Optimising plants with hydro-meteorological data10 September 1999
Norbert Beltz and Horst Weber* look at how hydro power plant operators can refine their water level forecasting techniques to increase efficiency
The efficiency of hydro power plants installed on rivers depends on water levels. For this reason, level gauges are used to plot expected trends so that electricity consumption can be managed according to the resources that are available for power production.
The quality of this information depends on the quality and the reliability of the data used to forecast water levels. This data can be collected by a network of water gauges at different stations along a river, and along the main side rivers in its catchment area. The data from the different stations are transmitted to the power plant where they are combined with statistical and empirical models to draw up a short-term water level forecast. To increase the quality of the data and the length of the forecast it is necessary to introduce improved hydro-meteorological data.
The use of hydro-meteorological data will also be valuable when managing water resources in large river basin areas with high temporal and spatial varieties of precipitation. In order to augment the flow of the river, large storage reservoirs need to be constructed in the headwaters of the basin. A network of stream gauges and rain gauges, in combination with historical rainfall probabilities, will help to increase the quality and effectiveness of hydro-meteorological data.
The use of hydro-meteorological data combined with precipitation forecast models is recognised as an important tool for early warning systems in flood forecasting. Such systems operate in many countries. Hydro-meteorological data extends the time base, the quality of the forecast and can be adapted to suit small catchment areas up to mid-sized regions of some hundred square kilometres.
Cascade power plants
Taking the example of a cascade of hydro power plants comprising eight individual plants with two to three units each, the co-ordination of water resources with electricity demand depends on unit availability and on the water level upstream of the power plant.
To optimise the use of water resources, plant operators used to take the level measurement of the plants to find the available quantity, and compare it with electricity demand, started and stopped units and spilled water.
For this procedure there were software tools available. These were based on commercial programs providing penalty points for each reservoir. The level in each power plant reservoir gave information about the available energy in the entire cascade.
The diagram below left shows how control optimisation for such a hydro power plant is now carried out. According to the daily power demand given by a load dispatch centre, the required production energy per power plant is calculated. The calculation takes into account the number of available units at each plant and the priorities of the cascade. This means the upper plant provides its maximum generation first to avoid wasting water over the spillways.
Further parameters are established by the economic operating procedures of the hydro power units, which indicate that a unit should operate for at least 30min to avoid the start/stop losses and maintenance problems occurring from too many starts and stops. Also, the production of one unit has to be outside the vibration range.
A study was carried out on site to test, simulate and compare the results from both methods of optimising water resources.
During the test good fit was found between the 24-hour forecast and execution. Longer time frames are not yet possible as the water inflow in the cascade forecast has to be measured earlier. This means that rainfall and inflow from side rivers has to be measured and registered to achieve a forecast of a few days to several weeks. An improvement will only be possible by the extended use of hydro-meteorological data and information.
Availability of information
The successful integration of hydro-meteorological data and information is based on the availability of appropriate measuring data and forecast models, such as:
•An automatic precipitation measuring network and data input in real time.
•A quantitative determination of regional precipitation in real time.
•A quantitative regional precipitation forecast.
•A quantitative forecast of snow cover development.
•Weather radar and satellite data.
In general, national weather or hydro-meteorological services operate meteorological data processing systems which integrate actual measuring data and forecast models to produce appropriate hydro-meteorological data and products. The data assimilation starts with the collection of international data from ground stations, vertical soundings and remote observations. Data is combined and integrated in different models to generate products that are tailored to the user. In the future, IT improvements will allow national meteorological and hydrological services to provide such data and information in real-time to the users.
Development of a project
The development of a project which uses improved hydro-meteorological products in the water management of hydro power plant requires close co-operation of the plant operator with the appropriate authorities.
Precipitation forecasting and assessing its influence on the water levels requires a basic knowledge of the hydrological conditions in the region under investigation. This comprises:
These properties will be integrated with the measured and predicted precipitation and other relevant data, such as soil moisture, into runoff models to determine the part of the precipitation in a catchment area which contributes to the river water levels.
Based on the information gathered during the data collection phase, in the next project phase the quantitative prediction methods and forecast models should be redeveloped and adopted to specific conditions.
In the next project phase the precipitation quantification and forecast models should be verified and, if necessary, adjusted by comparing the model data with real measurements. The most convenient and cost-effective way will be simulation using historical data. The measuring data should include precipitation measurements at already existing representative sites which will be used later on for model inputs and water level measurements.
In the final project phase the extension of the measuring network (precipitation network, water level gauge system) must be installed according to the requirements of the necessary input data for the forecast models.
Economics and operation
The integration or the extended use of hydro-meteorological information into water resource management of a hydro power plant depends on the costs for such a system in relation to the benefits, and how fast a return on investment can be achieved. Water management systems with level measurements at the reservoirs and refurbished control systems like the cascade system described were believed to increase the efficiency of hydro power plants by about 4-5%. The integration of improved hydro-meteorological products will give the opportunity for further efficiency increases of at least 4-7%.
The operation of hydro-meteorological systems needs strong support from national weather services. The development of the necessary tools has to be integrated in the infrastructure of the weather service. National weather services operate a verified forecast model hierarchy in which the regional precipitation forecasts will be nested. The most important operational aspect will be the real-time data transfer to the water management facilities of a power plant cascade.
There are many prerequisites available which will give the opportunity that proposed projects with the use of hydro-meteorological data will be successful. The integration of the existing information and available products from the national weather services, in connection with extended tailored hydro-meteorological information and the necessary technical upgrade of hydro plants’ control facilities, gives the chance to make hydro power production more efficient.