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Markets in Real Electric Networks Require Reactive Prices

William W. Hogan

Year: 1993
Volume: Volume14
Number: Number 3
DOI: 10.5547/ISSN0195-6574-EJ-Vol14-No3-8
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Abstract:
Differences in locational spot prices in an electric network provide the natural measure of the price for transmission. The ubiquitous problem of loop flow requires different economic intuition for interpreting the implications of spot pricing. The DC-Load model is the usual approximation for estimating spot prices, although it ignores reactive power effects. This approximation is best when thermal constraints create congestion in the network. In the presence of voltage constraints, the DC-Load model is insufficient, and the full AC-Model is required to determine both real and reactive power spot prices.



Economic Inefficiency of Passive Transmission Rights in Congested Electricity Systems with Competitive Generation

Shmuel S. Oren

Year: 1997
Volume: Volume18
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol18-No1-3
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Abstract:
The main thesis of this paper is that passive transmission rights such as Transmission Congestion Contracts (TCCs) that are compensated ex-post based on nodal prices resulting from optimal dispatch by an Independent System Operator (ISO) will be preempted by the strategic bidding of the generators. Thus, even when generation is competitive, rational expectations of congestion will induce implicit collusion enabling generators to raise their bids above marginal costs and capture the congestion rents, leaving the TCCs uncompensated. These conclusions are based on a Cournot model of competition across congested transmission links where an ISO dispatches generators optimally based on bid prices. We characterize the Cournot equilibrium in congested electricity networks with two and three nodes. We show that absent active transmission rights trading, the resulting equilibrium may be at an inefficient dispatch and congestion rents will be captured by the generators. We also demonstrate how active trading of transmission rights in parallel with 42 competitive energy market can prevent the price distortion and inefficient dispatch associated with passive transmission rights.



A Market Power Model with Strategic Interaction in Electricity Networks

William W. Hogan

Year: 1997
Volume: Volume18
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol18-No4-5
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Abstract:
When transmission constraints limit the flow of power in an electric network, there are likely to be strong interaction effects across different parts of the system. A model of imperfect competition with strategic interactions in an electricity transmission network illustrates a possible exercise of market power that differs from the usual analysis of imperfect competition in more familiar product markets. Large firms could exercise horizontal market power by increasing their own production, lowering some prices, and exploiting the necessary feasibility constraints in the network to foreclose competition from others. This behavior depends on the special properties of electric networks, and reinforces the need for market analysis with more realistic network models.



Short Term Energy Forecasting with Neural Networks

J. Stuart McMenamin and Frank A. Monforte

Year: 1998
Volume: Volume19
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol19-No4-2
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Abstract:
Artificial neural networks are beginning to be used by electric utilities, to forecast hourly system loads on a day ahead basis. This paper discusses the neural network specification in terms of conventional econometric language, providing parallel concepts for terms such as training, learning, and nodes in the, hidden layer. It is shown that these models are flexible nonlinear equations that can be estimated using nonlinear least squares. It is argued that these models are especially well suited to hourly load forecasting, reflecting the presence of important nonlinearities and variable interactions. The paper proceeds to show how conventional statistics, such as the BIC and MAPE statistics can be used to select the number of nodes in the hidden layer. It is concluded that these models provide a powerful, robust and sensible approach to hourly load forecasting that will provide modest improvements in forecast accuracy relative to well-specified regression models.



Forecasting Nonlinear Crude Oil Futures Prices

Saeed Moshiri and Faezeh Foroutan

Year: 2006
Volume: Volume 27
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol27-No4-4
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Abstract:
The movements in oil prices are very complex and, therefore, seem to be unpredictable. However, one of the main challenges facing econometric models is to forecast such seemingly unpredictable economic series. Traditional linear structural models have not been promising when used for oil price forecasting. Although linear and nonlinear time series models have performed much better in forecasting oil prices, there is still room for improvement. If the data generating process is nonlinear, applying linear models could result in large forecast errors. Model specification in nonlinear modeling, however, can be very case dependent and time-consuming.In this paper, we model and forecast daily crude oil futures prices from 1983 to 2003, listed in NYMEX, applying ARIMA and GARCH models. We then test for chaos using embedding dimension, BDS(L), Lyapunov exponent, and neural networks tests. Finally, we set up a nonlinear and flexible ANN model to forecast the series. Since the test results indicate that crude oil futures prices follow a complex nonlinear dynamic process, we expect that the ANN model will improve forecasting accuracy. A comparison of the results of the forecasts among different models confirms that this is indeed the case.



A Quantitative Analysis of the Relationship Between Congestion and Reliability in Electric Power Networks

Seth Blumsack, Lester B. Lave and Marija Ilic

Year: 2007
Volume: Volume 28
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol28-No4-4
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Abstract:
Restructuring efforts in the U.S. electric power sector have tried to encourage transmission investment by independent (non-utility) transmission companies, and have promoted various levels of market-based transmission investment. Underlying this shift to �merchant� transmission investment is an assumption that new transmission infrastructure can be classified as providing a congestion-relief benefit or a reliability benefit. In this paper, we demonstrate that this assumption is largely incorrect for meshed interconnections such as electric power networks. We focus on a particular network topology known as the Wheatstone network to show how congestion and reliability can represent tradeoffs. Lines that cause congestion may be justified on reliability grounds. We decompose the congestion and reliability effects of a given network alteration, and demonstrate their dependence through simulations on a 118bus test network. The true relationship between congestion and reliability depends critically on identifying the relevant range of demand for evaluating any network externalities.



Modeling Optimal Economic Dispatch and System Effects in Natural Gas Networks

Kjetil T. Midthun, Mette Bjorndal and Asgeir Tomasgard

Year: 2009
Volume: Volume 30
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol30-No4-6
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Abstract:
In this paper we present a modeling framework for analyzing natural gas markets, taking into account the specific technological issues of gas transportation. We model the optimal dispatch of supply and demand in natural gas networks, with different objective functions, i.e., maximization of flow, and different economic surpluses. The models take into account the physical structure of the transportation networks, and examine the implications it has for economic analysis. More specifically, pressure constraints create system effects, and thus, changes in one part of the system may require significant changes elsewhere. The proposed network flow model for natural gas takes into account pressure drops and system effects when representing network flows. Pressure drops and pipeline flows are modeled by the Weymouth equation. A linearization of the Weymouth equation makes economic analyses computationally feasible even for large networks. However, in this paper, the importance of combining economics with a model for pressure drops and system effects is illustrated by small numerical examples.



The Short and Long Term Impact of Europe’s Natural Gas Market on Electricity Markets until 2050

Jan Abrell and Hannes Weigt

Year: 2016
Volume: Volume 37
Number: Sustainable Infrastructure Development and Cross-Border Coordination
DOI: https://doi.org/10.5547/01956574.37.SI3.jabr
View Abstract

Abstract:
The interdependence of electricity and natural gas is becoming a major energy policy and regulatory issue in all jurisdictions around the world. The increased role of gas fired plants in renewable-based electricity markets and the dependence on natural gas imports make this issue particular important for the European energy market. In this paper we provide a comprehensive combined analysis of electricity and natural gas infrastructure with an applied focus: We analyze three different scenarios of the long-term European decarbonization pathways, and analyze the interrelation between electricity and natural gas markets on investments in the long run and spatial aspects in the short run.



Regulating Heterogeneous Utilities: A New Latent Class Approach with Application to the Norwegian Electricity Distribution Networks

Luis Orea and Tooraj Jamasb

Year: 2017
Volume: Volume 38
Number: Number 4
DOI: 10.5547/01956574.38.4.lore
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Abstract:
Since the 1990s, electricity distribution networks in many countries have been subject to incentive regulation. The sector regulators aim to identify the best performing utilities as frontier firms to determine the relative efficiency of firms. This paper develops a nested latent class (NLC) model approach where unobserved differences in firm performance are modelled using two `zero inefficiency stochastic frontier' (ZISF) models nested in a `latent class stochastic frontier' (LCSF) model. This captures the unobserved differences due to technology or environmental conditions. A Monte Carlo simulation suggests that the proposed model does not suffer from identification problems. We illustrate the proposed model with an application to Norwegian distribution network utilities for the period 2004-2011. We find that the efficiency scores in both LCSF and ZISF models are biased, and some firms in the ZISF model are wrongly labelled as inefficient. Conversely, inefficient firms may be wrongly labelled as being fully efficient by the ZISF model.



A Spatial Stochastic Frontier Model with Omitted Variables: Electricity Distribution in Norway

Luis Orea, Inmaculada C. Álvarez, and Tooraj Jamasb

Year: 2018
Volume: Volume 39
Number: Number 3
DOI: 10.5547/01956574.39.3.lore
View Abstract

Abstract:
An important methodological issue in efficiency analysis for incentive regulation of utilities is how to account for the effect of unobserved cost drivers such as environmental factors. We combine a spatial econometric approach with stochastic frontier analysis to control for unobserved environmental conditions when measuring efficiency of electricity distribution utilities. Our empirical strategy relies on the geographic location of firms as a source of information that has previously not been explored in the literature. The underlying idea is to utilise data from neighbouring firms that can be spatially correlated as proxies for unobserved cost drivers. We illustrate this approach using a dataset of Norwegian distribution utilities for the 2004-2011 period. We show that the lack of information on weather and geographic conditions can be compensated with data from surrounding firms. The methodology can be used in efficiency analysis and regulation of other utilities sectors where unobservable cost drivers are important, e.g. gas, water, agriculture, fishing.




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