Panagiotis Pediaditis

Although the large-scale integration of distributed energy resources constitutes a fundamental pillar of low-carbon energy systems, it creates an increasing stress in distribution networks, requiring mobilisation of the flexibility of those resources to resolve network congestion. To this end, two alternative mechanisms that have recently gained attention are temporally- and spatially-differentiated distribution use-of-system tariffs and local flexibility markets. However, existing research has investigated these two mechanisms in silos, neglecting their potential synergies in mobilising distributed energy resources flexibility. This paper aims at addressing this research gap by proposing a bilevel optimisation model which captures the interactions between a distribution system operator designing variable distribution use-of-system tariffs and operating a local flexibility market based on capacity limitations, and aggregators of electric vehicles with smart charging capability reacting to the designed tariffs and procured capacity limitations. The model is tested on a real 47-node distribution network. The presented studies illustrate cases where the two mechanisms synergetically resolve congestion phenomena and demonstrate that their combination results in a significant reduction of total system costs.

The ongoing energy transition has caused a paradigm shift in the architecture of power systems, increasing their sustainability with the installation of renewable energy sources (RES). In most cases, the efficient utilization of renewable energy requires the employment of energy storage systems (ESSs), such as batteries and hydro-pumped storage systems. The need for ESS becomes more apparent when it comes to non-interconnected power systems, where the incorporation of stochastic renewables, such as photovoltaics (PV) systems, may more frequently reduce certain power quality indicators or lead to curtailments. The purpose of this review paper is to present the predominant core technologies related to ESSs, along with their technical and life cycle analysis and the range of ancillary services that they can provide to non-interconnected power systems. Also, it aims to provide a detailed description of existing installations, or combinations of installations, in non-interconnected European islands. Therefore, it provides an overview and maps the current status of storage solutions that enhance the sustainable environmentally friendly operation of autonomous systems.

Frequency Restoration Reserves are traditionally sized using deterministic methods. The constant growth in non-dispatchable renewable energy, however, is increasing the importance of probabilistic methods for reserve sizing. In addition, as the geographical scope of reserve sizing expands, overall power imbalance stochasticity is reduced. In this paper, we propose a probabilistic method for shared cross-border frequency restoration reserve commitment and sizing, based on the concept of system generation margin and employing mathematical optimization. The aim is to reduce overall reserve volumes and costs. The cross-border interconnection capacities among countries are taken into account, and the shared uncertainty across interconnections is addressed via a novel robust approach. The method is tested on the cross-border system of south-east Europe that includes 9 countries. 5 different operational scenarios are used and a detailed calculation of the uncertainty distributions in each country is employed. Results show that cross-border shared sizing can significantly reduce overall reserve volumes and costs in a secure way.

The design of distribution use-of-system tariffs has been traditionally driven by long-term cost recovery considerations. However, the emerging large-scale integration of distributed energy resources motivates the value of tariffs that are more adaptive to short-term conditions, in order to exploit the inherent flexibility of distributed energy resources and consequently increase the economic efficiency of distribution network operation. This paper addresses the problem of short-term distribution use-of-system tariffs design through a bilevel optimization model, capturing the interaction between a distribution system operator at the upper level and prosumers with distributed energy resources at the lower level. In contrast to previous relevant literature, this model considers a detailed representation of the power flow constraints, different levels of temporal and spatial granularity in the designed tariffs, as well as discrete tariff levels for preserving intelligibility. Furthermore, instead of relying on exogenous typical days, the developed model employs a clustering approach to design tariffs that adapt to the forecasted conditions of the upcoming day. The examined case studies demonstrate the impacts of different levels of tariff granularity on economic efficiency, and test the performance of the proposed clustering approach through out-of-sample simulations involving different scenarios regarding the selected number of clusters.