Primary frequency control is the automatic mechanism implemented on power systems to regulate the power balance through frequency and hence, its action should be taken into account when modeling any contingency state leading to a modification of the active power balance (e.g. generator failures). This paper presents a fully distributed method to solve the DC security constrained power flow (DC-SCOPF) that takes into account the automatic primary frequency response of generators after an incident. In more detail, we extend existing distributed DC-SCOPF formulations by: (1) introducing a new variable representing the frequency deviation; and (2) enhancing the local problem of each generator to consider how it adjusts its production after each contingency following its primary frequency regulation curve. The computation of the frequency deviations in the DC-SCOPF problem is formulated into a suitable form (i.e. in the form of a general consensus problem) so that smaller problems, corresponding to individual sub-regions or actors, can be solved and coordinated via the alternating direction method of multipliers (ADMM) in a distributed manner. In this way, actors of the system do not need to exchange any confidential information with other actors during the optimization procedure. A salient feature of our approach is that it can consider contingencies that lead to area separation without any prior specification of the topology and thus can adapt to many kinds of situations that are of interest in interconnected systems. Extensive simulation results on several standard IEEE systems show the good performance of the proposed model and algorithm in terms of convergence speed and accuracy as well as its capacity to deal with the disconnection of areas in interconnected systems.
Local energy communities are identified as a promising approach to efficiently integrate distributed generation whereas keeping costs down for prosumers. In this context, we propose a multi-agent system to collectively optimise the energy flows of a local community of prosumers. The novelty and strength of our approach resides in the use of decentralised decision making algorithms, based on the alternating direction method of multipliers, to orchestrate the demand and supply of a large number of homes. Our preliminary results show how the proposed approach can significantly increase the self-consumption level of the community while significantly reducing the energy bills of its members.
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