Exploring near-optimal energy systems with stakeholders: a novel approach for participatory modelling

This paper introduces a novel participatory modelling framework that engages stakeholders in energy system planning by allowing them to explore a continuum of near-optimal designs through an interactive interface, thereby revealing their prioritization of factors like emissions and costs beyond mere economic efficiency while fostering deeper understanding of complex trade-offs.

The Gist

Imagine you are trying to design the perfect pizza for your neighborhood.

In the past, energy planners acted like a strict chef who said, "The cheapest pizza is the only good pizza." They would calculate the absolute lowest cost for ingredients and say, "Here is your menu. Take it or leave it."

But people aren't just about saving money. Some neighbors want extra cheese (even if it costs more), some want no pepperoni because they hate the smell, and others are worried about the delivery truck getting stuck in the snow. If the chef only offers the "cheapest" pizza, the neighborhood might reject it, and the transition to a better way of eating fails.

This paper is about a new way to design that pizza.

The Setting: Longyearbyen

The researchers went to Longyearbyen, a tiny, remote town in the Arctic (Svalbard). It's a place where the ground is frozen, the winters are dark, and the energy system is currently running on diesel because it's too far from the mainland to plug into a power grid. They need to switch to cleaner energy, but it's a tricky balancing act.

The Old Way vs. The New Way

• The Old Way (Cost-Optimal): A computer model calculates the single "best" solution based purely on money. It says, "To save the most cash, we should use 100% diesel and maybe a little bit of wind."
• The New Way (Near-Optimal): The researchers realized that there isn't just one best solution. There are thousands of solutions that are only slightly more expensive but might be much better for the environment, safer, or more popular.

Think of it like a mountain range.

• The "Cost-Optimal" solution is the very bottom of the deepest valley.
• The "Near-Optimal" solutions are the gentle hills surrounding that valley. You are only a few steps up, so you aren't paying a fortune, but from the top of the hill, you can see a much better view (lower emissions, less risk, etc.).

The Interactive Tool: The "Energy Slider"

The team built a video-game-like interface for the townspeople. Instead of reading a boring report, residents could play with sliders on a screen.

• Slider 1: How much wind power?
• Slider 2: How much solar power?
• Slider 3: How much hydrogen storage?
• Slider 4: How much green fuel imports?

As they moved the sliders, the screen instantly updated to show the consequences:

• "If you add more wind, your bill goes up by 10%, but your carbon footprint drops by 50%."
• "If you add more storage, the system is safer if a storm hits, but it costs more."

Crucially, the tool was smart. It wouldn't let you pick a combination that would leave the town in the dark. It only allowed you to choose from the "gentle hills" (the feasible, near-optimal solutions) so you couldn't accidentally design a system that failed.

What Did They Find?

When the researchers asked 126 locals to design their ideal energy system, something surprising happened:

1. People didn't want the cheapest option. Most people chose systems that were 91% more expensive than the computer's "cheapest" solution.
2. They wanted a mix. They didn't just want to save money; they wanted to balance emissions, safety, and visual beauty.
3. They learned. By playing with the sliders, people realized, "Oh, I can't have everything I want. If I want zero emissions, I have to pay more. If I want it to be cheap, I have to accept more risk."

One participant said, "Good to see how complicated it is... it will always be a compromise."

Why This Matters

This study shows that energy planning shouldn't be a top-down order. It should be a conversation.

By letting people play with the options, the researchers found that:

• Legitimacy: People are more likely to accept a plan if they helped design it.
• Reality Check: It helps people understand that there is no "magic bullet." Every choice has a trade-off.
• Better Decisions: Policymakers can see what the community actually values (safety over cost, for example) and build a plan that fits those values, rather than just the math.

The Takeaway

Imagine if your city council didn't just present you with a finished budget, but gave you a sandbox where you could build your own city, seeing in real-time how your choices affected traffic, pollution, and taxes.

That's what this paper did for energy. It turned a complex, scary math problem into a game where the community could find their own "sweet spot" between cost, safety, and the environment. It proves that when we involve people in the design, we don't just get a cheaper system; we get a system people actually want to live with.

Technical Summary

1. Problem Statement

Energy transitions require not only technological innovation but also social legitimacy and political feasibility. Traditional energy system modelling often relies on cost-optimization (finding the single cheapest solution) or presents stakeholders with a limited set of pre-selected scenarios. This approach has several limitations:

• Opacity: The complex trade-offs between cost, emissions, and reliability are often hidden from non-expert stakeholders.
• Bias: Pre-selected scenarios may reflect researcher assumptions rather than diverse societal preferences.
• Inflexibility: Cost-optimal solutions often fail to account for non-monetized objectives (e.g., visual impact, energy security, or vulnerability) that are crucial for public acceptance.
• Complexity: Existing participatory tools often simplify system interactions (e.g., ignoring the complementarity of renewables and storage), leading to unrealistic expectations.

The paper addresses the need for a framework that allows stakeholders to holistically explore the near-optimal feasible space of an energy system, enabling them to navigate complex trade-offs while ensuring technical feasibility.

2. Methodology

The authors propose a novel participatory modelling framework based on Modelling-to-Generate-Alternatives (MGA), applied to the remote Arctic settlement of Longyearbyen, Svalbard.

A. The Energy System Model (PyPSA-LYB)

• Context: Longyearbyen is an isolated, off-grid system (78°N) currently transitioning from coal to diesel. It faces severe climate change impacts and geopolitical constraints.
• Model: An open-source energy system optimization model (PyPSA) covering electricity, heat, electrified transport, and industry.
• Technologies: Includes wind, solar, green fuel imports (ammonia, biogas, pellets), hydrogen infrastructure, various storage (battery, thermal, hydrogen), and backup diesel generators. Coal and nuclear were excluded based on political targets (2030/2050 climate goals) and techno-economic feasibility, respectively.
• Optimization: The model optimizes investment and operational decisions to minimize total system cost (capital + operational).

B. Near-Optimal Space Generation (MGA)

Instead of finding a single cost-optimal solution, the authors generated 56,050 feasible system configurations that are slightly more expensive than the optimum but offer diverse characteristics.

• Slack Level: Solutions were generated up to a 125% cost increase over the cost-optimal baseline to ensure a vast diversity of designs.
• Sampling: The authors used a method (following Grochowicz et al.) to approximate the convex polytope of the near-optimal space by solving the linear program with random objective functions.
• Dimensions: The space was projected onto five key decision variables (sliders) controllable by users:
  1. Onshore wind investment.
  2. Solar power investment.
  3. Green fuel imports.
  4. Heat storage investment.
  5. Hydrogen infrastructure investment.

C. Interactive Interface and Interpolation

A custom web interface was developed to allow 126 local participants to interact with these pre-computed solutions.

• Dynamic Constraints: The interface uses linear programming to dynamically restrict slider ranges. As a user moves one slider, the feasible range of the others is recalculated in real-time to ensure the selected combination remains within the feasible near-optimal space ($F_\epsilon$). This prevents users from selecting technically impossible configurations.
• Metric Interpolation: Since metrics (e.g., emissions, prices) are non-linear functions of the sliders, the interface uses heuristic interpolation. It identifies the nearest pre-computed solutions (using Delaunay triangulation of the 56,050 points) and linearly interpolates metric values for the user's specific selection.
• Metrics Displayed: Total system cost slack, electricity/heat prices, CO2 emissions, system vulnerability (weighted sum of weather dependency, imports, complexity, and storage), visual impact, and land use.

D. Data Collection

• Participants: 126 inhabitants of Longyearbyen (representing ~5% of the population) were recruited in public spaces.
• Process: Participants individually adjusted the five sliders to design a system, viewed the resulting metrics, and selected their top three priorities. They also provided qualitative feedback.

3. Key Contributions

1. Novel Framework: This is the first application of near-optimal MGA methods coupled with a real-time, interactive interface for real-world stakeholders (rather than synthetic preferences).
2. Feasibility Enforcement: Unlike previous tools that allow users to select any combination of technologies (often leading to infeasible grids), this tool dynamically enforces system constraints, ensuring all user choices are technically viable.
3. Holistic Trade-off Visualization: The tool captures complex system interactions (e.g., how increased wind affects storage needs and backup requirements) which are often lost in simplified portfolio tools.
4. Bridging the Gap: It successfully bridges the gap between high-resolution techno-economic modelling and participatory planning, allowing stakeholders to "learn by doing."

4. Key Results

• Deviation from Cost-Optimum: Participants consistently deviated from the cost-optimal solution. The median "slack" (additional cost) chosen was 91%, with a mean of 81%. This indicates a strong willingness to pay for non-cost objectives.
• Priority Divergence:
  • Stated vs. Revealed: While 47% of participants stated "electricity price" as a priority, their actual designs often resulted in much higher costs. When explicitly asked about acceptable cost increases in a post-study survey, the median was only 15%, highlighting a gap between revealed (action-based) and stated preferences.
  • Top Priorities: The most prioritized metrics were Emissions (69%), Vulnerability (49.2%), and Electricity Price (47%).
• Technology Preferences:
  • Solar & Hydrogen: Despite being outcompeted in the pure cost-optimal scenario, participants heavily invested in solar and hydrogen storage to meet their emission and security goals.
  • Wind: Participants prioritizing "visual impact" significantly reduced wind power investments.
• Complexity Management: 57% of participants prioritized three or more metrics simultaneously. Despite the complexity, participants reported feeling better informed and capable of navigating the trade-offs.
• Qualitative Feedback: Participants appreciated the complexity revealed by the tool. Some suggested technologies not in the model (e.g., returning to coal, nuclear power, or demand reduction), revealing local identity issues and knowledge gaps.

5. Significance and Implications

• Legitimacy in Planning: The study demonstrates that involving stakeholders in exploring the entire feasible space of near-optimal solutions increases the legitimacy of energy transition plans. It moves beyond "selling" a pre-determined solution to co-creating a path forward.
• Beyond Cost-Optimization: The results challenge the dominance of cost-optimality in energy policy. Stakeholders value resilience, low emissions, and local control enough to accept significantly higher costs, suggesting that policy frameworks must account for these multi-objective preferences.
• Tool for Policymakers: The methodology provides a way for policymakers to understand the "social cost" of specific priorities (e.g., how much extra money is needed to achieve a specific emission target that the public demands).
• Scalability: While tested in a small, isolated community, the conceptual framework and the open-source tool (available on GitHub) can be adapted for other contexts to facilitate transparent and inclusive energy planning.

In conclusion, the paper presents a robust, technically sophisticated approach to participatory energy modelling that respects both the physical constraints of energy systems and the diverse, often conflicting, values of the communities they serve.