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Ng S.K.K.,University of Hong Kong | Zhong J.,University of Hong Kong | Cheng J.W.M.,CLP Research Institute Ltd.
IEEE Power and Energy Society General Meeting | Year: 2012

This paper presents a comprehensive sizing methodology which could contain all key elements necessary to obtain a practical sizing result for a stand-alone photovoltaic (PV) system. First, a stochastic solar radiation model based on limited/incomplete local weather data is formulated to synthesis various chronological solar radiation patterns. This enables us to evaluate a long-term system performance and characterize any extreme weather conditions. Second, a stochastic load simulator is developed to simulate realistic load patterns. Third, two reliability indices, Expected-Energy-Not-Supplied (EENS) and Expected-Excessive-Energy-Supplied (EEES), are incorporated with an Annualized Cost of System (ACS) to form a new objective function called an Annualized Reliability and Cost of System (ARCS) for optimization. We then apply a particle swarm optimization (PSO) algorithm to obtain the optimum system configuration for a given acceptable risk level. An actual case study is conducted to demonstrate the feasibility and applicability of the proposed methodology. © 2012 IEEE. Source


Liang J.,CLP Research Institute Ltd. | Ng S.K.K.,CLP Research Institute Ltd. | Kendall G.,CLP Research Institute Ltd. | Cheng J.W.M.,CLP Research Institute Ltd.
IEEE Transactions on Power Delivery | Year: 2010

Load signature is the unique consumption pattern intrinsic to each individual electrical appliance/piece of equipment. This paper focus on building a universal platform to better understand and explore the nature of electricity consumption patterns using load signatures and advanced technology, such as feature extraction and intelligent computing. Through this knowledge, we can explore and develop innovative applications to achieve better utilization of resources and develop more intelligent ways of operation. This paper depicts the basic concept, features of load signatures, structure and methodology of applying mathematical programming techniques, pattern recognition tools, and committee decision mechanism to perform load disaggregation. New indices are also introduced to aid our understanding of the nature of load signatures and different disaggregation algorithms. © 2010 IEEE. Source


Liang J.,CLP Research Institute Ltd. | Ng S.K.K.,CLP Research Institute Ltd. | Kendall G.,CLP Research Institute Ltd. | Cheng J.W.M.,CLP Research Institute Ltd.
IEEE Transactions on Power Delivery | Year: 2010

Load signatures embedded in common electricity consumption patterns, in fact, could render much information pertaining to the nature of the appliances and their usage patterns. Based on the proposed disaggregation framework, we use three advanced disaggregation algorithms, called committee decision mechanisms (CDMs), to perform load disaggregation at the metering level. Three random switching simulators are also developed to investigate the performance of different CDMs under a variety of scenarios. Through Monte Carlo simulations, we demonstrate that all CDMs outperform any single-feature, single-algorithm-based disaggregation methods. With sensitivity analysis, we also show that the CDMs are less sensitive to any load dynamics and noise. We finally demonstrate some applications of this technology in terms of appliance usage tacking and estimated energy consumption of each appliance. © 2010 IEEE. Source

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