Grounded, Compute-Efficient LLM Policy Agents for Energy-Poverty Equity in Physically-Constrained Peer-to-Peer Energy Markets
EqGrid introduces a closed-loop simulation where a low-frequency open-weight LLM policy agent sets price, carbon bounds, and targeted subsidies for households in a peer-to-peer energy market The system combines grounded household personas (validated against real smart-meter data) with high-frequency multi-agent RL traders clearing a continuous double auction on a physically constrained IEEE-33-bus grid A decoupled-safety design (LLM sets bounds, a validate-and-project grid gate enforces constrai
Analysis
TL;DR
- EqGrid introduces a closed-loop simulation where a low-frequency open-weight LLM policy agent sets price, carbon bounds, and targeted subsidies for households in a peer-to-peer energy market
- The system combines grounded household personas (validated against real smart-meter data) with high-frequency multi-agent RL traders clearing a continuous double auction on a physically constrained IEEE-33-bus grid
- A decoupled-safety design (LLM sets bounds, a validate-and-project grid gate enforces constraints) eliminates all grid-constraint violations compared to 55 violations under direct LLM control
- The LLM policy reduces the Gini coefficient of energy burden from 0.351 to 0.305 and cuts mean energy burden by 28% while lowering costs versus a tuned rule baseline
- Model compression preserves most equity gains: a 3B-active model retains 95% of benefits at 9x lower inference energy, and a 0.8B on-device model retains 92% at 24x lower energy
Why It Matters
This work addresses a critical gap in NLP-for-social-good by tackling energy poverty—a domain nearly absent from the literature—while directly confronting the "computational irony" of using carbon-intensive cloud LLMs for humanitarian applications. It provides a concrete framework for measuring and optimizing the social impact of AI systems, demonstrating that compute-efficient, on-device models can achieve near-parity with massive teacher models in equity-focused interventions.
Technical Details
- Architecture: EqGrid is a closed-loop simulation with a two-tier agent structure: a low-frequency open-weight LLM policy agent (ranging from 235B teacher down to 0.8B on-device) sets price/carbon bounds and subsidies, while high-frequency multi-agent RL traders clear a continuous double auction
- Grid constraints: The market operates on a physical distribution grid modeled after IEEE-33-bus with Dynamic Operating Envelopes, ensuring real-world feasibility
- Grounded personas: Household personas are region-matched socio-demographically, with load curves validated for shape and level realism against real smart-meter data
- Equity metrics: Formal energy-poverty metrics include Energy Burden, Gini of Energy Burden, and LIHC (Low Income Home Energy Assistance Program thresholds)
- Decoupled-safety design: The LLM proposes policy bounds while a separate validate-and-project grid gate enforces physical constraints, achieving zero violations versus 55 under direct LLM control
- Compression frontier: Systematic evaluation from 235B teacher → 3B-active (95% benefit retention, 9x energy reduction) → 0.8B on-device (92% retention, 24x energy reduction)
Industry Insight
- The decoupled-safety architecture (policy LLM + constraint enforcement gate) offers a practical blueprint for deploying LLMs in safety-critical infrastructure domains where direct end-to-end control is risky
- The compute-efficiency frontier demonstrates that on-device sub-1B models can deliver near-optimal social impact, enabling deployment in resource-constrained regions where energy poverty is most prevalent—aligning AI's carbon footprint with its humanitarian goals
- The formalization of equity metrics (Gini of energy burden, LIHC compliance) provides a replicable template for measuring social impact in other NLP-for-social-good applications, moving the field beyond qualitative assessments toward quantifiable outcomes
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