Design and control of a hybrid power supply

Daniel, Fransisca Muriel (2020-03)

Thesis (MEng)--Stellenbosch University, 2020.

Thesis

ENGLISH ABSTRACT: A hybrid power supply (HPS) is the combination of two or more power sources as one single supply. An HPS is ideal for off-grid areas to provide sustainable and stable energy to improve the quality of life for the users. Due to the stochastic and intermittent nature of weather-dependent power sources, combining these sources increases the complexity of the design and control of an HPS. The different configurations in this thesis consider PV-modules, batteries, generators and a limited grid connection. To solve the design problem, a genetic algorithm (GA) is implemented. The results are compared with commercially available HOMER software to highlight the differences between the two design methods. Three objectives are considered as part of the optimisation: technical, financial and environmental. The GA assesses different equipment configurations and sizes to not only look for a viable option but also a feasible configuration of different power sources. The algorithm clearly shows how the addition of more power sources increases the HPS’s capacity factor and decreases the overall financial costs of the plant. A trade-off analysis between the different configurations is d one. The GA can be seen as more robust than HOMER as it allows for user-specified constraints. HOMER can only assess one type of component (PV-module, battery, etc.) at a time, rather than looking at various options of the component. The control system is implemented using a model-free Q-learning reinforcement learning (RL)-based controller which is compared to two baselines, random action and rule-based. The RL-based control system has no prior knowledge of how the system interacts and only learns through reinforcements such as penalties and rewards. An Internet of Things-approach is added to increase the efficiency of the controller by using weather predictions to aid the RL-controller. The RL-based controller did not outperform the rule-based controller but did show improvement over the random action controller. The results indicates that the RL control system successfully minimised the loss of power supply and optimised the costs by using as much PV as possible. RL-controllers can be used as a feasible means of controlling an HPS. IoTbased application increased the utilisation of the PV and reduced the loss of power supply. The IoT-based implementation did not outperform the rulebased controller, but showed that IoT-methods can be exploited to increase the efficiency of controllers.

AFRIKAANSE OPSOMMING: ‘n Hibriede kragbron (HKB) is die kombinasie van twee of meer kragbronne. Landelike en afgeleë areas is ideale voorbeelde waar HKBe elektrisiteit aan die verbruikers kan verskaf. Bronne wat afhanklik is van weersomstandighede se energie-uitset is onvoorspelbaar en afwisselend. Dit bemoeilik die ontwerp en beheer van ’n HKB. Die verskillende komponente van ‘n HKB wat in hierdie tesis oorweeg word is, onder andere, PV-modules, batterye, generators en ‘n beperkte kraglynverbinding. Vir die komplekse kragbronintegrasie is ’n genetiese algoritme (GA) geïmplimenteer. Die GA se resultate is vergelyk met die kommersiëel-beskikbare sagtewareproduk HOMER. Die optimeringsproses het drie doelwitte: tegnies, finansieël en omgewingsimpak. Dit ondersoek nie net die mees lewensvatbare opsie nie, maar ook ’n haalbare gebruik van verskillende kragbronne. Die resultate van die GA het duidelik aangetoon dat addisionele kragbronne die HKB se kapasiteitsfaktor verbeter, terwyl die totale finansiële koste verminder. Verdere analise van die verskillende HKB’s is ondersoek om meer duidelikheid te gee oor die verskillende aspekte vir beleggers betrokke by die keuse van hernubare projekte. Die GA is meer robuust en buigsaam vir ’n HKB-ontwerp omdat dit addisionele verbruikersbeperkinge in aanmerking neem. In teenstelling, ondersoek die program HOMER net een komponenttipe (bv. PV-modules, batterye, ens.) op ’n slag, pleks daarvan om verskillende komponentopsies te oorweeg. Die beheerstelsel is gebaseer ‘n modelvrye, Q-leer versterkingsleer (‘reinforcement learning’) (RL) algoritme. Hierdie beheerstelsel is met twee maatstaafbeheerstelsels, ewekansige (‘random’) en reël-gebaseerd, vergelyk. Die RL-beheerstelsel het geen kennis van die stelselinteraksie nie en die proses van leer is deur ‘n metode van sogenoemde ‘beloon-en-straf’. Die doeltreffenheid van die beheerstelsel kan verder verbeter word deur middel van ’n ‘Internetof-Things’-benadering (IoT) deur gebruik te maak van addisionele inligting soos weervoorspellings. Die resultate van die reël-gebaseerde beheerstelsel het aangetoon dat dit beter as die RL-beheerder presteer het om die kragverliese en kostes te verminder. Verdere ondersoek van die RL-beheerder teenoor die ewekansige beheerder het getoon dat die RL-beheerder die kragverliese beperk het, asook om die bedryfskostes te verminder deur die hernubare kragbron optimaal te benut. Dus kan die RL-beheerder as ’n lewensvatbare beheerstelsel vir ’n hibriede kragbron aangewend word. Die gebruik van IoT het die verbruik van PV teenoor die alleenlik RL-beheerder verbeter. Sodoende is die kragverliese van die IoT-implimentering ook verminder, maar nie tot op die vlak van dit wat verkry is deur die reël-gebaseerde beheerstelsel nie.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/107863
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