Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach

Wei, Xiangyu; Xiang, Yue; Li, Junlong; Zhang, Xin

doi:10.1109/tste.2022.3156426

Cited by 29 publications

(8 citation statements)

References 8 publications

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“…However, the solving difficulty, especially for solving time consumption, will exhibit an exponentially increasing tendency with the extension of the sharing network. To problems like this, employing intelligence algorithms such as reinforcement learning (Al-Abbasi et al, 2019;Wei et al, 2022) and genetic algorithms (Yang et al, 2019(Yang et al, , 2021a et al is a common way. Compared with existing algorithms, the method of reinforcement learning is more suitable for the dynamic dispatching issue of this article due to its excellent performance in solving sequential decision optimization problems.…”

Section: Methods Proposition 41 Algorithm Outlinementioning

confidence: 99%

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Sharing network features analysis and dispatching strategy design

Shi

2022

IMDS

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PurposeA vital job for one sharing business is dynamically dispatching shared items to balance the demand-supply of different sharing points in one sharing network. In order to construct a highly efficient dispatch strategy, this paper proposes a new dispatching algorithm based on the findings of sharing network characteristics.Design/methodology/approachTo that end, in this paper, the profit-changing process of single sharing points is modeled and analyzed first. And then, the characteristics of the whole sharing network are investigated. Subsequently, some interesting propositions are obtained, based on which an algorithm (named the Two-step random forest reinforcement learning algorithm) is proposed.FindingsThe authors discover that the sharing points of a common sharing network could be categorized into 6 types according to their profit dynamics; a sharing network that is made up of various combinations of sharing stations would exhibit distinct profit characteristics. Accounting for the characteristics, a specific method for guiding the dynamic dispatch of shared products is developed and validated.Originality/valueBecause the suggested method considers the interaction features between sharing points in a sharing network, its computation speeds and the convergence efficacy to the global optimum scheme are better than similar studies. It suits better to the sharing business requiring a higher time-efficiency.

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Section: Methods Proposition 41 Algorithm Outlinementioning

confidence: 99%

“…To problems like this, employing intelligence algorithms such as reinforcement learning (Al-Abbasi et al. , 2019; Wei et al. , 2022) and genetic algorithms (Yang et al.…”

Section: Methods Propositionmentioning

confidence: 99%

Sharing network features analysis and dispatching strategy design

Shi

2022

IMDS

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“…Actions in these efforts are charging/discharging power of energy storage, and reward is to add the power system operation-related indices on top of equation (9). For example, in [50], the penalty cost for real power deviations from the bidding volume is considered. In [51], both energy storage and renewable energy exist in a MG, and power exchange between MG and grid is reflected in reward.…”

Section: Energy Storage Controlmentioning

confidence: 99%

Application of reinforcement learning in planning and operation of new power system towards carbon peaking and neutrality

Sun¹,

Wang²,

Huang³

et al. 2023

Prog. Energy

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To mitigate global climate change and ensure a sustainable energy future, China has launched a new energy policy of achieving carbon peaking by 2030 and carbon neutrality by 2060, which sets an ambitious goal of building New Power System (NPS) with high penetration of renewable energy. However, the strong uncertainty, nonlinearity, and intermittency of renewable generation and their power electronics-based control devices are imposing grand challenges for secure and economic planning and operation of the NPS. The performance of traditional methods and tools becomes rather limited under such phenomena. Together with high-fidelity modeling and high-performance simulation techniques, the fast development of artificial intelligence (AI) technology, especially reinforcement learning (RL), provides a promising way of tackling these critical issues. This paper first provides a comprehensive overview of RL methods that interact with high-fidelity grid simulators to train effective agents for intelligent, model-free decision-making. Secondly, three important applications of RL are reviewed, including device-level control, system-level optimized control, and demand side management, with detailed modeling and procedures of solution explained. Finally, this paper discusses future research efforts for achieving the goals of full absorption of renewable energy, optimized allocation of large-scale energy resources, reliable supply of electricity, and secure and economic operation of the power grid.

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“…Equipping the wind farm with an energy storage system can alleviate the above problems to a certain extent [ 7 , 8 , 9 , 10 ]. Therefore, how to realize a high-efficient wind-storage cooperative decision-making is a key issue for promoting the full absorption of wind energy [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…(1) The flexible loads embedded in the wind-storage cooperative framework have not been developed sufficiently in the existing literature. In [ 11 , 19 , 20 , 21 ], the authors did not focus on the favorable effect of the flexible loads in the proposed wind-storage model. As an example, flexible loads were considered in [ 22 ], where the benefits from the suitable management of demand-side flexible loads were validated.…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems

Zhai

Qiu

et al. 2023

Entropy

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As a promising information theory, reinforcement learning has gained much attention. This paper researches a wind-storage cooperative decision-making strategy based on dueling double deep Q-network (D3QN). Firstly, a new wind-storage cooperative model is proposed. Besides wind farms, energy storage systems, and external power grids, demand response loads are also considered, including residential price response loads and thermostatically controlled loads (TCLs). Then, a novel wind-storage cooperative decision-making mechanism is proposed, which combines the direct control of TCLs with the indirect control of residential price response loads. In addition, a kind of deep reinforcement learning algorithm called D3QN is utilized to solve the wind-storage cooperative decision-making problem. Finally, the numerical results verify the effectiveness of D3QN for optimizing the decision-making strategy of a wind-storage cooperation system.

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Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach

Cited by 29 publications

References 8 publications

Sharing network features analysis and dispatching strategy design

Sharing network features analysis and dispatching strategy design

Application of reinforcement learning in planning and operation of new power system towards carbon peaking and neutrality

An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems

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