Abstract
Abstract New energy technologies like photovoltaics, electric vehicles, and heat pumps increasingly find their way to distribution networks. At the time the existing distribution networks were designed, only conventional loads were considered. The capacity of the (existing) distribution networks is therefore insufficient to handle the additional (bidirectional) peak load caused by these new technologies. The distribution system operator (DSO) is facing network congestion. Flexibility to shift and/or change power and energy in time and/or amount is considered as an option to mitigate network congestion with various implicit and explicit mechanisms. This leaves DSOs with the question on how to deploy such mechanisms seamlessly and effectively in daily business with uncertain congestion scenarios and complex integration processes. To tackle these operational challenges, this paper introduces a generic four-step approach to operationalize the flexibility need of a DSO, for any chosen implementation of an implicit or explicit flexibility mechanism (e.g. price-based schemes, flexibility markets, direct control). To this end, this paper addresses the steps: 1. Data acquisition, 2. Forecasting, 3. Decision-making, and 4. Flexibility mechanism interfacing. Furthermore, a particular implementation is described in relation to the Dutch’ H2020 InterFlex demonstrator, showing the field application of the proposed steps. In this demonstrator, a large amount of flexibility (26 electric vehicle charge points of 22kW, a 250kW/315kWh battery energy storage system, and a 260 kWp photovoltaic installation) is connected to two 630kVA transformers in a residential area with approximately 350 apartments. The results of the implementation show that the proposed steps enable the DSO to predict congestion, put a monetary value on flexibility, and use this value to evaluate flexibility offered through – in this case study – a flexibility market.
Highlights
This paper focuses on flexibility for congestion management, from a distribution system operator (DSO) perspective
This paper introduces a four-step method to operationalize the DSO’s decision to obtain flexibility in order to mitigate network congestion
The decision-making model evaluates the monetary value of flexibility based on two aspects: the loss of life of a transformer and the DSO’s financial risk of a blackout due to an overloading
Summary
In [3], an implicit and an explicit method are integrated, introducing a day-ahead dynamic price and a real-time curtailment request. This is done using a multi-agent system, and its performance is simulated based on a modified European LV test network. In [7], the viability of different implicit and explicit flexibility mechanisms is evaluated with respect to their ability to resolve congestion in the distribution network. The local market enables synergies between all local energy carriers (electricity, heating and cooling), while local operation strategies of the (electric) distribution network are managed by an energy management system with a model-predictive control strategy, depending on the project laying at the base of the proposed and tested solutions. The model is shown to work in a simulated Dutch residential LV network, but not implemented in a day-to-day operational decision-making setup
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