Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning

Yu, Zekuan; Ruan, Guangchun; Wang, Xinyue; Zhang, Guanglun; He, Yiliu; Zhong, Haiwang

doi:10.1109/ei256261.2022.10116685

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…RL algorithms can learn to optimize the scheduling of generators, transmission lines, and energy storage systems by maximizing the efficiency of the system and minimizing the operational costs. Main RL applications in power system scheduling are Unit commitment [21], Economic dispatch [22], Reserve scheduling [23] and Demand response [24].…”

Section: Reinforcement Learningmentioning

confidence: 99%

Reinforcement Learning in Power System Control and Optimization

Bernadić¹,

Kujundžić²,

Primorac

2023

B&H Electrical Engineering

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Reinforcement learning (RL) is area of Machine Learning (ML) and part of wide-range portfolio of the Artificial intelligence (AI) methods. Besides the explanations of the concepts and principles of RL, in the paper are presented practical RL models for control and optimizing operation of power system – controlling tap-changers for maintain voltage levels and model for techno-economical optimizing operation of energy storages of households in microgrid. Trained RL agent in the practical example synchronizes operation of tap-changers to maintain satisfactory voltage level for the consumers, even in the network with distributed generation. Energy storages are in wide use in households, especially in the combination with PV. In the second example, microgrid’s energy management system (EMS) RL agent after learning process act in the simulated environment with variable electrical energy prices, variable load profiles and efficiency of PV modules of households to maximize profit for the houseowners in the microgrid. Agent controls charging and discharging of energy storages and obtain maximal benefit in randomly determined conditions of microgrid operation and different tariff situations. Models are implemented in the Python programming environment Python with specialized power system simulation software (Pandapower) and RL libraries (RLib, OpenAI).

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Section: Reinforcement Learningmentioning

confidence: 99%

Reinforcement Learning in Power System Control and Optimization

Bernadić¹,

Kujundžić²,

Primorac

2023

B&H Electrical Engineering

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Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach

Wang

Zhong

Zhang

et al. 2023

2023 IEEE 6th International Electrical and Energy Conference (CIEEC)

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Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

Pesántez,

Guamán,

Córdova

et al. 2024

Energies

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The efficient planning of electric power systems is essential to meet both the current and future energy demands. In this context, reinforcement learning (RL) has emerged as a promising tool for control problems modeled as Markov decision processes (MDPs). Recently, its application has been extended to the planning and operation of power systems. This study provides a systematic review of advances in the application of RL and deep reinforcement learning (DRL) in this field. The problems are classified into two main categories: Operation planning including optimal power flow (OPF), economic dispatch (ED), and unit commitment (UC) and expansion planning, focusing on transmission network expansion planning (TNEP) and distribution network expansion planning (DNEP). The theoretical foundations of RL and DRL are explored, followed by a detailed analysis of their implementation in each planning area. This includes the identification of learning algorithms, function approximators, action policies, agent types, performance metrics, reward functions, and pertinent case studies. Our review reveals that RL and DRL algorithms outperform conventional methods, especially in terms of efficiency in computational time. These results highlight the transformative potential of RL and DRL in addressing complex challenges within power systems.

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Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning

Cited by 3 publications

References 14 publications

Reinforcement Learning in Power System Control and Optimization

Reinforcement Learning in Power System Control and Optimization

Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

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