Generally, four successive and linked parts comprise optimization problems. (1) Problem statement at which you clearly and briefly state linguistically your objective(s) and surrounding constraints associated with your problem. (2) Problem formulation at which you transform the problem statement to either mathematical statements, or physical experiment(s). (3) Optimally conditions at which you derive the conditions that makes your objective(s) optimal (i.e minimum, or maximum) under the given constraints. In this stage you may consider only the equality constraints, while the satisfaction of the inequality constraints are to be considered in the next stage using appropriate techniques. For example, you may read the Kuhen-Tacker optimally conditions for the economic dispatch problem. In the last stage i.e the fourth stage, a suitable method is used for determining the problem solution based on the determined optimality conditions. These methods may be of any classical, or advanced iterative based AI or meta-heuristic methods.
The stated approach can be applied to the DG allocation problem considering a set of objectives and constraints. Searching the literature will result in a huge number of very useful publications. Good luck
How to model cost function of DG? In literature, the cost function of conventional generator is modeled quadratically but wind,solar etc is free, how can their cost function be modeled?