Optimal Scheduling Approach on Smart Residential Community Considering Residential Load Uncertainties

Nan, Sibo; Zhou, Ming; Li, Gengyin; Xia, Yong

doi:10.1007/s42835-019-00094-0

Cited by 16 publications

(11 citation statements)

References 24 publications

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“…Using this bidirectional technique, which not only brings benefits but has also allowed us to limit uncertainties, it has been possible to implement stochastic dynamic programming with at most two levels of estimation: the target variable plus an additional level with stochastic variables, which greatly increases the accuracy of the predictions as will be seen in the results section. The following references show up to six different strategies for stochastic optimization: stochastic optimization, robust optimization, chance-constrained optimization, stochastic dynamic programming, stochastic fuzzy optimization, and stochastic model, which generates synthetic consumption profiles [ 65 , 68 , 79 , 80 , 81 , 82 , 83 , 84 , 85 ]. For example, in [ 79 ] a stochastic energy consumption scheduling algorithm based on time-varying prices known in advance (similar to the one used in the HERMES system) is described as achieving a 24% to 41% reduction in simulations in billing costs.…”

Section: Methodsmentioning

confidence: 99%

Energy Management Expert Assistant, a New Concept

Linan-Reyes

Garrido-Zafra

Gil-de-Castro

et al. 2021

Sensors

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In recent years, interest in home energy management systems (HEMS) has grown significantly, as well as the development of Voice Assistants that substantially increase home comfort. This paper presents a novel merging of HEMS with the Assistant paradigm. The combination of both concepts has allowed the creation of a high-performance and easy-to-manage expert system (ES). It has been developed in a framework that includes, on the one hand, the efficient energy management functionality boosted with an Internet of Things (IoT) platform, where artificial intelligence (AI) and big data treatment are blended, and on the other hand, an assistant that interacts both with the user and with the HEMS itself. The creation of this ES has made it possible to optimize consumption levels, improve security, efficiency, comfort, and user experience, as well as home security (presence simulation or security against intruders), automate processes, optimize resources, and provide relevant information to the user facilitating decision making, all based on a multi-objective optimization (MOP) problem model. This paper presents both the scheme and the results obtained, the synergies generated, and the conclusions that can be drawn after 24 months of operation.

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Section: Methodsmentioning

confidence: 99%

Energy Management Expert Assistant, a New Concept

Linan-Reyes

Garrido-Zafra

Gil-de-Castro

et al. 2021

Sensors

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“…In most power systems applications, the Gaussian copula became the default approach for calculating stochastic variables correlation and scenario generation [29], [31], [37], [38] without any further consideration on the type of data to be modeled. Nevertheless, the MVT copula can benefit the individual RLPs modeling due to its ability to capture high values variations [39]; this property has not been explored for the highly volatile RLPs.…”

Section: A Literature Review and Contributionsmentioning

confidence: 99%

Conditional Multivariate Elliptical Copulas to Model Residential Load Profiles From Smart Meter Data

Duque

Vergara

Nguyen

et al. 2021

IEEE Trans. Smart Grid

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The development of thorough probability models for highly volatile load profiles based on smart meter data is crucial to obtain accurate results when developing grid planning and operational frameworks. This paper proposes a new top-down modeling approach for residential load profiles (RLPs) based on multivariate elliptical copulas that can capture the complex correlation between time steps. This model can be used to generate individual and aggregated daily RLPs to simulate the operation of medium and low voltage distribution networks in flexible time horizons. Additionally, the proposed model can simulate RLPs conditioned to an annual energy consumption and daily weather profiles such as solar irradiance and temperature. The simulated daily profiles accurately capture the seasonal, weekends, and weekdays power consumption trends. Five databases with actual smart meter measurements at different time resolutions have been used for the model's validation. Results show that the proposed model can successfully replicate statistical properties such as autocorrelation of the time series, and load consumption probability densities for different seasons. The proposed model outperforms other multivariate state-of-the-art methods, such as Gaussian Mixture Models, by one order of magnitude in two different distance metrics for probability distributions.

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“…8) The dishwasher is scheduled every day of the horizon. Daily dishwasher load scheduling is imposed in (12).…”

Section: Objectivementioning

confidence: 99%

“…Concerning the scheduling horizon, day-ahead (DA) controllable appliance scheduling is usually favoured among mathematical optimisation-based strategies [10]- [14]. For modelling electricity demand from non-controllable appliances, in some works, this contribution is completely neglected [10], [11], while in other works, it is explicitly accounted for [12]- [16]. Within non-controllable demand modelling, stochastic and robust programming are emphasised in explicit uncertainty mod-elling [17].…”

Section: Introductionmentioning

confidence: 99%

“…This effect results from a finite supply power, which, if surpassed, may damage existing infrastructure and cause power outages. Stochastic programming-based and robust non-controllable demand modelling, as suggested in [12], [15], [25], prevents these situations and guarantees a minimum probability of meeting supply bounds. Second, DA scheduling is favoured over longer scheduling horizons, namely, one week.…”

Section: Introductionmentioning

confidence: 99%

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