Primary energy demand in Canada

A regional energy demand model for Canada is described and its properties analysed. The model contains a consistent set of estimated equations that describe the end-use demand for energy within the major consuming regions. A thermal electric submodel is also described which determines the primary energy required to generate thermal electricity within each of the regions. Using the historical model structure together with forecasts of the exogenous variables, a base-case forecast of energy demand is produced. The base-case results are compared to the results of sensitivity experiments and policy experiments involving price changes for crude oil and natural gas.     

The potential of demand-side management in energy-intensive industries for electricity markets in Germany

This paper investigates the technical and economic potential of energy-intensive industries to provide demand-side management (DSM) in electricity and balancing markets through 2030. Increasing shares of renewables will lead to a rising demand for ancillary services at the same time that less conventional plants will be available to provide these services. This paper makes projections on the extent to which DSM from industrial processes can provide economic benefits in electricity markets with renewables by providing tertiary reserve capacity. Different industrial processes and their specific technical and economic properties are investigated and compared with other storage devices and electricity generation technologies. Based on an extension of an existing European electricity market model, simulations are used here to make long-term forecasts for market prices, dispatch and investments in the electricity markets through linear optimization.     

Regional analysis of electricity demand growth

This paper presents the regional electricity demand-forecasting model developed at the Oak Ridge National Laboratory. The model forecasts electricity demand and price by sector and by state. Econometric models are estimated for each of the nine census regions separately, using pooled time-series and cross-sectional (state) data. Thus, the estimated demand elasticities used in the forecasting model vary from region to region.The paper also presents and analyzes the most recent forecasts for nine selected states. The results show that electricity demand will continue to grow in all sectors and in all states. However, the rates of growth will be considerably lower than those observed in the 1950s and 1960s. Furthermore, the forecasted rates of growth in electricity demand vary considerably from state to state. The reasons for these variations being the different patterns of projected future growth in population, income, and industrial activities, as well as the different degrees of demand responsiveness to price changes among states. These regional variations of electricity demand growth suggest that forecasts at the national level should not be used as the basis for making energy policies at the regional or state level.     

Incorporating energy efficiency into electric power transmission planning: A western United States case study

Driven by system reliability goals and the need to integrate significantly increased renewable power generation, long-range, bulk-power transmission planning processes in the United States are undergoing major changes. At the same time, energy efficiency is an increasing share of the electricity resource mix in many regions, and has become a centerpiece of many utility resource plans and state policies as a means of meeting electricity demand, complementing supply-side sources, and reducing carbon dioxide emissions from the electric power system. The paper describes an innovative project in the western United States to explicitly incorporate end-use efficiency into load forecasts – projections of electricity consumption and demand – that are a critical input into transmission planning and transmission planning studies. Institutional and regulatory background and context are reviewed, along with a detailed discussion of data sources and analytical procedures used to integrate efficiency into load forecasts. The analysis is intended as a practical example to illustrate the kinds of technical and institutional issues that must be addressed in order to incorporate energy efficiency into regional transmission planning activities.     

Econometric analysis of 15-minute intraday electricity prices

The trading activity in the German intraday electricity market has increased significantly over the last years. This is partially due to an increasing share of renewable energy, wind and photovoltaic, which requires power generators to balance out the forecasting errors in their production. We investigate the bidding behaviour in the intraday market by looking at both last prices and continuous bidding, in the context of a reduced-form econometric analysis. A unique data set of 15-minute intraday prices and intraday-updated forecasts of wind and photovoltaic has been employed. Price bids are explained by prior information on renewables forecasts and demand/supply market-specific exogenous variables. We show that intraday prices adjust asymmetrically to both forecasting errors in renewables and to the volume of trades dependent on the threshold variable demand quote, which reflects the expected demand covered by the planned traditional capacity in the day-ahead market. The location of the threshold can be used by market participants to adjust their bids accordingly, given the latest updates in the wind and photovoltaic forecasting errors and the forecasts of the control area balances.     

Impacts of day-ahead versus real-time market prices on wholesale electricity demand in Texas

The somewhat recent nodal market structure in Texas impacts wholesale day-ahead market (DAM) and real-time market (RTM) prices. However, comparative insights on consumer responses to both these prices have not received attention. This paper attempts to fill this void by developing a system-wide demand response model to better understand price elasticities under DAM and RTM pricing. These insights may also assist grid operators to develop improved short-term forecasts of electricity demand. Using a large dataset from the Electric Reliability Council of Texas and a hierarchical Bayesian population model, we offer new insights on how DAM and RTM pricing shapes demand for electricity, and the related consequences for maintaining a reliable electricity market.     

Benefits of solar forecasting for energy imbalance markets

Short term electricity trading to balance generation and demand provides an economic opportunity to integrate larger shares of variable renewable energy sources in the power grid. Recently, many regulatory market environments are reorganized to allow short term electricity trading. This study seeks to quantify the benefits of solar forecasting for energy imbalance markets (EIM). State-of-the-art solar forecasts, covering forecast horizons ranging from 24 h to 5 min are proposed and compared against the currently used benchmark models, persistence (P) and smart persistence (SP). The implemented reforecast of numerical weather prediction time series achieves a skill of 14.5% over the smart persistence model. Using the proposed forecasts for a forecast horizon of up to 75 min for a single 1 MW power plant reduces required flexibility reserves by 21% and 16.14%, depending on the allowed trading intervals (5 and 15 min). The probability of an imbalance, caused through wrong market bids from PV solar plants, can be reduced by 19.65% and 15.12% (for 5 and 15 min trading intervals). All EIM stakeholders benefit from accurate forecasting. Previous estimates on the benefits of EIMs, based on persistence model are conservative. It is shown that the design variables regulating the market time lines, the bidding and the binding schedules, drive the benefits of forecasting.     

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.     

Electricity markets volatility: estimates,regularities and risk management applications

The recent deregulation of the market for electric power in many parts of the US and Canada has expanded the set of potential tools for managing the types of risks faced by both generators and consumers of electric power. In particular manufacturing and other firms whose operations are powered by electricity now face, on a continuing basis, the engineering management decisions concerning whether they should buy or produce electricity, and if they are to buy or sell electricity, what types of contracts are optimum. These types of risk management decisions typically involve futures, forwards, options and other financial derivatives. The price and volatility of electric power are known to play an essential role in determining which of these instruments should be used. However, electricity as a commodity possesses certain special features not shared by other commodities and hence its risk properties are not yet well understood. In this paper we consider and test certain hypotheses about the properties of electricity price using recent market data. We find that electricity prices possess certain volatility and other systematic properties that can be characterized by the type and method of delivery of electricity. These properties can be used by firms in formulating their optimal demand and supply schedules of electric power.     

过渡时期发电企业竞价模式研究

自2002年国家提出建立竞争性的电力市场的目标以来,多个采用不同竞价机制的区域电力市场试点已初步形成。但是由于我国的电力产业自身条件的特殊性和电价衍变过程的复杂性,决定了竞价机制改革必须在充分考虑历史遗留因素的条件下稳步推进。本文通过分析电价的衍变过程和电力市场化改革的区域试点工作,针对向市场化过渡的特殊时期所必须面临的问题,提出了一种新的发电企业竞价模式。该模式采用了新的两部制电价计算方法和电力市场运营模式,在有效解决电力产业的一些历史遗留问题的条件下,为不同技术类型、不同时期建设的发电机组提供了一个统一的竞争平台,同时为该模式提供了一些配套的政策建议。     

Electric vehicles in imperfect electricity markets: The case of Germany

We use a game-theoretic model to analyze the impacts of a hypothetical fleet of plug-in electric vehicles on the imperfectly competitive German electricity market. Electric vehicles bring both additional demand and additional storage capacity to the market. We determine the effects on prices, welfare, and electricity generation for various cases with different players in charge of vehicle operations. Vehicle loading increases generator profits, but decreases consumer surplus in the power market. If excess vehicle batteries can be used for storage, welfare results are reversed: generating firms suffer from the price-smoothing effect of additional storage, whereas power consumers benefit despite increasing overall demand. Strategic players tend to under-utilize the storage capacity of the vehicle fleet, which may have negative welfare implications. In contrast, we find a market power-mitigating effect of electric vehicle recharging on oligopolistic generators. Overall, electric vehicles are unlikely to be a relevant source of market power in Germany in the foreseeable future.     

MPC-based interval number optimization for electric water heater scheduling in uncertain environments

In this paper, interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling. First of all, interval numbers are used to describe uncertain parameters including hot water demand, ambient temperature, and real-time price of electricity. Moreover, the traditional thermal dynamic model of electric water heater is transformed into an interval number model, based on which, the day-ahead load scheduling problem with uncertain parameters is formulated, and solved by interval number optimization. Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices. Furthermore, the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day. Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand, ambient temperature, and real-time price of electricity, enabling customers to flexibly adjust electric water heater control strategy.     

Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae

We consider the problem of modelling and forecasting the distribution of a vector of prices from interconnected electricity markets using a flexible class of drawable vine copula models, where we allow the dependence parameters of the constituting bivariate copulae to be time-varying. We undertake in-sample and out-of-sample tests using daily electricity prices, and evidence that our model provides accurate forecasts of the underlying distribution and outperforms a set of competing models in their abilities to forecast one-day-ahead conditional quantiles of a portfolio of electricity prices. Our study is conducted in the Australian National Electricity Market (NEM), which is the most efficient power auction in the world. Electricity prices exhibit highly stylised features such as extreme price spikes, price dependency between regional markets, correlation asymmetry and non-linear dependency. The developed approach can be used as a risk management tool in the electricity retail industry, which plays an integral role in the apparatus of modern energy markets. Electricity retailers are responsible for the efficient distribution of electricity, while being exposed to market risk with extreme magnitudes.     

Analysis of imperfect competition in natural gas supply contracts for electric power generation: A closed-loop approach

The supply of natural gas is generally based on contracts that are signed prior to the use of this fuel for power generation. Scarcity of natural gas in systems where a share of electricity demand is supplied with gas turbines does not necessarily imply demand rationing, because most gas turbines can still operate with diesel when natural gas is not available. However, scarcity conditions can lead to electricity price spikes, with welfare effects for consumers and generation firms. We develop a closed-loop equilibrium model to evaluate if generation firms have incentives to contract or import the socially-optimal volumes of natural gas to generate electricity. We consider a perfectly-competitive electricity market, where all firms act as price-takers in the short term, but assume that only a small number of firms own gas turbines and procure natural gas from, for instance, foreign suppliers in liquefied form. We illustrate an application of our model using a network reduction of the electric power system in Chile, considering two strategic firms that make annual decisions about natural gas imports in discrete quantities. We also assume that strategic firms compete in the electricity market with a set of competitive firms do not make strategic decisions about natural gas imports (i.e., a competitive fringe). Our results indicate that strategic firms could have incentives to sign natural gas contracts for volumes that are much lower than the socially-optimal ones, which leads to supernormal profits for these firms in the electricity market. Yet, this effect is rather sensitive to the price of natural gas. A high price of natural gas eliminates the incentives of generation firms to exercise market power through natural gas contracts.     

Generation of synthetic sequences of electricity demand: Application in South Australia

We have developed a model to generate synthetic sequences of half-hourly electricity demand. The generated sequences represent possible realisations of electricity load that could have occurred. Each of the components included in the model has a physical interpretation. These components are yearly and daily seasonality which were modelled using Fourier series, weekly seasonality modelled with dummy variables, and the relationship with current temperature described by polynomial functions of temperature. Finally the stochastic component was modelled with autoregressive moving average (ARMA) processes. These synthetic sequences were developed for two purposes. The first one is to use them as input data in market simulation software. The second one is to build probability distributions of the outputs to calculate probabilistic forecasts. As an application several summers of half-hourly electricity demand were generated and from them the value of demand that is not expected to be exceeded more than once in 10 years was calculated.     

Electricity demand in Kazakhstan

Properties of electricity demand in transition economies have not been sufficiently well researched mostly due to data limitations. However, information on the properties of electricity demand is necessary for policy makers to evaluate effects of price changes on different consumers and obtain demand forecasts for capacity planning. This study estimates Kazakhstan's aggregate demand for electricity as well as electricity demand in the industrial, service, and residential sectors using regional data. Firstly, our results show that price elasticity of demand in all sectors is low. This fact suggests that there is considerable room for price increases necessary to finance generation and distribution system upgrading. Secondly, we find that income elasticity of demand in the aggregate and all sectoral models is less than unity. Of the three sectors, electricity demand in the residential sector has the lowest income elasticity. This result indicates that policy initiatives to secure affordability of electricity consumption to lower income residential consumers may be required. Finally, our forecast shows that electricity demand may grow at either 3% or 5% per year depending on rates of economic growth and government policy regarding price increases and promotion of efficiency. We find that planned supply increases would be sufficient to cover growing demand only if real electricity prices start to increase toward long-run cost-recovery levels and policy measures are implemented to maintain the current high growth of electricity efficiency.     

The demand for energy in Greece: assessing the effects of the Community Support Framework 1994–1999

The paper estimates the demand for energy in Greece in the tradable and non-tradable sectors and for the three main types of energy, namely oil, electricity and solid fuel. Using the estimated demand functions, forecasts of the demand for energy until the year 2010 are derived by incorporating the system of demand and energy price equations into a fully-fledged annual macroeconometric model. The model is subsequently used to forecast the demand for energy and the shares of various forms under alternative assumptions about the effects of the Community Support Framework (CSF). It is found that the demand for energy will be significantly affected by the presence of externalities in the implementation of CSF that give rise to positive supply-side effects and boost growth. In the absence of supply-side externalities, total energy demand rises by 1.4% above the benchmark non-CSF scenario. With growth-inducing externalities, total demand will increase at year 2010 by 6.0% above baseline.     

High penetration wind generation impacts on spot prices in the Australian national electricity market

This paper explores wind power integration issues for the South Australian (SA) region of the Australian National Electricity Market (NEM) by assessing the interaction of regional wind generation, electricity demand and spot prices over 2 recent years of market operation. SA's wind energy penetration has recently surpassed 20% and it has only a limited interconnection with other regions of the NEM. As such, it represents an interesting example of high wind penetration in a gross wholesale pool market electricity industry. Our findings suggest that while electricity demand continues to have the greatest influence on spot prices in SA, wind generation levels have become a significant secondary influence, and there is an inverse relationship between wind generation and price. No clear relationship between wind generation and demand has been identified although some periods of extremely high demand may coincide with lower wind generation. Periods of high wind output are associated with generally lower market prices, and also appear to contribute to extreme negative price events. The results highlight the importance of electricity market and renewable policy design in facilitating economically efficient high wind penetrations.     

Solar PV electricity and market characteristics: two Canadian case-studies

To determine whether solar electricity (that is, electricity generated by photovoltaics) is, on an average, more valuable—in market terms—than the electricity generated in power systems as a whole, this article investigates the extent to which solar resource availability in two Canadian locations is associated with peak electricity market demand and peak electricity market price. More specifically, solar radiation and electricity market data for the period 1 May 2002 to 30 April 2004 are examined for Calgary, Alta. and Guelph, Ont. A variety of visual and statistical investigations reveal that solar radiation values coincide closely with peak electricity market demand and, though to a somewhat lesser extent, peak electricity market prices during the summertime in each location. While more detailed investigation is needed in order to determine the specific impact of different levels of PV penetration upon provincial electricity markets, the article provides ample encouragement for further research. The article also shows how different techniques can be used—in any location—to investigate the relationship among solar electricity potential, system-wide demand and market prices. With electricity industries being restructured around the world, it continues to be important for solar energy proponents to participate in discussions regarding economic costs and benefits. Techniques used in this article can help them advance the solar electricity case more effectively and thus catalyse the deployment of photovoltaics in markets around the world.     

The renewable energy targets of the Maghreb countries: Impact on electricity supply and conventional power markets

Morocco, Algeria and Tunisia, the three countries of the North African Maghreb region, are showing increased efforts to integrate renewable electricity into their power markets. Like many other countries, they have pronounced renewable energy targets, defining future shares of “green” electricity in their national generation mixes. The individual national targets are relatively varied, reflecting the different availability of renewable resources in each country, but also the different political ambitions for renewable electricity in the Maghreb states. Open questions remain regarding the targets’ economic impact on the power markets. Our article addresses this issue by applying a linear electricity market optimization model to the North African countries. Assuming a competitive, regional electricity market in the Maghreb, the model minimizes dispatch and investment costs and simulates the impact of the renewable energy targets on the conventional generation system until 2025. Special emphasis is put on investment decisions and overall system costs.