An approach for wind power integration using demand side resources

Renewable energy resources, especially wind power, are expected to provide a considerable portion of the world energy requirements in the near future. Large-scale wind power penetration impacts the electricity industry in many aspects and raises a number of technical challenges for the electricity network. A day-ahead network-constrained market clearing formulation is proposed which considers demand side resources. The proposed approach can provide flexible load profile and reduce the need for ramp up/down services by the conventional generators. This method can potentially facilitate a large penetration of wind power by shifting the wind power generation from the off-peak periods to the high-peak hours. The validity of the proposed approach has been verified using the IEEE 30 bus and 57 bus test systems.

Network modelling for Townsville distribution system

Drinking water quality guidelines are becoming increasingly stringent. To comply with these guidelines and to manage water quality in a distribution system, improved understanding of the movement and fate of drinking water constituents within the system is required. This study illustrates the construction and calibration of an electronic model of the Townsville drinking water distribution system. Being in the tropics, the temperature of the water in the distribution system changes little throughout the year (usually between 20 and 25°C); also, water is supplied to the system from two sources, the location of the blending of these waters is varies with demand.

Neural network ensemble : evaluation of aggregation algorithms for electricity demand forecasting

This paper examines and analyzes different aggregation algorithms to improve accuracy of forecasts obtained using neural network (NN) ensembles. These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). The predictive performance of these algorithms are evaluated using Australian electricity demand data. The output of the aggregation algorithms of NN ensembles are compared with a Naive approach. Mean absolute percentage error is applied as the performance index for assessing the quality of aggregated forecasts. Through comprehensive simulations, it is found that the aggregation algorithms can significantly improve the forecasting accuracies. The BMA algorithm also demonstrates the best performance amongst aggregation algorithms investigated in this study.

Impacts of DG penetration in the Bamaga and Gununa isolated distribution networks

Isolated distribution systems are dispersed throughout regional Queensland to supply small isolated communities that are distant from the main supply grid. The costs of maintaining the electricity supply to these areas is costly; mainly due to the cost of diesel fuel. Furthermore, there is a community focus on climate change, and Ergon Energy aims to reduce the reliance on fossil fuels whilst optimising cost efficiencies and greenhouse gas emissions. The objective of this study is to examine the impacts of renewable energy sources in isolated power systems. For the locations studied, viable renewable energy sources have been integrated into these networks. Anticipated challenges and issues with the integration of the intermittent renewable energy sources were addressed, using mitigation techniques, including energy storage solutions. The investigation and findings demonstrated that network improvements can be achieved by an ideal level of renewable penetration, which has been the main focus of the project. The project involved the development and simulation of MATLAB Simulink and SINCAL models of the two isolated networks at Gununa and Bamaga. The subsequent analysis of these systems has shown a modest penetration level of renewables can be combined with energy storage solutions, which reduces fuel consumption and greenhouse gas emissions at these locations.

Voltage regulation in renewable energy integration into the distribution network

Large-scale renewable energy (RE) integration into the distribution network (DN) causes uncertainties due to its intermittent nature and is a challenging task today. In general RE sources are mostly connected near the end user level, i.e., in the low voltage distribution network. RE integration introduces bi-directional power flows across distribution transformer (DT) and hence DN experiences with several potential problems that includes voltage fluctuations, reactive power compensation and poor power factor in the DN. This study identifies the potential effects causes due to large-scale integration of RE into the Berserker Street Feeder, Frenchville Substation under Rockhampton DN. From the model analyses, it has clearly evident that voltage of the Berserker Street Feeder fluctuates with the increased integration of RE and causes uncertainties in the feeder as well as the DN.

Influences of wind energy integration into the distribution network

Wind energy is one of the most promising renewable energy sources due to its availability and climate-friendly attributes. Large-scale integration of wind energy sources creates potential technical challenges due to the intermittent nature that needs to be investigated and mitigated as part of developing a sustainable power system for the future. Therefore, this study developed simulation models to investigate the potential challenges, in particular voltage fluctuations, zone substation, and distribution transformer loading, power flow characteristics, and harmonic emissions with the integration of wind energy into both the high voltage (HV) and low voltage (LV) distribution network (DN). From model analysis, it has been clearly indicated that influences of these problems increase with the increased integration of wind energy into both the high voltage and low voltage distribution network, however, the level of adverse impacts is higher in the LV DN compared to the HV DN.

Impact of dynamic PHEVs load on renewable sources based distribution system

In this paper, charging effect of dynamic Plug in Hybrid Electric Vehicle (PHEV) is presented in a renewable energy based electricity distribution system. For planning and designing a distribution system, PHEVs are one of the most important factor as it is going to be a spinning reserve of energy, and also a major load for distribution network. A dynamic load model of PHEVs is introduced here based on third order battery model. To determine the system adequacy, it is necessary to do a micro level analysis to know the PHEVs load impact on grid. Scope of such analysis will cover the performance of wind and solar generation with dynamic PHEVs load, as well as the stability analysis of the power grid to demonstrate that it is important to consider the dynamics of PHEVs load in a renewable energy based distribution network.

Integration of roof-top solar photovoltaic systems into the low voltage distribution network

The advancement in solar photovoltaic (PV) technology, the cost and efficiency of PVs have encouraged users worldwide to adopt more and more PVs as it is free from greenhouse gas emissions and unlimited in nature. Integration of roof-top solar PV systems is currently emerging rapidly in Australia as the governments are giving attractive incentives and encouraging households to build a sustainable climate-friendly society for the future. The key major barriers to the integration of roof-top solar PV systems are the uncertainties in the performance of the low voltage distribution network due to the intermittent nature of solar PV sources. In this paper, a model was developed to investigate the potential technical impacts of integrating roof-top solar PV systems into the low voltage distribution network in a subtropical climate. The results show that integration of roof-top solar PV in the customer premises causes uncertainties such as voltage fluctuations, phase unbalance, distribution transformer overloading, reactive power compensation, and harmonic injections that detract the overall power quality of the typical distribution network. © 2014 AIP Publishing LLC.