Operational hydropower scheduling with post-spot distribution of reserve obligations

The algorithm is implemented with a policy network (the action to take for a given state), a value network (describing how advantageous each state is), a target value network, and

Therefore, the main goal of this paper is to calculate and analyse the value of perfect information (VPI) of the residual reserve curves (hereafter referred to as the VPI-RRCs) and

4 show the total income from the spot market and the reserve capacity market for upward and downward reserve capacity respectively, for all hydropower plants delivering

In this context, we apply an algorithm based on stochastic dual dynamic programming (SDDP) to solve the nonconvex MTHS problem and show that the use of strengthened Benders (SB) cuts

5) The upward and downward expected regulation energy in bars and the upward and downward RTURs (expressed in percentage) in solid and in dashed lines, respectively, assumed

This paper describes how the recently developed stochastic dual dynamic integer programming (SDDiP) method can incorporate nonconvex environmental constraints in the medium- and

Stochastic dual dynamic programming (SDDP) is a state- of-the-art algorithm for long- and medium-term hydropower scheduling with a linear problem formulation.. A recently pro-

1) Forward Iteration: In the HPMS forward iteration the root node MS problem described in (2) is solved first to obtain a trial maintenance schedule. An upper bound is obtained from