A Consumer Dissatisfaction Model Linking Dynamic Pricing With Shifted Product-Use in Residential Electricity Markets

2021 ◽  
Author(s):  
Samuel Dunbar ◽  
Scott Ferguson
Keyword(s):  
Electricity Markets ◽  
Dynamic Pricing ◽  
Product Use ◽  
Residential Electricity ◽  
Consumer Dissatisfaction

A Consumer Dissatisfaction Model Linking Dynamic Pricing With Shifted Product-Use in Residential Electricity Markets

2020 ◽  
Author(s):  
Samuel Dunbar ◽  
Scott Ferguson
Keyword(s):  
Electricity Markets ◽  
Dynamic Pricing ◽  
Financial Incentives ◽  
Energy Demand ◽  
Consumer Preferences ◽  
Population Level ◽  
System Level ◽  
Renewable Energy Resources ◽  
Product Use ◽  
Consumer Dissatisfaction

Abstract Demand Response (DR) is the implementation of a specific strategy or set of strategies, with the goal of altering consumer energy demand, such that some system level objectives are improved. These strategies typically include dynamic pricing, direct load control, policy implementation, or other financial incentives. DR will become a crucial tool for managing growing global energy demand in conjunction with higher penetration rates of intermittent renewable energy resources. Effective implementation of a DR strategy requires a realistic understanding of how consumers will respond to that strategy and how they will be affected by it. Here, a product-based decision model for residential consumers, that links consumer decisions directly to product-use, is revisited and adapted from a continuous time formulation to discrete time. The relationship between financial incentives, consumer preferences, and demand flexibility at the population level is then quantified. The model is used for exploring the tradeoffs between typical objectives for a dynamic pricing residential DR program and evaluating the characteristics of well-performing pricing solutions.

scholarly journals The Risk of Residential Peak Electricity Demand: A Comparison of Five European Countries

2017 ◽  
Author(s):  
Jacopo Torriti
Keyword(s):  
Time Use ◽  
Electricity Markets ◽  
Electricity Market ◽  
Dynamic Pricing ◽  
Risk Model ◽  
European Countries ◽  
Peak Demand ◽  
Time Use Survey ◽  
Control Dynamic ◽  
Different Levels

The creation of a Europe-wide electricity market combined with the increased intermittency of supply from renewable sources calls for an investigation into the risk of aggregate peak demand. This paper makes use of a risk model to assess differences in time-use data from residential end-users in five different European electricity markets. Drawing on the Multinational Time-Use Survey database, it assesses risk in relation to the probability of electrical appliance use within households for five European countries. Findings highlight in which countries and for which activities the risk of aggregate peak demand is higher and link smart home solutions (automated load control, dynamic pricing and smart appliances) to different levels of peak demand risk.

The Impact of Consumer Preference Distributions on Dynamic Electricity Pricing for Residential Demand Response

2019 ◽  
Author(s):  
Samuel Dunbar ◽  
Scott Ferguson
Keyword(s):  
Demand Response ◽  
Dynamic Pricing ◽  
Consumer Preferences ◽  
Consumer Preference ◽  
Population Level ◽  
Economic Incentives ◽  
System Level ◽  
Renewable Energy Resources ◽  
Product Use ◽  
The Impact

Abstract Demand Response (DR) is the adjustment of consumer electricity demand through the deployment of one or more strategies, e.g. direct load control, policy implementation, dynamic pricing, or other economic incentives. Widespread implementation of DR is a promising solution for addressing energy challenges such as the integration of intermittent renewable energy resources, reducing capacity cost, and improving grid reliability. Understanding residential consumer preferences for shifting product usage and how these preferences are distributed amongst a population are key to predicting the effectiveness of different DR strategies. In addition, there is a need for a better understanding of how different DR programs, system level objectives, and preference distributions will impact different segments of consumers within a population. Specifically, the impacts on their product use behavior and electricity bill. To address this challenge, a product based approach to modeling consumer decisions about altering their electricity consumption is proposed, which links consumer value to their products, instead of directly to the amount of electricity they consume. This model is then used to demonstrate how population level preference distributions for altering product use impact system level objectives.

scholarly journals Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6130
Author(s):  
Minseok Jang ◽  
Hyun-Cheol Jeong ◽  
Taegon Kim ◽  
Sung-Kwan Joo
Keyword(s):  
Dynamic Pricing ◽  
Mixed Logit ◽  
Electricity Consumption ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Customer Segmentation ◽  
Smart Meters ◽  
Load Profile ◽  
Residential Electricity

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.

scholarly journals Misunderstanding Nonlinear Prices: Evidence from a Natural Experiment on Residential Electricity Demand

2020 ◽  
Vol 12 (3) ◽  
pp. 433-461
Author(s):  
Blake Shaffer
Keyword(s):  
British Columbia ◽  
Economic Policy ◽  
Electricity Markets ◽  
Natural Experiment ◽  
Electricity Demand ◽  
Welfare Loss ◽  
Average Price ◽  
Residential Electricity ◽  
Marginal Price

This paper examines how consumers respond to nonlinear prices. Exploiting a natural experiment with electricity consumers in British Columbia, I find evidence that some households severely misunderstand nonlinear prices—incorrectly perceiving that the marginal price applies to all consumption, not simply the last unit. While small in number, the exaggerated responses by these households have a large effect in aggregate, masking an otherwise predominant response to average price. Largely unexplored in the literature, this type of misunderstanding has important economic, policy, and methodological implications beyond electricity markets. I estimate the welfare loss for these households to be the equivalent of 10 percent of annual electricity expenditure. (JEL D12, L11, L94, Q41)

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