Results:

• A data set of a Norwegian energy community
• A Feasibility Study of Blockchain Technology As Local Energy Market Infrastructure
• A systematic review of machine learning techniques related to local energy communities
• Methods for Cost Allocation Among Prosumers and Consumers Using Cooperative Game Theory
• Optimal coordination of renewable sources and storage in energy-constrained power systems
• Quantifying the benefits of shared battery in a DSO-energy community cooperation
• The impact of degradation on the investment and operation of a community battery for multiple services

Microgrids/local energy systems
CINELDI's Knowledge base

Microgrids and Local Energy Communities can be formed to serve a multitude of needs for end-users and grid companies. A key question is how economic attractive such advanced local solutions are in comparison with traditional energy supply strategies.

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.

Comprehensive research has been conducted within CINELDI on the topic of optimal utilization of energy storage in microgrids, under different technical configurations and market opportunities [1]. Here, it was shown how crucial it is to represent battery degradation in control strategies and economic evaluation of microgrid resources. Moreover, new methods inspired by long-term hydropower planning were develop to represent weather uncertainties in the operation strategies of storages and other flexible resources in isolated and weakly connected microgrids.

In CINELDI, a techno-economical evaluation at the Skagerak EnergiLab [2] was performed [3]. 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 [4]. 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 [5] explored the market value of batteries using local electricity trading. End-user economic benefits are analyzed and compared with traditional schemes. In [6], 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 [3]. Moreover, the evaluation of peer-to-peer trading can also show the potential economic benefit in local energy communities [7].

Selected publications from CINELDI:

1. P. Aaslid, “Optimal coordination of renewable sources and storage in energy-constrained power systems”, Doctoral thesis, NTNU, 2022.
2. Skagerak Energilab - Forside
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.