Network Voltage Profile Research Articles

Tariff structure for regulation of reactive power and harmonics in prosumer-enabled low voltage distribution networks

The exponential growth of renewable energy generation, electronic loads, and electromobility in active distribution networks has severe consequences on the network's power quality (PQ) and economic impacts on the utility. The conventional active energy tariff for low-voltage (LV) consumers do not consider the cost associated with the PQ parameters, specifically, power factor and harmonic distortion. Besides, there is no standard tariff structure in practice for PQ regulation in the prosumer-enabled LV distribution network. This paper proposes a tariff scheme for LV customers (consumers or prosumers) aiming to regulate their reactive power and harmonic injection. The proposed tariff scheme allows customers to participate in network voltage profile improvement for the benefit of both utility and customers. Further, we propose a technique to evaluate the individual contribution of consumers, prosumers, and utility to harmonic pollution at PCC, which is crucial in finding harmonic penalties. The proposed tariff scheme is validated on a modified CIGRE LV radial active distribution network with diverse loads. Finally, the tariff scheme is evaluated on a laboratory-scale system comprising a three-phase consumer with diverse loads and a variable supply. The results elucidate that the tariff scheme is realistic and appropriately penalizes/incentivizes the consumers and prosumers according to their reactive and harmonic profiles.

An efficient capuchin search algorithm for allocating the renewable based biomass distributed generators in radial distribution network

It is essential to keep the stability of electric distribution network especially during abnormal operating conditions, this guarantees service continuity to the customers. Installing small-scale generating units named distributed generators (DGs) can enhance the stability of the grid via minimizing the system losses and improving the network voltage profile. Many approaches, conventional and metaheuristic, have been conducted in installing the DGs in distribution network, however some shortages like slow convergence and stuck in local optima represent obstacles in identifying the appropriate locations and sizes of DGs. Therefore, this paper proposes an efficient methodology of capuchin search algorithm (CapSA) which is presented for the first time to determine the optimum locations, sizes, and power factors of biomass-based DGs (BDGs) in the radial distribution network. The proposed methodology is implemented via adapting the first rows of CapSA population to integer numbers assigned to the BDGs’ locations. The target is minimizing the network active power loss with keeping the power flow, bus voltage, and transmission line in their normal ranges. Two distribution networks of IEEE 33-bus and IEEE 69-bus are analyzed, the CapSA results are compared to chimp optimizer (CO), heap-based optimizer (HBO), seagull optimization algorithm (SOA), differential evolution (DE), and sine cosine algorithm (SCA). Moreover, installing different numbers of BDGs are presented to validate the proposed approach. Furthermore, statistical tests of Friedman and ANOVA are conducted to assess the CapSA performance. In IEEE 33-bus network, the proposed approach minimized the active and reactive power losses to 7.9745 kW and 10.4134 kVAr respectively via installing four BDGs. While for IEEE 69-bus system, the active and reactive losses are minimized to 2.1092 kW and 1.5065 kVAr respectively via the proposed CapSA. The obtained results confirmed the preference of the proposed CapSA in installing the BDGs in radial networks.

A Mathematical Programming Approach for Allocation and Analysis of TCSC in Power Transmission Systems

In this paper, a mathematical programming approach based on a branch and bound (BB) algorithm is proposed in order to determine the optimal allocation of thyristor-controlled series compensators (TCSC) in electrical power transmission systems, considering line thermal limits. The TCSC modeling is added in the Y-bus matrix, facilitating the computational implementation. BB-Y is the methodology used in this paper aiming to maximize the loading of the system considering a certain quantity of TCSC. In addition, the proposed algorithm is implemented for another test, using a determined number of TCSC, in order to improve the network voltage profile and minimize the total transmission losses. The methodology uses an interior point method to solve the problem relaxed, modeled as a reactive optimal power flow problem. Simulations are performed on the IEEE 118-bus test system, allocating different quantities of TCSC to find the maximum load the system can support. Given a certain number of TCSC, previously defined for a specific load, the TCSC are reallocated considering different objectives. For two TCSC the time for relocation is shorter than 4 seconds. The results also show that from three TCSC onwards, the maximum load increasing is not relevant, being around 0.3%. Therefore, the conclusion is that the algorithm has a low processing time, and conservative solutions allied with the presented methodology can be used in the optimal allocation of TCSC, what the aim is to optimize the systems performance.

Optimal sizing and placement of shunt capacitors on the distribution system using Whale Optimization Algorithm

This paper aims at power loss minimization and reduction of cost due to losses in the distribution network as well as improvement of the network voltage stability and profile via optimal shunt capacitor allocation. The optimal shunt capacitor allocation was obtained using Whale Optimization Algorithm considering technical (real power loss and VSI) and economic objective functions as sub-optimal allocation may result in undesired effects on the network. The obtained optimal sizes of the shunt capacitors were placed on the obtained location of the distribution networks to ascertain the behavior of the networks before and after their placements taking power loss, cost due to losses, and voltage stability index as the performance metrics. A comparison of these performance metrics under four cases, namely base case (case one), installation of one SC (case two), installation of two SCs (case three), and installation of three SCs (case four) were demonstrated using standard IEEE 33-bus and Dada 46-bus distribution networks. The results gave a percentage loss reduction of 33.74% with 27.60% annual net saving for the IEEE 33-bus network and 22.24% loss reduction with 25.60% annual net saving for the Dada 46-bus network. Hence, reduction in the total power losses and costs. Also, the voltage profile and voltage stability index were significantly improved.

Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion

This paper develops a computationally efficient algorithm which speeds up the probabilistic power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage profile in electrical power distribution networks. The algorithm is accordingly termed the Accelerated-PPF (APPF), since it can accelerate “any” sampling-based PPF solver. As the APPF runs, it concurrently generates a low-dimensional subspace of orthonormalized solution vectors. This subspace is used to construct and update a reduced order model (ROM) of the full nonlinear system, resulting in a highly efficient simulation for future voltage profiles. When constructing and updating the subspace, the power flow problem must still be solved on the full nonlinear system. In order to accelerate the computation of these solutions, a Neumann expansion of a modified power flow Jacobian is implemented. Applicable when load bus injections are small, this Neumann expansion allows for a considerable speed up of Jacobian system solves during the standard Newton iterations. APPF test results, from experiments run on the full IEEE 8500-node test feeder, are finally presented.

Impact assessment of grid tied rooftop PV systems on LV distribution network

Due to the high demand for electricity in the world today and the need to reduce carbon footprint, the power system is undergoing a profound change and this change is paving way for renewable electricity generation into the traditional grid, making the conventional network become smarter. The convention which has been in existence over several decades has changed; thus, consumers of electricity have now become prosumers; generating and feeding electricity into the low voltage network. Despite the benefits of prosuming, some researchers say their presence create stability, reliability and power quality (over and under-voltages, and high harmonic distortion levels) problems which in effect lead to overloading of transformers and feeders, extreme line losses, and failure in network protection exceeding the limits in the low voltage grid.The objective of this paper is to evaluate the effect of rooftop PV generations on distribution losses (power losses) and network voltage profile (voltage regulation on LV network) in a typical traditional grid setup. The study employs an empirical analysis of the power system, forward/backward sweep based on BIBC and BCBV matrices and python simulation to arrive at its conclusion under two different scenarios, nine different loading conditions and six PV (from 1kWp to 6kWp) injections.The results obtained show that a grid connected with rooftop PV systems have the potential of reducing distribution losses substantially and also do not violate standardized voltage limits. The net power loss recorded on the network with PV injections from 1kWp to 6kWp were in the range of 1.1kW–88.9kW at poor power factors and 0.7kW and 89.1kW at improved power factors, respectively. The variation in the voltage profile ranges between 0.97pu and 1.05pu for both the conventional and prosumer grids.

The random varying loads and their impacts on the performance of smart grids

This paper applies simple model of load profile by using a minimum number of the probability variables to explore the impacts of time varying loads on the smart network losses and voltage profiles. For this purpose, three scenarios are taken into considerations. These scenarios of loads include the loads without deviation, the random loads at fixed probability factor, and random loads at variable probability factor. 86-node distribution network in Turkey has been chosen in this study. This study yields, when the load deviations and their distribution probability increase, the losses increase, and the nodes’ voltage decreases.

Power Quality Improvement of a Distribution Network for Sustainable Power Supply

This paper presents a heuristic technique for improving the voltage profile and reducing power loss in a distribution network. These components are to be maintained at a certain standard to boost the electricity supply power of a distribution network. The shunt capacitor placement technique is applied to achieve a sustainable electric power supply. The issue of power loss has been a major threat to the distribution network. A distribution network is expected to operate at a certain voltage level to meet consumers’ energy demand. Power flow studies have been conducted using Newton Raphson’s technique on the 30 bus, 11 kV Onuiyi-Nsukka distribution network. It was found that the voltage profile on buses 19 and 26 was critically violated with voltage amplitudes of 0.72 pu and 0.79 pu respectively. The feeder power quality was improved using a shunt capacitor placement technique and the installation of a 1200KVAr shunt capacitor to keep bus voltage amplitudes within the legal limit of (0.95-1.05) pu. The voltage profile, active and reactive power losses on the network were determined. Active power loss and reactive power loss are reduced from 0.27MW to 0.12MW and 0.76Mvar to 0.14Mvar, respectively. Therefore, the distribution network voltage profile is improved and the power loss on the network significantly reduced.

A power loss minimization strategy based on optimal placement and sizing of distributed energy resources

AbstractDue to the enhanced price of electricity, the gradual depletion of fossil fuels, and the global warming concerns, power loss minimization through deployment of distributed generators (DGs) has attracted significant attention in recent decades. This paper proposes a genetic algorithm (GA) based strategy for minimization of active and reactive power losses through optimal location and size of DGs. It also quantifies and tallies the total network power losses for the cases with random as well as optimal allocation of DGs. To validate the accuracy of the obtained results from GA, another nature‐inspired optimization algorithm, cuckoo search, is also deployed. The simulation results on IEEE 30 and 118 bus systems indicate that the proposed strategy not only can effectively reduce the total network active and reactive power losses but also lead to the improvement of network voltage profile.

Operation of a Technical Virtual Power Plant Considering Diverse Distributed Energy Resources

Virtual power plants (VPPs) have emerged as a way to coordinate and control the growing number of distributed energy resources (DERs) within power systems. Typically, VPP models have focused on financial or commercial outcomes and have not considered the technical constraints of the distribution system. The objective of this article is the development of a technical VPP (TVPP) operational model to optimize the scheduling of a diverse set of DERs operating in a day-ahead energy market, considering grid management constraints. The effects on network congestion, voltage profiles, and power losses are presented and analyzed. In addition, the thermal comfort of the consumers is considered and the tradeoffs between comfort, cost, and technical constraints are presented. The model quantifies and allocates the benefits of the DER operation to the owners in a fair and efficient manner using the Vickrey–Clarke–Grove mechanism. This article develops a stochastic mixed-integer linear programming model and various case studies are thoroughly examined on the IEEE 119 bus test system. Results indicate that electric vehicles provide the largest marginal contribution to the TVPP, closely followed by solar photovoltaic (PV) units. Also, the results show that the operations of the TVPP improve financial metrics and increase consumer engagement while improving numerous technical operational metrics. The proposed TVPP model is shown to improve the ability of the system to incorporate DERs, including those from commercial buildings.

Voltage stability improvement of wind farms by self-correcting static volt-ampere reactive compensator and energy storage

Maintaining synchronism and voltage stability, especially in the presence of wind farms, has a crucial role in confirming the reliability requirements of the power grid, as the natural fluctuations of wind speed result in intermittency and uncontrollability of power generation. To this end, an optimized Battery Energy Storage System (BESS) was suggested to make an equilibrium between the generation and consumption in wind farms under harsh conditions. Additionally, the instability of wind farms is very likely after transmission line faults occur. To overcome this problem, in this paper, the impact of a constant capacitor bank, connected in parallel with the wind generators, is investigated. Also, a novel self-corrective Static Volt-ampere reactive Compensator (SVC) is proposed to overcome the variations of voltage and damp the reactive power oscillations after occurring the faults. The proposed SVC compares the network voltage profile as the reference voltage with the voltage deviations in a control block diagram. For this purpose, three similar wind farms of Permanent Magnet Synchronous Generator (PMSG) type are considered in a wind park. The simulation case studies are performed in Matlab/Simulink under different conditions of transient and steady-state voltage stabilities in the presence of proposed BESS including, only capacitor bank (without self-corrective SVC), and with self-corrective SVC. The results authenticate the ability of the proposed SVC strategy to correct the voltage deviations, and voltage stability improvement of the under study system.

Improvement of the Voltage Profile and Loss Reduction in Distribution Network Using Moth Flame Algorithm: Wolaita Sodo, Ethiopia

Voltage stability and line losses are inevitable issues even in modern power systems. There are several techniques that emerged to solve problems in the power system to provide quality and uninterrupted supply to customers. The algorithms used in this paper to determine the appropriate location and size of the Static Var Compensator (SVC) in the Distribution Network (DN) are Moth Flame Optimization (MFO) and Particle Swarm Optimization (PSO). The objective function is defined to minimize voltage deviation and power loss. The burning problem of voltage stability improvement current scenario is because of a rise in electricity demands in all sectors. Paramount duties of power engineers are to keep the system stable and maintain voltage magnitude constant even during peak hours. The results were checked with the aid of MATLAB on Wolaita Sodo radial distribution of 34 bus data networks. The potential use of SVC is key to solve distribution system power quality issues and estimating the advantage of the installation. The results obtained from the test system were compared with PSO results. This comparison was done to know the computational time of proposed techniques. The performance of the MFA based SVC was superior in distribution system and highlighted the importance of device.

Mitigating unbalance and improving voltage considering higher penetration ofEVsandDGusing hybrid optimization technique

In this manuscript, an efficient hybrid method is proposed aimed at mitigating imbalance, improving the voltage in the higher penetration of electric vehicles (EVs) and distributed generation (DG). The proposed hybrid method is the combination of the mayfly algorithm (MA) and the radial basis function neural network (RBFNN), called the mayfly algorithm-radial basis function neural network (MA-RBFNN) method. These major contributions of the proposed work mitigate network unbalancing and voltage profile with the loss of network energy, utilize obtainable network capacity and enlarge the comfort level for EV consumers. The MA is utilized for solving the multi-objective optimization issue of coordinating EV together with DG in the distribution grid. The RBFNN agrees with the distribution system operators to mutually enhance the phase sequence with the optimum dispatch of DGs for improving the performance of the network. The proposed system allows distribution system operators to mutually enhance the phase sequence with an optimum DG dispatch for recovering the performance of the network and solve the multi-objective optimization issue of coordinating EV and DG on the distribution network. This proposed technique is implemented on the MATLAB/Simulink platform as well as evaluated for efficiency, likened to the existing methods such as genetic algorithm (GA), particle swarm optimization (PSO) and MA technique. The performance of the proposed method was analysed in terms of quality of solution comparison, integral of the time-weighted absolute error (ITAE), integral squared error (ISE) and loss reduction (%). These comparative outcomes display the eminence of the proposed method more than any other existing technique.

Double-layer stochastic model predictive voltage control in active distribution networks with high penetration of renewables

The high penetration of renewable energy into distribution networks poses increasing challenges on voltage control. To address this issue, this paper presents a double-layer stochastic model predictive control algorithm to regulate voltage profile in active distribution networks. In the proposed algorithm, voltage regulation is achieved by coordination of an upper layer controller and a lower layer controller. In the upper layer, the number of operation of mechanical voltage regulation devices, including transformer with on-load tap changer and capacitor banks, is minimized in an hourly timescale. In the lower layer, the controller minimizes the active power curtailments and power losses with a control period of 5 min. The proposed double-layer stochastic model predictive voltage control utilizes not only the reactive power control, but also the active power curtailment to regulate bus voltages. In addition, mechanical voltage regulation devices and distributed generations are controlled in two different timescales. Case studies on a modified IEEE-33 bus system demonstrate that compared with traditional control and two-stage stochastic voltage control, the proposed algorithm can achieve an improvement of 8.05% and 7.43%, respectively.

Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm

In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cleared that optimal allocation and planning of the DGSCs can be improved the distribution network operation with reducing the power losses and also enhancing the voltage profile. The obtained results confirmed superiority of the MRFO compared with well-known particle swarm optimization (PSO) in the DGSCs allocation. The results also showed that increasing the number of DGSCs reduces more losses and improves more the network voltage profile. The achieved results demonstrated that the energy loss in smart network is less than the network with conventional load. In other words, any error in forecasting load demand leads to non-optimal operating point and more energy losses.

Optimal placement of facts devices to reduce power system losses using evolutionary algorithm

<p>The rapid and enormous growths of the power electronics industries have made the flexible AC transmission system (FACTS) devices efficient and viable for utility application to increase power system operation controllability as well as flexibility. This research work presents the application of an evolutionary algorithm namely differential evolution (DE) approach to optimize the location and size of three main types of FACTS devices in order to minimize the power system losses as well as improving the network voltage profile. The utilized system has been reactively loaded beginning from the base to 150% and the system performance is analyzed with and without FACTS devices in order to confirm its importance within the power system. Thyristor controlled series capacitor (TCSC), unified power flow controller (UPFC) and static var compensator (SVC) are used in this research work to monitor the active and reactive power of the carried out system. The adopted algorithm has been examined on IEEE 30-bus test system. The obtained research findings are given with appropriate discussion and considered as quite encouraging that will be valuable in electrical grid restructuring.</p>

MPC Based Centralized Voltage and Reactive Power Control for Active Distribution Networks

The paper presents an approach for online centralized control in active distribution networks. It combines a proportional integral (PI) control unit with a corrective control unit (CCU), based on the principle of Model Predictive Control (MPC). The proposed controller is designed to accommodate the increasing penetration of distributed generation in active distribution networks. It helps in continuously satisfying the reactive power requirements of the transmission system operators (TSOs), while maintaining an acceptable voltage profile in the active distribution network, and simultaneously minimizing the total active power losses. The controller also ensures compliance to operation requirements of distribution network operators (DNOs). By replacing the full load flow (LF) calculation with sensitivities, derived from a linearized model of the network, the controller can work in real-time applications. Moreover, the computational burden of the proposed controller is reduced since the CCU is activated only when a voltage violation or considerable change of operation condition occurs. The performance of the proposed controller is demonstrated on a 11-kV test network with 75 buses and 22 distributed generators.

Optimal planning of a soft open point in a distribution network subject to typhoons

While Soft Open Point (SOP) shows its strong capability to control load transfer and regulate network voltage profile in a distribution network, there are limited considerations of the high investment costs of these special electric devices. This gap may hinder optimal planning of an SOP and even affect its wide usage in a distribution network. This paper proposes an optimal planning method for an SOP that fully considers the cost of an SOP in a coastal network where typhoon frequently lands. The benefit of the SOP consists of the reduction of energy loss in daily operations and the post-fault supply restorations under extreme typhoon environment. The evaluation of the benefit of the SOP from the reduction of energy loss in daily operations is based on simulations of multiple scenarios in the long-term life cycle of the SOP. In order to evaluate the damages to the distribution network from typhoon, the virtual typhoon samples are generated from historical data in the entire period of the life cycle of the SOP. A practical algorithm is designed for the scanning of the possible formations of islands that can be restored by an SOP when typhoon lands. In the end of the paper, both the location and the capacity of an SOP are optimized considering the balance among the benefit of energy-loss reduction in daily operations, the benefit of supply restoration after typhoon lands, and the investment and operation costs of the SOP. From numerous simulations, it is validated that implementing only one SOP in the distribution network is both optimal and practical in most simulation cases due to the restrictions of the extremely high implement costs of SOP, and this may greatly simplify the complexity of the optimal planning problem for the SOP in a distribution network.

Significant Affect of EV Charging on Grid with Renewable Technologies

While pollution is increasing as a result of greenhouse gases and global warming; the number of hybrid cars is expected to increase. Although many rechargeable cars are designed for metropolitan usages, and these cars have to be resupplied in the evening or even during the night. So the electric cars would then communicate with the grid throughout that period. This process will have an impact on the network voltage profiles as well as the loading. Established grids have been intended several decades ago, and therefore it is necessary to take note of all this whether they'll be capable of supporting one such increased loading or if there is a need for rebuilding to fulfill one such demand. Most consumers will prefer to charge at their premises during the night, which is the Low Voltage (LV) system. And it operates at 230V. The LV grid is therefore taken into account to understand the impact of charging electric vehicles. As loads are connected to different phases, there will be a phase asymmetry in the network. This asymmetry can be reduced by proper grid planning. The electrical vehicle injection into the grid will also have an impact on the power factor. As some consumers turn their electric vehicles on or off, the overall power factor changes. This has to be considered as having an impact on reactive power consumption. As the number of electric cars increases, the loading characteristics of the transformer change. The grid without the need for an electric vehicle and with an electric vehicle is considered for this work and Grid parameters are evaluated. Simulations performed in the MATLAB environment show that the driving and charging patterns of EV’s can have a substantial effect on the grid. Hence nonconventional sources with controlled charging can reduce the necessity of the import of energy from the grid.

Optimal Placement of Distribution Generation Sources in Hybrid Generation Network

The ideal places and size of the distribution generators were determined by reducing the loss of power in the distribution networks. The ideal positioning of various kinds of DGs has been suggested in the current job. In this job, the ideal power factor for DG supply has been acquired, both the active power as well as the reactive power. In the proposed approach, different types of distribution generation (DG) supply both reactive and real power. For the optimal placement of DG sources, particle swarm optimization techniques have been used in this job. Each of these innovations has its own strengths and drawbacks. Most of the methods that have been proposed so far to formulate DG’s optimum placement problem only consider Type-I DGs, Type-II and Type-III DGs that are considered for optimal position in the existing research. In the reference, artificial bee colony algorithm was used to determine sites of DGs and condenser combinations and optimal size. The author used PSO method in the reference to determine the appropriate positioning of the DG’s and to maximize the savings of power loss and voltage profile in the distribution network.