# Modelling and characterization of flexible resources

Utilization of flexible resources can be a cost-effective alternative to grid investment [1], and also enhances the security of electricity supply: energy availability, power capacity, reliability of supply and power quality [2]. To enable large-scale utilization of flexible resources, it is useful to establish unified definitions of the properties which characterize them. This is important both for modelling the resources, but also for facilitating the information exchange between stakeholders. Degefa *et al*. [3] conceptualized a comprehensive classification framework for characterizing flexible resources.

Modelling the flexible resources in the distribution grid is a useful way of demonstrating their potential impact. The most central flexible resources considered in CINELDI so far, has been demand side flexibility in building operation [4]–[6] and households [7]–[9], battery storage systems [10]–[18] and electric vehicle (EV) charging [19]–[23].

Battery storage systems can be utilized as flexible resources in the power system. Although many such systems are presently considered to be too expensive solutions in many cases, the prices are expected to drop. Thus, battery storage systems have been considered for many case studies in CINELDI: optimization of battery system operation, as an alternative to grid reinforcement, for enhancing security of supply, cost-optimal operation in prosumer villas, considering battery degradation and optimal operation of such systems in large buildings, such as a football stadium, for example.

Fast charging stations for EVs is another important resource and can be considered as both a flexibility resource and as a load causing grid problems. As a resource, EV charging can be used both for reactive power supply and as a demand-side spatial flexibility resource.

The BATTPOWER toolbox was also developed in collaboration with CINELDI [24]. It is a multi-period AC optimal power flow (OPF) solver which takes various flexible resources such as stationary energy storage systems and EV charging into account and aims to be highly computationally efficient compared with traditional OPF solvers [25].

**References:**

[1] M. Löschenbrand, “A transmission expansion model for dynamic operation of flexible demand,” *International Journal of Electrical Power & Energy Systems*, vol. 124, p. 106252, Jan. 2021, doi: 10.1016/j.ijepes.2020.106252.

[2] I. B. Sperstad, M. Z. Degefa, and G. Kjølle, “The impact of flexible resources in distribution systems on the security of electricity supply: A literature review,” *Electric Power Systems Research*, vol. 188, p. 106532, Nov. 2020, doi: 10.1016/j.epsr.2020.106532.

[3] M. Z. Degefa, I. B. Sperstad, and H. Sæle, “Comprehensive classifications and characterizations of power system flexibility resources,” *Electric Power Systems Research*, vol. 194. p. 107022, 2021. doi: https://doi.org/10.1016/j.epsr.2021.107022.

[4] K. E. Thorvaldsen, M. Korpås, K. B. Lindberg, and H. Farahmand, “A stochastic operational planning model for a zero emission building with emission compensation,” *Applied Energy*, vol. 302, p. 117415, Nov. 2021, doi: 10.1016/j.apenergy.2021.117415.

[5] K. E. Thorvaldsen, M. Korpås, and H. Farahmand, “Long-term Value of Flexibility from Flexible Assets in Building Operation,” *International Journal of Electrical Power & Energy Systems*, vol. 138, p. 107811, Jun. 2022, doi: 10.1016/j.ijepes.2021.107811.

[6] K. Emil Thorvaldsen, S. Bjarghov, and H. Farahmand, “Representing Long-term Impact of Residential Building Energy Management using Stochastic Dynamic Programming,” in *2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)*, Liege, Belgium, Aug. 2020, pp. 1–7. doi: 10.1109/PMAPS47429.2020.9183623.

[7] M. Z. Degefa, H. Sæle, I. Petersen, and P. Ahcin, “Data-driven Household Load Flexibility Modelling: Shiftable Atomic Load,” presented at the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Oct. 2018. doi: 10.1109/ISGTEurope.2018.8571836.

[8] B. Fjelldal, M. Fodstad, G. Rosenlund, H. Sæle, and M. Z. Degefa, “Exploring household’s flexibility of smart shifting atomic loads to improve power grid operation and cost efficiency,” presented at the 2020 17th International Conference on the European Energy Market (EEM), Sep. 2020. doi: 10.1109/EEM49802.2020.9221872.

[9] V. Lakshmanan, H. Sæle, and M. Z. Degefa, “Electric water heater flexibility potential and activation impact in system operator perspective – Norwegian scenario case study,” *Energy*, vol. 236, p. 121490, Dec. 2021, doi: 10.1016/j.energy.2021.121490.

[10] A. A. Seijas, P. C. del Granado, H. Farahmand, and J. Rueda, “Optimal battery systems designs for Distribution Grids: What size and location to invest in?,” in *2019 International Conference on Smart Energy Systems and Technologies (SEST)*, Sep. 2019, pp. 1–6. doi: 10.1109/SEST.2019.8849119.

[11] I. B. Sperstad and M. Korpås, “Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties,” *Energies*, vol. 12, no. 7, Art. no. 7, Jan. 2019, doi: 10.3390/en12071231.

[12] F. Berglund, S. Zaferanlouei, M. Korpås, and K. Uhlen, “Optimal Operation of Battery Storage for a Subscribed Capacity-Based Power Tariff Prosumer—A Norwegian Case Study,” *Energies*, vol. 12, no. 23, p. 4450, Nov. 2019, doi: 10.3390/en12234450.

[13] I. B. Sperstad *et al.*, “Cost-Benefit Analysis of Battery Energy Storage in Electric Power Grids: Research and Practices,” *2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)*. pp. 314–318, 2020. doi: 10.1109/ISGT-Europe47291.2020.9248895.

[14] C. A. Andresen, H. Sæle, and M. Z. Degefa, “Sizing Electric Battery Storage System for Prosumer Villas,” in *2020 International Conference on Smart Energy Systems and Technologies (SEST)*, Sep. 2020, pp. 1–5. doi: 10.1109/SEST48500.2020.9203343.

[15] P. Aaslid, F. Geth, M. Korpås, M. M. Belsnes, and O. B. Fosso, “Non-linear charge-based battery storage optimization model with bi-variate cubic spline constraints,” *Journal of Energy Storage*, vol. 32, p. 101979, Dec. 2020, doi: 10.1016/j.est.2020.101979.

[16] M. R. Brubæk and M. Korpås, “A Norwegian Case Study on Battery Storage as Alternative to Grid Reinforcement,” presented at the 2021 IEEE Madrid PowerTech, Jun. 2021. doi: 10.1109/PowerTech46648.2021.9495054.

[17] E. Haugen, K. Berg, B. N. Torsater, and M. Korpas, “Optimisation model with degradation for a battery energy storage system at an EV fast charging station,” in *2021 IEEE Madrid PowerTech*, Madrid, Spain, Jun. 2021, pp. 1–6. doi: 10.1109/PowerTech46648.2021.9494979.

[18] K. Berg, M. Resch, T. Weniger, and S. Simonsen, “Economic evaluation of operation strategies for battery systems in football stadiums: A Norwegian case study,” *Journal of Energy Storage*, vol. 34, p. 102190, Feb. 2021, doi: 10.1016/j.est.2020.102190.

[19] H. Sæle *et al.*, “Electric vehicles in Norway and the potential for demand response,” presented at the 2018 53rd International Universities Power Engineering Conference (UPEC), Glasgow, UK, 2018. doi: 10.1109/UPEC.2018.8541926.

[20] M. Marinelli *et al.*, “Electric Vehicles Demonstration Projects - An Overview Across Europe,” in *2020 55th International Universities Power Engineering Conference (UPEC)*, Sep. 2020, pp. 1–6. doi: 10.1109/UPEC49904.2020.9209862.

[21] M. Garau and B. N. Torsæter, “Agent-Based Analysis of Spatial Flexibility in EV Charging Demand at Public Fast Charging Stations,” presented at the 2021 IEEE Madrid PowerTech, Jun. 2021. doi: 10.1109/PowerTech46648.2021.9494818.

[22] I. Ilieva and B. Bremdal, “Flexibility-Enhancing Charging Station to Support the Integration of Electric Vehicles,” *World Electric Vehicle Journal*, vol. 12, no. 2, Art. no. 2, Jun. 2021, doi: 10.3390/wevj12020053.

[23] I. Ilieva and B. Bremdal, “Implementing local flexibility markets and the uptake of electric vehicles – the case for Norway,” in *2020 6th IEEE International Energy Conference (ENERGYCon)*, Sep. 2020, pp. 1047–1052. doi: 10.1109/ENERGYCon48941.2020.9236611.

[24] S. Zaferanlouei, H. Farahmand, V. V. Vadlamudi, and M. Korpås, “BATTPOWER Toolbox: Memory-Efficient and High-Performance Multi-Period AC Optimal Power Flow Solver,” *IEEE Transactions on Power Systems*, vol. 36, no. 5, pp. 3921–3937, Sep. 2021, doi: 10.1109/TPWRS.2021.3055429.

[25] S. Zaferanlouei, M. Korpås, J. Aghaei, H. Farahmand, and N. Hashemipour, “Computational Efficiency Assessment of Multi-Period AC Optimal Power Flow including Energy Storage Systems,” in *2018 International Conference on Smart Energy Systems and Technologies (SEST)*, Sep. 2018, pp. 1–6. doi: 10.1109/SEST.2018.8495683.