Robust Demand Response for Device Scheduling under False Data Injection Attacks in Smart Grids

Dayaratne, Thusitha; Rudolph, Carsten; Liebman, Ariel; Salehi, Mahsa

doi:10.1109/isgt-europe47291.2020.9248790

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…One critical distributed resource is demand response programs (DRPs), which balance supply and demand without changing power generation. DRPs incentivize changes in electricity usage patterns, reducing operating costs, greenhouse gas emissions, improving grid efciency, and defending against cyberattacks such as false data injection (FDIA) [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Consumers bear costs such as installing smart meters, telecommunications infrastructure, peak load control, HEMS, and investing in renewable energy. Tey are also responsible for operation and maintenance costs [6]. Meanwhile, system operators or service providers should pay for training consumers and telecommunications infrastructure on the control side, among others [12].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Peak Shaving through Nonlinear Incentive-Based Demand Response: A Consumer-Centric Utility Optimization Approach

Eslaminia,

Mashhadi

2023

International Transactions on Electrical Energy Systems

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This paper presents a comparative study on the implementation of incentive-based demand response programs in power grid management, comparing the results obtained from linear and nonlinear models. The challenges faced by power grids in balancing supply and demand and managing price spikes during peak periods are addressed, and demand response programs are proposed as an effective strategy. The focus is on implementing incentive-based programs where load serving entities determine the optimal incentive price and demand reduction. A novel approach is presented for simulating consumer behavior based on the price elasticity of demand and consumers’ utility function, incorporating both linear and nonlinear economic models. The calculation of demand reduction aims to maximize consumers’ welfare, while the determination of the optimal incentive price maximizes the profit of the load serving entities. Real data are utilized, and the proposed models are implemented using the mixed-integer nonlinear programming (MINLP) method. The results demonstrate the effectiveness of providing incentives to consumers during peak hours compared to other approaches, with a comparison between the linear and nonlinear models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%