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