Load Optimization Through Scale Level Monitoring and Real Time Response: A Case Study of Olkaria II

Abstract

Kenya targets industrialization status by the year 2030 which needs reliable and adequate source of energy. Previously, half of the electricity supply in Kenya was met by hydropower but demand for Country’s house hold energy mainly wood and charcoal put pressure on Country’s forest cover, reducing it to 3% of the total land and this has severely affected electrical hydropower potential. Geothermal power is thus preferred as it offers dependable, reduced green gas emissions, meet diversification needs and provides least cost base load mode of energy generation. However, exploitation of this resource is face with challenges of scale formation on the steam lines and most surface equipment, leading to reduced and expensive production. To benefit effectively from geothermal resources, careful management of steam field is crucial to ensure that the resources are not depleted and that hazardous chemicals are properly managed using effective maintenance strategies. However, the practice in most geothermal fields is, waiting for scales buildup, ‘manually’ monitoring the change in parameters until production reduces to prompt removal. Dependency on human intervention to determine when ‘enough scales’ have formed so that they can be removed, is unreliable, costly and has led to plant shut down and in some cases, complete abandoning of wells due to clogging. This study determines how changes in geothermal parameters due to scaling can be used to quantify energy losses. Purposive sampling will be used to collect data from Production Data Sheets of Unit 1 of Olkaria II for the period 1 Nov 2018-19th Jan 2019, when scales had formed and for the period 28th Feb -14th March 2019 after the scales were removed for information comparison. Data collected will be analyzed using MATLAB software version R2017 to come up with a model that will help determine significant parameters associated with reduced production due to scaling. The model can be used to reliably come up with an automated monitoring and scale formation detection system in the geothermal system, that will prompt cleaning at the ‘right’ time to improve efficiency of production and reduce wastages.