Roles energy storage can play in a smart grid

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Table of contents

Abstract
Abbreviations
Some Notations
List of Figures
List of Tables
1 Introduction
1.1 Motivation
1.2 Contributions of the Thesis
1.3 Organization of the Thesis
1.3.1 Part I of the thesis: energy storage arbitrage
1.3.2 Part II of the thesis: energy storage co-optimization
1.3.3 Part III of the thesis: large-scale application
1.3.4 Conclusion and future directions
1.4 Publications
2 Challenges and Literature Review
2.1 Future Power System Challenges
2.1.1 Need for innovative billing
2.1.2 Need for ancillary services
2.2 Roles energy storage can play in a smart grid
2.2.1 Energy Arbitrage
2.2.2 Dynamic Regulation and Reserves
2.2.3 Peak Demand Flattening
2.2.4 Fast ramping and low response time
2.2.5 Power Quality
2.2.6 Case Study : EV Charging in Pasadena, California
2.2.7 Increasing Reliability and Inertia
2.2.8 Congestion and Voltage Support
2.2.9 Infrastructure Deferral
2.3 Bottlenecks with energy storage: Li-Ion battery
2.3.1 High Cost
2.3.2 Battery life and parameters
2.4 Uncertainty in parameters
2.4.1 AutoRegressive Forecasting and Model Predictive Control
2.5 Notation and battery model
3 Energy Arbitrage – Net Metering 1.0
3.1 Introduction
3.2 Arbitrage under NEM 1.0
3.2.1 Optimal Energy Arbitrage Problem: NEM 1.0
3.2.2 Proposed Algorithm under NEM 1.0
3.2.3 Open Source Code
3.2.4 Stylized Example of Proposed Algorithm
3.3 Numerical Evaluation
3.4 Case Study 1: Feasibility of Energy Arbitrage
3.4.1 Net Average Available Battery Capacity
3.4.2 Evaluation
3.5 Case Study 2: Quantifying length of a sub-horizon
3.5.1 Case Study: CAISO 2017 for equal buying and selling price of electricity
3.6 Case study 3: Effect of Uncertainty on Arbitrage
3.6.1 Threshold based structure for negative prices
3.6.2 Point Forecast with MPC
3.6.3 Scenario-Based MPC
3.6.4 Simulation Results
3.6.5 Key Observation
3.7 Conclusion and Perspectives
4 Energy Arbitrage – Net Metering 2.0
4.1 Introduction
4.1.1 General applicability of proposed algorithm
4.1.2 Contributions of the chapter
4.2 Optimal Arbitrage Problem
4.2.1 Threshold Based Structure of the Optimal Solution
4.2.2 Proposed Algorithm
4.2.3 Open Source Codes
4.3 Online Implementation of Proposed Algorithm
4.4 Numerical Results
4.4.1 MPC with incrementally improving forecast
4.5 Comparing Run-Time of Algorithms
4.6 Case Study 1: Intermediate ramp rate
4.7 Case study 2: Sensitivity analysis for varying
4.7.1 Deterministic Simulations
4.7.2 Results with Uncertainty
4.8 Conclusion and Perspectives
5 Battery Degradation and Valuation
5.1 Introduction
5.2 Battery degradation and mathematical model
5.2.1 Battery Degradation
5.2.2 Battery Model
5.2.3 Tuning Cycles of Operation
5.3 Eliminating Low Returning Arbitrage Gains
5.3.1 Cycle Life
5.3.2 Calendar Life
5.3.3 Optimal Storage Control
5.3.4 Limiting Cycles of Operation
5.3.5 Numerical Results
5.3.6 Observations
5.4 Controlling Arbitrage Cycles
5.4.1 Energy Storage Arbitrage Algorithm with Negative Prices
5.4.2 Controlling Cycles of Operation
5.4.3 Open Source Codes
5.5 Battery Participating in Ancillary Service
5.5.1 Compensation Mechanism
5.5.2 Controlling the Cycles
5.6 Numerical Results
5.6.1 Short Time-Scale: A typical Day
5.6.2 Long Term Simulation – One Year
5.7 Conclusion and Perspectives
6 Arbitrage & Power Factor Correction
6.1 Introduction
6.1.1 Literature Review
6.1.2 Contribution
6.2 System Description
6.2.1 Energy Arbitrage
6.2.2 Power Factor Correction
6.3 Arbitrage and PFC with Storage
6.3.1 McCormick Relaxation based approach
6.3.2 Receding horizon arbitrage with sequential PFC
6.3.3 Arbitrage with penalty based PFC
6.3.4 Minimizing converter usage with arbitrage and PFC
6.3.5 Open Source Codes
6.4 Modeling Uncertainty
6.4.1 Model Predictive Control
6.5 Numerical Results
6.5.1 Results with uncertainty
6.6 Case Study: Degradation of PF at a substation
6.7 Power Factor Correction with Solar Inverter
6.8 Conclusion and Perspectives
7 Co-optimizing Storage for Prosumers
7.1 Introduction
7.2 System Description
7.2.1 Billing Structure
7.3 Co-Optimization of Energy Storage
7.3.1 Energy Arbitrage
7.3.2 Arbitrage with PFC
7.3.3 Peak Demand Shaving with PFC and arbitrage
7.3.4 Co-optimization with control of cycles
7.3.5 Open Source Codes
7.4 Real-time implementation
7.4.1 AutoRegressive Forecasting
7.4.2 Model Predictive Control
7.5 Numerical Results
7.5.1 Controlling and Tuning Cycles of Operation
7.5.2 Real-time Implementation
7.6 Conclusion and Perspectives
8 Co-optimizing Storage in Madeira
8.1 Introduction
8.2 Power System Norms in Madeira
8.2.1 Overview of the Madeira Electric Grid
8.2.2 Peak Power Contracts, Tariffs and Billing Cycles
8.2.3 Self-Consumption and Renewables in Madeira
8.3 Co-optimizing Energy Storage
8.3.1 ToU pricing + zero feed-in-tariff + Peak-Shaving
8.3.2 Storage for BackUp with Arbitrage + Peak Shaving
8.3.3 Open Source Codes
8.4 Real-time Control under Uncertainty
8.4.1 Modeling Uncertainty: ARMA Forecasting
8.4.2 Model Predictive Control
8.5 Numerical Results
8.5.1 Deterministic Solution for Popt
8.5.2 Co-optimizing with Power Backup
8.5.3 Real-Time Implementation (Forecast plus MPC)
8.6 Conclusion and Perspectives
9 Storage for low voltage consumers in Uruguay
9.1 Introduction
9.2 Energy Landscape in Uruguay
9.3 Electricity Consumer Contracts
9.3.1 Fixed and Active Energy Cost
9.3.2 Peak Power Contract for LV Consumers
9.3.3 Billing of Reactive Energy
9.3.4 Cost of Consumption
9.3.5 Net-Metering in Uruguay
9.4 Storage for LV Prosumers in Uruguay
9.4.1 Active Power Management
9.4.2 Compensation Strategy for Reactive Power
9.5 Control Algorithm for Storage in Uruguay
9.5.1 Storage Operation Immune to Uncertainty
9.6 Numerical Experiments
9.6.1 Arbitrage Potential
9.6.2 Consumer gains with/without storage
9.6.3 Energy Storage Profitability
9.7 Conclusion and Perspectives
10 Effect of Electricity Pricing on Ancillary Service
10.1 Introduction
10.1.1 Related Work
10.1.2 Contributions
10.1.3 Key Observations
10.2 System Description
10.2.1 Consumer Model
10.2.2 Generation Scheduling
10.2.3 Price Model
10.2.4 Real time Operation
10.3 Indices Used for Measurement
10.3.1 Volatility Indices
10.3.2 Measuring Ancillary Service Required
10.4 Numerical Results
10.4.1 Results with only schedulable generations
10.4.2 With RES Generation
10.5 Conclusion and Perspectives
11 Control of a fleet of batteries
11.1 Introduction
11.2 Distributed control design
11.2.1 Nominal model design
11.2.2 Controlled Markov model for an individual battery
11.2.3 Mean Field Model
11.3 Numerical results
11.3.1 Tracking and SoC Performance
11.3.2 Impact of efficiency loss
11.4 Conclusions and Perspectives
12 Drift Control for a Fleet of Batteries
12.1 Introduction
12.1.1 Frequency Regulation in PJM
12.2 Drift compensation for a fleet of batteries
12.2.1 Lossless batteries with zero mean tracking signal
12.2.2 Lossy batteries with zero mean tracking signal
12.2.3 Why drift compensation?
12.3 Drift Compensation Controller design
12.3.1 Linearized System Model
12.3.2 Least Square Fitting
12.3.3 Discrete to Continuous Transformation
12.3.4 Augmented State Matrix
12.3.5 Linear Quadratic Regulator Gain
12.3.6 Gain Scheduling
12.3.7 Optimal Controller Gain
12.3.8 Test Simulation
12.4 PJM Performance Scores
12.5 Numerical Evaluation
12.6 Conclusion and Perspectives
13 Phase Balancing using Storage
13.1 Introduction to Phase Balancing
13.1.1 Cause of unbalance in three-phase power network
13.1.2 Effect of unbalance in three-phase power network
13.1.3 Indices for measuring of unbalance in three-phase power network
13.2 Understanding the effects of phase unbalance
13.2.1 Simulation Results
13.2.2 Honeymoon and Divorce Cases
13.3 Architectures of Storage Solutions
13.3.1 Solution with one battery and phase selector
13.3.2 Solution with three storage with each dedicated to each phase
13.3.3 Solution with three storage with phase selector for each battery
13.3.4 Phase Balancing with Storage: Stylized Example
13.4 Case Study : Madeira Substation
13.4.1 Overview of the Local Grid
13.4.2 Case of a Distribution Substation in Madeira
13.4.3 Q&A with the EEM Madeira
13.5 Conclusion and Perspectives
14 Conclusions and Future Directions
14.1 Conclusions
14.2 Future Directions
14.2.1 Selection of best-suited energy storage
14.2.2 Optimal lookahead horizon for hydro generation facilities
14.2.3 Storage/DG/Load placement based on voltage profile
14.2.4 Modeling flexible loads as batteries
14.2.5 Minimizing renewable energy curtailment
14.2.6 Extension of topics covered in thesis
A Appendix 1
A.1 Proof of Theorem 3.2.1
A.2 Convex Optimization: Conditions of Optimality
A.2.1 Convex Function and Subdifferential
A.2.2 Optimality condition for unconstrained problem
A.2.3 Constrained Optimization Primal Problem (P)
A.3 Proof of Theorem 4.2.1
A.3.1 For zi > 0
A.3.2 For zi < 0
A.4 Proof of Theorem 4.2.2
A.5 Proof of Remark 6
A.6 Proof of Remark 7
A.7 Arbitrage with NEM 1.0
A.8 Arbitrage with Convex Optimization using CVX
A.8.1 Only storage with NEM and losses
A.8.2 Arbitrage with load, renewable generation with NEM and losses
A.9 Proof of Theorem 9.4.1
A.10 Control Design Using LQG in Matlab
A.10.1 System Type I: Open Loop System
A.10.2 System Type II: Closed Loop Full-feedback
A.10.3 System Type III: Limited Measurement Feedback System
A.10.4 System Type IV: Limited Measurement Noisy Feedback System
B Arbitrage using Linear Programming
B.1 Introduction
B.2 Optimal Arbitrage with Linear Programming
B.2.1 Epigraph formulation of Linear Programming
B.3 Formulating LP Matrices
B.3.1 Lossless Storage with equal buy and sell price
B.3.2 Only Storage Case with net-metering and efficiency losses
B.3.3 Storage performing arbitrage with inelastic load and renewable generation under net-metering and storage losses
B.4 Real-time implementation
B.5 Conclusion
Bibliography