Agent skill

eptr2-imbalance-costs

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npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/development/eptr2-imbalance-costs

SKILL.md

Turkish Electricity Imbalance Cost Calculations with eptr2

Overview

This skill helps you calculate and analyze electricity imbalance costs in Turkey's energy market using the eptr2 Python library. Imbalance costs are critical for market participants who deviate from their scheduled positions.

Quick Start

python
from eptr2 import EPTR2
from eptr2.composite import get_hourly_price_and_cost_data

# Initialize
eptr = EPTR2(use_dotenv=True, recycle_tgt=True)

# Get comprehensive imbalance data
df = get_hourly_price_and_cost_data(
    eptr,
    start_date="2024-07-29",
    end_date="2024-07-29"
)

# Columns include: pos_imb_price, neg_imb_price, pos_imb_cost, neg_imb_cost, kupst_cost
print(df[['date', 'mcp', 'smp', 'pos_imb_cost', 'neg_imb_cost', 'kupst_cost']])

Key Concepts

System Direction (Sistem Yönü)

Turkish English Meaning sd_sign
Enerji Açığı Energy Deficit System is short, up-regulation -1
Enerji Fazlası Energy Surplus System is long, down-regulation +1
Dengede Balanced System is balanced 0

Imbalance Types

Type Turkish When Applied
Positive Imbalance Pozitif Dengesizlik You have surplus energy (produced more or consumed less than scheduled)
Negative Imbalance Negatif Dengesizlik You have deficit energy (produced less or consumed more than scheduled)

Imbalance Price Formulas

Current Regulation (2024)

python
# Penalty margin = 3%
penalty_margin = 0.03

# Positive imbalance price (you receive)
pos_imb_price = (1 - penalty_margin) * min(MCP, SMP)
pos_imb_price = 0.97 * min(MCP, SMP)

# Negative imbalance price (you pay)
neg_imb_price = (1 + penalty_margin) * max(MCP, SMP)
neg_imb_price = 1.03 * max(MCP, SMP)

Imbalance Cost (Per MWh)

python
# Cost of positive imbalance (revenue foregone)
pos_imb_cost = MCP - pos_imb_price

# Cost of negative imbalance (penalty paid)
neg_imb_cost = neg_imb_price - MCP

KUPST (Deviation Cost from Production Plan)

KUPST applies to deviations from scheduled production plans (KGÜP).

Formula

python
kupst_multiplier = 0.03  # 3%
kupst_floor_price = 750  # TL/MWh

kupst_cost = max(MCP, SMP, kupst_floor_price) * kupst_multiplier

Tolerance Rates by Source

Source Tolerance
Wind 17%
Solar 10%
Other 5%

Cost Calculation Functions

Using eptr2 Utility Functions

python
from eptr2.util.costs import (
    calculate_unit_imbalance_cost,
    calculate_unit_kupst_cost,
    calculate_imb_cost,
    calculate_imbalance_amounts
)

# Unit imbalance cost
mcp, smp = 2500, 2800
unit_costs = calculate_unit_imbalance_cost(mcp=mcp, smp=smp)
print(f"Positive imbalance cost: {unit_costs['pos']:.2f} TL/MWh")
print(f"Negative imbalance cost: {unit_costs['neg']:.2f} TL/MWh")

# KUPST cost
kupst = calculate_unit_kupst_cost(mcp=mcp, smp=smp)
print(f"KUPST cost: {kupst:.2f} TL/MWh")

Full Imbalance Cost Calculation

python
from eptr2.util.costs import calculate_imb_cost

result = calculate_imb_cost(
    actual=100,       # Actual generation/consumption (MWh)
    forecast=90,      # Scheduled position (MWh)
    mcp=2500,         # Market Clearing Price
    smp=2800,         # System Marginal Price
    is_producer=True, # True for generators
    imb_tol=0.1,      # 10% tolerance
    return_detail=True
)

print(f"Total Imbalance Cost: {result['costs']['total_imb_cost']:.2f} TL")

Common Use Cases

1. Estimate Daily Imbalance Exposure

python
from eptr2.composite import get_hourly_price_and_cost_data

df = get_hourly_price_and_cost_data(eptr, "2024-07-15", "2024-07-15")

# Example: 5 MWh surplus each hour
surplus_mwh = 5
daily_surplus_cost = (df['pos_imb_cost'] * surplus_mwh).sum()
print(f"Daily cost of 5 MWh/h surplus: {daily_surplus_cost:,.0f} TL")

# Example: 5 MWh deficit each hour
deficit_mwh = 5
daily_deficit_cost = (df['neg_imb_cost'] * deficit_mwh).sum()
print(f"Daily cost of 5 MWh/h deficit: {daily_deficit_cost:,.0f} TL")

2. Analyze System Direction Impact

python
df = get_hourly_price_and_cost_data(eptr, "2024-07-01", "2024-07-31")

# Group by system direction
direction_analysis = df.groupby('system_direction').agg({
    'pos_imb_cost': 'mean',
    'neg_imb_cost': 'mean',
    'mcp': 'count'
}).rename(columns={'mcp': 'hours'})

print("Average Imbalance Costs by System Direction:")
print(direction_analysis)

Authentication

Set credentials in .env file:

EPTR_USERNAME=your_email@example.com
EPTR_PASSWORD=your_password

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