# Peak Shaving with Battery Energy Storage Systems in Distribution Grids: A Novel Approach to Reduce Local and Global Peak Loads

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## 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

_{PE}= 96.9%. In accordance with the type of battery cell, the maximum ${\mathrm{e}}^{\mathrm{rate}}$ is set to 1 ${\mathrm{h}}^{-1}$.

#### 2.3. Simulation Setting

^{®}programming language.

## 3. Problem Formulation and Applied Methods

#### 3.1. Peak Shaving Operation Strategy: Strategy $\alpha $

#### 3.2. Grid-Centered Peak Shaving: Strategy $\beta $

#### 3.3. Combined Peak Shaving Approach: Strategy $\gamma $

#### 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

${E}_{t}^{\mathrm{actual},\mathrm{b}}$ | actual energy content of a battery energy storage system at a specific node b for a specific time step t |

${E}_{t}^{\mathrm{charge},\mathrm{b}}$ | charged energy of a battery energy storage system at a specific node b for a specific time step t |

${E}_{t}^{\mathrm{discharge},\mathrm{b}}$ | discharged energy of a battery energy storage system at a specific node b for a specific time step t |

${P}_{t}^{\mathrm{charge},\mathrm{b}}$ | charging power of a battery energy storage system at a specific node b for a specific time step t |

${P}_{t}^{\mathrm{discharge},\mathrm{b}}$ | discharging power of a battery energy storage system at a specific node b for a specific time step t |

${P}^{\mathrm{rated}}$ | rated power of the power electronics |

${\mathrm{BESS}}_{\mathrm{tp}}$ | energy throughput of a battery energy storage system |

${\mathrm{S}}_{t}^{\mathrm{b}}$ | apparent power at a specific node b for a specific time step t |

${\mathrm{S}}_{t}^{\mathrm{comb}}$ | combined apparent power including the power at a specific node b and the apparent power at the point of common coupling |

${\mathrm{S}}^{\mathrm{thresh},\mathrm{PCC}}$ | peak shaving threshold power for a battery energy storage system operating with the grid-centered approach |

${\mathrm{S}}^{\mathrm{thresh},\mathrm{b}}$ | peak shaving threshold power for a specific node b |

${\mathrm{S}}^{\mathrm{thresh},\mathrm{comb}}$ | peak shaving threshold power for a battery energy storage system operating with the combined approach |

${\sigma}_{\mathrm{b}}$ | scaling factor: peak power at the point of common coupling in relation to the peak load at a specific node b |

${\mathbf{S}}^{\mathrm{PCC}}$ | vector of the apparent power at the point of common coupling for each time step t |

${\mathbf{S}}^{\mathrm{Scaled,b}}$ | scaled apparent power of the point of common coupling in relation to the peak load at a specific node b |

$\mathbf{S}$ | matrix for the apparent power at each node b for each time step t |

${\mathrm{e}}^{\mathrm{rate}}$ | energy rate of the battery energy storage system |

${\mathrm{P}}_{\mathrm{BESS}}^{\mathrm{invest}}$ | storage investment costs per kWh |

${\mathrm{P}}_{\mathrm{peak}}$ | annual peak demand charge per kVA |

${\mathrm{t}}_{\mathrm{proj}}$ | project operation/depreciation period in years |

${\mathrm{w}}_{\mathrm{tp}}$ | throughput penalty costs |

${\eta}_{\mathrm{PE}}$ | 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 |

$\mathbf{N}$ | 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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**Figure 1.**Graphical overview of the simulated grid and battery energy storage systems (BESSs), as well as the investigated operation strategies. The BESS, modeled in detail, located at various nodes in a test grid, is operated in three different operation strategies to reduce the local peak load (Strategy $\alpha $) or the peak load at the point of common coupling ($\beta $) or both ($\gamma $).

**Figure 2.**Overview of all open-source simulation tools, which have been adapted for use in this study. The open_BEA tool operates as both a central control unit and as a configuration unit. The eDisGo tool conducts the power flow analysis and SimSES operates as a validation unit for the battery energy storage systems’ behavior.

**Figure 3.**Graphical representation of the test distribution grid. The open circuit breakers are marked in gray, the MV/LV transformers are marked in light blue, the PCC in red, and all branch tees in dark blue.

**Figure 4.**Graphical representation of all three energy management strategies used in this study. Subplot (

**a**) shows an exemplary load profile for an industrial consumer at a specific node b. The related power at the point of common coupling is displayed in subplot (

**b**) and subplot (

**c**) shows the combined profile. The solid line marks the results of the power flow analysis without the battery energy storage system (BESS) at a specific node b and the dashed line marks the results of the power flow analysis including the BESS. The black solid line associated to the right y-axis shows the difference in power, covered by the BESS.

**Figure 5.**Results of the component sizing optimization. The upper plot (

**a**) shows the peak shaving limits S

^{thresh,b}in % of the original peak power for all 32 battery energy storage system (BESS) with a capacity above 10 kWh. The lower plot (

**b**) shows the capacity for just these BESS.

**Figure 6.**Relative peak load reduction for each simulation with various operating strategies for the battery energy storage system (BESS). The reduction of the peak load at the local node b (= location of the BESS) is plotted on the abscissa and the reduction of the peak load at the point of common coupling (PCC) can be seen on the ordinate. The red crosses show the reduction if the BESS is operated with strategy $\alpha $. The blue crosses show the results for strategy $\beta $ and the green ones for the combined approach (Strategy $\gamma $). The filled circles show the reduction of the peak load at the PCC if all 32 BESSs are integrated into the grid simultaneously.

**Figure 7.**The upper plot (

**a**) shows the absolute peak load reduction for each simulation if the battery energy storage system (BESS) is operated with strategy $\alpha $. Plot (

**b**) shows the results for the centralized approach (Strategy $\beta $). The lower plot (

**c**) shows the absolute peak load reduction for each simulation if the BESS is operated with the newly developed combined approach (Strategy $\gamma $). The red bars show the difference in peak load at a local node b, the blue bars show the peak load reduction at the point of common coupling (PCC).

**Figure 8.**Detailed results about the additional stress on the battery energy storage systems (BESSs) for a six month simulation period. Subplot (

**a**) shows the number of full equivalent cycles and the mean round-trip efficiency in % is displayed in subplot (

**b**). Subplot (

**c**) shows the average cycle depth in discharge direction in % and the average resting time in minutes between two actions is illustrated in subplot (

**d**). The number of alternations between charging and discharging (sign changes) per day is indicated in subplot (

**e**). Finally, subplot (

**f**) shows the energy in relation to the bess capacity that is charged between these sign changes.

**Table 1.**Parameters and settings of the simulated battery energy storage system (BESS) comprising battery cells, a power electronics unit, and a battery management system (BMS).

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 e^{rate} | 1 | ${\mathrm{h}}^{-1}$ |

Starting state of energy (SOE) | 100 | % |

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## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Kucevic, 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