Multi-Agent Based Automation of Peer-to-Peer (P2P) Energy Trading

Prosumer agents autonomously set prices based on local scarcity, forecasted generation, and personal preferences, capturing 5-15% higher value per kWh compared to static feed-in tariffs or simple net metering. By continuously evaluating grid export prices against local peer demand, the system maximizes revenue for solar and storage owners while providing cheaper energy to buyers within the microgrid.

Manual reconciliation of bilateral trades, invoice generation, and payment processing is eliminated. Smart contracts or a centralized settlement engine automatically clear transactions, validate meter data, and initiate payments through integrated financial systems. This reduces the administrative cost of operating a local energy market by 60-80%, making it viable even for small community microgrids.

By facilitating local energy consumption, the platform reduces the volume of power imported from the main grid during high-price periods. For a community or campus, this directly translates to lower peak demand charges and transmission fees, which can constitute 30-50% of a commercial energy bill. The multi-agent system proactively shifts loads and dispatches storage to keep local net demand flat.

Automated onboarding, telemetry validation, and compliance checking allow new prosumers and assets to join the trading pool in days, not months. The agent-based architecture scales linearly; adding 1,000 new trading nodes requires no proportional increase in manual oversight. This enables rapid expansion of the Virtual Power Plant (VPP) portfolio and quicker monetization of new DER installations.

A self-balancing local market reduces strain on distribution transformers and feeders. For utilities or microgrid operators, this automated, decentralized coordination acts as a non-wires alternative, deferring or avoiding costly grid upgrades. The system provides real-time visibility and control, allowing operators to use the P2P network as a dynamic grid resource for voltage support or congestion management.

The workflow embeds governance directly into the trading logic: price caps, trade size limits, and participant credit checks are enforced automatically. Suspicious trading patterns are flagged by surveillance agents for human review. This reduces regulatory and financial risk, ensuring the market operates within defined policy guardrails and maintains audit-ready records for regulators or auditors.

Autonomous software agents representing each participant (solar homeowner, battery owner, EV fleet). Each agent ingests local asset telemetry (solar output, state-of-charge, load forecast) and internal value functions to autonomously generate buy/sell orders with price limits. They communicate via a standardized API to a central order book or perform direct peer-to-peer negotiation using predefined rulesets.

The core logic that matches buy and sell orders based on price-time priority or alternative mechanisms (e.g., double-auction, continuous trading). This component calculates clearing prices, determines transaction volumes, and initiates settlement. It integrates with a distributed ledger (e.g., Hyperledger Fabric) or a centralized settlement system to record immutable transactions and trigger financial settlements via pre-registered payment rails or credit accounts.

A safety-critical component that ingests real-time data from the distribution grid operator or local microgrid controller (voltage, transformer loading). It applies power flow models to validate that proposed P2P trades do not violate physical network constraints. If a constraint is forecasted, it can dynamically adjust trade limits or re-dispatch storage assets to provide non-wires alternatives, preventing costly grid upgrades.

The human-facing control plane built on frameworks like LangGraph or CrewAI for defining agent interaction workflows. It provides operators with a real-time view of market depth, agent activity, network constraints, and settlement status. Includes approval gates for manual intervention on large trades or system overrides, and generates audit trails for regulatory compliance (FERC, PUC). Integrates with utility DERMS and SCADA systems.

The data backbone that aggregates second-by-second readings from smart meters, inverters, and battery management systems. This pipeline feeds real-time state data to the trading agents and clearing engine. It also runs hyper-local solar irradiance and load forecasting models to give agents predictive intelligence, improving bid strategy and the platform's overall economic efficiency. Data quality checks and anomaly detection automatically route faulty sensor data for review.

The unified interface that connects disparate hardware and enterprise systems. It normalizes communication between inverter APIs (SolarEdge, Enphase), BMS protocols, utility demand response signals, and legacy building management systems (BMS/BAS). This component handles authentication, protocol translation (e.g., Modbus to REST), and command routing, ensuring the multi-agent system can execute physical control actions (charge, discharge, curtail) based on cleared trades.

The economic buyer who owns the distributed energy resources (DERs) and is accountable for ROI. They define the commercial strategy (e.g., maximize revenue, minimize costs, achieve sustainability targets) and approve the budget. Their success metrics are increased asset utilization, reduced energy bills, and new revenue streams from market participation. They provide the capital and sign off on the functional requirements and performance guarantees.

The technical lead responsible for integrating the multi-agent trading logic with existing physical and software infrastructure. They design the architecture connecting agent APIs to SCADA systems, inverter controls, battery management systems (BMS), and meter data management (MDM) platforms. They ensure low-latency communication (using protocols like IEEE 2030.5, OpenADR, MQTT) and are accountable for system reliability, data integrity, and cybersecurity of the control layer.

Ensures the automated trading platform adheres to local energy market rules, tariff structures, and regulatory frameworks (e.g., FERC, state PUCs). They define the guardrails for the trading agents—price caps, reporting requirements, data privacy (for prosumer data), and audit trail specifications. Their sign-off is required before go-live, and they oversee ongoing surveillance for market manipulation (MMS) risks. They work with legal to interpret complex, evolving regulations.

Builds and maintains the core agentic orchestration layer, typically using frameworks like LangGraph or CrewAI to model prosumer agents, a market-clearing agent, and a settlement agent. They develop the forecasting models (solar, load, price), the matching algorithms (continuous double auction, blockchain-based settlement), and the observability dashboards. They are responsible for model retraining, performance monitoring, and handling exception routing (e.g., failed transactions, data gaps).

Represents the end-users (commercial buildings, residential participants) in the microgrid. They define the user interface requirements for the trading portal—showing real-time prices, trade history, savings, and providing manual override controls. They design the onboarding flows, communication templates for rule changes, and handle support escalations. Their goal is to ensure participant trust and engagement, which is critical for network liquidity and the platform's long-term success.

Manages the interface with the local utility or balancing authority. They ensure the P2P platform correctly accounts for net energy metering (NEM) credits, manages grid export limits, and integrates with wholesale market settlement systems (e.g., via an ISO API). They oversee the automated generation of settlement statements, validate payments, and reconcile discrepancies. This role is crucial for financial accuracy and maintaining a positive relationship with the grid operator.