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Price-induced decisions that cause electricity demand response: the need for a computational tool

When devising electricity pricing policy, policymakers are interested in the potential demand and expenditure responses that follow. Statistical, or econometric, estimates of past behavior are often used for this task; however, two arguments for the complementary use of alternative methods can be made:

  • Econometric estimates are based on historical data. As argued by the Nobel laureate Robert Lucas in the 1970s, if future policies are different from those experienced in the past, then econometrics should not be the method upon which to rely.
  • Analysts and policymakers may be interested in the specific end-user decisions that cause lower electricity demand in light of higher prices. Decisions made by the household could be purely behavioral, purely energy efficiency, or a combination of both. Additionally, they may want to know the costs associated with energy efficiency subsidies versus the benefits that could arise from the adoption of the measure(s) for the power utilities.

In this respect, we developed a residential electricity use framework that combines the physics that govern the amount of electricity we consume, with the microeconomic foundations that guide our decisions. The physical side incorporates local climate conditions, construction attributes of a dwelling, the technologies used for lighting and appliances, and household characteristics. The microeconomic component evaluates behavioral response (i.e., conservation) and energy efficiency purchase decisions, and identifies the mix that maximizes our satisfaction.

When it comes to behavioral response, the framework focuses on thermostat adjustments in the summer peak and off-peak hours, thermostat adjustments in the spring and fall, and the modified use of lighting and appliances. A household can react differently based on its preferences, income level, and the characteristics of the installed equipment and lighting, when faced with different electricity pricing schemes; like real-time pricing, time-of-use pricing, or progressive block tariffs. The household may choose from the combinations of behavioral adjustment and energy efficiency purchases that results in highest satisfaction in a given price setting. In the papers below, I perform the analysis for various electricity pricing schemes, income levels, preferences, and efficiency cases.

Moreover, the proposed method is able to quantify the extent to which an energy efficiency subsidy may cause indirect rebound. Indirect rebound is defined as higher household expenditure on other goods in the economy due to lower spending on energy, possibly as a result of subsidized efficiency. The user can also make a judgment on which efficiency measure should be subsidized based on its potential for welfare-loss mitigation for the household.

As an example, residential electricity prices in Saudi Arabia have been low and fixed in nominal terms from October 2000 until December 2015. Naturally, households did not conserve electricity or adopt high energy efficiency. Based on a study performed for the then Saudi Ministry of Water and Electricity, it was found that the average household air conditioner in the United States, South Korea, and China was about 60% more efficient than that in Saudi Arabia; even though space cooling constitutes the majority of Saudi residential electricity use. The low starting base for energy efficiency means that as electricity prices are initially raised past an initial threshold, one would expect a larger marginal benefit minus marginal cost of energy efficiency improvement. As energy efficiency purchases are further made, lower marginal net benefits materialize.

Although I would argue a significant electricity price elasticity is expected, the historical data statistically show a muted long-run value for Saudi Arabia. For some of the other Gulf Cooperation Council countries, their own-price elasticities were econometrically found to be zero (literature review in the below papers). Thus, analyses were performed for residences in Saudi Arabia, with the climate and archetypical residence characteristics of the country taken into account. Results are available in the papers below.

For more information on the methodology, two peer-reviewed papers, a conference paper, and a working paper are available online:

  • Matar, W. “A household’s power load response to a change in the electricity pricing scheme: An expanded microeconomic-physical approach.” The Electricity Journal 32. 2019.
  • Matar, W. “Households' response to changes in electricity pricing schemes: bridging microeconomic and engineering principles.” Energy Economics 75. 2018.
  • Matar, W. “Residential Energy Efficiency Investment and Demand Response under Different Electricity Pricing Schemes: a Hybrid Physical-Microeconomic Approach.” 16th IAEE European Conference. 2019.
  • Matar, W. “A Household’s Power Load Response to a Change in the Electricity Pricing Scheme: A Microeconomic-Physical Approach.” USAEE Research Paper Series 19-383: https://ssrn.com/abstract=3330927. 2019.
Walid Matar's picture

Thank Walid for the Post!

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Matt Chester's picture
Matt Chester on Aug 6, 2019 6:29 pm GMT

When it comes to behavioral response, the framework focuses on thermostat adjustments in the summer peak and off-peak hours, thermostat adjustments in the spring and fall, and the modified use of lighting and appliances. A household can react differently based on its preferences, income level, and the characteristics of the installed equipment and lighting, when faced with different electricity pricing schemes; like real-time pricing, time-of-use pricing, or progressive block tariffs. The household may choose from the combinations of behavioral adjustment and energy efficiency purchases that results in highest satisfaction in a given price setting. 

This breakdown of how behavior response to DR/EE programs can/do vary based on the demographic and characteristic of the house are really interesting, and I'm surprised I haven't heard more from it. Do you know if there are any companies actively engaging in this type of analysis in a meaningful way?

Thanks for sharing, Walid!

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