How Can Data Center Planning Cut Costs and Boost Efficiency?

Background and Current Situation

Data centers are a high‑energy‑consumption industry; in China their electricity use has grown >10% per year over the past decade, exceeding 2000 billion kWh in 2020 (≈2.71% of national consumption) and is expected to keep rising, increasing carbon emissions.

Traditional planning starts with business demand, followed by server‑package configuration, electrical and HVAC design, and multiple reviews before construction. Business units often treat the data center as a cost base and ignore optimization opportunities, leading to resource waste.

As data‑center scale and energy‑efficiency regulations increase, the conventional planning model causes serious power waste. Existing research proposes demand‑response or load‑shifting, but few deployments succeed because they overlook reliability requirements and operational complexities.

Approach and Method

The ByteDance SYS‑DCD R&D team distilled practical experience into a planning tool that balances cost, reliability, delivery speed, and environmental impact while satisfying diverse business uncertainties.

Methodology Overview

Data‑center planning is a mixed‑integer programming problem that combines continuous and discrete variables to model real‑world constraints. Established energy‑system models such as MESSAGE, TIMES/MARKAL, and BIG3 illustrate the relevance of linear and integer optimization.

The DCD team defines “packages” for different scenarios, each consisting of a set of decision variables (e.g., building, cooling, electrical solutions) and associated scores for reliability, delivery speed, energy use, and water use.

Model Input

Inputs are divided into planning objectives and quantifiable technical options. Typical objectives include:

Default scenario: best total cost of ownership (TCO)

Water‑less scenario: minimum water‑use efficiency (WUE) while balancing TCO

Fast‑deploy scenario: quickest delivery with acceptable TCO

Green scenario: lowest PUE with strong policy incentives

High‑reliability scenario: stringent redundancy for finance/TOB workloads

High‑compatibility scenario: flexible for air‑ and liquid‑cooling

High‑performance scenario: compute‑oriented designs for peak IT performance

For each scenario, cost, reliability, and energy‑saving metrics of candidate solutions are broken down to form the optimization variables.

Mathematical Modeling and Solving

The model translates the planning problem into decision variables, an objective function (e.g., minimize total cost), and constraints (reliability, delivery speed, energy efficiency, water use). The resulting linear program can be solved with simplex, interior‑point, or other LP solvers.

Implementation

The following Python code uses Pyomo to build and solve a linear‑programming model for three regions (North China, East China, South China) based on a sample Excel dataset.

<code>from pyomo.environ import *
import pandas as pd

# Read Excel file
excel_file = pd.ExcelFile('/mnt/图片截图.xlsx')
sheet_names = excel_file.sheet_names
df = excel_file.parse('Sheet2', header=[0, 1])

# Merge column headers
df.columns = ['维度', '子维度权重', '华北地区', '华北地区_电气方案2子维度分值',
              '华东地区', '华东地区_电气方案2子维度分值',
              '华南地区', '华南地区_电气方案2子维度分值']

df['子维度权重'] = df['子维度权重'].fillna(method='ffill')
for region in ['华北地区', '华北地区_电气方案2子维度分值',
               '华东地区', '华东地区_电气方案2子维度分值',
               '华南地区', '华南地区_电气方案2子维度分值']:
    df[region] = df[region].fillna(df['子维度权重'])

df = df.drop(1).reset_index(drop=True)

# Create model
model = ConcreteModel()
model.x = Var(['华北地区_电气方案1', '华北地区_电气方案2'], within=Binary)

data = df[['维度', '华北地区', '华北地区_电气方案2子维度分值']]
data_dict = data.set_index('维度').to_dict(orient='index')

model.objective = Objective(expr=
    data_dict['成本']['华北地区'] * model.x['华北地区_电气方案1'] +
    data_dict['成本']['华北地区_电气方案2子维度分值'] * model.x['华北地区_电气方案2'],
    sense=minimize)

model.reliability = Constraint(expr=
    data_dict['可靠性']['华北地区'] * model.x['华北地区_电气方案1'] +
    data_dict['可靠性']['华北地区_电气方案2子维度分值'] * model.x['华北地区_电气方案2'] >= 3)

model.delivery = Constraint(expr=
    data_dict['交付性']['华北地区'] * model.x['华北地区_电气方案1'] +
    data_dict['交付性']['华北地区_电气方案2子维度分值'] * model.x['华北地区_电气方案2'] >= 4)

model.cost = Constraint(expr=
    data_dict['成本']['华北地区'] * model.x['华北地区_电气方案1'] +
    data_dict['成本']['华北地区_电气方案2子维度分值'] * model.x['华北地区_电气方案2'] >= 3)

model.green = Constraint(expr=
    data_dict['绿色']['华北地区'] * model.x['华北地区_电气方案1'] +
    data_dict['绿色']['华北地区_电气方案2子维度分值'] * model.x['华北地区_电气方案2'] >= 3)

solver = SolverFactory('glpk')
solver.solve(model)

print('选择方案：')
for var in model.component_objects(Var):
    print(f'{var}: {var.value}')
</code>

Results

The optimization recommends region‑specific packages that balance cost, reliability, and delivery speed for a generic business case.

Conclusion and Future Work

Packaging‑based planning enables rapid matching of business demands to suitable data‑center configurations, reducing cost and accelerating deployment. Future iterations will extend the framework to support deeper cost, agility, and reliability analyses for broader business scenarios.