## 3 Optimization Methods  
The objective problem refers to obtaining the optimal solution of objective function through certain optimization algorithm and mathematical models [32]. The objective function of the single-objective problem (SOP) is unique. For example, in the traditional power system dispatching, the total amount of coal consumed is minimized by controlling the output and start-stop of the generator set. When there are multiple objective functions for optimization, it is called a multi-objective problem (MOP). The focus of each goal in a multi-objective problem is often inconsistent, that is, when optimizing one objective, it is often at the expense of another or other objectives. Therefore, the multi-objective problem is to maximize the optimization of each sub-objective under the limitation of several conflicting or interacting sub-objectives [33]. For hydropower photovoltaic joint dispatching, it is a very complex decision-making system to consider the benefit and risk allocation of multi-agent and multi-time scale in the joint dispatching process. To ensure the basic benefits of all parties involved in dispatching on the basis of controllable risk, it is necessary to introduce a \"hedging mechanism\" to balance the risk and benefit in the dispatching decision-making process. On the basis of ensuring that the risk is controllable, it is necessary to introduce a \"hedging mechanism\" to balance the risks and benefits in the scheduling decision-making process to ensure the basic benefits of all parties involved in the scheduling.  
In hydropower photovoltaic joint dispatching, there is not only the problem of time-type two-stage conflict, but also the problem of multi-subject conflict in space. The traditional multi-objective optimization algorithm can only find a set of non-inferior optimization schemes and cannot comprehensively consider the hedging problem in hydropower photovoltaic joint scheduling. Therefore, it is necessary to change the solution process of the traditional optimal scheduling model and find a hydropower photovoltaic joint scheduling model and solution method suitable for the multi-objective hedging mechanism. It is necessary to ensure the reliability of the decision-making scheme, but also to ensure the optimality of the scheme, so as to solve the conflict problem in the joint scheduling to the greatest extent and obtain the balance point of the interests of each subject.  
According to the above description, the solution process of the hydropower photovoltaic joint dispatching model under the hedging mechanism established in this paper is divided into two parts. In the first part, the non-inferior set scheme of hydropower photovoltaic multi-objective joint dispatching is obtained through the high-dimensional MOEA/D optimization algorithm of the greedy strategy. This scheme is a series of feasible results and optimization decisions need to be made. The second part is to calculate the marginal benefit value of hydropower and photovoltaic according to the decision variables corresponding to non-inferior sets under the hydropower photovoltaic joint dispatching hedging mechanism, so as to obtain the optimal dispatching scheme under the hedging mechanism.