Employing demand prediction in pump as turbine plant design regarding energy recovery enhancement

Abstract

Nowadays, Pump as Turbine (PAT) technology is broadly accepted as a proper renewable generation tool, especially in small capacities. PAT plant design is dependent on the hydraulic characteristic of the site. Flow measurement devices are not widely used in water systems and average values are estimated to be used in the design procedure, which can harm the anticipated performance of the plant. In this work, demand prediction is employed to provide a full demand pattern for the studied network. The objective function of PAT selection process in Genetic Algorithm is defined as the combination of capability, adaptability, and reliability obtained as 0.4427 and 0.4121 in the predicted demand and average demand cases, respectively. The maximum daylight generated power in the predicted demand case is 66.18 kW that increased 19.37% compared to 55.44 kW in average demand. The prediction of demand pattern offers the possibility of minimizing the variation of plant discharge and avoiding drastic efficiency drops of PAT in off-design situations. A fixed discharge is provided by using a flow control valve, which considerably improves the energy recovery. The power generation and efficiency enhanced 39.04% and 36.09% compared to the variable flow rate, respectively.