Agent-Based Model of Intermittent Renewables: Simulating Emerging Changes in Energy Markets in Transition Emile Chappin1,2, Peter Viebahn1,*, Jörn Richstein2, Stefan Lechtenböhmer1, Arjuna Nebel1 1
Wuppertal Institute for Climate, Environment, Energy, Döppersberg 19, D-42103 Wuppertal, Germany 2 Delft University of Technology, Jaffalaan 5, 2628 BX Delft, the Netherlands *Corresponding Author:
[email protected]
Summary The energy transition is taking shape in the German and, to a lesser extent also its neighbouring electricity markets. We have proposed adaptations to an existing model to represent the increasing shares of intermittent renewables, that may alter the structure of the market and the viability of strategies of energy companies. The proposed model uses weather data from 15 regions in a long-term ABM of two interconnected electricity markets covering Germany and the Benelux. With the model, the merits of various strategies for energy companies – flexible operation of existing and new thermal power plants, shutdown renewables, carbon capture and storage and concentrated solar power – can be assessed. With sustained developments on this topic, we may be able to generate valuable insights for technology, economy, policy, and management with respect to the expected transition in our complex electricity infrastructure systems. Keywords: Intermittent renewables; Energy Transition; Agent-Based Modelling
1. Introduction Electricity systems are crucial for low-carbon strategies in industrialised countries. They aim to take a leading role in the development of renewable energy supply and decarbonisation as it is demonstrated, for example, by the Energy Roadmap 2050 of the European Commission (2011) or the German government’s Energy Concept (BMU and BMWi 2011). Making electricity systems rely on high shares of intermittent renewables, however, imposes new challenges on the technical systems in place, the organization of the sector, and the functioning of electricity markets. This is particularly true for the German electricity system which has ramped up its renewable share in production from 5% to more than 20% in the last decade (BMU 2012) and a further doubling over the next 10 years is expected according to the National Renewable Energy Action Plan (BMU 2010). The basis for this deployment will be wind and photovoltaics, so that before 2025, in the optimal case, one third of the total German electricity supply will result from intermittent renewables (Lechtenböhmer and Samadi 2012). Such a fast transition – apart from several technical challenges – has significant impacts on electricity markets, i.e. prices and the regimes of conventional power plants as well as their respective economy in Germany and in neighbouring electricity markets. Similar challenges for energy transition will emerge for the surrounding countries, although they may become prominent some years later. Therefore, an initial focus on Germany – and its interconnections to neighbouring markets – is worthwhile. A swift and successful energy transition requires to carefully consider the interests and options of all players involved in the current and the future regime. In order to be able to analyse emerging interests of diverse and potentially new market players and potential strategies they might pursue, we propose to develop a long-term focused Agent-Based Model (ABM) that is capable to catch important
structural characteristics of future interacting electricity systems increasingly dominated by intermittent renewable generation. In this extended abstract, we will give an overview of these plans.
2. Requirements for adapting and extending an existing agent-based model of conventional and intermittent power generators The approach presented draws upon the model developed by Chappin (2011), de Vries and Chappin (2012) which is an ABM representing market actors in a traditional liberalized electricity market, with mainly conventional power plants. The model has been implemented in the ABM modelling framework AgentSpring (Chmieliauskas et al., 2012). In this model, actors operate and invest in a portfolio of power plants on an annual timescale. The model consists of two interconnected electricity markets, with a common CO2 market. Electricity demand is modelled as a stepwise approximation of the load duration curve. The challenge to be solved now is to adapt and extend the model to the realworld changes that are necessarily introduced by the increasing shares of intermittent renewables. The main objective of the presented approach is to explore possible strategies by energy companies that remain viable in the transition from the fossil-based to a renewable-based power generation system. For this we need to be able to reflect the main characteristics of intermittent renewables in the market and capture these in the model, while keeping it computationally feasible. These characteristics include various levels of stochastic production that lead to time periods with oversupply and undersupply of electricity in the market. This is going to provoke price reactions and the need for balancing demand and supply. Achieving balance in such a system may be achieved by ramping thermal power plants up and down more frequently than today or to temporarily shut down renewable generation. Such measures influence the operation and economy of conventional as well as renewable power plants. The following strategies for energy companies are currently discussed. Each of them could be integrated in the model in order to explore their merits under various future developments regarding technology, policy and economy. (1) A first strategy would be to deploy flexible natural gas power plants. The last years have shown that both in the Netherlands and in Germany planned coal-fired power plants were converted into ones operating on natural gas. (2) Additionally, flexible operation of existing coal-fired power plants, originally designed for base load, is possible if the negative effects on their operation and (economic and environmental) performance are accepted (Mills 2011). (3) To moderate these implications, the concept of flexible, newly-built coal-fired power plants has been proposed (Scheier and Jeschke 2012, Balling 2010). (4) Another strategy is to accept lower renewable electricity supply by switching off intermittent power generators at times of oversupply. (5) In case a low-carbon strategy does not only rely on renewable energies but also on carbon capture and storage (CCS) based power plants, coal power stations with post-combustion capture may be flexible enough to balance short-run fluctuations by switching off the capture process and therefore producing more electricity (Ludig et al. 2011). (6) Concentrated solar power (CSP) could offer base-load power at a competitive price level by implementing heat storage (Trieb and Müller-Steinhagen 2009, Viebahn et al. 2011), an option which is also discussed in future energy scenarios for Germany.
3. First approach for representing renewables In order to assess the merits of strategies of energy companies in the system that is currently in transition in Germany and, less prominently, its neighbouring markets, we propose a number of additions and extensions to the existing ABM. Two of the key aspects of the agent-based model, as it exists today, are the segment-based load curve and the interaction between electricity and CO2 markets. Electricity demand is currently modelled as a
band of 20 segments with fixed decreasing loads and varying length (from peak to base-load), representing a load duration curve in a stepwise approximation. Agents bid into each of the 20 segments, based on their variable costs, among them fuel costs and the last known carbon price. Each segment is than cleared and the total carbon emissions determined. If the carbon emissions are above the cap, the carbon price is raised, if they are below the cap, the carbon price is lowered. With a new found carbon price the segments are cleared again. This is repeated in an iterative fashion, until an equilibrium between the electricity market and the carbon market is found. In this process, the interconnector capacity between the two modelled countries is taken into account (also known as market coupling). Due to the fundamental changes in the electricity markets in the transition to a renewable power generation system, the representation of varying production via annual demand of 20 different load bands (as described above) becomes invalid. The task is now to make the model capable of simulating the alternating operation of power plants and – at a later stage – the economies of electricity storage and demand side management. For this purpose, the existing ABM is further detailed with data and by means of the following steps to represent the penetration of intermittent renewables: First, the global load curve is determined, based on a hourly load per region. In the model, with global we mean the simulated countries together; with regions, we mean each of the German states, the Netherlands, Belgium and Luxembourg. The data used to determine the load curve includes demand growth estimates and regional load data (from ENTSO-E, the European Network of Transmission System Operators for Electricity). The total existing capacity of intermittent technologies per type, per region is determined. Next, the hourly load of intermittent technologies, on the basis of their capacities and regional weather data is determined. These intermittent loads are subtracted from the global load curve to obtain the hourly residual load summed for all regions and countries. The next step is to convert the previous result into a load-duration curve. This is done by ordering the hours in the global residual load curve by (the amount of) load: the hour with peak load first, the hour with the lowest load last. The curve is then split up in segments. This is fundamentally different from the approach existing today, because now we are taking into account the intermittent character of renewables in the shape of the curve in each time step (i.e. simulated year). Moreover, it still allows for a segment-based model, which is attractive in terms of performance and fits with the rest of the existing model. The residual load curve is then split up in segments on the basis of its shape. The largest variability in residual load (super peak load minus lowest base load, for instance 200-100=100 GW) is divided by the number of segments (for instance 20, that is 100/20=5 GW load difference covered in each segment). That means that the hours with the top 5 GW of residual load (between 195 and 200 GW) are put in the first segment, and the hours of the next 5 GW of residual load are put in the next segment, and so on. The load in each of the segment is the average of the load in the hours covered. The duration of each of the segments is determined by number of hours with a residual load in the range covered. The mapping of each hour to segments (for example hour 1 goes to segment 5, hour 2 goes to segment 14, etc.) is stored. After this step, the original model is continued by clearing the electricity markets (spot, long-term contracts) for all conventional power plants, determine revenues, pay for maintenance, invest in new capacity regarding different investment strategies as described in section 2.
4. Conclusions and outlook The energy transition is already significantly changing the rules of the game in the German and, to a lesser extent also its neighbouring electricity markets. In order to make sound analyses of actors’ longterm interests and strategies as well as possible interferences with technical and regulatory developments, existing agent-based models are insufficient. Therefore, we have proposed adaptations to an existing model. The main challenge is to deal with the significantly increasing shares of intermittent renewables. Therefore, we are currently developing a representation of hourly fluctuating load based on weather data of 15 regions, in a long-term ABM of two interconnected electricity markets covering Germany and the Benelux by converting developing time-dependent residual load curves per region and converting that into 20-segment based residual load duration curve. Current developments show the potential of this approach. There are many possible extensions to the existing model possible that may improve the understanding of how we might manage the enormous changes in European energy markets we face today. For instance, we could develop a better spatial representation of the electricity system by a more detailed grid and its constraints, including a proper load-flow analysis. Such work would enable to analyse the effects of demand-side management, significant expansions of the expansion of European transmission grid and the role of various options for electricity storage. Furthermore, we could simulate scenarios with the various existing datasets on power plant technology and existing installations in European countries. In addition, the model could be extended to include electricity markets which are linked to Germany and the Benelux. With sustained developments on this topic, we may be able to generate valuable insights for technology, economy, policy, and management with respect to the expected transition in our complex electricity infrastructure systems.
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