Voltage Profile Improvement Research Articles

Optimal Operational Planning of RES and HESS in Smart Grids Considering Demand Response and DSTATCOM Functionality of the Interfacing Inverters

With countries moving toward renewable energy sources (RES), the need for dispatchability and storage solutions has become more prevalent. The uncertainty associated with wind turbine (WT) units and photovoltaic (PV) systems further complex a system with a high level of intermittency. This work addresses this problem by proposing an operational planning approach to determine the optimal allocation of WT units, PV systems, and hybrid energy storage systems (HESS) in smart grids. The proposed approach considers the uncertainties of the RES and load demand, price-based demand response, and distribution static compensator (DSTATCOM) functionality of the RES interfacing inverters. The operational planning problem is divided into two subcategories: (1) optimal long-term planning and (2) optimal operation. In the first problem, probabilistic models of RES and load reflect on the sizes and locations of the used RES and storage technologies. This allocation is further optimized via the optimal operation of the smart grid. The proposed operational planning approach is formulated as a nested optimization problem that guarantees the optimal planning and operation of the RES and HESS simultaneously. This approach is tested on the IEEE 33-bus distribution system and solved using meta-heuristic and mathematical algorithms. The effectiveness of the proposed approach is demonstrated using a set of case studies. The results demonstrate that the proposed approach optimally allocates the RES and HESS with a 30.4% cost reduction and 19% voltage profile improvement.

Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement

A number of algorithms that aim to reduce power system losses and improve voltage profiles by optimizing distributed generator (DG) location and size have already been proposed, but they are still subject to several limitations. Hence, new algorithms can be developed or existing ones can be improved so that this important issue can be addressed more appropriately and effectively. This study proposes a reconfiguration methodology based on a hybrid optimization algorithm, consisting of a combination of the genetic algorithm (GA) and the improved particle swam optimization (IPSO) algorithm for minimizing active power loss and maintaining the voltage magnitude at about 1 p.u. The buses at which DGs should be injected were identified based on optimal real power loss and reactive power limit. When applying the proposed optimization algorithm for DGs allocation in power system, the search space or number of iterations was reduced, increasing its convergence rate. The proposed reconfiguration methodology was test in an IEEE-30 bus electrical network system with DGs allocations and the simulations were conducted using MATLAB software compared to other optimization algorithms, such as GA, PSO, and IPSO, the combination of GA and IPSO or Hybrid GA & IPSO (HGAIPSO) method has a smaller number of iterations and is more effective in optimization problems. The effectiveness of the proposed HGAIPSO has been tested on IEEE-30 bus network system with DGs allocations, and the obtained test results have been compared to those from other methods (i.e., GA, PSO, and IPSO). The simulation results show that the proposed HGAIPSO can be an efficient and promising optimization algorithm for distribution network reconfiguration problems. The IEEE-30 bus test system with DGs integrated at various location revealed reductions in overall real power loss of 40.7040%, 36.2403%, and 42.9406% for type 1, type 2, and type 3 DGs allocation, respectively. The highest bus voltage profile goes to 1.01 pu in the IEEE-30 bus.

A novel methodology for optimal location of reactive compensation through deep neural networks

The present investigation proposes a methodology for the optimal location of reactive compensation in an electrical power system (EPS) through deep neural networks for voltage profile improvement. One of the main parameters to consider regarding EPS reliability is the voltage profile, a parameter that can be affected due to unexpected increases in impedance and loads in the system that translate as overloads in the system and an increase in the number of users. A voltage profile below the minimum or above the maximum accepted in the regulations of each country puts at risk the correct operation of equipment connected to the electrical network and, in turn, can cause economic losses and human lives (e.g by not guaranteeing reliability for hospitals and similar institutions).Economically, one of the most viable alternatives for improving voltage profiles is reactive compensation which in itself is carried out through capacitor banks. Therefore, this work proposes to find the correct location of capacitor banks in an electrical power system (using IEEE 14, 30 and 118 bus-bars systems as cases of study). In each system, the highest reactive load is identified, thus three values for reactive compensation are established as 80%, 50% and 25% of this maximum.Then, with these values, power flows are generated by locating each one of the reactive compensators’ possible values in each one of the bars of the system, hence generating a large number of training data so that finally the neural network is capable of providing a quantitative classification highlighting which compensation and in which bus-bar produces the best result. The result is assessed by applying a modified standard deviation which evaluates the separation of the voltage profiles from the ideal desired value of 1pu.

Peer-to-Peer electricity trading of interconnected flexible distribution networks based on Non-Cooperative games

With the integration of power electronic devices represented by soft open points (SOPs), distribution networks have gradually evolved into interconnected flexible distribution networks (FDNs). Considering the deregulation of electricity market and user privacy, multiple stakeholders have participated in the operation of FDNs. Peer-to-peer (P2P) electricity trading is promising to alleviate operational problems of interconnected FDNs. As multiple regions pursue the maximum profits individually, non-cooperative game methods can be utilized to realize fair profit allocation in P2P trading. In this paper, a non-cooperative game-based P2P trading method is proposed to meet the electricity trading needs of multi-region interconnected FDNs. First, based on non-cooperative games, a two-layer P2P electricity trading framework is established to realize cost reduction and voltage profile improvement of multi-region interconnected FDNs. Then, a P2P trading adjustment mechanism is designed to improve the operational profits of SOP, in which spatial active power trading adjustment, temporal dispatching of energy storage (ES) link and reactive power support are incorporated. Finally, the effectiveness of the proposed method is verified based on a practical distribution network with four-terminal SOP in Tianjin. The results show that the proposed P2P electricity trading method can promote the economic operation performance of interconnected FDNs and improve the operational profit of SOP.

Predictability and Voltage Profile Improvement of Distribution Networks with a D-STATCOM Model

This paper presents a modeling for D-STATCOM device into Monte Carlo probabilistic load flow algorithm. The model is used to improve the predictability and voltage profile of distribution networks. In this model, the D-STATCOM’s bus is represented as a special generator bus which injects reactive power only. The injected reactive power of D-STATCOM is updated during the iterative process to maintain the voltage magnitude at the specified value. The model can be easily implemented in the original PLF code without deeply modifications. The probabilistic load flow (PLF) with D-STATCOM model is validated using various standard IEEE tests systems; 33-bus, and 69-bus systems. The PLF algorithm with DSTATCOM model has been evaluated based on MATLAB package. However, the obtained results prove the effectiveness of D-STATCOM implementation in the test systems regarding to predictability and voltage profile issues. Moreover, the standard deviation of the systems is decreased.

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.

Optimal power flow using archimedes optimizer algorithm

<span>This article proposes a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) for solving optimization problems of optimal power flow (OPF) utilizing the renewable energy sources (RES) for minimizing different single-objective and multi-objective functions based on minimization of fuel cost, power losses of transmission lines, emission and voltage profile improvement. Also, mathematical formulation of (OPF) is introduced by converting the function with multiple objectives based on price and weighting parameters into a single objective function. Also, the effect of optimal RES is merged into the OPF problem. Notably, optimal RES placement yields even more effective solution. AOA was inspired by an intriguing physical law known as Archimedes' Principle. To prove the effectiveness of the AOA proposed algorithm, it compared with different recent algorithms for solving the optimal power flow problems and testing them to one standard system of the IEEE30-bus test system. The superiority of the proposed AOA algorithm is proven also by applying them on the IEEE30-bus modified system with optimal allocation of renewable energy source (RES). The results demonstrate that the proposed algorithm is more successful and efficient than the other optimization methods in the title of resolving OPF problems.</span>

Performance Improvement of Power Transmission Networks Under Stressed Condition

The traditional method of power generation is not capable of fulfilling the increasing electricity demand. The main objective of installing DG is to put less pressure on the centralized power system and to install them nearer to the load or the consumer. DG and FACTS devices allocation in distribution systems has proved to improve the system performance that is the reduction in power loss, improved voltage profile, controlled power transfer capability, active and reactive power flow, improved transient and dynamic stability, better voltage regulation, etc. In this paper, simultaneous allocation of DG and UPFC is done by using an exhaustive search technique to study the impact on the system in overloading conditions. The main objective is to minimize losses. The power flow analysis is done by using the Newton Raphson method and tested on the IEEE 3-bus system and IEEE 14-bus system in Mi Power 9.0 software.

Voltage Profile improvement by optimal DG allocation using atom search optimisation in radial and mesh distribution system

Recent years have seen a considerable increase in the generation of power using renewable energy sources. This paper deals with the allocation of distributed generations (DG) in radial as well as mesh distribution network (RDSm&MDSm, respectively). The allocation of DG is been done through a meta-heuristic technique known as Atom Search Optimisation. In the paper, the power loss is reduced, along with simultaneous improvement of voltage profile of the system. The proposed algorithm has been tested on IEEE 33 & 69 radial and weakly meshed system with 5 tie lines is considered for power loss minimisation using single and multiple DGs. Two power factors are considered for the simulation 0.8 lag and 0.9 pf lag. The simulation results are carried out in MATLAB.

Power loss minimization and voltage profile improvement of radial and mesh distribution network using sine cosine optimization based DG allocation

Due to the increasing population and emerging technologies, energy requirements are increasing progressively. Distributed generation (DG) is an excellent option to handle the increased power demand. A well optimized DG can help to reduce power losses, CO2 emission and improve voltage profiles. This paper uses sine cosine algorithm to find the optimal location and best size ofDG with power loss minimization as its objective function. It presents the comparison of power loss reduction and voltage profile enhancement due to allocation of DGoperating at 0.85, 0.95 power factor lag andthe optimal power factor that has been obtained by sine cosineoptimization. IEEE 15, 69 bus radial and 33, 69 bus weakly meshed systems with five tie lines are used for power loss analysis. Annual energy saving due to allocating DG with different power factors have been analyzed. All the results are simulated in MATLAB 2021a.

Participation of Grid-Connected Energy Hubs and Energy Distribution Companies in the Day-Ahead Energy Wholesale and Retail Markets Constrained to Network Operation Indices

In this paper, the optimal scheduling of energy grids and networked energy hubs based on their participation in the day-ahead energy wholesale and retail markets is presented. The problem is formulated as a bilevel model. Its upper level minimizes the expected energy cost of electricity, gas, and heating grids, especially in the form of private distribution companies in the mentioned markets, in the first objective function, and it minimizes the expected energy loss of these networks in the second objective function. This problem is constrained by linearized optimal power flow equations. The lower-level formulation minimizes the expected energy cost of hubs (equal to the difference between sell and purchase of energy) as an objective function in the retail market. Constraints of this model are the operation formulation of sources and active loads and the flexibility limit of hubs. The unscented transformation approach models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage. Then, the Karush–Kuhn–Tucker approach and Pareto optimization technique based on ε-constraint are adopted to extract the single-level single-objective formulation. Finally, obtained results verify the capability of the present method in improving the economic status of hubs and the economic and operation situation of the mentioned networks simultaneously so that the proposed scheme by managing the power of energy hubs compared with power flow studies has been able to reduce operating costs by 8%, reduce energy losses by 10%, and improve voltage profile and temperature by 36% and 30%.

Optimal DG allocation and sizing in distribution systems with Thevenin based impedance stability index

The applications of Distributed Generation (DG) play a significant role to provide the benefit to conventional distribution systems. However, the sizing and location of these DG units should be taken into consideration to get maximum gain and benefit. If DGs are not located and sized properly, the distribution system can be adversely affected. So, optimal allocation and sizing are needed to avoid instability problems and more costs to increase the efficiency and quality of energy in distribution systems. This paper addresses the optimal sizing and allocation of DGs for power losses, voltage profile and stability improvement, introducing a proposed stability index based on Thevenin impedance in a distribution network. The stability index is calculated by the proposed approach based on Thevenin. Thevenin is obtained based on a two-bus system by reduction of more than two bus systems. The optimal sizing and location of DGs are also obtained by operating DGs under different power factors. The reactive capability of DGs is also shown by simulations with DGs operated under different power factors. The problem of optimal sizing and location of DGs, which is a nonlinear optimization problem with discrete and continuous variables, is solved based on the mixed integer Genetic Algorithm (GA). Also, the proposed approach is verified by using Grey Wolf Optimization (GWO) method. The problem contains multi-objectives with three objective functions: system power losses, voltage deviation and the Thevenin based stability index. The proposed approach has been tested on the IEEE-69 and 118-bus test system to demonstrate its effectiveness. The comparison with some other methods shows that the proposed approach gives better results than other methods while reducing the power losses and improving voltage and system stability in all suggested scenarios by finding the optimal placement and sizing of DG units. It is also shown that the reduction in power loss, voltage and stability improvements are increased more by using the reactive power capability of DGs. The results show that optimum located and sized DGs not only decreases the power loss, but also improve the voltage profile and stability of the system.

Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method

A key component of the design and operation of power transmission systems is the optimal power flow (OPF) problem. To solve this problem, several optimization algorithms have been developed. The primary objectives of the program are to minimize fuel costs, reduce emissions, improve voltage profiles, and reduce power losses. OPF is considered one of the most challenging optimization problems due to its nonconvexity and significant computational difficulty. Teaching–learning-based optimization (TLBO) is an optimization algorithm that can be used to solve engineering problems. Although the method has certain advantages, it does have one significant disadvantage: after several iterations, it becomes stuck in the local optimum. The purpose of this paper is to present a novel adaptive Gaussian TLBO (AGTLBO) that solves the problem and improves the performance of conventional TLBO. Validating the performance of the proposed algorithm is undertaken using test systems for IEEE standards 30-bus, 57-bus, and 118-bus. Twelve different scenarios have been tested to evaluate the algorithm. The results show that the proposed AGTLBO is evidently more efficient and effective when compared to other optimization algorithms published in the literature.

Power loss mitigation and voltage profile improvement by optimizing distributed generation

In a developing country, electricity has become the necessity of the growth industries; thus, the distribution system power quality and reliability are crucial. With low carbon initiatives, renewable energy or distributed generation (DG) is a promising source of electricity and leads the complex distribution system. Vital rises in DGs in power grids will significantly impact the system reliability and security, especially in power losses and voltage profiles parameters. This research focuses on an optimization placement and size of DGs in distribution systems to minimize power loss and improve voltage profile using the Modified Lightning Search Algorithm (MLSA). This research has modelled the practical 69-bus radial distribution system. Then MLSA with a weight summation approach is used to identify the suitable location and size for the DGs in the design proposal stage. The optimization objectives are to reduce power losses and improve the voltage profile, especially at the connection point of DGs. Besides that, load profile, DGs constant load and the solar load in distribution system modelled using MATLAB software. The results of the simulation using MLSA indicated that the optimization allocation and sizes of solar DGs applied with current load and load changes can minimize the power losses and improve voltage profile. These results verify the proposed approach’s effectiveness and success in determining the optimal location and sizing of solar DGs to reduce power losses as well as improve voltage profiles.

Enhancing Voltage Profile and Power Loss Reduction Considering Distributed Generation (DG) Resources

In recent years, Distributed Generation (DG) has received attention due to its benefits to the distribution network. In this paper, the influence of DG along with several techniques for mitigating the detrimental impact on voltage profile and power losses was examined. The test system of 132 KV residential test feeder was selected, examined, and modeled in the Electrical Transient Analyzer Program (ETAP). Various tests were carried out to determine the influence of DG on the distribution network. Results were compared with, and without DG, taking into account the voltage profile. When injecting DG with unity power factor at different buses in a radial test system, it was discovered that when the DG of the right size and type is injected at the ideal position, the voltage profile improves while the power losses are reduced. When an un-deterministic DG is injected at multiple points on the test feeder, no improvement in voltage profile was observed. When the cross-sectional area of conductors is increased and a DG is injected at optimal locations, a positive impact on voltage profile is observed while the detrimental impact on power losses was also analyzed. The findings of this study may be useful to distribution firms regarding the future expansion of the power systems and the proliferation of DG.

Impact of shunt capacitor penetration level in radial distribution system considering techno-economic benefits

Shunt Capacitor (SC) integration in radial distribution networks is a method utilized to minimize power losses and huge voltage drops. In other to obtain these maximum benefits, they should be optimally allocated. To this effect, researchers have used various optimization methods for solving SC allocation problems but most have not considered how the penetration level of SC into the radial distribution system affects these benefits. The goal of the paper, therefore, is to determine the most sensitive buses to reactive compensation and investigate the effect of the penetration level of SC on the techno-economic benefits. Load flow was performed for the base case to determine the steady-state performance of the system and the most sensitive buses were selected using New Voltage Stability Index (NVSI) technique. The size of the SC was increased in step of these buses, in turn, to determine SC penetration level impact on the techno-economic benefits. The approach was implemented on IEEE 33-bus and practical Nigerian radial distribution networks. The results showed that appropriate penetration of SC on networks leads to reduction of power losses, voltage profile improvement as well cost reduction resulting in high net savings.

Optimal Scheduling of Reconfigurable Microgrids in Both Grid-Connected and Isolated Modes Considering the Uncertainty of DERs

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in dealing with uncertainty and improving system reliability.

Improving voltage profile of unbalanced Low‐Voltage distribution networks via optimal placement and operation of distributed energy storage systems

Distributed energy storage system (DESS) has flexible operating characteristics, and DESSs can be properly configured to effectively serve the voltage regulation of the active distribution network. The existing voltage regulation-oriented DESSs optimization configuration studies are usually based on the balanced network model to analyze the impact of energy storage operation characteristics on the system voltage, but the distribution network is actually three-phase unbalanced. To this end, a method for the sequence optimization of DESSs in unbalanced distribution networks based on voltage sensitivity analysis is proposed, and the optimal configuration of DESSs in unbalanced distribution networks is studied from the perspective of improving the network voltage performance. Firstly, the DESSs placement is determined based on the proposed comprehensive voltage sensitivity analysis method; secondly, taking into account the economy of system operation, optimize the capacity of DESSs with the goal of minimizing the investment and operation and maintenance cost of DESSs, and the model solved by the improved grey wolf optimization algorithm. Finally, numerical simulations are analyzed by two unbalanced distribution testing networks. The results show that the proposed method can effectively improve the voltage profile of the unbalanced distribution network while ensuring the economy of system operation.

Optimized management of reactive power reserves of transmission grid-connected photovoltaic plants driven by an IoT solution

This paper presents a methodology for the analysis and simulation of the effect of operating large photovoltaic (PV) plants, in coordination, as static synchronous compensators (STATCOM). The goal is to improve voltage profiles at different load nodes and reduce power losses in transmission lines. The proposed approach takes into account the varying reactive power capacity in PV inverters, which depends on weather conditions.To implement the proposed method, proper Internet of Things (IoT) hardware and software solutions are required. In this context, the grid status and weather data need to be transmitted continuously, via wireless communication technology, to an edge computer. Based on the transmitted data, and using the system mathematical model, an optimization algorithm is then responsible for finding out the optimal reactive power setpoint for each plant in real time.The proposed method is implemented and tested successfully using MATLAB platform with the MATPOWER IEEE 30-bus test grid model. When only five 20 MW PV plants are connected to different locations in the grid with a penetration rate lower than 25 percent, the simulation shows the effectiveness of the optimal coordination of PV plants to deal with the effect on the transmission grid of instantaneous operation of multiple loads. In this context, a daily load profile of heat pumps, operating in winter scenario in multiple households, is approved. An improvement up to 68 percent in the global voltage profiles in the load buses for one-day scenario is achieved. Furthermore, total accumulated active and reactive power losses are reduced by 24.1 percent.

Optimal Placement and Sizing Problem for Power Loss Minimization and Voltage Profile Improvement of Distribution Networks under Seasonal Loads Using Harris Hawks Optimizer

Improving efficiency with sustainable radial distribution networks (RDNs) is challenging for larger systems and small grid-connected RDNs. In this paper, the optimal placement of DGs with the Harris hawks optimizer (HHO) under seasonal load demands is proposed to simultaneously reduce total active and reactive power losses and minimize bus voltage drops with the consideration of operational constraints of RDNs. HHO is a newly inspired metaheuristic optimization algorithm primarily based on the Harris hawks’ intelligent behaviors during the chasing of the prey. Furthermore, the authors have investigated four stages of DGs. The first stage involves the optimal allocation of one DG. The second stage includes an investigation with two DGs, the third stage considers three DGs, and the fourth stage investigates the integration of four DGs. The effectiveness of the applied HHO is validated on IEEE 33 and 69 bus RDNs, and results are analyzed by comparing with the standard optimization methods. The Big-O test is also executed for statistical analysis with standard algorithms. The simulation results reveal the better performance of the applied HHO under different circumstances than other algorithms. Furthermore, the total active and reactive power losses and bus voltage drops are improved by adding more DGs into IEEE 33 and 69 bus RDNs.