Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems
Abstract
:1. Introduction
1.1. Motivation
1.2. Related Literature
1.3. Summary of Proposed Research Contributions
- The presented GML-FOL-STL-DER hierarchical scheme represents a novel, scalable, and practically implementable approach to the Market DSO’s task of coordinating DERs while accounting for individual device and AC grid constraints;
- The scheme employs optimization-based methods within each layer to ensure that DERs are utilized optimally and in a “grid-aware” manner, and then integrates the layers with feedback-based control schemes to be robust against model-mismatch and forecast errors.
- Simulation-based analysis is conducted based on realistic network models from a New York DSO which validates the coupled GML-FOL-STL operations and highlights the role and value of the proposed hierarchical scheme.
2. System Models and Consideration
2.1. Market Signals for the GML
2.2. Grid Signals for the FOL
Modeling Unbalanced Feeders
2.3. Device Signals
3. Overview of Hierarchical DER Control Scheme
- Grid market layer (GML) employs a TSO’s market signals to optimize the dispatch of available, aggregated flexibility from all feeders and deliver economically optimal power set-points for each feeder’s headnode in the DSO’s system. Since we use market signals from New York’s TSO (NYISO), we consider the GML on a timescale of 5 minutes, which matches the update rate of NYISO’s “real-time market.”
- -
- Input: market signals (from TSO); bounds on flexibility for aggregated feeders (from FOL)
- -
- Output: economic feeder power reference (to FOL)
- Feeder operational layer (FOL) employs the GML’s desired power reference trajectory at each headnode, the DSO’s unbalanced distribution network models, and the STL’s VB model to optimize the dispatch of controllable assets within a feeder so as to minimize power deviations from the headnode reference. The controllable assets include groups of DERs (i.e., a VB) and PV inverters that together track the GML’s economic power reference at the feeder’s head-node while maintaining an acceptable voltage profile throughout the feeder. Since the FOL responds to forecast errors and that solar PV variability is on the order of minutes, the FOL’s timescale has been selected as 1 min.
- -
- Input: economic feeder head-node power reference (from GML); VB model parameters and VB state of charge (from STL)
- -
- Output: bounds on flexibility for aggregated feeders (to GML); VB power set-points (to STL)
- Service transformer layer (STL) employs the FOL’s optimal resource dispatch signal at each (primary) node in the feeder along with DER data to coordinate small, local groups of DERs while accounting for local device constraints on power and energy (e.g., temperature bounds prescribed by users). Since we need to update the DER dispatch often to reject any un-modeled disturbances (e.g., inflexible, background demand), we have selected a timescale of 1 s for the STL’s dispatch loop.
- -
- Input: VB power set-points (from FOL); DER data (from DER)
- -
- Output: updated VB state of charge estimate (to FOL); DER control signal (to DER)
4. Grid Market Layer (GML)
4.1. Operational Constraints
4.2. GML Power Flow Model
4.3. GML Formulation and Implementation
4.4. Peak-Shaving Mode
4.5. Illustration of GML
- Scenario #1: This baseline scenario assumes that no VB is available, i.e., , and that all solar runs at full capacity, i.e., , for both the day-ahead and real-time markets.
- Scenario #2: In this GML scenario, the GML has the ability to curtail the solar usage and charge/discharge the VB.
- Scenario #3: In this GML+peak-shaving scenario, the peak-shaving mode is implemented, and the unit price for peak demand charge is set to be MW.
5. Feeder Operational Layer (FOL)
5.1. FOL Multi-Period Formulation
Ensuring AC Feasible Optimal Solution
5.2. Robust FOL Formulation
5.2.1. Nature of Uncertainty in Solar PV Forecasts
5.2.2. Chance-Constraints
5.3. Illustration of FOL with Solar PV Forecasts
6. Service Transformer Layer (STL)
7. Inter-Layer Communication and Control
- The full network data for the FOL’s optimization-based dispatch of VBs.
- Live SCADA and power flow information from distribution substations.
- Secure communication infrastructure for corrective inter-feeder and intra-feeder control.
7.1. Communications between Layers
7.2. Feedback Control between Layers
7.2.1. Inter-Feeder Control System
7.2.2. Intra-Feeder Control System
7.3. Proof of Concept: Inter-Layer Feedback Control
7.4. Proof of Concept: Communications between Layers
8. Large-Scale Coupled Simulation Results
8.1. Simulation Setup
8.2. Results
Peak Shaving
9. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Without VB | VB: 75 MW + 187.5 MWh | VB: 150 MW + 375 MWh | |||
---|---|---|---|---|---|
Scenario | #1 | #2 | #3 | #2 | #3 |
Real-time cost ($) | 428,330 | 425,981 | 426,053 | 424,322 | 424,486 |
Solar curtailment cost ($) | 0 | 0 | 0 | 0 | 0 |
Peak cost ($) | 12,609,000 | 13,299,920 | 12,150,240 | 14,061,360 | 11,881,330 |
Total cost ($) | 13,037,330 | 13,725,901 | 12,576,293 | 14,485,682 | 12,305,816 |
VB: 75 MW + 187.5 MWh | VB: 150 MW + 375 MWh | |||
---|---|---|---|---|
Scenario | #2 | #3 | #2 | #3 |
Real-time saving | 12.59 $/MWh | ∖ | 10.72 $/MWh | ∖ |
Peak saving | ∖ | 2448.80 $/MWh | ∖ | 1941.49 $/MWh |
Capacity (MWh) | Power Rating (MW) | |
---|---|---|
Feeder 1 | 0.45 | 2.26 |
Feeder 2 | 0.29 | 1.45 |
Feeder 3 | 0.80 | 3.66 |
Tracking RMSE | Voltage u.b. with VB (95th Percentile) | Voltage u.b. without VB (95th Percentile) | |
---|---|---|---|
Feeder 1 | 14 kW | 1.053 p.u | 1.058 p.u |
Feeder 2 | 20 kW | 1.035 p.u | 1.041 p.u |
Feeder 3 | 90 kW | 1.038 p.u | 1.047 p.u |
Mean Curtailment with VB | Mean Curtailment without VB | |
---|---|---|
Feeder 1 | 0.2 MW | 0.5 MW |
Feeder 2 | 1.2 MW | 1.6 MW |
Feeder 3 | 0.3 MW | 0.6 MW |
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Almassalkhi, M.; Brahma, S.; Nazir, N.; Ossareh, H.; Racherla, P.; Kundu, S.; Nandanoori, S.P.; Ramachandran, T.; Singhal, A.; Gayme, D.; Ji, C.; Mallada, E.; Shen, Y.; You, P.; Anand, D. Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems. Energies 2020, 13, 6399. https://doi.org/10.3390/en13236399
Almassalkhi M, Brahma S, Nazir N, Ossareh H, Racherla P, Kundu S, Nandanoori SP, Ramachandran T, Singhal A, Gayme D, Ji C, Mallada E, Shen Y, You P, Anand D. Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems. Energies. 2020; 13(23):6399. https://doi.org/10.3390/en13236399
Chicago/Turabian StyleAlmassalkhi, Mads, Sarnaduti Brahma, Nawaf Nazir, Hamid Ossareh, Pavan Racherla, Soumya Kundu, Sai Pushpak Nandanoori, Thiagarajan Ramachandran, Ankit Singhal, Dennice Gayme, Chengda Ji, Enrique Mallada, Yue Shen, Pengcheng You, and Dhananjay Anand. 2020. "Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems" Energies 13, no. 23: 6399. https://doi.org/10.3390/en13236399