This paper introduces a novel, open-source MARL simulation framework for studying implicit cooperation in LEMs, modeled as a decentralized partially observable Markov decision process and implemented as a Gymnasium environment for MARL. Our framework features a modular market platform with plug-and-play clearing mechanisms, physically constrained agent models (including battery storage), a realistic grid network, and a comprehensive analytics suite to evaluate emergent coordination. The main contribution is a novel method to foster implicit cooperation, where agents' observations and rewards are enhanced with system-level key performance indicators to enable them to independently learn strategies that benefit the entire system and aim for collectively beneficial outcomes without explicit communication. Through representative case studies (available in a dedicated GitHub repository in https://github.com/salazarna/marlem, we show the framework's ability to analyze how different market configurations (such as varying storage deployment) impact system performance. This illustrates its potential to facilitate emergent coordination, improve market efficiency, and strengthen grid stability. The proposed simulation framework is a flexible, extensible, and reproducible tool for researchers and practitioners to design, test, and validate strategies for future intelligent, decentralized energy systems.
The global energy sector is undergoing a fundamental paradigm shift, transitioning from a historically centralized generation model, reliant on a few large-scale, dispatchable fossil-fuel power plants, towards a highly decentralized system characterized by the extensive proliferation of Distributed Energy Resources (DERs) [1,2]. This category of assets includes residential and commercial rooftop solar photovoltaics, battery energy storage systems, electric vehicles, and controllable loads [3]. This transformation is driven by a confluence of factors: international climate policy mandates for decarbonization, the declining costs of renewable and storage technologies making them economically viable for consumers, and a rising demand for energy autonomy and resilience against large-scale grid disruptions [2].
This evolution marks the rise of the prosumer, an active market participant who not only consumes energy but also produces, stores, and potentially manages it [4]. While this paradigm shift presents significant opportunities for a more efficient, resilient, and sustainable energy future, it concurrently introduces operational challenges, particularly for distribution networks [5]. The traditional unidirectional flow of power from central generators to passive consumers is being replaced by complex, bidirectional flows involving numerous, heterogeneous actors. This necessitates more sophisticated methods of coordination and control to manage potential issues like grid congestion, voltage instability, and the inherent variability of renewable generation [5].
In this context, Local Energy Markets (LEMs) have emerged as a promising framework for managing this emergent complexity at the distribution level [1,2]. By offering a platform for peer-to-peer (P2P) energy trading and flexibility services within a specific geographic community, LEMs aim to enhance local grid stability, promote the efficient utilization of local renewable resources, reduce reliance on bulk transmission systems (thereby minimizing losses), and unlock the full economic potential of distributed assets by allowing prosumers to directly monetize their flexibility [1,2].
Despite their potential, the effective design and operation of efficient, stable, and scalable LEMs present a complex scientific and engineering challenge, often conceptualized as a trilemma [1,2]. First, achieving efficient and scalable coordination among a potentially massive population of autonomous, self-interested agents is a problem of immense complexity [6]. Each agent (representing a home, a building, an electric vehicle, or a community battery) makes decisions based on private objectives (e.g., minimizing costs, maximizing revenue) and limited, local information. This inherent decentralization creates a non-stationary environment where the optimal strategy for any single agent depends on the concurrent, often unobservable, actions of all other participants [2,7].
Second, the economic transactions constituting the market must not compromise the physical integrity of the underlying distribution network [1]. Energy trades correspond to physical power injections and withdrawals. Uncoordinated actions, even if economically rational for individual agents (e.g., simultaneous battery discharging during peak prices), can lead to localized voltage violations, thermal overloading of lines, or unacceptable power quality degradation, threatening grid security [1,2]. Therefore, any viable LEM design must inherently respect or be co-managed with these physical constraints. LEM simulation software must thus tightly couple economic decision-making with physical grid simulation to capture these techno-economic interactions.
Third, a practical LEM architecture must preserve the privacy and autonomy of its participants [6]. Centralized control solutions, which require agents to divulge sensitive consumption data or cede control authority to a central entity, often face significant barriers due to privacy concerns, cybersecurity risks, and the potential creation of single points of failure [2,6]. Scalability also becomes a major issue, as a central controller managing potentially a large number (hundreds or thousands) of DERs faces immense computational burdens. Thus, truly decentralized approaches that respect agent autonomy and data privacy are highly desirable, if not essential, necessitating software capable of modeling and rigorously evaluating such paradigms.
The central problem, therefore, lies in discovering mechanisms and system designs, and the appropriate simulation software to develop and test them, through which desirable system-level goals (supply-demand balance, aggregate economic efficiency, physical grid security) can emerge from the uncoordinated, self-interested actions of independent entities operating under partial observability, without resorting to untenable centralized control.
Addressing the LEM trilemma requires sophisticated modeling and simulation softw
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