Incorporation of a hybrid cooling system in water cooled power stations to reduce water and energy consumptions

Water shortage is a major problem facing the power industry in many nations around the world. The largest consumer of water in most power plants is the wet cooling tower. To assist water and energy saving for thermal power stations using conventional evaporative wet cooling towers, a hybrid cooling system is proposed in this paper. The hybrid cooling system may consists of all or some of an air pre-cooler, heat pump, heat exchangers, and adsorption chillers together with the existing cooling tower. The hybrid cooling system described in the paper, consisting of a metal hydride heat pump operating in conjunction with the existing wet cooling tower, is capable of achieving water saving by reducing the temperature of warm water entering the cooling tower. Cooler inlet water temperatures effectively reduce the cooling load on existing towers. This will ultimately reduce the amount of water lost to the air by evaporation whilst still achieving the same cooling output. At the same time, the low grade waste energy upgraded by the metal hydride heat pump, in the process of cooling the water, can be used to replace the bleed of steam for the lower stage feed heaters which will increase overall cycle efficiency.

Construction of optimal prediction intervals for load forecasting problems

Short-term load forecasting is fundamental for the reliable and efficient operation of power systems. Despite its importance, accurate prediction of loads is problematic and far remote. Often uncertainties significantly degrade performance of load forecasting models. Besides, there is no index available indicating reliability of predicted values. The objective of this study is to construct prediction intervals for future loads instead of forecasting their exact values. The delta technique is applied for constructing prediction intervals for outcomes of neural network models. Some statistical measures are developed for quantitative and comprehensive evaluation of prediction intervals. According to these measures, a new cost function is designed for shortening length of prediction intervals without compromising their coverage probability. Simulated annealing is used for minimization of this cost function and adjustment of neural network parameters. Demonstrated results clearly show that the proposed methods for constructing prediction interval outperforms the traditional delta technique. Besides, it yields prediction intervals that are practically more reliable and useful than exact point predictions.

Short term load forecasting using a hybrid neural network

Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression.

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.

Quasi-decentralized functional observers for the LFC of interconnected power systems

This letter presents a novel approach to the load-frequency control (LFC) of interconnected power systems. Based on functional observers theory, quasi-decentralized functional observers (QDFOs) are designed to implement any given global PI state feedback controller. The designed functional observers are decoupled from each other and also of low-order; thus, they are cost effective and easy to implement. Although the proposed approach is applicable to N- area power systems, an example of a two-area interconnected power system with reheat thermal turbines is considered for simplicity.

Analyzing track sprint cyclists' performances using position-specific maximal-torque and power-cadence relationships

Previous studies have demonstrated the importance of maximal Torque-Cadence (T-C) and Power-Cadence (P-C) relationships, for the performances of world class track sprint cyclists. If these relationships are affected by the function of the lower limb muscles, the ability of cyclists to generate torque and power at a given cadence may vary depending on their riding position. During sprint events (individual and team sprints and Keirin), cyclists alternate between standing and seated positions. The T-C and P-C relationships may change with the position adopted by the cyclists. PURPOSE: The aim of this study was to evaluate the necessity to define position specific maximal T-C and P-C relationships. METHODS: Eight junior elite track cyclists from the National Talent Identification squad undertook two inertial-load tests that consisted of four all-out sprints each. One test was undertaken at the velodrome in a standing position on a carbon fibre track bike, and the other test was completed in a seated position on an air-braked stationary ergometer. A calibrated SRM power meter interfaced to a custom instrumentation package was used for all mechanical measurements. Maximal T-C and P-C relationships were analysed to calculate maximal Torque (T0), maximal Power (Pmax) and optimal pedalling cadence (PCopt). RESULTS: All individual T-C and P-C relationships obtained for both body positions were fitted by linear regressions (r2=0.95 ± 0.02) and second order polynomials (r2=0.96 ± 0.01), respectively. T0 was higher (209 ± 2.2N.m vs. 177.0 ± 3.9N.m, p<0.05), PCopt was lower (112.5 ± 11.4rpm vs. 120.1 ± 6.7rpm, p<0.05), and Pmax was higher (1261 ± 235W vs. 1076 ± 183W, p<0.05) in standing position compared to seated position. CONCLUSION: Analysis of track sprint cyclists’ performances can be improved by the determination of position-specific maximal T-C and P-C relationships .

Construction of neural network-based prediction intervals for short-term electrical load forecasting

Short-term load forecasting (STLF) is of great importance for control and scheduling of electrical power systems. The uncertainty of power systems increases due to the random nature of climate and the penetration of the renewable energies such as wind and solar power. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in datasets. To quantify these potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for construction of prediction intervals (PIs). A newly proposed method, called lower upper bound estimation (LUBE), is applied to develop PIs using NN models. The primary multi-objective problem is firstly transformed into a constrained single-objective problem. This new problem formulation is closer to the original problem and has fewer parameters than the cost function. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Two case studies from Singapore and New South Wales (Australia) historical load datasets are used to validate the PSO-based LUBE method. Demonstrated results show that the proposed method can construct high quality PIs for load forecasting applications.

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.

RF rectifiers for EM power harvesting in a deep brain stimulating device

A passive deep brain stimulation (DBS) device can be equipped with a rectenna, consisting of an antenna and a rectifier, to harvest energy from electromagnetic fields for its operation. This paper presents optimization of radio frequency rectifier circuits for wireless energy harvesting in a passive head-mountable DBS device. The aim is to achieve a compact size, high conversion efficiency, and high output voltage rectifier. Four different rectifiers based on the Delon doubler, Greinacher voltage tripler, Delon voltage quadrupler, and 2-stage charge pumped architectures are designed, simulated, fabricated, and evaluated. The design and simulation are conducted using Agilent Genesys at operating frequency of 915 MHz. A dielectric substrate of FR-4 with thickness of 1.6 mm, and surface mount devices (SMD) components are used to fabricate the designed rectifiers. The performance of the fabricated rectifiers is evaluated using a 915 MHz radio frequency (RF) energy source. The maximum measured conversion efficiency of the Delon doubler, Greinacher tripler, Delon quadrupler, and 2-stage charge pumped rectifiers are 78, 75, 73, and 76 % at -5 dBm input power and for load resistances of 5-15 kΩ. The conversion efficiency of the rectifiers decreases significantly with the increase in the input power level. The Delon doubler rectifier provides the highest efficiency at both -5 and 5 dBm input power levels, whereas the Delon quadrupler rectifier gives the lowest efficiency for the same inputs. By considering both efficiency and DC output voltage, the charge pump rectifier outperforms the other three rectifiers. Accordingly, the optimised 2-stage charge pumped rectifier is used together with an antenna to harvest energy in our DBS device.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Modelling and power quality analysis of a grid-connected solar PV system

Increased concern about global warming coupled with the escalating demand of energy has driven the conventional power system to be more reliable one by integrating Renewable Energies (RE) in to grid. Over the recent years, integration of solar PV forming a gridconnected PV is considered as one of the most promisingtechnologies to the developed countries like Australia to meet the growing demand of energy. This rapid increase in grid connected photovoltaic (PV) systems has made the supply utilities concerned about the drastic effects that have to be considered on the distribution network in particular voltage fluctuations, harmonic distortions and the Power factor for sustainable power generation. However, irrespective of thefact that the utility grid can accommodate the variability of load or irregular solar irradiance, it is essential to study the impact of grid connected PV systems during higher penetration levels as the intermittent nature of solar PV adversely effects the grid characteristics in meeting the load demand. Hence, keeping this in track, this paper examines the grid-connected PV system considering a residential network of Geelong region (38◦.09' S and 144◦.21’ E) and explores the level of impacts considering summer load profile with a change in the level of integrations. Initially, a PV power system network model is developed in Matlab-Simulink environment and the simulations are carried out to explore the impacts of solar PV penetration at low voltage distribution network considering power quality (PQ) issues such as voltage fluctuations, harmonics distortion at different load conditions.