CityLearn v3 simulates realistic renewable energy community controls
CityLearn v3: A Configurable Simulation and Evaluation Framework for Realistic Control Studies of Renewable Energy Communities
Multiagent SystemsArtificial Intelligence
Summary
Managing groups of buildings with solar panels, batteries, and electric vehicles is complex, especially as members and equipment change over time. The authors created CityLearn v3, a tool that simulates these dynamic renewable energy communities with detailed rules about energy use and sharing. This lets people test how different control approaches handle real-world challenges like equipment failures and strict service deadlines. The tool also tracks differences between requested and actual energy actions to help understand how well systems work.
What this means in practice
- •For energy system controllers: Test and compare control strategies for managing renewable energy communities under realistic conditions including changing resources and equipment failures.
- •For smart grid operators: Evaluate the impact of demand response and local energy sharing policies on community-level power limits and service reliability.
Authors
Tiago Fonseca, Luis Lino Ferreira, Armando Sousa, Ava Mohammadi, Zoltan Nagy
Abstract
Renewable energy communities (RECs) coordinate buildings, photovoltaic generation, batteries, electric vehicles and flexible loads. Controller studies often simplify changing participation, equipment availability, service deadlines and data quality, so lower cost or peak demand can conceal missed services or infeasible power requests. This paper presents CityLearn v3, a configurable simulation and evaluation framework for REC control studies under these conditions. It represents changing members and assets, flexible-load deadlines, demand-response requests, local energy sharing, and data or equipment failures within one simulation environment. Building and phase power limits constrain controllable requests, while a declared timestep preserves consistent power-to-energy accounting. The framework records controller inputs and distinguishes requested actions from those applied to the simulated equipment. Reference controllers, service- and constraint-aware performance indicators, and trajectory exports support comparisons within and across communities. Software checks and application examples examine service delivery, electrical constraints, settlement and changing scenarios; a synthetic high-frequency trace replay illustrates how aggregation can conceal short peaks without changing annual energy. Together, these records allow aggregate performance to be interpreted alongside service failures, action reductions and participant-level outcomes.