This subtopic is about the techno-economic evaluation of microgrids and local energy communities (LECs). One of the main economic challenges in microgrids is to establish business models that can enable participation in the markets and offer services. In the long run, together with technical advancements and regulations, this will make the microgrids a solution to provide more flexibility and efficient and resilient operation in the distribution grid. On the other hand, other challenges are related to the regulations for how to sell and buy electricity locally in LECs. Some concepts are still in the early stages, such as peer-to-peer energy trading schemes.

In CINELDI, a techno-economical evaluation at the Skagerak EnergiLab was performed [1]. Here, the performance of an existing infrastructure, which is a football stadium with solar panels and batteries, under a variety of operation strategies for peak-shaving, self-consumption maximization, and energy arbitrage was tested.  A Model Predictive Control (MPC) for the Skagerak EnergiLab is also proposed in [2]. The control seeks to find a trade-off between control objectives and decrease the operation cost while constraints in a probabilistic sense are satisfied.

In CINELDI, there is also research related to peer-to-peer trading. For example, reference [3] explored the market value of batteries using local electricity trading. End-user economic benefits are analyzed and compared with traditional schemes. In [4] a framework was proposed to integrate prosumer communities into the existing electricity market.

CINELDI results can show how technical-economic analysis can be done and suggest new business models that can assist the implementation of new microgrids [1]. Moreover, the evaluation of peer-to-peer trading can also show the potential economic benefit in local energy communities [5].

[1]          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.
[2]          J. P. Maree, S. Gros, and V. Lakshmanan, ‘Low-complexity Risk-averse MPC for EMS’, in 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Aachen, Germany, Oct. 2021, pp. 358–363. doi: 10.1109/SmartGridComm51999.2021.9632329.
[3]          A. Lüth, J. M. Zepter, P. Crespo del Granado, and R. Egging, ‘Local electricity market designs for peer-to-peer trading: The role of battery flexibility’, Applied Energy, vol. 229, pp. 1233–1243, Nov. 2018, doi: 10.1016/j.apenergy.2018.08.004.
[4]          J. M. Zepter, A. Lüth, P. Crespo del Granado, and R. Egging, ‘Prosumer integration in wholesale electricity markets: Synergies of peer-to-peer trade and residential storage’, Energy and Buildings, vol. 184, pp. 163–176, Feb. 2019, doi: 10.1016/j.enbuild.2018.12.003.
[5]          S. Bjarghov, M. Askeland, and S. Backe, ‘Peer-to-peer trading under subscribed capacity tariffs - an equilibrium approach’, in 2020 17th International Conference on the European Energy Market (EEM), Sep. 2020, pp. 1–6. doi: 10.1109/EEM49802.2020.9221966.