Peak Shaving with Battery Energy Storage Systems in Distribution Grids: A Novel Approach to Reduce Local and Global Peak Loads
Abstract
:1. Introduction
1.1. Scope of the Study
- With accurate co-simulations of BESSs and distribution grids, results for various operation strategies aiming to reduce both the local peak load and the global peak load are acquired.
- The storage systems are economical optimally sized using linear optimization.
- These storage systems are operated with a state-of-the-art peak shaving strategy as well as with a centralized approach and compared according to the peak load reduction at a specific node and the PCC.
- A newly combined approach is developed aimed to reduce the peak power at the PCC in an example distribution grid while not significantly influencing the peak load reduction for the individual industrial consumer.
- The stress on the storage system for the various operation strategies is derived from a detailed analysis based on six key characteristics.
1.2. Outline of the Paper
2. Simulation Settings and System Configurations
2.1. Example Grid and Denotations
2.2. Battery Energy Storage System Setting
2.3. Simulation Setting
3. Problem Formulation and Applied Methods
3.1. Peak Shaving Operation Strategy: Strategy
3.2. Grid-Centered Peak Shaving: Strategy
3.3. Combined Peak Shaving Approach: Strategy
3.4. Battery Energy Storage System: Component Sizing
4. Case Studies and Discussion
5. Conclusions and Outlook
Future Work and Outlook
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Parameters & Variables
actual energy content of a battery energy storage system at a specific node b for a specific time step t | |
charged energy of a battery energy storage system at a specific node b for a specific time step t | |
discharged energy of a battery energy storage system at a specific node b for a specific time step t | |
charging power of a battery energy storage system at a specific node b for a specific time step t | |
discharging power of a battery energy storage system at a specific node b for a specific time step t | |
rated power of the power electronics | |
energy throughput of a battery energy storage system | |
apparent power at a specific node b for a specific time step t | |
combined apparent power including the power at a specific node b and the apparent power at the point of common coupling | |
peak shaving threshold power for a battery energy storage system operating with the grid-centered approach | |
peak shaving threshold power for a specific node b | |
peak shaving threshold power for a battery energy storage system operating with the combined approach | |
scaling factor: peak power at the point of common coupling in relation to the peak load at a specific node b | |
vector of the apparent power at the point of common coupling for each time step t | |
scaled apparent power of the point of common coupling in relation to the peak load at a specific node b | |
matrix for the apparent power at each node b for each time step t | |
energy rate of the battery energy storage system | |
storage investment costs per kWh | |
annual peak demand charge per kVA | |
project operation/depreciation period in years | |
throughput penalty costs | |
efficiency of the power electronics |
Abbreviations
AC | alternating current |
BESS | battery energy storage system |
BMS | battery management system |
C | carbon/graphite |
DC | direct current |
eDisGo | software for electric distribution grid optimization |
LFP | lithium-iron-phosphate |
LIB | lithium-ion battery |
LV | low voltage |
MV | medium voltage |
open_BEA | open battery models for electrical grid applications |
PCC | point of common coupling |
SimSES | simulation of stationary energy storage systems |
SOE | state of energy |
Sets & Indices
B | total number of nodes b in the distribution grid |
H | time vector for the simulation period (time horizon) |
T | time horizon |
vector for all industrial consumers in the distribution grid | |
b | nodes with industrial consumers in the distribution grid |
t | specific time step |
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Parameter/Setting | Description/Value | Unit |
---|---|---|
Battery cell manufacturer | Murata | - |
Battery cell type | US26650FTC1 | - |
Battery cell chemistry | LFP:C | - |
Battery cell capacity | 2850 | mAh |
Nominal cell voltage | 3.2 | V |
Cell voltage range | 2–3.6 | V |
Maximum efficiency of power electronics | 96.9 | % |
Maximum erate | 1 | |
Starting state of energy (SOE) | 100 | % |
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Kucevic, D.; Semmelmann, L.; Collath, N.; Jossen, A.; Hesse, H. Peak Shaving with Battery Energy Storage Systems in Distribution Grids: A Novel Approach to Reduce Local and Global Peak Loads. Electricity 2021, 2, 573-589. https://doi.org/10.3390/electricity2040033
Kucevic D, Semmelmann L, Collath N, Jossen A, Hesse H. Peak Shaving with Battery Energy Storage Systems in Distribution Grids: A Novel Approach to Reduce Local and Global Peak Loads. Electricity. 2021; 2(4):573-589. https://doi.org/10.3390/electricity2040033
Chicago/Turabian StyleKucevic, Daniel, Leo Semmelmann, Nils Collath, Andreas Jossen, and Holger Hesse. 2021. "Peak Shaving with Battery Energy Storage Systems in Distribution Grids: A Novel Approach to Reduce Local and Global Peak Loads" Electricity 2, no. 4: 573-589. https://doi.org/10.3390/electricity2040033
APA StyleKucevic, D., Semmelmann, L., Collath, N., Jossen, A., & Hesse, H. (2021). Peak Shaving with Battery Energy Storage Systems in Distribution Grids: A Novel Approach to Reduce Local and Global Peak Loads. Electricity, 2(4), 573-589. https://doi.org/10.3390/electricity2040033