Economic Dispatch of the Combined Cycle Power Plant Using Machine Learning

Abstract: Combined Cycle Power Plant (CCPP)s play a key role in modern powersystem due to their lesser investment cost, lower project executiontime, and higher operational flexibility compared to other conventionalgenerating assets. The nature of generation system is changing withever increasing penetration of the renewable energy resources. Whatwas once a clearly defined generation, transmission, and distributionflow is shifting towards fluctuating distribution generation. Because ofvariation in energy production from the renewable energy resources,CCPP are increasingly required to vary their load levels to keep balancebetween supply and demand within the system. CCPP are facingmore number of start cycles. This induces more stress on the gas turbineand as a result, maintenance intervals are affected.The aim of this master thesis project is to develop a dispatch algorithmfor the short-term operation planning for a combined cyclepower plant which also includes the long-term constraints. The longtermconstraints govern the maintenance interval of the gas turbines.These long-term constraints are defined over number of EquivalentOperating Hours (EOH) and Equivalent Operating Cycles (EOC) forthe Gas Turbine (GT) under consideration. CCPP is operating in theopen electricity market. It consists of two SGT-800 GT and one SST-600 Steam Turbine (ST). The primary goal of this thesis is to maximizethe overall profit of CCPP under consideration. The secondary goal ofthis thesis it to develop the meta models to estimate consumed EOHand EOC during the planning period.Siemens Industrial Turbo-machinery AB (SIT AB) has installed sensorsthat collects the data from the GT. Machine learning techniqueshave been applied to sensor data from the plant to construct Input-Output (I/O) curves to estimate heat input and exhaust heat. Resultsshow potential saving in the fuel consumption for the limit on CumulativeEquivalent Operating Hours (CEOH) and Cumulative EquivalentOperating Cycles (CEOC) for the planning period. However, italso highlighted some crucial areas of improvement before this economicdispatch algorithm can be commercialized.