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This study proposes a new demand-side management (DSM) technique, which is characterised by low computational requirements. The proposed technique relies on developing an operational matrix by the device local controller based on the device characteristics and the customer preferences. This matrix is sent to the energy management system (EMS) without the need to send any further information about the device or the customer preferences; then, the EMS chooses the optimal schedule for the device. To demonstrate the effectiveness of the proposed DSM technique, it is incorporated in an EMS that consists of three units controlled by a centralised microgrid controller (MGC). The three units managed by the MGC are the data collection and storage engine, the forecasting engine, and the optimisation engine. The EMS utilises the rolling horizon concept to manage real-time information and to provide the plug-and-play option for all controllable devices. Simulation results on a typical microgrid system show that the proposed DSM technique outperforms conventional DSM approaches in terms of the computational time.

This paper focuses on the comprehensive restoration of both voltage and frequency in a standalone microgrid (SAMG). In a SAMG, the power balance is achieved through traditional methods such as droop control for power sharing among distributed generators (DGs). However, when such microgrids (MGs) are subjected to perturbations coming from stochastic renewables, the frequency and voltage parameters deviate from their specified values. In this paper, a novel hybrid-type consensus-based distributed controller is proposed for voltage and frequency restoration. Data-based communication is ensured among the DGs for controlling voltage and frequency parameters. Different parameters such as voltage, frequency, and active and reactive power converge successfully to their nominal values using the proposed algorithms, thereby ensuring smooth operation of inverter-dominated DGs. Additionally, the machine-learning-based long short-term memory (LSTM) algorithm is implemented for renewable power forecasting using historical data from the proposed location for visualising the insolation profile. The effectiveness of our approach is demonstrated through a SAMG, which consists of four inverters, showing that the proposed approach can improve system stability, increase efficiency and reliability, and reduce costs compared to traditional methods. The complete study is performed in Python and MATLAB environments. Our results highlight the potential of data-driven approaches to revolutionise power system operation and control.

The increase in energy-efficient DC appliances and electronic gadgets has led to an upheaval in the usage of AC–DC power convertors; hence, power loss in converter devices is cumulatively increasing. Evolving microgrid technology has also become deeply integrated with the conversion process due to increased power converters in its infrastructure, significantly worsening the power loss situation. One of the practical solutions to this disturbance is to reduce conversion losses in domestic distribution systems through the optimal deployment of the battery storage system and solar PV power using microgrid technology. In this paper, a novel energy management system is developed that uses a new control algorithm, termed Inefficient Power Conversion Elimination Algorithm (IPCEA). The proposed algorithm compares the Net Transferable Power (NTP) available on the DC side with the loss rate across the converter. The converter is switched off (or disconnected from the grid and load) if the NTP is less than 20% of the converter rating to avoid low-efficiency power conversion. The solar PV system is connected to the DC bus to supply the DC loads while the AC loads are supplied from the AC source (utility power). An auxiliary battery pack is integrated to the DC side to feed DC loads during the absence of solar energy. A battery energy storage system (BESS) is deployed to manage energy distribution effectively. The power distribution is managed using a centralized microgrid controller, and the load demand is met accordingly. Thereby, the power generated by the solar PV can be utilized effectively. Microgrid technology’s effectiveness is emphasized by comparative analysis, and the achievements are discussed in detail and highlighted using a prototype model.

This study proposes flexible controllers for the interlinking converter (ILC) and interfacing converters (IFCs) used in coupled hybrid AC/DC microgrids (HMGs). Proposed controllers are specifically designed for the multiple stacked bidirectional DC–AC ILCs/IFCs based microgrid outlays, to omit the droop power flow and system stability issues. The ILC and IFC grid supportive converter controllers focus on the wide-spread AC/DC bus parameters control for both DC and AC bus voltage regulation and superfluous power sharing while operating in the grid forming and feeding modes. Proposed controllers minimise the need for the controller parameter tuning as opposed to the conventional controllers used in zonal HMG systems. This makes the system stable for a much wider operating conditions as opposed to the widely used higher-order PLL integrated PQ and dq0 control algorithms. The proposed HMG also integrates the centralised battery energy stack through bidirectional dual active bridge DC–DC converter for achieving the high-power transfer efficiency and omitting the isolation issues between medium-voltage and low-voltage DC buses. The HMG system performance is evaluated using the simulation studies for various strategical operational modes. Further, the proposed controllers have also been tested individually on experimental platform.

Hybrid microgrids struggle to manage electricity due to renewable source, storage, and load demand variability. This paper proposes a centralized controller employing hybrid deep learning and evolutionary optimization to overcome these issues. Solar panels, BESS, EVs, dynamic loads, steady loads, and a switching main grid make up the hybrid microgrid. To capture spatial and temporal patterns, a centralized controller uses a deep learning model with a CNN–LSTM architecture. The imperialist competitive algorithm (ICA) optimizes neural network hyperparameters for more accurate controller outputs. The controller controls grid switching, voltage source converter power, and EV reference current. R2 values of 0.9602, 0.9512, and 0.9618 show reliable controller output predictions. A typical test case, low sunshine, and no EV or BESS initial charging are validation situations. Its constant power flow, uncertainty management, and adaptability make this controller better than others. Even with intermittent energy and limited storage capacity, the ICA-optimized hybrid deep learning controller stabilized smart-grids.

In this article, an approach to balance the State of Charge (SoC) of two batteries connected to the DC bus of a fuel cell (FC) electric aircraft by Droop-controlled converters is described. The proposed algorithm is based on shifting the Droop reference voltages and prevents the simultaneous charging and discharging of the batteries. This approach is not only practical but also highly versatile, as it is compatible with all converters as long as the Droop voltage can be changed remotely, and a current measurement is provided to a central controller. No further programming access to the DC/DCs is necessary. There is no need for nonlinear or different-valued Droop resistances for charging and discharging. The balancing approach is validated via simulation in MATLAB/Simulink 2024a.The results show that the proposed approach achieves SoC balancing without degrading the dynamic performance of the grid. The delays added by the slower communication with the central controller have a minimal impact on performance.

The abundant use of solar energy in Indonesia has the potential to become electrical energy in a microgrid system. Currently the use of renewable energy sources (RESs) in Indonesia is increasing in line with the reduction of fossil fuels. This paper proposes a new microgrid DC configuration and designs a centralized control strategy to manage the power flow from renewable energy sources and the load side. The proposed design uses three PV arrays (300 Wp PV module) with a multi-battery storage system (MBSS), storage (200 Ah battery). Centralized control in the study used an outseal programmable logic controller (PLC). In this study, the load on the microgrid is twenty housing, so that the use of electrical energy for one day is 146.360 Wh. It is estimated that in one month it takes 4.390.800 Wh of electrical energy. The new DC microgrid configuration uses a hybrid configuration, namely the DC coupling and AC coupling configurations.The results of the study show that the DC microgrid hybrid configuration with centralized control is able to alternately regulate the energy flow from the PV array and MBSS. The proposed system has an efficiency of 98% higher than the previous DC microgrid control strategy and configuration models.

DC microgrid (DCMG) is a promising technology for integrating distributed resources, such as solar generation and energy storage devices, that are intrinsically DC. Recently, model predictive control (MPC) is one of the control techniques that has been widely used in microgrid applications due to its advantages, such as transient response and flexibility to nonlinearity inclusion. MPC applications can be centralized, distributed, or decentralized based on the communication architecture. A major disadvantage of the centralized model predictive control (CMPC) is the high computational effort. This paper proposes a CMPC for DCMG stabilization that uses the admittance matrix of a reduced DCMG in the prediction equation and the one-step prediction horizon to decrease the computational effort. The proposed CMPC also replaces the hierarchical architecture primary and secondary controls, achieving voltage or power regulation. A hardware-in-the-loop (HIL) tool, known as RT-Box 2, has been used to emulate an 8-node DC microgrid with versatile buck–boost converters at the supply and power consumption nodes. The proposed predictive control exhibited better performance in comparison with the averaged voltage control in the HIL experiments.