Optimal sizing and placement of synchronous condensers with adaptive search-space reduction

To achieve net‐zero targets in many counties, renewable energy generators (REGs) are integrated with grids where less fault current is produced compared to synchronous generators. Accordingly, fault level availability, known as ‘system strength’, is reduced at point of coupling (POC) buses. A minimum system strength level is crucial for REGs to accurately detect and ride‐through faults. To maintain adequate system strength in renewable energy‐based weak grids, researchers and engineers have recommended supplementary devices, such as synchronous condensers (SynCons) or static synchronous compensators (STATCOMs), to provide the required fault‐current. Because SynCons and STATCOMs are costly devices, their optimal sizes and placement were investigated in existing literature. However, no long‐term techno‐economic analysis was conducted to confirm appropriate selection between SynCons and STATCOMs. To address the research gap, this paper investigates the long‐term financial feasibility of SynCons and STATCOMs by considering their optimal allocation and sizes. A hedge feedforward feedback‐based online gated recurrent unit (HFF‐OLGRU) optimisation learning algorithm is developed and compared with existing learning approaches in order to determine the best solution. The technical and economic findings suggest that SynCons improve voltage recovery time approximately 0.06 s and achieve net profit USD$42.68M over STATCOMs in renewable energy‐based weak grids.

This study mentions the problem of determining optimal placements and sizes for capacitor banks by comparing different schemes that allow implemented buses at both high and low voltage side, only high voltage side or low voltage side of transformers in distribution systems. Criterions evaluated for each scheme are active and reactive power losses in whole system, voltage value at all buses, installation and operation cost and accumulative profit saving. Standard with and without transformer branches in distribution systems and Newton-Raphson method are introduced to make clear about the way to analyze power flows in whole system. Optimal placements and sizes for capacitor banks are determined by using a proposed algorithm and optimal capacitor placement tool in ETAP software. Simulation results can help to show the comparisons, evaluations and make decision about optimal capacitor placements and sizes. The contribution shows the best scheme that install capacitors at low voltage side of transformers because it has the lowest implemented capacity, highest economic factors while voltage quality is always in allowable range. Achieved results can be applied in any distribution system, help managers have overall review and decide optimal placements and sizes for capacitor banks. Keywords: Capacitor bank, OCP Tool, ETAP software, Optimal placement, Optimal size, Voltage quality, Optimal cost.

With converter-based renewable energy sources (RES) increasingly integrated into the power system and conventional power plants gradually phased out, the future power system will experience reduced inertia level, making the grid frequency more vulnerable and prone to instabilities, potentially leading to blackouts. The deployment of synchronous condensers (SC) can serve as a potential solution for inertia support; however, the primary frequency reserve is still required. One promising solution to provide the required primary frequency reserve is to use a Battery Energy Storage System (BESS). The optimal size, location, and operation rules of BESS result in efficient transmission capacity utilization and help to adjust the power flow, increase the renewable penetration, and reduce the power loss. This paper proposes an optimization model to access BESS’s optimal location and size to support the primary frequency and coordinate with SCs for secure frequency stability in renewable-based systems. Simulation case studies on Sao Vicente Island’s high wind energy integration system have been demonstrated to validate BESS’s optimal size and location in the Real-Time Simulator (RTDS). Simulated results have shown that BESS’s optimal location and size and repurposed SC can enhance system frequency during disturbances.

One of the most beneficial and effective methods for reducing the power losses of the distribution networks (DNs) is using distributed generations (DGs). The issue of optimal placement and sizing of DGs is a challenge that needs to be investigated carefully, as an improper location and sizing lead to a negative effect on the DN. In this work, an IEEE 33‐bus is used as a test system for optimal placement and sizing of four DGs, three of them being photovoltaic (PV) sources and the fourth is a wind turbine (WT). The environmental data (irradiance, temperature, and wind speed) of Baghdad city (latitude: 33.29°, longitude: 44.38°) are used for training the artificial neural networks (ANNs) to forecast the day ahead values of the environmental variables for calculating the power production of PVs and WT. Particle swarm optimization (PSO) technique is used to optimize the location and sizing of the DGs. The operation cost of the system is optimized using genetic algorithm (GA) depending on the optimized sizing and placement of the DGs. Four electrical vehicles charging stations (EVCSs) are interconnected to the implemented DN with considering the uncertainty of hourly charging power demand using the queuing model. The optimal cost of the EVCSs is determined by using fuzzy logic system (FLS) to optimize the energy management of the daily power dispatch and peak power shifting to meet the peak power production of the DGs. The power losses are minimized by 50%, enhancing the voltage profile of the distribution system, and the operation cost is minimized by 19%. The annual operation cost saving of EVCSs is found to be 44.3%.

Integration of various on-site distributed generators in a micorgrid, which can either be operated in grid-connected or islanded mode, helps to satisfy the local demand. The optimum sizing and placement with a techno-economical optimization of a microgrid is done with Particle Swarm Optimization (PSO). The problem of optimum placement and size is formulated as a nonlinear integer minimization problem which minimizes the sum of the total capital, operational, maintenance and replacement cost of Distributed Generators (DGs), subject to constraints such as voltage, energy limits of each DG, exchanged power with utility and the constraints of system reliability. The exchanged power with the main grid, economics of DG units and penalty for not supplying the loads are taken into consideration. In this paper, some notions of reliability indices are calculated in order to evaluate the performance of a microgrid in both grid-connected and islanded mode, and the effect of reliability on total cost of microgrid is evaluated. The uncertainty of PV output is also considered which plays an important role in optimization technique.

<span lang="EN-US">Doubly fed induction generators (DFIG) based wind farms are capable of providing reactive power compensation. Compensation capability enhancement using reactors such as distributed static synchronous compensator (D-STATCOM) while connecting distribution generation (DG) systems to grid is imperative. This paper presents an optimal placement and sizing of offshore wind farms in a coastal distribution system that is emulated on an IEEE 33 bus system. A multi-objective formulation for optimal placement and sizing of the offshore wind farms with both the location and size constraints is developed. Teaching learning algorithm is used to optimize the multi-objective function constraining on the capacity and location of the offshore wind farms. The proposed formulation is a multi-objective problem for placement of the wind generator in the power system with dynamic wind supply to the power system. The random wind speed is generated as the input and the wind farm output generated to perform the optimal sizing and placement in the distributed system. MATLAB based simulation developed is found to be efficient and robust.</span>

Non-renewable energy resources cause high carbon emissions and Global Warming, threatening the environment. Due to these reasons, the Government of India has set a target of achieving 38000 MW of Roof Top Solar Plants by 31st December 2022. This will lead to remarkably high PV penetration at the distribution level. The generation through solar PV is not constant throughout the day due to the variable intensity of sunlight during the daytime and the absence of sunlight during the night. There are also seasonal variations. This results in degradation of voltage profile, increase in losses, increase in peak demand on the substation, and unbalances in the distribution system. Battery energy storage systems (BESS) play an important role in mitigating these worse impacts. However, because of the high cost of the battery storage system, its optimal sizing and placement is very important. Optimally placed BEES can make the distribution system more balanced, improve voltage profile, and reduce losses in the distributed network. In this paper, a new multi-objective technique using Particle Swarm optimization has been proposed for optimal placement and sizing of BESS. The proposed approach has been tested on a practical three-phase unbalanced 19-bus distribution feeder. The obtained results show that optimal placement of BESS using the proposed approach leads to reduced power losses, improved voltage profile and reduction in feeder load during peak-load hours.

Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality

Optimization of damping controller for PSS and SSSC to improve stability of interconnected system with DFIG based wind farm

Optimal placement and sizing of multi-type FACTS devices in power systems using metaheuristic optimisation techniques: An updated review

Power system frequency stability using optimal sizing and placement of Battery Energy Storage System under uncertainty

Optimal Placement and Sizing of Distributed Generation Using Shuffled Artificial Bee Colony Algorithm

Optimal placement and sizing of distributed energy resources (DERs) in radial distribution networks are vital for improving efficiency, reliability, and economic viability. However, conventional optimization techniques often face slow convergence, limited adaptability, and poor uncertainty handling. To overcome these challenges, this study introduces a hybrid framework that integrates a modified cheetah optimizer (MCO) with a regression-based machine learning model. The MCO, inspired by the hunting dynamics of cheetahs, adaptively balances exploration and exploitation, while the ML model predicts voltage profiles to guide the search process and enhance convergence speed. The framework jointly optimizes power loss, voltage deviation, and voltage stability, and incorporates a region-specific economic model that includes capital costs, tariffs, and grid penalties. Renewable energy variability is modelled using NASA-SSE solar and wind data via Weibull distributions. A multi-run strategy with 30 independent executions ensures robustness, while ANOVA analysis validates the statistical superiority of the method over existing algorithms. Tested on the IEEE 33-bus system, the approach achieves a 94.2% reduction in power losses, improves voltage stability index to 0.951, and delivers annual cost savings exceeding $77,000. Convergence curves, boxplots, and sensitivity analyses confirm the framework’s reliability, scalability, and practical value for DER planning under uncertainty.

In the current electricity paradigm, the rapid elevation of demands in industrial sector and the process of restructuring are the main causes for the overuse of transmission systems. Hence, the evolution of novel technology is the ultimate need to avoid the damages in the available transmission systems. An appreciable volume of renewable energy sources is used to produce electric power, after the implementation of deregulation in power system. Even though, they are intended to improve the reliability of power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand and the sudden failures of vital equipment cause transmission congestion in one or some transmission lines. Generation rescheduling and load shedding can be used to alleviate congestion, but some cases require quite few improved methods. With the extensive application of Distributed Generation (DG), congestion management is also performed by the optimal placement of DGs. Therefore, this research employs a Line Flow Sensitivity Factor (LFSF) and Particle Swarm Optimization (PSO) for the determination of optimal location and size of multiple DG units, respectively. This proposed problem is formulated to minimize the total system losses and real power flow performance index. This approach is experimented in modified IEEE-30 bus test system. The results of N-1 contingency analysis with DG units prove the competence of this proposed approach, since the total numbers of congested lines get reduced from 15 to 2. Hence, the results show that the proposed approach is robust and simple in alleviating transmission congestion by the optimal placement and sizing of multiple DG units.

The integration of BESS and RES is crucial for modern power grids, yet their optimal placement and sizing remain complex challenges due to the interdependent goals of minimizing power losses and maintaining voltage stability. This paper proposes a novel multi-agent deep reinforcement learning framework to jointly optimize the location and sizing of BESS, photovoltaic, and wind resources in a grid-connected distribution network. The primary objectives are to minimize active power loss and enhance voltage profile stability, as directly measured by the Voltage Stability Index and Voltage Deviation Index. To demonstrate the framework’s efficacy, we implement and compare three advanced MADRL algorithms Multi-Agent Deep Deterministic Policy Gradient (MADDPG), Multi-Agent Soft Actor-Critic (MASAC), and Multi-Agent Advantage Actor-Critic (MAA2C) on a modified IEEE 33-bus test system under residential, commercial, and mixed load profiles. The key contribution of thiswork is a scalable, model-free coordination strategy that directly addresses the coupled location and sizing problem for multiple DER types, overcoming the limitations of traditional iterative or centralized optimization methods. Comprehensive simulations reveal that the MADDPG-based agent consistently achieves the strongest technical performance across all test scenarios. Specifically, it reduces energy losses by up to 45.23% and improves voltage stability indices by over 15% compared to baseline scenarios, while maintaining a favourable balance between technical superiority and economic feasibility. The results underscore the potential of the proposed MADDPG-driven framework as a robust and effective tool for real-world distribution system planning, enabling enhanced operational resilience through intelligent DER coordination.