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Conservation Voltage Reduction: Implementation and Assessment

There have been many successful experiences with CVR, but it still is viewed with some skepticism because of its somewhat uncertain and possibly negative effects on system operations. The ever-increasing penetration of distributed generation brings additional challenges. The main methods of CVR implementation and assessment are discussed.

Conservation Voltage Reduction (CVR) lowers the voltage level of the electrical distribution system to reduce peak demands and energy consumption. As an established and economical way to save energy, CVR has been adopted by many utilities.

There are two ways to conduct CVR: short-term demand reduction and long-term energy reduction. In short-term CVR, the voltage is reduced during peak hours to reduce peak demand; in long-term CVR, the voltage is lowered permanently to save energy. There have been numerous CVR tests by utilities, and they confirm that load consumption can be cut significantly by lowering voltage levels.

CVR impact is stated in terms of a conservation voltage reduction factor (CVRf), which is defined as the percentage of load consumption reduction resulting from a 1 percent reduction in voltage. (The typical CVRf range is 0.3-1 percent.) A recent review of CVR implementation and assessment discusses the latest development, technical barriers and future research directions of CVR technologies.

CVR can benefit both consumers and utilities. Consumers see reductions in energy consumption and electricity bills. Utilities benefit in four major areas: (1) peak loading relief of distribution systems; (2) net loss reduction considering both the transformers and distribution lines; (3) increased social welfare, such as fuel consumption and emission reduction, and (4) potential incentives and requirements from regulatory bodies. The California Public Utilities Commission (CPUC) has encouraged utilities to implement CVR; the Pennsylvania Public Utility Commission (PPUC) requires utilities to reduce consumption and demand levels of consumers; and the Northwest Power and Conservation Council has performed extended research on CVR incentives.

While there have been many successful experiences with CVR, it is still viewed with some skepticism because of uncertain and possibly negative effects on system operations. In particular, the ever-increasing penetration of distributed generators poses new challenges to the voltage regulation of distribution systems, which may further affect the implementation of CVR. Thus, the technical barriers related to CVR fall into two categories: implementation and assessment.

The currently dominant technique to implement CVR is open-loop reduction without voltage feedback, such as load-tap-changer or LTC-based reduction and capacitor-based reduction. The installation of advanced metering infrastructure (AMI) has led many utilities to implement closed-loop voltage/var control (VVC): It incorporates dynamic information on distribution network configuration from Geographical Information Systems (GISs), detailed real-time measurements from AMI and advanced optimal power flow algorithms. CVR becomes an operation mode in these closed-loop VVCs. Utilities choose how to implement CVR based on operational, economic, and security considerations.

Assessing the performance of CVR on feeder circuits has always been a critical issue in deciding how to implement it. One issue concerns selection of target feeders to apply voltage reduction and performance of cost-benefit analyses. As noted, skepticism regarding the effect of CVR remains a barrier to its acceptance. The major challenge in quantifying CVR effects is that load consumption in the absence of voltage reduction during the CVR period may not be measured accurately and thus cannot be used as a benchmark for comparison.

The methodologies for assessing CVR effects can be classified as comparison-based, regression-based, synthesis-based and simulation-based.

Comparison-based methods compare load consumptions of the voltage-reduction group (test group) and normal-voltage group (control group). The control group can be a different feeder than the test group with a similar load composition, or the same feeder but on a different day with similar operating conditions. A good control group may not exist, however, since there are no two feeders or two days whose operating conditions are exactly the same.

Regression-based methods assume a regression model for the load and its impact factors. Multivariate regression is often used to detect sensitivities of load to its impact factors. The problem with this method is that the regression errors may bias the CVRf, which is usually small itself (not more than a few percent).

Synthesis-based methods aggregate the CVR effects of different customer types based on load composition information. However, these methods assume that the CVR effects on each customer type are deterministic. Moreover, it is difficult to collect accurate load composition information for a feeder.

Simulation-based methods require accurate system models to simulate system behaviors with and without voltage reduction.

In addition to these existing methods, an alternative way to assess CVR effects is to measure load-to-voltage (LTV) sensitivities. CVR effects will decrease when the LTV changes from a constant-impedance type to a constant-power type.

In summary, the challenges CVR faces are the coordination of different voltage/var devices and distributed generation sources to reduce voltage in a reliable and optimal way, and credible assessment and verification of CVR effects. Although a lot of work is needed to analyze and improve the performance of CVR, as an easy and cost-effective way to save energy, its deployment has a promising future with the help of smart meters, communication infrastructures and optimal controls.

Contributor

  • Jianhui WangJianhui Wang, a senior member of IEEE, is a computational engineer with the Decision and Information Sciences Division at Argonne National Laboratory, in Illinois.

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  • Zhaoyu WangZhaoyu Wang, a student member of IEEE, received a B.S. degree in Electrical Engineering from Shanghai Jiaotong University, Shanghai, China, in 2009, an M.S. degree in Electrical Engineering from Shanghai Jiaotong University in 2012, and an M.S. degree in Electrical and Computer Engineering from the Georgia Institute of Technology in 2012.

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About the Smart Grid Newsletter

A monthly publication, the IEEE Smart Grid Newsletter features practical and timely technical information and forward-looking commentary on smart grid developments and deployments around the world. Designed to foster greater understanding and collaboration between diverse stakeholders, the newsletter brings together experts, thought-leaders, and decision-makers to exchange information and discuss issues affecting the evolution of the smart grid.

Contributors

Farrokh AlbuyehFarrokh Albuyeh an IEEE life member, is Vice President, Smart Grid Projects, at Open Access Technology International (OATI).
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Amro M. FaridAmro M. Farid is an IEEE member and assistant professor of engineering systems and management.
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Jianhui WangJianhui Wang is a computational engineer with the Decision and Information Sciences Division at Argonne National Laboratory.
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Zhaoyu WangZhaoyu Wang is working towards a Ph.D. degree in the School of Electrical and Computer Engineering, Georgia Institute of Technology.
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Mahmud HasanMahmud Hasan is currently pursuing a Ph.D. in electrical and computer engineering at the University of Ottawa.
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Hussein T. MouftahHussein T. Mouftah is a university distinguished professor in the School of Electrical Engineering and Computer Science at the University of Ottawa.
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