Browsing by Author "De Villiers, Almero"

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    Development of a software application utilising classical efficiency theory, regression and Data Envelopment Analysis in the evaluation of thermal power plant performance.
    (Stellenbosch : Stellenbosch University, 2015-12) De Villiers, Almero; Vermeulen, H. J.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Recent capacity constraints on the South African power grid, coupled with the economic and environmental implications of increasing energy requirement, has given rise to major efforts to implement energy management initiatives in the industrial, commercial and residential load sectors. These efforts are supported by the construction of multiple new power plants, both thermal and renewable in nature. Additionally, the Energy Efficiency (EE) of existing plants is being optimised, which requires accurate performance evaluation and benchmarking as part of plant diagnostic and Measurement and Verification (M&V) exercises. Energy management exercises require accurate tracking of power plant efficiency. In this project a South African coal-fired power plant is used as a test case, and is analysed utilising both classical and Data Envelopment Analysis (DEA) based EE evaluation methods in an attempt to track plant efficiency over time and in relation to similar US plants. DEA is a non-parametric linear programming-based benchmarking technique used to comparatively evaluate multiple peer branches. The historical plant data used in this project is provided in monthly intervals, but is of low quality, with measured fuel consumption values out of sync with actual fuel consumption values. For this reason data averaging is also considered. A software application is developed to analyse historical plant data, supported by the development of a relational database. This database allows for permanent storage and access of historical plant data while the software application incorporates all relevant analysis methodologies and graphic user interface. The classical efficiency evaluation methods are found to provide a general overview of actual plant performance, but do not consider plant context, often making results ambiguous. The methods are also limited to energy datasets, and cannot incorporate additional factors that may be relevant to plant performance. Higher quality data is recommended to increase the accuracy of results. M&V interventions include an energy audit before and after an EE implementation. Preimplementation data is referred to as the baseline and is used to evaluate the positive impact of the implementation. Regression analysis is investigated as a means of gaining additional insight into the effect of additional factors on overall plant efficiency, but also as a means of baseline adjustment in an M&V context. The regression analysis study does not produce significant results, but increasing the quality of measured plant datasets may allow for more useful results. The DEA efficiency tracking methodology is found to be of use when additional factors are incorporated with energy data, and can provide a brief overview of performance between plants. When a single plant is evaluated over time the process can also easily identify inefficient periods, although additional insight is required to establish the sources of these inefficiencies. DEA is thus not a complete replacement for classical EE methods, but rather a useful supplementary tool in efficiency evaluation. The accuracy of its results is highly susceptible to the quality of data used. Evaluation of individual plant component inputs and outputs rather than overall plant inputs and outputs would make for a useful future study.