Open access peer-reviewed chapter

Overview of Technical Challenges, Available Technologies and Ongoing Developments of AC/DC Microgrids

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Reza Sabzehgar

Submitted: October 8th, 2016 Reviewed: April 24th, 2017 Published: August 16th, 2017

DOI: 10.5772/intechopen.69400

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Gradual depletion of fossil fuel resources, poor energy efficiency of conventional power plants, and environmental pollution have led to a new grid architecture known as smart microgrid. The smart microgrid concept provides a promising solution that enables high penetration of distributed generation from renewable energy sources without requiring to redesign the distribution system, which results in stable operation during faults and disturbances. However, distributed generators/loads and interaction between all nodes within a microgrid will substantially increase the complexity of the power system operation, control, and communications. Many innovative techniques and technologies have been proposed to address the complexity and challenges of microgrids including power quality, power flow balancing, real‐time power management, voltage and frequency control, load sharing during islanding, protection, stability, reliability, efficiency, and economical operation. All key issues of the microgrids, different solutions, and available methods and technologies to address such issues are reviewed in this chapter. Pros and cons of each method are discussed. Furthermore, an extensive comprehensive review for researchers and scholars working on microgrid applications is provided in this chapter to help them identify the areas that need improvements and innovative solutions for increasing the efficiency of modern power distribution grid.


  • AC microgrid
  • DC microgrid
  • microgrid power quality
  • power management
  • microgrid modeling
  • power electronics
  • renewable energies

1. Introduction

The smart microgrid concept was first developed in Refs. [1, 2]. The capability of integration of a large number of distributed power generation sources and high penetration of renewable sources such as photovoltaic panels, wind turbines, and fuel cells within a smart microgrid made it very attractive. In fact, management of multiple distributed power generation sources including renewable energy sources in a smart microgrid offers many advantages such as significant reduction in the need for a transmission and high voltage distribution system, satisfying the growing demand of electricity, and improving energy utilization efficiency and reliability. However, interaction between all nodes within a microgrid including both conventional and renewable energy generation sources will not only increase the complexity of the power system considerably, but it will also raise challenging issues of reliability and power quality due to intermittent nature of renewable sources. Therefore, many technical challenges must yet be overcome to ensure safe, secure, reliable, optimized, efficient, and cost effective operation of the microgrid.

Academic scholars and industry experts offered many innovative techniques and technologies to address the challenges of microgrids such as power quality and power flow balancing [312], voltage and frequency control [1335], power management [3648], optimization [4962], stability [6366], reliability and protection [6777], and dynamic modeling [7895]. In this chapter, the concept of microgrid is first presented. All key issues of microgrid and the proposed innovative techniques addressing theses issues are then briefly reviewed. Finally, dynamic modeling and control of microgrid in different circumstances is discussed.

The rest of this chapter is organized as follows: Section 2 presents the microgrid concept. Power quality and power flow balancing of microgrid is discussed in Section 3. Section 4 reviews different control methods, power management strategies, and optimization techniques for microgrid. The key issues and technical challenges of microgrid stability, reliability, and protection are then summarized in Section 5. Section 6 discusses dynamic modeling of microgrid. Finally, conclusion and discussion are presented in Section 7.


2. Microgrid concept

A microgrid is a local group of electricity sources and loads that are intelligently managed through power electronics interfaces. A microgrid normally operates in parallel with utility electrical macrogrid, which is denoted as grid‐tied mode of operation. It is capable of operating independently in islanded mode. In grid‐tied mode, the microgrid becomes part of the main utility grid, and serves the grid with the excess power from distributed renewable generation sources. In this mode of operation, system control and scheduling a transition to islanded mode are done based on the system operation information such as generation outputs, demands, voltages, and the status of protection relays. In islanded mode, however, the microgrid must provide load balance by load shedding and load management. In this mode of operation, some loads might be shutdown to guarantee that there is enough power for critical loads. To restore the transition back to grid‐tied mode, the frequency, voltages, and phase angles should be within acceptable limits and must be synchronized.

A microgrid can be generally classified into AC and DC types. The AC microgrid is the main type of microgrid where all distributed power generation sources and loads are connected to a common AC bus through power electronic interfaces as sown in Figure 1. However, in DC microgrids a DC bus is used as a common bus especially for small‐scale commercial and residential applications, due to higher efficiency and controllability as the extra power conversion stages are eliminated and synchronization and reactive power compensation are not needed in this configuration [96101].

Figure 1.

Schematic of an AC microgrid.

Integration of various distributed generators/loads and interaction among them within a smart microgrid remain in place regardless of the type of microgrid due to the complexity of power systems technologies, control and communication techniques. In the following sections, many challenges raised by smart microgrid applications such as power quality, protection, stability, reliability, and efficiency are briefly discussed.


3. Microgrid power quality

Power quality is an important issue in a microgrid due to intermittent nature of the integrated distributed renewable energy sources within the microgrid, the transition between grid‐tied and islanded modes of microgrid, nonlinear loads, injected current harmonics by power electronic devices, and loads with considerable reactive power demand.

Energy storage, filtering, and proper control schemes are three main categories utilized by scholars to improve the power quality of a microgrid. In Ref. [3], a two‐level and a three‐level controlled voltage structure were developed as the active power conditioners (APC) to improve the power quality. Their satisfactory results validate the ability of the controlled APCs in improving the power quality even in a weak microgrid context with strongly nonlinear loads and voltage unbalance. A micro‐source grid‐connected inverter control method based on an advanced fP, VQdroop control was proposed in Ref. [4] for microgrid power quality management. Authors in Ref. [5] employed a digital power processor in a closed loop scheme to remove and compensate for unwanted harmonic content of power system variables such as voltages and currents, and thus improve the power quality. Zhang et al. [6] took advantage of both energy storage and filtering by combining a flywheel energy storage system and an active power filter for power quality improvement of microgrid. Their simulation results show that the combined system can maintain short‐term uninterrupted power supply and meet harmonic content standard. Belov et al. [7] created a virtual prototype of a microgrid including AC/DC/AC converters and energy storage devices. They developed a mathematical model of the converter with energy storage device on the basis of the bridge‐element (B‐element) concept. Their experimental results demonstrate low‐voltage distortion caused by the AC/DC/AC converter. In [8], a cooperative control approach is applied to distribute switching power interfaces in a low‐voltage residential smart microgrid to optimize exploitation of local energy sources and improve power quality. It is assumed that the low‐voltage residential smart microgrid has limited or no communication capability to neighbor units. Cheng et al. [9] employed distributed multiple active filter system to remove power system’s harmonics. They used multiple active‐filter units instead of a centralized large‐rated active filter to reduce the harmonics in the power system and thus improve the power quality. In Ref. [10], Pulse Width Modulation (PWM) converters are utilized to improve the generators’ power factor and produce less harmonics. By changing voltage amplitude and phase of PWM converters, generators’ active and reactive powers are controlled to meet the required power quality. A combined system of active power filter and static var compensator is employed in Ref. [11] to reduce the harmonic current produced by power electronic devices and load with considerable reactive power demand, and thus improve power quality of microgrid. In Ref. [12], an optimal power control strategy is presented for a microgrid operating in islanded mode. The proposed control strategy is based on particle swarm real‐time self‐tuning method.


4. Microgrid control strategies, power management, and optimization

Generally, the control strategies of microgrid can be classified in three different levels including primary (local), secondary (power management), and tertiary (optimization) control level [13].

Primary control, which is also known as local or internal control, is simply based on local measurements. In this local control level, no communication is needed [14]. The frequency and voltage deviation in microgrid depend on active and reactive power mismatch, the load characteristic, and the droop model of distributed generation sources. Therefore, frequency/voltage droop control and inverter output control strategies are the general methods being utilized in the primary level of control [1526].

Unlike the primary control, the secondary control level approaches strongly need fast and reliable communication systems set by IEC 61850 standard [34, 35]. Both centralized and decentralized control approaches can be used in secondary control level [2730]. Non‐model‐based fuzzy and neural network controllers [30, 31] and model‐based predictive controllers are the examples of centralized control approaches. Multi‐agent based control approaches [32, 33] are the examples of decentralized control methods in secondary control level of microgrid. Also, peak shaving, load following, frugal discharge, state of charge set‐point, full power/minimum run time, and ideal predictive dispatch strategies [3638] are other examples of secondary level control algorithms that have been proposed based on the main power management strategies.

Coordination of multiple microgrids based on the requirements of the main utility system is done in the tertiary control level. Tertiary control is not done by the microgrid itself, and is normally considered part of the main utility grid.

In addition to the developed control strategies for secure, reliable, and stable operation of microgrid, many energy management approaches have also been proposed to handle the characteristics of different power generators and storage systems within the microgrid. In Ref. [39], a microcontroller‐based power management system is utilized for the online operation of an experimental low‐voltage microgrid to control the battery state‐of‐charge. In Ref. [40], a dynamic energy management strategy is proposed for a photovoltaic (PV)‐based micro grid with combined energy storage. The combined storage system includes batteries and super capacitors. Batteries have low power density and their charge and discharge rate are low causing severe stress under quick load fluctuations. Super capacitors, on the other hand, have high power density and they can easily tolerate quick load fluctuations, but cannot be used as an energy storage system alone as they cannot supply load for a longer time. Combining batteries and super capacitors provides a high‐power density storage system. In Ref. [41], authors developed an online power energy management strategy for a hybrid fuel cell/battery as another type of energy storage system utilized in a microgrid. Their proposed method includes three layers, where in first layer, all possible operation modes of microgrid are captured. The power split between batteries and fuel cells is done in the second layer by a fuzzy controller. The set points of each subsystem are regulated in the third layer. In Ref. [42], an energy management system is proposed for a microgrid, including advanced PV generators with embedded storage units and a gas microturbine. The power management is done both centrally at the microgrid side and locally at the customer side by exchanging data and order through a communication network. The proposed power management strategy relies on PV power predictions, load forecasting, and distributed battery storage system. In Ref. [43], an overall power management strategy is proposed to manage power flows among the different energy sources and the storage system within the microgrid in which the primary power sources of the system are wind and PV, and a fuel cell‐electrolyzer combination is used as a backup and a long‐term storage system. Katiraei and Iravani [44] investigated three power management strategies based on voltage‐droop characteristic, voltage regulation, and load reactive power compensation to address real and reactive power management of a multiple distributed generation microgrid system.

In addition to control and power management, optimization is the other pillar of efficient and reliable operation of the microgrid. An optimal allocation methodology and economic analysis of energy storage system within a microgrid is proposed in Ref. [45] using genetic algorithm on the basis of net present value (NPV). In Ref. [46], the optimization is done by a coordinated control approach in two layers including the schedule layer and the dispatch layer. The schedule layer ensures the economical operation of microgrid based on forecasting data, and dispatch layer provides power and voltage regulation based on real‐time data. The control and coordination between two layers satisfies both the economical benefit and technical constraints of microgrid on long‐time operation. Authors in Ref. [47] optimized a microgrid economically by considering a multi‐objective cost function including the operational cost of distributed generators, start‐up and shut‐down costs, and the cost of interrupted loads. A multi‐objective optimization is also implemented in Ref. [48] in order to minimize the emissions of the three main pollutants coming from the gas turbines, i.e., CO2, COand NOx. Another multi‐objective multi‐scenario optimization method is presented in Ref. [49] to evaluate and optimize the performance of the microgrid under various scenarios from different aspects consisting of construction and operation cost, customer outage cost and environment. In Refs. [50, 51], a centralized hierarchical optimization strategy is presented for economic evaluation of a typical microgrid. A decentralized agent‐based strategy is also developed in Refs. [5254] for microgrid power management and optimization. In Ref. [55], an optimization scheme is developed based on the heuristics using a fuzzy neural network. Another neural network‐based optimization method is presented in Ref. [56]. Genetic algorithm [57], particle swarm optimization (PSO) [5861], and ant colony optimization (ACO) [58, 62] are also utilized as intelligent computational methods for microgrid power management and optimization.

Despite all scholarly works and proposed strategies, it is still necessary to develop a comprehensive control and power management strategy to consider all aspects of energy managements such as different modes of microgrid operation and transition modes, voltage and power flow, coordination of controllable units, economical operation, and stability due to the complexity of microgrid.


5. Microgrid stability, reliability, and protection

Stability, reliability, and protection are the key issues of microgrids due to reverse power flows of distributed generation units, local oscillations, transient modes of microgrid, severe frequency deviations in islanded mode operation, and economical and supply‐demand uncertainties of microgrid. To address these issues, various strategies have been proposed and developed [6366]. In Ref. [63], the stability constraints imposed by droop characteristics in an islanded microgrid are identified using a small‐signal approach. It was shown that droop gains have significant impact on the stability and microgrids dynamic performance. In Ref. [64], an adaptive feedforward strategy is proposed to change the dynamic coupling between a distributed resource unit and the host microgrid in a way to make the system stability less sensitive and more robust to the droop coefficients and network dynamics. The conflicting goals of proper load sharing and stability of an islanded microgrid is investigated in Ref. [65]. Suitable load sharing requires high values of angle droop specially under weak system conditions. However, overall stability of the system is negatively impacted by high droop gains. To stabilize the system while ensuring proper load sharing, a supplementary control loop around the primary droop control loop is proposed in this chapter. Stability issues of high gains of droop controller are also investigated in Ref. [66]. In this chapter, a reduced‐order mathematical model of the microgrid is proposed for prediction of stability of microgrids with large number of inverters. The results show that the stability of the microgrid can be analyzed with each inverter transformed into an equivalent network separately assuming that the interconnection cables are predominantly inductive and the droop laws can be decoupled.

Protection is another major challenge for microgrids. An overview of microgrid’s protection techniques and strategies is presented in Refs. [67, 68]. All the proposed protection systems ensure to respond to both main grid (utility) and microgrid faults as fast as possible for proper isolation of the microgrid from the main grid. Fast operation of protection system plays a crucial role in stability of microgrid after transition to islanded operation. Protection against overcurrent is one of the fundamental elements of electricity grid. However, this is a challenging task in microgrid as the total short‐circuit current capacity of a microgrid in islanded and grid‐tied modes is different. In fact, in grid‐tied mode of operation the protection is simplified by the potentially large fault currents, whereas these fault currents may have relatively low values in islanded mode due to integrated power electronics interfaces in microgrid. This low current capacity in islanded mode is not sufficient to trip conventional overcurrent protection. Therefore an adaptive protection system is needed to change relay settings in real time to guarantee that the microgrid is always protected [69, 70]. Another solution is utilizing digital relays equipped with a communication network [71] to protect the microgrid. An easier solution to the protection issue is designing the microgrid to enter islanded mode in a faulty situation before any protection action could take place [72]. To prevent the flow of large line currents during a voltage sag, two current‐limiting algorithms are employed in Ref. [73]. These algorithms are used with a voltage‐source inverter (VSI) connected in series between the microgrid and main utility grid to imitate a large virtual RL or L impedance for limiting the large line current during utility voltage sags.

To address the issues and limitations of traditional relaying overcurrent protection techniques in microgrid with bidirectional power flow, a voltage‐based fault detection and protection strategy is proposed in Ref. [74]. The proposed protection scheme provides reliable and fast detection for different types of faults within the microgrid in which any output disturbance is detected and the strategy for the isolation of the faulty section is initiated. A state observer is utilized in Ref. [75] to detect and identify faults that occur within the protection zone. Fault detection in a DC microgrid and the method for discriminating the stable abnormal operating condition from the faults is presented in Ref. [76]. In Ref. [77], different fault‐detection and issues associated with grounding aspect of protection system is discussed.


6. Microgrid modeling and dynamic characteristics

Dynamic characteristics and suitable mathematical model of the microgrid is needed to design an efficient control and power management strategy. However due to high order model of microgrid and its complexity, many studies are performed based on digital computer simulation software packages such as Power Systems Computer Aided Design (PSCAD)/Electromagnetic Transients including DC (EMTDC) and MATLAB/Simulink/Simpowersystem to select appropriate control parameters on a trial and error basis [7883]. These scholarly works emphasize that simulation‐based modeling approaches facilitate a powerful tool to investigate the behavior of microgrid. However, simulation‐based models cannot provide a comprehensive prediction of all microgrid scenarios resulting in poor power quality or instability.

To this end, small‐signal dynamic model of a microgrid is presented to provide an accurate and valid representation of microgrid for designing and optimizing the proper control strategies [8487]. To develop an accurate and valid model of microgrid, individual model of each distributed generation source is obtained in its local dq0reference frame and then all individual models are transformed to the microgrid global dq0frame to form an integrated model of microgrid. For instance, a practical model of energy storage system as one of the most imperative components of the microgrid is developed in Ref. [88].

Although, a comprehensive model of a microgrid provides accurate dynamic characteristics of the grid, it is very complex and increases the computational burden of the designed controller and power management strategy. Therefore, reduced‐order model of microgrids is used [8990] to address the associated issues of complex comprehensive model. It is shown in Ref. [84] that microgrid low frequency modes are highly related to the network configuration and inverter external power loop, whereas the high frequency modes are largely relative to the inverter inner loop, the dynamic characteristics of loads and network. Thus far, a reduced small‐signal model of a microgrid operating in islanded mode is derived in Ref. [89] by neglecting the effect of microgrid high‐frequency modes. It should be noted that model order reduction and linearization is done around an operating point, assuming small signal variation around the desired operation point, to apply linear system theory to the power grid. However, network variables would not always remain in small neighborhood of the desired operating point. In these cases, nonlinear model [91, 92] or large‐scale system model [93] provide a suitable model of the microgrid. In Ref. [93], linearized state variable models of DC‐DC converters and system interconnections at different system operating points and changing interconnections are used to develop a large‐scale system model of a DC microgrid. Also, a modular and scalable model for a DC microgrid is presented in Ref. [94] which is independent of the type of renewable energy sources. The authors extend the proposed model of 40‐house village equipped with PV panels and energy storage system to an islanded DC microgrid. In Ref. [95], the model of a hybrid power generation system is developed using transfer function model of integrated power electronics converters within the microgrid.


7. Conclusion and discussion

The power delivery system has gradually changed from large‐scale unidirectional conventional power generation sources to small‐scale bidirectional distributed power generation sources/loads including new plugged‐in electric vehicles as bidirectional loads and renewable energy sources such as solar photovoltaic and wind energy. The microgrid concept provides promising solution for the transition from a conventional delivery system to the future grid with high penetration of distributed generation from renewable energy sources without requiring to redesign the distribution system. However, distributed generators/loads and interaction between all nodes within a microgrid substantially increases the complexity of power systems technologies, control techniques, and communications among grid’s components.

This chapter provided an overall vision of microgrids and their main requirements. An overview of several proposed methods and developed competing technologies for seamless deployment of microgrid and their pros and cons were also presented. Table 1 summarizes these methods and technologies and their key features. Despite the progresses made over the last few years, technologies remain immature and not yet ready for commercial stage as shown by a large number of different methods, strategies, and policies. To transform the current microgrid into fully commercial, reliable, and cost‐effective power grid, a combination of targeted research, development, engineering work, and government incentives is still necessary.

Microgrid challengesMethods/technologiesComments/examples
Power qualityEnergy storageElectrical batteries, flywheel mechanical storage, thermal storage
FilteringActive power conditioners (APC)
Proper control methodsfP, VQdroop control, optimal power control
Energy storage + FilteringFlywheel storage and active power filter
Control strategiesPrimary (local) controlFrequency/voltage droop control
Secondary centralized controlNon‐model‐based fuzzy and neural network controllers and model‐based predictive controllers
Secondary decentralized controlMultiagent‐based control approaches
Tertiary (optimization) controlPart of the main utility grid and not microgrid itself
Energy managementCombined energy storageCombined batteries and super capacitors, hybrid fuel cell/battery
Power generation prediction and load forecastingManaging power flow among the different energy sources and the storage system within the microgrid
Voltage‐droop characteristicVoltage regulation, and load reactive power compensation
Energy optimizationMulti‐objective optimization using intelligent methodsGenetic algorithm, fuzzy neural networks, particle swarm optimization (PSO), and ant colony optimization (ACO)
StabilityProper control strategiesSupplementary control loop around the primary droop control loop
ProtectionAdaptive protection system
Digital relays
Current‐limiting algorithms
Voltage‐based fault detection
State observer
ModelingSoftware‐based modelPSCAD/EMTDC and MATLAB/Simulink
Comprehensive small‐signal modelAccurate, but very complex
Reduced small‐signal modelModel order reduction and linearization around an operating point
Nonlinear model

Table 1.

Summary of different microgrid technologies.


  1. 1. Lasseter B. Microgrids [distributed power generation]. In: IEEE Power Engineering Society Winter Meeting. Vol. 1. 2001; pp. 146-149
  2. 2. Marnay C, Robio FJ, Siddiqui AS. Shape of the microgrid. In: IEEE Power Engineering Society Winter Meeting. Vol. 1. 2001; pp. 150-153
  3. 3. Balanuta C, Vechiu I, Gurguiatu G. Improving micro‐grid power quality using three‐phase four‐wire active power conditioners. In: IEEE 16th International Conference on System Theory, Control and Computing (ICSTCC). 2012. pp. 1-5
  4. 4. Li Y, An L, Chun Ming T, Fei R, Shuang Jian P. A micro power quality management technology based on grid‐connected inverter. In: IEEE International Conference on Electricity Distribution (CICED). 2010. pp. 1-8
  5. 5. Rafiei S, Moallem A, Bakhshai A, Yazdani D. Application of a digital ANF‐based power processor for micro‐grids power quality enhancement. In: IEEE 29th Annual Applied Power Electronics Conference and Exposition (APEC). 2014. pp. 3055-3059
  6. 6. Zhang L, Li L, Cui W, Li SH. Study on improvement of micro‐grid’s power quality based on APF and FESS. In: IEEE Innovative Smart Grid Technologies – Asia (ISGT Asia). 2012. pp. 1-6
  7. 7. Belov V, Butkina A, Bolschikov F, Leisner P, Belov I. Power quality and EMC solutions in micro grids with energy‐trading capability. In: IEEE International Symposium on Electromagnetic Compatibility (EMC Europe). 2014. pp. 1203-1208
  8. 8. Tenti P, Costabeber A, Mattavelli P. Improving power quality and distribution efficiency in micro‐grids by cooperative control of Switching Power Interfaces. In: IEEE International Power Electronics Conference (IPEC). 2010. pp. 472-479
  9. 9. Cheng P‐T, Lee T‐L. Distributed active filter systems (DAFSs): A new approach to power system harmonics. IEEE Transactions on Industry Applications. 2006;42(5):1301-1309
  10. 10. Wei H, Jianhua Z, Qinghua X, Ziping W. The impact on power quality by PWM converter in micro‐grid. In: IEEE International Conference on Sustainable Energy Technologies (ICSET). 2008. pp. 239-243
  11. 11. Dong J, Li L, Ma Z. A combined system of APF and SVC for power quality improvement in microgrid. In: IEEE Power Engineering and Automation Conference. 2012. pp. 1-4
  12. 12. Al‐Saedi W, Lachowicz SW, Habibi D, Bass O. Power quality improvement in autonomous microgrid operation using particle swarm optimization. In: IEEE PES Innovative Smart Grid Technologies. 2011. pp. 1-6
  13. 13. Olivares DE, Mehrizi‐Sani A, Etemadi AH, Caizares CA, Iravani R, Kazerani M, Hajimiragha AH, Gomis‐Bellmunt O, Saeedifard M, Palma‐Behnke R, Jimnez‐Estvez GA, Hatziargyriou ND. Trends in Microgrid Control. IEEE Transactions on Smart Grid. 2014;5(4):1905-1919
  14. 14. Karimi H, Nikkhajoei H, Iravani MR. Control of an electronically‐coupled distributed resource unit subsequent to an islanding event. IEEE Transactions on Power Delivery. 2008;23(1):493-501
  15. 15. De Brabandere K, Bolsens B, Van den Keybus J, Woyte A, Driesen J, Belmans R. A voltage and frequency droop control method for parallel inverters. IEEE Transactions on Power Electronics. 2007;22(4):1107-1115
  16. 16. Zeineldin HH, Kirtley JL. Micro‐grid operation of inverter based distributed generation with voltage and frequency dependent loads. In: IEEE Power and Energy Society General Meeting. 2009. pp. 1-6
  17. 17. Xiu Y, Xiang Z, Fei Y, Haiyang Z. A research on droop control strategy and simulation for the micro‐grid. In: IEEE International Conference on Electrical and Control Engineering. 2011. pp. 5695-5700
  18. 18. Guerrero JM, Vasquez JC, Matas J, de Vicua LG, Castilla M. Hierarchical control of droop‐controlled AC and DC microgrids – A general approach towards standardization. IEEE Transactions on Industrial Electronics. 2011;58(1):158-172
  19. 19. Mohamed Y, El‐Saadany EF. Adaptive decentralized droop controller to preserve power sharing stability of paralleled inverters in distributed generation microgrids. IEEE Transactions on Power Electronics. 2008;23(6):2806-2816
  20. 20. Bahrani B, Saeedifard M, Karimi A, Rufer A. A multivariable design methodology for voltage control of a single‐DG‐unit microgrid. IIEEE Transactions on Industrial Electronics. 2013;9(2):589-599
  21. 21. Brabandere KD, Bolsens B, den Keybus JV, Woyte A, Driesen J, Belmans R. A voltage and frequency droop control method for parallel inverters. IEEE Transactions on Power Electronics. 2007;22(4):1107-1115
  22. 22. Yu X, Khambadkone AM, Wang H, Terence STS. Control of parallel‐connected power converters for low‐voltage microgrid – Part I: A hybrid control architecture. IEEE Transactions on Power Electronics. 2010;25(12):2962-2970
  23. 23. Li YW, Kao C‐N. An accurate power control strategy for power‐electronics‐interfaced distributed generation units operating in a low‐voltage multibus microgrid. IEEE Transactions on Power Electronics. 2009;24(12):2977-2988
  24. 24. Vandoorn TL, Meersman B, Degroote L, Renders B, Vande‐velde L. A control strategy for islanded microgrids with DC‐link voltage control. IEEE Transactions on Power Delivery. 2011;26(2):703-713
  25. 25. Marwali MN, Jung J‐W, Keyhani A. Control of distributed generation systems ‐ Part II: Load sharing control. IEEE Transactions on Power Electronics. 2004;19(6):1551-1561
  26. 26. Delghavi MB, Yazdani A. Islanded‐mode control of electronically coupled distributed‐resource units under unbalanced and nonlinear load condition. IEEE Transactions on Power Delivery. 2011;26(2):661-673
  27. 27. Mehrizi‐Sani A, Iravani R. Potential‐function based control of a microgrid in islanded and grid‐connected modes. IEEE Transactions on Power Systems. 2010;25(4):1883-1891
  28. 28. Prodanovic M, Green TC. High‐quality power generation through distributed control of a power park microgrids. IIEEE Transactions on Industrial Electronics. 2006;53(5):1471-1482
  29. 29. Dimeas AL, Hatziargyriou ND. Operation of a multiagent system for microgrid control. IEEE Transactions on Power Systems. 2005;20(3):1447-1455
  30. 30. Pilo F, Pisano G, Soma G. Neural implementation of microgrid central controllers. In: IEEE 5th International Conference on Industrial Informatics. Vol. 2. 2007. pp. 1177-1182
  31. 31. Jia‐jun Y, Li‐xiao Y, Dong‐meng T, Bo L, Dong L. Study on the fuzzy control strategy based on back‐ to‐back micro grid connection. In: EEE Asia Pacific Power and Energy Engineering Conference. 2012. pp. 1-5
  32. 32. Wang Z, Yang R, Wang L. Intelligent multi‐agent control for integrated building and micro‐grid systems. In: IEEE PES Innovative Smart Grid Technologies. 2011. pp. 1-7
  33. 33. Zheng W‐D, Cai J‐D. A multi‐agent system for distributed energy resources control in microgrid. In: IEEE 5th International Conference on Critical Infrastructure (CRIS). 2010. pp. 1-5
  34. 34. Ustun T, Ozansoy C, Zayegh A. Modeling of a centralized microgrid protection system and distributed energy resources according to IEC 61850‐7‐420. IEEE Transactions on Power Systems. 2012;27(3):1560-1567
  35. 35. Colet‐Subirachs A, Ruiz‐Alvarez A, Gomis‐Bellmunt O, Alvarez‐ Cuevas‐Figuerola F, A. Sudria‐Andreu. Centralized and distributed active and reactive power control of a utility connected microgrid using IEC 61850. IEEE Systems Journal. 2012;6(1):58-67
  36. 36. Prema V, Rao KU. Predictive models for power management of a hybrid microgrid – A review. In: International Conference on Advances in Energy Conversion Technologies (ICAECT). 2014. pp. 7-12
  37. 37. Dursun E, Kilic O. Comparative evaluation of different power management strategies of a stand‐alone PV/Wind/PEMFC hybrid power system. International Journal of Electrical Power and Energy Systems. 2012;34:81-89
  38. 38. Madhu sudhakar P, Murali Mohan B. Predictive and optimizing energy management of photo voltaic fuel cell hybrid systems with short time energy storage. International Journal of Engineering Research and Applications. 2012;2(6):550-556
  39. 39. Belvedere B, Bianchi M, Borghetti A, Nucci CA, Paolone M, Peretto A. A microcontroller‐based power management system for standalone microgrids with hybrid power supply. IEEE Transactions on Sustainable Energy. 2012;3(3):422-431
  40. 40. Satishkumar R, Kollimalla SK, Mishra MK. Dynamic energy management of micro grids using battery super capacitor combined storage. In: Annual IEEE India Conference. 2012. pp. 1078-1083
  41. 41. Hajizadeh A, Golkar MA. Intelligent power management strategy of hybrid distributed generation system. Elsevier Electrical Power and Energy Systems. 2007;29:783-795
  42. 42. Kanchev H, Lu D, Colas F, Lazarov V, Francois B. Energy management and operational planning of a microgrid with a PV‐based active generator for smart grid applications. IEEE Transactions on Industrial Electronics. 2011;58(10):4583-4592
  43. 43. Wang C, Hashem M. Power management of a stand‐alone wind/photovoltaic/fuel cell energy system. IEEE Transactions on Energy Conversion. 2007;23(3):957-967
  44. 44. Katiraei F, Iravani MR. Power management strategies for a microgrid with multiple distributed generation units. IEEE Transactions on Power Systems. 2006;21(4):1821-1831
  45. 45. Chen C, Duan S, Cai T, Liu B, Hu G. Optimal allocation and economic analysis of energy storage system in microgrids. IEEE Transactions on Power Electronics. 2011;26(10):2762-2773
  46. 46. Jiang Q, Xue M, Geng G. Energy management of microgrid in grid‐connected and stand‐alone modes. IEEE Transactions on Power Systems. 2013;28(3):3380-3389
  47. 47. Nasrolahpour E, Doostizadeh M, Ghasemi H. Optimal management of micro grid in restructured environment. In: IEEE Second Iranian Conference on Renewable Energy and Distributed Generation. 2012. pp. 116-120
  48. 48. Kanchev H, Lu D, Francois B, Lazarov V. Smart monitoring of a microgrid including gas turbines and a dispatched PV‐based active generator for energy management and emissions reduction. In: IEEE Innovative Smart Grid Technology Conference Europe (ISGT Europe). 2010. pp. 1-8
  49. 49. Wang C‐S, Yu B, Xiao J, Guo L. Multi‐ scenario, multi‐objective optimization of grid‐parallel microgrid. In: IEEE 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. 2011. pp. 1638-1646
  50. 50. Tsikalakis AG, Hatziargyriou ND. Centralized control for optimizing microgrids operation. IEEE Transactions on Energy Conversion. 2008;23(1):241-248
  51. 51. Milo A, Gaztaaga H, Etxeberria‐Otadui I, Bilbao E, Rodriguez P. Optimization of an experimental hybrid microgrid operation: Reliability and economic issues. In: IEEE International Conference on Power Technology. 2009. pp. 1-6
  52. 52. Oyarzabal J, Jimeno J, Ruela J, Engler A, Hardt C. Agent based micro grid management system. In: IEEE International Conference on Future Power Systems. 2005. pp. 6-11
  53. 53. Hatziargyriou ND, Dimeas H. Operation of a multi‐agent system for microgrid control. IEEE Transactions on Power Systems. 2005;20(3):1447-1455
  54. 54. Eddy FYS, Gooi HB. Multi‐agent system for optimization of microgrids. In: IEEE 8th International Conference on Power Electronics. 2011. pp. 2374-2381
  55. 55. Chakraborty S, Weiss MD, Simes MG. Distributed intelligent energy management system for a single‐phase high‐frequency AC microgrid. IEEE Transactions on Industrial Electronics. 2007;54(1):97-109
  56. 56. Celli G, Pilo F, Pisano G, Soma GG. Optimal participation of a microgrid to the energy market with an intelligent EMS. In: IEEE 7th International Power Engineering Conference, Vol. 2. 2005. pp. 663-668
  57. 57. Deng Q, Gao X, Zhou H, Hu W. System modeling and optimization of microgrid using genetic algorithm. In: IEEE 2nd International Conference on Intelligent Control and Information Processing. 2011. pp. 1540-544
  58. 58. Colson CM, Nehrir MH, Pourmousavi SA. Towards real‐time microgrid power management using computational intelligence methods. In: IEEE Power and Energy Society General Meeting. 2010. pp. 1-8
  59. 59. Chung Il‐Y, Liu W, Cartes DA, Schoder K. Control parameter optimization for a microgrid system using particle swarm optimization. In: IEEE International Conference on Sustainable Energy Technologies (ICSET). 2008. pp. 837-842
  60. 60. Hassan MA, Abido MA. Optimal design of microgrids in autonomous and grid‐connected modes using particle swarm optimization. IEEE Transactions on Power Electronics. 2011;26(3):775-769
  61. 61. Prommee W, Pongprapunt N, Ongsaku W. Improved reliability model and optimal protective device placement in micro grid by improved binary particle swarm optimization. In: IEEE 8th International Conference on Power Electronics. 2011. pp. 1514-1519
  62. 62. Colson C, Nehrir M, Wang C. Ant colony optimization for microgrid multi‐objective power management. In: IEEE Power Systems Conference and Exposition (PSCE). 2009. pp. 1-7
  63. 63. Barklund E, Pogaku N, Prodanovic M, Hernandez‐Aramburo C, Green TC. Energy management in autonomous microgrid using stability‐constrained droop control of inverters. IEEE Transactions on Power Electronronics. 2008;23(5):2346-2352
  64. 64. Delghavi MB, Yazdani A. An adaptive feedforward compensation for stability enhancement in droop‐controlled inverter‐based microgrids. IEEE Transactions on Power Delivery. 2011;26(3):1764-1773
  65. 65. Majumder R, Chaudhuri B, Ghosh A, Majumder R, Ledwich G, Zare F, Improvement of stability and load sharing in an autonomous microgrid using supplementary droop control loop. IEEE Transactions on Power Systems. 2010;25(2):796-808
  66. 66. Iyer SV, Belur MN, Chandorkar MC. A generalized computational method to determine stability of a multi‐inverter microgrid. IEEE Transactions on Power Electronronics. 2010;25(9):2420-2432
  67. 67. Wei J, Zheng‐you H, Zhi‐qian B. The overview of research on microgrid protection development. In: IEEE International Conference on Intelligent System Design and Engineering Application (ISDEA). 2010. pp. 692-697
  68. 68. Laaksonen H. Protection principles for future microgrids. IEEE Transactions on Power Electronics. 2010;25(12):2910-2918
  69. 69. Oudalov A, Fidigatti A, Degner T, Valov B, Hardt C, Yarza JM, Li R, Jenkins N, Awad B, van Overbeeke F, Hatziargyriou N, Lorentzou M. Advanced architectures and control concepts for more microgrids: Novel protection systems for microgrids. IDeliverable DC2. 2009
  70. 70. Islam M, Gabbar HA. Study of micro grid safety & protection strategies with control system infrastructures. Scientific Research, Smart Grid and Renewable Energy. 2012;3(1):1-9
  71. 71. Sortomme E, Venkata SS, Mitra J. Microgrid protection using communication‐assisted digital relays. IEEE Transactions on Power Delivery. 2009;25(4):2789-2796
  72. 72. Nikkhajoei H, Lasseter RH. Microgrid fault protection based on symmetrical and differential current components. In: Prepared for Public Interest Energy Research, California Energy Commission. 2006
  73. 73. Vilathgamuwa DM, Chiang LP, Wei LY. Protection of microgrids during utility voltage sags. IEEE Transactions on Industrial Electronics. 2006;53(5):1427-36
  74. 74. Al‐Nasseri H Redfern MA, Li F. A voltage based protection for micro‐grids containing power electronic converters. In: IEEE Power Engineering Society General Meeting. 2006. pp. 1-7
  75. 75. Esreraig M, Mitra J. An observer‐based protection system for microgrids. In: IEEE Power and Energy Society General Meeting. 2011. pp. 1-7
  76. 76. Lee W‐S, Kang S‐H. Protection for distributed generations in the DC micro‐grid. In: IEEE International Conference and Exhibition on Innovative Smart Grid Technologies. 2011. pp. 1-5
  77. 77. Salomonsson D, Sannino A. Protection of low voltage DC microgrids. IEEE Transactions on Power Delivery. 2009;24(3):1045-1053
  78. 78. Katiraei F, Iravani MR, Lehn PW. Micro‐grid autonomous operation during and subsequent to islanding process. IEEE Transactions on Power Delivery. 2005;20(1):248-257
  79. 79. Zhou J, Chen Y, Huang M, Tong Y. Study on energy control strategies in microgrid‐modeling and simulation. In: IEEE Power and Energy Engineering Conference (APPEEC). 2012. pp. 1-4
  80. 80. Brissette A, Hoke A, Maksimovic D, Pratt A. A microgrid modeling and simulation platform for system evaluation on a range of time scales. In: IEEE Energy Conversion Congress and Exposition (ECCE). 2011. pp. 968-976
  81. 81. Mok KY, Norman CFT, Lau WH, Leung MC. Experiment‐based simulation for distortion behavior in LV networks for microgrid modeling. In: IEEE Power and Energy Society General Meeting. 2011. pp. 1-6
  82. 82. Chang GW, Zeng GF, Su HJ, Hsu LY, Chang YR, Lee YD, Lin CH. Modelling and simulation for INER AC microgrid control. In: IEEE PES General Meeting Conference and Exposition. 2014. pp. 1-5
  83. 83. Bae S, Kwasinski A. Dynamic modeling and operation strategy for a microgrid with wind and photovoltaic resources. IEEE Transactions on Smart Grid. 2012;3(4):1867-1876
  84. 84. Katiraei F, Iravani MR, Lehn PW. Small‐signal dynamic model of a micro‐grid including conventional and electronically interfaced distributed resources. IET Generation, Transmission and Distribution. 2007;1(3):369-378
  85. 85. Raju PESN, Jain T. Small signal modelling and stability analysis of an islanded AC microgrid with inverter interfaced DGs. In: IEEE International Conference on Smart Electric Grid (ISEG). 2014. pp. 1-8
  86. 86. Valdivia V, Diaz D, Gonzalez‐Espin F, Foley R, Chang NC. Systematic small signal modeling and stability analysis of a microgrid. In: IEEE 5th International Symposium on Power Electronics for Distributed Generation Systems (PEDG). 2014. pp. 1-5
  87. 87. Zhu M, Li H, Li X. Improved state‐space model and analysis of islanding inverter‐based microgrid. In: IEEE International Symposium on Industrial Electronics (ISIE). 2013. pp. 1-5
  88. 88. Bahramirad S, Camm E. Practical modeling of Smart Grid SMSTM storage management system in a microgrid. In: IEEE PES Transmission and Distribution Conference and Exposition. 2012. pp. 1-7
  89. 89. Wang Y, Lu Z, Min Y, Wang Z. Small signal analysis of microgrid with multiple micro sources based on reduced order model in islanding operation. In: IEEE Power and Energy Society General Meeting. 2011. pp. 1-9
  90. 90. Rasheduzzaman MD, Mueller JA, Kimball JW. Reduced‐order small‐signal model of microgrid systems. IEEE Transactions on Sustainable Energy, Early Access, 2015
  91. 91. Nazaripouya H, Mehraeen S. Modeling and nonlinear optimal control of weak/islanded grids using FACTS device in a game theoretic approach. IEEE Transactions on Control Systems, Early Access. 2015
  92. 92. Meza C, Biel D, Jeltsema D, Scherpen JMA. Lyapunov‐based control scheme for single‐phase grid‐connected PV central inverters. IEEE Transactions on Control System Technology. 2012;20(2):520-529
  93. 93. Tulpule P, Yurkovich S, Wang J, Rizzoni G. Hybrid large scale system model for a DC microgrid. In: American Control Conference (ACC). 2011. pp. 389-3904
  94. 94. Farooq R, Mateen L, Ahmad M, Akbar SQ, Khan HA, Zaffar NA. Smart DC microgrids: Modeling and power flow analysis of a DC Microgrid for off‐grid and weak‐grid connected communities. In: IEEE PES Asia‐Pacific Power and Energy Engineering Conference (APPEEC). 2014. pp. 1-6
  95. 95. Kim SK, Jeon JH, Cho CH, Ahn JB, Kwon SH. Dynamic modeling and control of a grid‐connected hybrid generation system with versatile power transfer. IEEE Transactions on Industrial Electronics. 2008;55(4):1677-1688
  96. 96. Gu Y, Xiang X, Li W, He X. Mode‐adaptive decentralized control for renewable DC microgrid with enhanced reliability and flexibility. IEEE Transactions on Power Electronics. 2014;29(9):5072-5080
  97. 97. Ahmadi R, Ferdowsi M. Improving the performance of a line regulating converter in a converter‐dominated DC microgrid system. IEEE Transactions on Smart Grid. 2014;5(5):2553-2563
  98. 98. Kumar M, Srivastava SC, Singh SN. Control strategies of a DC microgrid for grid Connected and islanded operations. IEEE Transactions on Smart Grid. 2015;6(4):1588-1601
  99. 99. Gu Y, Li W, He X. Frequency‐coordinating virtual impedance for autonomous power management of DC microgrid. IEEE Transactions on Power Electronics. 2015;30(4):2328-2337
  100. 100. Shadmand MB, Balog RS. Multi‐objective optimization and design of photovoltaic‐wind hybrid system for community smart DC microgrid. IEEE Transactions on Smart Grid. 2014;5(5):2635-2643
  101. 101. Yu X, She X, Zhou X, Huang AQ. Power management for DC microgrid enabled by solid‐state transformer. IEEE Transactions on Smart Grid. 2014;5(2):954-965

Written By

Reza Sabzehgar

Submitted: October 8th, 2016 Reviewed: April 24th, 2017 Published: August 16th, 2017