# Survey of Sustainable Energy Sources for Microgrid Energy Management: A Review

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## Abstract

**:**

## 1. Introduction

## 2. Control of AC Microgrid

#### 2.1. Primary Control

_{p}(s) and G

_{q}(s) are linear transfer functions.

#### 2.2. Secondary Control

_{p}, K

_{i}, ${K}_{p}^{\prime}$, and ${K}_{i}^{\prime}$ are the secondary control compensator’s parameters. In this instance, δω and δΕ must be constrained to stay within the range of permitted amplitude and frequency variations.

#### 2.3. Third Control

_{p}, K

_{i}, ${K}_{p}^{\prime}$, and ${K}_{i}^{\prime}$. In this situation, they are saturated if δE and δω are outside the permitted limits.

## 3. Methods of Microgrid Optimization

#### 3.1. Stochastic Optimization Techniques

#### 3.2. Dynamic Programming

#### 3.3. Mixed Integer Programming and Non Linear Programming

#### 3.4. Artificial Intelligence

## 4. Description of the Benefits and Drawbacks of Various Energy Management Strategies

#### 4.1. Comparison of Some Common Energy Strategies and Principles

#### 4.2. Tools and Modes of Microgrid Operating

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

MG | Microgrid |

AC | Alternating current line |

ARMA | Autoregressive moving average model |

CSA | Crow search algorithm |

DC | Direct current line |

DG | Distributed generation |

DER | Distributed energy resources |

EEMS | Expert system for energy management |

EMS | Energy management system |

GAMS | General algebraic modeling system |

HMI | Human machine interfaces |

HOGA | Hybrid optimization by genetic algorithms |

HOMER | Hybrid optimization model for multiple energy resources |

IHOGA | Improved hybrid optimization by genetic algorithms |

JADE | Java platform for agent developers |

MGSC | Microgrid supervisory controllers |

MILP | Mixed integer linear programming |

MO | Multiobjective |

MPC | Model predictive control |

PSO | Particle swarm optimization |

PV | Photovoltaic |

VPP | Virtual power plant |

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**Figure 1.**An integrated microgrid system [15].

**Figure 4.**Energy management methods [29].

**Figure 5.**Management of a microgrid [29].

Model | Advantages | Disadvantages |
---|---|---|

MILP | It resolves complicated issues with straightforward actions. It has a benefit compared to the MILP formulation in that it can obtain many optimal solutions. | Economic stochastic analysis and reliability. Restricted capabilities for applications with continuous or nondifferentiable objective functions. |

MINLP | The mathematical function is nonlinear, or one of its parameters is non-linear. | Numerous iterations (high computational effort). |

Dynamic programming (DP) | Dived to minor problems to solve a large one sequentially. | Complicated implementation due to numerous recursive methods. |

Genetic algorithms (GA) | Population-based evolutionary algorithms search for the best answer using mutation, crossover, and selection. A sufficient convergence rate. Widely utilized throughout many industries. | Mutation and crossover parameters must be determined. |

Particle swarm optimization (PSO) | Excellent brings about scattering and optimization challenges. | High complexity in computation. |

Artificial bee colony | Easy to implement a population-based algorithm. A fast enough convergence. | Intricate formulation |

Artificial Fish Swarm | Precision, rapid convergence, fewer parameters and flexibility | Maintains the benefits of GA but without its drawbacks (crossover and mutation) |

Bacterial foraging algorithm | The problem’s size and nonlinearity have less effects. Converge to the best solution compared to analytical techniques | Wide and complex search area |

Reference | Optimization Strategy | Contributions | Constraints | Drawbacks | Multi/Single-Goal |
---|---|---|---|---|---|

[31] | mixed integer and non-linear | The Given each generator, includes dump and deferrable loads, the ideal power scheduling, is obtained using a robust optimal EMS MPC-based method. | Power ratio Battery Generator Renewable Sources Loads | Power losses and demand are not assessed | Multigoals |

[32] | mixed integer and linear linear programming | an approach of energy management that combines three optional approaches (Power sharing, ON/OFF, and continuous run modes). | Battery Generation dispatch | Battery degradation costs are not taken into consideration | Multigoals |

[33] | Non-linear programming | Decreased total operating expenses while preserving the safe operation of the standalone MG | AC power DC power Converter power Load Distributed generators power | Systematic battery storage is not examined. The cost of emissions for distributed biomass generation is not evaluated. | Mono-goal |

[34] | Linear programming | Integration of AI-based linear programming techniques to solve multiobjective optimization | Limitations of dispersed generation in power balance | High computational complexity. Degradation of the battery is not assessed | Multigoals |

[35] | Particle swarm algorithm (PSO) | Merge of two energy storage units that are ideals. less time for computation than GA | The generators’ power Power transfer to the grid Charge/Discharge of the storage units Supply and demand balance | The traditional generator’s emission costs are not evaluated. | Multigoals |

[36] | Particle swarm algorithm (PSO) with Gaussian mutation | PSO variant new algorithm. | Active power Voltage Current | Power losses and Emissions of distributed generation are not assessed. | Mono-goal |

[37] | Artificial bee colony | A two-layer control model is utilized to reduce a microgrid’s operating expenses. | Power equilibrium Accessibility to resources Non-dispatchable resources Storing components | The formulation is difficult. The cost of emissions from a dispatchable microturbine is not calculated | Mono-goal |

[38] | Fuzzy logic (Gray Wolf Optimization) | Optimization of the battery size, storage, and generation plan | Power balance Generators power Battery load | The cost of battery deterioration is not estimated | Mono-goal |

[39,40] | Evolutionary algorithm (EA) and PSO Algorithm | Application of an energy hub model for optimization of a multicarrier MG. | Power balance Voltage in the transformer | Deterministic condition is a limitation. | Multigoals |

[41] | Artificial fish swarm optimization | A MG’s energy management schedule, which considers storage for the entire day and dynamic pricing, is optimized | Power equilibrium traditional methods of generating power standard power generators | Battery degradation cost is not assessed | Mono-goal |

[42] | Particle swarm algorithm (PSO) | It considers Three different objectives: Reliability, Operation cost, and Environmental impact. | Indefinite | Degradation cost of battery is not counted. | Multigoals |

[43] | Bacterial foraging algorithm | Optimized the power exchange with the grid, the battery and the generator setpoints. Quick convergence. | Power balance Generation limits of distributed generators Storage limits | Power losses are not counted | Multigoals |

[44] | Mixed-integer nonlinear programming (MINLP) | less reliance on forecast data. Various battery models compared. | Charge flow Dispatch generators Programming of the generator on/off Battery charge and discharge | Prediction of battery life is disregarded | Multigoals |

[45] | Dynamic Rules | Different restrictions are used by the MG management system for the batteries bank state of charge (SOC). | Battery Power balance | Battery cost and degradation are not considered. | Mono-goal |

[46] | Dynamic programming | Energy management strategy for PV. Batteries to stabilize and permit PV to run at a constant and stable output power | Charge/Discharge of batteries | Battery degradation and lifetime prediction are not evaluated | Multiobjective |

[47,48,49] | Multiagents | Reliable technique for real-time energy storage management used to adjust power imbalance optimally. Control system with many layers and coordinated control. Battery energy storage system, optimization problem based on distributed intelligence, and a multiagent system | Battery charge and discharge Power Equilibrium and Load Scheduling | Battery lifetime and degradation are not assessed Complex control scheme | Multigoals |

[50] | Stochastic | A straightforward way to include the influence of stand-alone scheduling on the grid-connected operation. | Power balance Dispatchable Distributed generation Renewable power generation Load Charge/Discharge of batteries | The battery aging model and the cost of DG emissions are not evaluated | Multigoals |

[51] | Robust programming | hybrid wind-battery-diesel system load management is optimized | Battery bank with wind turbine power source for the diesel generator | Shifting of controllable loads may be inefficient. | Mono-goal |

[52] | Mixed Integer Quadratic Programming | Demand side management and unit commitment for generators are evaluated by an integrated stochastic energy management model. | Power balance Generation Demand Reserve capacity | The deterministic model requires more processing time. Costs associated with emissions from traditional generators and DG are not assessed | Mono-goal |

[53] | Model predictive control | Automatic load shedding of noncritical loads when expected power imbalances threaten the MG’s stability. | Power distributed generators | The battery’s charging and discharging rates are not taken into account. Similarly, communication lags | Multigoal |

[54] | Model predictive control | a detailed mathematical description of the ideal EMS for standalone microgrids considering restrictions of power flow and unit commitment | Power balance Reserve Unit commitment Energy storage Grid | High computational effort The cost of emissions for traditional sources is not assessed. | Mono-goal |

[55] | Model predictive control | The main contribution of this work is daily optimizer that considered capacity losses while calculating the lead-acid battery’s deterioration | Not specified | The model of the lithium battery is not evaluated | Multigoal |

[56] | Genetic algorithm | A unique cost function includes the startup costs of distributed resources as well as the costs of selling and purchasing power. | Balance of power emissions battery power generator start | Distributed sources and battery state of charge are not considered. Customers’ uncertainty as well as the MGs’ uncertainty in their energy generation are not taken into account. | Mono-goal |

[57] | Game theory | Distributed energy management schedule in various MGs. | Energy transfer to the grid and the MG’s generation capacity | Computational complexity is not counted. | Multigoal |

[58] | Artificial Intelligence (Fuzzy logic) | easy implementation, enhanced power profile quality of the grid | discharge/charge of batteries | Only the battery charger/grid-connected inverter is controlled. Battery degradation is not evaluated. | Multigoal |

[59] | Game theory | Reduce the cost of fuel and trading power. | Power balance DG Traditional generator power The power that can be transferred between the main grid and the MG is limited | The conventional generators’ emission costs are not assessed | Multigoal |

[60] | Markov decision process | Linear model to evaluate the MG lifetime cost. | Gas turbine capacity Gas turbine emissions | Limited number of sizes’ possible combinations | Mono-goal |

[61] | Rule-based | Study of the predictive expenses of hybrid system including battery degradation. After developing a hybrid-operating regime, a levelized cost of electricity study is conducted (LCOE). Accuracy of energy storage degradation costs | Power balance SOC battery | The capacity fade modeling of temperature is not considered Conditions for dynamic state-of-charge cycling are not counted | Multigoal |

Reference | Microgrid Mode Operation |
---|---|

[11,20,30,31,32,33,36,39,45,49,51,52,53,55,56,58,59,63] | Grid-Connected |

[9,31,34,40,42,44,47,48,50,54,57,60,61,62,63,64] | Off-Grid |

[8,15,19,35,43,46,61,65] | Grid-Connected/Off-Grid |

References | Tools | Characteristics of Tools |
---|---|---|

[61] | PSCAD/EMTDC | HVDC, power electronics, power systems, FACTS, and control systems emulation software |

[11,32,33,35,38,62] | MATLAB/Simulink MATPOWER | Engineers specialized in control, telecommunications, power electronics, and power systems use matrix based programming languages (C++, Java, and Fortran) |

[30,63] | GAMS (GAMS Development Corp., Fairfax, VA, USA) | High-level programming mixed-integer nonlinear and linear optimization |

[64] | C++ | C++ development application for Windows environment |

[40] | TRNSYS based in Madison, WI, USA (Thermal Energy System Specialists, LLC) based in Madison, WI, USA (Thermal Energy System Specialists, LLC) HOMER\sHOGA | Modeling hybrid energy production systems. Genetic Algorithm-Based Hybrid Optimization |

[65] | RSCAD (RTDS Technologies Inc., Canada (Winnipeg, MA, USA) JADE (Jade, Christchurch, New Zealand) | Power systems simulator in real time |

[61,66] | JADE | Multiagent platform in Java environment |

[30] | HOMER | Simulation of energy hybrid system model |

[36] | CPLEX (IBM, Armonk, NY, USA) | Optimization Compatible to C, C++, Java and Python |

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**MDPI and ACS Style**

Hoummadi, M.A.; Bouderbala, M.; Alami Aroussi, H.; Bossoufi, B.; El Ouanjli, N.; Karim, M. Survey of Sustainable Energy Sources for Microgrid Energy Management: A Review. *Energies* **2023**, *16*, 3077.
https://doi.org/10.3390/en16073077

**AMA Style**

Hoummadi MA, Bouderbala M, Alami Aroussi H, Bossoufi B, El Ouanjli N, Karim M. Survey of Sustainable Energy Sources for Microgrid Energy Management: A Review. *Energies*. 2023; 16(7):3077.
https://doi.org/10.3390/en16073077

**Chicago/Turabian Style**

Hoummadi, Mohammed Amine, Manale Bouderbala, Hala Alami Aroussi, Badre Bossoufi, Najib El Ouanjli, and Mohammed Karim. 2023. "Survey of Sustainable Energy Sources for Microgrid Energy Management: A Review" *Energies* 16, no. 7: 3077.
https://doi.org/10.3390/en16073077