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The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational algorithms to identify the optimal solution. In this paper, the potential of metaheuristic methods is explored for solving complex optimization problems specific to power systems. In this regard, an improved salp swarm algorithm is proposed to solve the ORPD problem for the IEEE-14 and IEEE-30 bus systems, by approaching the reactive power planning as both a single- and a multi- objective problem and aiming at minimizing the real power losses and the bus voltage deviations. Multiple comparison studies are conducted based on the obtained results to assess the proposed approach performance with respect to other state-of-the-art techniques. In all cases, the results demonstrate the potential of the developed method and reflect its effectiveness in solving challenging problems.

Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

Microgrids are about to change the architecture and the operation principles of the future power systems towards smartness and resiliency. Power electronics technologies are key enablers for novel solutions. In this paper we analyze the benefits of a “microgrid by design” architecture (MDA), using a solid-state transformer (SST) as a low-voltage grid-former and inverter-based generation only. In this context, the microgrid stability is maintained with the help of “electrostatic energy inertia” that can be provided by the capacitor connected to the DC busbar behind the SST inverter topology. This happens in a natural way, alike the mechanical inertia in power systems with synchronous machines, however without depending on frequency and without the need of a rotational inertia. This type of microgrid always operates (both fully connected to the main grid or in islanding mode) with all the necessary mechanisms needed to maintain the microgrid stable—no matter of the perturbations in the upstream of the point of common coupling (PCC). In the case of microgrids with inverter-based generation only (including the energy storage systems), there is no mechanical inertia and different stability mechanisms need to be applied compared to the stability principle of the classical power systems. Our proposed mechanism differentiates from the recently proposed stability assessments of microgrids based on virtual synchronous generators from the control theory perspective. This paper is a continuation of our previous work where the MDA was first introduced. The use-cases and scenarios are based on realistic and yet reasonable complexities, by coupling the disturbance magnitude with the voltage stability limit in power grids. The paper finds meaningful disturbances to test the electrostatic energy inertia at the boundaries of grid stability, as guidance to understand the range of voltage variation for extreme conditions. The results show that in microgrids with inverter-based generation only and passive loads (RLC type) the operation is no longer frequency dependent. The energy of the DC busbar capacitor as electrostatic energy inertia of the MDA has a role similar to that of the rotational machines in classical grids in terms of maintaining dynamic stability, however impacting two different types of stability.

In recent years, a pronounced transition to the exploitation of renewable energy sources has be observed worldwide, driven by current climate concerns and the scarcity of conventional fuels. However, this paradigm shift is accompanied by new challenges for existing power systems. Therefore, the hosting capacity must be exhaustively assessed in order to maximize the penetration of distributed generation while mitigating any adverse impact on the electrical grid in terms of voltage and the operational boundaries of the equipment. In this regard, multiple aspects must be addressed in order to maintain the proper functioning of the system following the new installations’ capacities. This paper introduces a sequential methodology designed to determine the maximum hosting capacity of a power system through the optimal allocation of both active and reactive power. To achieve this goal, an Improved Salp Swarm Algorithm is proposed, aiming to establish the appropriate operational planning of the power grid considering extensive distributed generation integration, while still ensuring a safe operation. The case study validates the relevance of the proposed model, demonstrating a successful enhancement of hosting capacity by 14.5% relative to standard models.

In the electric distribution systems, the “Smart Grid” concept is implemented to encourage energy savings and integration of the innovative technologies, helping the distribution network operators (DNOs) in choosing the investment plans which lead to the optimal operation of the networks and increasing the energy efficiency. In this context, a new phase load balancing algorithm was proposed to be implemented in the low voltage distribution networks with hybrid structures of the consumption points (switchable and non-switchable consumers). It can work in both operation modes (real-time and off-line), uploading information from different databases of the DNO which contain: The consumers’ characteristics, the real loads of the consumers integrated into the smart metering system (SMS), and the typical load profiles for the consumers non-integrated in the SMS. The algorithm was tested in a real network, having a hybrid structure of the consumption points, on a by 24-h interval. The obtained results were analyzed and compared with other algorithms from the heuristic (minimum count of loads adjustment algorithm) and the metaheuristic (particle swarm optimization and genetic algorithms) categories. The best performances were provided by the proposed algorithm, such that the unbalance coefficient had the smallest value (1.0017). The phase load balancing led to the following technical effects: decrease of the average current in the neutral conductor and the energy losses with 94%, respectively 61.75%, and increase of the minimum value of the phase voltage at the farthest pillar with 7.14%, compared to the unbalanced case.

As current global trends aim at the large-scale insertion of electric vehicles as a replacement for conventional vehicles, new challenges occur in terms of the stable operation of electric distribution networks. Microgrids have become reliable solutions for integrating renewable energy sources, such as solar and wind, and are considered a suitable alternative for accommodating the growing fleet of electrical vehicles. However, efficient management of all equipment within a microgrid requires complex solving algorithms. In this article, a novel two-stage scheme is proposed for the optimal coordination of both active and reactive power flows in a microgrid, considering the high penetration of renewable energy sources, energy storage systems, and electric mobility. An improved sine-cosine algorithm is introduced to ensure the day-ahead optimal planning of the microgrid’s components aiming at minimizing the total active energy losses of the system. In this regard, both local and centralized control strategies are investigated for multiple generations and consumption scenarios. The latter proved itself a promising control scheme for the microgrid operation, as important energy loss reduction is encountered when applied.

As disturbances due to natural disasters or man-made attacks intensify awareness regarding power systems’ resilience enhancement, the scientific community concentrates on exploring state-of-the-art technologies for emergency supply restoration strategies. Recent studies are increasingly focusing on the expanded flexibility of soft open points (SOPs) compared to conventional tie-switches to increase the restoration rate of critical loads; however, the potential of this novel technology is not limited to this aspect, with SOPs being used to improve the voltage level and increase the hosting capacity of renewable energy sources (RESs). This paper proposes a deterministic model for the optimal coordination of SOPs and distributed resources in an active distribution network (ADN) aiming at re-establishing the energy supply to critical loads after a prolonged interruption occurrence. At the same time, the support of DC microgrids with integrated RESs, embedded in SOPs, for the restoration process is explored. The efficiency of the proposed optimization model is verified based on a 24-h analysis performed on the modified IEEE 33-bus system, while considering the load and generation uncertainties as well.

As climate changes intensify the frequency of severe outages, the resilience of electricity supply systems becomes a major concern. In order to simultaneously combat the climate problems and ensure electricity supply in isolated areas, renewable energy sources (RES) have been widely implemented in recent years. However, without the use of energy storage, they show low reliability due to their intermittent output. Therefore, this article proposes a methodology to achieve the optimal sizing of an energy storage system (ESS) to ensure predefined periods of safe operation for an ensemble consisting of multiple loads, renewable energy sources and controllable generators, located in a remote microgrid. In this regard, a mixed integer linear programming (MILP) model has been proposed to reduce the outages impact of critical loads by calculating the optimal ESS capacity and defining the proper resources management within the off-grid microgrid, while ensuring a cost-effective operation of its components.