IEEE 33-bus Radial Distribution System Research Articles

Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) Technique for Loss Minimization in Distribution System

Distribution system is the final section of the electrical power system, apart from the transmission and generation system. The distribution system acts as the interface that connects the high-voltage network to the low-voltage consumer service point. Consequently, the distribution system contributes the most of the overall losses in the electrical power system. Hence, evolutionary computation was introduced to accomplish a reduction of the previously said losses. The evolutionary computation involves optimization techniques to allocate distributed generation (DG) to the distribution system. Unfortunately, several optimization techniques are not capable of achieving accurate results and are trapped at local optima. This paper presents the Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) technique for loss minimization in a distribution system. In this study, Evolutionary Programming (EP), Ant Lion Optimization (ALO) and HALCEP optimization engines are developed. The validation process was simulated on the IEEE 33-bus radial distribution system (RDS) model. Comparative studies were performed to gain a clear observation of the advantage of the developed HALCEP over the conventional EP and the ALO techniques, effectively showing its capability to outperform them.

Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques

This study introduces a highly effective technique to address the load flow challenge in Radial Distribution Networks (RDNs). The proposed approach leverages two matrices derived from the topological features of distribution networks to provide an optimal solution to handle load flow challenges. To assess the efficacy of this technique, simulations were executed on an IEEE 33-bus radial distribution system using MATLAB. Deep Learning (DL) has become a powerful artificial intelligence technique that excels at interpreting power grid datasets. Thus, a data-driven methodology is presented that incorporates an advanced Long-Short-Term-Memory (LSTM) network. Employing the Recurrent Neural Network with the LSTM (RNN-LSTM) technique based on these simulations, the study precisely identifies the optimal placement of an integrated PV generator within the radial network. The application of DL techniques, specifically LSTM networks, exemplifies the potential of data-driven approaches in enhancing decision-making processes. The results of this study highlight the potential of RNN-LSTM for the optimal integration of PV generators and for ameliorating the reliability of RDNs.

Optimal allocation of open unified power quality conditioner in integrated distribution system environment with renewable energy sources

ABSTRACT The growing utilization of renewable energy sources (RES) that rely on inverters has adverse impacts on distribution systems, resulting in challenges such as power quality (PQ) issues and increased losses in the distribution network. One potential remedy for these issues involves the strategic allocation of open unified power quality conditioner (UPQC-O). This paper introduces a comprehensive two-layer integrated optimization model designed for the allocation of UPQC-O and RES in an active distribution network. The primary objective is to enhance energy efficiency, PQ, and economic benefits simultaneously. Furthermore, an innovative solver known as the multi-improved whale optimization method (multi-IWOA) has been implemented to address the optimization problem at hand. The efficacy of this approach was validated through testing on both the standard IEEE 69-bus radial distribution system and a real-world Indian 52-bus system. The evaluation results underscore the significance of optimally distributing UPQC-O resources while taking into consideration the integration of RES within the distribution system. It’s worth noting that the proposed methodology achieved a substantial 56.72% cost reduction for the IEEE 69-bus radial distribution system and a significant 76.092% cost reduction for the Practical System Indian 52-bus system. This highlights the substantial advantages of the proposed methodology for the efficient management of UPQC-O and RES integration in distribution systems.

Enhancing the performance of radial distribution systems via optimal integration of electric vehicles

<span lang="EN-US">Electric vehicles (EVs) and their associated charging stations (CSs) are a promising avenue for greenhouse gas reduction and energy security. However, improper location and size of the EV charging station (CSs/EVs) negatively affect the power system. It results in some technical challenges such as increased real power-loss, decreased voltage stability and increased voltage deviation. This paper suggests optimum planning of CSs/EVs locations to conserve voltage, improve power-loss and reduce the effect of CSs/</span><span lang="EN-US">EVs in electrical distribution systems. The multi objective genetic algorithm method (MOGA) is utilized to solve the optimization problem and reduce the impact of the random location of CSs/EVs. <span>The simulations are</span> performed on IEEE 33-bus and IEEE 69-bus radial distribution systems (RDS). In addition, the optimal EV allocation is carried out with two fixed levels of loading from 100% to 150% of the candidate EV load. A comparison between the proposed MOGA technique and other optimization techniques is carried out. The results demonstrated the capability of the proposed technique for optimal placement of CSs/EVs in RDS. </span><span lang="EN-US">Moreover, it is providing an objective value to solve a complex multi-objective nonlinear optimization problem to reduce the total power-loss, improve the voltage profile and extend the voltage stability.</span>

Single and multi-objectives based on an improved golden jackal optimization algorithm for simultaneous integration of multiple capacitors and multi-type DGs in distribution systems

This paper proposes a novel placement technique based on the improved golden jackal optimization (IGJO) algorithm for multiple capacitor banks (CBs) and multi-type DGs in a distribution network considering single and multi-objective problems. The proposed algorithm incorporates memory-based equations and random walk strategy to enhance the performance of the recent golden jackal optimization in terms of accuracy and convergence speed. The optimization problem is formulated as a weighted multi-objective that seeks to enhance the voltage profiles, boost stability, and minimize the total active power loss. An index named reactive loss sensitivity (QLSI) is also employed with the developed IGJO to identify the candidate nodes for the DGs and CBs installation to reduce the search space of the optimization algorithm. The robustness of the developed IGJO algorithm is evaluated through the CEC 2020 benchmark functions, and a comparison study is conducted with the original GJO and the other nine fresh competitors using various statistical tests to confirm its dominance and superiority. Then, the proposed IGJO is implemented in single and multi-objectives for the optimal deployment of multiple CBs individually and simultaneously with multiple DGs with different operating modes to enhance the performance of the IEEE 69-bus radial distribution system (RDS). The fetched outcomes are compared with the original GJO, weevil optimizer algorithm (WeevilOA), skill optimization algorithm (SOA), and Tasmanian devil optimization (TDO) to further measure its efficacy using different statistical tests. The IGJO algorithm is also applied to deploy multiple DGs for the IEEE 118-bus RDS with the aim of minimizing active loss. The simulation findings affirmed that the proposed IGJO technique beats the other rivals in all investigated situations, qualifying for the optimal inclusion of DGs in the presence of generation and demand uncertainties. Specifically, the integration of three units of CBs synchronously with three DGs Type-I and DG Type-III reduces the active power loss to 4.2664 kW and 3.4178 kW, respectively. The lowest power loss, approximately 2.7989 kW, is achieved with the simultaneous integration of three DG Type-I and DG Type-III using the developed IGJO algorithm.

Modelling and Allocation of Hydrogen-Fuel-Cell-Based Distributed Generation to Mitigate Electric Vehicle Charging Station Impact and Reliability Analysis on Electrical Distribution Systems

The research presented in this article aims at the modelling and optimization of hydrogen-fuel-cell-based distributed generation (HFC-DG) to minimize the effect of electric vehicle charging stations (EVCSs) in a radial distribution system (RDS). The key objective of this work is to address various challenges that arise from the integration of EVCSs, including increased power demand, voltage fluctuations, and voltage stability. To accomplish this objective, the study utilizes a novel spotted hyena optimizer algorithm (SHOA) to simultaneously optimize the placement of HFC-DG units and EVCSs. The main goal is to mitigate real power loss resulting from the additional power demand of EVCSs in the IEEE 33-bus RDS. Furthermore, the research also investigates the influence of HFC-DG and EVCSs on the reliability of the power system. Reliability is crucial for all stakeholders, particularly electricity consumers. Therefore, the study thoroughly examines how the integration of HFC-DG and EVCSs influences system reliability. The optimized solutions obtained from the SHOA and other algorithms are carefully analyzed to assess their effectiveness in minimizing power loss and improving reliability indices. Comparative analysis is conducted with varying load factors to estimate the performance of the presented optimization approach. The results prove the benefits of the optimization methodology in terms of reducing power loss and improvising the reliability of the RDS. By utilizing HFC-DG and EVCSs, optimized through the SHOA and other algorithms, the research contributes to mitigating power loss caused by EVCS power demand and improving overall system reliability. Overall, this research addresses the challenges associated with integrating EVCSs into distribution systems and proposes a novel optimization approach using HFC-DG. The findings highlight the potential benefits of this approach in terms of minimizing power loss, enhancing reliability, and optimizing distribution system operations in the context of increasing EV adoption.

Optimal Location and Sizing of Photovoltaic-Based Distributed Generations to Improve the Efficiency and Symmetry of a Distribution Network by Handling Random Constraints of Particle Swarm Optimization Algorithm

Distributed generators (DGs) are increasingly employed in radial distribution systems owing to their ability to reduce electrical energy losses, better voltage levels, and increased dependability of the power supply. This research paper deals with the utilization of a Particle Swarm Optimization algorithm by handling its random constraints to determine the most appropriate size and location of photovoltaic-based DG (PVDG) to keep the asymmetries of the phases minimal in the grid. It is thus expected that this algorithm will provide an efficient and consistent solution to improve the overall performance of the power system. The placement and sizing of the DG are done in a way that minimizes power losses, enhances the voltage profile, i.e., bringing symmetry in the voltage profile of the system, and provides maximum cost savings. The model has been tested on an IEEE 33-bus radial distribution system using MATLAB software, in both conditions, i.e., with and without PVDG. The simulation results were successful, indicating the viability of the proposed model. The proposed PSO-based PVDG model further reduced active power losses as compared to the models based on the teaching–learning artificial bee colony algorithm (TLABC), pathfinder algorithm (PFA), and ant lion optimization algorithm (ALOA). With the proposed model, active power losses have reduced to 17.50%, 17.48%, and 8.82% compared to the losses found in the case of TLABC, PFA, and ALOA, respectively. Similarly, the proposed solution lessens the reactive power losses compared to the losses found through existing TLABC, PFA, and ALOA techniques by an extent of 23.06%, 23%, and 23.08%, respectively. Moreover, this work shows cost saving of 15.21% and 6.70% more than TLABC and ALOA, respectively. Additionally, it improves the voltage profile by 3.48% of the power distribution system.

A Novel Electric Vehicle Placement Index for Sustaining Power Quality in a Distribution Network: Proper Allocation of Electric Vehicle Charging Stations

Large-scale deployment of electric vehicles (EVs) has adverse effects on distribution network operations. Inappropriate installation of EV charging stations (EVCS) degrades the distribution network’s power quality, affecting smooth functioning. This article proposes a novel strategy for properly allocating EVCS in a distribution system by analyzing the impact of power quality issues, namely harmonics and loss of life of the distribution transformer. The analysis is carried out in a standard IEEE-33 bus radial distribution system and a real-time distribution system in Kerala, India. The impact of power quality issues is highlighted by perceiving the effect of slow/moderate and fast chargers in the system under study. The results substantiate the potency of the reliability index (RI) strategy. The severity level for planning the charging station (CS) in numerous buses in the distribution network without infrastructural upgrades is determined by employing a modified novel EV placement index (EVPI <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> _m </i> ). The novelty of the index lies in the fact that it incorporates all of the operational parameters with a specific focus on the power quality issues in the distribution network. The severity level of the EVPI <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> _m </i> maintains the power quality without increasing the rating of the components used or any infrastructural upgrades. The sensitivity analysis results prove the robustness of the modified EVPI-based strategy. Moreover, the proposed strategy is validated using real-time experiments on a hardware-in-loop real-time simulator, OPAL-RT OP4510. The results substantiate the efficacy of the strategy and the modified index.

Power Distribution System Outage Management Using Improved Resilience Metrics for Smart Grid Applications

Smart grid systems play a significant role in improving the resilience of distribution systems (DSs). In this paper, two strategies are proposed for implementation of a smart grid application: (a) a network reconfiguration and (b) a network reconfiguration with mobile emergency generator (MEGs) deployment. An improved set of resilience metrics to quantify and enhance the resiliency of distribution systems (DSs) is developed for the proposed optimization. The metrics aim to determine a suitable strategy and the optimal number and capacity of MEGs to restore the disconnected loads through the development of several microgrids. These metrics are then aggregated with the proposed strategy to develop an automated solution provider. The objective is to maximize system resilience considering the priority loads. The proposed resilience metrics are tested on the IEEE 33-Bus radial DSs. The case studies conducted proved the performance of the proposed power outage management strategy and resilience metrics in maximizing system resiliency for smart grids.

Optimal Utilization of Bidirectional EVs for Grid Frequency Support in Power Systems

Bidirectional onboard chargers introduce electric vehicles (EVs) as flexible actors in the power grids that can operate in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. Despite the benefits of the bidirectional EVs in the event of grid contingencies, they may have adverse impacts on the normal operation of the power systems. This article investigates the impacts of bidirectional EVs on power quality, voltage profile, and frequency stability of the power grid. For this purpose, a detailed model of practical bidirectional onboard charger is presented to measure the total harmonic distortion (THD) in the grid current caused by the EVs. The proposed study accounts for various EV power levels, grid voltage levels, and EV modes of operation. Furthermore, an optimal frequency support strategy is developed that takes advantage of available EVs in V2G mode to improve the frequency stability of the grid in the event of frequency fluctuations. The effects of bidirectional EVs on the voltage profile is also explored by implementing current summation method (CSM)-derived power flow analysis for IEEE 33-Bus radial distribution system. The results show that with an optimal planning of EVs, it is possible to improve voltage profile and frequency stability of the grid while the power quality of the grid is maintained in the allowed range according to the IEEE 519-2014 standard.

DSO-Aggregator Demand Response Cooperation Framework towards Reliable, Fair and Secure Flexibility Dispatch

Unlocking flexibility on the demand side is a prerequisite for balancing supply and demand in distribution networks with high penetration levels of renewable energy sources that lead to high volatility in energy prices. The main means of fully gaining access to the untapped flexibility is the application of demand response (DR) schemes through aggregation. Notwithstanding, to extract the utmost of this potential, a combination of performance-, financial-, and technical-related parameters should be considered, a balance rarely identified in the state of the art. The contribution of this work lies in the introduction of a holistic DR framework that refines the DR-related strategies of the aggregator towards optimum flexibility dispatch, while facilitating its cooperation with the distribution system operator (DSO). The backbone of the proposed DR framework is a novel constrained-objective optimisation function which minimises the aggregator’s costs through optimal segmentation of customer groups based on fairness and reliability aspects, while maintaining the distribution balance of the grid. The proposed DR framework is evaluated on a modified IEEE 33-Bus radial distribution system where a real DR event is successfully executed. The flexibility of the most fair, reliable and profitable sources, identified by the developed optimisation function, is dispatched in an interoperable and secure manner without interrupting the normal operation of the distribution grid.

Reduction of Emission Cost, Loss Cost and Energy Purchase Cost for Distribution Systems With Capacitors, Photovoltaic Distributed Generators, and Harmonics

In this paper, a bonobo optimizer (BO) and two other methods, particle swarm optimization (PSO) and salp swarm algorithm (SSA), are implemented to determine the location and sizing of photovoltaic distributed generators (PDGs) and capacitors in IEEE 69-bus radial distribution system with many nonlinear loads. The objective of the study is to minimize the costs for purchasing energy from main grid for load demand and power loss on transmission lines as well as cost for emission fines from fossil fuel generation units of the grid under considering strict constraints on penetration, voltage, current and harmonic distortions. The results have shown that BO is the best and most stable method in solving the considered optimization problem. With the use of the optimal solution from BO, the total cost is significantly reduced up to 80.52%. As compared to base system without capacitors and PDGs, the obtained solution can reduce power loss up to 94.48% and increase the voltage profile from the range of [0.9092 1.00] pu to higher range of [0.9907 1.0084] pu. In addition, total harmonic distortion (THD) and individual harmonic distortion (IHD) are also much improved and satisfied under the IEEE Std. 519. Thus, BO is a suitable method for the application of installing capacitors and PDGs in distribution systems.

Load Flow Analysis and the Impact of a Solar PV Generator in a Radial Distribution Network

The distribution system acts as a conduit between the consumer and the bulk power grid. Due to characteristics such as a high resistance/reactance ratio, distribution networks cannot be solved using conventional methods, such as the Gauss-Seidel and Newton–Raphson. This research proposes a method for the calculation of the power flow in radial networks that considers their wide range of resistance and reactance values, PV generator characteristics, and radial structure. An iterative methodology is employed, with each iteration beginning with the branch that has the highest accurate power flow solution. The procedure is reliable and effective over various workloads and network configurations. To confirm the effectiveness of the suggested technique on the simple and IEEE 33-bus radial distribution system, simulations were carried out in MATLAB. The implications of including a renewable energy source, such as a PV generator, in the network under consideration are investigated by simulation result comparison. The optimal location of the PV generator was also determined using an Artificial Neural Network (ANN) controller. The results of the identification process improve the already exceptional efficacy and performance of the ANN controller.

Optimal allocation of a wind turbine and battery energy storage systems in distribution networks based on the modified BES-optimizer

Recently, incorporating renewable energy resources (RERs) like wind turbines (WTs) in a distribution network is rapidly increased to meet the load growth. However, distribution networks have been facing many challenges to withstand the intermittent output power of RERs. Battery energy storage (BES) is used with RERs to smoothly inject the output power to the grid by RERs. Therefore, this paper proposes an effective strategy for optimal allocation of WT and BES in RDS to decrease the total system losses. In addition, a modified bald eagle search (BES-optimizer) is proposed to obtain the preferable allocations of WT and BES simultaneously in the radial distribution system (RDS) considering the probabilistic distribution of the WT and load demand. IEEE 69-bus RDS is utilized as a test system. Based on the obtained results, installing WTs with BES gives better results than installing WTs alone in the RDS. However, the proposed algorithm proved its efficiency to obtain the best global results compared with other well-known techniques.

Optimal distributed generation allocation in distribution system for power loss minimization and voltage stability improvement

Voltage instability and power loss are significant problems in distribution Systems (DS). However, these problems are usually mitigated by the optimal integration of distributed generation (DG) units in the DN. In this regard, the optimal location and size of the DGs are crucial. Otherwise, System performance will deteriorate. This study is conducted to place the DGs in the radial DS. Mayfly Algorithm (MA) is used to determine the optimal placement and size of the DGs to minimize power loss, increase voltage stability in radial DS. The simulation results showed a reduction in the percentage of power loss is 69.14% for three PV-type DG unit integration. The corresponding percentage of power loss reduction is 98.09 % for three WT-type DG units by installing DG units to the test System. Similarly, the minimum bus voltage stability improves to 0.959 per unit for three PV type DG unit integration. The VSI after DG allocation increases to 0.989 per unit for three WT type DG units by optimal installing DG units. Comparative studies have been conducted, and the results have shown the effectiveness of the proposed method in reducing the power loss and improving the voltage stability of the DS. The proposed algorithm is evaluated in the IEEE-69 bus radial DS using MATLAB.

An Improved Equilibrium Optimizer for Optimal Placement of Distributed Generators in Distribution Systems considering Harmonic Distortion Limits

This paper proposes an improved equilibrium optimizer (IEO) for determining optimal location and effective size of distributed generation units (DGUs) in the distribution systems in order to minimize the total power loss on distribution branches, investment cost, and operation and maintenance cost. In a good obtained solution, limits of voltage, current, and harmonic flows are also seriously considered, exactly satisfying predetermined ranges. Especially, individual harmonic distortion (IHD) and total harmonic distortion (THD) of bus voltage must fall into IEEE Std. 519. The proposed IEO is developed from the original equilibrium optimizer (EO), which was motivated by control volume mass balance models. This novel algorithm can effectively expand the search area and avoid the premature convergence to low-quality solution spaces. With the determined solutions from IEO, not only are the voltages well improved but also the harmonics are mitigated from the violated values down to the allowable values of IEEE Std. 519. Moreover, the total power loss is significantly reduced from 0.2110 MW to 0.0815 MW, 0.2245 MW to 0.07197 MW, and 0.3161 MW to 0.1515 MW for IEEE 33-bus, IEEE 69-bus, and IEEE 85-bus radial distribution systems, respectively. Not only that, the total cost of DGUs is also more economical and consumes only $3.4753 million, $3.2840 million, and $3.0593 million corresponding to the three systems for a 20-year planning period. The performance of the proposed algorithm is compared to three other implemented methods consisting of artificial bee colony (ABC) algorithm, salp swarm algorithm (SSA), and EO and eight previously published methods for the three test systems. The comparisons of results imply that IEO is better than other methods in terms of performance, stability, and convergence characteristics.

Distribution Networks Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Hybrid TLBO-BH Algorithm

In this paper, a new method based on the combination of the Teaching-learning-based-optimization (TLBO) and Black-hole (BH) algorithm has been proposed for the reconfiguration of distribution networks in order to reduce active power losses and improve voltage profile in the presence of distributed generation sources. The proposed method is applied to the IEEE 33-bus radial distribution system. The results show that the proposed method can be a very promising potential method for solving the reconfiguration problem in distribution systems and has a significant effect on loss reduction and voltage profile improvement.

A Comprehensive Modeling for Wind Turbine Generator and DSTATCOM into Backward/Forward Load Flow Algorithm

Backward/forward load flow algorithm is commonly used in the power system, particularly in radial distribution networks. However, the rapid development in the power electronics technologies makes power systems more flexible with the integration of Renewable Energy Resources (RESs) and compensation devices. Consequently, this paper presents simple models for wind turbine generators based on Squirrel Cage Induction Generator (SCIG) and Distribution Static Synchronous Compensator (DSTATCOM) into a backward/forward load flow algorithm. In the wind generator model, the mechanical powers are collected from the manufactures data where the electrical power is calculated through the SCIG equivalent circuit and injected as negative loads at the connected buses. The second model in this paper is the DSTATCOM devices which are utilized to recover the SCIG reactive power. In the proposed model, the connected DSTATCOM bus is converted to a voltage-controlled bus (PV-bus type) with zero active power. Moreover, the reactive power of the DSTATCOM is updated during the iterative process until the load flow convergence. The simplified models for both wind generators and DSTATCOM are validated based on standard IEEE 33-bus and 85-bus radial distribution systems. The obtained results show the feasibility and effectiveness of the proposed models.

Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

A Novel Technique for Optimal siting and Rating of Shunt Capacitors Placed to the Radial Distribution Systems

The overall efficacy of power distribution is reduced through power loss in distribution systems owing to branch resistance. For providing quality power to customers, power losses must be diminished, and voltages must be enhanced. This paper introduces a novel improvement for the traditional Salps Swarm Algorithm (SSA) called Enhance SSA (ESSA) for selecting Optimal Shunt Capacitor Ratings and their Locations (OSCRL). ESSA is viewed as an enhanced version of SSA with the aim of improving the SSA's poor performance. A multi-objective function was designed in this work for reducing real power loss and Total System Cost (TSC), as well as, improving feeder voltage profile and maximizing Net Savings (NSs) per year under security restrictions. For validating and evaluating the proposed ESSA technique, a practical 33-bus local Iraqi and standard IEEE 69-bus Radial Distribution Systems (RDSs) were used as test systems through utilizing MATLAB. The experimental results attained by the proposed ESSA are compared with other approaches currently available in the literature for addressing OSCRL problem. The experimental results prove the efficacy and supremacy of the ESSA to other literature algorithms with respect to diminishing power loss as well as TSC, reinforce bus voltage profile and increase NSs per year for multiple RDSs.