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This paper develops optimal bidding strategy for operating multi-unit pumped storage power plant in day-ahead electricity market. Based on forecasted hourly market clearing price, a multistage looping algorithm to maximize the profit of multi-unit pumped storage plant is developed considering both spinning and non-spinning reserve bids and meeting the technical operating constraints. The proposed model is adaptive for the nonlinear three-dimensional relationship between the power produced, the energy stored, and the head of the associated reservoir. Evolutionary tristate particle swarm optimization (ETPSO) based approach is also proposed to solve the same problem, combining basic particle swarm optimization (PSO) with tri-state coding technique and mutation operation. The discrete characteristic of a pumped storage plant is modeled using tri-state coding technique and genetics based mutation operation is used for faster convergence in getting global optimum. The proposed approaches are applied with an actual utility consisting of four units. Experimental results for different operating cycles of the storage plant indicate the attractive properties of the ETPSO approach in a practical application, namely, a highly optimal solution and robust convergence behaviour.