The continuously increasing complexity of modern and sustainable power and energy systems leads to a wide range of solutions developed by industry and academia. To manage such complex system-of-systems, proper engineering and validation approaches, methods, concepts, and corresponding tools are necessary. The Smart Grid Architecture Model (SGAM), an approach that has been developed during the last couple of years, provides a very good and structured basis for the design, development, and validation of new solutions and technologies. This review therefore provides a comprehensive overview of the state-of-the-art and related work for the theory, distribution, and use of the aforementioned architectural concept. The article itself provides an overview of the overall method and introduces the theoretical fundamentals behind this approach. Its usage is demonstrated in several European and national research and development projects. Finally, an outlook about future trends, potential adaptations, and extensions is provided as well.

Distributed generation (DG) systems are growing in number, diversifying in driving technologies and providing substantial energy quantities in covering the energy needs of the interconnected system in an optimal way. This evolution of technologies is a response to the needs of the energy transition to a low carbon economy. A nanogrid is dependent on local resources through appropriate DG, confined within the boundaries of an energy domain not exceeding 100 kW of power. It can be a single building that is equipped with a local electricity generation to fulfil the building’s load consumption requirements, it is electrically interconnected with the external power system and it can optionally be equipped with a storage system. It is, however, mandatory that a nanogrid is equipped with a controller for optimisation of the production/consumption curves. This study presents design consideretions for nanogrids and the design of a nanogrid system consisting of a 40 kWp photovoltaic (PV) system and a 50 kWh battery energy storage system (BESS) managed via a central converter able to perform demand-side management (DSM). The implementation of the nanogrid aims at reducing the CO₂ footprint of the confined domain and increase its self-sufficiency.

Accurate photovoltaic (PV) power forecasting is an essential tool that enables the efficient integration of solar electricity and energy trading. This work proposes a novel methodology for the implementation of a forecasting tool that provides aggregated PV production day‐ahead forecasts for the PV systems installed at a distribution level. Specifically, the tool is a cloud‐based platform, comprising of a data quality block, a weather forecasting model, a machine learning power prediction step and an up‐scaling aggregation stage. In this context and in the absence of a fully observable distribution system, the aggregated PV generation was estimated using a clustering approach and up‐scaling the measured generation of reference systems to the aggregated installed capacity. The results demonstrated that the implemented tool exhibited high forecasting accuracies, lower than 10% given by the mean absolute percentage error when applied to the distribution system of Cyprus. Furthermore, the comparative benchmarking of the up‐scaling techniques against the estimated aggregated PV generation demonstrated that the best‐performing approach was the hybrid model, which provided a normalised root mean square error of 10.29% and mean absolute percentage error of 9.11%. Finally, useful information is provided for establishing a robust day‐ahead forecasting methodology that is based on an optimal supervised learning and hybrid up‐scaling approach.

Along with the increasing share of non-synchronous power sources, the inertia of power systems is being reduced, which can give rise to frequency containment problems should an outage of a generator or a power infeed happen. Low system inertia is eventually unavoidable, thus power system operators need to be prepared for this condition. This paper addresses the problem of low inertia in the power system from two different perspectives. At a system level, it proposes an operation planning methodology, which utilises a combination of power flow and dynamic simulation for calculation of existing inertia and, if need be, synthetic inertia (SI) to fulfil the security criterion of adequate rate of change of frequency (RoCoF). On a device level, it introduces a new concept for active power controller, which can be applied virtually to any power source with sufficient response time to create synthetic inertia. The methodology is demonstrated for a 24 h planning period, for which it proves to be effective. The performance of SI controller activated in a battery energy storage system (BESS) is positively validated using a real-time digital simulator (RTDS). Both proposals can effectively contribute to facilitating the operation of low inertia power systems.

The high penetration of the Renewable Energy Sources and other emerging technologies likely to be installed in future power grids will pose new operational challenges to grid operators. One of the main issues expected to affect the operation of the power grid is the impact of inverter-based technologies to the power system inertia and, hence, to system stability. Consequently, the main challenge of the future grid is the evaluation of the frequency stability in the presence of inverter-based systems and how the aforementioned technology can support frequency stability without the help of the rotating masses of the traditional power grid systems. To assess the above problem, this paper proposes a methodology to evaluate the frequency stability in a projection of the real distribution grid in Cyprus with the time horizon to be the year 2030. The power grid under investigation is evaluated with and without the presence of smart hierarchical controllers for providing support to the power system under disturbance conditions. The advanced controllers were applied to manage the available power resource in a fast and effective manner to maintain frequency within nominal levels. The controllers have been implemented in two hierarchical levels revealing useful responses for managing low-inertia networks. The first is set to act locally within a preselected area and the second level effectively supporting the different areas for optimal operation. After undertaking a significant number of simulations for time-series of one year, it was concluded from the results that the local control approach manages to minimize the frequency excursion effectively and influence all related attributes including the rate of change of frequency (RoCoF), frequency nadir and frequency zenith.

This book is the result of a concerted effort to shed scientific light on the timely theme of “Smart Photovoltaic Energy Systems for a Sustainable Future”