Deep Reinforcement Learning for Multiobjective Optimization

Li, Kaiwen; Zhang, Tao; Wang, Rui

doi:10.1109/tcyb.2020.2977661

Cited by 191 publications

(117 citation statements)

References 27 publications

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“…Simulation results show that, compared with the traditional rule-based ESS size optimization approaches, the optimal operation strategy-based ESS capacity optimization model can significantly reduce battery capacity degradation and microgrid operation cost. To solve the ESS capacity optimization problem, some coevolutionary algorithms [19], deep learning algorithms [20], and meta-heuristic algorithms in land microgrids or large power system are used. Although these algorithms have superior modeling construction and searching the optimal objectives, however, the computation burden of these algorithms will be very large if the ESS operation strategy is considered in the ESS sizing optimization problems and a long time will be used to obtain the final result.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal Sizing of Battery Energy Storage System in a Shipboard Power System with considering Energy Management Optimization

Bao

Zhang

et al. 2021

Discrete Dynamics in Nature and Society

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Due to the increasing concerns about the environmental and economic issues of traditional ships, all-electric ships with energy storage and renewable energy integration have become more and more appealing for the forthcoming future. In this paper, an optimal energy storage system (ESS) capacity determination method for a marine ferry ship is proposed; this ship has diesel generators and PV panels. ESSs sizing optimization and power system scheduling optimization are simultaneously conducted and it is converted to a mixed-integer quadratic programming (MIQP) model with special modeling techniques. The case study shows that the proposed method is flexible and effective, and the relationships between the ESSs size and the discharge rate, life cycle times, or initial investment cost are investigated.

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Section: Literature Reviewmentioning

confidence: 99%

“…In (20), the startup cost C stu i,DG (t) is defined as the product of a single time startup cost c stu i,DG , which is a constant value, and the index value of startup action δ stu i,DG (t). e definition of shutdown cost is similar.…”

Section: Ips Operationmentioning

confidence: 99%

Optimal Sizing of Battery Energy Storage System in a Shipboard Power System with considering Energy Management Optimization

Bao

Zhang

et al. 2021

Discrete Dynamics in Nature and Society

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“…e simulation results showed that the proposed NBBO has higher stability, better convergence accuracy, and faster convergence speed than other method for iris classification. How to use NBBO algorithm to solve other multiobjective optimization problem compare with multiobjective optimization method [30][31][32][33] and realize better applications for engineering practice will be the focus of our next work.…”

Section: Discussionmentioning

confidence: 99%

A Novel BBO Algorithm Based on Local Search and Nonuniform Variation for Iris Classification

Wei

Wang

2021

Complexity

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In order to improve the iris classification rate, a novel biogeography-based optimization algorithm (NBBO) based on local search and nonuniform variation was proposed in this paper. Firstly, the linear migration model was replaced by a hyperbolic cotangent model which was closer to the natural law. And, the local search strategy was added to traditional BBO algorithm migration operation to enhance the global search ability of the algorithm. Then, the nonuniform variation was introduced to enhance the algorithm in the later iteration. The algorithm could achieve a stronger iris classifier by lifting weaker similarity classifiers during the training stage. On this base, the convergence condition of NBBO was proposed by using the Markov chain strategy. Finally, simulation results were given to demonstrate the effectiveness and efficiency of the proposed iris classification method.

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“…Moreover, learned policies based on the above-mentioned way can not support flexible operation of building energy systems, e.g., switching flexibly between low energy cost mode and high comfort mode. To avoid deciding weighted parameters for multiple objectives and support flexible operation, a possible way is to design building energy optimization algorithms based on the framework of multi-objective DRL [78] [79] or multi-objective meta-DRL [80].…”

Section: B Multi-objective Building Energy Optimizationmentioning

confidence: 99%

Deep Reinforcement Learning for Smart Home Energy Management

Xie

et al. 2020

IEEE Internet Things J.

286

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In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.

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Deep Reinforcement Learning for Multiobjective Optimization

Cited by 191 publications

References 27 publications

Optimal Sizing of Battery Energy Storage System in a Shipboard Power System with considering Energy Management Optimization

Optimal Sizing of Battery Energy Storage System in a Shipboard Power System with considering Energy Management Optimization

A Novel BBO Algorithm Based on Local Search and Nonuniform Variation for Iris Classification

Deep Reinforcement Learning for Smart Home Energy Management

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