An Optimal and Learning-Based Demand Response and Home Energy Management System

Zhang, Dong; Li, Shuhui; Sun, Min; O’Neill, Zheng

doi:10.1109/tsg.2016.2552169

Cited by 212 publications

(88 citation statements)

References 40 publications

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“…The authors in [16] considered using reinforcement learning to learn the home appliances of a single household, and the related work of designing an automated system in [17] include energy scheduling under uncertainty in price. The authors in [18] considered a data-driven approach to learn customer behavior using historical consumption profile.…”

Section: A Literature Reviewmentioning

confidence: 99%

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A Distributed Online Pricing Strategy for Demand Response Programs

Pan

Wang

Zhang

2019

IEEE Trans. Smart Grid

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We study a demand response problem from utility (also referred to as operator)'s perspective with realistic settings, in which the utility faces uncertainty and limited communication. Specifically, the utility does not know the cost function of consumers and cannot have multiple rounds of information exchange with consumers. We formulate an optimization problem for the utility to minimize its operational cost considering time-varying demand response targets and responses of consumers. We develop a joint online learning and pricing algorithm. In each time slot, the utility sends out a price signal to all consumers and estimates the cost functions of consumers based on their noisy responses. We measure the performance of our algorithm using regret analysis and show that our online algorithm achieves logarithmic regret with respect to the operating horizon. In addition, our algorithm employs linear regression to estimate the aggregate response of consumers, making it easy to implement in practice. Simulation experiments validate the theoretic results and show that the performance gap between our algorithm and the offline optimality decays quickly.

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Section: A Literature Reviewmentioning

confidence: 99%

“…For example, we can writeγ t 1 as a result from least square estimation in the linear regression model presented in (16), where α i = 0:γ…”

Section: A Preliminaries For Proving Theorems 1 Andmentioning

confidence: 99%

A Distributed Online Pricing Strategy for Demand Response Programs

Pan

Wang

Zhang

2019

IEEE Trans. Smart Grid

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“…Significant research efforts have been focusing on developing demand response and energy management for HVAC [3,4]. Particularly, Zhang et al [3] developed a learning-based HVAC energy management system mechanism that can identify and update energy consumption model daily for an HVAC to determine an optimal DR policy, which makes the DR management of HVAC adaptive to seasons, users and house condition changes and therefore more efficient. However, except for HVACs, learning-based DR researches on other DR capable appliances and are limited.…”

Section: Introductionmentioning

confidence: 99%

Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

Lin

Xiao

2017

Energies

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This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.

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“…Demand-side management (DSM) is a promising responsive system for smartgrids to manage consumer demand [7][8][9][10][11][12][13][14][15]. For instance, a supplier can directly manage its consumer demand to maximize its revenue or improve energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Demand-Side Management over Pricing Competition of Multiple Suppliers Having Heterogeneous Energy Sources

2017

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Abstract:This study investigates a demand-side management problem in which multiple suppliers compete with each other to maximize their own revenue. We consider that suppliers have heterogeneous energy sources and individually set the unit price of each energy source. Then, consumers that share a net utility react to the suppliers' decisions on prices by deciding the amount of energy to request, or how to split the consumers' aggregated demand over multiple suppliers. In this case, the consumers need to consider the power loss and the price to pay for procuring electricity. We analyze the economic benefits of such a pricing competition among suppliers (e.g., a demand-side management that considers consumers' reaction). This is achieved by designing a hierarchical decision-making scheme as a multileader-multifollower Stackelberg game. We show that the behaviors of both consumers and suppliers based on well-designed utility functions converge to a unique equilibrium solution. This allows them to maximize the payoff from all participating consumers and suppliers. Accordingly, closed-form expressions are provided for the corresponding strategies of the consumers and the suppliers. Finally, we provide numerical examples to illustrate the effectiveness of the solutions. This game-theoretic study provides an example of incentives and insight for demand-side management in future power grids.

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An Optimal and Learning-Based Demand Response and Home Energy Management System

Cited by 212 publications

References 40 publications

A Distributed Online Pricing Strategy for Demand Response Programs

A Distributed Online Pricing Strategy for Demand Response Programs

Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

Evaluation of Demand-Side Management over Pricing Competition of Multiple Suppliers Having Heterogeneous Energy Sources

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