# A Generic Framework for the Definition of Key Performance Indicators for Smart Energy Systems at Different Scales

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## Abstract

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## 1. Introduction

#### 1.1. Smart Energy Systems and Future Trends

#### 1.2. The Role of KPIs and Background of KPI Frameworks for SESs

#### 1.3. Main Contributions and Paper Structure

- emphasis is given to integrating all relevant stakeholders involved in the deployment and operation of SESs;
- the methodology is based not on general thematic domains, but on mapping between the key SES requirements and the involved stakeholders’ objectives;
- the framework can be applied in application areas of different spatial scales.

## 2. KPI Methodology

#### 2.1. Main SES Application Areas

#### 2.1.1. Building

_{2}emissions in Europe; hence, they possess one of the highest untapped potentials for energy management [25]. In this direction, the implementation of nearly zero-energy buildings (nZEBs) is required for all new buildings by 2020 in Europe [6]. The energy used in nZEBs is provided by on-site or nearby RES units. This transition to an nZEB level for all buildings can also contribute on the mitigation of grid stress, reduction in greenhouse gases (GHGs) and energy bills, and better living conditions.

#### 2.1.2. Community

#### 2.1.3. City

#### 2.1.4. Region

#### 2.2. Main Groups of Stakeholders

#### 2.2.1. System Operators

#### 2.2.2. Energy Service Suppliers

#### 2.2.3. End Customers

#### 2.2.4. Local/Regional authorities

#### 2.2.5. Other Third-Party Providers

#### 2.3. SES Requirements—Stakeholders’ Objectives

#### 2.3.1. Network Operation Optimization

#### 2.3.2. Improved Network Development

#### 2.3.3. Increased Flexibility

#### 2.3.4. Enhanced System Feasibility

#### 2.3.5. Improved Interoperability

#### 2.3.6. Improved Model Accuracy

## 3. SESs under Evaluation

#### 3.1. Austrian Use Case

#### 3.2. Swedish Use Case

#### 3.3. Swiss Use Case

## 4. Results—KPI Identification

#### 4.1. Network Operation Optimization

#### 4.2. Improved Network Development

#### 4.3. Increased Flexibility

#### 4.4. Enhanced System Feasibility

#### 4.5. Improved Interoperability

- 1.
- IEC 61850 is a domain-specific standard with well-defined semantic models to describe the domain of energy networks. IEC 61499, on the other hand, is a domain-neutral standard, which can be used to model the specification and implementation of automation systems irrespective of domain.
- 2.
- There is no direct communication or message passing between IEC 61850 and IEC 61499 systems. The two standards complement each other, with IEC 61850 addressing the domain specific communication modeling, while IEC 61499 addresses the implementation of IEC 61850 systems.

- 1.
- IEC 61850 and EDIEL are both domain-specific standards with well-defined semantic models that model the communication and the message passing of their respective domains (i.e., the energy grid for IEC 61850 and the energy market for EDIEL). These two standards operate independently from each other and only communicate when message passing is required between the two standards. Therefore, the structural aspect of the information model in these two standards is not important with respect to interoperability.
- 2.
- Since message passing exists between the two standards, it’s more important to focus on the semantic interoperability of the two standards rather than the structure. Since both EDIEL and IEC 61850 were developed independently by two different task forces, there can be situations where two signals with identical terminologies (i.e., names) can refer to different things, or vice versa, where signals with the same meaning have different naming terminologies. In addition, only a very small subset of messages on the boundaries of the two standards are expected to interoperate.

#### 4.6. Improved Model Accuracy

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Primary energy mix and RES share in DERs at EU-28 level for the: (

**a**) distributed energy scenario, and (

**b**) global ambition scenario [8].

**Figure 2.**KPI framework used for various application areas based on the SES requirements and the stakeholders’ objectives.

**Figure 3.**Different application areas of SESs and main groups of stakeholders that can be engaged in their development and operation.

SES Requirements | Stakeholder Objectives | Interested Stakeholders | ||
---|---|---|---|---|

R1.1 | Network Operation Optimization | R1.1 | Peak Power Shaving | A, B, C |

R1.2 | Voltage Support | A, B | ||

R1.3 | Reduced Grid Losses | A, B, D | ||

R1.4 | Improved RES—Load Prediction | A, B | ||

R1.5 | Forecasting Error Compensation | A, B | ||

R1.6 | Reduced Grid Operational Costs | A, B | ||

R1.7 | Improved Reliability—Resilience | A, B, D | ||

R2 | Improved Network Development | R2.1 | Improved Grid Assets’ Usage | A, B, D |

R2.2 | Deferral of Grid Upgrades | A, B, D | ||

R3 | Increased Flexibility | R3.1 | Increased Demand Flexibility | A, B, C |

R3.2 | Increased RES Hosting Capacity | A, B, D, E | ||

R3.3 | Increased RES Self-Consumption | A, B, C | ||

R3.4 | Increased RES Self-Sufficiency | A, B, C, D, E | ||

R4 | Enhanced System Feasibility | R4.1 | Technical Feasibility | A, B, C, E |

R4.2 | Economic Sustainability | A, B, C, D, E | ||

R4.3 | Environmental Sustainability | A, B, C, D, E | ||

R4.4 | Social Sustainability | D, E | ||

R4.5 | Legal Feasibility | A, B, C, D, E | ||

R5 | Improved Interoperability | R5.1 | Systems interoperability | A, B, E |

R5.2 | Structural Interoperability | A, B, E | ||

R5.3 | Semantic Interoperability | A, B, E | ||

R6 | Improved Model Accuracy | R6.1 | Electrical System modeling | A, B, E |

R6.2 | Thermal System modeling | A, B, E |

**Table 2.**SES requirements and stakeholder objectives for the SONDER project and mapping to the national project scenarios (X—evaluated; O—affected, but not evaluated; “-”—not evaluated).

SES Requirements | Stakeholder Objectives | AT | CH | SE | ||
---|---|---|---|---|---|---|

R1 | Network Operation Optimization | R1.1 | Peak Power Shaving | O | X | - |

R1.2 | Voltage Support | O | X | X | ||

R1.3 | Reduced Grid Losses | O | X | - | ||

R1.4 | Improved RES—Load Prediction | X | X | - | ||

R1.5 | Forecasting Error Compensation | - | X | - | ||

R1.6 | Reduced Grid Operational Costs | - | X | - | ||

R1.7 | Improved Reliability—Resilience | - | O | - | ||

R2 | Improved Network Development | R2.1 | Improved Grid Assets’ Usage | O | X | - |

R2.2 | Deferral of Grid Upgrades | O | X | - | ||

R3 | Increased Flexibility | R3.1 | Increased Demand Flexibility | X | X | X |

R3.2 | Increased RES Hosting Capacity | X | X | - | ||

R3.3 | Increased DER Self-Consumption | X | - | X | ||

R3.4 | Increased DER Self-Sufficiency | X | - | X | ||

R4 | Enhanced System Feasibility | R4.1 | Technical Feasibility | X | X | - |

R4.2 | Economic Sustainability | X | X | - | ||

R4.3 | Environmental Sustainability | X | - | - | ||

R4.4 | Social Sustainability | O | - | - | ||

R4.5 | Legal Feasibility | O | O | - | ||

R5 | Improved Interoperability | R5.1 | Systems interoperability | X | - | - |

R5.2 | Structural Interoperability | - | - | X | ||

R5.3 | Semantic Interoperability | - | - | X | ||

R6 | Improved Model Accuracy | R6.1 | Electrical System modeling | - | - | X |

R6.2 | Thermal System modeling | - | - | X |

Objective | KPI | Description | Formula | Unit | |
---|---|---|---|---|---|

R1.1 | Peak power shaving | R1.1.1. Peak load reduction | Reduction of the maximum peak load on daily basis | $\Delta \widehat{P}={\widehat{P}}^{\prime}-\widehat{P}$ $\widehat{p}=\frac{{\widehat{P}}^{\prime}-\widehat{P}}{\widehat{P}}$ $\widehat{P},{\widehat{P}}^{\prime}$ = Daily peak power without and with the SES | [MW] [%] |

R1.1.2. Load factor | Variability of load consumption | $LF=100\xb7\frac{{P}_{avg}}{\widehat{P}}$ $\widehat{P},{P}_{avg}$ = Peak and average powers on daily or monthly basis | [%] | ||

R1.2 | Voltage support | R1.2.1. Voltage variations | Difference between the actual and nominal voltage at node i when applying the SES | $\Delta {v}_{i}\left(t\right)=100\xb7\frac{{V}_{i}\left(t\right)-{V}_{nom}}{{V}_{nom}}$ $V}_{i}\left(t\right),{V}_{nom$ = Actual and nominal voltages at node i applying the SES | [%] |

R1.2.2. Voltage improvement | Improvement of voltage at node i applying the SES | $\Delta {V}_{imp}={V}_{i}\left(t\right)-{V}_{i}^{\prime}\left(t\right)$ ${V}_{i}\left(t\right),{V}_{i}^{\prime}\left(t\right)$ = Actual voltages at node i without and with the SES | [V] | ||

R1.3 | Reduced grid losses | R1.3.1. Grid losses | Power losses on distribution cables and transformers when applying the SES | $P}_{loss}\left(t\right)=\sum {r}_{ij}\xb7{\left|{I}_{ij}\left(t\right)\right|}^{2$ $r}_{ij$ = Resistance of branch $ij$ ${I}_{ij}\left(t\right)$ = Complex current on branch $ij$ | [kW] |

R1.3.2. Reduction of grid losses | Reduction of grid losses when applying the SES | $\Delta {P}_{loss}=\sum ({P}_{loss}\left(t\right)-{P}_{loss}^{\prime}\left(t\right))$ ${P}_{loss}\left(t\right),{P}_{loss}^{\prime}\left(t\right)$ = Power losses at time t without and with the SES | [kW] | ||

R1.4 | Improved RES– load prediction | R1.4.1. Mean absolute error | Average of the absolute residuals (prediction errors) | $MAE=100\xb7\sum \frac{({z}_{f}\left(t\right)-{z}_{m}\left(t\right))}{{N}_{s}}$ ${z}_{f}\left(t\right),{z}_{m}\left(t\right)$ = Forecast and measurement $N}_{s$ = Number of timesteps t per day | [MW] |

R1.4.2. Root mean square error | Standard deviation of the residuals (prediction errors) | $RMSE=100\xb7\sqrt{\sum \frac{{({z}_{f}\left(t\right)-{z}_{m}\left(t\right))}^{2}}{{N}_{s}}}$ ${z}_{f}\left(t\right),{z}_{m}\left(t\right)$ = Forecast and measurement $N}_{s$ = Number of timesteps t per day | [MW] |

Objective | KPI | Description | Formula | Unit | |
---|---|---|---|---|---|

R1.5 | Compensation of forecasting errors | R1.5.1. BESS energy utilization | Total SES energy used for compensation of forecasting errors on daily, monthly, or annual basis | ${E}_{comp,i}=\sum {E}_{comp,i}\left(t\right)$ $e}_{comp,i}=100\xb7\frac{{E}_{comp,i}}{{E}_{ses,i}$ ${E}_{comp,i}\left(t\right)$ = SES energy used for compensation of forecasting errors at time t of day, month, or year i $E}_{ses,i$ = Total SES utilized energy per day, month, or year i | [MWh] [%] |

R1.6 | Reduction of grid operational costs | R1.6.1. T&D power cost | Total monthly charge for the DSO due to the monthly peak power demand | $C}_{p}={c}_{\widehat{p}}\xb7\widehat{P$ $c}_{\widehat{p}$ = Active power tariff due to monthly peak power demand $\widehat{P}$ | [CHF] |

R1.6.2. T&D energy cost | Total daily energy cost due to the usage of the T&D infrastructure | ${C}_{use}=\sum {c}_{use}\xb7{E}_{grid}\left(t\right)$ $c}_{use$ = fixed fee due to T&D use ${E}_{grid}\left(t\right)$ = Energy purchased at time t | [CHF] | ||

R1.6.3. Energy purchase cost | Energy purchase costs from energy transactions with a regional energy provider | ${C}_{grid}=\sum {c}_{price}\left(t\right)\xb7{E}_{grid}\left(t\right)$ ${c}_{price}\left(t\right)$ = Energy (EPEX) price at time t ${E}_{grid}\left(t\right)$ = Energy purchased at time t | [CHF] | ||

R1.6.4. Total grid operational costs | Sum of T&D power and energy costs and energy purchase cost | $C}_{tot}^{\prime}={C}_{grid}+{C}_{p}+{C}_{use$ $C}_{tot}^{\prime$ = Total cost with the SES | [CHF] | ||

R1.6.5. Reduction of grid operational costs | Reduction of grid operational costs when operating the SES | $\Delta {C}_{tot}={C}_{tot}^{\prime}-{C}_{tot}$ $\Delta {c}_{tot}=100\xb7\frac{{C}_{tot}^{\prime}-{C}_{tot}}{{C}_{tot}}$ $C}_{tot$ = Total cost without the SES | [CHF] [%] |

Objective | KPI | Description | Formula | Unit | |
---|---|---|---|---|---|

R2.1 | Improved utilization of grid assets | R2.1.1. Average capacity factor | Fraction of the average | $C{F}_{avg}=100\xb7\frac{{I}_{th,avg}}{{I}_{th,nom}}$ | [%] |

yearly thermal loading | $I}_{th,avg},{I}_{th,nom$ = Average yearly | ||||

to the rated value for | and nominal thermal loading | ||||

cables and transformers | of cables and transformers | ||||

R2.1.2. Peak capacity factor | Fraction of the peak | $C{F}_{peak}=100\xb7\frac{{I}_{th,peak}}{{I}_{th,nom}}$ | [%] | ||

yearly thermal loading | $I}_{th,peak},{I}_{th,nom$ = Peak yearly | ||||

to the rated value for | and nominal thermal loading | ||||

cables and transformers | of cables and transformers | ||||

R2.2 | Deferral of grid upgrades | R2.2.1. Reduction of grid upgrade costs | Reduction of costs related to grid reinforcement | $\Delta {C}_{upg}={C}_{upg}^{\prime}-{C}_{upg}$ $c}_{upg}=100\xb7\frac{{C}_{upg}^{\prime}-{C}_{upg}}{{C}_{upg}$ $C}_{upg},{C}_{upg}^{\prime$ = grid upgrade costs without and with the SES | [CHF] [%] |

Objective | KPI | Description | Formula | Unit | |
---|---|---|---|---|---|

R3.1 | Increased demand flexibility | R3.1.1. Power demand flexibility | Ability of SES to reduce demand | $\Delta {P}_{df}\left(t\right)={P}_{df}^{\prime}\left(t\right)-{P}_{df}\left(t\right)$ $\Delta {p}_{df}\left(t\right)=100\xb7\frac{{P}_{df}^{\prime}\left(t\right)-{P}_{df}\left(t\right)}{\widehat{P}}$ ${P}_{df}\left(t\right),{P}_{df}^{\prime}\left(t\right)$ = Load power without and with the SES at time t $\widehat{P}$ = Daily peak demand | [MW] [%] |

R3.1.2. Energy demand flexibility | Percentage of energy demand served by SES on daily basis | $\frac{\Delta {E}_{dem,i}}{{E}_{dem,i}}=100\xb7\frac{{E}_{dem,i}-{E}_{dem,i}^{\prime}}{{E}_{dem,i}}$ $E}_{dem,i},{E}_{dem,i}^{\prime$ = Energy demand on the MV side of the primary distribution transformer at day i without and with the SES | [%] | ||

R3.2 | Increased RES hosting capacity | R3.2.1. RES hosting capacity | Hosting capacity of RES units in the investigated grid | $H{C}_{res}=\sum _{i=1}^{{N}_{der}}{P}_{nom,i}$ $h{c}_{res}=100\xb7\frac{H{C}_{der}}{{\widehat{P}}_{yr}}$ $P}_{nom,i},{\widehat{P}}_{yr$ = Nominal power of i RES, and peak power of year $yr$ $N}_{res$ = Total number of RES units | [MW] [%] |

R3.2.2. Hosting capacity increase | Increase of hosting capacity owing to SES operation | $\Delta H{C}_{res}=H{C}_{res}^{\prime}-H{C}_{res}$ $\Delta h{c}_{res}=100\xb7\frac{H{C}_{res}^{\prime}-H{C}_{res}}{H{C}_{res}}$ $H{C}_{res},H{C}_{res}^{\prime}$ = RES hosting capacity without and with the SES | [MW] [%] | ||

R3.3 | Increased RES self- consumption | R3.3.2. Self- consumption rate increase | Increase of self- consumption rate when operating the SES | $\Delta SCR=SC{R}^{\prime}-SCR$ $SCR,SC{R}^{\prime}$ = Self-consumption rate without and with the SES | [%] |

R3.4 | Increased RES self- sufficiency | R3.4.1. Self- sufficiency rate increase | Increase of self- sufficiency rate when operating the SES | $\Delta SSR=SS{R}^{\prime}-SSR$ $SSR,SS{R}^{\prime}$ = Self-consumption rate without and with the SES | [%] |

Objective | KPI | Description | Formula | Unit | |
---|---|---|---|---|---|

R4.1 | Technical feasibility | R4.1.1. Self- consumption rate | Part of local RES production that is locally consumed | $SCR=100\xb7\left(1-\frac{{E}_{exp,i}}{{E}_{res,i}}\right)$ $E}_{exp,i},{E}_{res,i$ = Energy exported to the grid and energy produced by PV at day, month, or year i | [%] |

R4.1.2. Self- sufficiency rate | Part of total load covered by local RES production | $SSR=100\xb7\left(\frac{{E}_{res,i}-{E}_{exp,i}}{{E}_{imp,i}+{E}_{res,i}-{E}_{exp,i}}\right)$ $E}_{exp,i},{E}_{res,i$ = Energy exported to the grid and energy produced by PV at day, month, or year i | [%] | ||

R4.1.3. BESS degradation rate | Capacity losses on BESS due to cycling and calendar effects | $BDR=100\xb7\frac{B{C}_{i}-B{C}_{o}}{i\xb7B{C}_{o}}$ $B{C}_{o},B{C}_{i}$ = Battery capacity initially, and after i periods | [%] | ||

R4.2 | Economic sustainability | R4.2.1. Energy cost savings factor | Difference of energy costs for EC members when operating the SES | $C}_{s}=100\xb7\frac{{C}_{ec}-{C}_{ec}^{\prime}}{{C}_{ec}$ $C}_{ec},{C}_{ec}^{\prime$ = energy costs for the EC members without and with the SES | [%] |

R4.2.2. Annual cost savings | Savings on annual grid operational costs for the DSO with the SES | $\Delta {c}_{tot}=100\xb7\frac{{C}_{tot}^{\prime}-{C}_{tot}}{{C}_{tot}}$ $C}_{tot},{C}_{tot}^{\prime$ = Total operational cost for the DSO without and with the SES | [%] | ||

R4.3 | Environmental sustainability | R4.2.1. $C{O}_{2}$/GHG emission reduction factor | Reduction of $C{O}_{2}$/GHG emissions owing to the RES use in the EC | $C{O}_{2,R}=100\xb7\frac{C{O}_{2,i}^{\prime}-C{O}_{2,i}}{C{O}_{2,i}}$ $C{O}_{2,i},C{O}_{2,i}^{\prime}$ = $C{O}_{2}$/GHG emissions in period i without and with the SES | [%] |

Objective | KPI | Description | Formula | Unit | |
---|---|---|---|---|---|

R5.1 | Systems interoperability | R5.1.1. Number of new integration profiles | Newly created integration profiles based on the IES methodology | Not applicable | [-] |

R5.1.2. Number of tested integration profiles | Integration profiles tested in the context of “Connectathon Energy” | Not applicable | [-] | ||

R5.2 | Structural interoperability | R5.2.1. Interoperability capacity | Structural interoperability between two standards | $C}_{a}=100\xb7\frac{T}{S$ $S,C$ = number of source | [%] |

R5.3 | Semantic interoperability | R5.3.1. Interoperability level between | Measured with the five- point likert scale standards | Not applicable | [-] |

Objective | KPI | Description | Formula | Unit | |
---|---|---|---|---|---|

R6.1 | Electrical system modeling | R6.1.1. Mean absolute error | Average magnitude of the absolute variations between measured and predicted values | $MAE=\frac{1}{N}\sum _{i=1}^{N}|{P}_{m,i}-{P}_{r,i}|$ $P}_{m,i},{P}_{r,i$ = Measured and predicted value of DC power consumption (W) at time i N = number of timesteps | [kW] |

R6.1.2. Root mean absolute error | Average magnitude of the squared variations between measured and predicted values | $RMSE=\frac{1}{N}\sqrt{\sum _{i=1}^{N}{({P}_{m,i}-{P}_{r,i})}^{2}}$ $P}_{m,i},{P}_{r,i$ = Measured and predicted value of DC power consumption (W) at time i N = number of timesteps | [kW] | ||

R6.1.3. Mean absolute error | Relative error between the measured and predicted values | $MAE=\frac{1}{N}\sum _{i=1}^{N}\frac{|{P}_{m,i}-{P}_{r,i}|}{{P}_{r,i}}$ $P}_{m,i},{P}_{r,i$ = Measured and predicted value of DC power consumption (W) at time i N = number of timesteps | [kW] | ||

R6.1.4. Mean square error | Average squared difference between the measured and predicted values | $MSE=\frac{1}{N}\sum _{i=1}^{N}{({P}_{m,i}-{P}_{r,i})}^{2}$ $P}_{m,i},{P}_{r,i$ = Measured and predicted value of DC power consumption (W) at time i N = number of timesteps | [kW] | ||

R6.2 | Thermal system modeling | R6.2.1. Mean absolute error | Average magnitude of the absolute variations between measured and predicted values | $MAE=\frac{1}{N}\sum _{i=1}^{N}|{T}_{m,i}-{T}_{r,i}|$ $T}_{m,i},{T}_{r,i$ = Measured and predicted value of DC temperature (°C) at time i N = number of timesteps | [kW] |

R6.2.2. Root mean absolute error | Average magnitude of the squared variations between measured and predicted values | $RMSE=\frac{1}{N}\sqrt{\sum _{i=1}^{N}{({T}_{m,i}-{T}_{r,i})}^{2}}$ $T}_{m,i},{T}_{r,i$ = Measured and predicted value of DC temperature (°C) at time i N = number of timesteps | [kW] | ||

R6.2.3. Mean absolute error | Relative error between the measured and predicted values | $MAE=\frac{1}{N}\sum _{i=1}^{N}\frac{|{T}_{m,i}-{T}_{r,i}|}{{T}_{r,i}}$ $T}_{m,i},{T}_{r,i$ = Measured and predicted value of DC temperature (°C) at time i N = number of timesteps | [kW] | ||

R6.2.4. Mean square error | Average squared difference between the measured and predicted values | $MSE=\frac{1}{N}\sum _{i=1}^{N}{({T}_{m,i}-{T}_{r,i})}^{2}$ $T}_{m,i},{T}_{r,i$ = Measured and predicted value of DC temperature (°C) at time i N = number of timesteps | [kW] |

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## Share and Cite

**MDPI and ACS Style**

Efkarpidis, N.; Goranović, A.; Yang, C.-W.; Geidl, M.; Herbst, I.; Wilker, S.; Sauter, T. A Generic Framework for the Definition of Key Performance Indicators for Smart Energy Systems at Different Scales. *Energies* **2022**, *15*, 1289.
https://doi.org/10.3390/en15041289

**AMA Style**

Efkarpidis N, Goranović A, Yang C-W, Geidl M, Herbst I, Wilker S, Sauter T. A Generic Framework for the Definition of Key Performance Indicators for Smart Energy Systems at Different Scales. *Energies*. 2022; 15(4):1289.
https://doi.org/10.3390/en15041289

**Chicago/Turabian Style**

Efkarpidis, Nikolaos, Andrija Goranović, Chen-Wei Yang, Martin Geidl, Ingo Herbst, Stefan Wilker, and Thilo Sauter. 2022. "A Generic Framework for the Definition of Key Performance Indicators for Smart Energy Systems at Different Scales" *Energies* 15, no. 4: 1289.
https://doi.org/10.3390/en15041289