Distributed Generation In Distribution System Research Articles

Effect of DGs on Power Quality of Distribution System: An Analytical Review

Abstract This article offers an overview of distributed generation (DG) in distribution systems (DS). The primary goal of this study is to assess the performance of DGs in DS. Due to the rise in electrical energy consumption, it is anticipated that DG sources would be essential to DS. Future power generating networks have a bright outlook on consideration of DG’s potential for utilising alternative energy sources. The quality of power systems is a crucial concern for energy providers and consumers. In order to decrease reliance on fossil fuels for the production of electricity, distributed generations are gaining importance in the energy supply networks in many countries. Distributed generators are small units that generate electricity close to customer sites. These DGs use renewable energy methods such as wind energy, solar energy and geothermal energy. The incorporation of DGs into a conventional power supply system evolves in a number of side effects, including an increase in the number of short circuits, higher power losses, a decrease in the quality of the energy produced, voltage transients, problems with voltage stability, coordination issues regarding voltage regulation and protection, the possibility that system protection will not function correctly, and the fact that there is less residual current input as a result of the DG bidirectional power flows. This review paper discusses the impacts of the penetration of DG into DS and provides various strategies to mitigate these effects.

Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization

Objective: This article presents an analysis of different optimization methodologies, which aims to make an objective comparison between metaheuristic and convex optimization methods in distribution networks, focusing on the inclusion of distributed generation (DG). The MATLAB software is used as a tool for implementation and obtaining results. The objective was to determine the optimal size of the DGs to be integrated into the networks, with the purpose of reducing the active power losses (objective function). Methodology: Based on the specialized literature, the methodologies are selected, and the bases and conditions for the implementation of the optimization techniques are determined. In the case of second-order cone programming (SOCP), the relaxation of the nonlinear optimal power flow (OPF) problem is performed in order to use convex optimization. Then, the structures of each technique are established and applied in the MATLAB software. Due to the iterative nature of metaheuristic methods, the data corresponding to 100 compilations for each algorithm are collected. Finally, by means of a statistical analysis, the optimal solutions for the objective function in each methodology are determined, and, with these results, the different methods applied to the networks are compared. Results: By analyzing 33- and 69-node systems, it is demonstrated that metaheuristic methods are able to effectively size DGs in distribution systems and yield good results that are similar and comparable to SOCP regarding the OPF problem. Genetic algorithms (GA) showed the best results for the studied implementation, even surpassing the SOCP. Conclusions: Metaheuristic methods proved to be algorithms with a high computational efficiency and are suitable for real-time applications if implemented in distribution systems with well-defined conditions. These techniques provide innovative ideas because they are not rigid algorithms, which makes them very versatile methods that can be adapted to any combinatorial optimization problem and software, yielding results even at the convex optimization level.

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.

Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System under a 24 h Load Horizon

Load variations in any power system result in loss escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in existing methods that, ideally, the allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and the allocation of DGs must be done in accordance with the variations of the connected load. Therefore, the current paper addresses the aforementioned problem by the distinctive optimal allocation of DGs for each variability of 24 h load horizon. However, a single exclusive solution is considered among all allocations of 24 h. The min-max regret concept has been utilized in order to deal with such a methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royale optimization (BRO), accelerated particle swarm optimization (APSO), and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises active and reactive power losses and voltage deviation. Hence, in order to find the robustness of the proposed technique, Conseil international des grands reseaux electriques’ (CIGRE) MV benchmark model incorporating 14 buses has been used considerably as a test network. In the end, the results of three proposed algorithms have been compared.

Locating and sizing of capacitor banks and multiple DGs in distribution system to improve reliability indexes and reduce loss using ABC algorithm

DG sources have been introduced as one of the most widely used and effective methods among various methods providing losses reduction in power systems. In this paper, the artificial bee colony algorithm has been employed with the aim of determining location and capacity of distributed generations (DGs) and capacitor banks (CBs) in distribution systems. The proposed objective function includes power losses and ENS reliability index, which is used by deploying weight coefficients as objective function in the algorithms. Accordingly, the standard 37-bus networks have been used for studies. The simulation results demonstrate that the artificial bee colony algorithm is more effective in all sections and has higher capability in reducing losses and improving reliability as well.

Impact analysis of demand response on optimal allocation of wind and solar based distributed generations in distribution system

ABSTRACT The optimal integration of renewable-based distributed generations (DGs) in distribution system offers several techno-economic and environmental benefits. The demand response (DR) can further augment the benefits associated with DGs. This paper investigates the impact of DR on joint allocation of wind-based and solar-based DGs in distribution system. The integration of renewable-based DGs in coordination with the DR becomes a multi-constraints complex optimization problem. Therefore, in this paper a new methodology is proposed for optimal accommodation of renewable-based DGs in coordination with DR. The optimization problem is formulated as mixed-integer second-order cone programming (MISOCP) problem to minimize energy loss. The solvers which can solve MISOCP problem are commercially available and the optimal solution obtained using such solvers is guaranteed to be global. The proposed framework is investigated on standard 33-bus distribution system under different case studies to evaluate the impact of integrating DR on the allocation of DGs.

Probabilistic generation model for optimal allocation of wind DG in distribution systems with time varying load models

Renewable energy-based Distributed Generation (DG) is the most imperative part of modern power system and offers many potential benefits. To attain maximum benefits offered by DG integration, it is important to model the time varying characteristics of both load and generation. Therefore, this paper presents a new Weibull distribution-based time-coupled Probabilistic Generation model for optimal placement and sizing of wind DG with time varying voltage dependent (TVVD) loads. At first, Probabilistic model is proposed for wind speed uncertainty modeling to calculate the hourly output power from wind DG. Afterwards, the values of output power are considered for determining optimal allocation and penetration of wind DG in distribution network to minimize the Average Multi-Objective Index (AIMO) using Particle Swarm Optimization (PSO). The strength of the proposed methodology is validated on IEEE 33 and 69-bus systems. Results depict that, proposed methodology is appropriate for wind speed modeling and is suitable for implementing in power system planning. • Time-varying characteristics of load and generation are assumed for DG allocation. • Weibull distribution-based PG model is proposed to determine hourly wind DG output. • Hourly output power values are considered for the optimal allocation of DG. • The penetration level of wind DG is calculated for different TVVD load models.

Temperature impact assessment on multi-objective DGs and SCBs placement in distorted radial distribution systems

In smart grid distribution systems, the applications of Shunt Capacitor Banks (SCBs) and Distributed Generations (DGs) are widely known and installed for system loss reduction, voltage stability improvement of system buses, and to free up the system loading. However, the installation of SCBs and DGs in distribution systems with distorted waveforms will increase the harmonic distortion level if they are not placed at appropriate locations. In this paper, a Multiobjective Grey Wolf Optimizer (MOGWO) is proposed to find the optimal size and locations of SCBs and DGs simultaneously with regard to the impact of ambient temperature on distorted distribution systems. Electric utilities will be able to reduce system active losses, enhance the voltage stability for system buses, release the section loading, and reduce the total harmonic distortion level. Fuzzy set theory is utilized to choose the optimum compromise solution from the Pareto front solutions of the MOGWO method. The proposed method is applied to IEEE 69-bus and real data taken from the Saudi Electricity Company (SEC). The results show a higher capability in finding optimum solutions for SCBs and DGs placement in distorted Radial Distribution Systems (RDSs) compared to conventional methods.

Multi‐objective optimal planning of FCSs and DGs in distribution system with future EV load enhancement

Current trends suggest that electrical vehicle (EV) is a promising technology for road transportation. There is a substantial increase in the number of EVs due to improved energy efficiency and reduction in environmental impact as compared with internal combustion engine vehicles. The improper planning of fast charging stations (FCSs) and distributed generations (DGs) hurts the distribution system. So the distribution system operator has a significant challenge to identify the optimal location and sizing of FCSs in the distribution power network. This study presents optimal planning of FCSs and DGs with the account of the present and future increase in EV population. A multi-objective optimisation problem is formulated for optimal planning of FCSs and DGs with the objective of minimising the voltage deviation, distribution network power loss, DGs cost and the energy consumption of EV users. This problem is solved for different levels of increase in EV population for different cases. A novel hybrid shuffled frog leap-teaching and learning based optimisation algorithm is proposed and implemented to solve the considered multi-objective problem. The performance of the proposed algorithm is compared with prior-art algorithms in the literature.

Multi-objective optimal sizing of distributed generation by application of Taguchi desirability function analysis

Distributed Generation (DG) is modular power generating technology. DG has beneficial impact on tail end voltages, line losses and operating cost. The sizing and location of DG in distribution system is a vital undertaking. In this paper Taguchi Desirability Function Analysis (TDFA) is presented. The remarkable highlight of TDFA is the skillful handling of simultaneous optimization of nearly thousand objectives and its ability to generate multiple optimal solutions. It facilitates the effortless addition of each objective. TDFA is used for finding the optimal size of single as well as multiple DG. Size estimation is carried out for multiple objectives of minimizing the line losses, improving the voltage profile and improving voltage stability index (VSI). The objectives may be minimized, maximized or assigned target values simultaneously. In the scope of this paper more than nine objectives have been optimized simultaneously. TDFA has been implemented to determine optimal size of DG for different load conditions. The proposed approach is tested and verified on IEEE 33-bus and IEEE 85- bus radial distribution system (RDS).

Optimal location-allocation of storage devices and renewable-based DG in distribution systems

This paper proposes a mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems. The proposed planning framework focuses on the optimal siting and sizing of wind turbines, photovoltaic panels, gas turbines, and energy storage devices (ESD). Inherently, renewable energy sources and electricity demands are subject to uncertainty. To handle such probabilistic situations in decision-making, the MICP model is extended into a two-stage stochastic programming model. To obtain more practical results, annual historical data are used to generate the scenarios. For the sake of tractability, the k-means clustering technique is used to reduce the number of scenarios while keeping the correlation between the uncertain data. Due to convexity, the proposed MICP model guarantees to find the global optimal solution. To show the potential and performance of the proposed model a 69-bus radial distribution system under different conditions is dully studied and a sensitivity analysis is conducted. Results and comparisons approve its effectiveness and usefulness.

Multi-objective simultaneous optimal planning of electrical vehicle fast charging stations and DGs in distribution system

The large-scale construction of fast charging stations (FCSs) for electrical vehicles (EVs) is helpful in promoting the EV. It creates a significant challenge for the distribution system operator to determine the optimal planning, especially the siting and sizing of FCSs in the electrical distribution system. Inappropriate planning of fast EV charging stations (EVCSs) cause a negative impact on the distribution system. This paper presented a multi-objective optimization problem to obtain the simultaneous placement and sizing of FCSs and distributed generations (DGs) with the constraints such as the number of EVs in all zones and possible number of FCSs based on the road and electrical network in the proposed system. The problem is formulated as a mixed integer non-linear problem (MINLP) to optimize the loss of EV user, network power loss (NPL), FCS development cost and improve the voltage profile of the electrical distribution system. Non-dominated sorting genetic algorithm II (NSGA-II) is used for solving the MINLP. The performance of the proposed technique is evaluated by the 118-bus electrical distribution system.

Multi-Objective Jaya Algorithm for Optimal Scheduling of DGs in Distribution System Sectionalized into Multi-Microgrids

ABSTRACTIn this paper, the power dispatch problem of distributed generators (DGs) for optimal operation of the islanded distribution system has been addressed. In order to mitigate the number of customers being affected by a fault at any remote location of the system, the islanded distribution system is sectionalized into several self-supplied microgrids such that reliable power can be supplied to maximum loads. These microgrids can be operated individually or joining with other microgrids such that rescheduling of DGs can be performed for attaining desired objective. The objectives considered in this paper are minimization of operating cost of DGs, system losses, voltage deviation individually. Furthermore, a multi-objective problem is formulated considering minimization of operating cost & losses, operating cost & voltage deviation and losses & voltage deviation simultaneously. While scheduling the distribution system, dispatchable DGs are used. A novel, algorithm-specific parameter-free meta-heuristic algorithm named Jaya algorithm is used in this paper for solving the stated optimization problem. The feasibility of the proposed technique is verified with the modified 33 bus distribution network and obtained results have been validated by comparison with prior-art algorithm available in the literature.

Protection Coordination of Properly Sized and Placed Distributed Generations–Methods, Applications and Future Scope

The radial distribution networks are designed for unidirectional power flows and are passive in nature. However, with the penetration of Distributed Generation (DG), the power flow becomes bidirectional and the network becomes active. The integration of DGs into distribution network creates many issues with: system stability, protection coordination, power quality, islanding, proper placement and sizing etc. Among these issues, the two most significant are optimal sizing and placement of DGs and their protection coordination in utility network. The proper coordination of relays with high penetration of DGs placed at optimal location increases the availability and reliability of the network during abnormal operating conditions. This research addresses most of the available methods for efficient sizing and placement of DGs in distribution system (numerical, analytical and heuristic) as well as the developed protection coordination techniques for utility networks in the presence of DGs (Artificial Intelligence (AI), adaptive and non-adaptive, multi-agent, hybrid). This paper indicates the possible research gaps and highlights the applications possibilities and methods’ limitations in the area of DGs.

A hybrid teaching–learning-based optimization technique for optimal DG sizing and placement in radial distribution systems

Distributed generation (DG) technology has proved to be an efficient and economical way of generation of power. DGs are intended to generate power near the load centers. Optimal allocation of DG resources enhances the overall performance of distribution systems. This paper presents a hybrid teaching–learning-based optimization (HTLBO) technique for the optimal allocation of DGs in distribution systems. The proposed technique is proficient in handling continuous as well as discrete variables and has the capability to escape strong local minima/maxima trappings. The validity and effectiveness of HTLBO are tested on well-defined standard mathematical benchmark functions. The proposed method is further implemented for optimal allocation of DGs in the IEEE 33-bus, 69-bus and 118-bus radial distribution test systems for minimization of power losses, voltage deviation and maximization of voltage stability index. The multi-objective function for DG allocation uses the ɛ-constraints approach. The obtained results reveal improved convergence characteristics over both teaching–learning-based optimization and quasi-oppositional teaching–learning-based optimization.

Impact assessment of DG in distribution systems from minimization of total real power loss viewpoint by using optimal power flow algorithms

This paper presents the enhancement and impacts of the system losses, voltage profile and cost by using distributed generations (DGs) of different size in distribution network. Distributed Generation (DGs) is expected to play a key role in the residential, commercial and industrial sectors of the power system. Distributed Generation (DG) provides an alternative to the traditional electricity sources and can also be used to enhance the current electrical system. DG produces a series of important influence on the distribution network. It mainly introduces four effect areas, namely, distribution network real power loss, voltage, distribution grid planning and relay protection. In this analysis, load flow method is used for optimization of objective function for minimization of total real power loss and reactive power loss of the system. The work is focused on analyzing the impact of DG installation on distribution network operation including system voltage profile analysis, real and reactive power losses and cost of the system. First, various sizes of DG penetration levels and the impact of distributing the DG across the bus where load is maximum are explored. Secondly, the real power losses of the system are analyzed. Thirdly, the voltage profile and cost of the system are analyzed. The proposed methodology has been tested for IEEE-14 bus distribution test system. This work is also helpful for practitioner whose are working in the fields of renewable energy sources.

Fault Current Limiter Placement to Reduce Recloser ‑ Fuse Miscoordination in Electric Distribution Systems with Distributed Generation using Multiobjective Particle Swarm Optimization

This paper presents a method to minimize protective devices in electric distribution systems with Distributed Generation (DG). With the growing penetration of DG in distribution systems, the main practice to maintain protective devices coordination during a fault is to disconnect all DG sources. This practice nullifies the benefits brought by DG in distribution systems, mainly because the majority of faults are proven to be temporary. To solve this problem, this paper proposes the allocation of fault current limiters (FCL), which consist in inert devices on system's normal operation, but insert a high impedance value in series with feeders when a fault occurs, thus, limiting is magnitude. FCL placement is treated as a multiobjective optimization problem, aiming to reduce fault currents difference between situations with DG and without DG penetration. Also, FCL size must be minimized, aiming to reduce costs. The search for the optimal solution is made using multiobjective particle swarm optimization and the method is tested on the Canadian Benchmark Test System, showing great efficiency.

A Review on Optimal Allocation and Sizing Techniques for DG in Distribution Systems

Distributed Generation (DG) offers the reliable and economic source of electricity to consumers. These are connected directly to the distribution system at consumer load points. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. However, Optimal DG (ODG) allocation and sizing is always a challenging task for utilities as well as consumers. The major objective of ODG allocation and sizing is to improve system overall efficiency with minimum power loss, maximum system security, voltage stability, and reliability. Analytical techniques are performing well for small and simple systems, not suitable for a system with large and complex networks. However, various meta-heuristic techniques are performing better in terms of accuracy and convergence for extensively large and complex networks. A hybrid optimization is a combination of two or more optimization techniques. This technique offers efficient and reliable global optimum solutions for complex multi-objective problems. In this context, a comprehensive literature review of DG fundamentals and the different technical approaches for DG integration into the distribution system are analyzed here. Furthermore, an attempt has been made for comparison of analytical, classical (non-heuristic), meta-heuristic and hybrid optimization techniques with respect to objective function, test system, advantages, and disadvantages. This present study will give in-depth knowledge and acts as a forthright reference for imminent investigators and investors for ODG allocation and sizing in a distribution system.Â

Probablistic generation model for optimal allocation of PV DG in distribution system with time-varying load models

Distributed generation (DG) planning studies are generally conducted while assuming constant generation and load models. However, in this paper, optimal allocation of photovoltaic (PV) based DG is carried out while considering probabilistic generation and time varying voltage dependent load models. First, a novel probabilistic generation model is proposed for solar irradiance uncertainty modeling to compute the hourly output power produced via PV units. Subsequently, the obtained power values are used to determine the optimum PV allocation in the distribution system through particle swarm optimization to minimize the multi-objective function. The proposed algorithm has been validated on 33-bus and 69-bus distribution test systems. The results demonstrate that the proposed model is suitable for solar irradiance modeling and can be employed in power system planning studies.

Switching strategy for DG optimal allocation during repairing fault periods on loop distribution networks

Summary It is expected that the use of distributed generation (DG) will increase sharply in distribution systems, especially with the realization of smart grids. In spite of the benefits of DG in distribution systems, high penetration level of DGs could cause some challenges for the network. Therefore, the optimal sizing and siting of DGs should be identified carefully. In addition, different operating conditions have to be taken into account in this process. The DGs are usually installed depending on optimal siting and sizing techniques considering normal operating periods. During repairing fault periods, different locations and capacities of DGs may be preferable. In this paper, 2 scenarios of switching strategies are presented for attaining the optimal sizing and siting of DGs during repairing fault period starting from the optimal allocation obtained for normal operating period. Genetic algorithm is used for solving the optimization problem that is implemented for an actual loop distribution system in Egypt.