An Improved Voltage-Shifting Strategy to Attain Concomitant Accurate Power Sharing and Voltage Restoration in Droop-Controlled DC Microgrids

Silva, Waner Wodson A. G.; Oliveira, Thiago Ribeiro de; Donoso-Garcia, P.F.

doi:10.1109/tpel.2020.3009619

Cited by 46 publications

(52 citation statements)

References 44 publications

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“…Moreover, reactive power flows, voltage unbalances, and harmonics are absent from the DC microgrid, makes it easier to control [3]. With the continuous advancement of control theory, many advanced control algorithms [4] have been developed recently. In this context, researchers have proposed various control frameworks such as centralized control [5], distributed control [6], and hierarchical control [7], to meet the communication requirements in practical projects to ensure normal operations of the microgrids.…”

Section: Introductionmentioning

confidence: 99%

“…In AC and hybrid AC/DC microgrids, the MPC-based strategies have been proposed for the control of AC/DC [8], [9], and DC/DC bidirectional interlinking converters (BICs) [1]. In DC microgrids, different MPC-based techniques have been proposed for stability improvement [15], [16] optimal energy management [3], improving power-sharing [4], control of DC microgrids including constant power loads (CPLs) [12], [17], [18], highperformance control of DC microgrid with a good transient tracking error [21], control of naval DC microgrids supplying pulsed power loads [13], [14], mitigating distribution power loss of DC microgrids with DC electric springs [22], control of DC/DC BICs [1], [20], and maximum power point tracking of photovoltaic (PV) sources [19]. As known, MPCs possess some features such as the basic inclusion of systems constraints and nonlinearities, flexibility to implement, and time-consuming behavior due to progressing optimization and prediction processes to manage the plant [23].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control

Akpolat

Habibi

Baghaee

et al. 2022

IEEE Trans. Energy Convers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control

Akpolat

Habibi

Baghaee

et al. 2022

IEEE Trans. Energy Convers.

View full text Add to dashboard Cite

show abstract

“…Where the output voltage ripples, charging current ripples overheat the battery and shorten its lifespan [24]. With the blossoming development of EVs, DC-DC converters have been utilized to regulate the output voltage and alleviate the battery current ripples [3,6,25]. However, converters are still facing challenges to rapidly reach the desired output voltage with minimum error, such as load variation, disturbances in the input voltage, parameter deviation, and pulse width modulation (PWM) saturation constraints of the converters [4,19,26].…”

Section: Introductionmentioning

confidence: 99%

Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery

2022

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Electric vehicles are rapidly infiltrating the power grid worldwide, initiating an immediate need for a smart charging technique to maintain the stability and robustness of the charging process despite the generation type. Renewable energy sources (RESs), especially photovoltaic (PV), are becoming the essential source for electric vehicle charging points. The stochastic behavior of the PV output power affects the power conversion for regulating the battery charger voltage levels, which influences the battery to overheat and degrade. This study presents a PV standalone smart charging process for off-board plug-in electric vehicles, represented by a small-scale lithium-ion battery based on the multistage charging currents (MSCC) protocol. The charger comprises a DC–DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory recurrent neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. Additionally, it was used as an alarm flag for any possible PV output shortage during the charging process in the long- and short-term prediction to be supported by any other electricity source. The NNPC–LSTM controller was compared with the fuzzy logic and the conventional PID controllers while varying the input voltage and implementing the MSCC protocol. The proposed charging controller perfectly ensured that the minimum battery terminal voltage ripple and charging current ripple reached 1 mV and 1 mA, respectively, with a very high-speed response of 1 ms in reaching the predetermined charging current stages. The present simulated and experimental results are in good agreement with the previous related work in the literature survey.

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“…However, effective utilization of source power is affected because it does not have power management methods. In [19][20][21][22], the voltage regulation improvement with the reduction of circulating current is addressed. Table I highlights the state-of-the-art for the control system in DC microgrid in terms of various parameters, based on a comprehensive review.…”

Section: Introductionmentioning

confidence: 99%

An Improved Voltage Regulation and Effective Power Management by Coordinated Control Scheme in Multibus DC Microgrid

et al. 2022

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The available power is not always constant in renewable energy sources (RESs). It varies depending on the environmental conditions and time interval. These raise the problems of mismatch in power-sharing among the RESs and bus voltage regulation. The novelty of this article is that it presents a new control scheme for a multibus DC microgrid, in which it improves voltage regulation at the load bus under different network topologies and various system operating conditions. The proposed control method achieves adequate power-sharing proportional to the available power in RESs connected to the DC microgrid. Therefore, it accomplishes effectual power management and reduces the burden on the energy storage system (ESS) in the DC microgrids. It adapts to the inclusion and removal of sources under different network topologies. This control scheme is tested experimentally for various network topologies under diverse operating conditions. The results prove the feasibility of the proposed method.INDEX TERMS Availability index, multibus DC microgrid, network topology, power management, voltage regulation.

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An Improved Voltage-Shifting Strategy to Attain Concomitant Accurate Power Sharing and Voltage Restoration in Droop-Controlled DC Microgrids

Cited by 46 publications

References 44 publications

Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control

Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control

Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery

An Improved Voltage Regulation and Effective Power Management by Coordinated Control Scheme in Multibus DC Microgrid

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