Location Of Distributed Generation Research Articles

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

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

An optimization approach for optimal location & size of DSTATCOM and DG

This manuscript proposes a novel method to decide the optimum location and size of Distribution static compensator (DSTATCOM) and Distribution Generation (DG) are examined. For lessening the loss of power, voltage profile improvement and operation costs of system, the objective function is used under the constraints of equality and inequality. The aim of proposed method is diagnosis the dynamic issues present in the power system under various environments, like healthy conditions and unhealthy conditions. The primary location of both Distribution Generation and Distribution static compensator are detected by using the loss sensitivity factor (LSF). The final optimum location is determined by using Dwarf Mongoose Optimization (DMO) method. The DMO is used to decide the optimum size of Distribution Generation and Distribution static compensator. The aim of the proposed method is an integrated approach of loss sensitivity factor and Dwarf Mongoose Optimization to decide the optimum location and size of Distribution Generation and DSTATCOM for diminishing the loss of power, voltage profile improvement and operation cost. The Distribution Generation and DSTATCOM are simultaneously placed in radial DG. The LSF is used earlier to detect the optimum location of Distribution Generation and Distribution static compensator. The proposed system is performed in MATLAB or Simulink site and the performance of dynamic stability is tested with IEEE standard bench mark systems.

Location and size selection using hybrid Jaya‐Luus‐Jaakola algorithm for decentralized generations in distribution system considering demand‐side management

AbstractIn the distribution system, decentralized generation is incorporated to maximize the system reliability and economic stability, and to avoid redundancy in system. The efficient implementation of distributed‐generations (DGs) in a distribution‐system (DS) can be done by employing demand‐side management (DSM). In this research proposal, location and size selection of DGs in a DS considering DSM is accomplished using a hybrid Jaya‐Luus‐Jaakola (JLJ) algorithm. The hybridization of Jaya algorithm (JA) with Luus‐Jaakola algorithm (LJA) is done with a view to improving the rate of convergence of the algorithm. Three design objectives of minimization of active power loss and voltage deviation along with maximization of voltage stability index are considered in this research proposal. These design objectives under consideration are integrated together to form a problem of a single objective by assignments of weights. The formulated problem is minimized using proposed hybrid algorithm subjected to system constraints such as balance of power, limits on bus voltage and line capacity, and generation level of DGs. A static penalty is added to the formulated problem in case of any constraint violations. A photo‐voltaic‐based DG is used in the 33‐bus standard radial‐distribution‐system (RDS). A DSM scenario is created by shifting some loads of peak hours to off‐peak hours in view of minimizing the total cost of generation. The potency and superiority of the proposed algorithm is validated with the help of comparison of the results obtained by exploiting proposed method with results acquired from available state‐of‐art algorithms in literature. The comparison proves the comparative superior performance of the proposed algorithm.

Effects of Integration of Multi Type DGs on Voltage stability and Loss Minimization in the Distribution System

In recent days in distribution system the more importance is giving to Loss minimization techniques since electricity demand is continuously increasing and utility companies are struggling to meet this rapid increase in load due constraints on energy resource to increase the generation. Furthermore, voltage instability phenomena is also playing important role in distribution systems and caused a major blackout in the network. To solve the above problem new technology that is drawing more attention is Distributed Generation (DG). Placing DG in the distribution system inherently reduces the power loss and improves the system voltage. The selection of type of DG, proper placement and sizing of DG plays an important role. This paper presents an effective methodology to identify the optimum location of multi type DG in the distribution system in order to reduce the losses and improve the voltage. In this work the IEEE 33-Bus system is simulated in Power World Simulator (PWS) and the voltage stability and system losses are analyzed with different types of DGs and combination of different types of DGs are studied. Simulation result shows that optimal placement and sizing of DG will reduce the system losses and improve the voltage profile within the acceptable limits and the loss reduction and voltage profile improvement are depending on the types of DGs and combination of DGs placed in the distribution system. Index Terms: Distributed generation, Optimum size, Optimum location, Power loss, Power world simulator, Sensitivity analysis, Types of DG, Voltage stability.

Placement and Sizing of Distributed Generations and Shunt Capacitors in Radial Distribution Systems Using Hybrid Optimization Technique

This article presents a hybrid method based on analytical and metaheuristic optimization techniques for the optimal placement of distributed generations (DGs) and shunt capacitors (SCs) into radial distribution systems (RDSs) to minimize power loss. In the hybrid method, a meta-heuristic called the Salp Swarm Algorithm is utilized to determine the optimal locations of DGs and SCs, while an efficient analytical technique is applied to calculate the optimal sizes. To check the robustness of the proposed method, two standard IEEE systems are employed; IEEE 33-bus and 69-bus, and compared with existing optimization techniques. Also to confirm the ability of the proposed method on improving performance, it applied to an actual 94-bus Portuguese RDS. The obtained results show the feasibility of the proposed method in optimal allocating of the DG and SC into RDS.

Determination of location and capacity of distributed generations with reconfiguration in distribution systems for power quality improvement

The use of non-linear loads and the integration of renewable energy in electricity network can cause power quality problems, especially harmonic distortion. It is a challenge in the operation and design of the radial distribution system. This can happen because harmonics that exceed the limit can cause interference to equipment and systems. This study will discuss the determination of the optimal location and capacity of distributed generation (DG) and network reconfiguration in the radial distribution system to improve the quality of electric power, especially the suppression of harmonic distribution. This study combines the optimal location and capacity of DG and network reconfiguration using the particle swarm optimization method. In addition, this research method is implemented in the distribution system of Bandar Lampung City by considering the effect of using nonlinear loads to improve power quality, especially harmonic distortion. The inverter-based DG type used considers the value of harmonic source when placed. The combination of the proposed methods provides an optimal solution. Increased efficiency in reducing power losses up to 81.17% and %total harmonic distortion voltage (THDv) is below the allowable limit.

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

<span lang="EN-US">Power demand in the current days is increasing more and more where the conventional power generation systems are failing to meet these power demands due to less availability of non-renewable resources. Hence, many of the researchers are working on the distributed generation (DG) by using renewable resources like wind and solar. The penetration towards wind, solar DG faced challenging situations during power generation due to uncertainty in the wind speed and solar radiation. Recent studies have predicted that the combination of both solar and wind can lead to better performance. However, the sizing and placement of DG systems is necessary to achieve efficiency otherwise the systems may lead to adverse effects in distribution networks. This paper introduced the solar DG, wind DG and hybrid (solar and wind) DG system. The particle swarm optimization technique is used to size and place the DG because of its parallel search capability. Also, the combination of wind-solar DG gives better DG sizing in the respective DG location. The voltage profile of these DG systems has shown better results for the efficient power system. In comparison to conventional DG systems, the suggested hybrid DG system is capable of minimizing power loss and maintaining voltage profile.</span>

Optimal DG Location and Sizing to Minimize Losses and Improve Voltage Profile Using Garra Rufa Optimization

Distributed generation (DG) refers to small generating plants that usually develop green energy and are located close to the load buses. Thus, reducing active as well as reactive power losses, enhancing stability and reliability, and many other benefits arise in the case of a suitable selection in terms of the location and the size of the DGs, especially in smart cities. In this work, a new nature-inspired algorithm called Garra Rufa optimization is selected to determine the optimal DG allocation. The new metaheuristic algorithm stimulates the massage fish activity during finding food using MATLAB software. In addition, three indexes which are apparently powered loss compounds and voltage profile, are considered to estimate the effectiveness of the proposed method. To validate the proposed algorithm, the IEEE 30 and 14 bus standard test systems were employed. Moreover, five cases of DGs number are tested for both standards to provide a set of complex cases. The results significantly show the high performance of the proposed method especially in highly complex cases compared to particle swarm optimization (PSO) algorithm and genetic algorithm (GA). The DG allocation, using the proposed method, reduces the active power losses of the IEEE-14 bus system up to 236.7873%, by assuming 5DGs compared to the active power losses without DG. Furthermore, the GRO increases the maximum voltage stability index of the IEEE-30 bus system by 857% in case of the 4DGs, whereas GA rises the reactive power of 5DGs to benefit the IEEE-14 bus system by 195.1%.

A Search Group Algorithm for Optimal Distributed Generation Placement

The integration of renewable-based distributed generation (DG) in distribution networks is increasingly common worldwide due to fossil fuel depletion, environmental pollution, and global warming. This paper proposes a Search Group Algorithm (SGA) method to determine the optimal location, capacity, and quantity of DGs in distribution networks. The SGA method has a good ability to find optimal solutions thanks to the ability to balance between the global search and local search in the optimization process. The objective of the problem is to minimize the power loss of the distribution networks while meeting the constraints of the networks, such as power balance, bus voltage limit, branch current limit, DG power limit, and DG penetration limit. The proposed method is applied to the IEEE 33-node and 69-node distribution networks with two different cases. After integrating DGs into distribution networks, the power loss are reduced by 70.14% and 70.23% for the 33-node and 69-node distribution networks, respectively. The results obtained from the SGA method were compared with other methods in the literature. The simulation results have proved that the SGA method has good performance for the optimal DG placement problem in distribution networks.

Cloud-Edge Collaboration-Based Local Voltage Control for DGs With Privacy Preservation

The increased distributed generators (DGs) have exacerbated voltage violations in active distribution networks (ADNs). Local reactive power control of DG inverters can realize a fast response to frequent voltage fluctuations. However, commonly used model-based voltage control depends upon accurate network parameters and entire ADN data, which may cause the sensitive information leakage of ADN and DG behaviors in practical operation. In this article, a cloud-edge collaboration-based local voltage control strategy for DGs is proposed with privacy preservation. First, a local voltage control framework is established based on cloud-edge collaboration, in which a surrogate model is built based on the graph convolutional neural networks to estimate the ADN voltages. By transferring the surrogate model, the edge side can obtain the exact voltage estimation in the local curve tuning process without the authority of the whole ADN data, preserving the network parameters of ADN. Then, the interarea coordination based on federated learning is proposed to realize the parameter updating of DG control curves, which can achieve better voltage control performance. By updating surrogate submodels based on private data distributed across multiple edge devices, federated learning can effectively preserve DG behaviors. Finally, the effectiveness and adaptability of the proposed control strategy are validated using the modified IEEE 33-node system. The proposed local DG control strategy can effectively cope with voltage problems and enhance the adaptability to variations in practical operation states while considering privacy preservation.

Strategic Integration of DG and ESS by using Hybrid Multi Objective Optimization with Wind Dissemination in Distribution Network

The importance of distributed generation (DG) has increased recently as a result of the growth in commercial and industrial loads, which has put more pressure on conventional energy sources and utilities. Alternative power generation methods that can handle the massive load without endangering the environment are therefore urgently needed. The installation of Energy Storage Systems (ESSs) may give a substantial opportunity to enhance the aesthetic appeal of the distribution system. DG is a practical substitute for conventional energy sources, which have drawbacks for both the economics and the environment. It goes without saying that there are situations in which a large amount of land and money are required. However, improper DG location or sizing increases safety issues because to the increasing power loss caused by larger reverse flow from the load to the supply. In order to reduce power losses and maintain voltage stability, the ideal location and dimensions for the DG and ESS in IEEE Radial Distribution systems are evaluated. To accomplish these objectives, a hybrid Shuffled Frog Leap Algorithm (SFLA) and Improved Firefly Algorithm (SFLA-IFFA) is suggested. The SFLA-IFFA produced better results in terms of the ideal location and DG size when compared to other existing algorithms.

Nodal Electricity Price Based Optimal Size and Location of DGs in Electrical Distribution Networks Using ANT LION Optimization Algorithm

Distribution system has been the weakest link in the entire power system supply chain. It is also one of the most vital parts of the power system. However, a lot of methods have been developed to improve the condition of the distribution system. The use of distributed generations (DGs) is one such method where the generated power is closer to the load center, and the DG is also providing ancillary services to the grid. The nodal electricity price for DGs location is determined based on the Locational Marginal Price (LMP). LMP implies the price to buy and sell power at each node within electrical distribution markets. In the nodal electricity market (EM), the cost of energy is determined by the location of DG to which it is provided. This paper presents a novel approach that utilizes nodal electricity price for optimal sizing and location (OSL) of DGs. A multi-objective ANTLION optimization (MOALO) has been utilized as an optimization approach to compute the OSL of DGs units. ANTLION optimization (ALO) is based on the unique hunting behaviour of antlions. Optimization has been done for social welfare maximization, loss minimization, and voltage profile improvement in distribution networks (DNs). The results of the proposed technique have been evaluated for IEEE 33 bus DNs.

Optimal Location of Electric Vehicles in a Wind Integrated Distribution System Using Reptile Search Algorithm

Distributed generation (DG) has been employed over the years in distribution systems to enhance system voltage profile, improve voltage regulation and minimise power losses leading to improved stability besides economic benefits. This work addresses an application of reptile search algorithm (RSA) based optimization technique to determine the optimal placement of electric vehicles (EVs) in distribution systems. A matrix approach based radial distribution load flow method is adopted to determine the optimal location of DGs with the heuristic intelligent search approach of RSA looking after the optimal placement of EV loads. This work presents a standard IEEE-33 and 69 bus system integrated with a wind turbine generating system (WTGS). The system is modeled for optimal placement of EV loads such that the system voltage is maintained within allowable limits by reducing overall system losses. The optimal placement of EV loads in a radial distribution network (RDN) implies establishing an efficient active distribution network satisfying several operating parameters like bus voltage limits and current capacity of feeders while maintaining network radiality with minimal system losses. The proposed technique is investigated on the benchmark IEEE-33 and 69 bus test systems. The simulated results depict a substantial improvement in convergence characteristics and reduction in system losses.

Multiobjective Salp Swarm Algorithm Approach for Transmission Congestion Management

In the newly emerged electric supply industry, the profit maximizing tendency of market participants has developed the problem of transmission congestion as the most crucial issue. This paper proposes a multiobjective salp swarm algorithm (MOSSA) approach for transmission congestion management (CM), implementing demand side management activities. For this, demand response (DR) and distributed generation (DG) have been employed. For willingly reducing the demand, demand response has been called by providing appropriate financial incentives that supports in releasing the congestion over critical lines. Distributed generation implementing wind plant as renewable independent power producer (RIPP) has also been included in order to reduce the load curtailment of responsive customers to manage transmission congestion. The proposed incentive-based demand response and distributed generation approach of CM, has been framed with various strategies employing different thermal limits over transmission lines and has resulted into significant reduction in congestion and in-turn improvement of transmission reliability margin. Diversity has been obtained in multiobjective optimization by taking two and three objective functions, respectively (minimization of overall operational cost, CO2 emission, and line loading). The by-products of the proposed algorithm for multiobjective optimization are minimized demand reduction, optimum size, and location of DG. To examine the proposed approach, it has been implemented on IEEE 30-bus system and a bigger power system IEEE 118-bus system; as well as the proposed technique of MOSSA has been compared and found better than reported methods and two other meta heuristic algorithms (multiobjective modified sperm swarm optimization and multiobjective adoptive rat swarm optimization).

Topology Optimization of Regional Power Grid Under Large-Scale Access of Distributed Photovoltaic Power Generation

In order to avoid the impact of large-scale access of distributed photovoltaic power generation on regional power grid and minimize power grid line loss, a regional power grid topology optimization method under large-scale access of distributed photovoltaic power generation is proposed. Obtain the factors related to the line loss consumption of regional power grid and the access location and capacity of large-scale distributed photovoltaic power generation, as well as the power grid network structure and load. Take the minimum bus loss after large-scale access to distributed photovoltaic power generation as the objective function, and take the continuity, network structure, line relationship, node voltage, etc. as constraints, build the topology model of low-voltage power grid, Using k-means clustering algorithm, the power supply range of regional power grid after large-scale access of distributed photovoltaic power generation is divided into several regions. In each divided small region, KPSO algorithm is used to optimize the network topology, so as to realize the topology optimization of the whole regional power grid. The experimental results show that when the number of clustering centers is 4, the clustering effect of this method is the best and can effectively reduce the power supply line loss.

Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network’s energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies.

Locational marginal price based optimal placement of DG using stochastic radial basis function

The electricity market (EM) plays a dynamic part in the economics of the electrical power system. An electricity market is a marketplace where buyers and sellers interact with each other. Buyers want to buy the electricity at the lowest price and seller wants to sell the electricity at the maximum price. In this process, the location of the generating unit in the transmission network (TN) from the transmission point of view plays an important role that maximizing the Distributed Generation (DG) owners’ economic benefit is carried out in the electricity market. In this paper, the authors have identified the location of DG based on locational marginal price (LMP) in order to improve the social welfare and voltage profile. LMP implies the price to buy and sell power at various positions within wholesale electricity markets. After finding LMP on each bus, the DG will be placed at the bus where LMP is highest. Stochastic-based radial basis function (SRBF) has been used for achieving the optimal placement of DG in the TN. SRBF algorithm predicts and endeavours to find precise solutions for minimisation problems. The results of the proposed method have been validated on 6 and 14 bus transmission systems.

The Role of Distributed Generation on The Performance of Electrical Radial Distribution Network

Purpose: This article provides available information on the role of distributed generation (DG) in the performance of a power distribution network. 
 Design/methodology/approach: The study reviewed articles about available methods for reducing technical losses in electrical distribution networks. The second step involved studying various researchers' views on renewable energy in some developing countries for introducing DG into a distribution network. The influence of DG on the economic performance of a distribution network. Finally, the study scouted for available information on the implementation of a demand response (DR) program on the performance of a distribution network in the presence of DG.
 Findings: Available information reveals that the reliability of DG for reducing the technical losses in a distribution network is higher than relying on alternating current controllers. There are indications of renewable energies in developing countries for introducing DG into a distribution network. According to the articles reviewed, the approach for the optimal location of DG did not include the combination of the voltage stability index and power loss reduction index. It is also worth considering using the power system analysis toolbox (PSAT) for DG sitting. The economic influence of DG on a distribution network's performance has not been evaluated based on the technical loss, generation cost, emission cost and reliability. It is also worth considering the benefits of demand response programs in the presence of DG.
 Research limitation: The review concentrated mainly on DG's influence in reducing technical loss. Articles relating to the effect of DG on other distribution network technical issues such as voltage stability, harmonics etc. also require attention
 Practical implications: Distribution network performance is essential for the operation of electrical gadgets. Therefore, improved distribution network performance will result in the economic development of a country.
 Originality/Value: This paper provides the platform that stimulates interest in using DG to improve the distribution network performance.

A Critical Review of Optimization Strategies for Simultaneous Integration of Distributed Generation and Capacitor Banks in Power Distribution Networks

This paper reviews the optimization strategies for the optimal simultaneous allocation of distributed generation (DG) and shunts capacitor banks (SCBs) in electrical distribution networks. These optimization strategies aim to determine the optimal size, location, and combination of DGs and SCBs to constitute a techno-economic system while satisfying the constraints and energy demand of the load. The optimization strategies explicitly reviewed include the problem formulations, optimization techniques, restrictions posed for optimization problems, decision variables, and network operating modes typically assumed while allocating the DGs and SCBs. In addition, there is an attempt to highlight the limitations of the existing literature and future research directions. This study undertakes a comprehensive review of the literature that systematically considers the simultaneous application of DGs and SCBs to advance the existing literature, which lacks such a review. Expectedly, this review will serve as a principle platform for researchers intending to explore the subject area for further improvement.

Coordination control of distributed generators and load resources for frequency restoration in isolated urban microgrids

Urban microgrids have become the main body of energy consumption in modern power systems. To reduce carbon emissions, distributed generators (DGs), such as roof photovoltaics (PVs), are increasing rapidly in urban microgrids. By utilizing the power output from local DGs, more microgrids have the ability to operate in isolated mode. However, isolated microgrids have less regulation capacities compared with large interconnected power systems, which significantly raises the difficulty for maintaining the stable operation of isolated microgrids. To address this issue, this paper proposes a coordination control method to tap flexibility from both DGs in supply-side and load resources in demand-side. First, the microgrid model with high-penetration DGs is developed considering virtual power plants (VPPs) by aggregating flexible loads. Then, a coordination control framework is proposed for DGs and VPPs in multi-scenarios, including stable operation, uncertain load disturbances, and accidental DG outages. Based on this framework, a distributed consensus algorithm (DCA) and a local control algorithm (LCA) are designed for DGs and VPPs, respectively. The DCA can achieve a quick regulation of DGs with high plug-and-play expansibility. The LCA can control VPPs considering both the quality of regulation services and comfortable requirements of heterogeneous users. Finally, numerical studies verify that the proposed models and methods can awaken fragmented DGs and VPPs to improve the stability and enhance the resilience of isolated microgrids.