AC-DC Microgrid Research Articles

Energy management of hybrid AC/DC microgrid considering incentive‐based demand response program

AbstractIncreasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive‐based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost‐saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive‐based demand response programming.

Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

The worldwide need for electrical energy is increasing, and integrating renewable energy sources (RES) into the power grid will enhance the efficient use of clean energy to fulfill the growing demand for energy. However, the uncertain nature of power from the RES like solar and wind, utility price, and load demand, necessitates accurate forecasting of the uncertain parameters (UP) to improve the reliability of the hybrid microgrid. In this work, optimal energy management (EM) of a hybrid AC-DC microgrid (HMG) is proposed which comprises of two phases, forecasting and scheduling. In the former phase, the uncertainties like day-ahead utility price, electrical demand, and power from the RES are forecasted using the support vector machine (SVM) algorithm and the results are compared with the artificial neural network (ANN) algorithm. In the second phase, the improved Teaching and Learning-Based Optimization (ITLBO) algorithm isused to reduce the generation costs over a 24-h period in a hybrid microgrid. The forecasted uncertain parameters are used as input in the second phase. Power trading occurs between the utility grid and the hybrid microgrid based on load demand and bidding costs, aiming to minimize generation costs. The proposed framework's viability and performance are assessed using IEEE standard test systems. The generating cost, as well as the optimal power dispatch of the HMG, is obtained using the ITLBO algorithm, and the results are compared with different meta-heuristic techniques such as the teaching and learning-based algorithm (TLBO), Ant Lion Optimization algorithm (ALO) and the artificial bee colony algorithm (ABC). The results obtained demonstrate the superiority of the SVM algorithm in forecasting and the ITLBO algorithm over other methods in minimizing operating costs.

Dynamic load reference based DQ-axis synchronous frame control method of grid connected PV hybrid microgrid

The hybrid AC-DC microgrid is a system that links AC and DC microgrids using a bidirectional AC-DC interface converter. The presence of dynamic loads is a major obstacle to the implementation of the microgrid concept, and those 50 % include asynchronous motors. This paper presents a control system for a hybrid microgrid that powers an electrical motor using the field-oriented control (FOC) method. This study investigates the significance of third-harmonic injected PWM (THIPWM) in reducing the necessary voltage across the DC bus while still achieving the required AC voltage at motor terminals. MATLAB/Simulink simulation tool and OPAL-RT for real-time simulations were used for validation of the proposed control. The outcomes show the tracking of the reference currents, achieving accurate torque control, managing various operating modes like grid-connected having SPWM and THIPWM, or operation with PV and battery sources. The research also highlights lower settling time values as well as lower total harmonic distortion (THD) compared to SPWM in different load conditions using THIPWM.

Recent trends and developments in protection systems for microgrids incorporating distributed generation

AbstractMicrogrids are integral to power grids; they enhance grid reliability by integrating distributed generators (DGs) to fulfill the local load requirements, lowering energy generation costs, and providing eco‐friendly energy resources to reduce carbon emissions. Despite their benefits, new capabilities also introduce protection‐related complexities, including bidirectional power flow, varying fault currents, false tripping, relay reach limitations, coordination issues, and the requirement for proper grounding. This article offers a detailed review of protection issues in AC, DC, and hybrid AC–DC microgrids, investigating existing approaches to address these issues. Furthermore, the constraints and hurdles associated with these approaches and the future trends and intelligent approaches required to develop a reliable and efficient protection strategy are also identified in this article.This article is categorized under: Energy and Power Systems > Microgrids Energy and Power Systems > Distributed Generation

A novel stochastic framework for optimal scheduling of smart cities as an energy hub

AbstractSmart cities consist of various energy systems and services that must be optimally scheduled to improve energy efficiency and reduce operation costs. The smart city layout comprises a power distribution system, a thermal energy system, a water system, and the private and public transportation systems. Additionally, several new technologies such as reconfiguration, regenerative braking energy of the metro, etc. are considered. This study is one of the first to consider all these technologies together in a smart city. The proposed power distribution system is a grid‐connected hybrid AC–DC microgrid. The biogeography‐based optimization algorithm was utilized to seek the best solution for scheduling micro‐turbines, fuel cells, heat pumps, desalination units, energy storage systems, AC–DC converters, purchasing power from the upstream, distributed energy resources, and transferring power amongst electric vehicle parking stations and metro for the next day. Also, the reduced unscented transformation layout was used to capture the system's uncertainty. The suggested layout is implemented on an enhanced IEEE 33‐bus test system to show the efficiency of the suggested method. The results show that costs and environmental pollution are reduced. By comparing the proposed smart city with other studies, the efficiency and completeness of the proposed smart city are shown.

An advanced meta metrics-based approach to assess an appropriate optimization method for Wind/PV/Battery based hybrid AC-DC microgrid

This paper discusses the growing influence of renewable energy and distributed generation, emphasizing the need for smart control systems to maximize benefits and optimize network performance. However, the absence of a standardized evaluation framework makes it challenging to compare different control systems effectively, especially in large-scale hybrid networks with both AC and DC components. While hybrid energy systems show promise for greener and more reliable power networks, they introduce complexity to control methods. Researchers are exploring innovative approaches, including linear and nonlinear techniques, to leverage renewable energy sources effectively in hybrid grids. The paper provides an overview of heuristic evolutionary optimization methods for microgrids (AC, DC, and hybrid AC-DC), highlighting promising techniques such as Crow Search Algorithm, Modified Crow Search Algorithm, Particle Swarm Optimization, and Genetic Algorithms. Comparative analysis suggests that the Modified Crow Search Algorithm performs best across various evaluation criteria, indicating its potential for optimizing microgrids effectively.

Energy Management Strategy of Microgrid based on Photovoltaic and Energy Storage System in Construction Area of Sichuan-Tibet Railway

The construction area of the Sichuan-Tibet Railway is located at a high altitude with thin air condition, but it benefits from excellent solar irradiance. To address issues such as insufficient power and low efficiency of traditional fuel-based equipment in such harsh environments, electric equipment is being employed as a replacement for conventional fuel-based equipment. In order to achieve sustainable development for the Sichuan-Tibet Railway, this article proposes that a microgrid can be conducted in the construction area of the Sichuan-Tibet Railway which will provide power to the electric equipment during the construction process and to electric locomotives upon completion. This article compares the characteristics of AC microgrids and DC microgrids, analyses and simulates the mathematical model of key converters, and introduces their control strategies. This article adopts a hybrid AC-DC microgrid for research purposes and proposes a time-period-controlled energy management strategy for the photovoltaic-storage hybrid AC-DC microgrid in the construction area of the Sichuan-Tibet Railway. The simulation results demonstrate that the AC-DC microgrid in the construction area based on the proposed time-period-controlled energy management strategy could effectively meet the load demands of different time periods and provides power to the electric equipment used in the construction area of the Sichuan-Tibet Railway.

Real-Time Testing Optimal Power Flow in Smart-Transformer-Based Meshed Hybrid Microgrids: Design and Validation

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST’s equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink® environment of Matlab® 9.12 is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model’s accuracy and the MHM’s accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach.

Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids

As the world grapples with the energy crisis, integrating renewable energy sources into the power grid has become increasingly crucial. Microgrids have emerged as a vital solution to this challenge. However, the reliance on renewable energy sources in microgrids often leads to low inertia. Renewable energy sources interfaced with the network through interlinking converters lack the inertia of conventional synchronous generators, and hence, need to provide frequency support through virtual inertia techniques. This paper presents a new control algorithm that utilizes the reinforcement learning agents Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) to support the frequency in low-inertia microgrids. The RL agents are trained using the system-linearized model and then extended to the nonlinear model to reduce the computational burden. The proposed system consists of an AC–DC microgrid comprising a renewable energy source on the DC microgrid, along with constant and resistive loads. On the AC microgrid side, a synchronous generator is utilized to represent the low inertia of the grid, which is accompanied by dynamic and static loads. The model of the system is developed and verified using Matlab/Simulink and the reinforcement learning toolbox. The system performance with the proposed AI-based methods is compared to conventional low-pass and high-pass filter (LPF and HPF) controllers.

Reliability enhancement of hybrid microgrid protection against communication data loss and converter faults using cubic-spline interpolation, Savitzky Golay filtering and GRU network

A primary challenge towards adoption of hybrid AC-DC microgrid pertains to the complexity of the protection scheme. Among other challenges, the high resemblance of voltage and current dynamics for converter faults and line faults, hinders use of threshold-based protection schemes. Further the high dependence of the protection mechanism on the communication network results in reduced reliability during data packet loss in the communication channel. In this regard, a protection scheme is proposed based on the combination of cubic spline interpolation, Savitzky-Golay (SG) filtering, maximum overlap discrete wavelet packet transform (MODWPT) and gated recurrent unit (GRU)-neural network. The impact of data packet loss (modelled using Markov chain model) in the communication channel has been minimized by reconstructing the original signal from lossy data using spline interpolation and SG filter. With the reconstructed signal, significant attributes are extracted using MODWPT, which are further utilized to perform fault detection, fault classification and faulty section identification using a set of GRU classifiers. Unlike the reported techniques whose fault detection and classification accuracy are severely compromised during packet loss, the signal reconstruction module in the proposed framework makes it immune to packet loss. The inclusion of a module for distinguishing between converter faults and line faults allows for avoiding relay maloperation during switching failures. The proposed scheme has been extensively validated for a wide range of operating and fault scenarios under converter faults, PV array faults, varying degrees of packet loss, burst length and dynamic loading conditions. For packet loss rate ranging from 5 % to 30 % and burst length from 0.0167 s to 0.1 s, the proposed scheme achieves dependability and security in excess of 99.85 % and 99.46 % respectively.

Quantifying Sustainable Urban Energy Solutions: Statistical Analysis of Renewable Adoption, Economic Viability, and Technological Innovations

In the context of quantifying sustainable and reliable urban microgrid energy solutions economic feasibility, and technological innovation, this study investigates the potential advantages of hybrid AC-DC microgrids (HMGs) over traditional AC microgrids by eliminating the need for voltage inverters and mitigating energy losses associated with AC-DC converters. We propose a probabilistic strategy for economic feasibility and optimizing the unit commitment of HMGs while considering the integration of renewable energy sources and batteries. To account for uncertainties stemming from load demand fluctuations, energy market price variations, and renewable energy source output power variability, we introduce a stochastic power flow model based on the 2 m point estimation method for economic feasibility and technological innovation of HMGs. Furthermore, we present a novel and robust hybrid optimization approach that combines the harmony search optimization method and genetic algorithm to explore the problem space and identify optimal solutions. This hybrid optimization method incorporates a unique two-stage modification technique, enhancing its search capabilities while preventing convergence to local optima. To assess the performance of our proposed method, we apply it to the IEEE test system. The results demonstrate the effectiveness of our approach in achieving optimal management of the microgrid system, emphasizing its relevance to the quantification of sustainable and reliable urban microgrid energy solutions.

Design and analyzing a hybrid hydro and solar photovoltaic system for rural areas in Malaysia

Some rural areas worldwide, including Malaysia, are not electrified due to geographical constraints, less infrastructure development, and isolation from the main power grid. In addition, renewable energy (RE) sources are considered alternatives to replace conventional (non-RE) sources that cause pollution. However, RE sources have limitations, such as the dependency of weather and geological conditions. To address this issue, a hybrid renewable energy system (HRES) is introduced by connecting one RE and non-RE source or more than one RE with or without non-RE sources. In this study, a stand-alone hybrid hydro and solar photovoltaic (PV) HRES was designed for Rumah Bada in Nanga Talong, Ulu Engkari. As the site has limited information on load consumption and geographical data, estimations were done based on similar studies. The available solar PV and hydro system at the site was identified with the respective parameters. The proposed system was based on the hybrid AC-DC microgrid, along with two alternatives based on the AC and DC microgrid, respectively. Technical analysis was done to find out the capability of the system by calculating the power generated during different periods and performing the load flow analysis using the PowerWorld Simulator. Apart from that, economic analysis was done to find out the most cost-efficient system by calculating the net present cost (NPC) and cost of energy (COE) using HOMER Pro.

ECS-Grid: Data-Oriented Real-Time Simulation Platform for Cyber-Physical Power Systems

ECS-Grid is the first data-oriented real-time electromagnetic transient simulation platform for cyber-physical power systems (CPPS). Traditional simulation tools are constrained by object-oriented programming (OOP) architecture, which is now a significant obstruction to creating a comprehensive cyber-physical simulation. Therefore, the proposed ECS-Grid platform follows a new data-oriented paradigm based on an Entity-Component-System (ECS) framework, which delivers higher flexibility, extensibility, scalability, and performance to support cyber-physical system research. ECS-Grid proposes a layer of virtual intelligent electronic devices (vIEDs) to model IEDs in CPPSs. The vIEDs directly talk to physical components and communicate asynchronously with cyber services via the proposed high-performance JSON-like binary protocol. Tests with the islanding and the man-in-the-middle cyber attack scenarios on a 711-node AC-DC microgrid cluster based on a modified CIGRE 15-Bus system are performed and give accurate results. A faster-than-real-time performance is achieved on the 10th Gen Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Core <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> i7 computer, and real-time performance is achieved on distributed embedded NVIDIA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Jetson platform. The ECS-Grid design and test results demonstrate the potential of the ECS data-oriented paradigm and may inspire the renovation of industrial simulation software.

Approaches to Building AC and AC–DC Microgrids on Top of Existing Passive Distribution Networks

The process of building microgrids on top of existing passive distribution networks warrants a multi-criteria analysis. Besides the calculation of the investment outlays needed for the modernization of distribution networks, such an analysis covers an assessment of the technological and economic effects of building microgrids. The resulting effects depend on the topology and configuration of distribution networks, specific microgrid features, the choice of the current type for the entire microgrid or its individual parts, the methods of connecting distributed energy resources (DERs), the availability and maturity of information and communications technology (ICT) infrastructure, and other factors. Comprehensive input data allow for designing an optimal microgrid configuration, but the main technological and economic effects are determined by the algorithms of operation and the parameter settings of the automatic control system (ACS) and the protection system. The known approaches to designing microgrids focus on addressing basic tasks while minimizing the investment required for their implementation. The above is fully justified when constructing new microgrids, but building microgrids on top of existing distribution networks, given the uniqueness of their topology and configuration, does not allow the use of standardized solutions. The development of approaches to the design of microgrids under such constraints, with minimized investment in the modernization of existing distribution networks, is an urgent task. The use of different types of current for individual microgrid segments determines the choice of the particular ACS and protection system, which depends on the availability of information and communications technology infrastructure. This article contributes a review of approaches to designing AC and AC–DC microgrids so as to maximize their technological and economic effects. We review techniques for analyzing the existing distribution networks aimed at choosing the type of current for the entire microgrid or its individual parts, the optimal points for the connection of microgrids to distribution networks, and the mix and capacity of DERs, with such choices informed by the conditions of the switching devices and information and communications technology infrastructure. This article presents the results of the analysis of approaches to choosing the optimal configuration of microgrids, microgrid ACS, and protection system, with an evaluation of the technological and economic effects subject to the minimization of investment in the modernization of the existing distribution networks.

Distributed Unified Control for Global Economic Operation and Resilience Reinforcement of Hybrid AC–DC Microgrids

The paralleled bidirectional interlinking converters (BILCs) of the hybrid AC/DC microgrid (HMG) provide a flexible and reliable power interaction way between AC and DC subgrids with high power density. A distributed unified control (DUC) is proposed for BILCs to achieve both resilience reinforcement and global economic operation of the HMG. For distributed generators (DGs), the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{ac}-\lambda _{ac}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$v_{dc}-\lambda _{dc}$</tex-math></inline-formula> economic droop controls are employed for AC DGs and DC DGs to decrease the individual subgrid's generation expense by equaling DGs' incremental expenses (IEs). For BILCs, the normalized AC subgrid's frequency and DC subgrid's voltage are coordinated to achieve the economic power interaction (EPI), which further decreases the total generation expense (TGE). Besides economic operation, the proposed DUC is capable of adopting different MG operation modes into a unified control structure without any mode switching. Paralleled BILCs are managed to reinforce the system resilience by supporting the DC voltage or AC voltage once all DC or AC DGs fail. In addition, the proposed DUC enables BILCs with plug-and-play characteristics, resistance to the maximum acceptable communication delay, and the communication disruption robustness. The corresponding hardware-in-the-loop (HIL) results verify the effectiveness of the proposed control strategy.

Isolated UIPC-intertied AC-DC microgrids operation with novel dynamic flexible loads model

This work discusses the islanding operation of hybrid microgrids (HµGs) interconnected by a unified interphase power controller (UIPC). It is illustrated that considering islanding operation of HµG, the problem of power dispatch among intertied AC and DC µGs, as well as voltage and frequency control, can be handled by UIPC. The power dispatch among various AC and DC µGs using UIPC is analyzed and formulated through defining a simple demand condition function (DCF). A new control structure is proposed for the series power converter of the UIPC which enjoys the robustness of sliding mode control (SMC) concept and adopts a new energy management system (EMS) with embedded decision-making. This provides a fast, robust, and simple power flow control unit which acts as a general supervisory system for the UIPC and islanding operation of HµG. Also, a novel generalized dynamic model for flexible loads has been proposed. These loads have been directly controlled through UIPC supervisory system here. The analytical response of the proposed flexible load model is discussed and further developed using the nonlinear autoregressive-moving-average artificial neural network (ANN) model. The Levenberg-Marquardt learning algorithm is used to train the network based on the model estimation error. Besides, a new model predictive-based optimal control scheme is proposed for the flexible load. Simulation results using MATLAB support the usefulness of the proposed UIPC-based islanded HµG platform.

A fuzzy-logic-based smart power management strategy for reliability enhancement of energy storage system in a hybrid AC-DC microgrid with EV charging station

Abstract As Electric Vehicles (EVs) are considered as the future of the automobile industry, the demand for EV charging stations immensely rises. Though grid support can be expected for a microgrid (MG), it is highly advantageous to be self-reliant and be prepared for standalone operation for a long duration. This paper proposes a hybrid ac/dc microgrid (HMG) with an EV charging station in standalone mode with a smart power management strategy that increases the reliability of operation. Different modes of operation are designed according to power consumption levels and the operation of MG is switched between these modes considering the solar irradiance and battery bank state of charge. The decision-making system uses a combination of (a) fuzzy logic approach based on the state of charge of the energy storage system (ESS) and solar irradiance, adding to the intelligence of the system and providing smart reasoning capability and (b) logical decisions based on solar power and battery state of charge, considering all possible operating scenarios. With the proposed power management strategy in place, the standalone hybrid microgrid system is found to be able to prolong the discharge of the energy storage system under critical conditions of low battery state of charge and PV power availability and also under highly critical situations will be able to meet the critical load demand making use of the power available in the EV connected. Emphasis has also been given to maintaining the stability of the system while performing shifts in operating modes and during sudden changes in the scenario. Different operating conditions are recognized by the power management module in the model and an appropriate power management strategy is applied. Real-time simulation of the model was done in OPAL-RT real-time simulator using RT-LAB software and simulation results were reproduced.

Distributed elastic recovery strategy of AC/DC hybrid microgrid under false data injection attack

AbstractThe AC/DC hybrid microgrid, which integrates AC microgrid and DC microgrid, greatly improves the flexibility of distributed energy. However, it is imperative to acknowledge that the microgrid in question is accompanied by a significantly elevated network security risk. To solve this problem, this article proposes a false data injection attack detection and alarm method based on active power output. The detection algorithm is capable of detecting attacks at any location within the microgrid and mitigating the impact of communication delay. Moreover, a distributed elastic recovery strategy based on the aforementioned detection algorithm is proposed. This strategy aims to expeditiously restore the compromised agent to its rated state. Finally, a simulation model is developed to demonstrate the effectiveness of the proposed strategy.

Comprehensive review on control schemes and stability investigation of hybrid AC-DC microgrid

The utility grid integration of distributed generation units is seen as one of the most accomplished through the use of microgrid. Control methods and stability analyses in the microgrid are continuously being investigated in the field of hybrid microgrid. The most reviewed control method is hierarchical control, consisting of primary, secondary, and tertiary layers. This paper reviews multiple schemes of primary control for current or voltage regulation, secondary control for voltage or current error correction, power-sharing in microgrid, and tertiary control for power flow and energy management in a hybrid microgrid system. Additionally, this study examines the benefits and drawbacks of several control architectures that function as distributed, centralized, and decentralized controls. The principle of operation and effectiveness of each microgrid architecture control method have been discussed. Specific control factors are investigated in this paper, including mode transition and coordinated control between numerous interlinking converter and energy storage systems. System stability is the main concern of microgrid for system engineer thorough knowledge of control strategies is the upmost requirement. Thus, this paper highlights control strategies, rotor angle, voltage and frequency instability in power systems, and methods for improving stability in microgrid are also reviewed.

An improvement of stability and dynamic response in hybrid AC-DC microgrids using optimal power control management

Microgrid topologies enable more effective use of renewable resources as well as the autonomous process. Microgrids are effective frameworks for managing dispersed assets such as renewable structures with small-scale distributed generator supplies. Smart grids are adaptable electricity networks that have arisen as a result of the introduction of new technologies and features. It is suggested that a three-terminal AC/DC hybrid microgrid with DC two and AC one terminal be used. A two dual active bridge (DAB) with cascaded H-bridge (CHB) converter with AC grid link-based interactions that connect isolated two DC buses are included in the suggested structure. To minimise the energy generation stages and power devices, the DAB converters are intrinsically linked to the CHB of DC rails based on the structure-specific requirements. To present the unnecessary grid currents and DC rail voltage issues exist by this revised structure topology by simply two power alteration stages, a better solution is suggested that employs zero-sequence voltage (ZSV) injection in the CHB converters. The impacts of managed settings on structure stability and dynamic responsiveness are investigated in order to reduce conflicts among ZSV injection by structure voltage/current regulation. This paper compares the performance of a PI and a PR for DC voltage regulation. A comparison of simulation results for both control approaches is provided. The major difficulty is to normalise the system model in order to identify the appropriate controller settings for robust control. The findings are confirmed and compared to the conventional regulator PI. The suggested 3-Φ AC current and DC rail voltage regulating approach's universal efficacy is proved by MATLAB/Simulink analytical model from the three and five-terminal hybrid AC/DC power structures.