The paper proposes a new method for optimal allocation of shunt capacitors in distribution network with renewable distributed generation units such as wind turbines and/or solar power plants. The proposed method, based on application of the Monte Carlo simulation methods, respects the uncertainties associated with load demand and renewable sources power production. The optimisation problem is modelled by two criterion functions with the aim of improving voltage profile in a network by optimal selection of the locations and installed powers of the shunt capacitors. The suggested criterion functions have been optimised simultaneously by applying the non‐dominated sorting genetic algorithm. On the basis of the measured data on wind speed and solar radiation, the method has been tested by the example of a real distribution network.

Battery energy storage systems (BESSs) can play a significant role in overcoming the challenges in Distribution Systems (DSs) with a high level of penetration from renewable energy sources (RESs). In this paper, the goal is to determine the optimal location, size, and charging/discharging dispatches of BESSs in DSs with a high level of photovoltaic (PV) installations. The problem of the location and size of BESSs is solved with multi-criteria optimization using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The criteria of the multi-criteria optimization are minimal investment costs for BESS and improvement of the network performance index. The network performance index includes the reduction in annual losses of active energy in DSs and the minimization of voltage deviations. The dispatch of a BESS is determined using auxiliary optimization. Linear Programming (LP) is used for auxiliary optimization, with the aim of dispatching the BESS to smooth the load profile in DS. The proposed optimization method differs from previous studies because it takes in its calculations all days of the year. This was performed using the K-means clustering technique. The days of one year are classified by the level of consumption and PV production. The optimization was performed for five different levels of PV penetration (60%, 70%, 80%, 90%, and 100%) and for two scenarios: the first with one BESS and the second with two BESSs. The proposed methodology is applied to the IEEE 33 bus balanced radial distribution system. The results demonstrate that with an optimal choice of location and parameters of the BESS, significant improvement in network performance is achieved. This refers to a reduction in losses of active power, improvement of voltage profile, smoothing the load diagram, and reducing the peak load. For the scenario with one BESS and PV penetration of 100%, the reduction in daily energy losses reaches a value of up to 10% compared to the base case (case without a BESS). The reduction in peak load goes to 20%. Further, the highest voltage during the day is significantly lower in all buses compared to the base case. Similarly, the lowest voltage during the day is considerably higher. The methodology from this paper can be applied to any radial distribution network with a variable number of BESSs. The testing results confirm the effectiveness of the proposed method.

Systematic consideration of various scenarios of operational planning, development and power system control is becoming a necessity and the only way to involve and compare different technologies that are now present in power engineering. Comparison of all the relevant characteristics of the considered technology is of great significance for power system designers or operators. However, the differences between characteristics and dimensions of variables in optimization procedure models lead to the fact that classical algorithms become computationally inefficient and more difficult to implement. In this paper a procedure that is based on a heuristic optimization method and Pareto optimality is proposed. The procedure enables comparison of all considered scenarios in the entire space of control variables, while their parallel processing ensures timely elimination of scenarios that do not provide a better solution for any criteria than the other scenarios. The proposed procedure is illustrated on the example of selecting type, size and location of FACTS devices in the IEEE 57 bus test system. The optimization results, with different number of objective functions, illustrate the effectiveness of the procedure and point out the possibility of application in solving optimization problems of realistic power systems.

This paper presents a novel hybrid algorithm for determining the optimal Phasor Measurement Units (PMU) configuration in power networks to ensure full topological and numerical observability through a multi-phase process. In the first phase, a graph-theoretic Heuristic Node Selector (HNS) is developed to rapidly establish topological observability via Core-Tree construction and node dominance evaluation. Unlike most existing studies that implicitly assume topological observability implies numerical observability, the second phase applies a Genetic Algorithm to refine and extend the initial solution from HNS, ensuring complete numerical observability while minimizing number of PMUs. This hybrid method significantly reduces the search space and improves convergence. The HNS procedure is further extended in this work to explicitly handle Zero Injection Buses (ZIB) through rule-based topological modifications, enabling a modified version of the algorithm applicable to real networks with complex structures. Real-world implementation practices from European Transmission System Operators are considered through the adoption of a “one PMU per feeder” configuration. The proposed method is validated on standard IEEE test systems and Serbian transmission networks. Results demonstrate high scalability, adaptability to various network topologies (with and without ZIB nodes), and efficient PMU allocation. Notably, the method consistently achieves high values of the System Observability Redundancy Index, indicating strong robustness and redundancy in measurement placement.