Finding a global solution to the optimal power flow (OPF) problem is difficult due to its nonconvexity. A convex relaxation in the form of semidefinite programming (SDP) has attracted much attention lately as it yields a global solution in several practical cases. However, it does not in all cases, and such cases have been documented in recent publications. This paper presents another SDP method known as the momentsos (sum of squares) approach, which generates a sequence that converges towards a global solution to the OPF problem at the cost of higher runtime. Our finding is that in the small examples where the previously studied SDP method fails, this approach finds the global solution. The higher cost in runtime is due to an increase in the matrix size of the SDP problem, which can vary from one instance to another. Numerical experiment shows that the size is very often a quadratic function of the number of buses in the network, whereas it is a linear function of the number of buses in the case of the previously studied SDP method.

In this paper, we publish nine new test cases in MATPOWER format. Four test cases are French very high-voltage grid generated by the offline plateform of iTesla: part of the data was sampled. Four test cases are RTE snapshots of the full French very high-voltage and high-voltage grid that come from French SCADAs via the Convergence software. The ninth and largest test case is a pan-European ficticious data set that stems from the PEGASE project. It complements the four PEGASE test cases that we previously published in MATPOWER version 5.1 in March 2015. We also provide a MATLAB code to transform the data into standard mathematical optimization format. Computational results confirming the validity of the data are presented in this paper.

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The transmission grid in Europe is interconnected to guarantee the security of supply and to facilitate the competition among different market players, thereby making the system highly meshed. It is a challenging task for the transmission system operators (TSOs) to manage the power flows in their system, especially in the light of integration of renewable energy generation sources into the transmission system. The intermittent nature of such generation sources creates variable power flows and loop flows, in turn, questing for installation of controllable devices to manage these flows. The TSOs are currently installing such devices to cope with the situation. A proper coordination is needed for the operation of these devices, since they can lead to adverse effects on power flows in a meshed system. Coordination among TSOs in Central Western Europe (CWE) is performed, however, not towards a full system-wide objective, since there is no regulatory framework that exists for such coordination. This paper focuses on the potential of coordination among TSOs with respect to operation of the controllable devices. Two aspects are investigated: management of constraints in the system in the dayahead scheduling process and wind in-feed optimization. Both approaches are implemented at the Regional Security Center and tested on a high-stress situation in the CWE region. Furthermore, a case study at the coordination center is performed using actual data for the month of January 2013 to assess the usefulness on a longer time period. Index Terms-Coordination, high-voltage direct current (HVDC), phase-shifting transformer (PST), transmission system operations, uncertainty management. NOMENCLATURE l Line index. t Time index. b Node index. N h Number of hours. c Contingency index. j PST index. N P ST Number of PSTs. N b Number of nodes.

The French transmission system operator (RTE) needs to face a significant congestion increase in specific zones of the electrical network due to high integration of renewable energies. Network reconfiguration and renewable energy curtailment are currently employed to manage congestion and guarantee the system security and stability. In sensitive zones, however, stronger levers need to be developed. Large battery storage systems are receiving an increasing interest for their potential in congestion management. In this paper, a model for local congestion management mixing batteries and renewable generation curtailment is developed. Subsequently, an energy management approach relying on the principles of Model Predictive Control is presented. Results of simulations on RTE data sets are presented for the analysis of the degrees of freedom and sensitive parameters of the design.

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Congestion problems are increasing in number in power transmission networks due to the increment of renewable power sources along it. To reduce their impact, Transmission System Operators (TSOs) as the French RTE use network reconfiguration or renewable power curtailment in complex subtransmission areas. The operators need enhanced methodological tools to better address the optimal power flow management problem by also using novel levers as for example the storage devices. This paper proposes mathematical models which integrate the possibility to partially curtail the renewable power in the form of a dynamical system representing the transmission network, also considering storage devices.