Abstract: In the medium-long term electricity market environment, how to meet the demands of its own market benefits and the end of period energy storage control has become one of the core issues of multi-objective decision-making for cascade hydropower stations, which is related to the stable operation of cascade hydropower stations and the safety of the power grid. Considering the impact of the uncertainty of incoming water within the year on the market benefits and end of period energy storage control goals of cascade hydropower stations, as well as the different dimensional factors between the goals, two objective functions are proposed: when the incoming water within the year meets the market benefits and end of period energy storage demand goals of cascade hydropower stations, the goal is to maximize the normalized value of the optimization results of both; On the contrary, with the goal of minimizing the sum of squared normalized values that deviate from the demand objectives, the "hedging" feature between demand objectives is utilized to reduce overall losses. Firstly, based on the historical runoff data of the watershed, analyze the frequency distribution characteristics of incoming water, and construct a scene set that conforms to the historical runoff characteristics as input conditions through the correlation solution set method; Secondly, nonlinear optimization models are constructed based on the proposed objective functions, and the generation characteristic curves of cascade hydropower stations are described through multiple term expressions, achieving efficient solutions; Finally, based on the objective function conditions, the optimization calculation results are selected to obtain the annual available water quantity target decision pair, and the market benefit target and end of period energy storage target optimization decision sets are constructed separately. A detailed analysis was conducted on a cascade power station group in a southwestern watershed, including target weight coefficients, comparison with conventional scheduling rules, prediction of electricity price errors, and different combinations of demand objectives. The results showed that the model proposed in this paper can provide optimal target decisions based on the degree to which the inflow conditions meet the demand objectives; different weights will affect the focus of the target in the hedging stage; The uncertainty of electricity price errors greatly affects decision-making during the hedging phase, increasing overall losses; The size of market targets has a significant impact on the hedging stage range of the curve.