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Ensuring a functioning critical infrastructure system is crucial for many industries, including energy. Due to the transformation of the energy sector, it is necessary to take into account the impact of the induced changes on critical infrastructure. The presented paper focuses on the assessment of the effects of small dispersed photovoltaic power plants (PV), which are gaining in popularity both in households and in other buildings. Specifically, it is an impact assessment within the emergence of island operation in the selected area with the aim of restoring power to the facilities included in the critical infrastructure system as soon as possible. The specificity of the considered small photovoltaic power plants is their autonomy and impossibility of control from the superior control system. The presented case study compares possible approaches to power recovery on the one hand and the impact of different levels of dispersed PV on the other. The results provide important conclusions for the expected development of the critical infrastructure system in connection with the increase in autonomous distributed electricity sources.

Reliable and resilient grid operations with high penetration levels of distributed energy resources (DERs) can be achieved with improved situational awareness and seamless integration of DERs with utility enterprise controls. This paper presents the details of the development of a data-enhanced hierarchical control (DEHC) architecture and the results of its evaluation. The DEHC is a hybrid control framework that enables the efficient, reliable, and secure operation of distribution grids with extremely high penetrations of solar photovoltaic (PV) generation by seamlessly integrating centralized utility controls, distributed controls for DERs, and autonomous grid-edge controls. In the DEHC architecture, the advanced distribution management system (ADMS) controls the legacy devices (such as load tap changers and capacitor banks), the PV smart inverters are dispatched by real-time optimal power flow, and the grid-edge devices regulate local voltages in coordination with each other. The DEHC is demonstrated using a commercial ADMS platform, real utility distribution feeder models, and grid-edge devices. The performance of the DEHC architecture is evaluated using simulations and hardware-in-the-loop experiments with voltage regulation as the control objective. The results show that the DEHC enables high penetration levels of PV in distribution feeders by effectively managing system voltages through the synergistic operation of ADMS, distributed PV smart inverter controls, and secondary-level grid-edge device control.

Distributed photovoltaic (PV) solar power plants are playing an increasing role as a power generation resource in the modern electricity grid. However, PVs pose significant challenges to grid planners, operators, owners, investors, aggregators, and other stakeholders. This is due to the high uncertainty of the PV output power, which is caused by its entire dependence on intermittent environmental factors. This has brought a serious problem to the power industry to integrate and manage power grids containing significant penetration of PVs. Thus, an enhanced PV power forecast is very important to operate these power grids efficiently and reliably. Most previous methodologies have focused on predicting the aggregate amount of potential solar power generation at the national or regional scale and ignored the distributed PVs that are installed primarily for local electric supply. Furthermore, a few research groups have carried out predictor selection before training predictive models. This paper proposes an adaptive hybrid predictor subset selection (PSS) strategy to obtain the most relevant and nonredundant predictors for enhanced short-term forecasting of the power output of distributed PVs. In the proposed strategy, the binary genetic algorithm (BGA) is applied for the feature selection process and support vector regression (SVR) is used for measuring the fitness score of the predictors. In order to validate the effectiveness of the proposed strategy, it is applied to actual distributed PVs located in the Otaniemi area of Espoo, Finland. The findings are compared with those achieved by other PSS techniques. The proposed strategy enhances the quality and efficiency of the predictor subset selection, with minimal chosen predictors to achieve enhanced prediction accuracy. It outperforms the other prediction selection methods. Besides, a configuration of an adaptive forecasting model is introduced and the performance tests are presented to further validate the impact of the PSS results for the PV power prediction accuracy enhancement.