Energy Models
The need for mid/long-term energy planning
at international, national and local level, the increasing attention
given to the reduction of greenhouse gas emissions, the optimal
exploitation of energy resources, the economic impacts of energy
systems, and the security of energy supply are some of the reasons
that make it important to develop energy forecasting models. These
models allow to investigate – over a long-term time horizon – the
effects of different energy, environmental and economic policies or
of possible future technological improvements on the considered
energy system, thus proving to be not only tools for scientific
analyses, but also decision support tools for policy makers.
Reference Energy System
The first step to build an energy model is to identify the Reference
Energy System (RES), which typically describes the whole
supply-demand chain for each region/area. Fuel mining, primary and
secondary production, trade of energy and material, transformation
plants and end-use sectors are represented in terms of processes
(technologies) and commodity flows. Each technology (both existing
or planned/possible) is characterized by economical (i.e. investment
costs, O&M costs, etc.), technical (capacity, efficiency, etc.) and
environmental (emission factors) parameters.
An example of a RES is shown in the following figure.
Classification of energy models
Energy models (and, more generally, systems analysis models) can be
divided into two categories: simulation models and
optimization models.
Simulation models predict, in a parametric way, the
response of a system to a given set of technical or policy variables
and identify the possible impacts and the probable costs and
benefits of the analyzed configuration. These models are not able to
find an optimal value for the above mentioned variables, but they
only allow to compare two or more different scenarios by executing a
single run for each scenario.
Among the simulation models, the PRIMES model and the POLES model
can be mentioned.
The LAME team has developed and implemented the EDPM-CN
and EfB simulation
models in the framework of the EC2 (Europe-China Clean Energy Centre)
activities, in order to evaluate China energy demand evolution until
2030.
Optimization models estimate, for all the system
variables, the values that lead to an optimal configuration of the
system, i.e. the configuration that minimizes/maximizes a defined
objective function (for example, an economic objective function
coinciding with the total discounted system cost). These models can
include constraint conditions, in order to restrict the range of
values that variables can assume.
Optimization models can be further classified on the basis of:
- the geographic extension of the system (local, regional, national or global);
- the approach (bottom-up, top-down, partial equilibrium, general equilibrium);
- the level of economic characterisation (technical-economical models, macroeconomical models).
Differences between bottom-up models and
top-down models will be now briefly described.
Bottom-up models
Bottom-up models have been used to analyse the dynamics of various
fields (f.i. climate, energy or agriculture), taking also into
account the introduction of new technologies. The macroeconomic data
are always exogenous and, as a consequence, these models are not
able to evaluate the feedback effects of technological improvements
on the economic system.
In these partial equilibrium models production, transformation and
end-use technologies (existing and planned/possible) are described
by using both technical (capacity, efficiency, life, availability
factor, fuel consumption etc.) and economical (investment cost,
fixed operating and maintenance costs, variable costs) parameters.
The optimization procedure allows to define, over the whole time
horizon and under user-defined constraints and scenarios, the global
mix of technologies (for the End-Use sectors, the power generation,
etc.) and fuel commodities (crude oil, natural gas, hard coal, etc.)
that – at the same time – meets the service demands and minimizes
the total discounted system cost.
Among the bottom-up model generators, The Integrated MARKAL-EFOM
System (TIMES) can be mentioned. According to its definition, TIMES
“is an economic model generator for local, national or
multi-regional energy systems, which provides a technology-rich
basis for estimating energy dynamics over a long-term, multi-period
time horizon”. This bottom-up model generator allows to define
partial equilibrium models based on the maximization of the total
surplus (i.e. the sum of consumers surplus and producers surplus),
which in the simpler models (with fixed energy service demands)
corresponds to the minimization of the total system cost.
The LAME team has contributed to the development of some TIMES
models, such as the Pan European TIMES model (PET – Needs project)
and the REACCESS CORridor model (RECOR – REACCESS project).
Top-down models
Top-down models essentially are general equilibrium econometric
models, able to evaluate, in an endogenous way, the responses of the
economic system to different policies and scenarios. They describe
the relationship between the primary factors (labour, capital and
natural resources such as energy) by using elasticities of
substitution.
Because of their market-oriented approach, these models have a
limited representation of the energy sector and lack detail on the
description of present and future technologies, which are typically
represented by means of aggregated production functions for each
sector of the economy.
So, top-down models are useful to analyse the evolution of energy
prices and macro-economic variables but not to compare the effects
of different energy policies.
The projections of the main macro-economic drives taken from a
top-down model can be used to project the demand for energy services
to be used as exogenous input data in a bottom-up model. Among
top-down general equilibrium models, the General Equilibrium Model
for Economy – Energy – Environment (GEM-E3) can be mentioned.
Instruments
CAPLEP – at urbanm scale,
tool for finding the shortest path and optimum of a district heating
network
WINGRAF – simulation tool of static behavior of the
Energy System Reference. It allows to analyze parametrically flows
of energy and matter, the costs of individual processes and the
overall and emissions of pollutants associated with energy
consumption
EDPM-CN (Energy Demand Projection Model) –
Simulation model for evaluating the consumption of primary resources
in China, starting from a demand projected over a time horizon and
with the application of alternative scenarios
EfB (Energy for Buildings) – Version of EPDM-CN
made for a more detailed analysis of the residential sector
RECOR – model of energy corridors to Europe. It can
be used alone, or with other models (TIMES based) that describe in
detail the supply and use areas of various energy commodities
(Europe)
DBT – database containing records able to describe
topologically the supply system of the main energy commodities to
Europe
DWGA –
web-based
GIS visualization tool for energy corridors to Europe, describing
characteristics and energy flows in a base year and in subsequent
years milestone
PAIVI – tool for viewing and management. GIS-based, allows
both to display information related to results of optimization
exercises, both to act as input interface to the optimization model
(eg. Allowing to set simulation scenarios)
Proceedings
R. Gerboni, D. Grosso, E. Lavagno,
“Modelling reliability and security of
supply: a revised methodological approach and its possible
application to the Chinese system”,
IEA-ETSAP Workshop, Beijing, China, June 2-3, 2014
Contributions
R. Gerboni, D.Grosso, E. Lavagno, A.Kanudia,
G.C. Tosato
Coupling World and European models: energy trade and energy
security in Europe in Informing Energy and Climate Policies using
Energy Systems Models - Insights from Scenario Analysis Increasing
the Evidence Base, Springer, Lecture Notes in Energy, 2015, to be
confirmed
R. Gerboni,
R.Gutpa, A. Basile, N. Veziroglu, G. Mills
Handbook of Hydrogen Energy. Vol. 2 Hydrogen storage,
transmisison, transportation and infrastructures, Woodhead
Publishing