Installation Of Distributed Generation Research Articles

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

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

Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning

With the rise in load demand, improving the voltage profile and reducing line loss is vital to ensure reliable power delivery to the customer. However, increasing power plant generation capacity is limited by environmental and economic factors, requiring a careful assessment of locally optimal options. Distributed Generation (DG) installation into the electricity grid is one of the reliable remedial actions to ensure a smooth power delivery. Installation of DG requires an optimization process to identify the appropriate placement and sizing. Inaccurate sizing and placement of the DG installation may result to over-compensation or under-compensation phenomena. This paper proposes a novel approach termed the Integrated Immune Moth Flame Evolutionary Programming technique (IIMFEP) for optimizing the installation of DG sources in distribution systems. It handles various scenarios, including multi-DG single-type and multi-DG multi-type installations, with the main objective of minimizing power loss in the system. This technique employs a hybrid approach that combines elements of immune algorithms, moth flame optimization, and evolutionary programming to achieve more accurate and efficient results. Two cases were considered in this study termed Case 1 and Case 2. Results in Case 1 discovered that the optimal sizing and placement of four Type III DGs exhibit the lowest power loss worth 2.74 kW (98.78 % reduction) for the 69-Bus RDS, while for the 118-Bus RDS the power loss is 319.89 kW (75.36 % reduction). In Case Study 2, the combination of DG Types I and III provided the highest power loss reduction. With one DG Type I and two DG Type III units installed, power loss was reduced by 97.15 % to 6.41 kW for the 69-Bus RDS and by 62.01 % to 493.21 kW for the 118-Bus RDS. The proposed IIMFEP managed to alleviate the setback experienced in the traditional EP, AIS and MFO which found to be stuck at local optimum. The IIMFEP method is compared to Moth Flame Optimization, Artificial Immune System and Evolutionary Programming and validated using the IEEE 69-Bus and 118-Bus Radial Distribution Systems, resulting in outstanding performance.

Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.

Optimal allocation of distributed generation on DC Networks

A consistent increase in the installation of distributed generation (DG) in response to rising energy cost and demand throughout the world, especially for the last two decades, primarily aims to obviate upgrading investments for transmission and distribution lines on the existing power grid while ensuring sustainable, resilient, and reliable service to end users. Random installation of DG, on the other hand, can invalidate the existing grid’s designated objectives, and operational boundaries due to the changes in network topology resulting in unforeseen load flow. Appropriate integration of DG units on DC networks should be remedy to high transmission/distribution losses in lines, instabilities in voltage profiles, issues in power quality parameters, and derogation in the protection schemes. Based on the behavior of load profiles, e.g., 24 h or longer period, operational and technical constraints, this study proposes a framework that minimizes transmission/distribution losses on a DC network, in which discrete variables stand for the location of DGs. Optimal power flow (OPF) problem formulation underpins the proposed optimization framework. Overall, this computationally challenging planning problem refers to a mixed-integer nonlinear program (MINLP) including discrete variables with non-convex power balance/flow representations. To deal with the non-convexities and discrete form of the formulation, a mixed-integer second-order cone programming (MISOCP) relaxation, and branch-and-bound search are assigned. The validation of the introduced approach is carried out on the IEEE modified benchmark systems. With the help of proposed method, optimal allocation of DG units on 9-bus, 14-bus, and 30-bus systems, respectively, lead up to 99.93%, 94.38%, and 79.09% total power losses reduction.

Optimal installation of DG in radial distribution network using arithmetic optimization algorithm

AbstractThe purpose of this work is an arrangement of distributed generation (DG) in radial distribution networks (RDNs) in an adequate way. An efficient method, named arithmetic optimization algorithm (AOA) is used for that purpose. The intention for effective DG posting is to decrease the power loss and upgrade the voltage shape and voltage steadiness magnification in RDNs. The demonstration of the suggested AOA method has been made on 33‐bus, 69‐bus, 85‐bus, and 118‐bus RDNs for optimum orientation and capacity of DGs with distinct power factors (unity, fixed, and optimal). By using this projected technique, the execution of RDNs is improved with respect to voltage stability maximization, voltage fluctuation mitigation, and also reduction of real power loss. The AOA method which is recommended here, grants a better solution than several optimization techniques found in the literature. This recommended AOA technique provides the percentage improvement of power loss for case 1 to case 3 (65.50%, 86.48%, and 94.43%), (69.16%, 90.80%, and 98.10%), (44.57%, 66.61%, and 80.26%), and (60.34%, 88.80%, and 90.31%) for 33, 69, 85, and 118‐bus systems, respectively. The percentage improvement in voltage deviation minimization for case 1 to case 3 are (99.53%, 99.83%, and 99.84%), (99.88%, 99.91%, and 99.92%), and (97.53%, 98.98%, and 99.16%) for 33, 69, and 118‐bus systems, respectively. For different test systems (33, 69, and 118‐bus) improvements in voltage stability index maximization for case 1 to case 3 are (31.33%, 31.80%, and 31.81%), (30.15%, 31.25%, and 31.25%), and (37.78%, 39.23%, and 40.88%), respectively.

Application of Modified Flow Direction Algorithm for Network Reconfiguration with Distributed Generator Installation

Application of Modified Flow Direction Algorithm for Network Reconfiguration with Distributed Generator Installation

PERANCANGAN JARINGAN DISTRIBUSI UNTUK PLTS TERAPUNG DAN PLTMH DI BENDUNGAN SIDAN

Blahkiuh Feeder is a 20 kV radial distribution network spanning 78.65 kms, which is the longest network in the Mengwi Network Unit. The length of the distribution lines also results in relatively high power losses. Therefore, the installation of distributed generation (DG) utilizing the potential of solar photovoltaic (PV) and micro-hydro power plants (MHP) available at the Sidan Dam has the potential to improve voltage drop and power loss values on the Blahkiuh Feeder. This research was conducted to analyze voltage and power losses due to DG installation at the Sidan Dam, with power flow simulation during peak daytime load at 14:00 and peak nighttime load at 19:00. Simulation results indicate that when interconnected with the daytime peak load at 14:00, DG interconnection results in a decrease in power loss values by 0,8% or 64 kW, and interconnection with the nighttime peak load at 19:00 also leads to a decrease in power loss values by 0.61% or 55 kW. Distributed Generation interconnection on the Blahkiuh Feeder also leads to improvements in voltage values, where interconnection during daytime at 14:00 results in voltage improvement at 41 busbars units under undervoltage condition, and interconnection during night time at 19:00 leads to voltage improvement at only 34 busbars units undervoltage condition out of a total of 50 undervoltage busbars.

Coordinated management of electricity storages and distributed generation plant with predictive controllers in a power supply system with reduced electricity quality

THE PURPOSE. A study of the influence of coordinated control of electricity storage devices and distributed generation installations on the operating modes of a network cluster, which is an electrical distribution network connected to the AC railway power supply system through a direct current insert (DCI).METHODS. The studies were carried out using a simulation model implemented in the MATLAB system.RESULTS. The following disturbing influences were considered: turning off the main power supply of the network cluster from the DCI side for 0.5 seconds; three-phase short circuit with a duration of 0.5 s at the end of a 6 kV cable line with a length of two kilometers. The simulation results showed the effectiveness of coordinated control of electricity storage devices and a turbogenerator with self-adjusting predictive controllers. On this basis, the values of overshoot of the rotor speed are reduced and the transient process time is reduced for all parameters in various modes. For example, the transient time for the generator rotor speed in the mode of connecting an additional load is reduced by three times, and for voltage - by two times.CONCLUSION. Based on computer modeling, it is shown that coordinated control of electricity storage devices and a turbogenerator with predictive regulators reduces the amount of rotor speed overshoot to almost zero and reduces the transient process time by almost 90%. It is advisable to conduct further research on the application of the method of coordinated control of active elements used in the power supply system to regulate the mode parameters.

Optimal PV Distributed Generators Allocation using Metaheuristic Algorithm to Enhance Voltage Profile

In this paper, the authors propose a methodology to identify the key locations to install integration of Solar & windbased distributed generators((DG) and the optimal amount of CapacityDGs required to maintain a steady voltage profile and voltage stability. The optimal amount of DG required is obtained via Gorilla troops optimizer(GTO)Metaheuristic algorithm and ,the locations where Integration of DG installation results in maximum benefit are obtained through the voltage stability index. The methodology tested in the Indian28 bus system shows that the proposed technique effectively identifies the critical locations and optimizes the required Integration DG capacity.

Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Radial Power Distribution Networks (PDNs) often suffer from limited reliability, flexibility, and efficiency, leading to service interruptions. Planning for radial PDNs is essential to enhance redundancy resilience, reduce disruptions, and improve overall efficiency. However, traditional PDN planning methods have become obsolete due to the proliferation of Distributed Generation (DG) resources and energy storage systems. Additionally, the rise of Electric Vehicles (EVs) demands sophisticated charging infrastructure planning. This article presents a Mixed-Integer Linear Programming (MILP) model for joint expansion planning of PDN and Electric Vehicle Charging Stations (EVCSs). The model takes into account the construction or reinforcement of substations and circuits, along with the integration of EVs, the installation of DGs, and the placement of capacitor banks, all regarded as traditional conventional expansion options alternatives. To address uncertainties associated with DG generation, conventional loads, and EV demand, our model identifies optimal installation and asset locations. We formulate this as a stochastic scenario-based program with chance constraints for Power Distribution Network Expansion Planning (PDNEP), minimizing investment, operational, and energy loss cost costs over a planning horizon. Through two deterministic and stochastic approaches, encompassing six case studies on an 18-node test system, we evaluate the effectiveness of our model. Results are further validated on a 54-node system, confirming the model’s robustness. Notably, the numerical findings underscore the substantial cost reduction achieved by including EVCSs in the stochastic expansion planning approach, demonstrating its cost-effectiveness. In case study I, where all EVs charge at home during peak hours, it’s the worst case for the PDN. The 54-node system, more complex, demands longer computational time. In the 18-node system, costs improve from 9.97% (case study II) to 3.96% (case study VI) versus the worst-case (case I). In the 54-node system, improvements range from 10.47% (case study II) to 1.40% (case study VI). As a result, In comparative analyses against deterministic and stochastic approaches, our model consistently outperforms in diverse test case studies. The proposed model’s adaptability to address uncertainties underscores its suitability for solving the PDNEP problem in PND.

Multiyear stochastic wind generation investment planning with demand response in distribution system using improved water evaporation optimization

ABSTRACT This article proposes a multiyear distributed generation (DG) investment planning model with the coordination of demand response for making the decision of investment under uncertainty. The aim is to reduce the net present value related to the energy purchasing cost from the grid, investment cost including operation, and maintenance cost, emission penalty cost and energy losses cost. In this article, two investment planning models are developed with a total planning horizon of 20 years: (i) static investment planning model and (ii) dynamic investment planning model. In the static investment, DG is installed in first year, whereas for dynamic investment model, time period of DG installation is considered flexible. The impact of real-time demand response is also analyzed for both cases. The optimal distribution network investment planning problem, which includes multiple DG and demand response, is a complicated combinatorial optimization problem that traditional optimization methods are unable to tackle efficiently. Therefore, an improved water evaporation optimization algorithm is proposed and compared with the genetic algorithm, basic water evaporation algorithm, and particle swarm optimizationin this article. Both DG investment models are applied to the standard 33-bus radial distribution network.

OPTIMIZATION OF DISTRIBUTED GENERATION INSTALLATION IN CILEDUG TASIKMALAYA FEEDER TO REDUCE POWER LOSS WITH GENETIC ALGORITHM (GA) METHOD

Distributed generation (DG) is a small-scale power plant that is generally located close to the load. The installation of DG can be an alternative to reduce power losses in the distribution system. In this study, the placement and capacity optimization of DG to Ciledug feeder is carried out. Optimization is carried out using the genetic algorithm (GA) method which aims to reduce power losses in the system. The simulation is divided into 2 stages. In the first simulation before the installation of DG, the power loss obtained was 755,807 kW, and the voltage profile experienced under voltage from bus 31 to bus 104. The second simulation will be carried out by adding DG to the network, the DG used amounted to 2, 3, and 4 DG units. The location and capacity of DG are searched using the genetic algorithm optimization technique. The optimization results using 2 DGs, showed that the DGs were connected at bus 95 and bus 97 with a capacity of 0.969 MW and 0.480 MW, respectively. The addition of 2 DGs to the network reduces the power loss to 398,899 kW. Next, the optimization results using 3 DGs, showed that DGs were connected at bus 96, bus 78, and bus 67 with a capacity of 0.977 MW, 0.977 MW, and 0.013 MW, respectively. The addition of 3 DGs to the network reduces the power loss to 236,920 kW. Finally, the optimization results using 4 DGs show that DGs are connected at bus 67, bus 80, bus 46, and bus 26 with a capacity of 0.951 MW, 0.799 MW, 0.616 MW, and 0.257 MW, respectively. The addition of 4 DGs to the network reduces the power loss to 141,465 kW.

THE INFLUENCE OF DISTRIBUTED GENERATION INSTALLATION ON THE REDUCTION OF POWER LOSSES IN MEDIUM HIGH VOLTAGE

This research investigates the impact of installing Distributed Generation (DG) on power losses in the feeder. The study uses the Newton-Raphson method in a power flow study to determine bus voltage values and power flow for all DG installation scenarios. The research is conducted on Raya 10 and Kijing Feeders, considering two DG installation scenarios: scenario 1 with 1 DG addition and scenario 2 with 2 DG additions. For Raya 10 feeder, DG installations at buses 91 and 91+92 result in reducing power losses by 35.64% and 55.35% for active and reactive power, respectively. In the Kijing feeder, DG installations at buses 35 and 35+48 lead to reductions of power losses by 40.98% and 67.02% for active power in scenarios 1 and 2, respectively. Reactive power also sees reductions of 41.6% and 68.73% in consecutive scenarios. In conclusion, DG installation in medium voltage networks (MVN) has a significantly positive impact on the voltage profile and power losses. More DG units in a feeder improve the voltage quality and reduce power losses. These findings highlight the potential of DG integration in enhancing the efficiency and sustainability of electrical power systems.

Sharing the grid: The key to equitable access for small-scale energy generation

Small-scale distributed generation (DG) installations, like rooftop solar photovoltaics, are adopted earlier in high-income households than in other portions of society. This can limit grid electricity export capacity for latecomers, causing inequitable access and slower renewables deployment. In search for an equitable solution, this work challenges current DG permissioning practices: instead of applying rules of thumb to assign DG, we propose a policy that represents grid limitations at all hierarchies of the grid (i.e., customer, distribution, sub-transmission and transmission levels). A method to predict interest in household DG installations over time based on demographic and socioeconomic variables is presented and validated. This is leveraged to test a novel policy for small-scale DG deployment: sharing the grid; and to explore its potential to improve equity in grid access and speed of renewables deployment. Proposed, current and alternative policy paths on small-scale DG adoption are tested to identify benefits and trade-offs. Using Ireland’s national utility as case study, we perform a long-term simulation of solar photovoltaic adoption. Sharing electricity export capacities increases participation from 77.9 % to 100 %, unlocking access to the grid for 364,064 electricity customers compared to current policy—without the need for infrastructure upgrades. Furthermore, the proposed policy achieves the same overall level of installed capacity as current policy 44 % faster, accelerating small-scale DG rollout, benefiting households and decarbonisation targets. Ultimately, to determine equitability, we connect these results to demographic variables and find that grid limits are not biased towards any demographic group studied, suggesting that policy can be tailored to prevent DG access inequity.

Strategic incorporation of DSTATCOM and distributed generations in balanced and unbalanced radial power distribution networks considering time varying loads

The article focuses on the optimal planning of distribution static synchronous compensator (DSTATCOM) and distributed generation (DG) to minimize power losses, and simultaneously enhance the voltage profile, and voltage stability at various nodes of the Radial Power Distribution Networks (RPDN). The study involves combinational choices in the use of the DSTATCOM and DG and it relies on heuristic algorithms namely the Enhanced artificial bee colony algorithm (EABC) which has been formulated based on a modification in the searching strategy of the traditional artificial bee colony algorithm (ABC) by integrating the particle swarm optimization (PSO). The proposed optimization model considers the multiobjective function, including the power loss reduction, branch current capacity limit violation, and node voltage deviation. In the proposed approach, the optimal rating of DG and DSTATCOM is estimated under various load steps by using the EABC algorithm. Two different test cases namely base case, and combined installation of DG and DSTATCOM units were considered to analyze the distribution network performance at different load factors. The power loss minimization, voltage profile, and voltage stability improvement of the network before and after the allocation of DG unit and DSTATCOM is depicted. The study examines the effectiveness of the proposed method through a simulation run on 33-node balanced and IEEE 13-node unbalanced RPDN under time varying load demand. Simulation results reveal that the combined installation of DG unit along with DSTATCOM at a suitable location in 33-node and 13-node distribution networks can minimize the power loss by 62% and 43% respectively under the time varying load demand considered along with significant improvement in voltage profile and voltage stability of the network.

Incorporating direct load control demand response into active distribution system planning

Nowadays, moving toward new technologies and smart grids is a successful way of overcoming distribution system challenges. Smart grids facilitate demand-side management (DSM) which affects the expansion of network resources. In this paper, distribution system planning (DSP) considering demand response (DR), as an effective DSM tool, is presented. The proposed model is based on direct load control (DLC) DR in which a utility offers financial incentives to the customers to control their consumption through installing smart meters and switches. The utility deploys the DR program to reduce network daily peak load, which is formulated as a linear programming model, where uncertainties associated with the DR program are captured by the spherical simplex unscented transformation method. Although the DR is implemented on the basis of hourly data, integrating its high-resolution results into the DSP can cause intractability. To mitigate this issue, scenario definition and scenario reduction models using the K-means clustering algorithm are proposed. Besides, feeder reinforcement as well as renewable and conventional distributed generation (DG) installation are considered as the other network expansion alternatives. The proposed method is applied to the modified IEEE 33 Bus test system and effects of the DR program and DG installation on DSP are investigated. The results reveals that the implementation of the DLC program in the DSP either reduces or postpones feeder reinforcement actions thereby resulting in lower overall DSP costs. Furthermore, DG installation can significantly reduce operating costs, particularly purchased energy from the main grid and energy loss. Eventually, the most cost-efficient solution is obtained by incorporating both DLC and DG into the DSP.

Simultaneous network reconfiguration and DG allocation in radial distribution networks using arithmetic optimization algorithm

AbstractThis article introduces a newly suggested meta‐heuristic arithmetic optimization algorithm (AOA) to reorganize the distribution network with the simultaneous distribution of distributed generators (DGs). The algorithm is designed to be flexible with a new approach that employs the scattering behavior of arithmetic operators in mathematics to discover the solution space more proficiently throughout the path of iterations. The usefulness of the AOA is demonstrated on 33‐bus, and 69‐bus radial distribution networks (RDNs) with network reconfiguration (NR) in the presence of DGs. The main objective of NR and effective DG arrangement in RDNs is to truncate the true power loss, reduce the voltage fluctuation, and voltage stability index enlargement. In order to increase the efficacy of the RDN, a multi‐objective function is created in view of active power loss, voltage deviation, and voltage stability index of the system with appropriate strategy without destroying the system restrictions. To illustrate the effectiveness of the RDN, four separate cases of NR and DG employment have been carried out. Four cases namely, base case (case‐1), reconfiguration (case‐2), simultaneous reconfiguration and DG installation with a unity power factor (case‐3), and simultaneous reconfiguration and DG installation with 0.95 lagging power factor (case‐4) are explored. The projected technique gives better results than other renowned methods, namely, fireworks algorithm (FWA), harmony search algorithm (HSA), refined genetic algorithm (RGA), and improved sine‐cosine algorithm (ISCA) strategy in view of 33, 69‐bus RDN at three dissimilar load conditions and its supremacy is confirmed.

Optimal Placement and Sizing of Renewable and Non-Renewable Resources in Smart Grid

Distributed Generators (DGs) play a key role in existing power distribution networks together with significant innovations in smart grid technology. It is now more essential to evaluate the various types of DGs within the system. Renewable energy types of DGs such as PV and wind promise low emissions and abundant availability. When it comes to the installation of DG, the size, location, and type of DG should be given high importance because improper placement of DG units leads to reducing the benefits of the distribution system and even endangers the entire system operation. If DG size exceeds a certain value limit, power loss at that bus becomes negative. This situation must be avoided. As a result, optimal DG placement aids in the reduction of losses, improvement of voltage profiles, reliability, and overall system efficiency. Considering this, in this paper, a Simultaneous Particle Swarm Optimization (PSO) algorithm is implemented for placement allocation and sizing of multiple types and multiple numbers of DGs in power distribution systems with the objectives of minimization of active and reactive power loss and enhanced voltage profile. Along with the meta-heuristic optimization algorithm, sensitivity techniques such as index vector, loss sensitivity factor, and voltage stability margin methods have been analysed. The outcomes are obtained using the aforementioned sensitivity-related methods on the IEEE 15, 33, 69, and 85-radial bus systems and compared with simultaneous PSO for efficacy.

Анализ влияния объектов распределенной генерации на системы защиты и режим напряжения: обзор

In recent decades, in the process of restructuring energy systems, centralized energy sources are being replaced by decentralized ones. This led to the emergence of a new concept (power generation) in power supply systems, especially in distribution systems, known as «distributed generation». Distributed generation was introduced into the power systems, especially at the low voltage level, to make them more reliable, safer and efficient. At the same time, various problems appeared in the power supply systems that require solving issues of ensuring the selectivity and sensitivity of the protection action, maintaining the required voltage level. Since the current and future trends in the development of electric power systems are aimed at increasing the integration of distributed generation, especially through installations, made based on renewable energy sources, it is required to conduct studies on the impact of these generation technologies on the distribution networks. When using distributed generation, setting up the protection system is associated with the problems associated with changes in the magnitude and direction of emergency currents. Distributed generation installations affect the fault current in a certain way, while influencing the overcurrent protection systems in terms of their coordination. In this regard, the alternative protection systems and algorithms can be used, such as directional overcurrent relays. In addition, highly penetrating distributed generation devices such as photovoltaic installations can cause overvoltage in the distribution systems, which must be effectively prevented. Thus, the paper considers the problems caused by the distributed generation and related to protection systems and voltage regulation in the distribution networks. Solutions for mitigating the negative impact of distributed generation on the power supply systems are proposed and considered in detail.

Optimal placement and sizing of photovoltaic power plants in power grid considering multi-objective optimization using evolutionary algorithms

During the past decade, the effect of renewable and non-renewable Distributed Generation (DG) sources of production has grown all over the world. Also, it has enhanced by national and international policies aimed at increasing the share of renewable energy sources and combining small high efficient heat and power plants to reduce greenhouse gas emissions, and global warming have been encouraged. Although the installation and operation of DGs have discussed for solving network problems in distribution networks, the fact is that in most cases, Distribution System Operator has no control or influence over DG placement and size. In this article, a meta-heuristic algorithm for management and decision-making for optimal selection is presented, and in choosing the optimal solution, the impact factor is suggested in the best case. Inappropriate DG placement may increase system losses, network investment, and operating costs. This paper determined the optimal capacity and placement of photovoltaic sources to reduce losses, improve the voltage profile, and increase the active power to reactive power lines in MATLAB software by the second version of the genetically engineered algorithm with unstable Non-dominated Sorting Genetic Algorithm (NSGA-II).