Equivalence of Multi-Time Scale Optimization for Home Energy Management Considering User Discomfort Preference

Liu, Yi; Zhang, Yun; Chen, Kairui; Chen, Si Zhe; Tang, Bin

doi:10.1109/tsg.2015.2510222

Cited by 25 publications

(18 citation statements)

References 21 publications

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“…In [10], Tsui et al proposed a convex optimization framework with L 1 regularization for the home energy management, which can handle appliances with ON and OFF operational statuses. In [11], Liu et al investigated the transformation from single-time scale into multitime scale to reduce the computational complexity for the optimization of home energy management. In [12], Huang et al formulated a chance-constrained programming optimization problem to minimize energy cost of appliances considering uncertainties in a smart home.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Proof: To prove the performance of the proposed algorithm, we first define some equations as follows, i.e., a = lim sup t=0 E{e t }. Then, we have y = 0 based on the constraint (11). Similarly, we have A η (T min − T outmax ) ≤ e ≤ A η (T max − T outmin ) based on the constraint (2).…”

Section: Appendix G Proof Of Theoremmentioning

confidence: 99%

“…Note that (2),(4) are replaced by (39c), (5),(8) are replaced by (39d), (11), (12) are replaced by (39e). Since any feasible solution of P1 is also feasible to P3, we have y 2 ≤ y 1 , where y 2 and y 1 are the optimal objective values of P3 and P1, respectively.…”

Section: Appendix G Proof Of Theoremmentioning

confidence: 99%

See 2 more Smart Citations

Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

Jiang

Zou

2019

IEEE Trans. Smart Grid

115

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In this paper, we investigate the problem of minimizing the sum of energy cost and thermal discomfort cost in a long-term time horizon for a sustainable smart home with a Heating, Ventilation, and Air Conditioning (HVAC) load. Specifically, we first formulate a stochastic program to minimize the time average expected total cost with the consideration of uncertainties in electricity price, outdoor temperature, renewable generation output, electrical demand, the most comfortable temperature level, and home occupancy state. Then, we propose an online energy management algorithm based on the framework of Lyapunov optimization techniques without the need to predict any system parameters. The key idea of the proposed algorithm is to construct and stabilize four queues associated with indoor temperature, electric vehicle charging, and energy storage. Moreover, we theoretically analyze the feasibility and performance guarantee of the proposed algorithm. Extensive simulations based on real-world traces show the effectiveness of the proposed algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Appendix G Proof Of Theoremmentioning

confidence: 99%

See 1 more Smart Citation

Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

Jiang

Zou

2019

IEEE Trans. Smart Grid

115

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“…Due to the microturbine response characteristics, MFR can be applied for longer time-scale dispatch schemes ranging from 15 min to 1 h in this paper, aiming at minimizing the operation costs in day-ahead scheduling and smoothing out the fluctuations in intra-hour scheduling [27]. (3) DRR refers to the equivalent storage that aggregates flexible demand-side loads with reasonable control strategies [28]. Considering the fast-response characteristics of DRR, three typical controllable loads, i.e., heat pump (HP), central air conditioning (CAC), and electric vehicle (EV) are studied herein for energy balance service.…”

Section: Functional Framework and Comprehensive Modeling Of Generalizmentioning

confidence: 99%

“…In contrast to the HP temperature curve, the charging state and energy status of EV determine its power consumption, as shown in Figure 4c. In addition to the operation characteristics of different DRRs, the general model of controllable load groups could be further described by the dynamic mechanism and physical-power coupling model [28]. A typical DRR dynamic mechanism is proposed in Equation (17).…”

Section: General Model Of Gmses-drrmentioning

confidence: 99%

Research on Modeling and Hierarchical Scheduling of a Generalized Multi-Source Energy Storage System in an Integrated Energy Distribution System

Wang

Liu

et al. 2019

Energies

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Energy storage systems play a crucial role in ensuring stable operation. However, the development of system-level energy storage is hindered due to the restrictions of economy, geography, and other factors. Transitions of traditional power systems into integrated energy distribution systems (IEDS) have provided new solutions to the problems mentioned above. Through intelligent control management methods, the utilization of multi-energy-type resources both on the supply and demand sides shows the potential for equivalent storage characteristics. Inspired by the aggregation principles, this paper aims at proposing a novel model named generalized multi-source energy storage (GMSES), including the modeling and cooperation of three kinds of available resources: conventional energy storage (CES), multi-energy flow resources (MFR), and demand response resources (DRR). Compared with the conventional means of storage, GMSES can be regarded as a more cost-effective and flexible participant in the proposed hierarchical energy scheduling framework that can realize system-level storage services in IEDS. On this basis, a multi-timescale energy scheduling strategy is proposed to reshape the regulation of IEDS operations and deal with the fluctuations caused by renewable energy and loads, where the general parameter serialization (GPS)-based control strategy is utilized to select and control the responsive loads in DRR. Furthermore, a hierarchical scheduling algorithm is developed to generate the optimal set-points of GMSES. Case studies are analyzed in an electricity-gas coupled IEDS. The simulation results show that the coupled co-optimization GMSES model is conducive to achieving the goal of self-management and economical operation, while the influence of the underlying IEDS on the upper energy system is reduced, as the tie-line power fluctuations are smoothed out.

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Multiple time-scale energy management strategy for a hydrogen-based multi-energy microgrid

et al. 2022

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Equivalence of Multi-Time Scale Optimization for Home Energy Management Considering User Discomfort Preference

Cited by 25 publications

References 21 publications

Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

Research on Modeling and Hierarchical Scheduling of a Generalized Multi-Source Energy Storage System in an Integrated Energy Distribution System

Multiple time-scale energy management strategy for a hydrogen-based multi-energy microgrid

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