Hybrid 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.

Dual grid energy management strategy for electric vehicles in hybrid microgrid utilizing matrix pencil method

Abstract This study introduces a cutting-edge Matrix Pencil-based Dual Grid Energy Management System (MP-DEMS) aimed at seamlessly integrating electric vehicles (EVs) into power grids while enhancing power quality (PQ) and reliability (PR). Operating within an AC-DC hybrid microgrid (HMG) framework, the MP-DEMS reduces the necessity for additional conversion devices, simplifying the integration of EVs and batteries. The core of the proposed DEMS comprises three key modules: the Wind Energy Management Module (WEMS), Smart Storage and EV Power Coordination (SS-EVPC), and Coordinated Power Conversion Control (CPCC). These modules work together to optimize energy usage, leverage renewable sources effectively, and manage EV charging/discharging schedules to minimize grid impact. An innovative aspect of the MP-DEMS is its use of Matrix Pencil-based techniques, offering several advantages over traditional Discrete Fourier Transform (DFT) methods. These advantages include swift dynamic response, resilience to voltage variations, and precise voltage phase estimation. Software simulations and Hardware-in-the-Loop (HIL-402) testing validate the efficacy of the MP-DEMS approach, showcasing enhanced power transfer capabilities, improved harmonic performance, and superior voltage/frequency regulation within EV-HMS applications. Overall, the MP-DEMS presents a promising solution for advancing the integration of EVs into microgrid systems, contributing to a more sustainable and reliable energy future.

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.

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.

Adaptive power-sharing strategy in hybrid AC/DC microgrid for enhancing voltage and frequency regulation

This paper introduces a new adaptive control strategy for power-sharing in a hybrid AC/DC microgrid (HMG). The existing interlink converter (ILC) control methods exhibit limitations under heavy load and contingency conditions. To overcome this limitation, a bidirectional control strategy is proposed for the ILC, which effectively coordinates battery energy storage systems (BESSs) in both sub-grids while mitigating stress on the ILC and BESSs. The control strategy utilises the state of charge (SoC) and instantaneous power of the BESSs to stabilise frequency and DC voltage on the AC and DC sub-grids, respectively, enabling autonomous operation and plug-and-play capability. Stability analysis employing root locus and a stress level relaxation index for BESSs are presented. Performance evaluation based on the moving mean square error criterion validates the effectiveness of the control strategy. Simulation results using MATLAB/SIMULINK demonstrate the superior performance of the proposed strategy in regulating both voltage and frequency within the system over the existing control approaches. Furthermore, the proposed approach significantly reduces stress levels on the BESSs in different operation conditions.

Research on the Hybrid Wind–Solar–Energy Storage AC/DC Microgrid System and Its Stability during Smooth State Transitions

The hybrid AC/DC microgrid is an independent and controllable energy system that connects various types of distributed power sources, energy storage, and loads. It offers advantages such as a high power quality, flexibility, and cost effectiveness. The operation states of the microgrid primarily include grid-connected and islanded modes. The smooth switching between these two states is a key technology for ensuring the flexible and efficient operation of the microgrid. In this paper, the typical structure of an AC–DC hybrid microgrid and its coordination control strategy are introduced, and an improved microgrid model is proposed. Secondly, an adaptive current–voltage–frequency integrated control method based on signal compensation is proposed to solve the impulse current and voltage generated during the switching between a grid-connected state and an off-grid state. Finally, in response to unplanned grid-connected scenarios, an improved pre-synchronization control strategy based on BP neural networks is introduced to rapidly restore stable operation. The proposed control strategies enhanced the steady-state and transient stability of the hybrid wind–solar–energy storage AC/DC microgrid, achieving seamless grid-connected and islanded transitions without disturbances. The simulation and experimental results validated the correctness and effectiveness of the proposed theories.

Multi-time Optimal Dispatch of AC-DC Hybrid Microgrid with Spatiotemporal Discrepancy

AC-DC hybrid microgrid with high permeability of renewable energy and its special system structure poses a challenge to the rational dispatch of AC-DC hybrid microgrid. To solve the problem, a multi-time period stochastic optimization of AC-DC hybrid microgrid scheduling strategy is proposed in this paper, which considers spatiotemporal differences. In the day-ahead optimal stage, the comprehensive consideration of the operating costs and characteristics of each distributional unit and bidirectional AC/DC power converter in the microgrid, a backup plan is developed to cope with the uncertainties of wind and AC/DC loads. In the intra-day scheduling stage, multi-scenario analysis technology is used to simulate the uncertainty of wind and AC and DC loads, and the intra-day rolling optimization dispatch model is constructed to ensure the validity of the day-ahead plan and fully suppress the power fluctuations caused by the day-ahead forecasting errors. Finally, by an example, the results show that the method can effectively deal with the influence of multi-uncertainties of the AC-DC hybrid microgrid.

Optimal allocation of multiple capacitors in a hybrid AC/DC microgrid for power quality improvement

Along with the various features for implementing the Hybrid AC/DC Microgrid (HMG), this article proposes an approach for optimal allocation of multiple capacitors which are investigated in a proposed modeling based on the IEEE 14-bus distribution system. The power quality of the HMG has been investigated during the urgent intermittent of Distributed Energy Resources (DERs) and Reactive Power Compensation (RPC) methods. Moreover, the investigation has been achieved in the presence of unbalanced loads and nonlinear loads with maximum and minimum demand scenarios. To cope with the power quality concerns in the studied cases, the fixed capacitor bank as an RPC system in Medium Voltage (MV) level load buses has been utilized. Although the performance indices of the power quality improved in MV-level buses, the Low Voltage (LV) level load buses still endure extensive operation performance deteriorations caused by unbalanced loads. Therefore, in this article, a compensation scheme applied in LV-level load buses and MV-level buses has been proposed consistent with the power flow computations. The Multi-Objective Grey Wolf Optimizer (MOGWO) algorithm is implemented to optimize both the size and location of capacitor banks over different voltage levels with high accuracy. The comprehensive assessment and discussion of the simulation results demonstrate the superiority of utilizing the proposed compensation scheme in both MV-level and LV-level load buses. Hence, the power quality is not only enhanced but also the installation cost is reduced. The complete model of the studied system has been validated using MATLAB/ Simulink.Article HighlightsThe reactive power compensation method is used to improve the power quality in a hybrid AC-DC microgrid.The applied RPC method verifies using the MOGWO algorithm to optimize both the location and size of multiple capacitor banks.Power quality challenges and future research trends are debated.

Research on the control strategy of DC microgrids with distributed energy storage

As a supplement to large power grids, DC microgrids with new energy access are increasingly widely used. However, with the increasing proportion of new energy in DC microgrids, its output fluctuations directly affect the overall stability of the microgrids. Distributed energy storage can smooth the output fluctuation of distributed new energy. In this paper, an AC-DC hybrid micro-grid operation topology with distributed new energy and distributed energy storage system access is designed, and on this basis, a coordinated control strategy of a micro-grid system based on distributed energy storage is proposed. To maintain the voltage stability of the DC bus and make each station have the power-sharing ability, the AC/DC flexibly interconnected converter should adopt two control strategies. The power can flow bidirectional in the power scheduling and distribution of the energy storage station; At the same time, different power distribution schemes will generate different scheduling costs. To optimize the operation of energy storage power stations, an improved particle swarm optimization algorithm is adopted in this paper to optimize the scheduling task allocation scheme. The optimization objective is the lowest scheduling cost, to realize the optimal scheduling of energy storage power stations. In this paper, based on a Matlab/Simulink environment, a microgrid system based on an AC-DC hybrid bus is built. The simulation results verify the effectiveness of the proposed microgrid coordinated control strategy.

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.

Distributed Predictive Secondary Control With Soft Constraints for Optimal Dispatch in Hybrid AC/DC Microgrids

Hybrid AC/DC microgrids (H-MGs) are a prominent solution for integrating distributed generation and modern AC and DC loads. However, controlling these systems is challenging as multiple electrical variables need to be controlled and coordinated. To provide flexibility to the control system, these variables can be regulated to specific values or within secure bands. This paper proposes a set of distributed model predictive control schemes for the secondary control level to control certain variables to specific values and other variables within secure pre-defined bands into H-MGs. Specifically, optimal dispatch of active and reactive power is achieved while frequency and voltages are regulated within secure bands in H-MGs. Dynamic models of AC generators, DC generators and interlinking converters along with their novel multi-objective cost functions are developed in constrained distributed predictive optimisation problems to simultaneously achieve the aforementioned objectives via information sharing. Extensive simulation work validates the performance of this proposal.

A Consensus-Based Distributed Secondary Control Optimization Strategy for Hybrid Microgrids

In this paper, a new consensus-based distributed secondary control (DSC) strategy is proposed for frequency control, dc-voltage regulation, and optimal dispatch (OD) in isolated hybrid ac/dc-microgrids (HMGs). At the same time, all the units are maintained within limits. The proposed control scheme dispatches the distributed generators (DGs) within the microgrid in compliance with the Karush-Kuhn-Tucker (KKT) conditions of a linear optimal power flow (OPF) formulation. Furthermore, the power through the interlinking converters (ICs) is also dispatched to help minimize the total operation cost of the microgrid. The controllers rely on local measurements and information from neighbouring devices at both sides of the microgrid (DGs and ICs). Thus, unlike conventional methods, the microgrid is considered a single entity and not as three independent systems interacting with one another, and the OD is calculated considering both the ac-DGs and dc-DGs. Extensive simulations demonstrate a good performance of the controller amid load step changes and unit congestion, driving the system to an optimal economic operation.

A virtual inertial control strategy for bidirectional interface converters in hybrid microgrid

Insufficient inertia is one of the urgent problems to be solved in the stability of AC-DC hybrid microgrid. In order to improve AC bus frequency and DC bus voltage inertia in hybrid microgrid, a virtual inertia control strategy for bidirectional interface converter (BIC) based on virtual synchronous generator (VSG) is proposed. The virtual inertia equations on both sides of the AC and DC are used to slow down the variation of the DC voltage and the AC frequency. When the load fluctuates, the dynamic response of the DC voltage, the AC frequency and the power transmission between the AC and DC subnets is improved, thus improving the stability of the system. This control strategy enables BICs to share active power equally under stable conditions, prevents circulating power, and achieves accurate power sharing among parallel BICs. The small signal model of BIC under virtual inertia control is established, the dynamic characteristics of the system under different control parameters are analyzed, and the influence of the control parameters on the system is discussed. The simulation model is established in PSCAD/EMTDC software to verify the correctness of the proposed control strategy.