3. Estado del arte
3.1. Sistema de Tratamiento de Aguas Residuales
As discussed for energy models generally, optimization models and agent-based models represent widely used approaches to analyze electricity systems. Both approaches are discussed in this section to further detail the selection of the model approach of this thesis. If electricity systems should be operated and planned on an economic evaluation, the electricity market can be optimized by maximizing profits or returns of companies or minimizing the overall system costs (Kallrath, 2009). In optimization models, an electricity system is implemented by the use of the graph theory with a directed graph. Nodes of the graph represent generation capacities, storage capacities or grid nodes. Electricity flows are realized by the edges. A central planner or a company optimizes the electricity system by implementing its decision variables in the objective function of the model. After solving the model, a closed solution is obtained by the optimization program (typical solvers are e.g. CPLEX, CONOPT, NLPEC, etc.). Several approaches based on mathematical optimization models are often used. An approach is usually chosen depending on the model task and the structure of variables:
22 CHAPTER 2. Modeling fundamentals for electricity systems with renewable energy sources
• Linear program • Nonlinear program • Mixed integer program • Dynamic optimization • Genetic algorithm • Network Flow algorithm
Optimization models normally assume a perfect market with perfect information availability (Held, 2010). The assumption of perfect foresight and perfect markets in optimization models can lead to results which are “too perfect” for the reality. For example, “option one” can be preferred by the model due a small advantage of the input parameters compared to “option two” which is only a bit worse. This minimal effect can considerably influence the results. The model can select only “option one” whereas in reality always a combination of option one and option two is chosen by decision makers.
In deregulated and liberalized energy market using increasing shares of RES-E, uncertainties increase as more independent stakeholders make decisions according to their preferences or cannot completely forecast electricity generation in a specific hour. Therefore, this increasing challenge of handling uncertainties lead to the use of stochastic elements in optimization models (but also in other models). Stochastic distributions of input parameters or the use of scenario trees are implemented in many models. Potential uncertainties range from volatile fossil fuel prices, fluctuating renewable energy sources, uncertain availability of power plants and rapidly changing political and regulatory framework conditions in the electricity system. Stochastic modeling is implemented for three different categories of problems according to Möst and Keles (2010). First, stochastic development of commodity prices is analyzed by modeling the temporal variation over days, weeks or years. Second, the development of analytical and simulative scenario generation includes a stochastic variation of the input parameters. Third, stochastic models optimize short-term market decisions (Swider and Weber, 2007) or long-term system planning (Schroeder, 2012).
A disadvantage of stochastic modeling is the higher computing time when stochastic distributions of parameters are included in the model approach. Nagl et al. (2012) compare the results of a deterministic and stochastic model approach for uncertain availability of wind and solar power plants. The stochastic investment and dispatch model gives a similar capacity development with and without modeled uncertainty of renewable resources (wind and solar). But the value of electricity from renewable energy sources is overestimated and total system costs are underestimated in the deterministic approach. Although stochastic models provide some advantages in terms of including uncertainties into the model, the high computing time reduces the possibility to implement a large amount of variables into the model. However, the findings of Nagl et al. (2012) should be considered when interpreting deterministic model results, especially the value of RES-E and total system costs.
To represent individual preferences, heterogeneity of actors and dynamic decision process in deregulated electricity markets, agent-based modeling concepts have been developed. An overview of different approaches and the use of agent-based models can be found in Sensfuß (2007). Agent-based models offer the potential to include the principles of evolutionary economics in the analysis of electricity markets. Optimization models require a stable equilibrium which is difficult to find for highly interdependent systems (Reeg et al., 2012). By modeling different agents such as single market players or companies, the interactions of these agents can be analyzed. Agents could embody utilities, grid operators, retail companies or
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market operators. In the NEMSIM model of the Australian power market, agents simulate their trading behavior on the electricity market (Grozev et al., 2005). In Germany, the PowerACE model covers the German spot market for electricity in which traders (agents) negotiate on the electricity pool price and/or the C02 emissions price (Sensfuß, 2007; Genoese, 2010). In Held
(2010), development path for renewable energy sources are included to PowerACE by using cost potential curves and agent learning. Reeg et al. (2012) present the AMIRIS model in which investors base their decisions to invest in RE power plants on the availability of support schemes. Based on these market decisions, long-term deployment scenarios are derived and consequently the efficiency of the support schemes can be tested.
As described before, (agent-based) simulation models require a range of parameters and relations to describe the behavior of the agents. If an (agent-based) simulation approach would be applied for the North African electricity systems, this model would consequently use very detailed information of the different stakeholders. As the literature of the North African electricity markets and relations between potential agents is very rare, an agent-based approach would be based on many assumptions which have to be made by the author.