Power to the Prosumers: Decentralized Energy Markets Emerge

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Author: Denis Avetisyan

A new approach to energy sharing leverages automated market making to efficiently coordinate distributed resources and empower individual producers.

The interplay between a power grid and its constituent prosumers reveals a dynamic of reciprocal exchange, where aggregated energy sales to the grid-indicated by increasing green values-are balanced against community energy purchases-represented by growing red values-with the resulting net interaction-traced by the blue line-highlighting periods of energy surplus and deficit.
The interplay between a power grid and its constituent prosumers reveals a dynamic of reciprocal exchange, where aggregated energy sales to the grid-indicated by increasing green values-are balanced against community energy purchases-represented by growing red values-with the resulting net interaction-traced by the blue line-highlighting periods of energy surplus and deficit.

This review demonstrates that decentralized energy markets utilizing Automated Market Makers can achieve welfare equivalence to centralized planning, offering a practical framework for prosumer coordination.

Decentralized energy sharing promises increased resilience and efficiency, yet coordinating prosumer behavior remains a significant challenge. This paper, ‘Automated Market Making for Energy Sharing’, introduces a novel market design leveraging Automated Market Makers (AMMs) to facilitate local energy exchange. We demonstrate that implementing AMMs-built upon principles from decentralized finance-can achieve welfare equivalence to a centralized social planner, ensuring both budget balance and ex-ante efficiency. Could this approach unlock a new era of peer-to-peer energy trading and empower more sustainable, localized energy systems?

The Shifting Sands of Energy: From Centralized Control to Distributed Complexity

The established architecture of energy markets was designed for a unidirectional flow – power plants generating electricity and consumers utilizing it. However, the rise of ‘prosumers’ – individuals and businesses that both consume and produce energy, often through solar panels or wind turbines – fundamentally disrupts this model. This influx of decentralized generation introduces significant complexities; traditional systems struggle to accurately forecast and balance supply and demand when energy is flowing from countless, intermittent sources. The sheer volume of data, coupled with the variability of renewable generation, overwhelms conventional grid management tools, leading to potential instability and economic inefficiencies. Consequently, adapting to this new paradigm requires innovative approaches to energy trading and grid operation, moving beyond centralized control towards more dynamic and localized solutions.

The increasing prevalence of distributed energy resources – rooftop solar, home batteries, and electric vehicles – introduces significant challenges to grid stability by complicating the traditional balance between energy supply and demand. Historically, large, centralized power plants provided predictable generation, allowing grid operators to anticipate and manage fluctuations. However, the granular and often intermittent nature of distributed generation makes precise forecasting difficult, leading to potential imbalances. When local supply exceeds demand, it can overwhelm distribution networks, while shortages necessitate reliance on more expensive and potentially carbon-intensive backup sources. This inherent volatility not only increases operational costs but also hinders the efficient integration of renewable energy, as excess clean power may be curtailed due to grid limitations, ultimately diminishing the environmental and economic benefits of a decentralized energy system.

The increasing prevalence of locally generated power – from rooftop solar and wind turbines to community batteries – necessitates a shift towards robust local energy trading systems. These systems aren’t merely about facilitating transactions; they are becoming essential for maintaining grid stability as centralized power sources are supplemented – and potentially displaced – by these distributed energy resources. By enabling peer-to-peer energy exchange and incentivizing local consumption of renewable energy, these markets can drastically reduce transmission losses, alleviate strain on grid infrastructure, and optimize the utilization of intermittent sources like solar and wind. Successful implementation requires advanced technologies for forecasting, pricing, and secure transactions, but the potential benefits – a more resilient, efficient, and sustainable energy future – are substantial and increasingly critical as the energy landscape evolves.

Traditional energy systems, built around large, centrally-located power plants, are increasingly challenged by the rise of distributed energy resources. These established frameworks struggle with the inherent variability and bi-directional energy flows introduced by rooftop solar, wind turbines, and energy storage systems operating at the grid edge. The conventional methods of forecasting demand and dispatching power, designed for predictable, one-way flows, prove inadequate in this dynamic landscape. Attempting to manage these decentralized resources with centralized control introduces significant latency and inefficiencies, hindering the full potential of renewable integration and creating barriers to a more resilient and cost-effective energy future. A fundamental shift toward more localized and adaptive management strategies is therefore essential to accommodate this evolving energy paradigm.

Community energy profiles in Paris reveal higher demand during the winter week (right) compared to the summer week (left), with solar and wind energy (blue and black lines, respectively) contributing to meet that demand (red line).
Community energy profiles in Paris reveal higher demand during the winter week (right) compared to the summer week (left), with solar and wind energy (blue and black lines, respectively) contributing to meet that demand (red line).

Automated Markets: A Fluid Response to Decentralized Energy

Automated Market Makers (AMMs) facilitate continuous and dynamic pricing in energy trading by directly responding to real-time supply and demand fluctuations. Unlike traditional energy markets relying on scheduled trades or fixed pricing, AMMs utilize algorithms to constantly adjust prices based on the ratio of buy and sell orders. This responsiveness is achieved without requiring a central order book; instead, prices are determined by a mathematical function correlating supply with demand. The benefit is a price discovery mechanism that reflects immediate market conditions, potentially increasing efficiency and reducing price discrepancies, and enabling transactions even with limited liquidity. This contrasts with conventional approaches where price adjustments may lag due to batch processing or manual intervention.

Traditional energy trading relies on centralized order books managed by intermediaries, incurring both operational costs and delays due to matching and settlement processes. Eliminating these intermediaries and the associated infrastructure reduces transaction fees and minimizes latency. By directly connecting prosumers and consumers, Automated Market Makers (AMMs) facilitate peer-to-peer energy transactions without the need for a central authority to validate and execute trades. This disintermediation streamlines the trading process, enabling faster response times to fluctuations in supply and demand, and lowering the overall cost of energy transactions for all participants. The removal of these traditional layers also reduces counterparty risk as trades are often executed via smart contracts, automating settlement and reducing the need for trust between parties.

Automated Market Makers (AMMs) utilize mathematical functions to establish price and liquidity within a trading system, mirroring techniques originating in decentralized finance (DeFi). These functions, such as bonding curves, define a relationship between the quantity of an asset – in this case, energy – and its price. A bonding curve mathematically defines this relationship, allowing traders to buy or sell energy at prices determined by the current supply and demand as reflected in the curve’s equation. This contrasts with traditional order-book systems; instead of matching individual buy and sell orders, AMMs use these functions to continuously adjust prices based on the total volume of energy traded. The resulting price discovery mechanism is fully automated and enables constant liquidity, even with limited trading activity, by algorithmically providing or absorbing energy based on the established mathematical parameters.

The Constant Function Market Maker (CFMM) is an Automated Market Maker (AMM) implementation particularly well-suited for local energy trading due to its simplicity and predictable pricing. It operates on the principle of maintaining a constant product between the quantities of two assets – in this case, energy and a reference asset, often a stablecoin or fiat currency. This relationship is mathematically expressed as x * y = k, where ‘x’ represents the quantity of energy, ‘y’ represents the quantity of the reference asset, and ‘k’ is a constant. Price is determined by the ratio of these quantities; as demand for energy increases (x increases), the price (y/x) increases to maintain the constant ‘k’, providing inherent price discovery. This design minimizes slippage for smaller trades and offers predictable liquidity provision, making it efficient for managing the granular, localized transactions characteristic of community energy systems.

Establishing upper and lower price boundaries effectively constrains the Automated Market Maker (AMM) within a defined operating range.
Establishing upper and lower price boundaries effectively constrains the Automated Market Maker (AMM) within a defined operating range.

Axiomatic Foundations: Building Trust into the Market Mechanism

The functionality of Automated Market Maker (AMM)-based energy markets relies on adherence to several foundational axiomatic principles. These principles, derived from mechanism design and game theory, establish predictable behaviors and outcomes crucial for market stability. Key among these are ‘Individual Rationality’, ensuring participants consistently benefit from engaging with the market; ‘Budget-Balance’, which dictates that total market revenue must equal total costs to prevent deficits or surpluses; and ‘Coalition-Proofness’, a condition that prevents any subgroup of participants from achieving a more favorable outcome by deviating from the established market rules. These axioms, when satisfied, contribute to a trustworthy and efficient energy trading environment.

The stable operation of an Automated Market Maker (AMM)-based energy market relies on several axiomatic principles. ‘Individual Rationality’ ensures that each prosumer receives sufficient benefit from market participation to incentivize continued engagement. ‘Budget-Balance’ dictates that total market revenue must equal total costs, preventing deficits or surpluses that could destabilize the system. Finally, ‘Coalition-Proofness’ guarantees that no subgroup of prosumers can collectively improve their outcomes by deviating from the established market mechanisms, thereby maintaining overall market integrity and preventing opportunistic behavior. These principles work in concert to establish a predictable and trustworthy environment for energy trading.

The implementation of homothetic and quasi-concave functions within the Automated Market Maker (AMM) energy market is critical for maintaining price stability and predictable behavior. A homothetic function ensures price scales proportionally with demand; if demand doubles, the price will also double, preserving the relative value of transactions. Quasi-concavity guarantees that the price curve exhibits diminishing marginal returns, preventing excessively steep price increases that could discourage participation or lead to market instability. Mathematically, this implies the second derivative of the price function with respect to demand is negative. These properties, when combined, result in a smooth and predictable price discovery mechanism, facilitating efficient matching of supply and demand within the energy market and promoting trust among prosumers.

The implementation of anonymity within the AMM-based energy market, where payments are strictly determined by individual contribution and are independent of participant identity, is a key factor in building market trust and promoting broader participation. This approach eliminates concerns regarding preferential treatment or discrimination, ensuring equitable compensation for all prosumers. By decoupling payment from identifying information, the system minimizes the potential for collusion or manipulation, and reduces the administrative overhead associated with identity verification. This contributes to a more transparent and predictable market environment, incentivizing a larger number of actors to engage in energy trading and contribute to overall market stability.

Simulated Automated Market Maker (AMM) price bands, represented by the interquartile range of buying (red) and selling (green) prices, demonstrate the system's price fluctuations relative to benchmark prices (dotted and dashed lines) across all epochs.
Simulated Automated Market Maker (AMM) price bands, represented by the interquartile range of buying (red) and selling (green) prices, demonstrate the system’s price fluctuations relative to benchmark prices (dotted and dashed lines) across all epochs.

From Individual Optimization to System-Wide Equilibrium

The core of this energy system lies in individual prosumer optimization, where each participant independently calculates the ideal balance between locally generated power production, direct consumption, and strategic energy trading. This isn’t a passive process; rather, each prosumer acts as a miniature energy enterprise, continuously assessing factors like predicted solar irradiance, time-of-use tariffs, and real-time market prices to refine their operational plan. Through this decentralized decision-making, the system moves beyond simple supply and demand; it actively shapes both. Each prosumer’s choices are not isolated, but collectively influence the broader energy landscape, driving a dynamic interplay that ultimately leads to a more efficient and responsive grid. The process involves evaluating various operating scenarios to maximize financial benefit while ensuring reliable energy access, effectively transforming consumers into active participants in the energy market.

Addressing the intricate challenges of prosumer optimization necessitates the application of sophisticated mathematical techniques. Dynamic Programming breaks down complex decisions into manageable stages, enabling the identification of optimal strategies over time. Complementing this, Linear Programming formulates the problem as a set of linear constraints and an objective function, efficiently determining the best allocation of resources. Furthermore, Rolling Horizon Optimization provides a practical approach by repeatedly solving the optimization problem over a finite time horizon, adapting to changing conditions and uncertainties. These methods, often implemented with advanced computational algorithms, allow each prosumer to navigate the trade-offs between production costs, consumption needs, and market prices, ultimately contributing to a more stable and profitable energy ecosystem.

The complexities of numerous prosumers simultaneously optimizing their energy strategies necessitate a population-level approach, and the Mean Field Game (MFG) framework provides just that. Rather than modeling each prosumer individually, MFG treats the collective behavior of prosumers as a continuous ‘field’, simplifying the calculations while retaining a high degree of accuracy. This allows researchers to predict emergent market behaviors – such as price fluctuations and overall system stability – by analyzing the interactions within this field. The framework considers each prosumer’s decision-making as influenced by the average behavior of all others, creating a dynamic system where individual strategies adapt to the collective. Ultimately, MFG facilitates a robust understanding of how decentralized energy markets self-organize and achieve equilibrium, offering valuable insights for system design and policy implementation.

A stable market equilibrium emerges from the collective optimization strategies of interconnected prosumers, fostering a remarkably resilient and efficient energy system. This dynamic balance of supply and demand, achieved through decentralized decision-making, yields substantial economic benefits; simulations reveal prosumers experience a significant 60% monetary gain compared to conventional fixed-price models. Furthermore, the broader community benefits from a 42% increase in overall profits, demonstrating the power of this prosumer-centric approach to not only enhance individual economic well-being but also to bolster the financial health of the energy network as a whole. This equilibrium isn’t static, however, but rather a continuously adapting response to fluctuating conditions, ensuring a robust and profitable energy landscape.

Net trading profiles reveal distinct patterns among consumer, solar prosumer, and wind prosumer agents, with the median (black line) and interquartile ranges (light/dark blue) illustrating the variability in their trading behavior.
Net trading profiles reveal distinct patterns among consumer, solar prosumer, and wind prosumer agents, with the median (black line) and interquartile ranges (light/dark blue) illustrating the variability in their trading behavior.

The pursuit of welfare equivalence in decentralized energy systems, as explored in this work, hinges on a rigorous understanding of dynamic equilibrium. It’s a complex game, one where prosumer behavior must be anticipated and coordinated without a central authority. This echoes Albert Camus’s observation: “The only way to deal with an unfree world is to become so absolutely free that your very existence is an act of rebellion.” The system doesn’t promise perfect optimization-it acknowledges the inherent uncertainty and strives for a robust equilibrium, constantly tested by the actions of individual actors. Every metric is an ideology with a formula, and the beauty of this approach lies in its ability to withstand the inevitable imperfections of real-world data, proving that even in complex systems, a degree of freedom-and the acceptance of its associated chaos-can lead to surprisingly stable outcomes.

Where Do We Go From Here?

The demonstration of welfare equivalence to a centralized planner is, predictably, not the end. It merely shifts the burden of proof. Every dataset is just an opinion from reality, and this framework, while elegant in its theoretical construction, exists presently as a simulation. The true challenge lies in reconciling its assumptions with the messy, stochastic realities of power grids – the intermittent renewables, the unpredictable demand spikes, the sheer diversity of prosumer behavior. The devil isn’t in the details-he’s in the outliers, those edge cases that will inevitably expose the limitations of current equilibrium analyses.

Future work should prioritize robustness testing under more realistic network topologies and cost structures. Mean field games offer a powerful simplification, but the heterogeneity of prosumers may demand more granular modeling. Beyond technical refinements, a critical question remains: does this decentralized approach genuinely incentivize participation, or does it simply redistribute existing benefits? Averages are comforting, but variance-the degree of inequality-will ultimately determine its societal impact.

Ultimately, the success of such a system will hinge not on mathematical perfection, but on pragmatic adaptability. The pursuit of theoretical optimality is a worthwhile endeavor, provided it doesn’t blind one to the inherent uncertainty of complex systems. The goal shouldn’t be to predict the future energy market, but to create a framework resilient enough to absorb its shocks.

Original article: https://arxiv.org/pdf/2512.24432.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-02 11:07