Design and control of photovoltaic/wind/battery based microgrid system

Küçüker, Ahmet; Kamal, Tariq; Hassan, Syed Zulqadar; Li, Hui; MaazMufti, Gussan; Waseem, MuhammadHamza

doi:10.1109/icee.2017.7893445

Cited by 17 publications

(4 citation statements)

References 10 publications

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“…In Küçüker et al (2017), a hybrid energy management strategy is proposed by using a hierarchical genetic algorithm (HGA) to alter the fuzzy inference system's rule base. The fuzzy-HGA method seems to be more effective than the conventional fuzzy-GA approach, even with just 47% of the total rules in the rule base.…”

Section: Hybrid Approachmentioning

confidence: 99%

“…Numerous studies have focused on the load control strategies used by DSM (Jabir et al 2018), the roles played by DSM in the electricity market (Morgan and Talukdar 1979), the economic benefits of DS (Conchado and Linares 2012), the impacts of DSM on the commercial and residential sectors (Esther and Kumar 2016;Shoreh et al 2016), the interactions between DSM and other smart grid technologies (Khan et al 2015b), the business strategies used by DSM (Behrangrad 2015), the impacts of DSM on the reliability of the power system (Kirby 2006), the optimization strategies used by DSM (Hussain et al 2015;Vardakas et al 2014), and the load control strategies (Khan et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

et al. 2023

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Demand-side management, a new development in smart grid technology, has enabled communication between energy suppliers and consumers. Demand side energy management (DSM) reduces the cost of energy acquisition and the associated penalties by continuously monitoring energy use and managing appliance schedules. Demand response (DR), distributed energy resources (DER), and energy efficiency (EE) are three categories of DSM activities that are growing in popularity as a result of technological advancements in smart grids. During the last century, the energy demand has grown significantly in tandem with the increase in the global population. This is related to the expansion of business, industry, agriculture, and the increasing use of electric vehicles. Because of the sharp increase in global energy consumption, it is currently extremely difficult to manage problems such as the characterization of home appliances, integration of intermittent renewable energy sources, load categorization, various constraints, dynamic pricing, and consumer categorization. To address these issues, it is critical to examine demand-side management (DSM), which has the potential to be a practical solution in all energy demand sectors, including residential, commercial, industrial, and agricultural. This paper has provided a detailed analysis of the different challenges associated with DSM, including technical, economic, and regulatory challenges, and has proposed a range of potential solutions to overcome these challenges. The PRISMA reviewing methodology is adopted based on relevant literature to focus on the issues identified as barriers to improving DSM functioning. The optimization techniques used in the literature to address the problem of energy management were discussed, and the hybrid techniques have shown a better performance due to their faster convergence speed. Gaps in future research and prospective paths have been briefly discussed to provide a comprehensive understanding of the current DSM implementation and the potential benefits it can offer for an energy management system. This comprehensive review of DSM will assist all researchers in this field in improving energy management strategies and reducing the effects of system uncertainties, variances, and restrictions.

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Section: Hybrid Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

et al. 2023

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“…Control and switching-based energy management system is recommended for the microgrid system consisting of wind, PV and battery in [23]. The recommended energy management system (EMS) is available with or without mains.…”

Section: Related Literaturementioning

confidence: 99%

“…For example, according to the strategy, while wind is used as the primary power source, PV is added to increase the reliability of the system in different weather conditions. The battery module is used as an energy storage system during backup power during excess power and/or demand in [23].…”

Section: Related Literaturementioning

confidence: 99%

Neuro-Fuzzy-Based Model Predictive Energy Management for Grid Connected Microgrids

et al. 2020

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With constant population growth and the rise in technology use, the demand for electrical energy has increased significantly. Increasing fossil-fuel-based electricity generation has serious impacts on environment. As a result, interest in renewable resources has risen, as they are environmentally friendly and may prove to be economical in the long run. However, the intermittent character of renewable energy sources is a major disadvantage. It is important to integrate them with the rest of the grid so that their benefits can be reaped while their negative impacts can be mitigated. In this article, an energy management algorithm is recommended for a grid-connected microgrid consisting of loads, a photovoltaic (PV) system and a battery for efficient use of energy. A model predictive control-inspired approach for energy management is developed using the PV power and consumption estimation obtained from daylight solar irradiation and temperature estimation of the same area. An energy management algorithm, which is based on a neuro-fuzzy inference system, is designed by determining the possible operating states of the system. The proposed system is compared with a rule-based control strategy. Results show that the developed control algorithm ensures that microgrid is supplied with reliable energy while the renewable energy use is maximized.

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Design and Control of Utility Grid Interfaced Solar Photovoltaic System for Bipolar DC Microgrid

Dodda

Sandepudi

2021

Lecture Notes in Electrical Engineering

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Design and control of photovoltaic/wind/battery based microgrid system

Cited by 17 publications

References 10 publications

A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

Neuro-Fuzzy-Based Model Predictive Energy Management for Grid Connected Microgrids

Design and Control of Utility Grid Interfaced Solar Photovoltaic System for Bipolar DC Microgrid

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