Total Active Power Losses Research Articles

Power Flow Analysis in Unbalanced Three-Phase Distribution Systems using Backward/Forward Sweep and Current Injection Methods

The electrical power distribution system is a part of the power system that distributes electricity from the transmission network to customers. In the distribution system, imbalances often occur due to the varying load profiles in each phase. This can cause voltage imbalances in the distribution system. This study aims to compare two power flow analysis methods, Backward/Forward Sweep and Current Injection. The study analyses the voltage and power loss conditions on each phase at each bus and line in the three-phase distribution system under unbalanced conditions. Simulations were conducted on two IEEE test buses, IEEE 19-Bus and IEEE 33-Bus with radial configurations. The power flow calculation results using the Backward and Forward Sweep method showed that in the IEEE 19-Bus system, the highest voltage drop percentage occurred on phase b at bus 19, at 3.14%, the highest voltage imbalance percentage occurred at bus 19, at 0.1409%, and the total active and reactive power losses were 7.352 kW and 3.164 kVAR. In the IEEE 33-Bus system, the highest voltage drop percentage occurred on phase c at bus 18, at 5.85%, the highest imbalance percentage occurred at bus 15, at 0.2077%, and the total active and reactive power losses were 19.107 kW and 8.22 kVAR. The percentage difference between the two methods used is less than one percent, indicating that both methods are sufficiently accurate in analyzing power flow in an unbalanced distribution system.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Solution of the Steiner problem in the search for optimal network structure using population optimization methods

Subject of research: This paper considers an example of solving the Steiner problem for the design of utilities, in particular, power lines. Purpose of research: The study is electrical networks. The study is to assess the suitability of using genetic algorithms to find the network connection scheme with the lowest total active power losses. Methods and objects of research: Heuristic methods of population optimization are applied as a research method. Main results of research: As a result of the work, the suitability of applying genetic algorithms to solve the Steiner problem on the example of an electrical network to minimize total power losses has been evaluated.

Real Power Losses Reduced by Network Reconfiguration the Distribution Systems using Modified BAT Algorithm

This research paper is proposed to achieving the minimum power losses in all the branches, minimum number of switching operations, maximizing the power flow through the placing the DG sources, minimizing the voltage deviations with satisfying all the constraints using the modified BAT algorithm. The effect of the offered method is tested on standard systems like IEEE 33, 69 buses and Indian standard 62 bus distribution systems. The mBAT effect is estimated with the recent algorithm including Shuffled Frog, Stud krill, Dingo, Grey Wolf, and Antlion algorithms. MATLAB results are proved that the total power active power losses and branch voltages and number of switches, capacity of DG sources and cost of the DG sources are drastically reduced. The results are compared with many techniques are tabulated. Moreover, the mBAT algorithm is more superior and confirmed with other animal-related algorithms like Antlion, Grey wolf, Gross Hopper, Dingo, sturd Krill, cuckoo crunch algorithm and Shuffled Frog algorithms. In view of more speed of convergence and high accuracy and processed in less number repetition. Also, the results of the proposed techniques are encouraging and helpful to future research.

A hybrid chaotic bat algorithm for optimal placement and sizing of dg units in radial distribution networks

Appropriate location and sizing of distributed generation (DG) units in a radial distribution system (RDS) play a significant role in the improvement of its overall performance. Indeed, proper DG placement and sizing help in maintaining the balance between power supply and demand, decreasing energy losses and enhancing voltage profile. Within this context, this paper presents an effective approach for the determination of the optimal location and the appropriate capacity of a photovoltaic distributed generation (PVDG) unit in RDSs. In the suggested approach, the best position of the PVDG is selected using the loss sensitivity factor (LSF). Meanwhile, a new optimization technique incorporating chaos, bats’ self-adaptive compensation, and Doppler effect into the original bat algorithm (BA) is developed to find the optimal size of the PVDG unit. The optimal PVDG size is optimally determined in such a way that total active power losses of the RDS is reduced and voltage profile is enhanced. The performance of the proposed optimization technique, symbolized by (CSA-DC)BA, is evaluated using various benchmark functions. Moreover, the applicability and effectiveness of the suggested approach are verified on the IEEE 33-bus and the IEEE 69-bus RDSs. Obtained results revealed that the proposed (CSA-DC)BA outperforms the comparison optimization techniques.

Optimal planning and partitioning of multiple distribution Micro-Grids based on reliability evaluation.

Power system researcher have turned to Micro-Grids (MG) for higher reliability, greater flexibility, lower operating costs and losses, and lower CO2 emissions at the distribution system. This paper presents a single-level stochastic optimization framework for planning and partitioning of a distribution system including Multiple Micro-Grids (MMGs). The main objective is to minimize the total cost of the system including investment, operation, total losses and reliability costs of the distribution network. The proposed model takes into account the viewpoints of MG owners and distribution system operators, simultaneously. The voltage stability index is introduced to identify the optimal site of MG investment. To deal with uncertainties caused by renewable generations, the Firefly Algorithm (FA) and probability-tree method is used to create various operation scenarios of Photo-Voltaic (PVs) and Wind Turbines (WTs). This model is solved through the genetic algorithm in MATLAB, and to evaluate its effectiveness, numerical studies have been carried out on the experimental IEEE distribution network with four specified locations for investing MGs and seven Tie Switches (TS) for network partitioning. Simulation results reveal that optimal locations for MG investment are determined in such a way all MGs connect to the buses near the beginning of the feeder and as a result, load point reliability is improved, total active power losses are reduced, and the energy program becomes more optimized.

An improved particle swarm optimisation algorithm for optimum placement and sizing of biogas-fuelled distributed generators for seasonal loads in a radial distribution network

Optimal allocation is critical for effective planning and operation of grid connected distributed generator(s) (DGs) subject to efficient optimisation approach(es). In this paper, an improved particle swarm optimisation (IPSO) algorithm based on weighted randomised acceleration coefficients and adaptive inertia weight for dynamic movement of the particles towards an optimal solution is proposed. The technique is expected to determine optimal placement, sizing and power factor of biogas-fuelled distributed generators (B-DGs such as internal combustion engine (ICE) and fuel cells (FC)) in a radial distribution network. The proposed IPSO is applied to simultaneously optimise three technical objectives namely total active power loss (TAPL), voltage deviation (VD) and voltage stability index (VSI) considering load growth and seasonal voltage dependence. The proposed IPSO is validated using an IEEE 33 bus radial distribution network to evaluate its effectiveness through various combinations of B-DGs for different scenarios and cases with a maximum of 3 B-DGs. Some of the key findings indicate that operating 3 ICEs at optimal power factor reduces TAPL by 93.52 %, reduces VD by 99.62 % and improves VSI by 23.32 % compared to 61.80 % TAPL reduction, 94.77 % VD reduction and 17.89 % VSI improvement for 3 FCs operating at unity power factor. The proposed IPSO gives better results than the standard PSO in terms of solution quality, convergence speed and statistical results. It is also perceived to be better compared to most of the recent state-of-the-art optimization algorithms found in literature.

OPTIMAL PLACEMENT OF UNIFIED POWER FLOW CONTROLLER USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

The load growth in recent times due to advancements in technology and population increase has led to shortage of reactive power in the power system resulting in key issues such as voltage instability, large line losses, power outages among others. In this work, Particle Swarm Optimization (PSO) technique was used for optimal placement of Unified Power Flow Controller (UPFC) for power loss minimization using the IEEE 6-bus system as a test network. The load flow analysis was performed using Gauss-Seidel iterative method with and without inclusion of PSO-based UPFC. The PSO algorithm was used to find the optimum point for UPFC location and determined using the line with the least active power loss among other lines randomly selected by the PSO algorithm, with p.u defined as the voltage statutory limit. The simulation results revealed that the optimum point obtained was line 4-5, and the voltage profile were within the statutory limit with and without optimally placed UPFC. The total active power loss reduced from 495.5 to 273.9 MW, giving a reduction of 44.72 with optimally placed UPFC. The total reactive power loss equally reduced from 1439 to 936.2 MVAr, producing a reduction of 34.94 with optimally placed UPFC. These results obtained showed that PSO-based UPFC placement is suitable for power loss minimization and enhancement of the system voltage profile.

Maximizing EV profit and grid stability through Virtual Power Plant considering V2G

The electrification of transportation through the widespread adoption of electric vehicles (EVs) has raised substantial concerns within the realm of power grid operations. This concern predominantly stems from the elevated electricity demand brought about by the surging population of EVs, consequently exerting strain on the power grid infrastructure which can be reduced with vehicle-to-grid (V2G) technology integration. To address this issue, this paper delves further into the realm of grid integration by introducing a Virtual Power Plant (VPP) concept to enhance the synergy between EVs and power grid. This study aims to compare different realistic objectives, ranging from total active power loss and voltage drop minimization to EV profit maximization and then optimize the balance between the distribution grid power quality and VPP profit through bi-level modeling. The presented model is devised as mixed-integer quadratically constrained programming (MIQCP) and incorporates Temporal Convolutional Network (TCN) based forecasting to handle the uncertain behavior of the residential loads using historical data. The experiments are conducted in IEEE 33-Bus and real-world 240-Bus distribution networks. The results indicate that enabling bidirectional power flow between the grid and VPP can yield significant profits for EV users while only marginally impacting the active power loss, approximately around 5%. This validation underscores how V2G not only presents various advantages for power system operators but also benefits EV users simultaneously.

Improvised direct torque control of a permanent magnet synchronous motor

Permanent magnet synchronous motor (PMSM) is widely used in different applications because of their different key operation features. But despite having important key features, PMSM suffers from torque ripple when simple control techniques like direct torque control (DTC) are employed. Several approaches have been raised by many researchers to lessen the ripple when DTC is used for controlling the PMSM drive. In this work, duty ratio-optimized DTC control, which has the same simplicity as conventional DTC (CDTC), and delay compensation for input signal prediction are proposed. In the proposed system, to determine the duty ratio, slope manipulation was made by considering the minimization of torque error and forcing the developed torque to be equal to the reference torque at the end of the span. An online slope determination approach was employed to compute the duty ratio. MATLAB 2021b is used for simulation purposes. The dynamics and steady-state performance of the proposed scheme were tested for both variable-load and variable-speed operations. In addition, the harmonic performance and ratio of harmonic power loss to total active power loss of the motor were evaluated. In general, the proposed scheme performs effectively in reducing steady-state ripple and harmonic.

Distributed Energy Resource Deployment to Mitigate Adverse Effects of High EV Penetration in Unbalanced Distribution System by Analytical Sensitivity-Based Optimization

The rapid integration of electric vehicles (EVs) into distribution systems in response to environmental concerns poses challenges for power system operators. This article addresses the operational difficulties faced by unbalanced distribution systems (UDS) when incorporating three-phase EV loads. We propose a novel method for supporting EV integration and enhancing UDS operational indicators, including voltage unbalance, bus voltage stability, and bus voltage deviation. Our approach utilizes analytical loss sensitivity index (ALSI)-based meta-heuristic wild horse optimization for the optimal allocation of distributed energy resources (DERs) and capacitor bank during EV load connections. The ALSI is employed to identify potential DER locations, streamlining the computation process and reducing search space. Tested on a modified 123-bus UDS, the proposed method demonstrates a 76.71% reduction in total active power loss and a 96.43% reduction in total voltage deviation. The results underscore the effectiveness and applicability of the proposed approach. While showcasing promising outcomes, the article also discusses the potential limitations and implications for practical implementation.

Optimal Allocation of Distributed Generations and Capacitor Banks in Distribution Systems Using Arithmetic Optimization Algorithm

In this paper, an optimization approach based on an arithmetic optimization algorithm (AOA) is proposed for specifying the optimal allocation of distribution generations/generators (DGs) and capacitor banks (CBs) in radial distribution systems. The AOA is a new population-based meta-heuristic algorithm that is essentially based on using basic arithmetic operators in mathematics. The proposed approach is employed to specify the optimum placement, capacity, and power factor of DGs and CBs to decrease the distribution systems’ total power loss and voltage deviation. To state the performance of the proposed approach, DGs and CBs are placed in IEEE 33-bus and 69-bus systems separately or together. When only DGs are used and the parameters of location, capacity, and power factor of DGs are determined simultaneously, the total active power loss reductions in the IEEE 33-bus and 69-bus systems are achieved at 94.42% and 98.03%, respectively. When the results of other optimization algorithms are examined, it is seen that better results are obtained with AOA.

Multi-Objective Optimal Allocation of Hybrid Photovoltaic Distributed Generators and Distribution Static Var Compensators in Radial Distribution Systems Using Various Optimization Algorithms

In recent years, considerable growth was about the integration of renewable energy sources in the Radial Distribution Systems (RDS), as Photovoltaic Distributed Generators (PVDG) due to their importance in achieving plenty desired technical and economic benefits. Implementation of the Distribution Static Var Compensator (DSVC) in addition to the PVDG would be one of the best choices that may provide the maximum of those benefits. Hence, it is crucial to determine the optimal allocation of the devices (PVDG and DSVC) into RDS to get satisfactory results and solutions. This paper is devoted to solve the allocation problem (locate and size) of hybrid PVDG and DSVC units into the standards test systems IEEE 33-bus and 69-bus RDSs. Solving the formulated problem of the optimal integration of hybrid PVDG and DSVC units is based on minimizing the proposed Multi-Objective Function (MOF) which is represented as the sum of the technical-economic parameters of Total Active Power Loss (TAPL), Total Reactive Power Loss (TRPL), Total Voltage Deviation (TVD), Total Operation Time (TOT) of the overcurrent relays (OCRs) installed in the RDS, the Investment Cost of PVDGs (ICPVDG) and the Investment Cost of DSVCs (ICDSVC), by applying various recent metaheuristic optimization algorithms. The simulation results reveal the superiority and the effectiveness of the Slime Mould Algorithm (SMA) in providing the minimum of MOF, including minimization of the power losses until 16.209 kW, and 12.11 kVar for the first RDS, 4.756 kW and 7.003 kVar for the second RDS, enhancing the voltage profiles and the overcurrent protection system. Moreover, the ability to reach the optimal allocation of PVDG and DSVC and maintain the voltage profiles in the allowable limit, whatever the load demand variation.

Optimizing energy management of hybrid wind generation-battery energy storage units with long-term memory artificial hummingbird algorithm under daily load-source uncertainties in electrical networks

Due to the unpredictable nature of renewable energy sources, it is necessary to have a storage system to store energy during off-peak hours and supply it during peak hours. However, if the storage units in a network are not properly connected, the benefits of the storage system cannot be realized. To improve the performance of radial distribution networks, this research proposes an optimal locating and sizing problem of battery energy storage (BES) and a renewable source of wind turbine distributed generation (WTDG). To solve this allocation problem, a multi-objective function (MOF) based on techno-economic parameters of total active power losses (TAPL), total voltage deviation (TVD), and investment cost of integrated devices is optimized using a modified version of the Artificial Hummingbird Algorithm (AHA) that incorporates a long-term memory component. With this enhancement, the algorithm can make decisions based on multiple past experiences, allowing it to consider a wider range of promising locations during the optimization process. This integration of long-term memory reduces the risk of premature convergence or stagnation and allows the algorithm to gain a broader perspective, potentially leading to improved performance and exploration of the solution space. The results demonstrate the effectiveness of the Long-term Memory Artificial Hummingbird Algorithm (LMAHA) in optimizing the MOF and enhancing the performance of the electrical distribution networks of IEEE 33 and 69 bus.

Dynamic optimisation of unbalanced distribution network management by model predictive control with Markov reward processes

In this work, a two-level control system is used to minimize the total active power losses of an active distribution system connected to the external grid and composed of a wind turbine, two photovoltaic power sources, and two batteries. At the first control level, a model-based predictive control (MPC) is run, using non-homogeneous Markov reward models for wind power prediction and homogeneous Markov reward models for photovoltaic power. At the second level, an algorithm is run for optimal management of voltage control assets, such as voltage regulating transformers, to minimize losses. Different scenarios have been considered, highlighting the advantages of using an MPC framework. This results in an optimization process that can be influenced by different time horizons depending on whether or not the MPC is applied. The predictions allow considering a long-horizon stepwise optimization process that leads to an increasing number of variables along with the decrease of total active power losses. When the MPC is not applied, a short-horizon analysis is performed with a decrease in both the number of variables and the quality of the results. Different cases are considered in which the nominal power of a photovoltaic unit and the battery capacity are modified.

Application of static thyristor compensators to increase the reliability of distribution networks

Currently, the use of FACTS devices for voltage regulation and optimal control of electric power system modes is widely used. Controlled FACTS devices, based on power electronics, allow obtaining the desired characteristic of not only power transmission, but also a dedicated part of the electrical system. The paper presents the results of a study of the operating modes of a real distribution network, taking into account the installation of a static thyristor compensator (STC), in order to increase its operating reliability. The efficiency of distribution networks in and the area of existence of modes significantly depend on the installation location and the law of power control of compensating devices. To solve this problem, a systems approach based on graph theory methods is proposed. The conditions for stabilizing nodal voltages to reduce total active power losses in the electrical network are studied. The ranges of change in the required powers with the corresponding STC control laws are determined to ensure a minimum of total power loss in the network

Swarm intelligence technique for solving optimal power flow problem in the Nigeria power system

This paper deals on the implementation of the particle swarm optimization method for solving power flow problems in the Nigeria power system. The difficulty of solving optimal power flow (OPF) problems increases significantly with increasing network size and complexity. Some of the weakness of the conventional methods include: limited ability in solving real-world large scale optimization problems, weakness in handling constraints, poor convergence and slow computational time. The results gotten by implementing the Particle Swarm Optimization (PSO) algorithm in Nigeria 330kV 52-Bus network show that the total active power and reactive power losses were substantially reduced to 0.03MW and 0.05MVAR. The system time of convergence was faster at 0.5seconds. Also, the maximum line active and reactive power losses on line 1 to 2 were reduced greatly to 0.00MW and 0.01MVAR respectively. These show that the system was better optimized with the PSO algorithm than the conventional method.

STUDY OF PHASE VOLTAGE AND LOAD SYMMETRATION IN ACTIVE-ADAPTIVE DISTRIBUTION ELECTRIC NETWORKS

Networks of 0.4 kV are characterized by a large unbalance of loads in phases. Current unbalance leads to voltage unbalance and additional losses of electrical energy. As a result, the voltage at the consumer may not meet the quality standards of electrical energy. In addition, due to unbalance, the service life of electrical equipment is reduced. Since the effect of stress balancing significantly depends on the place of balancing loads on the line, the paper proposes to determine the places of balancing loads by solving a multicriteria optimization problem. The paper proposes an objective function that minimizes active power losses and contains the total index of active power losses and indices of voltage unbalance coefficients in the negative and zero sequence.
 
 The purpose of the study is to obtain an effective objective function for determining the places of balancing loads and voltages in the network, which ensures a minimum of active power losses and the values of the voltage unbalance factors within the required limits; conduct a study of balancing loads and voltages depending on the places of balancing.
 
 Materials and methods. In the work, methods for calculating electrical networks were used, taking into account voltage losses and active power. To study the places of balancing loads and voltages, the method of multicriteria optimization with restrictions was used. The study of the objective function was carried out on a mathematical model of a low voltage overhead line. All calculations were carried out in MATLAB.
 
 Research results. A review and analysis of modern tools and methods for balancing loads and voltages in low voltage networks has been carried out. As a result of the analysis, it was concluded that there is no algorithm for determining the places of load balancing in low-voltage networks that provide minimal active power losses and the values of the voltage unbalance factors within the required limits. The task of finding places for balancing loads and voltages is a multiobjective optimization problem with constraints. Therefore, an objective function was proposed that minimizes active power losses in the network and contains the total index of active power losses and indices of voltage unbalance coefficients for the reverse and zero sequence. To study the proposed objective function, a model overhead line of a 0.4 kV network with specified phase loads and voltages was used. For the model line, the calculation of active power losses and the values of the voltage unbalance coefficients in the initial mode before balancing was carried out. All calculations were carried out for each phase separately. At the first stage, the calculation of the sensitivity coefficients of active power losses and the sensitivity coefficients for the voltage unbalance coefficients was carried out. To study the balancing of loads, nodes were selected that have the maximum values of the sensitivity coefficients. It follows from the calculation results that the best effect from balancing is observed when balancing loads simultaneously in two nodes: in the node with the highest value of the total sensitivity factor of active power losses, and in the node with the maximum value of the phase sensitivity factor of active power losses. When balancing loads in only one of the nodes, the most optimal of the selected ones will be the node most remote from the TS. We also obtained weight coefficients that provide a minimum of the objective function.
 
 Conclusions. The proposed objective function is effective for determining the places of load and voltage balancing in low voltage networks. In this case, the best effect is observed when balancing loads in nodes that have the highest values of the sensitivity coefficients of total active power losses and by phases. The node most remote from the TP will be more optimal. When balancing loads in places determined using the proposed objective function, it is possible to reduce power losses and ensure the values of the unbalance coefficients in the nodes on the line less than the maximum allowable value.

Enhancing PV distributed generator planning in medium‐voltage DC distribution networks: A multi‐design techno‐economic analysis with load demand response

AbstractDistributed generators have grown in significance for the technical and financial operation of electric power systems in recent years. The integration of these generators into the electrical distribution network (EDN) has experienced rapid growth, primarily driven by advancements in renewable energy sources (RESs), particularly photovoltaic distributed generators (PVDGs). With the increasing implementation of solar energy as a RES, designing the optimal integration of PVDGs into medium voltage direct current (MVDC) networks is crucial to comprehensively analyzing technical and economic factors. To address this issue, a new multiple objective function (MOF) is proposed, which combines various techno‐economic parameters such as total active power loss (APL), voltage deviation (VD), and the investment cost of PVDGs (ICPVDG) installed in the test system. The objective is to minimize the MOF simultaneously to achieve the optimal incorporation of PVDGs. This study aims to solve the allocation problems related to the location and size of PVDG units in the modified MVDC test systems IEEE 27 and 33‐bus. Simulation results demonstrate the superior accuracy and effectiveness of the equilibrium optimization algorithm (EOA) in achieving the minimum MOF. The utilization of multiple PVDG units reduces overall active power losses and improves voltage profiles across all buses.

Improving Voltage Stability in Kurdistan Power System in Areas with Deficit Power Production by Rescheduling the Active Power Based on PSS/E Simulation

This paper aims to improve the voltage profile using the Static Synchronous Compensator (STATCOM) in the power system in the Kurdistan Region for all weak buses. Power System Simulation studied it for Engineers (PSS\E) software version 33.0 to apply the Newton-Raphson (NR) method. All bus voltages were recorded and compared with the Kurdistan region grid index (0.95≤V ≤1.05), simulating the power system and finding the optimal size and suitable location of Static Synchronous Compensator (STATCOM)for bus voltage improvement at the weakest buses. It shows that Soran and New Koya substations are the best placement for adding STATCOM with the sizes 20 MVAR and 40 MVAR. After adding STATCOM with the sizes [20MVAR and 40MVAR] at Soran to the test, it is seen that the total average change in voltage profile for the system improved results in about 17.34 % in average per unit change for the 28 weakest buses, which provides a good improvement in stability. Also, the system’s total active power loss reduced from 123.8 MW without STATCOM to 102.8 MW with STATCOM. The results are encouraging for applying the approach to the power system. This approach stands out due to all bus voltages are within acceptable ranges.