focus on which of those abovementioned Smart Grid functionalities are going to facilitate the ever-growing need for enhanced demand response and load control.     

Applications of thermostatically controlled loads for demand response with the proliferation of variable renewable energy

More flexibility is desirable with the proliferation of variable renewable resources for balancing supply and demand in power systems.Thermostatically controlled loads (TCLs) attract tremendous attentions because of their specific thermal inertia capability in demand response (DR) programs. To effectively manage numerous and distributed TCLs, intermediate coordinators, e.g., aggregators, as a bridge between end users and dispatch operators are required to model and control TCLs for serving the grid. Specifically, intermediate coordinators get the access to fundamental models and response modes of TCLs, make control strategies, and distribute control signals to TCLs according the requirements of dispatch operators. On the other hand, intermediate coordinators also provide dispatch models that characterize the external characteristics of TCLs to dispatch operators for scheduling different resources. In this paper, the bottom-up key technologies of TCLs in DR programs based on the current research have been reviewed and compared, including fundamental models, response modes, control strategies, dispatch models and dispatch strategies of TCLs, as well as challenges and opportunities in future work.     

Dynamic temperature dependence patterns in future energy demand models in the context of climate change

Energy demand depends on outdoor temperature in a ‘u’ shaped fashion. Various studies have used this temperature dependence to investigate the effects of climate change on energy demand. Such studies contain implicit or explicit assumptions to describe expected socio-economic changes that may affect future energy demand.This paper critically analyzes these implicit or explicit assumptions and their possible effect on the studies' outcomes. First we analyze the interaction between the socio-economic structure and the temperature dependence pattern (TDP) of energy demand. We find that socio-economic changes may alter the TDP in various ways. Next we investigate how current studies manage these dynamics in socio-economic structure. We find that many studies systematically misrepresent the possible effect of socio-economic changes on the TDP of energy demand. Finally, we assess the consequences of these misrepresentations in an energy demand model based on temperature dependence and climate scenarios. Our model results indicate that expected socio-economic dynamics generally lead to an underestimation of future energy demand in models that misrepresent such dynamics. We conclude that future energy demand models should improve the incorporation of socio-economic dynamics. We propose dynamically modeling several key parameters and using direct meteorological data instead of degree days.     

Timing-based business models for flexibility creation in the electric power sector

Energy policies in many countries push for an increase in the generation of wind and solar power. Along these developments, the balance between supply and demand becomes more challenging as the generation of wind and solar power is volatile, and flexibility of supply and demand becomes valuable. As a consequence, companies in the electric power sector develop new business models that create flexibility through activities of timing supply and demand. Based on an extensive qualitative analysis of interviews and industry research in the energy industry, the paper at hand explores the role of timing-based business models in the power sector and sheds light on the mechanisms of flexibility creation through timing. In particular we distill four ideal-type business models of flexibility creation with timing and reveal how they can be classified along two dimensions, namely costs of multiplicity and intervention costs. We put forward that these business models offer ‘coupled services’, combining resource-centered and service-centered perspectives. This complementary character has important implications for energy policy.     

Regional electric power demand elasticities of Japan's industrial and commercial sectors

In the assessment and review of regulatory reforms in the electric power market, price elasticity is one of the most important parameters that characterize the market. However, price elasticity has seldom been estimated in Japan; instead, it has been assumed to be as small as 0.1 or 0 without proper examination of the empirical validity of such a priori assumptions. We estimated the regional power demand functions for nine regions, in order to quantify the elasticity, and found the short-run price elasticity to be 0.09–0.30 and the long-run price elasticity to be 0.12–0.56. Inter-regional comparison of our estimation results suggests that price elasticity in rural regions is larger than that in urban regions. Popular assumptions of small elasticity of 0.1, for example, could be suitable for examining Japan's aggregate power demand but not power demand functions that focus on respective regions. Furthermore, assumptions about smaller elasticity values such as 0.01 and 0 could not be supported statistically by this study.     

OPEC and demand response to crude oil prices

This paper investigates demand response to crude oil price movements before and after the recent global financial and economic crisis. It employs several market power indices to structurally estimate price elasticities. A newly developed market power index for crude oil markets is implemented. In this approach OPEC is the central player and acts as a dominant producer in the global oil market. We quantify how a change in market structure (such as changes in marginal cost of production) would contribute to market power exercise of OPEC and have an ultimate impact on price elasticity of demand for oil. Our price elasticity predictions fall in a range reported in the literature, however estimates for pre-crisis deviate from the post-crisis ones. In fact, demand response to crude oil prices has almost doubled during the crisis. This severe change in price response can be associated with record price levels caused by supply shortages and surge in alternative renewable energy resources. The key advantages of this methodology over the existing literature are that it is simple to use and estimates price elasticity using a competition framework without specifying demand/supply function(s), and utilizes commonly observable market variables that can be applied to any admissible data frequency.     

A high-resolution stochastic model of domestic activity patterns and electricity demand

Realistic time-resolved data on occupant behaviour, presence and energy use are important inputs to various types of simulations, including performance of small-scale energy systems and buildings’ indoor climate, use of lighting and energy demand. This paper presents a modelling framework for stochastic generation of high-resolution series of such data. The model generates both synthetic activity sequences of individual household members, including occupancy states, and domestic electricity demand based on these patterns. The activity-generating model, based on non-homogeneous Markov chains that are tuned to an extensive empirical time-use data set, creates a realistic spread of activities over time, down to a 1-min resolution. A detailed validation against measurements shows that modelled power demand data for individual households as well as aggregate demand for an arbitrary number of households are highly realistic in terms of end-use composition, annual and diurnal variations, diversity between households, short time-scale fluctuations and load coincidence. An important aim with the model development has been to maintain a sound balance between complexity and output quality. Although the model yields a high-quality output, the proposed model structure is uncomplicated in comparison to other available domestic load models.     

Conservation and deferral potentials of demand side management programmes: A case study

Demand side management options (DSMO) can reduce the peak electricity demand for utilities. This reduction in demand is helpful to the utility in at least two ways: first, it minimizes the penalty costs of not being able to meet the peak demand and thus it has the potential to reduce costs; second, it also can defer the need for building new power plants and hence it can release, at least for some period of time, the scarce capital (which is especially important for the developing countries) for use in development activities elsewhere. These two benefits have been considered in detail in the paper. An analytical model has been developed to estimate the conservation potential of the DSM programmes. The model is then used to illustrate the benefit derived by deferring the construction of a new power plant. The model has been applied to the Maharashtra State Electricity Board, an electric utility in India, as a case study. Several scenarios have been constructed to account for different levels of the DSM possibilites. A sensitivity analysis has been carried out to tackle some of the uncertainties associated with the assumptions in the analysis.     

Strategic real option and flexibility analysis for nuclear power plants considering uncertainty in electricity demand and public acceptance

Nuclear power is an important energy source especially in consideration of CO₂ emissions and global warming. Deploying nuclear power plants, however, may be challenging when uncertainty in long-term electricity demand and more importantly public acceptance are considered. This is true especially for emerging economies (e.g., India, China) concerned with reducing their carbon footprint in the context of growing economic development, while accommodating a growing population and significantly changing demographics, as well as recent events that may affect the public's perception of nuclear technology. In the aftermath of the Fukushima Daiichi disaster, public acceptance has come to play a central role in continued operations and deployment of new nuclear power systems worldwide. In countries seeing important long-term demographic changes, it may be difficult to determine the future capacity needed, when and where to deploy it over time, and in the most economic manner. Existing studies on capacity deployment typically do not consider such uncertainty drivers in long-term capacity deployment analyses (e.g., + 40 years). To address these issues, this paper introduces a novel approach to nuclear power systems design and capacity deployment under uncertainty that exploits the idea of strategic flexibility and managerial decision rules. The approach enables dealing more pro-actively with uncertainty and helps identify the most economic deployment paths for new nuclear capacity deployment over multiple sites. One novelty of the study lies in the explicit recognition of public acceptance as an important uncertainty driver affecting economic performance, along with long-term electricity demand. Another novelty is in how the concept of flexibility is exploited to deal with uncertainty and improve expected lifecycle performance (e.g. cost). New design and deployment strategies are developed and analyzed through a multistage stochastic programming framework where decision rules are represented as non-anticipative constraints. This approach provides a new way to devise and analyze adaptation strategies in view of long-term uncertainty fluctuations that is more intuitive and readily usable by system operators than typical solutions obtained from standard real options analysis techniques, which are typically used to analyze flexibility in large-scale, irreversible investment projects. The study considers three flexibility strategies subject to uncertainty in electricity demand and public acceptance: 1) phasing (or staging) capacity deployment over time and space, 2) on-site capacity expansion, and 3) life extension. Numerical analysis shows that flexible designs perform better than rigid optimal design deployment strategies, and the most flexible design combining the above strategies outperforms both more rigid and less flexible design alternatives. It is also demonstrated that a flexible design benefits from the strategies of phasing and capacity expansion most significantly across all three strategies studied. The results provide useful insights for policy and decision-making in countries that are considering new nuclear facility deployment, in light of ongoing challenges surrounding new nuclear builds worldwide.     

World transport energy demand modelling : Methodology and elasticities

This paper presents the International Energy Agency's (IEA) approach of modelling transport energy demand. Fuel demand, which is not a demand per se, is derived, whenever possible, from the economic activity in the transport sector and not estimated directly, ie using one equation or (simultaneous) equation system. In general, the transport models employ a ‘two-step-approach’: in the first step, transport activity, the sector's relevant energy service, is estimated econometrically. In the second step, the transport activity projections are then combined with estimates of efficiency improvements, car turnover rates and diesel/gasoline penetration assumptions in order to arrive at projections of fuel demand. The principal advantages of this approach are that the relevant energy services are modelled and that, for model simulation, efficiency improvements can be dealt with explicitly. The effectiveness of economic instruments is a function of the reaction of consumers (and businesses) to income and price changes. An in-depth understanding of income and price elasticities of transport demand and transport energy demand is important in order to be able to assess the effectiveness of policies considered. The paper also shows the underlying long-term income and price elasticities for OECD and non-OECD regions.     

Diagnostic testing in modeling energy demand

In recent years, a number of formal diagnostic tests for identifying misspecification of models and criteria for comparing alternative models have been proposed. Not many of them, however, have found common use among energy analysis. This paper provides a comprehensive listing of these techniques and describes them in a manner easily accessible to modelers of energy demand. The methods suggested are illustrated with an application to the modeling of peak electricity demand in a utility service area in the upper midwest.     

Technical demand response potentials of the integrated steelmaking site of Tata Steel in IJmuiden

Power generation from intermittent renewable energy sources in northwest Europe is expected to increase significantly in the next 20 years. This reduces the predictability of electricity generation and increases the need for flexibility in electricity demand. Data on demand response (DR) capacities of electricity-intensive consumers is limited for most countries. In this paper, we evaluate the DR potential that can be provided to the Dutch national grid by the integrated steelmaking site of Tata Steel in IJmuiden (TSIJ). TSIJ generates electricity from its works arising gases (WAGs). The DR potentials are evaluated by using a linear optimisation model that calculates the optimal allocation of WAGs of TSIJ in case of a call for DR by the transmission system operator. The optimisation is done subject to the technical constraints of the WAG distribution network, WAG storage capacities, the on-site demand for WAGs and the ramp-up rate of the power plant that runs on WAGs. Results show that TSIJ can supply 10 MW for two programme time units (equal to 15-min period in the Netherlands) of positive DR capacity (demand reduction) with an availability rate of 97%. This is not sufficient for participating in the current emergency capacity programs in the Netherlands, which require at least 20 MW for longer than one programme time unit. Tata Steel can provide 20 MW DR capacity with an availability rate of 65%. The negative DR capacity (demand increase) of Tata Steel in IJmuiden is found to be 20 MW supplied for three programme time units and four programme time units with doubling of blast furnace gas storage capacities.     

Industrial electricity demand in New York State

We will discuss electricity demand in manufacturing industries in upstate New York. Empirical results are presented based on data obtained at the electric utility service area level for the years 1969–1981. The equations are based on a partial adjustment model including relevant input prices and industrial output by SIC group. The estimated coefficients have the expected signs and are for the most part, statistically significant. There is considerable variation in price and output elasticities among SIC groups and between service areas, which suggests that energy policy and industrial policy at the state level should be tailored to specific industries and perhaps to specific subregions within the state. Forecasts of industrial electricity demand are based on assumptions of prices and industrial output growth made by the utility companies themselves. A comparison of these forecasts with those made by the utility companies indicates that electricity demand forecasts are sensitive to the inclusion of alternative fuel prices and to the level of sectoral disaggregation.     

Behavioral aspects of regulation: A discussion on switching and demand response in Turkish electricity market

Electricity sector has been transformed from state-owned monopolistic utilities to competitive markets with an aim to promote incentives for improving efficiency, reducing costs and increasing service quality to customers. One of the cardinal assumptions of the liberalized and competitive electricity markets is the rational actor, and decision-makers are assumed to make the best decisions that maximize their utility. However, a vast literature on behavioral economics has shown the weakness of economic theory in explaining and predicting individuals’ decision-making behavior. This issue is quite important for competition in electricity markets in which consumers’ preferences have a significant role. Despite its importance, this issue has almost been neglected in Turkey, which has taken major steps in electricity sector restructuring. Therefore, this paper aims to examine switching and demand response behavior in Turkish electricity market by using multiple correspondence and panel data analysis, and findings are discussed in light of the neoclassical and behavioral economics literature. Analyses’ results show that consumers’ switching and demand response behavior is consistent with the neoclassical literature to some extent; however, behavioral factors are also affecting consumers’ decisions. Furthermore, there are systemic problems that hinder effective functioning of the electricity market and restrict competition.     

Power system planning with high renewable energy penetration considering demand response

Electric system planning with high variable renewable energy (VRE) penetration levels has attracted great attention world-wide. Electricity production of VRE highly depends on the weather conditions and thus involves large variability, uncertainty, and low-capacity credit. This gives rise to significant challenges for power system planning. Currently, many solutions are proposed to address the issue of operational flexibility inadequacy, including flexibility retrofit of thermal units, inter-regional transmission, electricity energy storage, and demand response (DR). Evidently, the performance and the cost of various solutions are different. It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source. In this study, the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed. Two types of DRs, namely interrupted DR and transferred DR, were modeled. Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization. Clustered unit commitment constraints for accommodating variability of renewables were incorporated. Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.     

Smart electric storage heating and potential for residential demand response

Low-carbon transition plans for temperate and sub-polar regions typically involve some electrification of space heating. This poses challenges to electricity system operation and market design, as it increases overall demand and alters the temporal patterns of that demand. One response to the challenge is to ‘smarten’ electrical heating, enabling it to respond to network conditions by storing energy at times of plentiful supply, releasing it in response to customer demands and offering rapid-response ancillary services to the grid. Shared operation of domestic electrical heating, in such a scenario, may imply changes in everyday heating practices and will change the number of system stakeholders, their activities and how they relate to each other.This paper sets out some practical and theoretical issues relating to the potential for residential demand response via electric storage heating, drawing on academic and policy-related literature and on material from a current research project. It offers a brief history of residential storage heating and recent developments, paying particular attention to customer experience; considers the role of distributed storage in energy transitions and associated questions of value; outlines how agency and value in a smart system may be distributed between stakeholders; and assesses continuity and change in storage heating. While the paper focuses on storage heating, many of the issue raised apply to heat pumps, given their functional similarities with storage heaters and water heaters. The paper concludes with some conditions to be met if smart storage heating is to succeed in the twin tasks of providing effective customer service and demand response, and sets out questions for further research into demand response and heating practices.     

A Bayesian sampling approach to measuring the price responsiveness of gasoline demand using a constrained partially linear model

Partial linear models provide an intuitively appealing way to examine gasoline demand because one can examine how response to price varies according to the price level and people's income. However, despite their intuitive appeal, partial linear models have tended to produce implausible and/or erratic price effects. Blundell et al. (2012) propose a solution to this problem that involves using Slutsky shape restrictions to improve the precision of the nonparametric estimate of the demand function. They propose estimating a constrained partially linear model through three steps, where the weights are optimized by minimizing an objective function under the Slutsky constraint, bandwidths are selected through least squares cross-validation, and linear coefficients are estimated using profile least squares. A limitation of their three-step estimation method is that bandwidths are selected based on pre-estimated parameters. We improve on the Blundell et al. (2012) solution in that we derive a posterior and develop a posterior simulation algorithm to simultaneously estimate the linear coefficients, bandwidths in the kernel estimator and the weights imposed by the Slutsky condition. With our proposed sampling algorithm, we estimate a constrained partially linear model of household gasoline demand employing household survey data for the United States for 1991 and 2001 and for Canada for 2006–2009 and find plausible price effects.     

Synthetic demand functions for solar energy

There is as yet very little empirical data on solar energy demand with which to estimate demand functions econometrically. The approach of this paper is to use economic theory and engineering information to construct demand functions for solar energy installations. We present a general model of consumer choice of energy-using durable goods under uncertainty and energy rationing. From this we derive demand functions for solar water heating equipment which we quantify using technological data and assumptions about future variables such as electricity prices. Finally we conduct a sensitivity analysis which indicates that the main conclusions are robust to changes in the assumptions.