Octavian Bass

Fuzzy logic for smart utilisation of Storage Devices in a typical microgrid

Efficient utilisation of Storage Devices (SD) among multiple sources of dispatch within a typical microgrid have a substantial impact on reducing the economic and environmental generation costs in that particular microgrid. Eventually, managing the multiple sources that supply energy simultaneously is a big engineering challenge. The complexity rises due the uncertainty of demand, generation cost, availability of renewable energy sources and (charging/discharging) time and price for the installed SD. This paper introduces a utilisation method that makes the SD more efficient in supplying the electricity within a typical medium size enterprise microgrid. The method is simply targeting the dynamic charging price for the SD to achieve a profitable charging, and also to maximise the opportunity of participation during the SD lifetime. A fuzzy logic based adaptive charging price is set for charging the SD based on the microgrids local generation price at the time of charging, and the amount of the daily SD participation in the microgrid dispatch. By considering the economic and environmental generation costs in 30-minute operation intervals, a multi-objective Particle Swarm Optimisation (PSO) method is applied to optimise the energy dispatch for the managed microgrid. In addition, a switching mechanism based on the SD status is integrated with the proposed PSO to deal with the variable operation scenarios in the managed microgrid. The proposed optimisation technique has been tested on the realistic operation scenarios of the power grid of the Joondalup Campus of Edith Cowan University in Western Australia. The simulation results showed a reasonable amount of efficiency improvement with a range of benefits in cutting the generation cost for the targeted case study.

Superconducting Magnetic Energy Storage unit for increasing stability of a wind power generation system

A superconducting Magnetic Energy Storage (SMES) system includes a high inducting coil that can act as a constant source of direct current. A high temperature SMES (HTS) unit connected to a power system is able to absorb and store both active and reactive power from this system and to release these powers into this system in the demand periods. These injected powers can be controlled by adjusting the power conversion system of SMES by changing both the duty cycle of the dc-dc chopper switches and its operation modes. In this paper, an efficient design based on an SMES unit controlled by the combined the artificial neural network (ANN) and adaptive control method is presented to improve transient stability by regulating the dc link voltage and to damp and reduce the voltage and frequency fluctuations that are always associated with wind power generator. The authors propose using an SMES as an interface device between the wind power farm and the power grid connected through the DC Link capacitor to rapidly stabilize the voltage and frequency fluctuations in the power system. The system behavior is tested with three different faults/events for both voltage and frequency fluctuations of wind power supply with and without applying the SMES unit. The results show that both voltage and frequency stabilities are significantly increased when the SMES unit is applied in these three events.

Application of a superconducting magnetic energy storage unit for power systems stability improvement

A superconducting Magnetic Energy Storage (SMES) includes a high inductance coil that can act as a constant source of direct current. A SMES unit connected to a power system is able to absorb and store both active and reactive power from this system and to inject these powers into this system when they are needed. The injected power can be controlled by changing both the duty cycle of the dc-dc chopper switches and its operation modes. A SMES is always associated with a power conversion system consisting of two identical converters connected by a de link capacitor. This paper presents an efficient system, based on a SMES unit, to improve transient stability by regulating the dc link voltage to reduce the voltage and frequency fluctuations that occur after disturbances or rapid load changes. The authors propose using a SMES as an interface device connected to the DC Link capacitor and located between the power supply and load sides to act as a universal stabilizer of voltage and frequency on both sides. The system behavior is tested with three faults/events for both power supply and load with and without applying the SMES unit. The results show that the SMES unit increases the dc link voltage stability significantly whenever any of these three events occur.

Fuzzy Inference System in Energy Demand Prediction

Fuzzy Inference Systems (FIS) have been widely used in many applications including image processing, optimization, control and system identification. Among these applications, we would like to investigate energy demand modelling. Generally, developing an energy demand model is the challenge of interpreting the historical use of energy in an electric power network into equations which approximate the future use of energy. The developed model’s equations are coded and embedded into a processor based system, which predicts the output when a certain type of input occurs. However, the range and quality of prediction is still limited within the knowledge supplied to the model. The major concern about the energy demand modelling is to categorize the type of prediction in short or longterm prediction. In addition, it is crucial to categorize the type of the power network to be modelled. Since identifying the useful historical operation data for setting the model parameters is crucial in modelling, the operation history of the modelled systems must to be analysed. In simple terms, modelling energy demand is the art of identifying the right modelling technique and system’s operation parameters. The operation parameters differ based on the type and size of the modelled system. So, taking into consideration why the system is modelled will justify the selection of modelling techniques. Among the reasons for modelling energy demand is managing the use of energy through an Energy Management System (EMS).

PSO algorithm for an optimal power controller in a microgrid

This paper presents the Particle Swarm Optimization (PSO) algorithm to improve the quality of the power supply in a microgrid. This algorithm is proposed for a real-time selftuning method that used in a power controller for an inverter based Distributed Generation (DG) unit. In such system, the voltage and frequency are the main control objectives, particularly when the microgrid is islanded or during load change. In this work, the PSO algorithm is implemented to find the optimal controller parameters to satisfy the control objectives. The results show high performance of the applied PSO algorithm of regulating the microgrid voltage and frequency.