# Major Challenges towards Energy Management and Power Sharing in a Hybrid AC/DC Microgrid: A Review

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## Abstract

**:**

## 1. Introduction

#### Review Methodology

## 2. Hybrid Microgrids

#### Operational Challenges of Hybrid Microgrids

## 3. Power Flow Analysis

_{BUS}method is employed to tackle this power flow problem in a multi-grounded imbalanced hybrid microgrid. This generic method addresses a variety of challenges in hybrid microgrid power flow analysis, including unbalanced operation, bipolar DC, and multi-grounded systems. However, a critical examination of this study and all of the techniques summarized in Table 1 reveals that in an unbalanced multi-grounded hybrid microgrid with bipolar DC, this problem still necessitates a method to simultaneously investigate load models, voltage, and frequency stability due to load variation and renewables uncertainties.

## 4. Load and Energy Management (Power Sharing)

#### 4.1. Forecasting the Load and Generation Profile

#### 4.2. Bidirectional Power Converter Control

_{dc}

^{2}rather than V

_{dc}to linearize the voltage fluctuations. This scheme claims to have shifted an HMG from grid connected to islanded mode without requiring changes to the control algorithm. However, the power-sharing from the ESS is not optimized, and ineffective discharge causes voltage overshoots in underload conditions. Another d-q-0 axis control-based technique is designed to suppress the circulating currents (missing in [72]) in the parallel operation of power converters [73], which requires a separate control algorithm for islanding and grid-connected mode. The frequency droop control equivalent to Vdc is an alternative for suppressing the circulating currents [74]. The modified droop control for power sharing is based on Equation (1).

_{pu}is normalized DC voltage compared with PU frequency of AC subgrid for power sharing P

_{ILC}between the grids. A control strategy based on an accurate line impedance calculation is proposed in [75]. A small signal is used to estimate the impedance in this scheme, which reduces the dependence on a communication link. This approach may provide better power sharing than conventional control, but voltage regulation and hence reactive power are vulnerable. A modified current control mechanism for ILC (active power sharing) based on the common bus voltage instead of terminal voltages of DGs is presented [76]. However, this technique is only tested on a stable, modest, and balanced microgrid with only six busses, limiting its applicability. In grid-linked mode, ILC control can also be tweaked to improve power quality. The author in [77] proposed a modified ILC version that includes an additional parallel converter with ILC, which aids in the removal of harmonics and is depicted in Figure 7, but may not apply to islanded systems. On the other hand, the authors in [78] recommended using an ESS converter control to operate as a virtual synchronous generator (VSG) to help limit frequency and voltage fluctuations. However, because VSG is connected to ESS on the AC subgrid and the DC microgrid only has a PV source, the system’s applicability is in question.

_{pi}is optimized using a hybrid fuzzy-PSO technique considering the uncertainty of renewable generation for a small six-bus HMG. Another scheme in [88] suggests a modified control strategy for effective power-sharing based on input–output feedback linearization (IOFL). According to the authors, this DC bus voltage-based control, with an additional sliding mode controller for current error, achieves better power sharing than traditional droop control. In [89], a model predictive control-based approach is introduced that regulates the most deviated parameter (AC/DC current or voltage) using multivariable optimization. Another study in [90] suggests multiple control strategies to be adopted as a master–slave system for the parallel operation of ILC. A master converter adopts Q-V

_{dc}control, while the slaves follow P-Q control for power sharing. However, the validation of this scheme is carried out with a single DC-generating unit connected with multiple AC subgrids, questioning the applicability. An alternative approach to innovative ILC control is designed based on a flying capacitor multi-level converter to improve the AC power quality, while also reducing the filter size [91]. However, this technique is solely intended for grid-connected systems and has been tested on multiple DC-generating units connected to the grid through ILC.

#### 4.3. Battery Management

## 5. Converter Control and Protection in HMG

#### 5.1. Local and Global Control of DGs and Power Converters

- Conventional PQ or v-f control,
- P-f and Q-v control,
- Voltage real power droop/frequency-reactive power boost droop control,
- Reactive power differential of voltage Q-V droop,
- Angle droop control,
- Virtual frame transformation control,
- Virtual impedance-based control,
- Adaptive droop control (static and transient/dynamic droop gains),
- Unbalanced power flow control for nonlinear load sharing.

#### Optimization of Controller Parameters

#### 5.2. Effect on Protection Strategies

## 6. Discussion and Future Trends

#### 6.1. Future of Hybrid Microgrids

- Reliable and Immune: Immunity against the uncertainties of the renewables.
- Stable: Operational stability during multi-mode operation of HMG with a smooth transition of modes.
- Secure: Adaptive protection against unforeseen faults.
- Economical: Minimized cost by maximizing renewables to decarbonize the environment.

- A fast-converging power flow analysis scheme for HMG ticking all of the qualities listed in Table 1.
- Unidirectional power converters in a specified HMG environment require collaborative control (with bidirectional converters) for handling the transients in the system.
- The essential task in any electrical network (due to advancements in power electronics technology) is to reduce harmonic content and improve voltage stability (especially in power converters’ dominant systems).
- Bidirectional power converters (DC–AC/DC–DC) should be modified to provide the virtual inertia and impedance to liquefy the absence (lesser number) of synchronous DGs and improved fault response, respectively, in a system dominated by the inverter-based renewable DGs. Providing voltage support (reactive power support) and frequency stability through these converters is another trending option for successfully deploying the HMG concept.
- Mathematical modeling and AI-based intelligent schemes for supervising the forecasting-based power-sharing (smart pricing) techniques to cope with uncertainties and unplanned outages is another interesting topic. The same can also be developed and utilized with an advanced control structure of power converters to mitigate the supply and demand gap for standalone operation and uncertain loading (variable demand response).
- A comprehensive under-voltage (under-frequency) load curtailment method is yet to be developed to prevent blackouts during extreme contingencies (hurricanes) and to ensure power supply for critical loads.
- The hybrid microgrid has the greatest potential for the research and development of a technique capable of protecting the system in multiple modes, particularly during mode transitions. Hence, designing an adaptive protection scheme to compensate for lower fault currents due to inverter-based DGs (especially in islanding mode) by modifying the relay setting for a multimode HMG with minimized dependence on the communication is needed.

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 6.**Three-dimensional droop control of ILC (adapted from [67]).

**Figure 7.**Modified ILC version to increase power quality [77].

**Figure 10.**Classification of hierarchical control in an HMG. (Note: size of the circle shows the response time of the controller.)

**Figure 11.**Distribution of multiple techniques used for power management (

**a**). Interlinking converter control techniques (

**b**). Next-day forecasting-based supervisory control techniques.

Reference | Mode of Operation GC/ISO | Absence of Slack Bus | Unbalanced Operation of Subgrids | Parallel Operation of ILCs | Load Variation and Types | Uncertainty of Renewables | Convergence of Algorithm | Stability |
---|---|---|---|---|---|---|---|---|

[30] | Both | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |

[32] | GC only | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |

[33] | Both | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |

[34] | Both | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |

[35] | ISO only | ✓ | ✗ | ✗ | ✓ | ✓ | ✓ | ✗ |

[36] | GC only | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |

[37] | GC only | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |

[38] | Both | ✓ | ✓ | ✗ | ✗ | ✗ | ✓ | ✗ |

[39] | Both | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ |

[40] | ISO | ✓ | ✗ | ✓ | ✗ | ✗ | ✓ | ✓ |

[41] | ISO | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ |

Ref | Contribution | Technique Used | Limitation | System for Validation |
---|---|---|---|---|

[27] | Cost optimization with minimized mode switching | MILP and mathematical model | Assumed contingency scenarios only | Multiple subgrids with intermittent and dispatchable DGs |

[29] | Uncertainty handling by two-stage optimization to realize power management from forecasted curves | Non-Cooperative Game Theory Mathematical programming with complementarity constraints (MPCC) | Complex control that ignores the impact on local control of DGs and ILCs. No solution to the generation and load imbalance | Modified IEEE 33-bus system |

[45] | Minimizing cost by forecasting day-ahead hourly demand | MILP optimization to minimize the cost | No real-time validation | Hybrid framework for validation is just like a generation plant with AC and DC units |

[47] | Day-ahead forecasting based power sharing to optimize the cost | Firefly algorithm (FA) | Focused only to interface multiple DC RES’ with IEEE 33-bus system | IEEE 33-bus system with additional DC generation units as DC microgrid |

[49] | Optimizing the net power in microgrid using ESS to overcome the prediction error in load and generation profile | Fuzzy controller based on results of particle swarm optimization results | Quantitative net power balancing without considering real-time operation constraints | Modified DC subgrid with PV and wind source |

[50] | Cost optimization | Non-Cooperative Game Theory | No solution to the generation and load imbalance | Cluster of 3 MGS, part of Singapore power network |

[51] | Uncertainty modeling for day-ahead power management involving EVs | Piecewise linearization combined with quadratic Newton–Gregory interpolating polynomial technique MILP | Forecasting-based cost optimization only | Microgrid composed of diesel generator, wind, solar, and EVs |

[52] | Minimizing cost | MILP optimization | Problem-specified solution | 38-bus remote system |

[53] | Cost and emission minimizing | Fuzzy logic for two different objectives | Focused only on lowering emissions from a generation unit | 6-bus generation dominant HMG |

[54] | Optimized realistic cost and energy management | Modified Crow Search Algorithm | Solution for grid-connected system only | Modified IEEE 33-bus system with addition DC busses as DC subgrid |

[55] | Power management in PV and wind outages scenario | MILP as supervisory control | Realistic issue of a negative power imbalance is not addressed | PV and wind-based DGS on AC and DC sides, respectively, with local loads |

[56] | Optimal power management for grid-connected multiple microgrids (HMG) | MILP optimization | Only grid-connected mode of operation discussed | Multiple AC and DC MGs connected through a single ILC |

[57] | Increased stability of voltage for increased penetration of renewable generation | Modified supervisory control based on predicted uncertainties | Prediction-based uncertainties model only | Modified IEEE 33-bus system with additional DC busses and generation |

[59] | Minimized operational cost | Double uncertainty optimization theory, fuzzy stochastic optimization | Focused on cost only, stiff grid is assumed to be available always | HMG with PV, wind, diesel generator, and ESS |

Ref | Contribution | Technique Used | Limitation | System for Validation |
---|---|---|---|---|

[43] | Basic framework for sharing power between AC and DC subgrids | Modified droop control of IC | Conventional droop control | Hybrid framework for validation is just like a generation plant with AC and DC units |

[58] | Multiple nano-grids control Modified droop characteristics | P_{dc} − v_{dc}^{2} droop control strategy | Power sharing is based on active power only ignoring reactive power effect | Multiple generating units as subgrid with local loads |

[64] | Prototype of HMG with basic droop control | Decentralized basic droop control | Multiple assumptions were made about power flow and very basic control | Multiple DGs with ESS designed as an entitled subgrid |

[65] | Improved efficiency and reliability by operating converters at near to full rating | Centralized control of converters for parallel operation | Basic control and assumed communication links | Multiple DG types and ESS connected to utility |

[66] | Practical multi-level droop-controlled inverter | Secondary control to stabilize the voltage | Focused on active power-sharing | Only PV and ESS as DGs connected with the grid |

[67] | P-f and P-v droop control for active power sharing | 3D droop control | Active power sharing only | Basic HMG with 2 loads and DGs on each subgrid |

[68] | Improved stability of the system with optimized power sharing through parallel ILCs | Modified droop control | Active power sharing only | ESS as DGs on both sides with increased capacity |

[70] | Prototype of practical HMG | Reactive power compensation-based control of HMG | Frequency undershoot for such a small system is quite high, which may reach instability for a larger system | A DG on AC and PV on DC subgrid along with ESS |

[71] | Effective power sharing including reactive power | Modified mathematical-based droop coefficient | Limited validation in stable operation only, assumed subgrids with power flow only | AC and DC microgrids assumed |

[72] | Modified ILC control | d-q-0 three-axis control V _{dc}^{2} regulation instead of V_{dc} | Ineffective ESS discharge causing overshoot of voltage | One PV-based DG on DC and ESS on the AC side |

[73] | Suppressing circulating currents and power sharing considering capacity of subgrids | d-q-0 axis outer control P _{dc}-V_{dc}^{2} and f-P_{ac} droop for inner control | Separate control algorithm for islanding and grid-connected mode | HMG with PV as DG on both subgrids and an ESS |

[74] | Improved power sharing | Modified frequency droop control based on ${P}_{ILC}=K\left({V}_{pu}-{f}_{a{c}_{pu}}\right)$ | Voltage regulation and reactive power are not considered | Single DG on each subgrid with an accumulated single load |

[76] | Prototype of practical HMG | Modified current control droop strategy for ILC | Limited validation on a stable HMG | Independent microgrid with ESS |

[77] | Improving the power quality and increasing the power-sharing flexibility | Modified ILC control by adding an ESS in parallel through a bidirectional DC–DC half-bridge converter and a VSC | Focused more on harmonics mitigation in grid-connected mode | An HMG with a DG each on AC and DC subgrids along with a load connected to the grid. |

[82] | Robust mode transition in HMG with modified control | Model bank synthesis optimization to optimize droop coefficients | Provides voltage stability but lacks uncertainty management | Grid-connected HMG with a DG to be tested on both subgrids |

[85] | Minimized transients during switching due to variable loads | Decentralized integrated droop control of ILC and RESs converter control | Assumed communication link and validated for underload generation only | Lab-based HMG prototype with three DGs in AC and two in Dc subgrid with local loads |

[86] | ANN-based MPPT controller with fuzzy controller power management | ANN-based converter control and fuzzy-based power management | Grid-connected operation only limited validation with just DC generation | Wind, PV, fuel cell, and ESS as DC subgrid connected with utility |

[88] | Modified control of DC/AC converters | Input–output feedback linearization and sliding mode controller-based control to DC/AC converters | The interlinking bidirectional aspect is not discussed in the control | HMG with multiple DGs supplied AC grid through DC/AC converters and a major DC load |

[90] | Master–slave control for improved stability | Q-Vdc and P-Q control | Very basic control and validation for modern complex systems may be questionable | Three AC subgrids connected to the same DC-generating unit |

[91] | Innovative control to improve the power quality of AC voltage | Flying capacitor multi-level converter | Just a grid-connected system for multiple DC generating units | Four DC generating units are connected to grid through a single multi-leg converter |

[92] | Accurate active power and DC current sharing in AC and DC subgrid, respectively | Hierarchical control of the ILC, comprising primary and secondary control layers based on distributed consensus algorithm | Power sharing in stable conditions only | Prototype HMG in lab, consisting of 3 DGs on each subgrid |

[94] | Power sharing based on common bus voltage distributed secondary control | Droop control of ILCs based on common DC bus voltage | played in only stable conditions to validate with only 4 heavy loads | An HMG infrastructure composed of 4 DGs and 2 loads on each subgrid with an ESS connected to a common DC bus |

[95] | Compensating variable converter impedance and loading | Error minimization using passivity theory and surface mode controller | Improves the control of converter, but power instabilities are ignored | DC-generating units synchronized with AC grid |

[96] | Efficient power sharing | Set point weighting iterative learning method | Tested under stable conditions only | Wind PV and diesel generator as sources (Aichi Microgrid setup Japan) |

[97,98] | Compensating unbalanced loads by balancing the power between phases | Modified bilayer distributed control of converters | Focused only to balance the power between the phases | Two AC and DC subgrids with one specifically designed with one single- and two three-phase generators |

[100] | Improved voltage and frequency stability with additional secondary control | Hierarchical secondary control to stabilize the voltage and frequency | Basic primary control and no discussion about extreme contingencies | Validated on two large networks |

[101] | Enhanced upper limit of maximum power transfer through ILC with voltage and frequency support | Improved normalization of voltage and frequency with a new power reference generator | Validated with ESS only, presence of actual DGs will question the scheme | ESS as DG on both sides with a lumped load |

[103] | A unique model to directly connect AC and DC subgrids with a utility grid | Modified UIPC to reduce the number of power converters | Only grid-connected balanced grid is studied | No specified subgrids, just mentions the power supplied and extracted |

[104] | Reduced number of power converters for variable DC voltage requirements | Modified UDFB rectifier connected in parallel with ILC | A specific solution to supply DC distributed loads that require variable voltage | A simplified HMG with wind and PV as DGs for AC and DC subgrids, respectively |

Ref | Contribution | Technique Used | Limitation | System for Validation |
---|---|---|---|---|

[62] | Cost optimization by maximizing the solar and wind energy | MILP optimization to minimize the cost and maximize the renewables | Ideal converter is assumed | Hybrid framework for validation is just like a generation plant with AC and DC units |

[84] | Improved inertia response | Discharging control of ESS based on inertia constant of wind turbine | Compromised power-sharing with additional complex inertia control only | HMG with wind turbine PV and ESS on both subgrids |

[105] | Prototype of practical HMG with SOC equalization for multiple ESUs | Modified control of converters with additional SOC equalization | Limited test case No consideration of unforeseen outages and fluctuations | PV source with multiple ESUs |

[106] | Effective power sharing in ESS dominated HMG by SOC equalization of individual battery units | Multiple ESS as DGs in both subgrids | ||

[107] | Power sharing in HMG with SOC control | State machine approach for predefined state selection Fuzzy controller for battery | Basic pre-defined modes of power-sharing | Isolated HMG model with wind, diesel generator, PV, and ESS |

[108] | Enhances power stability by minimizing voltage transients | ANFIS-based battery controller | Only a grid-connected and stable system is assumed | Multiple generating units with local loads |

[109] | Optimal battery utilization | Fuzzy-based battery controller | Problem-specific solution to enhance battery SOC usage | Two PV panels with ESS on AC and DC side |

[110] | Minimizing operational cost for the next day | Consensus-based new algorithm with modified power system constraints | Real-time load and generation uncertainty is not discussed | Four DGs and loads on each subgrid with on ILC |

[111] | Optimized energy management with effective load curtailment and V2G technology | Multi-agent optimization based on multi-objective PSO | Multi-step load curtailments may cause protection to operate Heavily reliance on advanced communication | Modified IEEE 33-bus system |

[112] | Optimized energy management | Advanced metering and multi-objective PSO | Limited application due to the absence of advanced meters | Modified IEEE 33-bus system |

[114] | Power sharing compensation for the uncertainty of renewables | Lyapunov theory and input–output feedback linearization | No schedule for battery charging | HMG with multiple renewable-based DGs |

[115] | Compensating for the noise in the communication network for adaptive power-sharing | Lyapunov theory with martingale convergence theorem | Missing voltage support and reactive power sharing | Multiple nano-grid clusters |

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**MDPI and ACS Style**

Sarwar, S.; Kirli, D.; Merlin, M.M.C.; Kiprakis, A.E.
Major Challenges towards Energy Management and Power Sharing in a Hybrid AC/DC Microgrid: A Review. *Energies* **2022**, *15*, 8851.
https://doi.org/10.3390/en15238851

**AMA Style**

Sarwar S, Kirli D, Merlin MMC, Kiprakis AE.
Major Challenges towards Energy Management and Power Sharing in a Hybrid AC/DC Microgrid: A Review. *Energies*. 2022; 15(23):8851.
https://doi.org/10.3390/en15238851

**Chicago/Turabian Style**

Sarwar, Sohail, Desen Kirli, Michael M. C. Merlin, and Aristides E. Kiprakis.
2022. "Major Challenges towards Energy Management and Power Sharing in a Hybrid AC/DC Microgrid: A Review" *Energies* 15, no. 23: 8851.
https://doi.org/10.3390/en15238851