Agent Overview

The Agent represents an energy system participant, such as a household, commercial entity, or industrial consumer, that interacts with markets and the grid. Each agent follows a structured workflow to make decisions, optimize its energy usage, and engage in trading.

What Does an Agent Do?

Each agent in HAMLET: - Retrieves grid data to understand network constraints. - Obtains forecasts to predict future energy consumption, production, and prices. - Executes control strategies via the Energy Management System (EMS). - Trades energy by submitting bids and offers to the market.

Agent Execution Workflow

Each simulation step follows a structured sequence:

  1. Grid Data Retrieval - The agent gathers grid-related data to assess constraints and available capacity.

  2. Forecasting - Agents predict their future energy needs and availability using forecasting models. - Forecasts can be based on historical data, weather predictions, or machine learning techniques.

  3. Control Strategy Execution - The Energy Management System (EMS) defines how the agent manages its energy usage. - The EMS can follow:

    • Rule-based strategies

    • Optimization models (e.g., linear programming)

    • Reinforcement learning-based decisions

  4. Market Participation - Based on its forecast and EMS, the agent submits bids and offers to the market. - Market clearing determines how much energy is bought or sold.

Agent Structure

Each agent consists of the following components:

  1. Agent Type - The category of the agent (e.g., single-family home, multi-family home, industry). - Defines the agent’s properties such as load profiles, generation capacity, and flexibility.

  2. Energy Management System (EMS) - The EMS defines how the agent interacts with energy markets and storage systems. - Determines when to store, consume, or trade energy.

  3. Trading Strategy - Defines how the agent participates in energy trading. - Strategies include:

    • Retailer-based trading: Agents buy and sell at retailer prices.

    • Market-driven strategies: Agents bid dynamically based on forecasts.

    • Zero Intelligence (ZI) models: Randomized trading behaviors.

  4. Grid Interaction - Ensures that the agent’s transactions respect grid constraints. - If grid limitations exist, the agent may adjust its trading behavior.

Extending Agent Behavior

HAMLET allows customization of agent behavior:

  • Custom Forecasting Models - Users can integrate different forecasting techniques, from simple averages to deep learning models.

  • Advanced EMS Control - The EMS can be customized to include complex decision-making mechanisms.

  • New Trading Strategies - Users can define new trading mechanisms beyond the default strategies.

By modeling agents with autonomous decision-making capabilities, HAMLET provides a powerful simulation environment for decentralized energy markets.