In accordance with the 1997 white paper, the EU set a target for 2010 to meet 12% of energy consumption needs and 22.1% of electricity consumption from renewable sources. Indicative targets were established for each Member State in Directive 2001/77/EC. However, the results were far below expectations. Consequently, in its communication of 10 January 2007, entitled “Renewable Energy Road Map Renewable energies in the 21st century: building a more sustainable future”, the Commission proposed two binding targets for 2020: 20% of EU energy consumption should come from renewable energy sources, and 10% of fuel consumption for transport should come from biofuels. Furthermore, a new legislative framework was drawn up.
This framework has contributed to a sharp increase in use of renewable energies, particularly wind and solar power, in electricity generation. To compensate for the stochastic nature of these energy sources, which depend on meteorological conditions, energy sources are needed that can stabilize the electricity network, providing energy when there is no sun or wind, or storing energy at times of low consumption. Hydraulic turbines and pump turbines are key technical components to contribute to the stability of the grid and to introduce intermittent renewable energy.
In this context, hydroelectric energy contributes to renewable energy production and provides a highly dynamic form of energy storage that enables an injection of photovoltaic or wind energy into the grid whilst maintaining its stability. To meet these functions, turbines must be adjustable to enable minimum and maximum power output. This is known as “flexible power generation”. In this context, hydraulic power generation is a key factor, as it can provide energy with an immediate response time and adapt to the required amount of energy when the supply is insufficient. This can be achieved because a turbine in a hydroelectric power plant can start up in a short time and produce energy at the power that the controller determines.
As in all turbomachinery, hydroelectric turbines function in optimum conditions when they work around their design point. Outside of these power ranges and at extreme operating points such as low part load or overload, complex flow phenomena appear with high turbulence and cavitation, which produce extremely high dynamic forces on the machinery. Under these operating conditions, deformation and strains are produced in rotors, as well as vibrations that could cause damage or considerably reduce useful life.
The Centre for Industrial Diagnostics and Fluid Dynamics (CDIF UPC) has worked on the impact of this operation on turbines of the MICA hydroelectric plant, located 135 km from Revelstoke, in the area of British Columbia (Canada). MICA is operated by BCHydro and has the capacity to generate 2,805 MW from its six turbines. At the time of its construction, in 1974, it was the second largest hydroelectric power station in the world. The study is part of the European HYPERBOLE project.
One part of the project involved installing a monitoring system; the other part was comprised of experimental measurements of one of the power station’s turbines that can generate almost 500 MW at maximum power. Over 70 channels of vibrations, movement, strain, stress on the rotor, coupled fluctuations in the axis, noise and power were measured simultaneously to study the behaviour under various operating conditions. In addition, a numerical model was created to study the dynamic behaviour of the group when the dynamic forces of the fluid are applied to the turbine structure, to simulate the deformation and vibrations that are produced. This numerical model was compared with the experimental tests carried out with machinery in operation.
On the basis of these data and the model, an improvement was made in the turbine’s monitoring system. The information gathered by the sensors was recorded in the monitoring unit of the power plant, which can be accessed remotely. These data were then used to calculate control parameters and “indices of health” of the machine, which indicate for each area of operation the effects on the rotor (stresses), erosive cavitation, wear on bearings, and the early detection of unstable phenomena that could cause fluctuations in the power supplied to the grid.
In addition to detecting incipient faults and cutting maintenance costs, this smart monitoring can estimate for each operating area the effects on machinery in terms of wear, erosion or fatigue. As a result, the cost of flexibility can be assessed, that is, the deterioration and useful life can be estimated according to the operating conditions. This prescriptive analysis provides information so that the manager can make supply decisions depending on the market price, and take advantage of one off opportunities or reduce future risk to take better advantage of the groups.