Optimization and Simulation model coupling

5 December 2023

Master thesis summary – Menghua Prisse

This work has been further categorized into the following:

Introduction

Multi-models better handle complex issues than singular models due to the combination of the strength of individual models (`Duboz et al. 2003`_; `Quesnel, Duboz, and Ramat 2008`_). However, combining models is challenging on both technical and non-technical levels. Technical challenges involve differences in the system design and alignment of each model. Alignment involves several topics, the most common being formalism, resolution, and scale. There are predominantly two non-technical challenges. First, it becomes increasingly difficult to understand and interpret the meaning of the numbers and outcomes within a multi-model when they become more complex.

One of the critical challenges for multi-models is the coupling of individual models and studying how the coupled models might affect the outcome. This thesis focused on understanding which interaction structures exist for coupling optimization and simulation models and how the choices might affect the workings of the multi-model. Accordingly, the overall research question was formulated as follows:

What is the effect of coupling an agent-based model to an existing optimization model?

This thesis was conducted within the boundaries of the micro case of the multi-modeling project. The case study area was the industrial area of Tholen in Zeeland province. This thesis coupled two models: an optimization and an agent-based model (ABM).

State-of-the-art

An ABM can be constructed by following a ten-step method (`van Dam, Nikolic, and Lukszo n.d.`_). One of the main issues associated with coupling is interoperability (`Bollinger et al. 2017`_; `Nikolic et al. 2019`_; `Rezaeiahari and Khasawneh 2020`_). Coupling tightness refers to the fundamental concept of interdependence between models, how they are connected, and their variables intertwined. Five different levels of coupling tightness have been identified based on coupling methodologies (`Brandmeyer and Karimi 2000`_). Four levels of interoperability have been determined: technical, syntactical, semantic, and organizational (`van der Veer and Wiles 2008`_). Four model configurations for coupling a simulation and optimization model have been provided (`Figueira and Almada-Lobo 2014`_).

Methods, results, and findings

An existing optimization model named Techno-Economic Analysis Of Complex Option Spaces (TEACOS), developed by our project partner QUOMARE, was used. It is a long-term optimization tool designed to facilitate the transition towards low-carbon energy systems, identifying the most profitable investments while adhering to supply-demand balances and environmental constraints set by the modeler. Optional choices as input to TEACOS are converted to fixed investment decisions based on the total system cost minimization objective. Since TEACOS is not openly available, an adapter was created that communicated with the model through API calls (this creation was external to the master’s thesis scope). The standard communication occurred via Energy System Description Language (ESDL) files.

The industrial area of Tholen was selected because of its strong intention towards low-carbon energy system transition. For this study, the optional assets were limited to solar panels as the purpose was to test the feasibility of the multi-model within the micro case.

An ABM was created to simulate the buying behavior of the companies in Tholen. The Mesa modeling environment was chosen because of the relative ease of linking with ESDL files and sufficient documentation related to the tool. ABM aimed to simulate investment decisions in the optional assets selected by TEACOS. One key outcome of the model was the number and distribution of solar panels purchased by agents in a single simulation run. The conceptual model consisted of environment, agents, and time.

A short overview of model characteristics that are important for coupling was presented to create a meaningful interface between models. The modeling steps are conceptualization, implementation, verification and validation, and implementing data. Conceptualization hypothesized what a multi-model output might look like and how this would answer the overall research question. This coupling is loose, i.e., the modeler interfaces with each model using automated data transfer. The coupling was executed using a Python script to call TEACOS, followed by the ABM, for iterations. This allowed for the investigation into the ABM’s effects on the outcome of TEACOS. The implementation was performed using Python script in the modeler’s integrated development environment. Verification and validation ensured that the developed multi-model was created correctly and performed the way it should. Data implementation involved preparing ESDL files to be exchanged between the coupled models.

Two key performance indicators (KPIs) were devised to facilitate the multi-model investigation: investment trajectory returned by the multi-model and inflection point KPI (iteration at which the suggested optimum trajectory of investment alters). Dynamic experimental setup allowed for a cyclical, adaptive approach to conducting experiments. Observations indicated that KPIs stabilized after 6-8 iterations. When the general tipping point was reached, TEACOS did not recommend further investments, a little over 400 kW for PV array power output. Process analyses demonstrated challenges faced during different phases of the project. A two-dimensional array was created with axes representing interoperability categories and research phases. Organizational and technical interoperability proved to be the most cumbersome, with six and five challenges, respectively.

Conclusion and future work

The iterative process of conceptualizing the ABM and the multi-model resulted in a very fit ABM. When coupling models, high cohesion and low coupling are desired (`Hellhake et al. 2022`_), which this combination did. Overfitting of coupled models should be avoided (`Shahumyan and Moeckel 2015`_). Instead, a generic ABM should be created and connected via a wrapper or an adapter. This study highlights the need for more focus on the broader organizational, practical context within which the models operate, particularly related to different operating principles. The limitation is the simplicity and abstraction of the ABM, which might not capture the intricacies of human behavior. The ABM still successfully fulfilled its core function of making decisions, working with ESDL files, and interacting with the TEACOS model. This study shows that coupling an optimization and agent-based model is possible.

This research serves as a basis for integrating optimization and simulation models in a more complicated manner in the future. This can involve more interaction between models than only PV array capacity. ABM can incorporate more complicacies of human behavior. Multi-period optimization can be performed in a singular iteration.

A link to Menghua Prisse’s master thesis work follows: https://repository.tudelft.nl/islandora/object/uuid:53acc329-7990-4fe0-8374-3418d10c3f85

Bibliography

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van Dam, Koen H., Igor Nikolic, and Zofia Lukszo. n.d. Agent-Based Modelling of Socio-Technical Systems. https://link.springer.com/book/10.1007/978-94-007-4933-7.

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