Depletion of fossil resources and goals to reduce CO₂ emission encouraged governments to boost researches to construct energy planning & policy to increase the deployment of renewable energy (RE). For that purpose, governments need sufficient information on total amount required and the total capacity of RE to be installed both in the present and upcoming years. However, there is a lack of information required to measure the requirements to install RE technologies in certain areas in the upcoming years. Therefore, a method to collect information is essential to answer this problem. A method that can be used is the energy model that aims to project a country’s energy balance in the future. There are two types of energy model methods: single model and multiple model methods. The single model only uses one energy model for projecting energy balance. This is the most common method used by modellers since it takes a shorter time to generate the model compared to the multiple models. However, the use of a single model may generate only a single dataset, so it may give a false sense of certainty or correctness. In contrast, multiple models use two or more energy models simultaneously for projecting energy balance. It takes a longer time in generating the models. However, the use of multiple models highlights the potential variation in outcomes, consequently, producing more data outcomes and may provide a sense closest to certainty. In fact, there is only a very small number of energy modelling studies using multiple models. To give a brief explanation on the use of multiple models, the writer has conducted a research in 2015 as a master thesis on the use of multiple models to evaluate: the added value of simulating possible developments paths of the energy mix using multiple models simultaneously.

The approaches to conduct the study were as follows: a) identifying the most suitable energy balancing models to be compared with several existing models, then select two suitable models for the study; Long-range Energy Alternative Planning (LEAP) and Big Picture; b) taking a case study including country selection and an existing scenario of the selected country; c) using existing literature as a reference to construct base-year (2012) and end-year (2050) models. International Energy Agency (IEA) reports and other existing literature related to energy data of the selected country have been used as main references.

The use of multiple models as above resulted in different outcomes since they have different characteristics. Therefore, understanding each model characteristic is highly required because low differences and high similarities on the characteristic among selected models can minimize discrepancies in the simulation process. The differences in model outcomes may be produced; however, the differences have become added values. Throughout the study, it can be concluded that the added values of using multiple models simultaneously are as follows:

1. The different outcomes can be used to complement each result. An example can be seen in Table 1, which shows brief differences and similarities between the two selected models. In “Environmental Aspect” category, the LEAP model is not able to provide data on the total area (km²) of land and sea-use. In contrast, Big Picture does. Therefore, by combining these two model outcomes, the total area required (km²) for RE deployment may be produced. As a result, governments can collect information on the total area required (km²) for RE deployment for the purpose of constructing energy planning.
2. The uses of multiple models may increase model outcome trustworthiness. If the outcomes among the models produce minor deviations and the explanations on the model deviation can be correctly described, the model outcomes may give mutual benefits and increase the model outcome trustworthy.
3. The uses of multiple models may produce more data Example is given by comparing figure 1 and 2. Figure 1 shows energy demand on an annual basis which means long term energy projection is provided. Meanwhile, figure 2 shows on a daily basis meaning that detail information for short term projection is provided. By combining those two outcomes, the energy projection both in the short and long term can be produced. It can be concluded that more data means more detailed information. The more detailed information may give more user input and recommendations for the governments to construct a plan in RE deployment.

Photo Credit: Christophe Hautier.