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Improving the value of variable and uncertain Power Generation in Energy Systems (VaGe)

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VaGe project’s objective is to improve operational decision making in the power systems when considering the variability and uncertainty of wind, solar, water inflow, heat and electricity demand, their correlations and possible sources of flexibility.

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Improving the value of variable and uncertain Power Generation in Energy Systems (VaGe)

  1. 1. VAGE – IMPROVING THE VALUE OF VARIABLE AND UNCERTAIN POWER GENERATION IN ENERGY SYSTEMS VAGE FACTSHEET OCTOBER 2016 For more information, please contact VaGe Project Leader Hannele Holttinen / VTT Tel. +358 (0) 207225798 Email: hannele.holttinen@vtt.fi Sami Niemelä, Project Manager / Finnish Meteorological Institute (FMI) Email: sami.niemela@fmi.fi VaGe project consortium Project organizations are VTT Technical Research Centre of Finland Ltd and Finnish Meteorological Institute (FMI). http://clicinnovation.fi/activity/vage/ Project started 01.09.2015 and runs until 31.12.2018 The energy sector is under transformation. The share of variable power generation, such as wind power and photovoltaic (PV), is increasing rapidly. Their output is dependent on weather and therefore much more variable and uncertain than the output from more conventional power generation. Variability and uncertainty bring challenges to power system operators and lowers the value of wind power and PV for the overall energy system and therefore also for the society at large. Variability decreases value since it causes periods with surplus electricity and periods with high net demand (demand minus the generation from wind power and PV – i.e. what other power plants need to provide for). Uncertainty decreases value since decision making under uncertainty is more difficult. Uncertainty leads to suboptimal decisions concerning e.g. when to store energy and when to start up power plants. VaGe project’s objective is to improve operational decision making in the power systems when considering the variability and uncertainty of wind, solar, water inflow, heat and electricity demand, their correlations and possible sources of flexibility. Decision making under weather related variability and uncertainty is improved in two different time scales: 1) short-term power plant unit commitment and dispatch decisions (look-ahead up to 36 hours) and 2) medium-term optimization of storage use, consumer resources and other slow processes (look-ahead up to two weeks). Operational decision making will be improved when better and more comprehensive and all forecast information is used, quantifying the amount of uncertainty for each time step. Optimising over different time scales will also improve the use of all flexibility assets in an energy system. VARIABLE GENERATION VALUE WIND POWER SOLAR ENERGY FLEXIBILITY TRANSITION UNCERTAINTY FORECAST CALIBRATION BIG DATA SCENARIOS STORAGES CYCLING STOCHASTIC PHYSICS STOCHASTIC PHYSICS BALANCING DISPATCH UNIT COMMITMENT WEATHER DEPENDENCY ENSEMBLES RAMPING OPTIMISZATION ENERGY SYSTEM BIOMASS
  2. 2. WP1 – Forecasts and uncertainty: The first aim in WP1 is to develop methods for providing realistic weather dependent forecast uncertainty estimations at medium-term time scales. The development work relies on the existing data from global forecasting system, ECMWF-ENS. WP1 will develop a calibration model based on state-of-the-art statistical methods in order to further improve the forecast quality and their uncertainty estimates in general. The second aim in WP2 is to develop new methods to estimate the short-term forecast uncertainty that cannot be provided by global forecasting systems. Here the uncertainty estimate will be based on i) model error estimates by using stochastic physics and ii) boundary condition error by using forcing data from ECMWF-ENS. The new methods will be applied in an ensemble numerical weather prediction model HarmonEPS. Finally WP1 will develop automated conversion tools for casting forecasts and their uncertainty into energy terms. WP2 – Development of multi-scale optimisation methods The operation of power systems has been typically planned using forecasts up to 36 hours ahead. With increasing uncertainty and utilization of flexibility from time constrained resources (e.g. building heating or batteries) this time scale will not be long enough for cost efficient system operation. However, it is computationally difficult to extend the time horizon in power system optimization models, since these models can already be slow to solve. VaGe tries to address this problem by developing multi-scale optimization methods where it is possible to consider the longer time horizon (up to two weeks using WP1 data) by simplifying and aggregating temporal and geographical elements in the model. At the same time, the first day or so is kept at high resolution for accurate unit commitment and dispatch that will be much better informed by the longer time horizon uncertainty and variability. This can result in better allocation of resources and consequently improved cost efficiency. WP3 – Improving the value of wind power and PV in energy systems The last work package will utilize the data and the tools developed in the previous work packages. It will test and explore hypotheses about the possibilities of enhanced forecasts and models. It will also investigate market designs that would better accommodate the use of longer time horizons in operational planning. New forecasts for wind, solar, demand, hydro New multi time scale model “Backbone” Planning investments Operational planning with new forecasts Dispatch and operations Producing and calibrating ensemble forecasts Complete description of weather prediction in terms of a Probability Density Function (PDF) Forecast time Uncertainly estimate FORECASTINITIAL CONDITIONS Temperature Perturbed initial conditions Stochastic physics

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