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Oil Shocks and the Demand for Electricity

Edward C Kokkelenberg and Timothy D. Mount

Year: 1993
Volume: Volume 14
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol14-No2-6
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Abstract:
This paper uses a Structural Econometric Model - Time Series Analysis to forecast the demand for electricity in the United States. The main innovation is to incorporate price shocks for oil into the model. The results show that if forecasts had been made with this model in the mid-1970s, they would have predicted the drop in the growth of demand more promptly than did the electric utility industry forecasts. Using current data, forecasts of demand for the year 2000 from the model are higher than industry forecasts, suggesting a reversal of the situation that existed in the 1970s.

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Estimating Consumer Energy Demand Using International Data: Theoretical and Policy Implications

Dale S. Rothman, J. Ho Hong and Timothy D. Mount

Year: 1994
Volume: Volume15
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol15-No2-4
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Abstract:
In this paper, consumer energy demand is estimated as part of a complete demand system using a consistent set of international data on prices, and expenditures for 53 countries ranging from the poorest to the wealthiest. We compare three models: the Translog, the Deaton-Muellbauer Almost Ideal! Demand System (DM), and the Generalized Logit (Logit), and two levels of commodity aggregation (6-good and 9-good). The estimation results indicate that the model specification and level of aggregation are important. The Logit model performs better than the Translog and D-M models which provide illogical! elasticity estimates for many countries. The 9-good model shows that the demand for electricity is significantly more price and income elastic than the demand for primary energy.

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Winners and Losers in the Transition to a Competitive Electricity Industry: An Empirical Analysis

Robert G. Ethier and Timothy D. Mount

Year: 1997
Volume: Volume 18
Number: Special Issue
DOI: 10.5547/ISSN0195-6574-EJ-Vol18-NoSI-8
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Abstract:
The objective of this paper is to show how the treatment of strandable assets, constrained by industrial customers' access to distributed generation technology, affects the prices paid by different classes of customers and the corresponding level of electricity sales. Competitive electricity rates are likely to be shaped by the regulatory need to recover strandable costs. The choice of recovery method (i.e., the structure of rates charged to customers) and the size of recovered costs will affect both total sales of electricity and consumer welfare. The availability of new turbine technology will limit the design of effective rate structures by giving industrial customers a credible threat to self-generate. A dynamic model using a complete Generalized Logit demand system coupled with an electricity supply system is used to evaluate the effects of different rate structures. The results show that stranding some assets is the best way to improve the welfare of all classes of customer and simultaneously increase the need for new generating capacity.

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The Hidden System Costs of Wind Generation in a Deregulated Electricity Market

Timothy D. Mount, Surin Maneevitjit, Alberto J. Lamadrid, Ray D. Zimmerman, and Robert J. Thomas

Year: 2012
Volume: Volume 33
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol33-No1-6
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Abstract:
Earlier research has shown that adding wind generation to a network can lower the total annual operating cost by displacing conventional generation. At the same time, the variability of wind generation and the need for higher levels of reserve generating capacity to maintain reliability standards impose additional costs on the system that should not be ignored. The important implication for regulators is that the capacity payments ["missing money"] for each MW of peak system load are now much higher. Hence, the economic benefits of reducing the peak system load using storage or controllable demand will be higher with high penetrations of wind generation. These potential benefits are illustrated in a case study using a test network and a security constrained Optimal Power Flow (OPF) with endogenous reserves (SuperOPF). The results show that the benefits are very sensitive to 1) how much of the inherent variability of wind generation is mitigated, and 2) how the missing money is determined (e.g. comparing regulation with deregulation).Keywords: Electricity markets, Wind generation, Optimum dispatch, Endogenous reserve capacity, Missing money, Total annual system costs.

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Developing a Smart Grid that Customers can Afford: The Impact of Deferrable Demand

Wooyoung Jeon, Jung Youn Mo, and Timothy D. Mount

Year: 2015
Volume: Volume 36
Number: Number 4
DOI: 10.5547/01956574.36.4.wjeo
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Abstract:
With more electricity generated from renewable sources, the importance of effective storage capacity is increasing due to its capability to mitigate the inherent variability of these sources, such as wind and solar power. However, the cost of dedicated storage is high and all customers eventually have to pay. Deferrable demand offers an alternative form of storage that is potentially less expensive because the capital cost is shared between providing an energy service and supporting the grid. This paper presents an empirical analysis to illustrate the beneficial effects of Plug-in Hybrid Electric Vehicles (PHEV) and thermal storage on the total system cost using data for a hot summer day in New York City. The analysis shows how customers can reduce total system costs and their bills by 1) shifting load from expensive peak periods to less expensive off-peak periods, 2) reducing the amount of installed conventional generating capacity needed to maintain System Adequacy, and 3) providing ramping services to mitigate the variability of generation from renewable sources. Moreover, this paper demonstrates economic benefits of different types of customers with different deferrable demand capabilities under two bill payment policies, flat price payment and optimum price payment, and it finally shows how long it takes for customers to fully pay back their initial capital costs of PHEV or thermal storage under two different policies.

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The Economic Value of Distributed Storage at Different Locations on an Electric Grid

Wooyoung Jeon, Alberto J. Lamadrid, and Timothy D. Mount

Year: 2019
Volume: Volume 40
Number: Number 4
DOI: 10.5547/01956574.40.4.wjeo
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Abstract:
The objective of this article is to analyze the system benefits of distributed storage at different locations on a grid that has a high penetration of renewable generation. The chosen type of distributed storage modeled is deferrable demand (e.g., thermal storage) because it is relatively inexpensive to install compared to batteries and could potentially form a large component of the peak system load. The advantage of owning deferrable demand is that the purchase of energy from the grid can be decoupled from the delivery of an energy service to customers. Consequently, these customers can reduce costs by shifting their purchases from expensive peak periods to off-peak periods when electricity prices are low. In addition, deferrable demand can provide ramping services to the grid to mitigate the uncertainty of renewable generation. The primary economic issue addressed in this paper is to determine how the storage capacity is allocated between shifting load and providing ramping services. The basic economic tradeoff is between the benefit from shifting more load from peak periods to less expensive periods, and reserving some storage capacity for ramping to reduce the amount of conventional reserve capacity purchased. Our approach uses a new form of stochastic, multi-period Security Constrained Optimal Power Flow (SCOPF) that minimizes the expected system costs for energy and ancillary services over a 24-hour horizon. For each hour, five different levels of wind generation may be realized and these are treated as different system states with known probabilities of occurring. This model is applied to a reduction of the grid in New York State and New England and simulates the hourly load on a hot summer day, treating potential wind generation at different sites as stochastic inputs. The results determine the expected amount and location of conventional generating capacity dispatched, the reserve capacity committed to maintain operating reliability, the charging/discharging of storage capacity, and the amount of potential wind generation spilled. The results show there are major differences in how the deferrable demand at two large load centers, Boston and New York City, is managed, and we provide an explanation for these differences.

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