Data-Driven Uncertainty Quantification and Characterization for Household Energy Demand Across Multiple Time-Scales

Shi, Heng; Ma, Qiuyang; Smith, Nathan D.; Li, Furong

doi:10.1109/tsg.2018.2817567

Cited by 8 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The information collection & communication technology [33], the grid load [35], [36] & V2G capacity estimation [37], [38] approach and the smart charging pile technology [39] has been well studied in the existing literature. Therefore, in the rest part of this paper, we mainly focus on the V2G behavior management method.…”

Section: E System Operation Time Logicmentioning

confidence: 99%

Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

2020

IEEE Access

View full text Add to dashboard Cite

The bi-directional linkage between the power grid and electric vehicles (EVs) enables flexible, cheap and fast-responding use of vehicle batteries in the grid. However, the battery aging effects due to the additional operation cycles caused by Vehicle-to-Grid (V2G) service and the concern of the battery degradation are the main reason that keeps the customer from being the named prosumer of the grid. This paper proposes a novel active battery anti-aging V2G scheduling approach. Firstly, to evaluate the battery aging effect in V2G service, the battery degradation phenomenon is quantified by a novel use of rain-flow cycle counting (RCC) algorithm. Then, the V2G scheduling is modeled as a multi-objective optimization problem, in which the minimal battery degradation and grid load fluctuation are designed as the optimization objectives. Finally, a multi-population collaborative mechanism, which is particularly designed for the V2G scheduling problem, is also developed to improve the practicability and performance of the heuristic optimization based V2G scheduling method. The proposed methodologies are verified by numerical analysis, which highlights that the proposed V2G scheduling method can minimize battery charge/discharge cycles by optimizing the time and scale of each V2G participant while providing the same services to the grid as expected. INDEX TERMS Electric vehicle, vehicle to grid, active battery anti-aging and heuristic algorithm.

show abstract

Section: E System Operation Time Logicmentioning

confidence: 99%

Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The probability of each scenario is assumed based on the operational results (Table 1), and each probability is: P(σ 1 ) = 0.2, P(σ 2 ) = 0.6, P(σ 3 ) = 0.2 (17) From Equations (4), (5), (8), (10), (14), and (16), each player has equal probability conditions due to the symmetry:…”

Section: Simulation Condition and Expected Payoffmentioning

confidence: 99%

“…However, GUM approach has some critical limitations [11] and as a way of overcoming the limitations of GUM, Monte Carlo Simulation (MCS) is being considered as an advanced approach toward evaluating uncertainty [11][12][13][14]. For this reason, MCS is used to handle uncertainty in the electric field [15][16][17]. In [15,16], MCS is used to deal with the uncertainty problem in DR, and Shi [17] estimated uncertainty from household energy data with MCS.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-Cooperative Indirect Energy Trading with Energy Storage Systems for Mitigation of Demand Response Participation Uncertainty

Ryu

Kim

2020

Energies

View full text Add to dashboard Cite

This work focuses on the demand response (DR) participation using the energy storage system (ESS). A probabilistic Gaussian mixture model based on market operating results Monte, Carlo Simulation (MCS), is required to respond to an urgent DR signal. However, there is considerable uncertainty in DR forecasting, which occasionally fails to predict DR events. Because this failure is attributable to the intermittency of the DR signals, a non-cooperative game model that is useful for decision-making on DR participation is proposed. The game is conducted with each player holding a surplus of energy but incomplete information. Consequently, each player can share unused electricity during DR events, engaging in indirect energy trading (IET) under a non-cooperative game framework. The results of the game, the Nash equilibrium (N.E.), are verified using a case study with relevant analytical data from the campus of Gwangju Institute of Science and Technology (GIST) in Korea. The results of the case study show that IET is useful in mitigating the uncertainty of the DR program.

show abstract

“…When there is not enough prior system knowledge, kernel density estimation (KDE) is more suitable for uncertainty quantification [10]. In [11], a data-driven temporal-dependency Haar expansions approach is used to quantify the household energy demand. In [12], information gap decision theory (IGDT) is employed to model the load uncertainty.…”

mentioning

confidence: 99%

Probabilistic Revenue Analysis of Microgrid Considering Source-Load and Forecast Uncertainties

Yang

Geng³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Data-Driven Uncertainty Quantification and Characterization for Household Energy Demand Across Multiple Time-Scales

Cited by 8 publications

References 30 publications

Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

Non-Cooperative Indirect Energy Trading with Energy Storage Systems for Mitigation of Demand Response Participation Uncertainty

Probabilistic Revenue Analysis of Microgrid Considering Source-Load and Forecast Uncertainties

Contact Info

Product

Resources

About