Energy Arbitrage Competition

Algorithmic electric grid optimization

Energy storage arbitrage is a core problem in modern electricity markets: a battery operator can profit by purchasing power when prices are low and selling it back when prices are high, but must act under uncertainty as real-time prices deviate from day-ahead forecasts due to weather, demand shocks, and transmission congestion.

The Energy Arbitrage competition challenges miners to optimize battery dispatch decisions across a simulated electrical grid. Miners submit algorithmic policies that decide when to charge and discharge batteries at each time step to maximize profit, while respecting physical constraints including battery state-of-charge limits, network power flow limits, and transaction/degradation costs.

Evaluation Overview

Each evaluation runs the miner's policy across 100 challenge instances. Instances cycle through 5 scenarios of increasing difficulty:

Scenario	Nodes	Lines	Batteries	Time Steps	Duration
Baseline	20	30	10	96	1 Day
Congested	40	60	20	96	1 Day
Multiday	80	120	40	192	2 Days
Dense	100	200	60	192	2 Days
Capstone	150	300	100	192	2 Days

• Each time step represents 15 minutes. Scenarios increase in network size, congestion, price volatility, and battery heterogeneity.

• Each rounds's evaluation set is seeded to ensure determinism.

  • This seed changes from round to round.

Step by Step

At each time step, the miner's policy function receives:

• The current state: battery state-of-charge levels, real-time nodal electricity prices, exogenous grid injections, feasible action bounds per battery, and accumulated profit.

• The challenge view: network topology (nodes, lines, PTDF matrix, flow limits), battery parameters (capacity, power limits, efficiency), exogenous injection schedule for all time steps, and day-ahead prices.

• The policy returns a list of actions (MW), one per battery. Negative values charge; positive values discharge.

  • Actions must stay within the provided bounds.

• Real-time prices are generated stochastically at each step from a hidden seed -- miners cannot predict future RT prices.

  • Day-ahead prices are known in advance for the full horizon.

Constraints

• Battery SOC: Must remain between 10% and 90% of capacity. Starts at 50%.

• Charge/discharge efficiency: 95% each direction.

• Network flow limits: Actions must not cause line flows to exceed limits (DC power flow model). Violations cause the step to fail.

• Action bounds: Pre-computed at each step based on current SOC and battery power limits.

• Timeouts:

  • Per-step timeout = 30 seconds.

  • Total evaluation timeout = 1200 seconds.

Profit Calculation

At each time step, per battery:

  profit = revenue - transaction_cost - degradation_cost                                                                   
                                                                                                                         
  revenue           = action * rt_price * dt                                                                               
  transaction_cost  = 0.25 * |action| * dt
  degradation_cost  = 1.00 * (|action| * dt / capacity)^2                                                                  
                                                                                                                           
  Where dt = 0.25 hours (15 min). Total profit is the sum across all batteries and all time steps.                         
 

Scoring

• Each instance is scored by comparing the miner's total profit against a baseline (the better of two built-in heuristic policies -- greedy and conservative):

• quality = (miner_profit - baseline_profit) / (baseline_profit + 1e-6)

• quality_int = round(clamp(quality, -10, +10) * 1,000,000)

• The miner's final score is the average quality across all 100 instances.

• To surpass the current winner, a miner must earn a raw score > 1% higher than the current top raw score.

  • If there is no current winner, the miner must beat the baseline raw score by at least 1%.

• The score_to_beat is displayed in the Apex CLI dashboard under competition information.

Miner Submissions

• Miners submit a single .py file implementing:

  • def policy(challenge: PolicyView, state: State) -> list[float]:

• Maximum submission size: 50,000 characters.

• Default round length: 1 day.

• Submission Fee: $1.00 USD.

• 1% raw_score threshold to beat current top scorer.

• Miners code is revealed 1 day after evaluation.

• Logs are opened after the current round is completed.

• The submission rate limit is 4 submissions per hotkey within 24 hours, across all competitions.

• An example of baseline solver implementations can be found in the energy_arbitrage/python folder.

• The information about enabled packages is in requirements.txt. Only numpy is available beyond the standard library.