Microgrid Components Research Articles

Design Optimization of a Residential PV-Battery Microgrid With a Detailed Battery Lifetime Estimation Model

The interest in installing photovoltaic (PV)-based microgrids has increased significantly in the last few years due to the urgent need for reducing greenhouse gas emissions and improving the reliability as well as the quality of power supply, particularly in developing countries. The design of a microgrid is challenging since multiple components and complicated operating conditions have to be taken into account. In this article, a comprehensive method for optimal design of a class of residential PV-battery microgrids is proposed to determine the optimal number of lead-acid batteries and PV panels, the optimal battery bank depth of discharge (DOD) value, and the optimal tilt angle of the PV panels. The aim of the optimization is to minimize the levelized cost of energy (LCOE) where the limitation of the annual total loss of the power supply and the operational constraints are considered. In addition, a detailed model for battery lifetime estimation is introduced based on the physico-chemical mechanism of the lead-acid battery. Moreover, the effect of design parameters, e.g., the size and DOD of the battery bank on the battery capacity degradation, the PV-panel number and battery lifetime on LCOE, is investigated. Computation results demonstrate that the proposed method is capable of optimizing the size of the microgrid components and the other design parameters while satisfying all technical and operational constraints.

Innovative solution for microgrid components testing: Real time line impedance emulation

This paper proposes an enhanced control for a converter based three phase line impedance emulator in order to improve clean energy sources integration. In fact, the proposed emulator is used to perform tests related to the integration of renewable energy systems with public grids according to international codes and standards. The emulator control includes a power converter control and an impedance emulation algorithm. Conventional power converter controls for line impedance emulators are based on PI or resonant controllers. However, the stability region of this kind of controls is limited. In addition, existing impedance emulation algorithms are complicated, difficult to implement and very greedy in computing resources. In view of this, the presented work investigates the dead beat predictive control for the power converter associated to an optimized impedance emulation algorithm. The proposed control is characterized by its fast dynamic response, simple design and minimized computational resources, while the proposed impedance emulation algorithm is characterized by its precision, efficiency and decoupled inductive and resistive impedance components variation. Indeed, these characteristics allow flexible, precise and accurate impedance emulation based tests. Simulation and experimental results for the line impedance emulator are presented to prove the performances and the efficiency of the proposed control strategy.

Novel Implementation and Comparison of Active and Reactive Power Flow Control Methods in a Single Phase Grid-Connected Microgrid

Grid-connected inverters are essential microgrid components that interface a common dc bus, fed by several distributed resources, to the ac loads. This article presents different methods to control active and reactive power flow from the ac source in a single phase microgrid when it operates in grid-connected mode. A novel mixed-frame control strategy is presented and its performance evaluated using different methods to produce the orthogonal axis components. The different strategies are presented and compared with respect to their performance in the presence of harmonics in the main ac grid. Physics-based models and simulated plots are presented and validated by experimental waveforms measured on a laboratory prototype.

LoRa-based communication system for data transfer in microgrids

This paper proposes a LoRa-based wireless communication system for data transfer in microgrids. The proposed system allows connection of multiple sensors to the LoRa transceivers, and enables data collection from various units within a microgrid. The proposed system focuses on communications at the secondary communication level of the microgrid between local controllers of each distributed generation (DG) unit and the microgrid central controller due to the possibility of applying low-bandwidth communication systems at this level. In a proof of concept test bed setup, the data collected by the sensors are sent to the LoRa gateway, which serves as the central monitoring system from which control messages are sent to various microgrid components through their local controllers such as DG units, storage systems and load. In this work, to improve communication security, a private server has been developed using Node-Red instead of cloud servers that are currently used in most Internet-of-Things (IoT) monitoring systems. A range test of the proposed system is carried out to observe the rate of data delivery. It demonstrated over 90% data delivery at 4 km. Finally, a test bed experiment is conducted to validate key features of the proposed system by achieving one-directional data transfer in a grid monitoring system.

IEC 61850 Modeling of UPFC and XMPP Communication for Power Management in Microgrids

Integration of distributed renewable energy sources in microgrids presents many challenges to grid such as voltage, stability and power management issues. In literature different control strategies to improve the performance of microgrids integrated with renewable energy sources proposed. FACTS based controller such as Unified Power Flow Controller (UPFC) has also been employed in microgrids to improve the power management and transient stability. Coordinated control strategies between UPFC controller and DERs have been developed. However, the underlying communication for realizing these strategies has not been discussed. This paper presents IEC 61850 and XMPP based communication for coordinating UPFC and DERs in microgrid for its stable operation. IEC 61850 information model for UPFC controller is developed. Furthermore, the XMPP protocol is utilized to provide the scalability for communication network in microgrids with high penetration of DERs. Additionally, XMPP also provides improved security along with the scalability. IEC 61850 message exchanges over XMPP communication between different components of microgrid for power management is demonstrated.

Software-Defined Microgrid Control: The Genesis of Decoupled Cyber-Physical Microgrids

Nowadays, microgrid controllers are often embedded in specialized hardware such as PLC and DSP. The hardware-dependency and fit-and-forget design make it difficult and costly for microgrid controllers to evolve and upgrade under frequent changes such as plug-and-play of microgrid components. Furthermore, different distributed energy resources in a microgrid require customized controllers, leading to long development cycles and high operational costs for deploying microgrid services. To tackle the challenges, a software-defined control SDC) architecture for microgrid is devised, which virtualizes traditionally hardware-dependent microgrid control functions as software services decoupled from the underlying hardware infrastructure, fully resolving hardware dependence issues and enabling unprecedentedly low costs. A generic SDC prototype is designed to generate microgrid controllers autonomously in edge computing facilities such as distributed virtual machines. Extensive experiments verify that SDC outperforms traditional hardware-based microgrid control in that it empowers a decoupled cyber-physical microgrid and thus makes microgrid operations unprecedentedly affordable, autonomic, and secure.

Temporally Coordinated Energy Management for AC/DC Hybrid Microgrid Considering Dynamic Conversion Efficiency of Bidirectional AC/DC Converter

Multiple uncertainties from renewable energy sources, power loads and bidirectional AC/DC converter have brought great challenges to the energy management of AC/DC hybrid microgrid. In view of the issues, this paper proposes a temporally coordinated energy management strategy for AC/DC hybrid microgrid considering dynamic conversion efficiency of bidirectional AC/DC converter. According to the operation and loss characteristics of bidirectional AC/DC converter, a novel dynamic conversion efficiency model of bidirectional AC/DC converter is developed. To maintain high robustness at minimum operation cost, the proposed strategy is divided into two stages. The outputs of renewable energy sources, operation characteristics of microgrid components and time-of-use electricity price are comprehensively considered in the day-ahead economic energy management stage to minimize the daily operation cost. In the intraday rolling energy management stage, day-ahead schedules of controllable units are adjusted based on intraday ultra-short-term forecast data to suppress the intraday power fluctuations induced by day-ahead forecast errors. The simulation results demonstrate that the proposed strategy can effectively mitigate the impact of multiple uncertainties and realize the economic operation of AC/DC hybrid microgrid.

Continuous Nonsingular Terminal Sliding Mode Control of DC–DC Boost Converters Subject to Time-Varying Disturbances

DC-DC converters work as one of the crucial components in DC microgrid intergraded power systems. In this brief, the robust output voltage regulation problem of DC-DC boost converter system is addressed by using a continuous nonsingular terminal sliding mode control (CTSMC) technique based on finite-time disturbance observer. By integrating the disturbance estimations into the controller design, an improved sliding mode control (SMC) approach is developed to achieve better voltage tracking performance. The proposed control method admits the properties of fast transient responses, strong suppression ability against time-varying disturbances and small steady state oscillations of output voltage. Experimental results in the presence of both load variations and supplied voltage fluctuations are provided to validate the effectiveness of the proposed algorithm.

A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid

There has been growing interest in the development of sustainable energy systems using the potential of renewable energy sources. However, due to the intermittent nature of renewable energy sources, they must be accompanied by appropriate storage devices to be optimally integrated into so-calledsmartmicro-grid systems. The optimal design problem of sustainable micro-grids is associated with several nonlinearities and non-convexities, and therefore is not amenable to exact methods of optimization. Accordingly, this paper proposes a metaheuristic-based approach for optimizing the size and typology of the components of an off-grid hydrogen-based micro-grid, backed up with super-capacitors and stationary fuel cell systems, in the presence of a hydrogen refuelling station. The paper also compares the performance of six recent metaheuristics. The simulations are carried out for the climatic conditions of the Feilding area, New Zealand using MATLAB. Based on the comparative results, the moth-flame optimization algorithm is found to result in a significant reduction in the total net present cost of the system in comparison with other investigated algorithms. Notably, it outperforms the genetic algorithm and the particle swarm optimization in terms of solution quality by ~2.1% and ~3.2% (equating to cost savings of $123,910 and $188,129 from the target system). The results also demonstrate both the technical feasibility and cost-effectiveness of the proposed stand-alone micro-grid architecture. Particularly, the levelized costs of electricity and hydrogen of the conceptualized system are found to be $0.09/kWh and $4.61/kg, respectively, which are well below the current tariffs in New Zealand.

Operation Scheduling Optimization for Microgrids Considering Coordination of Their Components

Operation scheduling is one of the most practical optimization problems to efficiently manage the electric power supply and demand in microgrids. Although various microgrid-related techniques have been developed, there has been no established solution to the problem until now. This is because the formulated problem becomes a complicated mixed-integer programming problem having multiple optimization variables. The authors present a framework for this problem and its effective solution to obtain an operation schedule of the microgrid components considering their coordination. In the framework, trading electricity with traditional main power grids is included in the optimization target, and uncertainty originating from variable renewable energy sources is considered. In the solution, the formulated problem is reformulated to reduce the dimensions of its solution space, and, as a result, a combined algorithm of binary particle swarm optimization and quadratic programming is applicable. Through numerical simulations and discussions of their results, the validity of the authors’ proposal is verified.

Predictive active-reactive optimal power dispatch in PV-battery-diesel microgrid considering reactive power and battery lifetime costs

Microgrids are considered as a cost-efficient and reliable energy sources to rural areas and regions with unreliable power supply. The key potential for a low-cost operation and uninterrupted power supply lies in the optimal operation of such microgrids. Optimizing the microgrid operation consists of the optimal management of the power dispatched from the microgrid components for covering the load demand with a minimum cost while satisfying all technical and operational constraints. This leads to a complex optimization problem which is not easy to be solved. This paper presents an active-reactive optimal power dispatch strategy based on the concept of an economic model predictive controller (EMPC). In this operation strategy, the battery lifetime cost, the active-reactive power generation cost and the grid blackout problem are considered. In addition, a cost model for the dispatched reactive power from the PV-system and battery storage system is developed. Furthermore, a new cost model for reactive power generation from the diesel generator is introduced. The computation result shows that the proposed EMPC framework is able to manage the power dispatch in the microgrid in a cost-effective and reliable manner for both grid-connected and islanded mode. Moreover, the proposed operation strategy leads to a significant reduction in the total costs of the dispatched active-reactive power and battery lifetime loss. Furthermore, it is approved that the PV-inverter is able to generate reactive power with very low cost compared to other energy sources in the microgrid.

Robust Nonlinear Adaptive Feedback Linearizing Decentralized Controller Design for Islanded DC Microgrids

This paper presents a robust nonlinear decentralized control scheme for islanded dc microgrids, where the main control objectives are to achieve the desired voltage at the common dc bus and to maintain the power balance. The proposed control scheme uses the partial feedback linearization scheme to simplify the dynamical models of different components in dc microgrids. In this paper, the dc microgrid includes a solar photovoltaic unit, a fuel cell system, and a battery energy storage system along with dc loads. The robustness of the proposed controller against parametric uncertainties is ensured by the parameter, appearing in the control inputs as unknown, which is then estimated through adaptation laws. The inherent noise decoupling capability of the feedback linearization scheme is used to provide robustness against external disturbances in dc microgrids. The performance of the proposed controller is evaluated on a dc microgrid through simulation and experimental studies in order to demonstrate the effectiveness and robustness under different operating conditions, while considering the effects of parametric uncertainties and external disturbances. Simulation results are also compared with an existing proportional-integral controller.

Validating Response of AC Microgrid to Line-to-Line Short Circuit in Islanded Mode Using Dynamic Analysis

This paper is presented in an attempt to validate the dynamic response of a microgrid to line-to-line short circuit. The microgrid components include two identical Wind Turbine Generators (WTGs) tied to a 100MVA, 13.8kV utility via a Point of Common Coupling (PCC). The utility-microgrid testbed is modeled in SIMPOWERSystems® using two Doubly-Fed Induction Generators (DFIGs) in the microgrid side. While in islanded operating mode, line-to-line short circuit fault is applied at 6.0s and withdrawn at 8.0s, obtaining a 50.0s dynamic response of the system for different fault locations, under voltage and reactive power control regimes of the wind turbine controller. For measurement purpose, the absolute value of the stator complex voltage is transformed to reference frame. Bidirectional power flow between the two feeders is established in the study. The study also confirms that the microgrid composed of DFIGs offer reactive power management capability, particularly by presenting superior performance when stressed under Q control regime than under V control regime. Finally, the response of the testbed to line-to-line short circuit has been validated and shown to be consistent with established short circuit theory.

A Novel Predictive Fuzzy Logic-Based Energy Management System for Grid-Connected and Off-Grid Operation of Residential Smart Microgrids

In this paper, a novel energy management system (EMS) with two operating horizons is proposed for a residential microgrid application. The microgrid utilizes the energies of a photovoltaic, a fuel cell (FC), and a battery bank to supply the local loads through a combination of electric and magnetic buses. The proposed microgrid operates in a large number of grid-connected and off-grid operation modes. The EMS includes a long-term data prediction unit based on a 2-D dynamic programing and a short-term fuzzy controller. The long-term prediction unit is designed to determine the appropriate variation range of the battery state of charge and FC state of hydrogen. The efficiency performance of the microgrid components, predicted energy generation and demand, energy cost, and the system constraints are taken into account. The resultant data then are sent to the short-term fuzzy controller which determines the operation mode of the microgrid based on the real-time condition of the microgrid elements. A prototype of the proposed microgrid including the EMS is developed, and experimental tests are conducted for three different energy management scenarios. The proposed management technique is validated through energy distribution and cost analysis.

Microgrid Stability Definitions, Analysis, and Examples

This document is a summary of a report prepared by the IEEE PES Task Force (TF) on Microgrid Stability Definitions, Analysis, and Modeling, IEEE Power and Energy Society, Piscataway, NJ, USA, Tech. Rep. PES-TR66, Apr. 2018, which defines concepts and identifies relevant issues related to stability in microgrids. In this paper, definitions and classification of microgrid stability are presented and discussed, considering pertinent microgrid features such as voltage-frequency dependence, unbalancing, low inertia, and generation intermittency. A few examples are also presented, highlighting some of the stability classes defined in this paper. Further examples, along with discussions on microgrid components modeling and stability analysis tools can be found in the TF report.

Techno‐economic analysis of metal hydride‐based energy storage system in microgrid

AbstractHydrogen can be a promising option as an energy storage medium in renewable power generator‐based microgrid. However, the technologies used for the hydrogen storage such as high pressure in gaseous form and solid storages can significantly affect the cost performance of the hydrogen‐based microgrid. In this study, a techno‐economic assessment for solid hydrogen storage (metal hydride [MH])‐based microgrid in Indian operating conditions has been carried out in terms of levelized cost of electricity (LCOE), net present cost, and avoided environmental emissions. In the present microgrid, excess photovoltaic (PV) generator electricity is used to generate hydrogen using the alkaline water electrolyzer (EL). This generated hydrogen is stored in the MH cylinder. The stored hydrogen is converted back into electricity by fuel cell (FC) in case of insufficiency of PV electricity. The sizing of the microgrid components such as PV, FC, EL, and hydrogen storage tank are obtained for given load profile using HOMER optimization tool. An attempt has been made for the cost evaluation of the solid and high‐pressure gaseous storages in the microgrid. A comparative study between two energy storage mediums such as battery and hydrogen in microgrid is also performed. In case of battery and hydrogen storages, the LCOE is found to be 0.1293 and 0.383 $/kWh, respectively. It was found that battery storage system is more economical than hydrogen storage in microgrid. Nonetheless, the difference between the LCOE in hydrogen‐based microgrid is reduced considerably with optimized cost scenario of the EL, FC, and H2 storage tank as compared to the battery storage. Such technical, economic, and environmental assessment of the MH‐based microgrid system provides a firm basis to policymakers for shaping energy and environment policies for hydrogen activities.

Optimal sizing and siting of smart microgrid components under high renewables penetration considering demand response

The purpose of this article is to determine the size and place of different components in microgrids (MGs) including renewable energy resources (RERs). Various factors like reliability, the uncertainty of wind speed, solar irradiance, load, and load growth are considered. The Ekbatan residential complex is studied as the pilot case study placed in Tehran, Iran. Ekbatan complex has three separate sets of buildings called phase 1, 2, and 3 considered as smart MGs. The multi-objective optimisation problem is solved considering RERs uncertainties, improving reliability and power quality and minimizing power loss by particle swarm optimisation algorithm. Different constraints in terms of voltage, frequency, resources, and energy storage systems (ESSs) capacity are taken into consideration. The effect of load growth, photovoltaic (PV) and ESSs placement, changing the capital cost of RERs, and demand response of controllable loads are studied on optimal sizing and siting. The proposed method is tested on a wind turbine/PV/fuel cell (FC)/hydrogen tank MGs system and the optimal sizing and siting of mentioned sources could decelerate the rate of increase in the total cost of MG considering the load growth.

Voltage Source Operation of the Energy-Router Based on Model Predictive Control

The energy router (ER) is regarded as a key component of microgrids. It is a converter that interfaces the microgrid(s) with the utility grid. The energy router has a multiport structure and bidirectional energy flow control. The energy router concept can be implemented in nearly zero energy buildings (NZEB) to provide flexible energy management. We propose a concept where ER is working as a single grid-forming converter with a predefined voltage reference. The biggest challenge is to maintain regulated voltage and frequency inside the NZEB in the idle operation mode, where traditional regulators, e.g., proportional-resonant (PR), proportional-integral-derivative (PID), will not meet the control design requirements and could have unstable behavior. To gain the stability of the system, we propose model predictive control (MPC). The design of the MPC algorithm is explained. A simulation software for power electronics (PLECS) is used to simulate the proposed algorithm. Finally, the simulation results are verified on an experimental prototype.

Techno-economic optimization and social costs assessment of microgrid-conventional grid integration using genetic algorithm and Artificial Neural Networks: A case study for two US cities

Through two case studies, a methodology is presented that assessed the techno-economic and environmental performance of microgrid-conventional grid integration scenarios for fifty homes located in US cities of Fargo and Phoenix. The microgrid was composed of seven components - solar photovoltaics, wind-turbines, lead acid batteries, biodiesel generators, fuel cells, electrolyzers and H2 tanks. Firstly, mathematical models that predicted the hourly power generation were developed for every microgrid component. Secondly, Artificial Neural Networks were utilized to predict hourly electricity demand and its results were validated with actual available data. Thirdly, through an electricity dispatch strategy and a Genetic Algorithm optimization technique, microgrid configurations were determined that had lowest levelized cost of energy, $/kWh. From peak power standpoint, four microgrid-conventional grid integration scenarios were examined, namely, microgrid possessing penetration level of 25%, 50%, 75%, 100%. Based on the environmental life cycle assessment of power generation, three carbon taxes were imposed -$12, $48, $72/tonne carbon dioxide emitted. Microgrid's electricity cost was found to be $0.43–0.86/kWh. Imposing carbon taxes barely showed any effect on microgrid's electricity cost nor its optimum configuration, but conventional grid's electricity cost was found to increase by 7–33% as its carbon emissions were five times as that of microgrid.

Time-of-use pricing model based on power supply chain for user-side microgrid

The user-side microgrid offers great potential for improving energy efficiency. This flexible and small-scale power system is characterized by multiple types of clean power supplies. The power supply chain can reveal the supply-demand relationships in the series of steps from power generation to consumption. However, electricity prices tend to vary and can significantly affect the actual energy costs to end-users and other entities in the power supply chain. In this study, we propose an optimization model of time-of-use pricing for the user-side microgrid from the perspective of power supply chain management. The objective of this model is to minimize the total cost of the power supply chain and optimize the charging-discharging behaviors of end-users. First, to reduce the impact of demand amplification and variation, we investigated the bullwhip effect of the power supply chain. Then, we considered distributed energy storage as an important component of the user-side microgrid and how electric power companies can utilize pricing strategies to optimize the charging-discharging behaviors of end-users. We performed experiments based on two scenarios that assumed end-users with and without distributed energy storage devices. A comparative analysis of the modelling results indicates that optimal time-of-use pricing can support the charging-discharging behaviors of residential users and reduce the cost of the entire electric power supply chain. The optimized time-of-use price is important for stability, flexibility, and efficiency improvement in both the user-side microgrid and the entire power supply chain.