A computationally efficient algorithm for the provision of a day-ahead modulation service by a load aggregator

We study a decision making problem faced by an aggregator willing to offer a load modulation service to a Transmission System Operator (TSO). In particular, we concentrate on a day-ahead service consisting of a load modulation option, which can be called by the TSO once per day. The option specifies the maximum amplitude of a potential modification on the demand of the loads within a certain time interval.

We consider the specific case where the loads can be modeled by a generic tank model whose inflow depends on the power consumed by the load and outflow is assumed to be known the day before for every market period. The level of the reservoir at the beginning of the market day is also assumed to be known.

We show that, under these assumptions, the problem of maximizing the amplitude of the load modulation service can be formulated as a mixed integer linear programming problem (MILP). In order to solve this problem in a computationally efficient manner we introduce a novel heuristic-method. We test this method on a set of problems and demonstrate that our approach is orders of magnitude faster than CPLEX - a state-of-the-art software for solving MILP problems - without considerably compromising the solution accuracy.