A Modified Control Scheme for Power Management in an AC Microgrid with Integration of Multiple Nanogrids

Talapur, Girish G.; Suryawanshi, Hiralal Murlidhar

doi:10.3390/electronics8050490

Cited by 7 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Power generation and load fluctuate a lot. Reference [10] proposes a modified centralized control scheme for multiple NGs in an AC MG. The proposed system contains a photovoltaic (PV)/ BESS as a master unit and three other NGs.…”

Section: Power Management Strategies In DC Mngsmentioning

confidence: 99%

Adaptive control and management of multiple nano‐grids in an islanded dc microgrid system

Abadi

Khalili

Habibi

et al. 2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

This paper presents an adaptive control framework for the flexible and effective management and control of clustered DC nano-grids (NGs) in an islanded DC microgrid system. It is assumed that each NG contains a photovoltaic (PV) system, a battery energy storage system (BESS), local loads, and a gateway (GW) module. Each NG has a hierarchical control system consisting of a decision-making module and low-level controllers. The decisionmaking module ensures various desirable features including plug-and-play operation of NGs, maximum utilization of PV power generations, and avoiding state of charge (SoC) violation of batteries. Moreover, an adaptive model predictive control (AMPC) strategy is proposed to regulate the voltage of the NG local DC buses in the presence of nonlinear loads. This approach improves the performance of the NG voltage control system and reduces the current ripples of BESSs, thereby enhancing the lifetime of the batteries. In addition, a smart switching consensus-based control strategy is designed that provides flexible power sharing among the NGs to balance the SoC of BESSs in which the BESSs altogether imitate the behaviour of a single cloud energy storage system (ESS). Finally, the performance of the proposed control system is verified by simulating the DC microgrid in MATLAB/Simulink.

show abstract

Section: Power Management Strategies In DC Mngsmentioning

confidence: 99%

Adaptive control and management of multiple nano‐grids in an islanded dc microgrid system

Abadi

Khalili

Habibi

et al. 2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…A hierarchical topology is hybrid central control, a combination of centralized and decentralized control. Decentralized control can be used for immediate, source-level power-sharing to reduce the control pressure on the centralized controller [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis and simulation of optimized micro-grid

Ali¹,

Usman²,

Aziz³

et al. 2022

murjet

View full text Add to dashboard Cite

The centralized power grid bears a heavy burden in a time when consumers expect an uninterrupted reliable power supply, a reduction in carbon emissions, increased efficiency within the national grid, and power supplied to remote communities. One such structure is used to implement this type of control that is based on small- scale Distributed Generation (DGs), at a single house or a small building level: micro-grid (μG). A μG is an autonomous, sustainable hybrid framework that uses renewable and non-renewable energy resources to supply continuous electricity to the load. Taking this scenario into account, the three sources of generation that have been utilized in the proposed μG are Photovoltaic (PV) array, Fuel Cell (FC), and Wind Turbine (WT) in this research paper. The active and reactive power of all three generation resources has been controlled using various controllers, i.e., integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, Fractional Order Proportional Integral Derivative (FOPID), and Sliding Mode Controller (SMC). An advanced optimization technique based on a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm has been utilized to optimize all these controllers. The integral square error has been taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes has been evaluated to find the best optimal solution using MATLAB/Simulink. The performance of SMC has been surpassed the performance of all other optimized controllers for the power stability analysis. Moreover, a smart switching algorithm has been introduced for switching between the generation resources following the load demand and cost of the system to operate the μG more economically. Finally, a case study has been performed in which the smart switching algorithm has been utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads.

show abstract

“…The latter can allow the interconnected nanogrids to withstand power outages without compromising the operation of critical loads. In [39], a cluster of nanogrids in a microgrid configuration is considered, where the reactive power, current unbalance and harmonics are managed locally at each nanogrid. Therefore, transients between the microgrid and the utility grid are eliminated, as the microgrid can be deemed as a constant load in the main grid, resulting in smooth transitions between grid-connected and islanding operations.…”

Section: Adoption Of Nanogrids and Their Impactmentioning

confidence: 99%

Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings

et al. 2021

View full text Add to dashboard Cite

Distributed generation (DG) systems are growing in number, diversifying in driving technologies and providing substantial energy quantities in covering the energy needs of the interconnected system in an optimal way. This evolution of technologies is a response to the needs of the energy transition to a low carbon economy. A nanogrid is dependent on local resources through appropriate DG, confined within the boundaries of an energy domain not exceeding 100 kW of power. It can be a single building that is equipped with a local electricity generation to fulfil the building’s load consumption requirements, it is electrically interconnected with the external power system and it can optionally be equipped with a storage system. It is, however, mandatory that a nanogrid is equipped with a controller for optimisation of the production/consumption curves. This study presents design consideretions for nanogrids and the design of a nanogrid system consisting of a 40 kWp photovoltaic (PV) system and a 50 kWh battery energy storage system (BESS) managed via a central converter able to perform demand-side management (DSM). The implementation of the nanogrid aims at reducing the CO2 footprint of the confined domain and increase its self-sufficiency.

show abstract

A Modified Control Scheme for Power Management in an AC Microgrid with Integration of Multiple Nanogrids

Cited by 7 publications

References 25 publications

Adaptive control and management of multiple nano‐grids in an islanded dc microgrid system

Adaptive control and management of multiple nano‐grids in an islanded dc microgrid system

Analysis and simulation of optimized micro-grid

Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings

Contact Info

Product

Resources

About