Kristian Lindgren – Complex systems group
Department of Energy and Environment, Chalmers
A future energy system that to a larger extent depends on variable renewable energy sources, like wind and solar power, involves new challenges for securing a reliable supply of electricity. The economic basis for such an electricity system differs significantly from the one we have today, and one can expect that prices will be more volatile and risk for shortages in supply increases. The experience from countries where the expansion of solar and wind has been strong exemplifies this. At times with abundant solar electricity supply and low demand, prices have decreased, while at peak demand if supply is low one has seen the opposite (The Economist, 2013; Hirth, 2013). Many owners of fossil fuelled power plants make considerable losses, and some argue that the electricity market should be restructured in order to allow for investments to secure reliable electricity supply at times of high demand (Newbery, 2010).
In the transition towards a renewable energy system, foresight is needed in order to design policies that facilitate changes that are required for the transition. This also includes improved understanding of possible consequences of implemented measures and policies—also the unintended ones, like the increased price volatility in the example above. Another example of undesired consequences that has been discussed as a possible effect of climate policies is the food price peaks we have witnessed the passed ten years. The argument has been that the rapidly increased demand for bioenergy from agricultural land has led to increased prices on several agricultural commodities, further amplified by subsequent political decisions like export restrictions in certain countries, e.g., India and Vietnam.
The main tool for foresight in the transition of the energy system is numerical modelling. There is in this context a long tradition of optimisation models based on economic cost minimisation or utility maximisation under constraints, like we have done in the Global Energy Transition model GET (Azar et al, 2003, 2006, 2010; Hedenus et al, 2006); for an interactive online demonstration, see chalmers.se/ee/getonline. Most of the models that are used for assessments of policies are based on rational expectations and perfect knowledge of the future resulting in an equilibrium solution describing the transition. This is part of the equilibrium economics paradigm that in many cases has proven useful, but that in our view is insufficient for a proper analysis of the energy systems transition that is required in order to mitigate climate change. Equilibrium models do not well represent dynamic processes and are thus weak at capturing temporal effects in a system that undergoes a transition.
In a recently started project, we will develop a new set of models in order to be able to capture several dynamic effects in subsystems of high relevance for a transition of the energy system. The two examples mentioned above, (I) the increased variable supply in the electricity system, and (II) the bioenergy demand effect on the agricultural system, will be investigated in more detail as illustrative examples of our modelling approach, see, e.g., (Bryngelsson and Lindgren, 2013; Jonson et al, 2015; Lundberg et al, 2015; Lindgren et al, 2015).
The new models need to be able to handle short-term variability and mechanisms describing how this affects the economic basis in the system and thus also the investment decisions that determine the development of the system. These details are important in order to capture the effect of the short time scale variability on the long-term development of the whole system. We are thus developing a complement to the traditional energy systems modelling approach that builds on optimisation and equilibrium economics, with models that explicitly includes mechanisms for decisions—mechanisms that do not necessarily assume rational agents but may allow for bounded rationality as a basis for decisions. These models may be formulated on the level of individual agents (citizens, firms, regions, etc), so-called agent-based models, or projections of agents into aggregate variables leading to dynamic simulation models. With an agent-based model, assumptions on rationality and full informaiton may be relaxed, and one may study how non-rational and heterogeneous actors can influence a market (Farmer & Foley, 2009; Arthur, 2014; Page, 2012). One major aim with the project is to critically and carefully investigate the difference between the possible modelling approaches, (i) agent-based modelling, (ii) dynamic aggregate models, and (iii) optimisation or equilibrium modelling. Depending on what questions that are asked, different levels of modelling are appropriate. This will be exemplified with the two mentioned application areas of high relevance for the transformation of the energy system in a climate change perspective.
1. Arthur, W. B. (2014). Complexity and the Economy. (Oxford University Press).
2. Azar, C., Lindgren, K., Andersson , B. A. (2003). Global energy scenarios meeting stringent CO2 constraints – cost-effective fuel choices in the transportation sector. Energy Policy 31, 961-976.
3. Azar, C., Lindgren, K., Larson, E., and Möllersten, K. (2006). Carbon capture and storage from fossil fuels and biomass — Costs and potential role in stabilizing the atmosphere. Climatic Change 74, 47-79.
4. Azar, C., Lindgren, K., Obersteiner, M., Riah, K., van Vuuren, D.P., den Elzen, K.M.G.J., Möllersten, K., Larson, E. D. (2010). The feasibility of low CO2 concentration targets and the role of bio-energy with carbon capture and storage (BECCS). Climatic Change, 100 (1), 195-202.
5. Bryngelsson, D. K., Lindgren, K. (2013). Why large-scale bioenergy production on marginal land is unfeasible: A conceptual partial equilibrium analysis,” Energy Policy 55, 454-466.
6. Farmer, D. F., Foley, D. (2009). The economy needs agent-based modelling, Nature 460, 685-686.
7. Hedenus, F., Azar, C., and Lindgren, K. (2006) Induced technological change in a limited foresight optimization model. The Energy Journal 27 special issue 109-122.
8. Hirth, L. (2013). The market value of variable renewables. The effect of solar wind power variability on their relative price. Energy Economics 38, 218–236.
9. Jonson, E., Lundberg, L., Lindgren, K. (2014). Impacts on stability of interdependencies between markets in a cobweb model, paper presented at 10th Artificial Economics Conference, Barcelona, Sept. 1-2, 2014; published in Advances in Artificial Economics, sid 195-205 (2015).
10. Lindgren, K., E. Jonson, and L. Lundberg (2015). Projection of a heterogeneous agent-based production economy model to a closed dynamics of aggregate variables. Advances in Complex Systems.
11. Lundberg, L., Jonson, E., Lindgren, K., Bryngelsson, D., Verendel, V. (2014). A cobweb model of land-use competition between food and bioenergy crops. Journal of Economic Dynamics and Control 53, 1-14 (2015).
12. Newbery, D. (2010). Market design for a large share of wind power. Energy Policy 38, 3131-3134.
13. Page, E. S. (2012). Aggregation in agent-based models of economics. The Knowledge Engi- neering Review 27, 151–162.
14. The Economist (2013). “How to lose half a trillion euros,” downloadable via http://www.economist.com/news/briefing/21587782-europes-electricity-providers-face-existential-threat-how-lose-half-trillion-euros