The penetration of Distributed Renewable Energy Sources (DRES) in the distribution grid is increasing considerably in the last years. This is one of the main causes that contributed to the growth of technical problems in both transmission and distribution systems. An effective solution to improve system security is to exploit the flexibility that can be provided by Distributed Energy Resources (DER), which are mostly located at the distribution grids. Their location combined with the lack of power flow coordination at the system operators interface creates difficulties in taking advantage of these flexible resources. This paper presents a methodology based on the solution of a set of optimization problems that estimate the flexibility ranges at the TSO-DSO boundary nodes. The estimation is performed while considering the grid technical constraints and a maximum cost that the user is willing to pay. The novelty behind this approach comes from the development of flexibility cost maps, which allow the visualization of the impact of DER flexibility on the operating point at the TSO-DSO interface. The results are compared with a sampling method and suggest that a higher accuracy in the TSO-DSO information exchange process can be achieved through this approach.

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This paper presents two contributions developed in the framework of evolvDSO Project to support TSO-DSO cooperation. The Interval Constrained Interval Power Flow (ICPF) tool estimates the flexibility range at primary substations by aggregating the distribution network flexibility. The Sequential Optimal Power Flow (SOPF) tool defines a set of control actions that keep the active and reactive power flow within pre-agreed limits at primary substations level, by integrating different types of flexibility levers. Several study test cases were simulated using data of four real distribution networks from France and Portugal, with different demand / generation profiles and several degrees of flexibility.

Future electricity distribution grids will host a considerable share of the renewable energy sources needed for enforcing the energy transition. Demand side management mechanisms play a key role in the integration of such renewable energy resources by exploiting the flexibility of elastic loads, generation or electricity storage technologies. In particular, local energy markets enable households to exchange energy with each other while increasing the amount of renewable energy that is consumed locally. Nevertheless, as most ex-ante mechanisms, local market schedules rely on hour-ahead forecasts whose accuracy may be low. In this paper we cope with forecast errors by proposing a game theory approach to model the interactions among prosumers and distribution system operators for the control of electricity flows in real-time. The presented game has an aggregative equilibrium which can be attained in a semi-distributed manner, driving prosumers towards a final exchange of energy with the grid that benefits both households and operators, favoring the enforcement of prosumers' local market commitments while respecting the constraints defined by the operator. The proposed mechanism requires only oneto-all broadcast of price signals, which do not depend either on the amount of players or their local objective function and constraints, making the approach highly scalable. Its impact on distribution grid quality of supply was evaluated through load flow analysis and realistic load profiles, demonstrating the capacity of the mechanism ensure that voltage deviation and thermal limit constraints are respected.

The limited capacity of distribution grids for hosting renewable generation is one of the main challenges towards the energy transition. Local energy markets, enabling direct exchange of energy between prosumers, help to integrate the growing number of residential photovoltaic panels by scheduling flexible demand for balancing renewable energy locally. Nevertheless, existing scheduling mechanisms do not take into account the phases to which households are connected, increasing network unbalance and favoring bigger voltage rises/drops and higher losses. In this paper, we reduce network unbalance by leveraging market transactions information to dynamically allocate houses to phases using solid state switches. We propose cost effective mechanisms for the selection of households to switch and for their optimal allocation to phases. Using load flow analysis we show that only 6% of houses in our case studies need to be equipped with dynamic switches to counteract the negative impact of local energy markets while maintaining all the benefits. Combining local energy markets and dynamic phase switching we improve both overall load balancing and network unbalance, effectively augmenting DER hosting capacity of distribution grids.