Mithulananthan Nadarajah

DG Allocation in Primary Distribution Systems Considering Loss Reduction

Loss reduction in distribution systems has been a subject of great concern since the evolution of the interconnected power system. In the recent past, with increasing interest in climate change and energy security, renewable energy integration and energy efficiency, including loss reduction, have been considered as twin-pillars of sustainable energy solutions. When renewable energy is integrated by considering loss reduction as an additional goal, it would lead to multi-fold benefits. This chapter presents the application of distributed generation for loss reduction. The two key issues of the most suitable location and appropriate size of distributed generation for loss reduction have been discussed. Analytical expressions have been developed for finding the appropriate size of different types of distributed generations. Methodologies are presented for locating the DG in primary distribution feeders, assuming primary energy resources are evenly distributed along the feeder. The analytical expressions and placement methodologies have been tested in three test distribution systems of varying sizes and complexity.

Influence of renewable energy based microgrid on low frequency oscillation of power systems

Increase proliferation of microgrid (MG) technologies with extended use of renewable energy sources (RES) can affect the system stability performance since dynamic characteristics of this kind of MG are different from conventional generation. Sharing certain portion of or entire generated power from synchronous generator with MG results in decrease of total system inertia. On the other hand, connecting RES based MG near a central load eventually enhance system performance due to improvement in reliability, reduction of power loss and congestion on transmission line. This paper investigates the impact of RES based MG on local and inter-area oscillatory modes of power systems. This study focuses on low oscillatory eigenvalues in the frequency range of 0.1-2 Hz. Eigenvalues analysis is performed to observe damping ratios and stability margins of the system due to MG integration. Furthermore, time domain simulation is then carried out to validate the result from eigenvalues analysis.

Fuzzy embedded MPPT modeling and control of PV system in a hybrid power system

The literature is populated with different Maximum Power Point Tracking (MPPT) methods for Photovoltaic (PV) system to obtain maximum power from it. This piece of work provides an artificial intelligence-based fuzzy logic MPPT modeling and control of PV system in a grid connected hybrid power system under different weather patterns. The proposed technique uses seven fuzzy sets with seven linguistic variables applied to a DC-DC converter. Furthermore, a battery module is added as an energy storage system during surplus power and/or backup device during load demand. The overall operation of system is performed by classical logic power management switching algorithm. The performance of proposed method is compared with and without Proportional Integral Derivative (PID) MPPT controllers. MATLAB simulation results show better behavior of proposed method in terms of load tracking and reliability.

An improved neural ensemble framework for accurate PV output power forecast

A significant role of renewable energy resource such as solar photovoltaic (PV) is substantially important for the smart grid. One of a major challenge for large scale integration of PV into the grid is intermittent and uncertain nature of solar PV. Therefore, developing a framework for accurate PV output power forecast is utmost important. In this research, a novel ensemble forecast framework is purposed. A novel feed forward neural network (FNN) ensemble based forecast framework is proposed and trained with particle swarm optimization (PSO). The wavelet transform (WT) technique is applied to handle the sharp spikes and fluctuations in historical PV output data. Correlated variables such as PV output power data, solar irradiance, temperature, humidity and wind speed are applied as inputs to forecast the PV output power precisely. The performance of proposed framework was analyzed for one day ahead load PV output power forecast of summer (S), autumn (A), winter (W) and spring (SP) days. The selected days from each season are a clear day (CD), partial cloudy day (PCD) and cloudy day (CLD). The proposed forecast framework provides higher forecast accuracy compared to persistence and backpropagation neural network (BPNN) model.

A multivariate ensemble framework for short term solar photovoltaic output power forecast

In emerging renewable energy resources, solar photovoltaic (PV) is substantially important to fulfil the future electricity demand. One of the major challenges for large scale integration of PV into the grid is intermittent and uncertain nature of its output. Therefore, it is utmost important to forecast the solar PV output power with higher accuracy. In this paper, a novel ensemble forecast framework is proposed based on autoregressive (AR), radial basis function (RBF) and forward neural network (FNN) predictors. The neural predictor (FNN and RBF) are trained with particle swarm optimization (PSO) to enhance the prediction performance. Furthermore, wavelet transform (WT) technique is applied to remove the sharp spikes and fluctuations in it. In addition, correlated variables such as PV output power data, solar irradiance, temperature, humidity and wind speed are applied as inputs to multivariate ensemble network. The performance of proposed framework is analyzed for one day and week ahead case studies. The selected days from each season are a clear day (CD), partial cloudy day (PCD) and cloudy day (CLD). The proposed forecast framework provides a reduction in forecast nRMSE in seasonal daily and week ahead case studies.

Optimal scheduling of PHEVs in a PV based charging station

Applications of PHEV have been observed as popular in transportation sector, but, an uncoordinated charging of PHEV poses an extra load during peak times which brings up many technical problems in which durability of power system occurs first. Considering PHEV to stay for several hours in the parking lot may offer a unique opportunity to charge PHEV from photovoltaic power in those hours. The proposed charging facility is working under smart charging scheduling paradigm, which is independent from forecasting models of PHEVs charging. The proposed paradigm schedules of PHEV is in an optimal way based on two parameters i.e., priority levels of PHEVs charging, and variations in DC link voltage level. Furthermore, the priorities of PHEV charging are based on their power demand, maximum rating of distribution transformer and stay duration of PHEVs in the parking lot during high irradiation of solar effect. The sensing of proposed paradigm is based on change in DC link voltage. The DC link voltage changes due to variance in solar irradiance, temperature and real-time residential load. Such priorities are updated whenever smart charging scheduling paradigm detects changes in voltage and initiate/terminate PHEVs charging. Simulation results express the effectiveness of proposed charging station effectively.

Multifunctional control of single-phase transformerless PV inverter connected to a distribution network

Recently transformerless grid-tied photovoltaic (PV) inverters are getting popular in order to address several concerns; for example, poor efficiency, large size, and heavy weight compared to those with traditional inverter topologies. However, according to the several recently updated grid codes, the grid-tied PV inverter are required to provide full range of services such as maximum power injection, grid voltage regulation (VR), and fault ride through (FRT). In this paper, the performance of a transformerless PV system with a new droop based controller connected to the low voltage distribution system under different operating condition is investigated. In order to provide full-range of grid supporting services, three operation modes are proposed. A detail description of the transformerless PV inverter, droop based controller, and the control strategy are provided. The theoretical analysis is verified using nonlinear simulations in MATLAB/Simulink software environment. The results show that the presented system is capable of injecting maximum power when participate in the grid voltage regulation, and also can enhance fault ride through capability.

Renewable energy development in Australia: Regulatory to technical challenges

In Australia, grid integration of renewable energy (RE) resources has been very popular due to public awareness of climate change and favorable government policies. The initial mandatory renewable energy target (MRET) has been successful and there has been a revised target (RET) set for next decade. On the other hand, there are some regulatory and technical issues posing hindrances to the development of renewable resources. This paper discusses the opportunities and problems associated with renewable energy integration into Australias National Electricity Market NEM. Issues such as the regulatory frameworks, access standards as per National Electricity Rules (NER), connection process, and transmission pricing frameworks are discussed.