Using of a cost-based unit commitment algorithm to assist bidding strategy decisions

Abstract

Abstract-The paper describes a procedure developed to assist a generating company in choosing the most convenient bidding strategies for a day-ahead electricity energy market. According to the proposed method, the profit maximization problem is transformed into a minimization problem that can be solved by a traditional hydro-thermal unit commitment program after implementing a few modifications. The paper describes t he modifications introduced in a unit commitment program based on the Lagrangian relaxation approach and on a disaggregated Bundle method for the solution of the dual problem. It also presents some results obtained for a realistic data set of hydrothermal power plants. The results are discussed in order to emphasize how the method can be applied to assess the bidding strategy choice of a given company.

Tighter Approximated MILP Formulations for Unit Commitment Problems

“…The time period "0" is used for indicating the initial conditions of the power system; note that we assume knowledge of the complete state of each unit prior to the beginning of the current operation, that is, its commitment u i 0 and its generated power p i 0 . For the sake of minimum up-and down-time constraints (5), (6), as well as for the computation of time-dependent startup costs (if any), it is also necessary to know for how long each unit has been on or off prior to time period 0.…”

“…Closely representing the actual operating behavior of generating units within mathematical optimization models is crucial for being able to effectively coordinate the production of the generating system taking into account each unit's characteristics [16], which is of increasing importance in the ongoing liberalization of the electricity market in many countries [13]. Indeed, while UC, in the form treated in this paper originated from the era of monopolistic producers, it has numerous applications even in the liberalized regime; furthermore, algorithmic approaches developed for the "classical" UC can usually be easily extended to forms of the problem arising in a market environment [1,13,6].…”

“…Again, the introduction of these further elements should not impact on the relative efficiency of the different approximate formulations tested in this paper. The UC model here considered, while having been historically motivated by the centralized decision environments prevalent in the past, is well-suited also for being employed in today's free market regime, both at the stage where GenCos need to optimize their production schedule once that their own load profile has been established by the market procedures, and within approaches for computing optimal bidding strategies [1,13,9,6]. …”

Large-scale Unit Commitment under uncertainty

“…Finally, in Borghetti et al (2001) Lagrangian approaches are compared with Tabu Search heuristics, and an improved primal phase is proposed in Borghetti et al (2003a). The approach is later extended to the free-market regime (Borghetti et al 2003b) and to the handling of ramping constraints (Frangioni et al 2008) via the use of the specialized DP procedure of Frangioni and Gentile (2006b). An hybrid version also using MILP techniques is presented in Frangioni et al (2011).…”

Solving Nonlinear Single-Unit Commitment Problems with Ramping Constraints

“…These Lagrangian schemes are among the most efficient solution techniques for this class of difficult, largescale mixed-integer nonlinear problems; see, e.g., Bacaud et al (2001), Belloni et al (2003), Borghetti et al (2001), Borghetti et al (2003a), Madrigal and Quintana (1999), Zhuang and Galiana (1988) among others, not least because they are easily extended to accommodate contributions from other types of generating units, such as hydroelectric ones. Also, Lagrangian techniques can be relatively easily extended to consider constraints arising from selling the generated power on a free market as in Borghetti et al (2003b).…”