Optimal Control of a Microgrid with Distributed Renewable Generation and Battery Energy Storage

Alshehri, Jaber; Alzahrani, Ahmed; Khalid, Muhammad; Al–Ismail, Fahad Saleh

doi:10.1109/isgt45199.2020.9087685

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…PI controller in Figure 4 is a combination of proportional (Kp) and integral (Ki) [22]. The advantage of the combination of proportional and integral control is to speed up the reaction of a system and eliminate offsets [23].…”

Section: Pi Control Systemmentioning

confidence: 99%

Implementation of proportional–integral control in Baglog steamer temperature control

et al. 2022

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Sterilization is the oyster mushroom cultivation process. Sterilization is used to kill nuisance microorganisms that can inhibit mushroom growth. The sterilization process is 8 hours at a temperature of 70–95 oC. This process of frequent breakdown is caused by the unstable temperature sterilization space and is controlled manually. Based on these problems, the right solution is to use a steamer that can be controlled automatically using the proportional–integral (PI) control method. PI controller consists of proportional gain and integral gain. To determine the value of proportional gain and integral gain, this study used the Ziegler-Nichols tuning method using the S curve. The results of the PI control parameters obtained the value of Kp=25.2 and Ki=0.302. Thus, producing a transient response graph with Mp=94.5; Os=0.45; PO=0.47; Tr=16,440 s. The system can work according to setpoint 95 oC and maintain a stable temperature according to the setpoint with these results. And the sterilization time becomes fast from 8 hours to 6 hours.

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Section: Pi Control Systemmentioning

confidence: 99%

Implementation of proportional–integral control in Baglog steamer temperature control

et al. 2022

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An Optimized Planning Model for Management of Distributed Microgrid Systems

Kaur¹,

Singh²,

Manna³

et al. 2022

Smart Innovation, Systems and Technologies

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A review of control strategies for optimized microgrid operations

Juma,

Ayeng'o,

Kimambo

2024

IET Renewable Power Gen

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Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management.

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Optimal Control of a Microgrid with Distributed Renewable Generation and Battery Energy Storage

Cited by 4 publications

References 18 publications

Implementation of proportional–integral control in Baglog steamer temperature control

Implementation of proportional–integral control in Baglog steamer temperature control

An Optimized Planning Model for Management of Distributed Microgrid Systems

A review of control strategies for optimized microgrid operations

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