A Fuzzy Q-Learning Algorithm for Storage Optimization in Islanding Microgrid

Yu, Yunjun; Yang, Qin; Gong, Hancheng

doi:10.1007/s42835-021-00769-7

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…In Figure 3, Ich and Idis are the charging and discharging current. The theoretical domain of the wind speed is taken as [6, 13.5] m/s, the theoretical domain of the frequency difference rate of change is taken as [−0.6, 0] Hz/s, the theoretical domain of the frequency deviation amount is taken as [−1, −0.033] Hz, and the theoretical domain of both the virtual inertia coefficient and the sag coefficient is taken as [5,20]. The theoretical domain of the wind speed is taken as [6, 13.5] m/s, the theoretical domain of the frequency difference rate of change is taken as [−0.6, 0] Hz/s, the theoretical domain of the frequency deviation amount is taken as [−1, −0.033] Hz, and the theoretical domain of both the virtual inertia coefficient and the sag coefficient is taken as [5,20].…”

Section: Fuzzy Control Modelmentioning

confidence: 99%

“…Reference [18] proposed an adaptive control method using a sliding film to cope with the variation in the system parameters. References [19][20][21] used a fuzzy control method to control the frequency regulation power of the energy storage in the wind storage system to improve its immunity to disturbances. This paper investigates a new primary frequency regulation control method for wind energy storage systems.…”

Section: Introductionmentioning

confidence: 99%

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Primary-Frequency-Regulation Coordination Control of Wind Power Inertia and Energy Storage Based on Compound Fuzzy Logic

Ma,

Xin,

Jiang

et al. 2023

Batteries

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The increasing proportion of wind power systems in the power system poses a challenge to frequency stability. This paper presents a novel fuzzy frequency controller. First, this paper models and analyzes the components of the wind storage system and the power grid and clarifies the role of each component in the frequency regulation process. Secondly, a combined fuzzy controller is designed in this paper, which realizes the cooperative control of frequency regulation considering wind power running state, battery energy management, and power grid stability. Finally, this paper establishes typical operation scenarios of various time scales to verify the effectiveness and feasibility of the proposed control strategy.

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Section: Fuzzy Control Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Primary-Frequency-Regulation Coordination Control of Wind Power Inertia and Energy Storage Based on Compound Fuzzy Logic

Ma,

Xin,

Jiang

et al. 2023

Batteries

View full text Add to dashboard Cite

show abstract

“…In the work of Yoldas et al (2020), a pilot stochastic and dynamic MG on a university campus was studied, and a Q-learning algorithm guided by multistage mixed-integer nonlinear programming (MINLP) was proposed to optimize the operation of the MG. In the work of Yu et al (2021), a Q-learning algorithm based on fuzzy control was proposed to improve the economics of the MG with an ESS. In the work of Shang et al (2020), an RL approach was proposed for the optimal scheduling of an MG, and the Monte Carlo tree search method was combined with the proposed RL approach.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

et al. 2023

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The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios.

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Harmonic compensated individual-phase control of four-leg inverters for microgrid applications

Lee,

Lee

et al. 2023

J. Power Electron.

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A Fuzzy Q-Learning Algorithm for Storage Optimization in Islanding Microgrid

Cited by 3 publications

References 28 publications

Primary-Frequency-Regulation Coordination Control of Wind Power Inertia and Energy Storage Based on Compound Fuzzy Logic

Primary-Frequency-Regulation Coordination Control of Wind Power Inertia and Energy Storage Based on Compound Fuzzy Logic

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

Harmonic compensated individual-phase control of four-leg inverters for microgrid applications

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