Renewable Energy Integration for Bulk Power Systems: ERCOT and the Texas Interconnection

Preface
Contents
Chapter 1: Renewable Integration at ERCOT
1 Overview
1.1 Texas Power System
1.2 Wind Generation Development in Texas
1.3 Solar Generation Development in Texas
1.4 Interconnection Requirements for Generation Resources
2 Transmission Development and Capacity Adequacy
2.1 Transmission Access
2.2 Transmission Reinforcement
2.3 Operational Challenges with High Inverter-Based Resource Penetration
2.4 Capacity Adequacy and Wind Generation Resources
3 ERCOT Energy and Ancillary Services Market
3.1 Ancillary Services
4 Reliability and Security of Grid Operations
4.1 Requirement for Primary Frequency Response
4.2 ERCOT Frequency Performance
4.3 System Inertia Trend
4.4 Renewable Generation Forecasting
4.5 Operations Analysis and Studies
4.5.1 Day-Ahead Analysis and Studies
4.5.2 Real-Time Analysis and Studies
4.5.3 Situational Awareness Tools
4.5.4 Challenges for the Nearest Future
5 Conclusions
References
Chapter 2: Overview of Market Operation at ERCOT
1 Overview
1.1 Network Modeling
1.2 Day-Ahead Operations
1.3 Adjustment Period Operations
1.4 Real-Time Operations
2 Day-Ahead Market (DAM)
2.1 DAM Overview
2.2 ERCOT and QSE Activities in DAM
2.2.1 Ancillary Service Plan Posted in DAM
2.2.2 QSE Activities
2.2.2.1 Report Availability of Must-Run and Black Start Resources
2.2.2.2 Submit Bids for PTP Obligations
2.2.2.3 Submit Three-Part Supply Offers
2.2.2.4 Submit Energy Bids and Energy-Only Offers
2.2.2.5 Submit Self-Arranged Ancillary Service
2.2.2.6 Submit Ancillary Service Offers
2.2.2.7 Update and Submit Current Operating Plan
2.3 DAM Engine
3 RUC
3.1 RUC Process Overview
3.2 Input to RUC Process
3.2.1 Current Operating Plan (COP)
3.2.2 Network Operations Model
3.2.3 Generic Transmission Constraints
3.2.4 Load Forecast
3.2.5 Offers and Proxy Energy Offer
3.3 RUC Process
3.3.1 RUC Problem Formulation and Solution Algorithm
3.4 Energy Offer Curve for RUC-Committed Resource
4 Real-Time SCED
4.1 SCED: Energy Dispatch
4.1.1 Information Flows in SCED
4.1.2 Real-Time Network Security Analysis
4.1.3 Resource Limit Calculator (RLC)
4.1.4 How SCED Functions
4.1.4.1 The Texas Two-Step
4.1.4.2 SCED Schedule and Output
4.2 Load Frequency Control (LFC)
5 Conclusions
References
Chapter 3: Market Designs to Integrate Renewable Resources
1 Overview
2 ERCOT Nodal Market
3 Short-Term Wind/PVGR Generation Forecasting and Current Operating Plan (COP)
4 IRR Scheduling in DAM, RUC, and SCED
4.1 IRR Scheduling in DAM
4.2 IRR Generation Scheduling in RUC
4.3 IRR Generation Scheduling in Real-Time SCED
4.4 Base Point Deviation Charge for IRR
4.5 Effect on Management of Congestion
4.6 Effect on Market Prices
5 Management of Generic Transmission Constraints (GTCs)
5.1 Management of GTC Limits
5.2 Non-to-Exceedance (NTE) Method
5.3 Implementation of NTE Concept
6 Incorporation of 5-Minute Wind/Solar Ramp into SCED
6.1 Generation to Be Dispatched (GTBD)
6.2 Forecast of 5-Minute Solar Ramp
7 Conclusions
Chapter 4: Ancillary Services (AS) at ERCOT
1 Overview
1.1 Responsive Reserve Service (RRS)
1.2 Regulation Reserve Service
1.3 Non-spinning Reserve Service
2 Regulation Services
2.1 Short-Term Wind Generation Forecasting
2.2 Method to Determine Regulation Services Requirement
2.2.1 An ERCOT Dispatch Model for Calculating Regulation Requirement
2.2.2 Regulation Service Requirement
2.2.3 Including Effect of Wind Generation
2.2.4 Adequacy of Regulation Reserves
2.3 Procedures to Determine Regulation Service Requirement
2.3.1 Regulation Service Requirements
2.3.2 Evaluation of Exhaustion Rate
2.3.3 Performance Validation
3 Responsive Reserve Requirement
3.1 Quantification of PFR and FFR Requirement for Inertias
3.2 RRS Requirement
4 Non-spinning Reserve
4.1 Background
4.2 Net Load Forecast Error (NLFE) Analysis
4.3 Net Load Ramp-Up
4.4 Adjustment to Non-Spin Need by Considering Forced Outage
4.5 Procedures to Determine Non-Spin Need
5 Summary
References
Chapter 5: Design of New Primary Frequency Control Market for Hosting Frequency Response Reserve Offers from Both Generators a...
1 Introduction of Frequency Control
2 Impact of Renewable Resource over Inertia and Primary Frequency Control
3 Co-optimization of Energy and FRR in Day-Ahead Market
3.1 Day-Ahead Market Co-optimization Model
3.2 Solution of Day-Ahead Market Co-optimization
3.3 Case Studies
4 Stochastic Formulations of Co-optimization of Energy and FRR in Day-Ahead Market
4.1 Energy, PFR, and Inertia Scheduling Without Uncertainties
4.2 Energy, PFR, and Inertia Scheduling Under Uncertainties
4.2.1 Scenario Generation and Reduction
4.2.2 Interaction Between Day-Ahead Scheduling and Hour-Ahead Operation
4.2.3 An ERCOT Case Study
4.2.3.1 Deterministic Scheduling Solution
4.2.3.2 Stochastic Scheduling Solution
4.2.3.3 Comparison Between Deterministic and Stochastic Scheduling Solutions
5 Conclusions
References
Chapter 6: New Ancillary Service Market for ERCOT: Fast Frequency Response (FFR)
1 Introduction
2 Existing Ancillary Service Market at ERCOT
2.1 Regulation Service
2.2 Responsive Reserve Service (RRS)
2.3 Non-spin Reserve Service (NSRS)
3 Inertia Trend and Primary Frequency Control at ERCOT
3.1 Inertia Trend at ERCOT
3.2 Overview of Frequency Control Coordination at ERCOT
3.3 RRS at ERCOT Before Re-design of AS Market
4 New Ancillary Service Market
5 Fast Frequency Response (FFR)
5.1 Qualifications of FFR and Performance Evaluations
5.2 Telemetry Data Requirement for Deployment and Recall of FFR
6 Maximum Amount of FFR Allowed
7 Benefits of FFR
7.1 Impact of FFR over Critical Inertia
7.2 RRS Cost Saving with FFR Resources
8 Conclusions
References
Chapter 7: System Inertia Trend and Critical Inertia
1 Basics of Synchronous Inertia
2 Inertia at ERCOT
3 Historical Synchronous Inertia Trends
4 Determining Critical Inertia
5 ERCOT Tools to Monitor and Forecast System Inertia
6 Impact of Parameter Changes on Critical Inertia
6.1 ``Faster´´ Frequency Response
6.2 ``Earlier´´ Frequency Response
6.3 Lower UFLS Trigger
6.4 Reduction of Largest Possible Loss of Generation
7 International Review of Inertia-Related Challenges and Mitigation Measures
8 Summary of Potential Mitigation Measures to Lower Critical Inertia or Support Minimum Inertia Level
9 Conclusions
References
Chapter 8: Multiple-Period Reactive Power Coordination for Renewable Integration
1 Introduction
2 Literature Review
3 Renewable Integration and New Transmission Operators
4 Reactive Power Coordination (RPC) Tool
4.1 Architecture of RPC
4.2 Objective Function of RPC
4.3 Constraints of RPC
4.4 Mathematic Formulations of RPC
4.5 Solution Methodology
5 Special Considerations
5.1 Sensitivity of Reactive Power for a Regulating Bus
5.2 Reactive Device´s Temporal Constraint
5.3 Handling Special Capacitor Banks
6 Case Studies
6.1 ERCOT Network Model
6.2 Verification of Temporal Constraints
6.2.1 Initial State of Capacitor Is OFF
6.2.2 Sequence Constraints Between Master and Slave Capacitors
6.3 Simulation Results
7 Conclusions
References
Chapter 9: Renewable Forecast
1 Introduction
2 Wind Forecasting System
2.1 Wind Forecasting System Overview
2.2 Data Flow of Wind Forecasting System
2.3 Input Data for Wind Forecasting System
2.4 Design Approach of Wind Forecasting System
3 Solar Forecasting System and Forecast Errors
3.1 Solar Forecasting System
3.1.1 NWP Data
3.1.2 Machine Learning Algorithms Used by Solar Forecasting System
3.1.3 Satellite Cloud Tracking Algorithms
3.1.4 Plant Output Models
3.1.5 Ensemble Optimization Algorithm
3.1.6 Solar Forecast Delivery Mechanism
3.2 Solar Forecast Error Analysis
3.2.1 Source of Data
3.2.2 Probability Distribution Function of Solar Forecast Errors
3.2.3 Temporal Correlation of SFE
4 Summary and Conclusions
References
Chapter 10: Ensemble Machine Learning-Based Wind Forecasting to Combine NWP Output with Data from Weather Stations
1 Introduction
2 Numerical Weather Prediction
3 West Texas Mesonet (WTM)
3.1 Value of WTM Data: An Example
4 Machine Learning-Based Ensemble Method
5 Experimental Results
5.1 Performance of Three Machine Learning Algorithms
5.2 Performance of Base Algorithms for Large Wind Ramp
5.3 Performance of Ensemble Method
5.4 Robustness of Proposed Method
6 Conclusions
References
Index