The influence of demand-side flexibility and technology availability on the cost-optimal carbon-neutral Swiss energy system

Table of Contents

1 Introduction

2 Theory

2.1 Energy Systems Optimization

2.1.1 Mathematical Foundations of Optimization

2.1.2 Modeling Framework SecMOD

2.1.3 Multi-Objective Optimization

2.2 Literature Review on Heuristic Algorithms

2.2.1 Trajectory-based Methods

3 Implementation of Constraints

3.1 Technology Availabilities

3.1.1 Alpine PV

3.1.2 Wind Energy

3.1.3 Electrified Residential Heating

3.1.4 Carbon Capture and Storage

3.1.5 Hydrogen Storages

3.2 Demand-side Flexibilities

3.2.1 Electricity Demand Flexibility

3.2.2 Residential Heat Demand Flexibility

3.2.3 Mobility Demand Flexibility

3.2.4 Industrial Heat Storages

3.3 Market Integration Levels

3.3.1 Electricity Market

3.3.2 Hydrogen and Synthetic Gas Market

3.3.3 CO2 Compensation Abroad

3.4 Consideration of Measure Costs

4 Heuristic Algorithms

4.1 Considered Optimization Problem

4.2 Preparation of the Heuristic Algorithms

4.2.1 Total Cost Reduction Potential

4.2.2 Individual Cost Reduction Potentials

4.3 Heuristic Algorithm 1 – “The Intuitive”

4.4 Heuristic Algorithm 2 – “The Smart”

5 Case Study

5.1 Swiss Energy System Model

5.1.1 Spatial Aggregation

5.1.2 Model Setup

5.2 Model and Optimization Parametrization

5.3 Limited Resource Availabilities

6 Results and Discussion

6.1 Resulting Individual Cost Reduction Potentials

6.2 All-Conservative vs. All-Progressive

6.2.1 Cost Comparison

6.2.2 Utilization of Progressive Technology Availabilities

6.2.3 Carbon Balance

6.3 Heuristic Algorithms Results

6.3.1 “The Intuitive”

6.3.2 “The Smart”

6.4 Generation of Alternatives

6.5 Discussion

6.5.1 Interpretation

6.5.2 Limitations of our Study

7 Conclusion and Outlook

Research Objectives and Themes

This thesis aims to identify cost-effective, smaller subsets of controversial measures that can support Switzerland’s transition to a carbon-neutral energy system by 2050. The research addresses the combinatorial optimization challenge of selecting the most beneficial combinations of technology, flexibility, and market integration measures from a predefined catalog.

• Development of model-specific heuristic optimization algorithms (“The Intuitive” and “The Smart”).
• Technical implementation and cost-analysis of controversial energy system measures.
• Analysis of synergistic effects when combining different energy policy measures.
• Minimization of total annualized system costs through targeted sector coupling (electricity, heat, and mobility).

Excerpt from the Book

Summary of Chapters

1 Introduction: Provides background on the Swiss energy transition, defines the scope of controversial measures, and states the thesis objective to optimize the system cost-effectively.

2 Theory: Introduces mathematical principles of energy system optimization (MILP) and provides a literature review on heuristic algorithms.

3 Implementation of Constraints: Details the technical modeling of technology, demand-side, and market integration measures as constraints in SecMOD.

4 Heuristic Algorithms: Explains the conceptual development of the two custom heuristic algorithms, "The Intuitive" and "The Smart".

5 Case Study: Describes the Swiss energy system model configuration, the spatial grid aggregation, and the parameterization for the optimization.

6 Results and Discussion: Presents the findings regarding cost reduction potentials, compares conservative and progressive scenarios, and evaluates the performance of the generated alternatives.

7 Conclusion and Outlook: Summarizes the key findings and provides recommendations for future research directions in energy system modeling.

Keywords

Energy system optimization, carbon-neutrality, Switzerland, demand-side flexibility, heuristic algorithms, sector coupling, SecMOD, technology availability, cost-optimal design, energy policy, renewable energy, carbon capture and storage (CCS), grid aggregation, energy storage, system integration.

Frequently Asked Questions

What is the core focus of this Master’s thesis?

The work focuses on identifying cost-optimal combinations of "unpopular" energy policy measures to achieve a carbon-neutral Swiss energy system by 2050.

Which categories of measures are analyzed?

The measures are categorized into technology availability (e.g., Alpine PV, CCS), demand-side flexibilities (e.g., flexible charging for BEVs), and market integration levels (e.g., CO2 compensation abroad).

What is the primary objective of the optimization?

The primary objective is to minimize the total annualized cost (TAC) of the sector-coupled Swiss energy system while respecting carbon-neutrality constraints.

Which scientific methods are employed?

The thesis uses mixed-integer linear programming (MILP) within the SecMOD framework and develops two novel, model-tailored heuristic algorithms to solve the combinatorial optimization problem.

What is the main topic covered in the results section?

The results section evaluates the cost reduction potentials of individual measures, compares all-conservative against all-progressive scenarios, and discusses the performance and synergy effects generated by the proposed heuristic algorithms.

Which keywords categorize this research?

Key topics include energy system optimization, demand-side flexibility, heuristic algorithms, Swiss energy system, sector coupling, and carbon-neutrality.

How do "The Intuitive" and "The Smart" algorithms differ in their logic?

"The Intuitive" combines individually promising measures based on performance, whereas "The Smart" enforces combination constraints based on predefined categories to exploit synergistic effects between technologies.

What is the role of the "must-run" formulation for residential heat?

The must-run formulation ensures that all heat-generating technologies are operated in proportion to their installed capacity shares, preventing the model from exclusively favoring a single technology based on unrealistic merit-order principles.