Deep-Reinforcement-Learning-Based Capacity Scheduling for PV-Battery Storage System

Huang, Bin; Wang, Jianhui

doi:10.1109/tsg.2020.3047890

Cited by 87 publications

(29 citation statements)

References 17 publications

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“…Distributed control Proximal policy optimization 2020 [144] Capacity scheduling of PV-Battery system A proximal policy optimization-based DRL is studied to perform the capacity scheduling of PV-BSS where the method can readily leverage continuous action space and determine the specific amount of charging/discharging.…”

Section: Milpmentioning

confidence: 99%

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Li¹

2021

Preprint

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From self-driving vehicles, voice recognition based virtual digital assistants, smart thermostats to recommendation systems, Artificial Intelligence (AI) is becoming a crucial part of the carbon neutral society that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. The emergence of AI initiates numerous opportunities to transform energy industry to AI-powered smart system which can revolutionize traditional approaches of creativity thinking, strategical operation, and solution seeking, especially for accelerating carbon neutrality of our society. This survey provides a comprehensive overview of fundamental principles that underpin applications of big data analysis in Energy Internet (EI), such as smart energy supply and consumption, smart health and Fintech. Next, we focus on intelligent decision-making for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature. Subsequently, cybersecurity issues for AI system related to EI are discussed with recent advancements from vulnerability analysis of AI system to differential privacy and to blockchain based security technology. To our knowledge, this is one of the first academic, peer-reviewed works to provide a systematic review of AI applications for EI research and initiatives in terms of big data analysis, intelligent decision-making and AI related cybersecurity These initiatives were systematically classified into different groups according to the field of application, methodology and contribution Afterwards, potential challenges, limitations for existing research and opportunities for future directions are discussed, ranging from emerging explainable AI, to localized multi-energy marketplaces, self-driving electric vehicle charging and e-mobility. This paper can help us understand how to build smart cities and critical infrastructure for a climate-changed world towards the UN’s sustainable development goals.

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Section: Milpmentioning

confidence: 99%

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Li¹

2021

Preprint

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“…In [18], a hybrid interval robust optimization method is proposed and the optimal SoC interval is obtained. In order to improve the profitability of photovoltaic and energy storage system investors, reference [19] uses the Proximal Policy Optimization agent to allocate system capacity. Reference [20] proposes capacity optimization that takes into account the degradation of the battery.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The objective function of the lower-level is: E is the SOC of energy storage es in the x optimization cycle. The initial and final states of stored energy are limited by (18) and (19). The state of stored energy is calculated by (20).…”

Section: ) Lower Levelmentioning

confidence: 99%

Market Impact of Wind-Energy Storage Alliance Strategic Bidding Under Uncertainty

Li¹,

Wang

Jin

2021

IEEE Access

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“…In addition, compared to rule-based algorithms, RL methods can learn directly from experiences without constructing an expert dataset [2], [10]. Learning-based control paradigms have been proposed for a variety of operation tasks in power grids, including control of voltage and frequency [11]- [14], capacity scheduling of PV and energy storage [15], topology control [1] and many more. A more detailed review of RL for power system operation can be found in [10].…”

Section: A Related Workmentioning

confidence: 99%

Understanding the Safety Requirements for Learning-based Power Systems Operations

Chen¹,

Arnold²,

Shi³

et al. 2021

Preprint

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Recent advancements in machine learning and reinforcement learning have brought increased attention to their applicability in a range of decision-making tasks in the operations of power systems, such as short-term emergency control, Volt/VAr control, long-term residential demand response and battery energy management. Despite the promises of providing strong representation of complex system dynamics and fast, efficient learned operation strategies, the safety requirements of such learning paradigms are less discussed. This paper explores the design requirements on both data and model side of such learning algorithms by exploiting the impacts of adversarial attacks on safety critical system operations. Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.

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Deep-Reinforcement-Learning-Based Capacity Scheduling for PV-Battery Storage System

Cited by 87 publications

References 17 publications

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Market Impact of Wind-Energy Storage Alliance Strategic Bidding Under Uncertainty

Understanding the Safety Requirements for Learning-based Power Systems Operations

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