Safe Reinforcement Learning-Based Resilient Proactive Scheduling for a Commercial Building Considering Correlated Demand Response

Liang, Zheming; Huang, Can; Su, Wencong; Duan, Nan; Donde, Vaibhav; Wang, Bin; Zhao, Xianbo

doi:10.1109/oajpe.2021.3064319

Cited by 19 publications

(11 citation statements)

References 34 publications

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“…N S and N V denote the sizing of solar arrays and wind turbines; o S and o V are the operating and maintenance coefficients of solar arrays and wind turbines. Equation ( 22) calculates the degradation cost of the SB system, which depends on the charging and discharging power quantity in each time slot t [30], in which o E is the degradation cost coefficient. The penalty cost related to the load-shedding quantity in a voltage-based DR program is shown in Equation (23), in which o L is the penalty factor.…”

Section: Objective Functionmentioning

confidence: 99%

“…U de dc (t) is shown in Equation ( 29), which is defined by realizing the difference between the nominal voltage U lim dc (t) and the reduced voltage after load shedding, where D con L (t) is total controllable load magnitude, η DC is the DC-DC/AC inverter efficiency, δ L (t) is a continuous random variable in range [0; 1] to denote the percentage of load reduction, and x L (t) is a binary control variable for the voltage-based DR. The trigger condition for x L (t) is shown in Equation (30).…”

Section: Voltage-based Demand Responsementioning

confidence: 99%

“…Step 2: At time slot t, the DC bus input voltage 𝑈 𝑡 is checked against its nominal voltage reference 𝑈 𝑡 to consider for carrying out a voltage-based DR program. Equation (30) is applied in this step to make a control action related to voltage-based DR.…”

Section: Proposed Control Strategymentioning

confidence: 99%

“…The other system parameters are shown in Table 2, according to [32]. All remaining parameters of the algorithm, including the -greed, learning rate, and discount factor, are set to be 0.95, 0.05, and 0.95, respectively, according to [30]. The simulation tests are run by the Gurobi 9.1.2 optimization solver in the Python 3.8.8 environment on a dual-core 3.0 GHz computer with 8.0 GB of RAM.…”

Section: Simulation Settingmentioning

confidence: 99%

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Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch

Thanh

Wang

2022

Energies

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Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries’ optimal dispatch operation for minimizing the entire DC microgrid’s operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries’ charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method.

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Section: Objective Functionmentioning

confidence: 99%

Section: Voltage-based Demand Responsementioning

confidence: 99%

Section: Proposed Control Strategymentioning

confidence: 99%

Section: Simulation Settingmentioning

confidence: 99%

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Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch

Thanh

Wang

2022

Energies

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“…Application Objective Building Type Algorithm [141], [142] Other/Mixed Cost Residential DQN [143] Cost & Load Balance [94] EV, ES, and RG Cost [144] Other [145] Cost & Comfort [146] HVAC, Fans, WH Cost [147] Other/Mixed Commercial [148] Cost & Comfort [149], [150] HVAC, Fans, WH Mixed/NA [151], [152] Other/Mixed Cost [153], [154] P2P Trading Other Mixed/NA [163] EV, ES, and RG [164] Other/Mixed Cost [165] Cost & Comfort Residential TRPO [51], [168], [169], [170] Other/Mixed [171], [172] Cost & Load Balance [173] Cost [174] EV, ES, and RG [175] Other/Mixed Cost & Comfort Academic [176] Other [177], [178] EV, ES, and RG Commercial [179], [180], [181] HVAC, Fans, WH Cost & Comfort Mixed/NA [182], [183], [184] EV, ES, and RG Other [185], [186] Other/Mixed Cost & Load Balance Residential SAC [187], [188] HVAC, Fans, WH Cost Commercial [189], [190],…”

Section: Referencementioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The twin capabilities of learning from experience and learning at higher levels of abstraction, set reinforcement learning apart from other areas of machine learning and (within the broader context) all of artificial intelligence. It allows algorithmic agents to replace human beings in the real world, including in homes and buildings, in application domains that had hitherto been considered to be beyond today’s capabilities. This goal, specifically aimed at home energy automation that forms the backdrop of this article, which surveys the use of deep reinforcement learning in various HEMS applications. The article provides an overview of generic reinforcement learning. This is followed with discussions on the state-of-the-art methods for value based, policy gradient, and actor-critic methods in deep reinforcement learning. In order to make published literature in reinforcement learning more accessible to HEMS researchers, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using the same terminology as the machine learning community. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. The survey suggests that this research is still in its infancy.

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