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      • Economic Dispatch (ED)
      • Unit Commitment (UC)
      • DC Optimal Power Flow (DC-OPF)
      • AC Optimal Power Flow (AC-OPF)
      • Solver Comparison & Selection Guide

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Docs/kpg run/Solver Comparison & Selection Guide

Solver Comparison & Selection Guide

Choosing the right optimization model is critical for balancing accuracy, computational speed, and analysis goals. This guide helps you select among ED, UC, DC-OPF, and AC-OPF.

Quick Decision Tree

flowchart TD A[Start] --> B{Need <br/>commitment <br/>decisions?} B -->|Yes| C{Network <br/>constraints <br/>matter?} B -->|No| D{Network <br/>constraints <br/>matter?} C -->|Yes| E[UC + OPF <br/>Chained] C -->|No| F[Unit <br/>Commitment] D -->|Yes| G{Voltage/reactive <br/>critical?} D -->|No| H[Economic <br/>Dispatch] G -->|Yes| I[AC Optimal <br/>Power Flow] G -->|No| J[DC Optimal <br/>Power Flow]

Comprehensive Comparison

Feature Matrix

Feature

ED

UC

DC-OPF

AC-OPF

Problem Type

LP/QP

MIP

LP/QP

NLP

Network Model

None

None

DC (linearized)

AC (full)

Time Periods

Single

Multiple (24+)

Single

Single

Commitment Decisions

✗

✓ Binary

✗

✗

Transmission Limits

✗

✗

✓

✓

Voltage Constraints

✗

✗

✗

✓

Reactive Power

✗

✗

✗

✓

Transmission Losses

✗

✗

✗

✓

Startup Costs

✗

✓

✗

✗

Ramping Limits

✗

✓

✗

✗

Min Up/Down Time

✗

✓

✗

✗

Reserve Requirements

✗

✓

✗

✗

Solving Speed

Fastest

Slow

Fast

Medium

Detailed Model Comparison

Economic Dispatch (ED)

graph TB A[Input: Demand, Costs] --> B["ED Solver (LP/QP)"] B --> C[Output: Dispatch] D[Ignores: <br/>Network, <br/>Time, <br/>Commitment]
  • Strengths:

    • Fastest solving speed

    • Convex formulation

    • Easy to understand

    • Good for teaching

    • Merit order analysis

  • Weaknesses:

    • No network constraints

    • May be infeasible in reality

    • Cost is lower bound only

    • No temporal dynamics

  • Best for:

    • Screening studies

    • Fuel cost sensitivity

    • Technology comparisons

    • Educational purposes

→ ED Details


Unit Commitment (UC)

graph TB A["Input: 24h Profile"] --> B[UC Solver <br/>MIP] B --> C["Output: Schedule"] D[Includes: <br/>- Commitment u<sub>it</sub> <br/>- Startup costs <br/>- Ramping <br/>- Reserves] E[Ignores: <br/>- Network <br/>- Voltage <br/>- Reactive]
  • Strengths:

    • Realistic scheduling

    • Startup/shutdown costs

    • Temporal constraints

    • Reserve provision

    • Day-ahead markets

  • Weaknesses:

    • No network constraints

    • Slow (MIP)

    • May not be AC feasible

    • High memory usage

  • Best for:

    • Day-ahead planning

    • Generator scheduling

    • Cycling cost analysis

    • Reserve studies

→ UC Details


DC Optimal Power Flow (DC-OPF)

graph TB A[Input: Network, Demand] --> B[DC-OPF Solver<br/>LP] B --> C[Output: Dispatch + Flows] D[Includes:<br/>- Transmission limits<br/>- Power flow<br/>- LMPs<br/>- Congestion] E[Ignores:<br/>- Voltage<br/>- Reactive Q<br/>- Losses]
  • Strengths:

    • Network-aware

    • Fast (LP)

    • Locational prices (LMPs)

    • Congestion analysis

    • Always converges

  • Weaknesses:

    • Voltage fixed

    • No reactive power

    • Ignores losses

    • DC approximation errors

  • Best for:

    • Market clearing

    • LMP calculation

    • Congestion studies

    • Large-scale analysis

→ DC-OPF Details


AC Optimal Power Flow (AC-OPF)

graph TB A[Input: Full Network] --> B[AC-OPF Solver<br/>NLP] B --> C[Output: Complete Solution] D[Includes:<br/>- Full AC flow<br/>- Voltages V<sub>b</sub><br/>- Reactive Q<sub>i</sub><br/>- Losses] E[Challenges:<br/>- Nonlinear<br/>- Local optima<br/>- May not converge]
  • Strengths:

    • Complete physics

    • Voltage and reactive

    • Accurate losses

    • True feasibility

    • Highest accuracy

  • Weaknesses:

    • Slower (NLP)

    • May not converge

    • Local optima possible

    • Sensitive to initialization

  • Best for:

    • Feasibility validation

    • Voltage studies

    • Reactive planning

    • Detailed operations

→ AC-OPF Details

Selection by Application

Research Applications

  • Decarbonization Studies

    • Primary: UC (capture cycling costs)

    • Secondary: DC-OPF (transmission needs)

    • Validation: AC-OPF (feasibility check)

  • Renewable Integration

    • Primary: UC (ramping, reserves)

    • Secondary: DC-OPF (congestion from variable generation)

    • Detailed: AC-OPF (voltage impact)

  • Transmission Planning

    • Primary: DC-OPF (congestion, LMPs)

    • Validation: AC-OPF (true flow limits)

    • Optional: UC (temporal patterns)

  • Market Design

    • Primary: DC-OPF (locational pricing)

    • UC: Day-ahead commitment

    • Validation: AC-OPF (deliverability)

Operational Applications

  • Day-Ahead Markets

    • Stage 1: UC (commitment schedule)

    • Stage 2: DC-OPF (dispatch + LMPs)

    • Post-analysis: AC-OPF (feasibility)

  • Congestion Management

    • Primary: DC-OPF (identify constraints)

    • Validation: AC-OPF (verify relief)

Cost and Accuracy Trade-offs

Accuracy Ladder

graph LR A[ED <br/>Lower Bound] -->|+Network| B[DC-OPF <br/>Better] B -->|+Losses/Voltage| C[AC-OPF <br/>Accurate] D[UC <br/>+Temporal] -->|+Network| E[UC+DC-OPF <br/>Realistic] E -->|+AC| F[UC+AC-OPF <br/>Most Accurate]

Solver Capabilities

What Each Model Can Answer

1. Economic Dispatch (ED)

  • Questions ED can answer:

    • What is the minimum possible generation cost?

    • Which generators should run based on merit order?

    • What is the system marginal price?

    • How do fuel prices affect dispatch?

  • Questions ED cannot answer:

    • Is the dispatch AC feasible?

    • Are transmission lines overloaded?

    • What are the locational prices?

    • How should units be scheduled over time?

2. Unit Commitment (UC)

  • Questions UC can answer:

    • Which units should be online each hour?

    • When should units start up and shut down?

    • What are the total cycling costs?

    • Are reserve requirements met?

    • How much ramping capability is needed?

  • Questions UC cannot answer:

    • Are there transmission constraints?

    • What are locational prices?

    • Is reactive power adequate?

    • Are voltages within limits?

3. DC Optimal Power Flow (DC-OPF)

  • Questions DC-OPF can answer:

    • Which transmission lines are congested?

    • What are locational marginal prices?

    • Where should new transmission be built?

    • How much does congestion cost?

    • What is the optimal dispatch with network?

  • Questions DC-OPF cannot answer:

    • Are voltages acceptable?

    • Is reactive power sufficient?

    • What are the transmission losses?

    • How should units be scheduled over time?

4. AC Optimal Power Flow (AC-OPF)

  • Questions AC-OPF can answer:

    • Is the dispatch truly feasible?

    • What are the actual voltages?

    • How much reactive power is needed?

    • What are the transmission losses?

    • Are generator capability curves violated?

  • Questions AC-OPF cannot answer:

    • How should units be scheduled? (single period)

    • What are startup costs? (no commitment)

Speed vs. Accuracy

Pareto Frontier

graph TD A["Trade-off Space"] B["ED (Fastest, Least Accurate)"] C["DC-OPF (Fast, Medium Accuracy)"] D["UC (Slow, Good Temporal)"] E["AC-OPF (Medium, High Accuracy)"] F["UC+AC-OPF (Slowest, Best Overall)"] B --> C C --> E D --> F

Best Practices

Model Selection Checklist

  1. Time horizon?

    • Single period → OPF

    • Multiple periods → UC

  2. Network important?

    • No → ED/UC

    • Yes → DC-OPF/AC-OPF

  3. Voltage critical?

    • No → DC-OPF

    • Yes → AC-OPF

  4. Speed requirement?

    • Fast → ED/DC-OPF

    • Moderate → AC-OPF

    • Can wait → UC

  5. Accuracy need?

    • Approximate → ED/DC-OPF

    • High → AC-OPF

    • Complete → UC + AC-OPF

Validation Strategy

flowchart LR A[Simplest Model] --> B[Run & Analyze] B --> C{Need more<br/>accuracy?} C -->|Yes| D[Next Complex Model] C -->|No| E[Done] D --> B F[ED] --> G[DC-OPF] G --> H[AC-OPF] I[UC] --> J[UC + DC-OPF] J --> K[UC + AC-OPF]
  • Progressive refinement:

    1. Start simple (ED or DC-OPF)

    2. Check if results reasonable

    3. Add complexity if needed

    4. Validate with more accurate model

    5. Iterate until confidence achieved

Next Steps

  • Learn each model:

    • Economic Dispatch →

    • Unit Commitment →

    • DC Optimal Power Flow →

    • AC Optimal Power Flow →

  • Start using KPG Run: Getting Started →

← AC Optimal Power Flow (AC-OPF)Overview →

ON THIS PAGE

Quick Decision TreeComprehensive ComparisonFeature MatrixDetailed Model ComparisonEconomic Dispatch (ED)Unit Commitment (UC)DC Optimal Power Flow (DC-OPF)AC Optimal Power Flow (AC-OPF)Selection by ApplicationResearch ApplicationsOperational ApplicationsCost and Accuracy Trade-offsAccuracy LadderSolver CapabilitiesWhat Each Model Can AnswerSpeed vs. AccuracyPareto FrontierBest PracticesModel Selection ChecklistValidation StrategyNext Steps