Microgrid Power System Research Articles

Techno-economic analysis and dynamic power simulation of a hybrid solar-wind-battery-flywheel system for off-grid power supply in remote areas in Kenya

Sub-Saharan Africa (SSA) has the lowest energy access rates globally. The need for transformative energy sources ranging from solar off-grid and mini-grid solutions to hybrid micro-grid power systems has rapidly grown to deliver clean energy admittance. This research proposes a hybrid photovoltaic-wind turbine power system coupled to a hybridized storage system composed of a Lithium-Ion battery and a flywheel storage system which ensures reliability for off-grid electrification for rural and less accessible remote areas of Makueni County in Kenya. The optimal size of the proposed Hybrid Renewable Energy System (HRES) is estimated, using a multi-objective optimization modeling approach, taking into account the Levelized Cost of Electricity (LCOE) and reliability (energy index of self-reliance (EISR)) of the system as the main objective functions, using epsilon(ε)-constraint technique as the solving approach. A resultant Pareto front is analyzed to obtain the best compromise for COE at 0.519 USD/kWh and reliability indicator, energy index of self-reliance (EISR) at 0.997. The optimal size of the HRES was realized at 26 PV panels (330 W) and 3 wind turbines (1 kW) which satisfies the annual local load requirement of 37.94MWh. Next, the hourly performance of the proposed HRES, under different operating conditions, is evaluated, using a dynamic power control simulation model developed in Matlab-Simulink. The results revealed the proposed off-grid HRES is highly reliable, meeting the load demand sufficiently in all meteorological conditions experienced in the area of study. Furthermore, adopting a hybrid energy storage system (HESS) realized an annual potential of 858kWh storage capacity gain in the battery when coupled with the flywheel storage system.

Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid

The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day.

Development of economic processes utilizing solar energy

The concern of the electric utility companies to prioritize tight control on system performance is addressed in the paper as it immensely helps planning maintenance expenditure economically with customers’ expectations fully met. . The paper aims at making a comparative study of performance of the micro-grid power system with that of the interconnected micro-grid and national grid. A micro-grid that utilizes solar energy in sparse locations in hilly terrain for ensuring reliable electric supply to industrial houses engaged in product manufacturing processes has been considered. These manufacturing processes as a part of small and medium industries introduce goods and services necessary for securing economic growth and development in the country side where grid power supply is subject to frequent weather disturbances, and hence, very costly from the perspective of sustenance as well as maintenance. The solar energy trend has given scope for self-reliance and better livelihood of people who live away from cities but the products what they manufacture may be sent for consumption in the nearby cities. Thus, more and more dependence on the solar energy leads to nation’s economic growth policy. Moreover, it adds to uninterrupted and quality electric supply using clean energy in place of fossil fuels-based generation of electricity to protect environment; allows reducing health hazards and mitigating greenhouse gas emissions. Using reliability techniques, solar energy plant performance is quantitatively judged and compared with respect to the performance of the national grid supplying power supply to the remotely located population. The improved energy security, easy access and operational responses of the solar plant as a substitute to the grid power supply help develop economic growth of a nation.

A Novel Model for Enhancing the Resilience of Smart MicroGrids’ Critical Infrastructures with Multi-Criteria Decision Techniques

Microgrids have the potential to provide reliable electricity to key components of a smart city’s critical infrastructure after a disaster, hence boosting the microgrid power system’s resilience. Policymakers and electrical grid operators are increasingly concerned about the appropriate configuration and location of microgrids to sustain post-disaster critical infrastructure operations in smart cities. In this context, this paper presents a novel method for the microgrid allocation problem that considers several technical and economic infrastructure factors such as critical infrastructure components, geospatial positioning of infrastructures, power requirements, and microgrid cost. In particular, the geographic allocation of a microgrid is presented as an optimization problem to optimize a weighted combination of the relative importance of nodes across all key infrastructures and the associated costs. Furthermore, the simulation results of the formulated optimization problem are compared with a modified version of the heuristic method based on the critical node identification of an interdependent infrastructure for positioning microgrids in terms of the resilience of multiple smart critical infrastructures. Numerical results using infrastructure in the city of Pittsburgh in the USA are given as a practical case study to illustrate the methodology and trade-offs. The proposed method provides an effective method for localizing renewable energy resources based on infrastructural requirements.

The Architecture Optimization and Energy Management Technology of Aircraft Power Systems: A Review and Future Trends

With the development of More/All-Electric Aircraft, especially the progress of hybrid electrical propulsion or electrical propulsion aircraft, the problem of optimizing the energy system design and operation of the aircraft must be solved regarding the increasing electrical power demand-limited thermal sink capability. The paper overviews the state of the art in architecture optimization and an energy management system for the aircraft power system. The basic design method for power system architecture optimization in aircraft is reviewed from the multi-energy form in this paper. Renewable energy, such as the photo-voltaic battery and the fuel cell, is integrated into the electrical power system onboard which can also make the problem of optimal energy distribution in the aircraft complex because of the uncertainty and power response speed. The basic idea and research progress for the optimization, evaluation technology, and dynamic management control methods of the aircraft power system are analyzed and presented in this paper. The trend in optimization methods of engineering design for the energy system architecture in aircraft was summarized and derived from the multiple objective optimizations within the constraint conditions, such as weight, reliability, safety, efficiency, and characteristics of renewable energy. The cost function, based on the energy efficiency and power quality, was commented on and discussed according to different power flow relationships in the aircraft. The dynamic control strategies of different microgrid architectures in aircraft are compared with other methods in the review paper. Some integrated energy management optimization strategies or methods for electrical propulsion aircraft and more electric aircraft were reviewed. The mathematical consideration and expression of the energy optimization technologies of aircraft were analyzed and compared with some features and solution methods. The thermal and electric energy coupling relationship research field is discussed with the power quality and stability of the aircraft power system with some reference papers. Finally, the future energy interaction optimization problem between the airport microgrid and electric propulsion aircraft power system was also discussed and predicted in this review paper. Based on the state of the art technology development for EMS and architecture optimization, this paper intends to present the industry’s common sense and future trends on aircraft power system electrification and proposes an EMS+TMS+PHM to follow in the electrified aircraft propulsion system architecture selection

Short-term building electrical load forecasting using adaptive neuro-fuzzy inference system (ANFIS)

The aim of this study is to develop very short-term and accurate energy consumption forecasts for educational building. The purpose is to develop a predictive model for building energy planning to balance the supply from renewable power systems and the building electrical load demand). For the methodology, an adaptive neuro-fuzzy inference system (ANFIS) was used as a machine learning approach for building energy forecasting. The data for training (80%), testing (10%), and validation (10%) was used from smart energy meters installed in the building and weather data. A total of 20520 dataset instances was used for developing the forecasting model. The originality of this study is the ANFIS model was validated for the training, testing, and validation data, and the accuracy and reliability of the forecasting model was assessed over various very short time horizon of past data (0.5–4 h ahead). The main results reveal that the predictive model is extremely accurate in predicting the energy consumption of a building. The values of coefficient of correlation R for training, testing, and validation are 0.98017, 0.9778, and 0.97593, respectively, for the 30 min ahead energy forecasting. The R values for all the data are respectively 0.97951, 09854, and 0,96778 for the 0.5, 1, 4 h ahead energy forecasting. The low nMSE and nMAE errors for the ANFIS model demonstrate how well the model replicates the reported results from smart energy meters. The research findings are very important for the energy planning of microgrid power systems (energy purchase, building operations and maintenance, and demand side management).

Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm

This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction.

A Novel Mathematical Approach to Model Multi-Agent-Based Main Grid and Microgrid Networks for Complete System Analysis

Penetration of renewable energy resources in modern power systems has increased rapidly. The integration of different renewable and nonrenewable resources for the purpose of electricity generation is referred to as Distributed Generation (DG) units. The penetration of DG units gave birth to the concept of power microgrid. Power inverters play a major role in the integration of DGs in the power system. Control and stability analysis of microgrids in power systems is a challenging task for the control community. Dynamic microgrid modeling demands knowledge of fundamental engineering laws to detailed theoretical analysis. To model the dynamic behavior of the power microgrid, a basic understanding of the power converter operation modes and their control schemes is necessary. The main microgrid modeling components are power converters, power lines, transformers, protection systems, load, and faults. In this paper, preliminary concepts of power systems along with graph theoretic approach are used to develop the model of the microgrid and main grid networks. A mathematical model of a power microgrid in islanded mode, as well as the grid-connected mode, is developed and comprises of generation sources, power inverter interface, protection mechanism, load, faults, and transmission lines. The developed mathematical model can be used to address the stability issues as well as resilience in the power networks for complete system analysis. To validate the mathematical model, a renewable energy-based main grid and microgrid model is simulated. The graphical result of simulated model presents the generation and load curves.

Analysis of an Energy Management System of a Small Plant Connected to the Rural Power System

This paper presents the analysis of an energy management system (EMS) implemented to fulfill the requirements of a microgrid (MG) power supply owned by a small industrial company and connected to the rural power system. The main goal of this system is to ensure connection with the existing rural power system in terms of energy exchange, as well as to perform islanding mode operation of the microgrid based on the energy demand of the company. Power generated in the implemented microgrid is based on a hybrid system supported by renewable energy sources and an energy storage system. The aim of the developed energy management system implemented in this MG power system is to enable the microgrid to operate according to scheduled diagrams related to different load events. The presented investigation, analysis, and assessment of the implemented energy management system are based on the on-site measurements and observations of the long-term operation of the microgrid concerning the hybrid renewable energy-based generation system and the company energy demand, which is useful from perspective of the predicted deficiency of electrical energy in Poland, both for customers and transmission system operators. Furthermore, the observed drawbacks and failures of the implemented EMS and the MG are pointed out and discussed to overcome and minimize these disadvantages, and to improve the operational reliability of the whole system.

Economic optimal load management control of microgrid system using energy storage system

The microgrid (MG) implementation has changed the power system's configuration, and it has optimally permitted the integration of renewable energy resources (RERs). This paper addresses an optimal load management control method application for an economic load shedding problem. The control scheme is based on coordination of the diesel generator (DG) system combined with a standalone MG that contains RER (wind and photovoltaic (PV), two biomass generation systems and an energy storage system (ESS). The control model minimises the DG unit's fuel cost incorporated into an MG where a milling plant is part of the loads. The optimal control scheme has been executed to minimise the DG's operating cost, maximise the energy from RERs and finally increase the MG's profit during the load shedding operation. This strategy develops an under-frequency load shedding (UFLS) scheme to control the MG based-multiobjective optimally. An adaptive UFLS strategy is designed to coordinate the system behaviour by adjusting the distributed generation in the function of variation in the demand side. It is observed that it is economically reliable to solve the load shedding problem and save the fuel cost of the diesel generator in the MG power system by optimally fashioning the control scheme. The profitability of this adaptive strategy is impressive, which is about 100%. Besides, the application of different distributed energy structures in an MG impacts the demand side. It implies adjusting the total power supply fluctuations, which remarkably improves the MG power system's lifecycle cost (LC) and stabilise the system frequency and voltage.

Adaptive Neural Network Linear Parameter-Varying Control of Shipboard Direct Current Microgrids

To avoid pollution of transportation applications, renewable energies are deployed. Whereas they are uncontrolled, fully controlled pollution-free energy sources and storage units should be also considered. However, a complicated direct current (DC) microgrid (MG) is obtained, which suffers from nonlinearities, high order, and uncertainties of the power elements. Therefore, it is vital to use advanced nonlinear controllers to assure closed-loop stability and performance of the overall system. In this paper, a neural network (NN)-based adaptive linear parameter varying (LPV) controller is suggested for the whole DC MG power system. The main advantage of the developed controller is that it deploys the operating information of all power energy sources to manipulate each component. This enhances the DC MG stability margin and fast regulation of the power and voltage. Besides, the proposed controller has a systematic and fully offline design algorithm by combining numerical solvers and theoretical theories. The LPV controller is designed via the numerical linear matrix inequality (LMI) approach and the adaptation law for the NN parameters is designed based on the Lyapunov stability theory. A DC MG benchmark including a 5kW fuel cell, a 5kW solar plant, and a 2kW battery package is considered as the case study, which can be utilized in future fully electric boats (EBs). Numerical simulations with different scenarios are conducted to verify the performance of the proposed controller. Furthermore, comparative results are provided to show the advantages of the proposed method dealing with power fluctuations of the solar plant and DC loads over state-of-the-art nonlinear methods.

Fractional Order Piλdµ Controller for Microgrid Power System Using Cohort Intelligence Optimization

Fractional Order Piλdµ Controller for Microgrid Power System Using Cohort Intelligence Optimization

Weibull distribution-based analysis for reliability assessment of an isolated power micro-grid system

This study investigates Weibull Distribution-based Analysis for reliability assessment of an isolated power Micro-grid system. The Mean Time to Repair (MTTR) reliability indices were calculated using the outage data obtained from the micro-grid site between 2015 and 2019. The Weibull probability function is widely used in reliability analysis; in this method, the shape and scale parameters were calculated using the Maximum likelihood Estimator (MLE). MLE technique was deployed to check the problem of the MTTR data analysis from data collected to estimate the Weibull study. The research involved the percentage, cumulative percentage, density, cumulative probability, survival probability, and hazard probability. The scale parameter, which was observed as 0.625195, equally represents the mean failure time of 0.99728, which influenced the overall reliability performance of the micro-grid power system.

Probability-Based Customizable Modeling and Simulation of Protective Devices in Power Distribution Systems

Fused contactors and thermal magnetic circuit breakers are commonly applied protective devices in power distribution systems to protect the circuits when short-circuit faults occur. A power distribution system may contain various makes and models of protective devices, as a result, customizable simulation models for protective devices are demanded to effectively conduct system-level reliable analyses. To build the models, thermal energy-based data analysis methodologies are first applied to the protective devices’ physical properties, based on the manufacturer’s time/current data sheet. The models are further enhanced by integrating probability tools to simulate uncertainties in real-world application facts, for example, fortuity, variance, and failure rate. The customizable models are expected to aid the system-level reliability analysis, especially for the microgrid power systems.

Economic Feasibility Study on PV/Wind Hybrid Microgrids for Indonesia Remote Island Application

This paper presents the economic feasibility of hybrid microgrid power system for three remote islands of Sumatra, Indonesia. The microgrid system simulated and analysed using Homer Pro software. Optimization results showed that the combination of photovoltaic (PV), diesel generation (G) and batteries (Batt) for microgrid power system in Mandeh and Lagundri Island area were the most economical configuration. Meanwhile, for Mentawai area, the combination of PV, Wind Turbine (WT), G, Batt was the most optimal since it has higher wind speed then the other two areas. The Mandeh area has the highest solar radiation compared to the other two areas, resulting in the lowest CoE of $0.096/kWh as well as the lowest investment and operational costs. For the fixed PV 100 kW scenario, the optimal configuration is obtained with 86 kW supplied by WT for the Lagundri location, and 67 kW supplied by WT for the Mentawai area, while the WT installation area is not recommended for Mandeh location. The power management analysis showed that the average and patterns of weather parameters including solar radiation and wind speed effect both PV and Wind electrical power production.

Interval Type-2 Fuzzy Logic Control-Based Frequency Control of Hybrid Power System Using DMGS of PI Controller

The load frequency control of a microgrid is one of the emerging areas due to the changes in demand and supply in power system. So the controllers’ implementation must be changed accordingly. This paper proposes an interval type-2 fuzzy logic-based, dual-mode gain scheduling (DMGS) of the proportional and integral controller in which the gains of the PI controller werescheduled through the dynamic selector. This proposed controller was implemented ina hybrid microgrid power system in which nonconventional energy sources wereadded to each area of the conventional power plant, which madethe system much more prone to frequency variations. The controller was designed for three areas, consisting of a photovoltaic (PV) system, a wind power system, a fuel cell and a diesel engine/hydropower generator in which the generation rate constraint (GRC) was considered as a nonlinearity. The proposed power system was investigated under various load conditions in the MATLAB/SIMULINK environment. A comparative evaluation of changes in frequency, tie-line power fluctuations and variations in area control errors for the test system showed the effectiveness of the current approach over simple fuzzy PI and a conventional PI-controlling approach.

Decentralized Control of DC Microgrid Based on Droop and Voltage Controls with Electricity Price Consideration

In this paper, a power flow control strategy (PFCS) for the decentralized control of DC microgrids (DCMGs) is proposed to enhance the flexibility and scalability of the microgrid power system. The proposed scheme is achieved by combining the droop control and DC-link voltage control with the consideration of the electricity price condition. Generally, the droop control method can be used effectively in decentralized DCMGs to achieve power-sharing without additional communication links. However, the deviation of the DC-link voltage caused by the droop control affects the amount of power delivered to the load. As an alternative, the DC-link voltage control can be used to prevent such a deviation. To combine both control schemes in this study, the utility grid (UG) unit uses the DC-link voltage control to exchange the power between the DC-link and a UG in the grid-connected mode, while a distributed generator (DG) and energy storage system (ESS) units use the droop control method in the islanded mode. The operating modes of the UG, DG, ESS, and load units are determined by the deviation values of the DC-link voltage to maintain DCMG power balance. The overall PFCS is also developed for a decentralized DCMG system by taking into consideration several uncertainties such as DG power variation, battery state of charge (SOC) level, load demand, and grid availability. The proposed PFCS also considers electricity price conditions to adaptively change the DC-link voltage level for the purpose of minimizing the utility cost. When the DC-link voltage level is reduced due to the high electricity price condition, the proposed droop controller is designed such that the ESS unit operates with a discharging mode, which leads to the required minimum power support from the UG. The effectiveness of the proposed PFCS is demonstrated by comprehensive simulation and experimental results under various conditions. Those test results clearly confirm the control flexibility and overall performance of the proposed PFCS for a decentralized DCMG system.

Hierarchical multi-agent based frequency and voltage control for a microgrid power system

With the introduction of active devices such as inverters in the microgrid the system stability has been jeopardized. A primary controller fails to maintain the system frequency and hence an additional secondary controller is needed. In this paper, a control technique for an isolated smart microgrid in a multi-agent based infrastructure is studied. The agents are software abstractions that utilize advanced algorithms such as Particle Swarm Optimization (PSO) and Adaptive Dynamic Programming (ADP) to tune the control parameters. The proposed ADP based control approach is applied in different multi-agent structures in the inverter based microgrid which is tested against oscillations in the system. The results show improvement in control performance of the microgrid system using the proposed multi-agent based control method.

Design and Techno-economic assessment of a new hybrid system of a solar dish Stirling engine instegrated with a horizontal axis wind turbine for microgrid power generation

The increasing interest in renewable microgrids have motivated the exploration of more sustainable alternatives to traditional energy supply. In this study, a novel hybrid renewable energy-based microgrid power system is proposed, designed and techno-economically assessed. The system consists of a concentrated parabolic solar dish Stirling engine and a horizontal axis wind turbine integrated with a battery bank. The novelty of the study lies in replacing conventional hybrid systems, such as a typical photovoltaic/wind assembly, with a novel solar dish/wind turbine system that has the potential to achieve higher efficiencies and financial competitiveness. The solar dish Stirling engine serves as the primary source of electrical power generation while the horizontal axis wind turbine, in conjunction with a battery bank, supplies backup electricity when the primary source of power is unavailable. The system has been designed through advanced modelling in the MATLAB/ Simulink® environment that efficiently integrates the individual energy technologies. A technical sensitivity analysis has been performed for all the units in order to reduce the respective design limits and identify optimum operational windows. Further, the performance of the model has been tested at two locations in Jordan, and a thorough techno-economic analysis of the integrated system has been conducted. The simulation results show that at the optimal design point the efficiency of the Stirling engine is 37% with a net output power of 1500 kWe. For the horizontal axis wind turbine, a module of 100 kWe with a power coefficient of 0.2–0.24 is suitable for operation in terms of cost, power, torque and farm size. Also, two economic indicators, namely, the levelised cost of electricity and hourly cost, have been calculated. The levelised cost of electricity lies between 0.13 and 0.15 $/kWh while the hourly cost is found to be around 4 $/h. Thus, the economic evaluation revealed that the proposed system is very competitive with other integrated renewable energy technologies.

An Interoperable Communication Framework for Grid Frequency Regulation Support from Microgrids.

Renewable energy sources, which are controllable under the management of the microgrids with the contribution of energy storage systems and smart inverters, can support power system frequency regulation along with traditionally frequency control providers. This issue will not be viable without a robust communication architecture that meets all communication specification requirements of frequency regulation, including latency, reliability, and security. Therefore, this paper focuses on providing a communication framework of interacting between the power grid management system and microgrid central controller. In this scenario, the microgrid control center is integrated into the utility grid as a frequency regulation supporter for the main grid. This communication structure emulates the information model of the IEC 61850 protocol to meet interoperability. By employing IoT’s transmission protocol data distribution services, the structure satisfies the communication requirements for interacting in the wide-area network. This paper represents an interoperable information model for the microgrid central controller and power system management sectors’ interactions based on the IEC 61850–8–2 standard. Furthermore, we evaluate our scenario by measuring the latency, reliability, and security performance of data distribution services on a real communication testbed.