Aim and Objectives

The main goal of this thesis is to improve the stability performance and resilient operation of microgrids through the development of innovative, holistic power management of a microgrid network by addressing a unified, systematic framework, where the power management decomposes into modular blocks in chronological executions. Management consists of the interlocking functions of creating a corporate strategy, policy, and process tiers for organising, planning, controlling, and directing the microgrid and EVs’ sources to achieve a robust operation of the microgrid. The main contribution from the proposed functionalities relies on the coordination of power system sources, locally available sources, mobile energy storage systems in EVs, and controllable loads during microgrid autonomous operation. The management and control features are expected to increase the reliability, efficiency and

sustainability of the system, as well as make the system flexible enough to recover the supply and demand conditions in emerging electricity markets, such as after natural disasters, during demand congestion, or grid outage, and to avoid power flow congestion. In order to achieve this major goal, a set of intermediate objectives have been established:

Construct a microgrid from a realistic distribution network of a typical community, considering the advantages of uniform integrating distributed generators based on voltage stability analysis. Several kinds of DGs integrate within the existing distribution

network to balance loads of a typical community based on the weakest bus bar analysis to identify the advantages of microgrids over the previous network. Then, the effects of the microgrid on the conventional power system are analysed to determine the voltage stability enhancement of a smart grid over the previous system. To identify the EVs within the power flow analysis of the power system, a set of analytical equations links the EVs’ characteristics within the microgrid and determines the effect of integrating EVs on the voltage stability analysis and characteristics of the microgrid.

Design a holistic, systematic management and control framework constructed from several tiers to include the operation of each element connected to the microgrid. In order

to balance the electrical sources and demands, and maintain the stability of the microgrid, a chronological structure of the tri-level hierarchical management operation to form a modular power management implementation has been made in order to override the power flow congestion of the bidirectional power flow characteristics in the microgrid. The structure consists of MGO, CSO, and EVO, with the EVO being structured into three modular, hierarchical processes: EMS, PMS and PES.

The objective function of the MGO is the optimal scheduling operation of several types of DGs including renewable energy sources, mobile energy storage of EVs, and controllable loads, to minimise the total combined operation and treatment emission costs of the microgrid. Several scenarios of a typical community case study have been demonstrated including UC strategy, island, and connected operation within this stage of the unified structure.

The objective functions of the CSO either minimise the cost of charging power or maximise the cost of discharging power of aggregated EVs connected to the CSS of the microgrid. EVs can be represented as flexible loads or mobile storage devices to potentially provide frequency and voltage variation regulation during autonomous microgrid operation. A comprehensive case study operating in different scenarios is demonstrated to determine the viability of the proposed energy management concept.

The main objective function of the EVO synchronises the power conversion system of the EV to either provide standard waveforms or consume specific energy, based on the resources state of the EV. The EVO has been structured into three shells, with each shell having a particular objective function and execution period. The EMS is responsible for providing standard waveforms through detecting a positive sequence of an unbalanced waveform nature at a low voltage level network, and controlling it using droop controller and vector controller based on a set of analytical equations emerging from different levels of active and reactive power at different states of resources. The PMS is responsible for arbitrating power between the

resources of EV based on the fuzzy heuristic controller. The PES is accountable for the innovative switching modulation of the multilevel inverter based on a modified SVPWM strategy in order to combine multi-resources operation at minimum losses transition of switches.

The physical model of EV is powered by dual energy resources which are a battery as the main resource and supercapacitors as assistant resources. Power conversion circuitry is an

essential part of integrating the DC form of EVs’ resources to the AC form of the microgrid. To maintain the voltage balance of the microgrid as well as achieve good performance, high power and high efficiency, a modified three phase, multilevel, H-bridge cascade inverter has been chosen, which is able to operate multiple resources connected in series to synergize each other.

Laboratory implementation and testing of the multilevel inverter operation is necessary to validate the operation of the multilevel inverter by combining the dual resources

(battery and bank of supercapacitors). The experimental validation and specification of switching the multilevel inverter are of utmost importance for the deployment of EVs within the microgrid. The fact that the microgrid is a very complex system with hundreds of elements spread over a wide geographical area makes implementing a laboratory microgrid both difficult and expensive, but could be managed by a group of researchers who are generously funded. Therefore, exploited particular implementation part of the system for the development and proof of concept of switching solution envisioning the prototype construction for EVs integration within microgrid. The hierarchical management strategy that will be investigated for the microgrid operation is depicted in Figure 1-10.

Power and energy management system of a microgrid_1

Microgrid operator_1 (MGO_1) Charging station operator_N (CSO_N) Charging station operator_1 (CSO_1)

Power and energy management system of an electric vehicle 1

(EVO_1) Type_1 energy system

(battery)

Type_2 energy system (supercapacitor) Power conversion and

distribution

Power and energy management system of an electric vehicle N

(EVO_N)

Type_1 energy system (battery)

Type_2 energy system (supercapacitor) Power conversion and

distribution

Platform_1 Platform_N

Power and energy management system of an electric vehicle 1

(EVO_1)

Type_1 energy system (battery)

Type_2 energy system (supercapacitor) Power conversion and

distribution

Power and energy management system of an electric vehicle N

(EVO_N)

Type_1 energy system (battery)

Type_2 energy system (supercapacitor) Power conversion and

distribution

Platform_1 Platform_N

Distribution network operator (DNO)

Power and energy management system of a microgrid_N Microgrid operator_N (MGO_N) Distributed generator_1 Distributed generator_N Microcontroller_N (MCL_N) Microcontroller_1 (MCL_1) Load_1 Load_N Distribution management system (DMS)