Invited Session Tue.2.MA 550

Tuesday, 13:15 - 14:45 h, Room: MA 550

Cluster 18: Optimization in energy systems [...]

Stochastic programming models for electricity generation planning


Chair: Michel Gendreau



Tuesday, 13:15 - 13:40 h, Room: MA 550, Talk 1

Oscar Mauricio Carreno
Developing optimization software for Colombian power system planning

Coauthors: Jaime A. Castillo, Carlos M. Correa


Motivated by the liberalization process of electricity markets led by Chile in 1982 and followed by England and Wales in 1990 and Norway in 1991, Colombia restructured its electricity industry in 1995 evolving to a novel electricity market in the region based on price offers. From then until now, several market rules have changed and evolved, causing modifications in the optimization planning models used for system operation. As a result, the system operator has improved and developed new models and strategies in order to be timely at the forefront of the changing market. XM, Colombian Independent System Operator (ISO), has led this assignment, using state-of-the-art commercial optimization software. Therefore, the optimization models used by XM to plan short- and very short-term have been developed by its own I+D team. This paper presents both, the IT and mathematical formulation, for the most important models developed and daily used by XM, and also showing the benefits and gains derived from their usage that have allowed XM to be pioneer among others Latin America's ISOs.



Tuesday, 13:45 - 14:10 h, Room: MA 550, Talk 2

Raphael Eduardo Chagas Gonçalves
Analyzing multistage stochastic optimization methods to solve the operation planning problem of hydrothermal systems

Coauthors: Edson L. da Silva, Erlon C. Finardi


The operation planning of hydrothermal systems is, in general, divided into coordinate steps which have different horizons and prioritizes distinct details of the modeling. The medium-term operation planning (MTOP) problem, one of the operation planning steps of hydrothermal systems and the focus of this work, aims to define the weekly generation for each plant, regarding the uncertainties related to water inflows to reservoirs, to obtain the minimum expected operational cost over a specific period. Solving this problem requires a high computational effort and, consequently, the use of multistage stochastic programming algorithms. Therefore, the main purpose of this work is to present a comparative study about the performance of different multistage stochastic optimization methods applied to the MTOP: Nested decomposition (ND) and the progressive hedging (PH) method. With respect to PH method, the algorithm properties and the problem features are studied to assess suitable decomposition schemes to obtain lower CPU time. To evaluate the performance of the both algorithm regarding its particularities, the Brazilian hydrothermal system is studied.



Tuesday, 14:15 - 14:40 h, Room: MA 550, Talk 3

Michel Gendreau
Midterm hydro generation scheduling under inflow uncertainty using the progressive hedging algorithm

Coauthors: Fabian Bastin, Pierre-Luc Carpentier


Hydro-Québec, one of the largest electric utilities in North America, generates virtually all of its power supply using hydro plants. A key problem faced by planners is the midterm generation scheduling problem (MGSP), solved on a weekly basis, in which generation targets must be set for controllable hydro plants in order to manage reservoir energy storage efficiently over the coming months.
Reservoir inflows are the main source of uncertainty to account for in the decision-making process. In this paper, we model reservoir inflow uncertainty through scenario trees. We tackle the MGSP using the progressive hedging algorithm (PHA) (Rockafellar and Wets 1991). In our model, hydroelectric generation is given by concave piecewise-linear functions of the upstream reservoir storage and of water release. A key feature of our implementation of the PHA is a new penalty parameter update formula.
We assess our model and algorithm on Hydro-Québec’s power system (21 large reservoirs and 25 hydro plants) over a 93-week planning horizon with several load levels. Reservoir inflow uncertainty is modeled by a 16-scenario tree. Computational results show that the proposed approach is promising.


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