The Value of Residential Power Quality 

    For industrial and larger commercial customers, it is easier to measure why power quality is important. Machine downtime directly affects productivity, increased maintenance and replacement impacts the bottom line, and small inefficiencies multiply into huge wastes of energy at the meter. One of the selling points of demand response is improved power quality, which benefits both the customer and the utility. LaCommare and Eto from LBNL estimated the cost of power quality disturbances was $79 billion in 2004, with 67% due to momentary interruptions and 33% due to sustained interruptions. Of the $79 billion, nearly three-quarters was attributed to commercial customers and a quarter to industrial customers. This leaves very little to the residential sector, whose costs made up 2% of the total, or $2 billion. Nearly a decade of grid R&D has since passed, but has the impact of power quality on the residential customer changed significantly? With a greater focus on household consumption, companies and utilities are looking for ways to increase acceptance of new technologies and programs. Understanding the value of power quality may provide impetus to customer adoption, or highlight continuing challenges.
    The LBNL report recognizes the difficulty of quantifying residential costs and does its best to attribute costs to not just physical goods and appliances but to the experience of a disruption in service. The report states that: “…the other “costs” borne by residential customers are experiential in nature, such as resetting clocks, changing plans, and coping with inconvenience, fear, anxiety, etc. Analytical techniques to estimate these costs typically involve contingent valuation, which includes so-called “willingness to pay” and “willingness to accept” approaches as a means of addressing experiential costs in deriving outage costs for residential customers. The findings developed through application of contingent valuation methods have been controversial due to concerns regarding bias in the responses provided by customers to the hypothetical nature of situations they must rely on.”
    The report also attempted to integrate multiple utility studies using a Tobit regression model to form cost of interruption functions, known as customer damage functions. These functions represent outage costs based on “outage duration, season, time of day, annual electricity use, and depending on the customer class, household income or number of employees.” Surveys can ask for a customer’s willingness-to-pay to avoid a certain outage scenario, rank scenarios/payment options, or estimate costs for an itemized list of mitigating actions. Other attempts to quantify residential costs generally involve surveys, but can be plagued by non-response and a lack of knowledge of the electric system for customers to provide adequate responses.
    It is recognized that outage and power quality costs are non-homogenous. The distribution of these costs across different customer categories, times, service interruption types, and other characteristics is still an active field of study. Another LBNL study uses a newer set of utility survey studies to create a two-step model based on GLM rather than loglinear regression. For comparison, they demonstrate that the earlier Tobit model underestimates costs dramatically and that a Heckman two-step model underestimates C&I costs and overestimates residential costs. Nonetheless, the lack of consistent and relevant data limits the conclusions such mega-studies can draw.
    The issue of contention focuses on how to treat the multitude of zero valued responses. Regardless of the model, the data continues to show residential customers often do not place a cost or WTP for many categories of reductions in power quality or service. This acts to reduce the economic justification for improving reliability standards. A 2011 customer satisfaction survey showed that residential electric utility customers were more satisfied in the categories examined except power quality, reliability and price. These declined by less than 10 points in a 1000 point scale. At what point does satisfaction levels translate into a real cost? On the flip side, the findings prove that there is a buffer in case disruption events occur more frequently under some circumstances. Thus, from an economic point of view there is a non-technological resiliency in the system that already exists. It would be interesting to see if this could be leveraged for promoting grid development.

Sources:

[1] LaCommare, Kristina Hamachi and Joseph H. Eto, ”Understanding the Cost of Power Interruptions to U.S. Electricity Consumers,” Ernest Orlando Lawrence Berkeley National Laboratory, Sep 2004.

[2] Scarpa, Ricardo and Anna Alberini, Applications of simulation methods in environmental and resource economics, 2005.

[3] Sullivan, Michael J., Ph.D., Matthew Mercurio, Ph.D., Josh Schellenberg, M.A, Freeman, Sullivan & Co. “Estimated Value of Service Reliability for Electric Utility Customers in the United States,” Jun 2009.

[4] J.D. Power and Associates, “2011 Electric Utility Residential Customer Satisfaction Study,” Press Release, 13 July 2011. http://www.jdpower.com/news/pressRelease.aspx?ID=2011101