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🕵️ Introduction

The CityLearn Challenge 2023 addresses this multi-faceted nature of advanced control in buildings by blending the challenges of control algorithm design, forecast quality and grid-resilience. The CityLearn Challenge 2023 presents a control track as done in previous challenges as well as introduces an independent forecast track where, both tracks are run in parallel and utilize the same dataset.

In the control track, participants will develop energy management agent(s) and an optional custom reward function (in RLC solutions) to manage electrical and domestic hot water energy storage systems, and heat pump power in a synthetic single-family neighborhood under normal grid-operation and power outages.

📑 Problem Statement

In the control track, participants are to develop their own single-agent or multi-agent reinforcement learning control (RLC) policy and optional custom reward function OR a model predictive control (MPC) policy for electrical (battery) and domestic hot water storage systems, and heat pump control in the buildings with the goal of maintaining thermal comfort, reducing carbon emissions, increasing energy efficiency and providing resiliency in the event of power outages.

A single-agent setup will mean one policy is used to control all building resources whereas, multi-agent setup will mean each building's set of resources is controlled using a unique policy that may cooperate or compete with other policies. 

In Phase II, the environment will be updated to include stochastic power outages based on the Reliability Metrics of U.S. Distribution System where the control agent must adequately manage the available distributed energy resources to maintain comfort and energy demand.

💾 Dataset

Please find a detailed breakdown of the dataset over here. 
Refer to the dataset and schema files.

🚀 Submission

Make your first submission using the starter kit🚀!

🖊 Evaluation Criteria

The control track score (ScoreControl) is the weighted average of a thermal comfort score (ScoreControlComfort), an emissions score (ScoreControlEmissions), a grid score (ScoreControlGrid), and a resilience score (ScoreControlResilience). ScoreControlGrid and ScoreControlResilience are averages of four and two KPIs respectively.The control track score (ScoreControl) is the weighted average of a thermal comfort score (ScoreControlComfort), an emissions score (ScoreControlEmissions), a grid score (ScoreControlGrid), and a resilience score (ScoreControlResilience). ScoreControlGrid and ScoreControlResilience are averages of four and two KPIs respectively.

The weights are specified in the table below. In Phase I, the highest weight is given to ScoreControlGrid. By Phase II, ScoreControlResilience is introduced and has the same weight as ScoreControlComfort and ScoreControlGrid. ScoreControlEmissions has the lowest non-zero weight in both phases. The private leaderboard in Phase II makes use of the same weights as the public leaderboard.

All together, the four scores are made up of eight KPIs namely: carbon emissions (G), discomfort (U), ramping (R), 1 - load factor (L), daily peak (P_d), all-time peak (P_n) 1 - thermal resilience (M), and normalized unserved energy (S), which have been defined in the table below. G, U, M, and S are building-level KPIs that are calculated using each building's net electricity consumption (e) or temperature (T) then averaged to get the neighborhood-level value. R, L, P_d, and P_n are neighborhood-level KPIs that are calculated using the neighborhood's net electricity consumption (E). Except U, M, and S all KPIs are normalized by their baseline value where the baseline is the result from when none of the distributed energy resources (DHW storage system, battery, and heat pump) is controlled.

Name	Formula	Description
Carbon emissions		Emissions from imported electricity.
Unmet hours		Proportion of time steps when a building is occupied and indoor temperature falls outside a comfort band,
Ramping		Smoothness of the district’s consumption profile where low R means there is gradual increase in consumption even after self-generation is unavailable in the evening and early morning. High R means abrupt change in grid load that may lead to unscheduled strain on grid infrastructure and blackouts caused by supply deficit.
1 - Load factor		Average ratio of daily average and peak consumption. Load factor is the efficiency of electricity consumption and is bounded between 0 (very inefficient) and 1 (highly efficient) thus, the goal is to maximize the load factor or minimize (1 − load factor)
Daily peak		Average, maximum consumption at any time step per day.
All-time peak		Maximum consumption at any time step.Maximum consumption at any time step.
1 - Thermal resilience		Same as discomfort, UUU but only considers time steps when there is power outage.
Normalized unserved energy		Proportion of unmet demand due to supply shortage e.g. power outage.

Where:

• t: Time step index;
• n: Total number of time steps, t, in 1 episode;
• h: Hours per day (24);
• d: Day;
• i: Building index;
• b: Total number of buildings;
• e: Building-level net electricity consumption (kWh);
• E: Neighborhood-level net electricity consumption (kWh);
• A: Electricity rate ($/kWh);
• B: Emission rate (kgCO2e/kWh);
• T: Indoor dry-bulb temperature (oC);
• Tsetpoint: Indoor dry-bulb temperature setpoint (oC);
• b: Thermal comfort band (±Tsetpoint); and
• O: Occupant count (people).
• F: Power outage signal (Yes/No); and
• q: Building-level cooling, domestic hot water and non-shiftable load energy demand (kWh).

📅 Timeline

See the detailed breakdown of all challenge phases over here.

🏆 Prizes

This challenge has Leaderboard Prizes and Co-authorship Prizes

Leaderboard Prizes

Top three teams or participants on the private Phase II control track leaderboard will receive the following prizes:

• 🥇 1st Prize: 1000 USD
• 🥈 2nd Prize: 800 USD
• 🥉 3rd Prize: 700 USD

Co-Authorship

In addition to the cash prizes, we will invite the top three teams or participants from both tracks to co-author a summary manuscript at the end of the competition. At our discretion, we may also include honorable mentions for academically interesting approaches, such as those using exceptionally little computing or minimal domain knowledge. Honorable mentions will be invited to contribute a shorter section to the paper and have their names included inline.

📱 Contact

📲 Join the conversation with other participants on CityLearn Discord Server.
👥 Challenge is more fun with friends, find your teammates.
💬 Got a query or suggest? Share your feedback