To match supply from intermittent renewable energy sources (RES) with demand, it is proposed in literature to introduce flexibility in the electricity market of the future. Flexibility can be provided by energy storage, demand response and cross-border transmission. In this thesis flexibility is modeled explicitly through the price mechanism of demand and supply.
This price mechanism can be made explicit with the principles of Economic Engineering. With that price mechanism a price-dynamic bond graph model of the electricity market of the future is built. This model can be used with the various tools that control engineering has to offer to aid investors and regulators in designing the electricity market of the future.
It can aid specifically in determining the adequate generation capacity, but also in determining the necessary power and energy capacity of storage, demand response and cross-border transmission.

As an example of application, this thesis demonstrates the use of the price-dynamic model by simulating a future scenario. By simulating trading behavior of a market participant the change of prices for a market with flexibility can be quantified. It is shown that passive control does not represent realistic trading behavior, so optimal control is used. To this end, an Economic Model Predictive Controller (EMPC) is designed to simulate how market prices change when a trader maximizes his profits through energy arbitrage. Based on these price changes it is advised that the Transmission System Operator (TSO) implements an energy storage reserve market to account for risk and ensure grid stability in the electricity market
of the future.

Banks such as Rabobank depend on multi-year mortgage prepayment forecasts in order to make provisions for the associated prepayment risks. The econometric models they use are fitted to historical data, and as a consequence their models are fitted to a decreasing interest rate regime. Given the current economic climate of increasing interest rates, Rabobank has ascertained that their models are underperforming. They expressed the need for an alternative modeling approach that performs better in changing interest rate regimes.

This thesis takes a systems and control approach motivated by this need. We split the development into two parts; a dynamical system for modeling mortgage payments and prepayments, and a controller for simulating mortgagor behavior.

We model the dynamical system by following the principles of economic engineering. Economic engineering is based on the method of analogs, and we develop specific analogies applicable to the mortgage market. We first derive a continuous model describing the mortgage payment and partial prepayment dynamics. This model is then extended towards a hybrid model to include the dynamics of full prepayment. The parameters of this economic engineering model can be identified with historical data and are relatively constant. The resulting model is not affected by the variation of interest rates and performs well in any interest rate regime.

We design an Economic Model Predictive Controller (EMPC) to simulate mortgagor behavior that minimizes an objective function of its costs. This controller minimizes an economic objective which is needed to simulate the behavior of mortgagors in changing interest rate regimes. For different interest rate scenarios, we forecast prepayments with the model by simulating this minimizing behavior. We perform simulations for different kinds of mortgagors by varying the model parameters and the objective function. Based on these simulations, we describe for each mortgagor both the exact cause and dynamics behind the mortgage prepayments supplied.

This research paper describes the design and implementation of a Model Predictive Controller, using economic engineering principles, on a model of the US economy. The purpose of the Model Predictive Controller is to mimic the US
government policy by maximizing the Net Domestic Product. The Model Predictive Controller is integrated with a bond graph model which is modeled based on macroeconomic principles and used to simulate the US economy. In all cases, the Model Predictive Controller was able to successfully stabilize the US economy, while maximizing the Net Domestic Product e.g. the economic output of the economy. The model described in this paper provides a promising new way of generating more accurate prospects of future market movements.