Power system stability plays a significant role in the overall power system analysis. With the high penetration level of distributed generation (DG), especially large-scale wind farms, this problem needs to be addressed. This study investigates the system stability in case of a wind park (WP) integration using doubly fed induction generators (DFIGs) to transmission grid, while focusing on WP fault ride-through ability. The system was modelled for time-domain simulations. The results indicate that WP parallel operation with the high voltage network is possible if specific conditions are met, with fault clearance time being crucial. This is shown through scenarios, in which each of the overhead lines (OHL) was disconnected due to three-phase short circuit symmetrical fault, and the network parameters were observed for each case. The predefined control and protection configurations in the DFIG-based wind farm model simplify the analysis. The introduction of a battery energy storage system (BESS) with P and Q control strategies, improves WP stability during faults. Professional software tools, PSSE, and EMTP-RV, were employed for the analysis. The study showed that simulated WP and BESS connected to a real network, paired with appropriate fault clearance time and protection settings, can operate effectively while maintaining overall system stability. This research is significant for power system planning, especially with the growing integration of large-scale wind generation.

Abstract This paper presents a method for distributed generation (DG) allocation in medium voltage (MV) distribution system based on energy loss minimization. The main objective of the research is to design, implement and test a DG allocation (siting and sizing) method and to investigate how optimal DG allocation influence the operational parameters of the system from the Distribution System Operator (DSO) perspective. The problem is formulated as a single objective optimization problem solved by using both genetic algorithm and particle swarm optimization techniques. Model of a realistic Electric Power Distribution System (EPDS) and IEEE 37-bus EPDS are used as test systems. The results confirm that proposed algorithms can be used for practical DG allocation. The research presented contributes to the field as it provides a DG allocation method for energy loss reduction performed on a EPDS which can be applied in realistic planning and regulatory situations using open-source software.

Abstract Islanded microgrids with low-inertia distributed energy resources (DERs) are prone to frequency fluctuations. With the increasing integration of DERs in microgrids, the complexity of control and stability has also increased. Moreover, the integration of DERs into microgrids may result in a power imbalance between energy supply and demand during sudden changes in load or energy generation. This can cause frequency variations in the microgrid, which could have disastrous consequences such as equipment damage or even blackouts. This paper proposes a control strategy to ensure the efficient operation of an islanded hybrid microgrid consisting of a PV generator, battery energy storage system (BESS), and emergency diesel generator. According to Energy Exchange Model proposed in this paper, the hybrid system presented operates independently without being connected to the electrical grid, where the PV system and BESS act as the main energy sources, while the emergency diesel generator provides active power backup with voltage and frequency regulation. The novel in this paper is also that DER aids in frequency regulation during active power transients by delivering and absorbing active power in accordance with the inverter's suggested P droop control strategy. In this way inverter droop control decreases system frequency nadir emulating so called “synthetic inertia”. To design both the islanded hybrid system and the proposed control strategy, the MATLAB/Simulink environment is utilized. Based on the results, it can be concluded that the analyzed microgrid system is capable of maintaining stability and operating efficiently in a range of operating conditions.

Abstract Environmental issues and the current global energy crisis serve as further motivators for the promotion of renewable energy sources. However, integrating these sources into existing power grids presents numerous challenges. As the connection capacity approaches its limits, it is imperative to employ innovative engineering methods to integrate distributed generation (DG) into resilient, self-healing smart grids of the future. One such tool is Hosting Capacity (HC) analysis, which is an emerging power system-planning tool used to position investments toward parts of the network that can absorb additional generation and promote efficient use of energy sources, avoiding overloading, inefficiencies, DG misallocations, and network failures. In this study, a technique for calculating the ideal HC in a power system is presented. The goal of this research is to develop a replicable optimization methodology for determining HC in smart distribution systems using a single objective constrained optimization problem solved through the use of genetic algorithm (GA). Detailed power system load and generation modeling and the use of advanced open-source research tool for load flow optimization improve the confidence in the proposed model. This research contributes to collective knowledge of the subject matter and establishes a reliable optimization methodology for determining HC in power systems.

This paper presents the implementation of the Binary Search Algorithm (BSA) to determine the Maximum Power Point (MPP) of a photovoltaic (PV) system under variable weather conditions. Additionally, the conventional well-known Perturb and Observe (P&O) algorithm is also implemented to be compared with the binary search based Maximum Power Point Tracking (MPPT) algorithm. Both algorithms are implemented in real time in MATLAB/Simulink environment. The experimental study is performed using the two 260 W series connected PV modules, the buck converter, and Humusoft MF 634 card to enable real-time operation. The value of the duty cycle for the buck converter is being updated in each step moving the operation point closer to MPP. The obtained experimental results demonstrate that the binary search based MPPT algorithm is more efficient and accurate when compared to the P&O MPPT algorithm.

With the decreasing reserves of conventional sources and the high emission of harmful gases caused by them, the inclusion of renewable energy sources in power system is increasing. However, to best utilize them, different site location criteria for PV generator installment need to be considered in the decision-making process. This paper presents Fuzzy Analytical Hierarchical Process (AHP) method used in energy planning to find the best Photovoltaic (PV) system site location for the established criteria and factors. Eight criteria were identified and evaluated. These include the solar energy potential, distance to the transmission line, PV surface slope, sunshine duration, the total amount of energy/PV, the temperature ratio, site survey, and performing shading analysis. PVGIS software tool is used to collect necessary data. Evaluation criteria are prioritized by applying fuzzy AHP, fuzzifying the inputs of the decision matrix using triangular fuzzy numbers. The obtained results and the methodology show potential in finding the best location where the PV system can be best utilized.