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.

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. 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