Power Loss Reduction and Voltage Profile Improvement in Electrical Power Distribution Networks Using Static Var Compensators

Including renewable energy sources into contemporary power systems has greatly added unpredictability and presented problems including harmonic distortion, voltage instability, and degraded power quality. In response, we provide a new deep learning-enabled framework for the real-time adaptive control and optimal allocation of hybrid Flexible AC Transmission Systems (FACTS), specifically the Unified Power Flow Controller (UPFC) and Static VAR Compensator (SVC), in renewable-rich transmission networks. For multi-objective planning, the suggested method combines a Two-Point Estimation Method (2PEM)-based probabilistic load flow model with a hybrid Grey Wolf Optimization–Particle Swarm Optimization (GWO–PSO) algorithm. Trained offline on a scenario-rich dataset, a Long Short-Term Memory (LSTM) neural controller inferences control set-points in real time with sub-15 ms latency. Under operational contingency and changeable photovoltaic (PV) generation, this integration of offline probabilistic optimization with online artificial intelligence-driven control enables dynamic stabilization of voltage, harmonic mitigation, and power loss minimization. Application to the Southern Interconnected Grid of Cameroon shows compliance with IEEE-519 harmonic thresholds, a 47% increase in voltage stability index, and a 30.9% reduction in active power loss. These findings support the scalability and efficiency of the framework in improving power system resilience and quality by means of intelligent control, so fitting for both advanced and developing energy infrastructures. Graphical abstract

This paper introduces a smart coordinated allocation of distributed generation (DG) units, shunt-connected capacitors (SC), and static VAR compensators (SVC) for power loss minimization. The problem is formulated as a mixed-integer non-linear programming and is solved using a BONMIN solver embedded in General Algebraic Modeling System (GAMS). The proposed method’s evaluation was performed on the IEEE 33-bus test system, where three cases were discussed. GAMS/BONMIN was used to find optimal locations and power outputs of DGs in the first case. Simulation results, in this case, showed that losses were reduced by 48.42% with one DG unit, 58.58% with two DG units, and 65.44% with three optimally allocated DG units. In the second case, the proposed method was used to coordinate DGs and SCs, while in the third case, coordinated allocation of DG and SVC was investigated. The results showed that SC/SVC coordination further reduces active power losses. With three SCs allocated, the reduction in the active power losses was 93.5% compared to the base case, and 82.18% compared to losses in the case where only 3 DGs are connected, while in the case of three SVC allocated, it was 93.85% compared to the base case, and 82.21% compared to the case where 3 DGs are connected. The second and third cases also showed that the coordination of SC/SVC leads to an improved voltage profile, where the bus voltages are close to the acceptable range, and voltage fluctuation is significantly reduced. The comparison between different non-linear programming solvers embedded in GAMS for solving optimal power flow was also presented.

A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations. To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively.

Flexible AC transmission systems (FACTS) technology is widely adopted and utilized to maintain the performance of power systems. However, the improvements of power system performance achieved by FACTS devices depend on the right sizing and allocation of such devices. For technical and economic considerations, a FACTS device's location and size should be selected very carefully in order to maximize its benefits to the power system. In this paper, the sizing and location of a static VAR compensator (SVC) are optimally determined using a new optimization technique called lightning attachment procedure optimization (LAPO). The optimal allocation of the SVC is determined regarding the improvement of voltage deviation index and the reduction of total active power losses. The system is optimized in two cases: once without SVC installation in order to find out the optimal settings of the system that achieve the objective functions, and another time with SVC installation to determine its optimum sizes and locations by which the required objective functions are achieved. Then the system performance is analyzed after optimization with and without SVC devices to show the impact of the optimum sizing and location of the SVC on the system. The study is validated using the standard IEEE 30-bus system, while the developed LAPO is performed by MATLAB M-Files and the system performance analysis in different cases is performed by NEPLAN software.

A charge-discharge optimization strategy considering the spatiotemporal distribution of electric vehicles and the operational safety of the power distribution network in the power-transportation coupling network

Integration of multiple distributed solar PV (DSP) into the grid: The case of the distribution network in Freetown, Sierra Leone

Flexible alternating current transmission systems (FACTS) devices are emerging technologies that serve as a means of providing reactive power support to mitigate issues of active power loss and voltage instability on existing power grid. The optimal placement, locations and sizes of these devices influence its performance on the grid. This paper presents a comparative analysis of performance evaluation of SVC and TCSC for transmission loss reduction on Nigerian 330kV grid system using self- adaptive firefly algorithm. The results of the analysis showed that with the system reinforced with SVC, the total system loss reduced from 98.21MW to 92.44MW which is about 5.88% reduction. The reduction in active power loss with the optimal location of TCSCs is 94.259MW which amount to 4.02% reduction. An appreciable voltage enhancement occurred at Ayede, New Haven, Onitsha and Kano due to the system reinforcement with SVCs and TCSCs. In all, SVC gives better result than TCSC in term of active power reduction and voltage profile enhancement.

The rapid depletion of fossil fuel motivates researchers and policymakers to switch from the internal combustion engine (ICE) to plug-in electric vehicles (PEVs). However, the electric power distribution networks are congested, which lowers the accommodation of PEVs and produces higher power losses. Therefore, the study proposes an effective deterministic methodology to maximize the accommodation of PEVs and percentage power loss reduction (%PLR) in radial distribution networks (RDNs). In the first stage, the PEVs are allocated to the best bus, which is chosen based on the loading capacity to power loss index (LCPLI), and the accommodation profile of PEVs is developed based on varying states of charge (SoC) and battery capacities (BCs). In the second stage, the power losses are minimized in PEV integrated networks with the allocation of DG units using a recently developed parallel-operated arithmetic optimization algorithm salp swarm algorithm (AOASSA). In the third stage, the charging and discharging ratios of PEVs are optimized analytically to minimize power losses after planning PEVs and DGs. The outcomes reveal that bus-2 is the most optimal bus for accommodation of PEVs, as it has the highest level of LCPLI, which is 9.81 in the 33-bus system and 28.24 in the 69-bus system. The optimal bus can safely accommodate the largest number of electric vehicles, with a capacity of 31,988 units in the 33-bus system and 92,519 units in the 69-bus system. Additionally, the parallel-operated AOASSA mechanism leads to a reduction in power losses of at least 0.09% and 0.25% compared with other algorithms that have been previously applied to the 33-bus and 69-bus systems, respectively. Moreover, with an optimal charging and discharging ratio of PEVs in the IEEE-33-bus radial distribution network (RDN), the %PLR further improved by 3.08%, 4.19%, and 2.29% in the presence of the optimal allocation of one, two and three DG units, respectively. In the IEEE-69-bus RDN, the %PLR further improved by 0.09%, 0.09%, and 0.08% with optimal charge and discharge ratios in the presence of one, two, and three DG units, respectively. The proposed study intends to help the local power distribution companies to maximize accommodation of PEV units and minimize power losses in RDNs.

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)

The power distribution system has difficulties with regard to power loss and unacceptable voltage drops as a result of the rapidly expanding power system network, rising electrical energy consumption, and longer distances of power distribution. A typical strategy to address the issues with the distribution system is to perform distribution system reconfiguration. The study focuses on distribution feeder reconfiguration of the Katunje Feeder of Bhaktapur, Nepal where optimization problem is formulated to minimize the system active power loss and investment cost of the system. Genetic algorithm is employed in a co-simulation framework to solve the optimization problem where states of 26 different tie switches are to be altered to achieve the desired optimum results. Two cases are formulated: in case I active power loss is assigned more weight than investment cost whereas, in case II equal weights are assigned for active power loss and investment cost. The results showed the reduction in active power loss and investment cost for both the cases. Case I resulted in more active power loss reduction compared to case II, and case II resulted in more investment cost reduction compared to case I. From this, decision makers can obtain insights in adopting one of the cases for distribution feeder reconfiguration based on technical consideration (active power loss reduction) or economic consideration (investment cost reduction).

The application of optimal reactive power flow (ORPF) problem to the NB (New Brunswick) Power company are presented. The potential benefits and the real-time implementation problems of ORPF are discussed. The execution of ORPF program for NE Power network has shown two major benefits: (1) the improvement in the voltage profile and voltage stability, and (2) the savings in active power loss. The improvement in voltage profile can cause less violations and a more stable system from the voltage point of view. A reduction in active power loss gained from ORPF can save a significant amount of money. The total saving for year 1995 predicted in study was in excess of $1100000, and this was supported by the real-time test. These savings can be gained simultaneously with the improvement of the voltage profiles.

Distribution Electric power system is the largest and the most complex system made by the mankind. The distribution network power system is being encountered by quickly rising load demand and it is detected that under certain critical loading circumstances, the distribution system poses maximum power losses and poor voltage profile and collapse in convinced areas. To overcome these problems integrating distributed generation (DGs) on the grid near to the load center is the better solution as compared to others. Though, for the DG to serve its purpose, its location and size have to be determined optimally. In this paper, Grid-Based Multi-Objective Harmony Search Algorithm (GrMHSA) has been utilized to determine the size and location of DG in the distribution system in Debre Markos town. By placing DG optimally, in addition to the reduction of the power loss in the distribution network, the proposed mechanism improves the node (bus) voltage profile of the system under consideration. A MATLAB program is developed to mitigate power losses and improve the voltage profile by optimally sizing and placing a DG in the distribution network. After sizing and placing the DG in the network, the total voltage deviation, active and reactive power losses are reduced by 93.42%, 81.63% and 82.45% for Debre Markos Feeder 3 and 85.20%, 84.94% and 85.73% for Debre Markos Feeder 4 respectively. The performance comparison of GrMHSA and MOPSO has been made and GrMHSA has been found better in terms of reducing voltage deviation and power losses in the system.

Distributed energy resources (DERs) can improve the performance of radial distribution systems. The nonlinear power flow constraints, multi-objective trade-offs, and network reconfiguration scenarios for DER placement and sizing call for the formulation of optimization problems. Most of the times optimization algorithms suffer from premature convergence and poor exploration-exploitation balance. These problems exhibit an inherent internal structural symmetry. In order to overcome the above problem, this study uses the Multi-Objective Archimedes Optimization Algorithm (MAOA) to optimally allocate DERs in the Radial Distribution Networks (RDNs), moreover the performance of the proposed MAOA is compared with the other well established algorithms including Particel Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Shuffled Frog Leaping Algorithm (SFLA), Atom Search Optimization (ASO), and Butterfly Optimization Algorithm (BOA) on the IEEE-33 RDN. The comparison is made for the four cases (S1: DER Only), (S2—Network Reconfiguration Only), (S3—DER Followed by Reconfiguration), and (S4—Reconfiguration Followed by DER) considering factors like voltage profile, network reconfiguration, active and reactive power loss reduction, carbon emission DER utilization and Cost reduction. The MAOA is observed to provide better results among all the other benchmark algorithms. In S3, the active power loss is reduced by 68.41%, whereas the reactive power loss is reduced by 57.44% and the MAOA algorithm improves the voltage by 3.98%. The minimum voltage of the network is also improved by 6.28%. The algorithm improves convergence with a percentage of 18.50% enhancing the system’s operational symmetry and stability, while satisfying all constraints. At Bus 3 and Bus 6 of IEEE-33 bus radial distribution network (Baran–Wu test system), DG capacity is allocated to be 3.8 MW and 2.1 MW, respectively.

In Indonesia, electricity is a basic need with demand that continues to grow. PT PLN (Persero) projects an increase in electricity consumption of 8.9% by early 2022, highlighting the urgent need to address frequent problems such as blackouts, power losses, and voltage sags in the power distribution system. Effective solutions, including Static VAR Compensator (SVC) and Distributed Generation (DG), have been proposed to improve voltage stability and reduce power losses. This study evaluates and compares the performance of SVC and DG on a standard IEEE 14-bus system under increased load conditions. Using power flow analysis in ETAP, we simulate the installation of SVC at 15.99 Mvar and DG at 20.58 Mvar on bus 9, which shows optimal results. The findings show that DG slightly outperforms SVC in improving voltage stability and reducing power losses, with a 0.16% greater voltage increase and a 3.2 MW or 17.3% reduction in power losses. These results indicate that although both devices meet PLN’s voltage standards and improve power system efficiency, DG provides a slightly superior improvement in overall system performance.

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