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Exploitation of Distributed Energy Resources for local services on power distribution network

Publications - Paper

Exploitation of Distributed Energy Resources for local services on power distribution network

The electrification of residential and industrial air conditioning, combined with the increasing number of electric vehicles, is causing undesired situations in the distribution network, such as overloads or significant variations in power factor. One possible solution to these problems is the establishment of a market for local services, where distribution system operators can find the necessary resources to address these issues. This document demonstrates the potential of a local services market for managing network congestion in a real-world use case.

The electrification of residential and industrial air conditioning, coupled with the increasing number of electric vehicles, is leading to a substantial increase in electrical load on medium and low voltage distribution networks. This growth is partially offset by distributed generation, predominantly renewable and uncontrollable, which can also cause over-generation in the distribution network. All these events are causing undesirable situations in the distribution network, such as congestion or significant power factor variations.

 

One possible solution to these problems is the establishment of a market for local services, where distribution system operators (DSOs) can find the necessary resources to address these issues. This document demonstrates the potential of a Local Services Market (LSM) for managing network congestion within the context of a real-world use case. A real portion of the Milan network has been modeled and integrated into an LSM simulator.

 

The results show that the proposed approach represents a viable solution for alleviating distribution network congestion. Based on the assumptions made, the average annual cost for the DSO to purchase local services is approximately €94/MWh. This cost may vary depending on the load forecasting error, currently estimated at about 10%.

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