Optimization Days 2024
HEC Montréal, Québec, Canada, 6 — 8 May 2024
TC2 - Session industrielle III
May 7, 2024 03:30 PM – 05:10 PM
Location: Procter & Gamble (green)
Chaired by Amira Dems
3 Presentations
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03:30 PM - 03:55 PM
Generation planning and operation at Hydro-Québec
Hydro-Quebec (HQ) is a vertically integrated utility that produces, transmits and distributes most of the electricity in the province of Quebec. The power grid it operates has a particular architecture created by large hydroelectric dams located far North and the extensive 735kV transmission grid that allows the generated power to reach the majority of the load located thousand of kilometers away in the southern region of Quebec. The specificity of the grid has led HQ to develop monitoring tools responsible for generating so-called stability limits. Those stability limits take into account several non linear phenomena such as angular stability, frequency stability or voltage stability. Since generation planning and operation tools rely mostly on mixed integer linear programming formulation, HQ had to adapt its tools in order to integrate stability limits into them. In this presentation, we will present the challenges it faced, especially considering its reserve monitoring tool and unit commitment tool.
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03:55 PM - 04:20 PM
Black box optimization with virtual sensors for turbine deformations during startup events
During startup, hydroelectric turbine blades undergo important deformations, which significantly reduce the device’s Remaining Useful Life (RUL) through fatigue. Directly measuring these deformations can only be done for a dozen of trials during the commissioning of the turbines. Our objective during these trials is to find the optimal startup controller parameters which minimize the deformation peak-to-peak range while reaching the synchronous rotation speed in given time. After gathering some initial startups, we train a neural model to predict the deformation envelope from the turbine’s startup trajectory in the 2-D space defined by the rotational speed and the wicket gates opening. This model, coupled with a turbine dynamics surrogate simulator, defines a black box. Given a set of startup parameters, it outputs a cost based on the deformation amplitude, the model’s uncertainty, and the time to reach the synchronous speed. The NOMAD optimizer is then used to find the optimal parameters. First, in an active learning phase, the cost considers the model’s uncertainty so that the optimized parameters, when tested on the turbine, provide relevant data to improve the deformation model. Then, we only consider the deformation amplitude and time to synchronous speed to output the optimal startup parameters.
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04:20 PM - 04:45 PM
Resource-constrained transmission maintenance scheduling problem at HQ
The Transmission Maintenance Scheduling (TMS) is a strategic problem in asset management for electrical utilities around the worlds. It consists in generating an annual plan for the preventive maintenance of electric power transmission equipment. The resulting maintenance plan must ensure a continuous power flow during maintenance operations. It must also satisfy various constraints such as the stability of the network and the availability of skilled teams to fulfill the planned maintenance tasks. The proposed TMS problem is part of an important project for asset management at Hydro-Quebec (HQ). It is a variant addressing the human resource allocation constraints to ensure the feasibility of a maintenance plan (respecting the available number of technicians in each period). The problem is solved using a constraint-satisfaction approach and experiments are carried out on a HQ power subnetwork, with limits on the capacity of available electricians.