Smart Home Energy Management Optimization Method Considering Energy Storage and Electric Vehicle

Hou, Xuan; Jun, Wang; Huang, Tao; Wang, Tao; Wang, Peng

doi:10.1109/access.2019.2944878

Cited by 115 publications

(43 citation statements)

References 34 publications

(43 reference statements)

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“…The proposed scheme scheduled the smart home appliances; however, the integration of the photovoltaic panel and the use of mixedinteger linear programming increases the complexity and cost of the deployment. Similar approaches for smart home appliances scheduling is proposed in [16,17]. The proposed approach also adopted the flexibility of MILP to schedule the smart home appliances and the energy demand is fulfilled with the help of installing a PV system.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

2020

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The scheduling of the operational time of household appliances requires several parameters to be tuned according to the available energy supplied to a smart home. However, scheduling of operational time of multiple appliances in a smart home itself is the NP-hard problem and thus requires an intelligent, heuristic method to be solved in polynomial time. In this research work, we propose Real-time Scheduling of Operational Time of Household Appliances based on the well-known value iterative reinforcement learning called Quality learning (RSOTHA-QL). The proposed RSOTHA-QL scheme operates in two phases. In the first phase, the agents of the Q learning act by interacting with the smart home environment and obtain a reward. The reward value is further utilized to schedule the operational time of household appliances in the next state ensuring minimum energy consumption. In the second phase, the dissatisfaction arises due to scheduling of operational time of the household appliances of the home user is maintained by categorizing the household appliances into three groups: 1) deferrable, 2) non-deferrable, and 3) controllable. Besides, using the shared memory synchronization phenomenon, the agents attached to each appliance of the smart home become coordinated. The simulation and experiments are performed in a smart home scenario comprised of a single user and multiple appliances. As compared with our previous research work using the Least Slack Time (LST) scheduling algorithm and scheduling based on demand-response strategy, it is revealed that the operational time of the household appliances is efficiently scheduled to reduce the energy consumption and dissatisfaction level of the home users significantly.

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Section: Related Workmentioning

confidence: 99%

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

2020

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“…It is an undeniable fact that the high-energy storage capacity of batteries plays an important role in increasing the flexibility of microgrid [25], [26]. This stored energy in batteries is indicated with the state of charge (SOC) which is computed as [10]:…”

Section: A Quantifying Soc Levelsmentioning

confidence: 99%

Clustering-Based Penalty Signal Design for Flexibility Utilization

et al. 2020

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“…To implement this second function, an HEMS algorithm is generally formulated as a model-based optimization problem. Recently, numerous studies have been published on the development of HEMS optimization algorithms [2][3][4][5][6][7][8][9][10][11][12]. These studies address the scheduling of the energy consumption for home appliances and DERs, while maintaining the consumer's comfort level using mixed-integer nonlinear programming (MINLP) [2], the load scheduling using mixed-integer linear programming (MILP) for single and multiple households [3,4], robust optimization for scheduling of home appliances to resolve the uncertainty of consumer behavior [5], and distributed HEMS architectures consisting of local and global HEMSs [6].…”

Section: Introductionmentioning

confidence: 99%

“…A model predictive control-based HEMS algorithm was proposed using the prediction of the EV state [9]. An HEMS optimization model considering both ESS and EV was formulated for a single household [10,11] based on their bi-directional operation and multiple households with a renewable energy facility [12]. In addition, many studies proposed the methods to evaluate and preserve the consumer comfort during the HEMS process.…”

Section: Introductionmentioning

confidence: 99%

Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach

Lee

Choi

2020

Sensors

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This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources (DERs) including an energy storage system (ESS) and an electric vehicle (EV). Compared to Q-learning algorithms based on a discrete action space, the novelty of the proposed approach is that the energy consumptions of home appliances and DERs are scheduled in a continuous action space using an actor–critic-based DRL method. To this end, a two-level DRL framework is proposed where home appliances are scheduled at the first level according to the consumer’s preferred appliance scheduling and comfort level, while the charging and discharging schedules of ESS and EV are calculated at the second level using the optimal solution from the first level along with the consumer environmental characteristics. A simulation study is performed in a single home with an air conditioner, a washing machine, a rooftop solar photovoltaic system, an ESS, and an EV under a time-of-use pricing. Numerical examples under different weather conditions, weekday/weekend, and driving patterns of the EV confirm the effectiveness of the proposed approach in terms of total cost of electricity, state of energy of the ESS and EV, and consumer preference.

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Smart Home Energy Management Optimization Method Considering Energy Storage and Electric Vehicle

Cited by 115 publications

References 34 publications

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

Clustering-Based Penalty Signal Design for Flexibility Utilization

Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach

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