The increased diffusion of Renewable Energy Sources (RES) into energy distribution systems gives rise to a number of issues that the Distribution Network Operators (DNOs) need to face. The dependency of RES on weather, along with their intermittent and non-dispatchable nature, urges us to develop frameworks that can guarantee a stable network, despite the power fluctuations.
In this thesis, we develop a Data-Driven Optimal Power Flow (OPF) formulation in order to achieve voltage regulation against operational problems that may occur because of the high PV penetration into energy distribution systems. The unpredictable PV generation deteriorates the system’s reliability and introduces instability. Thus, we develop appropriate formulations to define a limit on exports from residential PV owners to the grid. For this purpose, we employ Distribunally Robust Chance Constraint Programming (DRCCP), as a method that can handle constraints that depend on the uncertain PV generation and
residential demand. We capture the distributional uncertainties with an ambiguity set and we utilize the Wasserstein metric to parameterize the range of this set.
We divide the thesis into two case studies. In the first case study, we perform the DRCCP optimization with only few recorded data. We evaluate the results of our algorithm in a dataset of 500 days of recorded data and we achieve to reduce significantly the overvoltage instances. We tune the DRCCP in a way that we achieve at least 95% satisfaction of constraints that emerge from the DRCCP, avoiding overconservative solutions and high generation cuts that
could harm the PV owners.
In the second case study, we intend to exploit plenty of historical data, but the computational burden hinders us from using them in the DRCCP. Therefore, we employ Wasserstein Barycenters, and we utilize the Wasserstein distance in order to cluster the data. With Wasserstein Barycenters we reduce significantly the running time and we provide an efficient and robust output, for at least 95% satisfaction of the model’s constraints.