Development of a software application utilising classical efficiency theory, regression and Data Envelopment Analysis in the evaluation of thermal power plant performance.

De Villiers, Almero (2015-12)

Thesis (MSc)--Stellenbosch University, 2015.

Thesis

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.

AFRIKAANSE OPSOMMING: Onlangse kapasiteitstekortkominge op die Suid-Afrikaanse kragnetwerk sowel as ekonomiese en omgewingsimplikasies van toenemende energiebehoeftes het aanleiding gegee tot 'n intensifisering van die pogings om energie bestuursinisiatiewe in die industriële, kommersiële en residensiële ladingsektore te implementeer. Hierdie pogings word vergestald deur onder andere die skepping van nuwe en alternatiewe opwekkingsfasiliteite. Verder word die bestaande sentrales se Energie- Doeltreffendheid (ED) geoptimaliseer, wat die verbetering van akkurate prestasie-evaluering en maatstawwe as onontbeerlike element van die sentrale se diagnostiese en Meting en Verifikasie (M&V) oefenginge vereis. Energie bestuuroefeninge vereis die akkurate begeleiding van kragsentrale doeltreffendheid. In hierdie projek word ‘n Suid-Afrikaanse steenkool-aangedrewe kragsentrale gebruik as ‘n toets onderwerp, en die analise van beide die klassieke en Data Omhulsel Ontleding (DOO) gebaseerde ED evalueringsmetodes in ‘n poging om die kragsentrale se doeltreffendheid na te spoor oor ‘n gegewe tydperk in verhouding met soortgelyke Amerikaanse kragsentrales. DOO is 'n nieparametriese lineêre programmering-gebaseerde maatstaf tegniek wat gebruik word om vergelyking te tref met verskeie ander soortgelyke takke. Die historiese kragsentrale data wat in hierdie projek gebruik word, is verskaf in maandelikse frekwensie. Die kwaliteit van die data word bevraagteken. Dit bevat gemete brandstofverbruik waardes wat uit verhouding is met die werklike verbruikswaardes. Om hierdie rede word die data wat verkry is vergemiddeld. ‘n Sagteware program is ontwikkel om historiese kragsentrale data te analiseer en word ondervang deur die ontwikkeling van ‘n verwante databasis. Hierdie databasis sorg vir permanente storing en toegang tot die historiese kragsentraledata, terwyl die sagteware program alle relevante analise metodes en ‘n grafiese gebruikerskoppelvlak insluit. Die klassieke doeltreffendheid-evaluering metodes verskaf ‘n algemene oorsig oor die werklike prestasie van die kragsentrale, maar neem nie die kragsentrale se unieke omstandighede in ag nie wat veroorsaak dat die resultate dubbelsinnig van aard kan wees. Die metodes word beperk tot energie datastelle en kan nie bykomende faktore wat relevant is tot die kragsentrale prestasie assimuleer nie. Hoër data kwaliteit word aanbeveel om die akkuraatheid van die resultate te verhoog. ‘n M&V intervensie bevat ‘n energie-oudit voor en na die ED implementering. Die basislyn is voorimplementerings data en word gebruik om die positiewe impak van die implementering te evalueer. Regressie-analise is ondersoek as ‘n metode tot die verkryging van bykomende insig in die effek van bykomende faktore op algehele opwekkingseenheid doeltreffendheid en ook as 'n middel om die basislyn aanpassing in 'n M&V konteks te bepaal. Die regressie-analise studie bied nie beduidende resultate nie. Die verhoging van die kwaliteit van die gemete kragsentrale datastelle mag moontlik bruikbare resultate verskaf. Die gebruik van die DOO doeltreffendheid metode is effektief wanneer daar bykomende faktore by gewerk word tot die energie data en kan as n kort vergelykende oorsig van die prestasie tussen die verskillende kragsentrales gebruik word. Wanneer ‘n enkele kragsentrale oor n tydperk evalueer word kan die proses ook maklik ondoeltreffende periodes identifiseer. Bykomende insig is nodig om die bronne van hierdie ondoeltreffendheid te bevestig. DOO is dus nie 'n volledige vervanging vir klassieke energie-doeltreffendheid metodes nie maar eerder 'n nuttige aanvullende hulpmiddel van doeltreffendheid evaluering en verifikasie. Die akkuraatheid van die resultate is baie vatbaar vir die gehalte van die data wat gebruik word. Evaluering van individuele kragsentrale komponent insette en uitsette eerder as algehele kragsentrale insette en uitsette sou as grondslag van n toekomstige studie-onderwerp kan dien.

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