scholarly journals A Multiarmed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids

2020 ◽  
Vol 34 (02) ◽  
pp. 2046-2053 ◽  
Author(s):  
Jain Shweta ◽  
Gujar Sujit
Keyword(s):  
Demand Response ◽  
Electricity Markets ◽  
Private Information ◽  
Dynamic Pricing ◽  
Smart Grids ◽  
Renewable Energy Resources ◽  
Incentive Compatible ◽  
Pricing System ◽  
The Stability ◽  
Selection Of

Demand response is a crucial tool to maintain the stability of the smart grids. With the upcoming research trends in the area of electricity markets, it has become a possibility to design a dynamic pricing system, and consumers are made aware of what they are going to pay. Though the dynamic pricing system (pricing based on the total demand a distributor company is facing) seems to be one possible solution, the current dynamic pricing approaches are either too complex for a consumer to understand or are too naive leading to inefficiencies in the system (either consumer side or distributor side). Due to these limitations, the recent literature is focusing on the approach to provide incentives to the consumers to reduce the electricity, especially in peak hours. For each round, the goal is to select a subset of consumers to whom the distributor should offer incentives so as to minimize the loss which comprises of cost of buying the electricity from the market, uncertainties at consumer end, and cost incurred to the consumers to reduce the electricity which is a private information to the consumers. Due to the uncertainties in the loss function (arising from renewable energy resources as well as consumption needs), traditional auction theory-based incentives face manipulation challenges. Towards this, we propose a novel combinatorial multi-armed bandit (MAB) algorithm, which we refer to as \namemab\ to learn the uncertainties along with an auction to elicit true costs incurred by the consumers. We prove that our mechanism is regret optimal and is incentive compatible. We further demonstrate efficacy of our algorithms via simulations.

scholarly journals Incentive compatible demand response games for distributed load prediction in smart grids

2014 ◽  
Vol 3 ◽  
Author(s):  
Yan Chen ◽  
W. Sabrina Lin ◽  
Feng Han ◽  
Yu-Han Yang ◽  
Zoltan Safar ◽  
...  
Keyword(s):  
Demand Response ◽  
Dynamic Pricing ◽  
Smart Grids ◽  
Demand Curve ◽  
Load Prediction ◽  
Incentive Compatible ◽  
Distributed Load ◽  
Power Substation ◽  
Game Theoretic ◽  
Response Scheme

While demand response has achieved promising results on making the power grid more efficient and reliable, the additional dynamics and flexibility brought by demand response also increase the uncertainty and complexity of the centralized load forecast. In this paper, we propose a game-theoretic demand response scheme that can transform the traditional centralized load prediction structure into a distributed load prediction system by the participation of customers. Moreover, since customers are generally rational and thus naturally selfish, they may cheat if cheating can improve their payoff. Therefore, enforcing truth-telling is crucial. We prove analytically and demonstrate with simulations that the proposed game-theoretic scheme is incentive compatible, i.e., all customers are motivated to report and consume their true optimal demands and any deviation will lead to a utility loss. We also prove theoretically that the proposed demand response scheme can lead to the solution that maximizes social welfare and is proportionally fair in terms of utility function. Moreover, we propose a simple dynamic pricing algorithm for the power substation to control the total demand of all customers to meet the target demand curve. Finally, simulations are shown to demonstrate the efficiency and effectiveness of the proposed game-theoretic algorithm.

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.

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.

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