Time-based electricity pricing for Demand Response implementation in monopolized electricity market

Nazar, Nazatul Shiema Moh; Abdullah, Md. Pauzi; Hassan, M. Y.; Hussin, Faridah

doi:10.1109/scored.2012.6518634

“…ToU tariffs and smart metering systems could incentive the consumers to change the consumption behavior scheduling the appliances' operation at lower rate intervals. Thus, designing the ToU peak/off-peak rates and setting the intervals are important steps in getting adequate demand response [9]. The major risk is to design a ToU tariff that leave the consumers indifferent.…”

Section: I Initial Consumption C Modmentioning

confidence: 99%

Setting the Time-of-Use Tariff Rates With NoSQL and Machine Learning to a Sustainable Environment

Oprea

¹

,

Bârã

²

2020

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The electricity consumption will continue to increase despite the overall efforts and tendencies of changing the old appliances to less energy intensive ones. The advancements of Electric Vehicles (EV) and public mobility, electric heating, and the abundance of smart appliances that enhance the comfort of modern life lead to an increasing consumption trend. On the other hand, prosumers raising the quota of distributed generation and storage capacity will balance the electricity consumption trend. These changes at the consumption and generation level lead to the necessity to increase the awareness and incentive the consumers' behavior to flatten the consumption curve and improve the savings. Such objectives could be reached by properly setting the Time-of-Use (ToU) tariff rates to encourage the consumption at off-peak hours when the rates are lower and unstress the grid loading. In this paper, we propose a methodology for setting the Time-of-Use (ToU) tariff rates and peak/off-peak intervals using big data technologies and machine learning, and verify the assumptions considering the large volume of consumption data of over 4200 residential consumers recorded in a smart metering implementation trail period that took place in Ireland from January to December 2010. We calculate the contribution to the peak/off-peak of the total consumption and use it in setting the ToU tariff rates starting from the flat tariff. Then, the consumers' sensitivity to tariff change from flat to ToU is considered to identify the consumption change. The results show that using ToU instead of flat tariff, the peak is reduced in average by 5 to 7.5% and annual savings are around 4%. Also, by clustering the consumers a better allocation of the tariffs is possible. Thus, clustering is proposed considering the importance of the tariff allocation in Demand Side Management (DSM). INDEX TERMS Time-of-use tariff rates, contribution to consumption peak/off-peak, big data, machine learning, tariff elasticity. NOMENCLATURE Symbol Description C off cluster Consumption at off-peak hour for a cluster of consumers C peak cluster Consumption at peak hour for a cluster of consumers C% Consumption variation in percentage c off Contribution to consumption off-peak coefficient c peak Contribution to consumption peak coefficient C h Hourly consumption The associate editor coordinating the review of this manuscript and approving it for publication was Zhiwei Gao .

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“…Lately, clever pricing schemes have been developed to meet the requirements of modern power systems [20], such as RTP, where consumers are charged with a price closest to the real price of generation at a specific interval in time. RTP plays an integral part in time-based DSM programs that make consumers choose the time of consumption of power as a response to prices [21]. Cyber-enabled DSM programs are an automated form of time-based DSM programs.…”

Section: Real-time Pricingmentioning

confidence: 99%

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Hatalis

¹

,

Zhao

²

,

Venkitasubramaniam

³

et al. 2020

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Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.

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“…Lately, clever pricing schemes have been developed to meet the requirements of modern power systems [20], such as RTP, where consumers are charged with a price nearest to the real price of generation at a specific interval in time. RTP plays an integral part in time-based DSM programs that make consumers choose the time of consumption of power as a response to prices [21]. Cyber-enabled DSM programs are an automated form of time-based DSM programs.…”

Section: Real Time Pricingmentioning

confidence: 99%

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Hatalis¹,

Zhao²,

Venkitasubramaniam³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.

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Time-based electricity pricing for Demand Response implementation in monopolized electricity market

Cited by 35 publications

References 5 publications

Setting the Time-of-Use Tariff Rates With NoSQL and Machine Learning to a Sustainable Environment

Setting the Time-of-Use Tariff Rates With NoSQL and Machine Learning to a Sustainable Environment

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

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