TY  - BOOK
AU  - Fawad Azeem, 
TI  - Optimized Implementation of Efficient Control Algorithm for Islanded Microgrid (PhD Thesis)
U1  - 621.31378242 
PY  - 2019///
CY  - Karachi : 
PB  - NED university of Engineering and Technology Department of Electronic Engineering, 
KW  - Islanded Microgrid Smart Powers Grids Thesis
KW  - Renewable Energy Sources Economic Aspect Thesis
N1  - YN
N2  - Abstract: 

Islanded microgrids are the independent low voltage power systems capable to electrify the far-flung underdeveloped rural areas. Such standalone power systems have own renewable distributed generation, limited storage, low voltage transmission system and defined loads. The independent operation of islanded microgrid demands rigorous monitoring and control when dealing with the intermittent renewable distributed generation aided with the limited storage capacity. To ensure the stable islanded microgrid operation, several control algorithms have been developed that utilizes load scheduling, load and generation forecasting, load shedding and power sharing techniques. All the techniques requires implementation of communication protocols, smart devices and internet information for the effective integration between intermittent distributed generations, limited storage, and variable load. The rural areas of the underdeveloped countries faces the challenges of affordability and accessibility. Such areas has no access to modem services like internet, communication architectures & protocols for adaptive monitoring and control of microgrid. Whereas the backup power sources like diesel generators and connection with the upstream network are beyond affordability in such areas. The lack of accessibility of state of the art expensive monitoring and control infrastructures causes inefficient load consumption and storage dispatch. The overall result is system-wide outages and blackouts. This research aims to reduce system-wide outages without employing networked communicational infrastructures, internet and smart devices commonly used for real time operation monitoring and control. A novel three-tier adaptive control algorithm is proposed using local microgrid system parameters for real-time monitoring, future microgrid state prediction, proactive smart load curtailment and scheduling. The prime objective is blackout reduction without using conventional backup sources and sophisticated monitoring and control communication architectures. The proposed control approach is a three tier feedback monitoring and proactive scheduling control approach. The fuzzy logic method is applied that makes proactive decisions to reduce load and is given the name as Demand Management (DM) i.e. tier one of the proposed load control. Microgrid system parameters such as remaining time of storage to completely drain (DT), charge state of storage (SoC), instantaneous power consumption and irradiations of solar are utilized as membership functions to the fuzzy demand management control. For efficient dispatch and energy conservation, Human Activities Tracking System (HA TS) has been introduced which offers smart load curtailment and referred to as tier two of the proposed adaptive control. To smartly curtail the load on priority, the load reduction from low human activity areas is started using HATS controller. Activities of residents are monitored using Pyroelectric sensors mounted on building entrance. To enhance resiliency in proposed scheme and to make it more vulnerable during night hours when solar power is not available, a control algorithm is designed which is one step ahead and control the residential load at an advanced level. This advance controller individually controls the distinctive house hold load like fans, lights, washing machines, water pumps and is referred to as tier three of the adaptive control system. The fuzzy controller takes decision for the activation or deactivation of any load based on the local input functions of the charge state of storage, solar power generation, and ambient temperature. 
To assess the effectiveness of fuzzy based demand management control in terms of effective prediction and load scheduling, the fuzzy simulation was performed followed by the lab scale experimentation. The system performance is compared with SoC controlling method (SCM) and prior research study. The blackouts were decreased to 96 to 100% under different conditions. Similarly, the rate of SoC degradation was noticeably less than SCM. 
To evaluate the effectiveness of distinctive load control, the experimental results gathered from tier 1 and tier 2 control approach was compared with the experimental results after adding the tier 3 controller into the system. After adding the distinctive load control, the overall load reduction was improved compared to that of tier 1 and tier 2 control. On average 19.43 % of load was reduced and SoC of the storage remains 12.7% better compared to the tier 1 and tier 2 control only.

ER  -