Re: MOST preventive security

[Carlos E Murillo-Sanchez]

Dear Igor, please see comments below. Igor Verbruggen wrote: Dear all, � I am attempting to use MOST to formulate a DC security constrained OPF, with preventive and corrective redispatch/curtailment. If I understand MOST�s approach to security correctly, it is aimed at determining a base case dispatch (e.g. a day-ahead schedule), and corresponding contingency state dispatches (e.g. a real-time dispatch) that are reachable from the base case (within ramping and reserve constraints). Hence, the approach is mostly corrective, yet some preventive action is also taken as the optimization attempts to converge to a secure base case dispatch. By assigning the PositiveActiveDeltaPrice and NegativeActiveDeltaPrice, it is possible to set a preference for the generators to use for corrective actions, by penalizing them more or less compared to the others. There is one key feature in MOST's treatment of the day-ahead optimal quantities for generators which is unlike other optimal stochastic planning models in the literature.� These day ahead optimal quantities (called contracted quantities in MOST) do not necessarily correspond to base case dispatches.� In MOST, there may be several base case dispatches for a given time period, each representing a realization of an uncertain parameter associated to continuous-varying uncertainty such as wind or solar resources.� Thus, there isn't a single base case day ahead dispatch.� Instead, MOST computes the optimal day-ahead contracted quantities (the optimal contract posture, Pc in MOST) from which it will be feasible to reach any realization of these uncertain parameters during operation.� This decoupling of the notion of "base case dispatch" and "optimal day ahead contracted quantities" is a major conceptual characteristic in MOST. Typically, when modeling wind, you will have several base cases in a time period, each with a different PMAX value for the wind generator.� If there is a curtailment penalty for wind, the corresponding cost function could even have negative marginal cost. � Now, in my case the grid is being supplied by a number of wind generators that can be curtailed to any value below PMAX. When I remove all corrective costs, and set the maximum contingency reserves to 0, the dispatch in base case and all contingency states will be �preventively� secure (and equal). However, the dispatch is made entirely on a market basis (all curtailments are driven by the generator cost functions). I would therefore like to add penalty costs, say, a curtailment cost per MW that the base case deviates from PMAX. That way, for preventive actions it is possible to control which generators are most likely to curtail. Reserves are meant to be used for dealing with the other kind of uncertainty considered in MOST: discrete events such as line and generator outages, but they also define a "range" over which a generator can be redispatched (from the contracted quantity) during operation in order to reach a dispatch that is appropriate for a given renewable source realization.� In MOST's model, it doesn't make much sense to set the limits of the reserves to zero for renewable sources, since they will vary their dispatch according to the realization of the resource.� If reserve limits are set to zero the only feasible solution will be to uniformly set the dispatch to the minimum of the PMAX values considered for the resource - hardly an optimal choice.� Instead, normally the reserve limits for these generators are� set free (very large values). It is also important to see that for the lowest marginal cost resources, their redispatch deltas actually constitute flexibility that will be exacted from the system in order to accommodate the low cost resource - the contrary of the usual notion of flexibility. Is there a straightforward/documented way to edit the objective function and achieve this? I have seen a similar question previously (https://www.mail-archive.com/matpower-l@cornell.edu/msg07231.html) where the response was to add new variables / constraints in the appropriate places of the most.m file, but I struggle to find documentation on how this works exactly. � Kind regards, Igor Verbruggen If you use the multiple scenario/base case mechanism in MOST to model different wind scenarios, and impose a negative cost (curtailment cost) on wind generators, you will likely get what you are looking for: wind generators generally dispatched to their maximum capacity across wind scenarios, with conventional generation providing the required flexibility for this to happen.� Unless, of course, some technical hard limit is hit and there is an actual feasibility-induced curtailment. Regards, Carlos.