The economic and technical requirements of current changes in the distribution system are reflected in the use of all available resources and the activation of mechanisms for local use of flexibility. Local flexibility markets are evolving and face numerous obstacles for which appropriate solutions must be found. The local flexibility market will be complemented by the development of a local flexibility register, which will contain all relevant information about the flexibility assets necessary for the efficient operation of the local flexibility market. In this paper, interpretation and quantification of the flexibility sources location on the flexibility service in the distribution grid is given. The information is derived from power flow simulation results and finally written down in the form of line coefficients, which are determined by applying the least squares method to the power flow results. We have developed a Python-based simulator to perform the methodology to determine the information and test it on a realistic medium voltage distribution grid in Bosnia and Herzegovina. This paper confirms the approximate linearity of the active power changes on the demand side to the line load and to the voltage at the nodes for a given operating condition of the distribution grid.

The growing penetration of renewable energy sources (RES) in the electrical power sector has increased the amount of distributed generation (DG) units connected at the distribution system level. In this context, new balancing challenges have arisen, creating the need for a novel use case methodology to enable an active role at the distribution system level such that transmission system operators (TSOs) can coordinate with distribution system operators (DSOs) with regard to connected resources for balancing purposes. In this study, the exploitation of the DSO-connected resources for balancing purposes in a market environment is proposed and evaluated via a novel business use case (BUC) methodology based on the categorization of IEC 62913-1. More specifically, in order to address different balancing market situations, two scenarios are considered with regard to the BUC. The first one represents the data exchange between the TSO, the DSO, and the balancing service provider (BSP). The second one represents an alternative scenario where data are exchanged directly between the TSO and the DSO, where the DSO also takes on the role of the BSP. The proposed BUC was also developed in order to validate the required data modeling and exchange mechanisms between DSOs and TSOs in order to exploit DSO-connected resources for overall system balancing purposes across different time scales.

There has been an increasing trend of integrating photovoltaic power plants (PVs). One of the important challenges for distribution system operators is to evaluate the total installed power of a PV that a particular network can host (or PV hosting capacity) while keeping voltage and element constraints within required limits. The major drawback of the existing methods for calculating PV hosting capacity is that they use the same installed power of the PV systems for all simulated PVs, as these methods do not use external data sources about building roofs. As a consequence, this has a significant impact on the final accuracy of the results. This paper presents a probabilistic methodology for calculating the PV hosting capacity in low voltage (LV) networks. The main contribution of this paper is the improved modeling of PV generation using actual building roof data when calculating the PV hosting capacity, as every building is treated according to its actual solar potential. Monte Carlo simulations with incorporated stochastic consumption and PV generation models are utilized for load flow calculations of the actual LV network. The simulation results presented in this paper prove that the proposed methodology increases the accuracy of the final PV hosting capacity calculations.