optimization_code stringclasses 3
values | scheduling_problem stringclasses 3
values |
|---|---|
import cvxpy as cp
import numpy as np
def optimize_profile_tracking():
demand = np.array([5.5, 5.2, 5.0, 5.4, 6.2, 6.8, 6.4, 5.8])
renewable = np.array([1.0, 1.2, 1.8, 2.4, 1.7, 0.8, 0.3, 0.2])
target_import = np.array([4.3, 4.0, 3.6, 3.4, 4.2, 5.0, 5.4, 5.0])
n = len(demand)
dt = 0.25
energy_... | Dispatch an 8 MWh flow battery in eight 15-minute intervals to track a requested site net-import profile. Site demand is [5.5, 5.2, 5.0, 5.4, 6.2, 6.8, 6.4, 5.8] MW, renewable generation is [1.0, 1.2, 1.8, 2.4, 1.7, 0.8, 0.3, 0.2] MW, and the target net import is [4.3, 4.0, 3.6, 3.4, 4.2, 5.0, 5.4, 5.0] MW. The battery... |
import cvxpy as cp
import numpy as np
def optimize_arbitrage():
prices = np.array([34, 30, 28, 31, 45, 72, 88, 61], dtype=float)
n = len(prices)
dt = 1.0
capacity = 12.0
energy_min, energy_max = 1.2, 10.8
initial_energy = 6.0
charge_max = discharge_max = 3.0
eta_charge = eta_discharge =... | Optimize an eight-hour energy-arbitrage schedule for a vanadium flow battery using the hourly electricity prices [34, 30, 28, 31, 45, 72, 88, 61] USD/MWh. The battery has 12 MWh of usable capacity, must remain between 1.2 and 10.8 MWh, starts at 6 MWh, and must finish with at least 6 MWh. Limit charging and discharging... |
import cvxpy as cp
import numpy as np
def optimize_peak_shaving():
demand = np.array([4.0, 4.2, 4.5, 5.8, 6.2, 5.0], dtype=float)
solar = np.array([0.5, 1.0, 2.5, 3.5, 2.0, 0.3], dtype=float)
price = np.array([42, 38, 35, 76, 91, 55], dtype=float)
n = len(demand)
dt = 1.0
energy_min, energy_ma... | Schedule a 10 MWh flow battery over six one-hour intervals to reduce electricity costs and the billing-period import peak at a site with demand [4.0, 4.2, 4.5, 5.8, 6.2, 5.0] MW and on-site solar output [0.5, 1.0, 2.5, 3.5, 2.0, 0.3] MW. The import tariff is [42, 38, 35, 76, 91, 55] USD/MWh, and the demand charge is 18... |
Flow Battery Energy Scheduling Optimization Code Dataset
This dataset pairs flow battery energy scheduling problems with corresponding optimization code in text-to-code examples. The problem statements cover key operational details such as scheduling objectives, state of charge, charging and discharging power, efficiency, and operating constraints. These examples turn storage requirements into optimization programs for code SFT, scheduling logic learning, and algorithm prototyping for flow battery energy storage systems.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| optimization_code | string | Optimization program for flow battery energy scheduling generated from the scheduling problem description. |
| scheduling_problem | string | Source text specifying requirements such as scheduling objectives, state of charge, charging and discharging power, efficiency, and operating constraints. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
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