Dear all,
I am trying to use callback functions in the formulation stage to consider a user-defined quadratic objective. Specifically, my objective function does not consider the generation cost, and it only considers maximizing the Euclidean distance between the decision variables and several given points (a detailed expression is given in the picture below). To realize the above goal, I set all the coefficients in mpc.gencost to 0. I also use om.add_quad_cost function in the formulation stage to add the quadratic function. My questions are: 1) Am I in the right way to ignore generation cost by setting all the coefficients to 0? 2) If I use om.add_quad_cost several times in my userfcn, does it mean that the objective is summed up sequentially? And does the Q matrix support the negative-definite matrix? In my case, I want to maximize the Euclidean distance and hence, the Q matrix will be negative definite. 3) Sometimes I will encounter this warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 2.691450e-17. However, this optimal power flow should have at least one solution because I will get one if I simply set the objective function as 0. Do you think this warning is caused by my objective function? And if so, does it arise from the nonconvexity of my quadratic objective? Any help will be appreciated. Thank you in advance. Best, Yuanxi Wu
