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Literatures review
Optimal sizing, control and energy management strategies are known as important research issues. First, several methods for optimal sizing have been proposed in the literature. Some of the authors use the artificial intelligent (AI) methods such as genetic algorithm (GA), Particle Swarm optimization (PSO), whereas others utilize the iterative method to find the optimal configuration of a microgrid which satisfies the optimal operation strategy. Second, the optimal for microgrid energy management is also presented in some researches. The Rule-based method, optimal global methods (Linear programming (LP), Mix-Integer-Linear-Programming (MILP) and dynamic programming (DP)) as well as the artificial intelligent (AI) methods are used to find the optimal energy management for a microgrid. Last, the local controllers for DER can enhance the efficiency of microgrid operation by using the conventional methods (single master (centralize control), master/slave and droop control). Furthermore, the variations of conventional droop control are also addressed in some publications.
Optimal sizing of a microgrid
Optimal sizing of an island microgrid
The optimization sizing of island microgrid has been presented in the literature. It has two main aspects, which are the architecture sizing and the energy fluxes. Dealing with these problems, various simulation and optimization software tools on PV hybrid systems have been reviewed in the literature [7]-[12]. On the other hand, two main methods of optimization that are iterative and artificial intelligent (AI) based methods have been proposed in [13]
Genetic Algorithm (GA) has been proposed for optimal sizing of a PV-diesel-battery system in [14]. The main objective is to define the optimum number of PV panels, battery banks and DG capacity. In [15], the GA is used to optimize a hybrid PV/diesel generator system which is divided into two parts. The first part aims to find the optimal configuration of the system. Then, the latter part optimizes the operation strategy by using each calculated configuration in the first part. The optimal configuration is the one that leads to the minimum cost of the system. A multi-objective optimization for a stand-alone PVWind –diesel system with battery storage by using Multi-Objective Evolutionary Algorithms (MOEAs) is described in [16]. The levelized cost of energy (LCOE) and the equivalent CO2 life cycle emission (LCE) are known as the objectives. In [17], a multiobjective evolutionary algorithms (MOEAs) and a GA have been used to minimize the total cost, pollutant emissions (CO2) and unmet loads.
An iterative optimization technique for a stand-alone hybrid photovoltaic/wind system (HPWS) with battery storage was proposed in [18]. The aim is to find the optimum size of system in order to respond to the demanded load and to analyze the impact of different parameters on the system size. In [19]-[20], another iterative optimization technique is used to optimize the capacity sizes of different components of hybrid solarwind power generation systems employing a battery bank. The sizing optimization of this hybrid system can be achieved technically and economically according to the system reliability requirements. An optimal sizing model based on iterative technique in order to optimize the capacity sizes of different components of hybrid photovoltaic/wind power generator system using a battery bank was proposed in [21].
Optimal sizing a grid-connected microgrid
The optimization of the grid-connected microgrid has been presented in the recent literature. For instance, in [22], a method is proposed to determine the size of battery storage for grid connected PV system. The objective is to minimize the cost considering the net power purchase from grid and the battery capacity loss (state of health). A methodology for the optimization sizing and the economic analysis dedicated to PV gridconnected systems is presented in [23]. In which, the number, type of the PV units and converters are given as the decision variable. In [24], an optimum sizing of PV-energy storage methodology for small autonomous islands is studied. The main parameters of this paper are the PV-rated power and the storage capacity. The artificial intelligent (AI) techniques are used to optimize such architecture based on PV Grid-connected systems. In [25], the author uses GA to determine the optimal allocation and sizing of PV grid connected systems. On the other hand, the Particle Swarm optimization (PSO) is used for optimal sizing of a grid connected hybrid system in [26]. A comparison between the two methods which are PSO and genetic algorithm (GA) is carried out in advance to evaluate the efficiency of the proposed method.
Energy management of microgrid
When a microgrid has more than two DERs, the energy management system (EMS) is needed to impose the power allocation among DER, the cost of energy production and emission.
The EMS in a microgrid is shown in the Figure I.13. As can be seen from this Figure, the forecast values of load demand, the distributed energy resources and the market electricity price in each hour on the next day are denoted as inputs. Furthermore, the operation objectives are considered to optimize the energy management, are given as follows:
– Economic option
– Technical option
– Environmental option
– Combined objective option.
Some algorithms for the optimization of microgrid energy management are proposed [27-40]. The optimal energy management of an island microgrid is presented in [27], [28] by using a rule-based management. The operation of the system depends on the developed rules; thus, the constraints are always satisfied, but the optimization is not global results. In [29], [30], fuzzy logic is used to estimate the rule to improve the rule-based technique. The linear programming (LP) and mix integer linear programming (MILP) are used to find the optimal energy management in [31], [32]. This method gives good results; however, the main limitation is known as the need of a specific mathematical solver [31]. In [33], [34], the optimal energy management for a grid connected with PV/battery and a vehicular electric power system is addressed by using the quadratic programming (QP). The good results achieved, however, the limit of this method is to need the objective function to be convex. In [35], the optimal energy management of a microgrid is solved by using Game Theory and multi-objective optimization. The operating cost and the emission level are given as two objectives functions. The Mesh Adaptive Direct Search (MADS) algorithm is used to optimize the microgrid operating cost function in [35]. A matrix real-coded genetic algorithm (MRC-GA) optimization module is used to search the optimal generation schedule in [36]. The particle swarm optimization (PSO) technique was proposed in [37], [38]. The dynamic programming (DP) and advance dynamic programming (ADP) are used to optimize the energy management in [39] and [40]. The achieved results are proved the efficiency of these methods.
Microgrid control
Microgrid control was addressed in some literatures.
– The European R&D project [1], [41], [42]
The microgrid control is presented in Figure I.14 [1], [41], [42].
The microgrid control includes:
Micro Source Controllers (MC) and Load Controllers (LC)
Microgrid System Central Controller (MGCC)
Distribution Management System (DMS).
+ The Micro Source Controller (MC):
Power electronics interfaces are the privileged mean for energy fluxes control. The local information is measured for controlling and monitoring DERs
+ Microgrid System Central Controller (MGCC)
The Microgrid Central Controller proposes the interface of a microgrid with the other actor such as distribution system operation (DSO) and optimizes the microgrid operation
+ Distribution Management System (DMS).
Distribution Management Systems (DMS) is used for distribution areas management and control, comprising several feeders including several Microgrids
Table of contents :
CHAPTER I : Introduction
I.1. Context
I.1.1. Development of photovoltaic
I.1.2. Development of Electrochemical Energy Storages
I.1.3. Microgrid
I.2. Literatures review
I.2.1. Optimal sizing of a microgrid
I.2.2. Energy management of microgrid
I.2.3. Microgrid control
I.3. Objective of the thesis
I.4. Thesis contributions
I.5. Thesis organization
CHAPTER II : Microgrid concept
II.1. Definition of microgrid
II.2. Microgrid structure and components
II.3. Microgrid operation
II.4. Microgrid control
II.5. Microgrid protection
CHAPTER III : Modeling of the microgrid components
III.1. Introduction
III.2. Photovoltaic system Modeling
III.2.1. Photovoltaic module
III.2.2. PV system sizing
III.2.3. PV system Modeling
III.3. Electrochemical storage Modeling
III.3.1. Battery Parameters
III.3.2. Battery Interface
III.4. Diesel Modeling
III.5. Load Modeling
III.6. Conclusion
CHAPTER IV : Optimal sizing of microgrid
IV.1. Introduction
IV.2. Optimal sizing of a microgrid in island mode
IV.2.1. System configuration
IV.2.2. System components
IV.2.3. Methodology
IV.2.4. Simulation results and discussion
IV.3. Optimal sizing of a microgrid in grid connected mode
IV.3.1. System configuration
IV.3.2. System components
IV.3.3. Methodology
IV.3.4. Simulation results and discussion
IV.4. Conclusion
CHAPTER V : Optimal energy management for microgrid
V.1. Introduction
V.2. Optimization methods
V.2.1. Dynamic Programming and Bellman Algorithm
V.2.2. Application of Bellman algorithm to finding the nominal state of charge (SOC) of atteries
V.3. Optimization of energy management for a microgrid in isolated mode
V.3.1. Objective function
V.3.2. Constraints
V.3.3. A rule‐based energy management strategy
V.3.4. Application Bellman algorithm in optimal energy management for an island microgrid
V.3.5. Simulation results and discussion
V.4. Optimization energy management for a microgrid in grid connected mode
V.4.1. Objective function
V.4.2. Constraints
V.4.3. A rule‐based energy management strategy
V.4.4. Application Bellman algorithm in optimal energy management for a grid connected microgrid
V.4.5. Simulation results and discussion
V.5. Conclusion
CHAPTER VI : Microgrid control
VI.1. Introduction
VI.2. Control strategies for DERs
VI.2.1. Master slave control
VI.2.2. Multi ‐ Master control
VI.2.3. An intelligent control strategy
VI.2.4. Simulation results
VI.3. Conclusion
CHAPTER VII : Conclusion and Future works
VII.1. Conclusion
VII.2. Future work
