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

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 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

- GSMA. Smart Energy Systems—Connectivity for a Zero-Emissions Future; Technical Report; GSM Association: London, UK, 2020; pp. 1–60. [Google Scholar]
- Redl, C.; Hein, F.; Buck, M.; Graichen, P.; Jones, D. The European Power Sector in 2020: Up-to-Date Analysis on the Electricity Transistion; Technical Report; Agora Energiewende: Berlin, Germany; Ember: London, UK, 2021; pp. 1–18. [Google Scholar]
- Lund, H.; Mathiesen, B.V.; Connolly, D.; Østergaard, P.A. Renewable energy systems—A smart energy systems approach to the choice and modeling of 100% renewable solutions. Chem. Eng. Trans.
**2014**, 39, 1–6. [Google Scholar] [CrossRef] - Mathiesen, B.V.; Lund, H.; Connolly, D.; Wenzel, H.; Ostergaard, P.A.; Möller, B.; Nielsen, S.; Ridjan, I.; Karnoe, P.; Sperling, K.; et al. Smart Energy Systems for coherent 100% renewable energy and transport solutions. Appl. Energy
**2015**, 145, 139–154. [Google Scholar] [CrossRef] - Smart Energy Networks. Vision for Smart Energy in Denmark—Research, Development and Demonstration; Technical Report; DTU Electro—Center for Electric Power and Energy: Kongens Lyngby, Denmark, 2015; pp. 1–8. [Google Scholar]
- Lund, H.; Østergaard, P.A.; Connolly, D.; Mathiesen, B.V. Smart energy and smart energy systems. Energy
**2017**, 137, 556–565. [Google Scholar] [CrossRef] - Dincer, I.; Acar, C. Smart energy systems for a sustainable future. Appl. Energy
**2017**, 194, 225–235. [Google Scholar] [CrossRef] - ENTSO-E. TYNDP 2020—Scenario Report; Technical Report; ENTSOG: Brussels, Belgium, 2020; pp. 1–64. [Google Scholar]
- Kampman, B.; Blommerde, J.; Afman, M. The Potential of Energy Citizens in the European Union; Technical Report; CE Delft: Delft, The Netherlands, 2016; pp. 1–34. [Google Scholar]
- DECC. Towards a Smart Energy System; Technical Report; Department of Energy & Climate Change: London, UK, 2015; pp. 1–21.
- Refaat, S.S.; Ellabban, O.; Bayhan, S.; Abu-Rub, H.; Blaabjerg, F.; Begovic, M.M. Smart Grid and Enabling Technologies; Wiley-IEEE Press: Chichester, UK, 2021; pp. 1–473. [Google Scholar]
- Salom, J.; Tamm, M.; Andresen, I.; Cali, D.; Magyari, Á.; Bukovszki, V.; Balázs, R.; Dorizas, P.V.; Toth, Z.; Mafé, C.; et al. An evaluation framework for sustainable plus energy neighbourhoods: Moving beyond the traditional building energy assessment. Energies
**2021**, 14, 4314. [Google Scholar] [CrossRef] - Yavor, K.M.; Bach, V.; Finkbeiner, M. Resource assessment of renewable energy systems—A review. Sustainability
**2021**, 13, 6107. [Google Scholar] [CrossRef] - Francis, C.; Costa, A.S.; Thomson, R.C.; Ingram, D.M. Developing a Multi-Criteria Assessment Framework for Smart Local Energy Systems; Technical Report; EnergyREV, University of Strathclyde: Glasgow, UK, 2020; pp. 1–16. [Google Scholar]
- Li, Y.; O’Donnell, J.; García-Castro, R.; Vega-Sánchez, S. Identifying stakeholders and key performance indicators for district and building energy performance analysis. Enery Build.
**2017**, 155, 1–15. [Google Scholar] [CrossRef] [Green Version] - Dakheel, J.A.; Pero, C.D.; Aste, N.; Leonforte, F. Smart buildings features and key performance indicators: A review. Sustain. Cities Soc.
**2020**, 61, 102328. [Google Scholar] [CrossRef] - Design4Energy. Report 2.1 B Indicators and Success Factors for Holistic Energy Matching; Technical Report; 2015; Available online: http://www.design4energy.eu/Download.html (accessed on 10 January 2022).
- Pramangioulis, D.; Atsonios, K.; Nikolopoulos, N.; Rakopoulos, D.; Grammelis, P.; Kakaras, E. A methodology for determination and definition of key performance indicators for smart grids development in island energy systems. Energies
**2019**, 12, 242. [Google Scholar] [CrossRef] [Green Version] - Ntafalias, A.; Papadopoulos, P.; Tsakanikas, S.; Menyktas, K.; Kentzoglanakis, K.; Kyriakopoulos, G.; Courouclis, I.; Papadopoulos, G.; Kousouris, S.; Tsitsanis, A. D2.1 Definition of SPARCS Holistic Impact Assessment Methodology and Key Performance Indicators; Technical Report; 2020; Available online: https://www.sparcs.info/about/deliverables/d201-definition-sparcs-holistic-impact-assessment-methodology-and-key (accessed on 10 January 2022).
- Yang, Y.; Jurasz, J.; Li, H.; Syrri, A.L.A.; Yan, J. Key Performance Indicators on Flexibility of a Multi-Energy System. In Proceedings of the Applied Energy Symposium: Low Carbon Cities and Urban Energy Systems (CUE), Xiamen, China, 16–18 October 2019; pp. 1–7. [Google Scholar]
- Walnum, H.T.; Sørnes, K.; Mysen, M.; Sørensen, Å.L.; Almås, A.J. Preliminary Toolkit for Goals and KPIs—PI-SEC, Planning Instruments for Smart Energy Communities; Technical Report; 2017; Available online: https://www.ntnu.edu/documents/21392748/1276440712/PI-SEC+-+Report+task+1.2+_Preliminary+toolkit+for+goals+and+KPIs.pdf/9231b522-f912-44ca-93f4-96bbf51f4929 (accessed on 10 January 2022).
- SONDER. Service Optimization of Novel Distributed Energy Regions. Available online: https://www.project-sonder.eu/about/ (accessed on 10 January 2022).
- Bosch, P.; Jongeneel, S.; Rovers, V.; Neumann, H.M.; Airaksinen, M.; Huovila, A. CITYkeys Indicators for Smart City Projects and Smart Cities; Technical Report; 2017; Available online: https://nws.eurocities.eu/MediaShell/media/CITYkeystheindicators.pdf (accessed on 10 January 2022).
- Franzl, G.; Wilker, S. Technical Framework on Local Energy Communities (TF-LEC) Vol.1 Version 0.5—First Trial Release; Technical Report; Forschungsbereich Software-Intensive Systems: Vienna, Austria, 2021; pp. 1–74. [Google Scholar]
- Groote, M.D.; Fabbri, M.; Volt, J.; Rapf, O.; Marian, C.; Faber, M.; D’angiolella, R. Smart Buildings in a Decarbonised Energy System—10 Principles to Deliver Real Benefits for Europe’s Citizens—BPIE; Technical Report; Buildings Performance Institute Europe: Brussels, Belgium, 2016; pp. 1–28. [Google Scholar]
- Benedettini, S.; Brugnetta, G.; Fumiatti, F.; Gentili, P.; Ghiglione, G.; Giordano, V.; Gidron, A.; Küpper, G.; Mandatova, P.; Masci, R.; et al. Assessment and Roadmap for the Digital Transformation of the Energy Sector Towards an Innovative Internal Market; Technical Report; European Commission: Brussels, Belgium, 2020; pp. 1–327. [Google Scholar]
- Bauwens, T.; Schraven, D.; Drewing, E.; Radtke, J.; Holstenkamp, L.; Gotchev, B.; Yildiz, Ö. Conceptualizing community in energy systems: A systematic review of 183 definitions. Renew. Sustain. Energy Rev.
**2022**, 156, 111999. [Google Scholar] [CrossRef] - Tounquet, F.; Vos, L.D.; Abada, I.; Kielichowska, I.; Klessmann, C. Energy Communities in the European Union—Revised Final Report; Technical Report; European Commission: Brussels, Belgium, 2019; pp. 1–142. [Google Scholar]
- THEMA Consulting Group; Multiconsult Norge AS. Descriptive Study of Local Energy Communities; Technical Report 1; Norwegian Water Resources and Energy Directorate: Oslo, Norway, 2019; pp. 1–49.
- IEA. Empowering Cities for a Net Zero Future: Unlocking Resilient, Smart, Sustainable Urban Energy Systems; Technical Report; International Energy Agency: Paris, France, 2021; pp. 1–111. [Google Scholar]
- Pellerin, B.; Puranik, S.; Tuiskula, H.; Kunze, C.; Willems, C.; Loock, M.; Murphy, B. D7.1—Market and Stakeholder Analysis; Technical Report; 2019; Available online: https://elandh2020.eu/wp-content/uploads/2020/09/D7.1-Market-and-stakeholder-analysis.pdf (accessed on 10 January 2022).
- Morandi, C.; Rolando, A.; Vita, S.D. From Smart City to Smart Region—Digital Services for an Internet of Places, 1st ed.; PoliMI SpringerBriefs: Milano, Italy, 2016; pp. 1–103. [Google Scholar]
- Sutriadi, R. Defining smart city, smart region, smart village, and technopolis as an innovative concept in indonesia’s urban and regional development themes to reach sustainability. IOP Conf. Ser. Earth Environ. Sci.
**2018**, 202, 012047. [Google Scholar] [CrossRef] [Green Version] - Calvillo, C.F.; Miralles, A.S.; Villar, J. Energy management and planning in smart cities. Renew. Sustain. Energy
**2016**, 55, 273–287. [Google Scholar] [CrossRef] [Green Version] - Matern, A.; Binder, J.; Noack, A. Smart regions: Insights from hybridization and peripheralization research. Eur. Plan. Stud.
**2020**, 28, 2060–2077. [Google Scholar] [CrossRef] [Green Version] - Zhao, Y.; Ma, L.; Li, Z.; Ni, W. The development of regional smart energy systems in the World and China: The concepts, practices, and a new perspective. Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
**2021**, 11, e1409. [Google Scholar] [CrossRef] - Energy Community. Electricity Market Functions—Short Overview and Description; Technical Report; Energy Community Secretariat: Vienna, Austria, 2020; pp. 1–9. [Google Scholar]
- Lavrijssen, S.; Marhold, A.; Trias, A. The Changing World of the DSO in a Smart Energy System Environment: Key Issues and Policy Recommendations; Technical Report; Centre on Regulation in Europe: Brussels, Belgium, 2016; pp. 1–98. [Google Scholar]
- Oosterkamp, P.V.D.; Koutstaal, P.; Welle, A.V.D.; Joode, J.D.; Lenstra, J.; Hussen, K.V.; Haffner, R. The Role of DSOs in a Smart Grid Environment; Technical Report; ECORYS: Rotterdam, The Netherlands, 2014; pp. 1–145. [Google Scholar]
- Khajeh, H.; Laaksonen, H.; Gazafroud, A.S.; Khah, M.S. Towards flexibility trading at TSO-DSO-customer levels: A review. Energies
**2019**, 13, 165. [Google Scholar] [CrossRef] [Green Version] - Klaassen, E.; Laan, M.V.D. Energy and Flexibility Services for Citizens Energy Communities. In USEF White Paper; 2019; pp. 1–19. Available online: https://www.usef.energy/new-white-paper-energy-flexibility-services-for-citizens-energy-communities/# (accessed on 10 January 2022).
- Gancheva, M.; O’Brien, S.; Crook, N.; Monteiro, C. Models of Local Energy Ownership and the Role of Local Energy Communities in Energy Transition in Europe; Technical Report; European Committee of the Regions: Brussels, Belgium, 2018; pp. 1–71. [Google Scholar]
- CENELEC EN 50160; Voltage Characteristics of Electricity Supplied by Public Electricity Networks. European Committee for Electrotechnical Standardization: Brussels, Belgium, 2010; pp. 1–44.
- Strbac, G.; Djapic, P.; Pudjianto, D.; Konstantelos, I.; Moreira, R. Strategies for Reducing Losses in Distribution Networks; Technical Report; Imperial College London: London, UK, 2018; pp. 1–87. [Google Scholar]
- Ochoa, L.F.; Harrison, G.P. Minimizing Energy Losses: Optimal Accommodation and Smart Operation of Renewable Distributed Generation. IEEE Trans. Power Syst.
**2011**, 26, 198–205. [Google Scholar] [CrossRef] [Green Version] - Strbac, G.; Pudjianto, D.; Aunedi, M.; Djapic, P.; Teng, F.; Zhang, X.; Ameli, H.; Moreira, R.; Brandon, N. Role and value of flexibility in facilitating cost-effective energy system decarbonisation. Prog. Energy
**2020**, 2, 042001. [Google Scholar] [CrossRef] - IEEE 610-1990; IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. The Institute of Electrical and Electronics Engineers: New York, NY, USA, 1991; pp. 1–218.
- CEN-CENELEC-ETSI. CEN-CENELEC-ETSI: Smart Grid Coordination Group—Smart Grid Reference Architecture; Technical Report; 2012; Available online: https://ec.europa.eu/energy/sites/ener/files/documents/xpert_group1_reference_architecture.pdf (accessed on 10 January 2022).
- Interoperability in Healthcare. Available online: https://www.himss.org/resources/interoperability-healthcare (accessed on 10 January 2022).
- Mateus, M.; Maló, P.; Almeida, B.; Teixeira, T. Towards measuring information interoperability based on model transformations. In Proceedings of the 6th Iberian Conference on Information Systems and Technologies (CISTI 2011), Chaves, Portugal, 15–18 June 2011; pp. 1–4. [Google Scholar]
- Berezovskaya, Y.; Yang, C.W.; Vyatkin, V. Towards Extension of Data Centre modeling Toolbox with Parameters Estimation. In Technological Innovation for Applied AI Systems; Camarinha-Matos, L.M., Ferreira, P., Brito, G., Eds.; Springer International Publishing: Cham, Switzerland, 2021; pp. 189–196. [Google Scholar]
- Energy Innovation Policy & Technology LLC. How Much Energy Do Data Centers Really Use? Available online: https://energyinnovation.org/2020/03/17/how-much-energy-do-data-centers-really-use/ (accessed on 10 January 2022).
- Yin, Y.; Wu, J.; Zhou, X.; Eeckhout, L.; Qouneh, A.; Li, T.; Yu, Z. COPA: Highly Cost-Effective Power Back-Up for Green Datacenters. IEEE Trans. Parallel Distrib. Syst.
**2020**, 31, 967–980. [Google Scholar] [CrossRef] - Brännvall, R.; Siltala, M.; Gustafsson, J.; Sarkinen, J.; Vesterlund, M.; Summers, J. EDGE: Microgrid Data Center with Mixed Energy Storage. In Proceedings of the Eleventh ACM International Conference on Future Energy Systems (e-Energy 2020), Melbourne, Australia, 22–26 June 2020; pp. 466–473. [Google Scholar]
- Herbst, I.; Lukovic, S.; Gasparin, A.; Schulz, N.; Witzig, J.; Kieber, S. Lv grid data analysis demonstrated at dso arbon energie. In Proceedings of the 25th International Conference and Exhibition on Electricity Distribution, Madrid, Spain, 3–6 June 2019; pp. 1–5. [Google Scholar]
- Franzl, G.; Goranovic, A.; Wilker, S.; Sauter, T.; Treytl, A. Initiating an IES based Technical Framework on Local Energy Communities. In Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria, 8–11 September 2020; Volume 1, pp. 1131–1134. [Google Scholar] [CrossRef]
- Gottschalk, M.; Franzl, G.; Frohner, M.; Pasteka, R.; Uslar, M. From integration profiles to interoperability testing for smart energy systems at connectathon energy. Energies
**2018**, 11, 3375. [Google Scholar] [CrossRef] [Green Version] - IEC 61850:2021; Communication Networks and Systems for Power Utility Automation. IEC: Geneva, Switzerland, 2021; Volume 1.0, pp. 1–7618.
- IEC 61499-1:2012; Function Blocks—Part 1: Architecture. IEC: Geneva, Switzerland, 2012; Volume 2.0, pp. 1–245.
- Wang, Q.; Huang, N.; Lin, H.; Li, H.; Wennersten, R.; Sun, Q. Potential of energy saving in a data center—Application of an agent-based modeling. Energy Procedia
**2017**, 105, 3903–3908. [Google Scholar] [CrossRef]

**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 | $SCR=100\xb7\left(\right)open="("\; close=")">1-\frac{{E}_{exp,i}}{{E}_{res,i}}$ $E}_{exp,i},{E}_{res,i$ = 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] |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## 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