🌍 The Rise of Local Energy Markets in the Global Energy Transition

Haoyang Zhang, Eindhoven University of Technology (TU/e)

Introducing Haoyang and his work at MEGAMIND

This article draws on the research of Haoyang Zhang, a final-year PhD student in Electrical Energy Systems at Eindhoven University of Technology (TU/e).

Haoyang is part of the MEGAMIND project— a Dutch national research initiative bringing together top scholars and sector partners – underscores the indispensable role of the collaborative integration of digital technologies, especially Artificial Intelligence (AI), by (local) electricity value-chain partners, operating within the existing legislative framework, as a decisive factor in achieving a successful energy transition.

A key goal of Haoyang’s work is to investigate and demonstrate the transformative potential of Artificial Intelligence (AI) and optimization techniques in shaping the future of energy systems, with a specific focus on developing more efficient and sustainable market mechanisms.

Through this research, Haoyang contributes to MEGAMIND’s broader mission of building AI-supported, data-driven infrastructures that empower communities and advance the energy transition in a responsible and scalable way.

Introduction

As the world accelerates toward sustainable energy, electricity markets are undergoing a major shift in how we produce, consume, and share energy. Renewable energy sources like solar panels and wind turbines are now common at both the household and community level. Today, millions of homes are equipped to generate and even store their own electricity.

In this evolving market, the flow of power is no longer one-way from big power plants to passive consumers. Instead, former consumers are becoming “prosumers” – both producers and consumers of energy – who actively participate in the energy system. This decentralization is paving the way for local energy markets, where neighbors and communities trade electricity among themselves. It’s a radical shift from the traditional top-down grid, aligning with a broader move to decentralized, consumer-centric models of energy management.

In this article, we explore how hybrid local markets operate, the roles of aggregators and prosumers within them, the pros and cons of different market structures, and how advanced AI techniques like reinforcement learning are optimizing energy trading and reliability. Throughout, we will simplify key findings from recent research in collaboration with the MEGAMIND project to illuminate how these concepts work in practice.

đź’ˇ This article is based on research from the MEGAMIND project, in collaboration with Haoyang Zhang, a PhD student at TU Eindhoven focusing on AI-driven market designs for decentralized and user-centric electricity markets.

The Rise of Local Electricity Markets     

A local electricity market (LEM) is a marketplace where prosumers in the same area (neighborhoods, villages, or communities) can buy and sell energy among themselves, rather than only relying on traditional utility companies. This local exchange can take several forms, the most common being peer-to-peer (P2P) markets and community-based markets.

🔄 Peer-to-peer (P2P) markets let individual prosumers trade directly with each other, offering maximum flexibility to each participant. In a P2P market, a home with surplus solar energy can sell it directly to a neighbor who needs it. Trading usually happens through an online platform that matches buyers and sellers, giving participants the freedom to set their own terms and find the best deals within the local network.

🧩 Community-based markets: In contrast, a community-based market operates through a central aggregator and/or community manager who mediates a defined group (e.g. an apartment complex or a neighborhood association). The aggregator optimizes the group’s combined energy resources, sets internal prices, and manages allocations to maximize the community’s overall financial benefit, creating a more structured and cooperative trading environment.

Both models offer distinct advantages: P2P markets maximize freedom and direct interaction, while community markets can offer more structure and stability within the group. However, these are not mutually exclusive options – and that’s where hybrid local markets come into play.

What are Hybrid Local Markets?

A hybrid local electricity market combines elements of both P2P and community-based markets. Aggregators manage local communities of prosumers (ensuring efficiency within each community) and then trade with other communities on a peer-to-peer network. By mixing centralized coordination with decentralized trading, hybrid markets enhance both local energy flexibility and overall market scalability.

Think of a hybrid market structure like a federation of energy communities. Each community optimizes energy use internally (maybe reserving some shared battery storage or prioritizing essential loads) and then sends any remaining surplus or demand into a larger marketplace that connects multiple communities.

Researchers have been studying hybrid LEM designs to understand how best to organize these markets. One key finding is that how you structure the hybrid market – the rules and hierarchy by which communities and individuals trade – can greatly impact performance in terms of economic efficiency and grid stability. Let’s examine two main structural approaches: distributed versus hierarchical markets.

Hierarchical vs. Distributed Market Structures

Designing a market where lots of small players trade energy can be done in more than one way. Researchers have been exploring two main structures for hybrid local markets: hierarchical and distributed​.

  • In the distributed structure, there is no single coordinating authority for peer-to-peer trades. Here, each aggregator (or sometimes even each prosumer) negotiates directly with others in a decentralized way. Essentially, the integrated hybrid market is broken into many small bilateral negotiations or small group agreements, rather than posting offers on one big marketplace board.
  • In the hierarchical structure, the market is split into two levels. At the lower level, we have multiple community markets (each run by an aggregator for a neighborhood or building complex). At the upper level, there’s a central P2P market platform that links all those communities. In this setup, each aggregator manages its community’s resources optimally first and thereafter trades with other aggregators in a central marketplace (or platform). It’s “hierarchical” because there’s a clear division: community decisions first, then a top-level market to connect communities. The advantage is that it simplifies the negotiation process: the central market can calculate the optimal trades much faster than multiple bilateral negotiations.

Which structure works better? According to recent studies, the hierarchical approach tends to be more reliable and scalable in practice. In one study, the hierarchical structure consistently achieved the most optimal trades at better prices and allocations for everyone under a variety of conditions​. In the distributed market, when every player tried to negotiate with every other player simultaneously, the process became slow and computationally heavy. The distributed model was also harder to scale up to many participants, whereas the hierarchical model handled growth more gracefully​.

💡 Hierarchical models often achieve optimal results with less complexity — like using a town hall instead of door-to-door deals.

Smarter Energy Trading with AI and Reinforcement Learning

In a hybrid local market, each aggregator or prosumer needs to decide how much energy to buy or sell, and at what price, to benefit themselves while ensuring grid constraints are met. These decisions are complex: they involve forecasting demand and supply, reacting to others’ bids, and obeying network constraints. Rather than hard-coding strategies, researchers are training “AI agents” to learn optimal strategies through simulation. Reinforcement learning is a technique where an agent learns by trial-and-error, getting “rewards” for good outcomes (like cost savings or profits) and penalties for bad ones. Over time, the agent figures out a strategy that maximizes its reward.

In the context of local energy markets, Multi-Agent Reinforcement Learning (MARL) is used, meaning multiple AI agents learn simultaneously while interacting with each other. This makes sense because in a market, no player acts alone – each home or aggregator’s decisions affect the others. MARL algorithms let agents learn and adapt their bidding strategies based on the behavior of others, rather than assuming others’ actions are fixed​.

Conclusion: Toward a Collaborative Energy Future

The rise of local energy markets signals that the future of energy is not only greener, but also smarter and more collaborative. While the global energy transition is often framed by large-scale projects like offshore wind farms and sweeping national policies, the quieter revolution happening within communities is just as vital.

🔑 Key Takeaways

  • ⚡ Local energy markets give prosumers more control and value from their own energy resources.
  • 🤝 Hybrid models combine P2P freedom with community coordination.
  • 🧭 Hierarchical market structures streamline decision-making by combining local control with centralized coordination
  • 🧠 Reinforcement learning can optimize bidding strategies and improve market outcomes through AI Agents.

References to Haoyang’s published articles: