Isolated Microgrid System Research Articles

Distributed dynamic economic dispatch of biogas-wind-solar-hydrogen multi-microgrid system considering individual selfishness

This paper proposes a biogas-wind-solar-hydrogen multi-microgrid system to address the issues of poor economy and reliability, as well as the waste of wind and solar energy, in single energy-based isolated microgrid systems. The study considers the coupling constraints of multiple time scales and establishes a dynamic economic dispatch model. Furthermore, a consensus-based distributed dynamic economic dispatch strategy is proposed. To tackle the challenge of unified economic dispatch caused by the interaction among multiple microgrids in the joint operation of the biogas-wind-solar-hydrogen multi-microgrid system, a microgrid selfishness impact model and elimination strategy are developed. Simulation results demonstrate the effectiveness and superiority of the proposed distributed dynamic economic dispatch strategy considering individual selfishness in the biogas-wind-solar-hydrogen multi-microgrid system.

A machine learning-based stochastic optimal energy management framework for a renewable energy-assisted isolated microgrid system

ABSTRACT This paper proposes a cost-based stochastic optimal energy management framework for a renewable energy-assisted isolated microgrid system. These microgrids encourage the integration of multiple distributed energy sources, including the penetration of renewable energy. For this purpose, the optimal day-ahead dispatch of the connected energy resources is obtained for an economically viable system by solving a nonlinear constrained optimization problem. The renewable energy and the load demand data forecasting are accomplished using the Gaussian process regression learning model in the MATLAB/SIMULINK® environment for obtaining the day-ahead dispatch. The optimal problem is solved through sequential quadratic programming and a hybrid function approach incorporating particle swarm optimization for a comprehensive techno-economical analysis. A comparative assessment of the results is accomplished to obtain a more feasible and economical system operation corresponding to different time horizons and other critical factors such as fast iterations, computational accuracy, solution feasibility, convergence rate, etc.

Adaptive Control Approach-Based Isolated Microgrid System with Alleviating Power Quality Problems

In this article, a dual third-order generalized integrator-based phase-locked loop (DTOGI-PLL) and an additional degree of freedom-based frequency locked loop (ADF-FLL) control approaches are utilized on a voltage source converter (VSC) to control an isolated microgrid system under different operating conditions. The proposed adaptive approaches provide a fast dynamic response with a precise estimation of frequency, phase angle, and fundamental load current component compared to that of the TOGI-PLL and least mean square (LMS) algorithms. An ADF-FLL allows closed-loop poles to perform more optimally, resulting in faster dynamic performance without compromising filtering capability. Moreover, the proposed adaptive approaches with VSC provide power support, load leveling, harmonic eliminations, and enhance the isolated microgrid’s overall power quality during dynamics in load and energy sources. A solar PV (SPV) array with perturb & observe (P&O) MPPT control is interfaced at the DC link of a bidirectional controller (BDC) to draw maximum solar power. Through dc-link voltage control, the BDC with energy storage system (ESS) ensures the appropriate power flow and power equilibrium between energy sources and load. The Simulation and test results show the dynamic performance of the isolated microgrid is examined under different operational scenarios.

Modified Droop Control for Microgrid Power-Sharing Stability Improvement

Isolated microgrid (IMG) power systems face the significant challenge of achieving fast power sharing and stable performance. This paper presents an innovative solution to this challenge through the introduction of a new droop control technique. The conventional droop controller technique used in inverter-based IMG systems is unable to provide satisfactory performance easily, as selecting a high droop controller gain to achieve fast power sharing can reduce the system’s stability. This paper addresses this dilemma by proposing a modified droop control for inverter-based IMGs that effectively dampens low-frequency oscillations, even at higher droop gain values that would typically lead to instability. The design is described step-by-step, and the proposed controller’s effectiveness is validated through time domain simulation analysis. The results demonstrate the significant improvement in stability and fast power sharing achieved with the proposed controller. This innovative technique presents a promising solution for achieving fast power sharing and stable performance in IMG power systems.

Emission-averse techno-economical study for an isolated microgrid system with solar energy and battery storage

Emission-averse techno-economical study for an isolated microgrid system with solar energy and battery storage

Composite Demand-Based Energy Storage Sizing for an Isolated Microgrid System

This paper presents a comprehensive model for optimal energy storage system (ESS) design for an isolated microgrid. The model presented is a mixed integer linear program (MILP) that considers seasonal varying generation (VG) demand, more specifically seasonal solar cell generator (SCG) demand, SCG maintenance (failure and restoration) rates, and practical operation of conventional generation (CG) while satisfying the required demand and reserve. The model is based on unit commitment (UC) to simulate real operations and physical constraints of CG units, the power balance, and reserve requirements. The objective function aims at minimizing the associated cost of CG, namely, production (fuel), costs of startup and shutdown procedures, and the investment cost of power and energy. The proposed model is assessed on a case study system consisting of multiple SCGs in addition to CG to meet a specific demand. The proposed model showed that the ESS sizing when considering Li-Ion technology and a SCG penetration of 25% was on average approximately 3 MWh and 1.70 MW. Meeting the demand and reserve requirements were the two major constraints when determining the optimal ESS sizing. Moreover, introducing the ESS substantially reduced the operating cost of the system.

A Novel Electric Spring With Improved Range of Operation for Isolated Microgrid Systems

A Novel Electric Spring With Improved Range of Operation for Isolated Microgrid Systems

Optimal sizing of an isolated microgrid with electric vehicles using stochastic programming

An isolated microgrid system with renewable sources and energy storage systems ensures sustainable access to electricity and is especially suitable to handle inadequate electrical infrastructure in rural areas. The capacity configuration of the system depends on the uncertainties of renewable energy sources and home user behaviour, and the stochastic behaviour of electric vehicle (EV) users. This study determines the optimal configuration of the isolated microgrid that comprises wind and photovoltaic generation systems as well as batteries and EVs by proposing a two-stage stochastic programming-based multi-objective optimization problem where the objective functions are minimization of the life cycle cost and reliability. The problem is solved using the deterministic equivalent of the stochastic programming model to find the exact solution. The scenarios in the model are generated for one year period to consider the effects of uncertainties throughout seasons. Cases for different roles and configurations of electric vehicles are simulated and analyzed to compare their impacts on the sizing of the microgrid. Also, the effects of charger size on the microgrid configuration are investigated through simulations. The simulation results show that a slight increase in LPSP values causes a drop in cost values of around 10%–20% in each EV scenario. The minimum cost is obtained when more efficient charge & discharge plugs for EVs are used. Furthermore, the robustness of the model results is observed through repeated experiments with different random parameters. • An isolated PV-wind-battery-EV-based microgrid system is designed by considering the uncertainties due to random demand, renewable generation and EV behaviour. • A two-stage stochastic multi-objective optimization problem is solved by minimizing the LCC and LPSP. • The impacts of EVs on the system design are investigated using different roles and configurations of EVs. • As bigger-sized chargers replace smaller ones, the cost of microgrid decreases.

Fuzzy Logic–Based Decentralized Voltage–Frequency Control and Inertia Control of a VSG-Based Isolated Microgrid System

This work proposes the use of fuzzy-logic-based voltage frequency control (VFC) and adaptive inertia to improve the frequency response of a virtual synchronous generator (VSG)-based isolated microgrid system. The joint VFC and inertial control scheme is proposed to limit frequency deviations in these isolated microgrid systems, mainly caused by the increasing penetration of intermittent distributed energy resources, which lack rotational inertia. The proposed controller uses artificial neural networks (ANN) to estimate the exponent of voltage-dependent loads and modulate the system frequency by adjusting the output voltage of the VSGs, which increases the system’s active power reserves while providing inertial control by adjusting the inertia of VSGs to minimize frequency and VSG DC-link voltage excursions. A genetic algorithm (GA)-based optimization strategy is developed to optimally adjust the parameters of the fuzzy logic controller to diminish the impact of disturbances on the system. In addition, the proposed technique is illustrated through simulations within the framework of a test system based on the CIGRE medium-voltage benchmark under various circumstances. The results of these simulations demonstrate that the proposed control strategy outperforms existing methods.

A mathematical framework of ANFIS tuned UPQC controlled RES based isolated microgrid system

This paper presented a nascent mathematical modeling of ANFIS tuned Unified Power Quality Conditioner (UPQC) controlled hybrid microgrid incoprating an Induction generator (IG) for a wind energy- system and an alternator for conventional generation. The mathematical frame of the hybrid microgrid system is presented using power equalities. A powerful Flexible AC Transmission system (FACTS) controller UPQC, generally used with conventional sources is considered to control the voltage and reactive power flow in the presented microgrid framework. Further Adaptive Neuro-Fuzzy Inference System (ANFIS) technique has been used to optimize the FACTS controller UPQC in MATLAB software.

Load Frequency Control of Microgrid System by Battery and Pumped-Hydro Energy Storage

Energy security is one of the main factors in the development and diffusion of microgrid applications. In networks operating without storage, the operation of their systems is greatly affected by sudden load demand and intermittent generation fluctuations. The main purposes of using energy storage systems in microgrids are stabilizing the intermittent generation of renewable energy sources locally, to ensure that energy production matches energy demands, participating in the frequency regulation process, maintaining the energy balance between generation and demand in renewable energy microgrids, and increasing energy reliability. This study investigates the frequency and power balance of an isolated microgrid system, by including storage systems (battery and pump-hydro). Realistic data for wind and solar sources are used for the optimal tuning of the proportional-integral controller, using the integral of the absolute error criterion multiplied by time, with a Quasi-Newton method. Simulation studies have been carried out, to investigate the performance of the microgrid system, by including the hydroelectric power plant system with pump storage for 24 h, under various operating conditions. The results reveal that by including the storage units in the system, it exhibits a more consistent and smooth dynamic performance, using renewable energy efficiently.

A New Parameter Tuning Technique for Noninteger Controllers in Low-Inertia Modern Power Grids

Since isolated microgrid systems are prone to unwanted frequency oscillations against load fluctuations and randomness of renewable sources, load frequency control (LFC) plays a vital role in restoring the frequency of such systems. In the microgrid with virtual inertias, the existing dynamic analysis of LFC assessment is not enough to meet the grid stability due to the virtual inertia control. Confronted with this problem, a noninteger (or fractional-order) type of proportional–integral controller design is developed in this work for LFC synthesis of the microgrid with the application of virtual inertia. In particular, a new analytical tuning scheme is adopted for the proper setting of the established noninteger controller to effectively damp the frequency oscillations of the concerned system under the virtual inertia. In the suggested analytical scheme, the stability boundary locus and geometric centroid methods are used to tune the coefficients of the noninteger LFC controller. The analysis of stability region curves has been conducted to study the dynamic effects of the virtual inertia and damping over the microgrid performance. Finally, the efficiency of the suggested noninteger controller design and accuracy of the stability analysis in regulating a microgrid with virtual inertia and damping are proved.

A Comparative Analysis of Peak Load Shaving Strategies for Isolated Microgrid Using Actual Data

Peak load reduction is one of the most essential obligations and cost-effective tasks for electrical energy consumers. An isolated microgrid (IMG) system is an independent limited capacity power system where the peak shaving application can perform a vital role in the economic operation. This paper presents a comparative analysis of a categorical variable decision tree algorithm (CVDTA) with the most common peak shaving technique, namely, the general capacity addition technique, to evaluate the peak shaving performance for an IMG system. The CVDTA algorithm deals with the hybrid photovoltaic (PV)—battery energy storage system (BESS) to provide the peak shaving service where the capacity addition technique uses a peaking generator to minimize the peak demand. An actual IMG system model is developed in MATLAB/Simulink software to analyze the peak shaving performance. The model consists of four major components such as, PV, BESS, variable load, and gas turbine generator (GTG) dispatch models for the proposed algorithm, where the BESS and PV models are not applicable for the capacity addition technique. Actual variable load data and PV generation data are considered to conduct the simulation case studies which are collected from a real IMG system. The simulation result exhibits the effectiveness of the CVDTA algorithm which can minimize the peak demand better than the capacity addition technique. By ensuring the peak shaving operation and handling the economic generation dispatch, the CVDTA algorithm can ensure more energy savings, fewer system losses, less operation and maintenance (O&M) cost, etc., where the general capacity addition technique is limited.

Voltage Restoration Control for Microgrid With Recurrent Wavelet Petri Fuzzy Neural Network

This study presents a voltage restoration control (VRC) based on battery energy storage system (BESS), which can be used for both supporting power source and voltage compensation. Voltage restoration is an important task for the power control of microgrid during utility disturbances. One of the disturbances is caused by short circuit on power line of the microgrid, which may lead to voltage sag and even blackout of the microgrid system. To tackle this problem, the recurrent wavelet petri fuzzy neural network (RWPFNN) controller is proposed in this study for the VRC of BESS to provide fast control response to mitigate the transient impact. Moreover, to examine the compliance with the requirements of low voltage ride through (LVRT) of the photovoltaic (PV) plant and investigate the performance of the proposed VRC, the microgrid built in Cimei Island in Penghu Archipelago, Taiwan, is investigated. Furthermore, the PV system, the wind turbine generator (WTG) system and the BESS are connected to the same point of common coupling (PCC) with separated step-up transformers in the microgrid. In addition, the diesel generators provide the main power sources and form the isolated microgrid system. Through the hardware in the loop (HIL) mechanism, which is built using OPAL-RT real-time simulator, with two floating-point digital signal processors (DSPs), the effectiveness of proposed intelligent controllers can be verified and demonstrated.

Voltage Regulation of an Isolated DC Microgrid with a Constant Power Load: A Passivity-based Control Design

Passivity-based nonlinear control for an isolated microgrid system is proposed in this paper. The microgrid consists of a photovoltaic array and a battery energy storage connected to a point of common converters, supplying a constant power load. The purpose of this control strategy is to maintain the output direct current voltage in its reference value under load variations, improving battery interaction. The system is represented by its state space averaged model and the proposed controller is designed using the interconnection and damping assignment strategy, which allows obtaining controller parameters while ensuring the closed-loop system stability. The unknown constant power load is estimated using an observer based on the energy function of the system. The behavior of the proposed control strategy is validated with simulation and experimental results.

A Decentralized Multi Agent ANN Based Control Technique for Operation Cost Optimization of Isolated Microgrid Systems

With an ever increasing electrical load demand and the associated fuel price to generate it, researchers are compelled to find alternate, cheap and environment friendly ways for power generation to cater to this techno-economic conundrum. Renewable Energy Sources (RES) are now being integrated in Distributed Generation (DG) based environment as a solution to this problem, hence resulting in Microgrids, with multiple sources and loads demarcating their footprint. Control and management of a diverse generation profile within a single microgrid is arduous and computationally intensive for a single centralized controller. This paper addresses this inherent problem and proposes a decentralized Multiagent based intelligent control technique to efficiently encompass the heterogeneous generation profile of Microgrids. Artificial Neural Network (ANN) based intelligent agents are deployed at the planning stage for each component of the Microgrid. These agents are responsible for maintaining individual local control parameters within the prescribed control margins, hence creating a multi-agent environment in a Microgrid structure. The proposed intelligent multi-agent control scheme is tested on a test system to furnish its merits over its traditionally employed counterparts. Simulation results show that ANN integration not only reduced the computational burden but also reduced the overall operating cost (around 10.2 %) by reducing the thermal generation.

A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system

A novel peak load shaving algorithm has been proposed in this study for peak shaving application in hybrid PV-BESS connected Isolated Microgrid (IMG) system. This algorithm will help an IMG system to operate its generation systems optimally and economically along with PV generation unit. An IMG model has been developed in MATLAB/Simulink environment with actual load data, conventional Gas Turbine Generator (GTG), Photovoltaic (PV) generation system and Battery Energy Storage System (BESS). The proposed algorithm has been tested with the designed IMG model. To evaluate the effectiveness of the algorithm, simulation case studies have been conducted with actual load data and actual PV generation data. The simulation results demonstrate that the algorithm can minimize the limitations of the existing methods and it can use the PV generation system effectively. Peak shaving service can be found from this algorithm as a simple and forcible way. A comparative analysis has been conducted of the proposed algorithm with the conventional methods. The comparison results can easily reflect that the proposed algorithm can ensure the optimal use of PV generation system and can serve peak shaving service effectively. It can also ensure an economic and environment friendly system where the existing algorithms are limited to serve these. The proposed algorithm can mitigate the available power issues for hybrid PV-BESS connected system and it can also minimize the operating cost by dispatching the generation units optimally.

Maximum sensitivity-constrained coefficient diagram method-based PIDA controller design: application for load frequency control of an isolated microgrid

This paper proposes an improved coefficient diagram method (CDM) for proportional integral derivative acceleration (PIDA) controller design. In the improved CDM, stability indices are obtained using a graphical approach based on the maximum sensitivity constraints. The stability indices play an important role in the designing of the controller and stability of the closed-loop control system. Further, the improved CDM is utilized to compute the analytical expressions for the coefficients gain of PIDA controller. From the application point of view, the proposed control approach is designed for load frequency control (LFC) of an isolated microgrid (IMG). In IMG, the generators cannot supply constant electrical power and sometimes cause an imbalance between power generation and demand. Thus, LFC scheme is applied to maintain the balance between power generation and demand. The effectiveness and robustness of proposed control approach are analyzed for IMG system under $$\pm 50$$ % parametric uncertainty, random load demand, random solar and wind power deviation profile. The efficacy and performance of proposed control approach are examined in comparison with the published PID, 2DOF-PID and PIDA control schemes.

Modeling of an isolated microgrid with hybrid PV-BESS system for peak load shaving simulation

In this paper, a simplified model of an isolated microgrid (IMG) with hybrid photovoltaic (PV)-battery energy storage system (BESS) is discussed. The concept of peak load shaving is also demonstrated. The developed model helps an isolated power system to minimize the demand and maximize the economic benefit. MATLAB/Simulink tool was used to simulate the electrical circuit of the IMG system. The IMG model contains four sub-systems, namely the PV model, time varying load profile model, BESS model and gas turbine generator (GTG) dispatch model. To evaluate the performance of the developed IMG, a simulation case study was conducted under realistic load conditions of Universiti Teknologi PETRONAS (UTP). Results indicated that simulation illustrates the precise situation of the real-time system, as found in the UTP microgrid. Results also revealed that the concept of peak load shaving broadly illustrated in this study. The implementation of the proposed IMG model may bring economic benefit by minimizing peak load demand. © 2021 Md Masud Rana et al.

Load Shedding in Microgrids with Consideration of Voltage Quality Improvement

Microgrids have become more and more popular their usefulness as a renewable energy resource has been recognized. The core ability and promise of microgrids is addressing the environmental concerns due to climate change that have been growing during recent years. The innovation of microgrids is that they are designed to operate either in island mode or interconnected with the main grid system. However, when the microgrid operates in islanded mode, faults may occur which can cause system collapse or even blackout. Load curtailment schemes can be utilized to decrease the quantity of associated load to a level that can be securely supported by accessible generation in isolated mode. The main goal of this research is to evaluate the optimal amount of shedding power considering sustainable power sources, with the help of primary and secondary adjustments of the generator to restore the frequency to the allowed range. Particle Swarm Optimization algorithm is applied in this paper to determine the distributed shedding power on each demand load bus which can improve the voltage quality of the isolated microgrid system. The effectiveness of the proposed method is demonstrated through the simulation of IEEE 16- bus microgrid.