Case study: Island energy transition

Toolkit: Complex Systems Toolkit.

Author: Onyekachi Nwafor (KatexPower).

Topic: Complex systems modelling in renewable energy transition.

Title: Island energy transition.

Resource type: Teaching – Case study.

Relevant disciplines: Electrical engineering; Systems engineering; Environmental engineering; Computer science; Energy.

Keywords: Available soon.

Licensing: This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. 

Downloads: Available soon.

Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness.    A free spreadsheet version of the framework can be downloaded.

This resource relates to the Systems Thinking, Systems Modelling and Analysis, Configuration Management, Requirements Definition, Communication, Verification, and Validation INCOSE Competencies. 

AHEP4 mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4):  Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems). In addition, this resource addresses the themes of Sustainability and Communication. 

Educational level:  Advanced.

Learning and teaching notes:

Overview: 

This multi-part case study guides students through the complex systems challenges of Prince Edward Island, Canada’s ambitious 100% renewable energy transition by 2030. Students will experience how technical, social, and economic factors interact through emergence, feedback loops, and multi-scale dynamics that traditional engineering analysis alone cannot capture. 

Learners have the opportunity to: 

• Identify complex systems characteristics (emergence, feedback loops, nonlinearity) in real energy systems. 

• Apply multiple modelling approaches (ABM, system dynamics, network analysis) to analyse system behaviour. 

• Evaluate how technical decisions create emergent social and economic consequences. 

• Synthesise insights from different modelling approaches to inform policy recommendations. 

• Communicate complex systems concepts and uncertainties to non-technical stakeholders. 

Teachers have the opportunity to: 

• Demonstrate complex systems concepts through hands-on modelling. 

• Facilitate discussions on emergence and system-level behaviours. 

• Evaluate learners’ ability to apply systems thinking to engineering problems. 

• Connect technical modelling to real-world policy and social implications. 

Learning and teaching resources:

• Materials required:

• • NetLogo Models Library

• • Vensim PLE Tutorials

• • NetworkX Documentation

• • IRENA Data Portal

• • Santa Fe Institute Complexity Explorer

• Suggested pre-reading:

• • Meadows, D. (2008). Thinking in Systems: A Primer - Chapters 1-3 (leverage points and system structure) 

• • Mitchell, M. (2009). Complexity: A Guided Tour - Chapter 1 (what is complexity?) 

• • Government of PEI (2018). Prince Edward Island Energy Strategy - Available at: https://www.princeedwardisland.ca/en/information/environment-energy-and-climate-action/energy-strategy 

• • Mulyono, Y.O., Sukhbaatar, U. and Cabrera, D. (2023). ‘Hard and Soft Methods in Complex Adaptive Systems (CAS)’, Systems Thinking, 3(1), pp. 1–33. 

• Energy systems data sources:

• • Statistics Canada Energy Data

• • International Renewable Energy Agency (IRENA)

• • Global Wind Atlas

• • Natural Resources Canada Renewable Energy Data 

• Case study context resources:

• • CBC News PEI Energy Archives 

• • Maritime Electric Annual Reports

• • University of Prince Edward Island Sustainable Design Engineering

Overview: Energy transition as a complex systems challenge:

Prince Edward Island (PEI), Canada’s smallest province, aims to achieve 100% renewable electricity by 2030. Its small grid, dependence on imported power, and growing renewable infrastructure make it a natural laboratory for systems thinking in energy transitions. 

This case invites students to explore how technical, social, and policy decisions interact to shape renewable integration outcomes. Using complexity-science tools, they will uncover how local actions produce emergent system behaviour, and why traditional linear models often fail to predict real-world dynamics. 

The complex challenge:
Traditional engineering approaches often treat energy systems as predictable and linear, optimising components like generation, transmission, or storage in isolation. However, energy transitions are complex socio-technical systems, characterised by feedback loops, interdependencies, and emergent behaviours. 

In PEI’s case, replacing stable baseload imports with variable wind and solar generation creates cascading effects on grid stability, pricing, storage demand, and social acceptance. The island’s bounded geography magnifies these interactions, making it an ideal context to observe emergence and system-level behaviour arising from local interactions. 

PEI’s energy system presents a fascinating case study of complex systems, where interactions between wind generation, energy storage, demand patterns, and grid infrastructure create emergent behaviours that cannot be predicted from individual components alone. Currently, PEI generates approximately 25% of its electricity from on-island wind farms, with the remainder imported via submarine cables from New Brunswick.  

Part one: Understanding system complexity 

The integration paradox:

PEI currently imports about 75% of its electricity via two 180 MW submarine cables, while 25% is produced locally through onshore wind farms (204 MW). Plans for offshore wind, community solar, and hydrogen projects have triggered debates around stability, affordability, and social acceptance. 

Taking on the role of an engineer at TechnoGrid Consulting, students are tasked to advise Maritime Electric, the island’s utility, on modelling strategies to guide $2.5 billion in renewable investments. 

Competing goals:

• • Maintain grid reliability while replacing fossil baseloads. 

• • Achieve policy targets without increasing public resistance. 

• • Balance economic cost, environmental benefit, and technological feasibility. 

Discussion prompt: 

In systems terms, where do you see tensions between policy, technology, and society? How might feedback loops amplify or mitigate these tensions? 

Part two: Mapping system complexity – What counts as ‘the system’?:

While Maritime Electric’s engineering team insists the project scope should stay strictly technical, limited to grid hardware, generation, and storage, policy advisors argue that social behaviour, market pricing, and community engagement are part of the system’s real dynamics.

Expanding boundaries makes the model richer but harder to manage; narrowing them simplifies computation but risks missing the very factors that determine success. 

Activity 1: Boundary definition: 

Map the PEI energy system by identifying: 

• • Physical boundaries: generation, transmission, storage, interconnections. 

• • Temporal boundaries: timescales from milliseconds (grid response) to decades (infrastructure). 

• • Organisational boundaries: stakeholders, regulations, and markets. 

Discuss how including or excluding elements (e.g., electric-vehicle uptake, community cooperatives, carbon policy) changes the model’s focus and meaning. 

Learning insight: 

Complex systems cannot be fully understood in isolation; boundaries are analytical choices that shape both perception and leverage. Every inclusion or exclusion reflects an assumption about what matters and that assumption determines which complexities emerge, and which stay hidden. 

Part three: Modelling the system: Multiple lenses of complexity:

(a) Agent-Based Modelling (ABM) with NetLogo: 

Students construct simplified models of households, businesses, and grid operators: 

• • Household agents: decide to adopt rooftop solar based on payback time and neighbour influence. 

• • Technology providers: adjust prices in response to market demand. 

• • Grid operator: balances reliability and cost. 

Emergent patterns such as adoption S-curves or network clustering illustrate how simple local rules generate complex collective dynamics. 

(b) System Dynamics (SD) with Vensim: 

Students then develop causal loop diagrams capturing key feedbacks: 

• • Adoption–Learning Loop: installations ↓ costs ↓ encourage more adoption. 

• • Reliability–Trust Loop: performance ↑ trust ↑ investment ↑ reliability. 

• • Cost–Acceptance Loop: higher bills ↓ public support ↓ investment capacity. 

This provides a macroscopic view of feedback, delay, and leverage points. 

(c) Network Analysis with Python (NetworkX):

Students model actor interdependencies: how households, utilities, industries, and regulators interact. Network metrics (centrality, clustering, connectivity) reveal where resilience or vulnerability is concentrated. 

Reflection prompt: 

Which modelling approach offered the most insight into system-level behaviour? What were the trade-offs in complexity and interpretability? 

Part four: Scenario exploration: Pathways to 2030:

Students explore three transition scenarios, each with distinct emergent behaviours: 

Activity 2: Comparative Scenario Analysis 

Run simplified models for each pathway. Track how feedback loops evolve over time and identify points of instability or resilience. 

Inquiry questions: 

• • How do small behavioural changes (e.g. adoption thresholds) affect system-wide outcomes? 

• • Which feedbacks drive adaptability vs. brittleness? 

• • How can system design encourage positive emergence? 

Part five. Dealing with uncertainty: 

Complex systems resist deterministic prediction. Instead, students use Monte Carlo simulations or sensitivity tests to explore uncertainty. 

Activity 3: Communicating uncertainty:

Students prepare short policy memos to government or utility executives: 

• • How can uncertainty be framed as a planning strength, not a weakness? 

• • What visual tools (e.g. fan charts, scenario envelopes) best express it? 

Learning outcome: 

Effective system modelers communicate uncertainty transparently and use it to support adaptive decision-making. 

References: 

• Sterman, J. (2000). Business Dynamics: Systems Thinking and Modeling 

• Bar-Yam, Y. (2003). Dynamics of Complex Systems - Available at: https://necsi.edu/dynamics-of-complex-systems 

• Santa Fe Institute Complexity Explorer

Modelling Software and Tutorials:

• • NetLogo – https://ccl.northwestern.edu/netlogo

• • Vensim PLE – https://vensim.com/vensim-personal-learning-edition/

• • NetworkX (Python) – https://networkx.org/

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.