Distributed Generation In Distribution Network Research Articles

Oppositional based artificial rabbits optimization applied for optimal allocation of nonlinear DG in distribution networks considering total harmonic distortion limit

This paper proposes a new optimization approach to identify the optimal placement and sizes of distributed generation (DG) units in a radial distribution network (RDN). Inverter-based DG also known as nonlinear DG (NLDG) units inject nonlinear current into the system, which can cause harmonic distortion. This harmonic distortion can limit the penetration of DG in the system by affecting the stability and reliability of the power system. Therefore, it is essential to consider harmonic distortion when identifying the optimal placement and sizes of DG units in RDNs to ensure DG’s safe and effective integration into the power system. The aim of the proposed work is to minimize real power loss, voltage deviation (VD), and total harmonic distortion limit (THD), and improve the voltage stability index (VSI) by integrating the search strategies of opposition-based learning and artificial rabbits optimization (ARO) to achieve better performance. The proposed algorithm, called opposition-based artificial rabbits optimization (OARO), considers different types of DG units like DG TYPE I and DG TYPE III and is suggested for two familiar RDNs: IEEE 33-bus, and 118-bus systems. The Pareto optimality concept has been introduced to solve the multi-objective problems of the systems. The simulation outcomes have been analyzed by comparing several optimization techniques in various cases. OARO has been shown to produce high-quality results with minimal iterations, reducing the time needed to solve the issue and conserving energy. Overall, the proposed approach offers a promising solution for identifying DG units’ optimal placement and sizes in RDNs while considering various operating scenarios.

A new optimal allocation of DGs in distribution networks by using coot bird optimization method

Energy is one of the most important topics in the world today and is considered as one of the most effective factors for the development of countries. Due to the limitation of non-renewable energy sources and undesirable effects of consuming these resources on the environment, the strategy of countries has changed towards the use of renewable energy. Renewable energy sources do not decrease over time and operate independently of price fluctuations and are more available, thus being able to play a greater role in modern power systems. Therefore, the optimal location and use of these resources will have an impact on modifying the parameters of the power grid. In this paper an analytical approach for optimal placement and sizing of distributed generation (DG) in power distribution networks to minimize the power loss, bus voltage limits, DG capacity limits, current limits, and DG penetration limit. In the first step, determines the DG capacity causing maximum benefit at different buses, and then selects the best location for DG placement which corresponds to highest benefit in the buses. This method is applicable for sizing and siting of single as well as multiple DG units. The coot bird optimization method (CBOM) is proposed for solving optimal placement, size, and power factor (PF) of DG in distribution network. The suggested method is tested on the IEEE 33-bus, 69-bus, Distribution Networks. The proposed CBOM method has good performance to find optimal placement, size, and PF of DG and it can be applied for various distribution system.

Optimal Dispatch of Battery Energy Storage in Distribution Network Considering Electrothermal-Aging Coupling

With the rapid development of distributed generation (DG), battery energy storage systems (BESSs) will play a critical role in supporting the high penetration of renewable DG in distribution networks. The traditional dispatching approach of BESSs commonly adopts linear models with constant operational characteristics and neglects the aging cost. However, the operational characteristics of BESSs are related to operational states, i.e., temperature, state of charge and current. Furthermore, the aging cost is also an important issue to optimize the economic profitability of BESSs, which is affected by the operational states as well. To this end, this paper proposes an optimal dispatch model of BESSs in distribution networks that considers the electrothermal-aging coupling relationship. The nonconvex original model is reformulated as a second-order cone programming problem, which can be effectively solved. Case studies show that the proposed method accurately captures the operational characteristics and synergistically optimizes the operational cost of distribution networks and the aging cost of BESSs. Compared to the traditional dispatching approach, the proposed method largely avoids the deviation between the scheduled power and actual power of BESSs and effectively saves the total cost, leading to a 48% extension of lifetime and a 30% increase in profitability of the BESSs.

Protective Relaying Coordination in Power Systems Comprising Renewable Sources: Challenges and Future Insights

This article provides a comprehensive review of optimal relay coordination (ORC) in distribution networks (DNs) that include distributed generators (DGs). The integration of DGs into DNs has become a real challenge for power system protection, as the power flow changes from unidirectional to bidirectional, which complicates the relay settings. The introduction of DGs in DNs requires changes and modifications in the protective schemes to maintain proper operation, reliability, stability, and security of the system. This paper focuses on the impacts of DGs penetration into DNs, including the effects on protective scheme coordination. Various expressions for characterizing the overcurrent (OC) coordination problem, as well as related solution attempts, are discussed. Several optimization strategies and techniques are suggested by scientists to deal with coordination optimization problems aiming to achieve less computation time and better accuracy. All these efforts ultimately aim to define optimal relay settings to achieve ORC by generating the optimal setting of cascading relative OC relays. This comprehensive review provides a broad overview of the contributions of scholars in recent publications in this field, with more than 210 articles reviewed and analyzed. It is a valuable resource for other researchers in the same field who aim to tackle ORC problems in their future endeavors.

Optimal Allocation and Sizing of Distributed Generation Using Interval Power Flow

Modern distribution systems and microgrids must deal with high levels of uncertainty in their planning and operation. These uncertainties are mainly due to variations in loads and distributed generation (DG) introduced by new technologies. This scenario brings new challenges to planners and system operators that need new tools to perform more assertive analyses of the grid state. This paper presents an optimization methodology capable of considering uncertainties in the optimal allocation and sizing problem of DG in distribution networks. The proposed methodology uses an interval power flow (IPF) that adds uncertainties to the combinatorial optimization problem in charge of sizing and allocating DG units in the network. Two metaheuristics were implemented for comparative purposes, namely, symbiotic organism search (SOS) and particle swarm optimization (PSO). The proposed methodology was implemented in Python® using as benchmark distribution systems the IEEE 33-bus and IEEE 69-bus test distribution networks. The objective function consists of minimizing technical losses and regulating network voltage levels. The results obtained from the proposed IPF on the tested networks are compatible with those obtained by the PPF, thus evidencing the robustness and applicability of the proposed method. For the solution of the optimization problem, the SOS metaheuristic proved to be robust, since it was able to find the best solutions (lowest losses) while keeping voltage levels within the predetermined range. On the other hand, the PSO metaheuristic showed less satisfactory results, since for all test systems, the solutions found were of lower quality than the ones found by the SOS.

Economic Analysis by Optimal Placing of DGs in Distribution Networks by Particle Swarm Optimisation and Gravitational Search Optimisation Algorithm

The particle swarm optimisation and gravitational search optimisation algorithm (PSOGSA) is a hybrid algorithm which is used to determine size of optimal Distributed Generation (DG) in this paper. The PSOGSA integrates the social thinking ability (gbest) in PSO to capability of local search in GSA. The algorithm combines the searching capability of PSO and with enhanced exploration ability of GSA. Distributed generations are connected in distribution systems to consumers to reduce losses, enhance the voltage profile, reliability and economic benefits. DG optimal positioning and loss minimisation have a significant role for economic operation and overall reduction of energy costs. For evaluation of proposed algorithm, the test bus sets IEEE15, 33 and 69 are chosen. For considered objectives i.e., optimal DG sizing and economic analysis, this PSOGSA algorithm gives better results as compared to other methods and better outcomes has been achieved when DG unit of type III operates at power factor of 0.9 lag

Optimal Operation of Distributed Generations in Four-Wire Unbalanced Distribution Systems considering Different Models of Loads

The distribution network is generally unbalanced due to the distribution of consumers in different phases and the different status of their energy consumption. Also, in recent years, the utilization of distributed generations (DGs) has flourished in the distribution network, thanks to their favorable operational and environmental benefits. Therefore, it is predicted that the optimal energy management of DGs in the unbalanced distribution network can improve the operation state of the network. Moreover, the consumer load model is generally a combination of constant impedance, constant current, and constant power conditions. Therefore, the current study focuses on the analysis of the operation of unbalanced four-wire distribution systems containing DGs, in which load models are also involved. An optimization structure is established to make the voltage deviation, power loss, neutral wire loss, and voltage unbalance minimized. Limitations of the problem include those related to the network and those concerning operating indices of the system like voltage magnitude and phase angle of buses, power flow through the distribution lines, constraints of operation of DGs, load model, and unbalanced conditions. The problem formed in this study is nonlinear and is solved using sequential quadratic programming (SQP) by implementing it in the GAMS simulation environment. To test the benefits of the introduced approach, a 13-bus distribution system has been adopted so that the optimal operation of DGs in the distribution network has been able to reduce the unbalanced situation of the network compared to the power flow studies. Also, this scheme has improved the operation of network, where energy losses and voltage drop have been reduced compared to power flow studies, and a smooth voltage profile has been extracted.

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.

Optimal Placement and Sizing of DGs in Distribution Networks Using Dandelion Optimization Algorithm: Case Study of an Algerian Distribution Network

The optimal placement of distributed generation (DG) in power distribution systems involves identifying the best locations for the generators to be installed and sizing them appropriately, to optimize the performance of the system. In this paper, the recently proposed nature-inspired optimization algorithm namely: Dandelion Optimizer (DO) has been used for the optimal placement and sizing of DG in the radial distribution network. The objectives are to minimize active power loss and voltage deviation and to enhance the voltage stability of the distribution network. The efficiency of the proposed method has been verified over the IEEE 33-bus and Algerian 112-bus distribution systems. The result comparisons indicated that the proposed method can obtain higher quality solutions than many other methods for the considered scenarios from the test systems. Therefore, the DO algorithm can be a very effective method for solving the optimal allocation of the DG problem.

Firefly analytical hierarchy algorithm for optimal allocation and sizing of DG in distribution network

<span lang="EN-MY">Distributed generation (DG) can be beneficially allocated in distribution power systems to improve the power system's efficiency. However, specious DG's allocation and sizing may cause more power loss and voltage profile issues for distribution feeders. Therefore, optimization algorithms are vital for future intelligent power distribution network planning. Hence, this study proposes a multi-objective firefly analytical hierarchy algorithm (FAHA) for determining the optimal allocation and sizing of DG. The multi-objective function formulation is improved further by integrating analytical hierarchy process (AHP) with FA to obtain the weight of the coefficient factor (CF). The performance of the proposed approach is verified on the 118-bus radial distribution network with different bus voltage at DG location (VDG) as regulated PV-bus during load flow calculations. The calculated CF and impact of the unregulated voltage at the PV-bus on the objectives function have been analysed. The findings show that the proposed techniques could allocate the DG at the most voltage deviation while minimizing the power loss and improving the radial distribution’s voltage stability index (VSI). The experimental results indicate that the approach is able to improve the overall voltage profile, especially at PQ-buses, minimize the power loss while improving the network's stability index simultaneously.</span>

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

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

Will power company be profitable from non-utility-owned distributed generators planning with peer-to-peer strategy?

With the high penetration of renewable energy integrated into distribution networks, distributed generators (DGs) are encouraged to participate in market competition with a “peer-to-peer” (P2P) strategy. The operation strategy of the distribution network is increasingly characterized by weak centralization and decentralization. The traditional centralized operation strategy of the distribution network is difficult to ensure the efficiency and effectiveness of terminal energy consumption calculation and equipment regulation, which increases the complexity of distributed energy interactive operation cost calculation and subsequent planning scheme evaluation. Consequently, to enhance the penetration of DGs in distribution networks and ensure their economic operation, the distribution network planning calls for market-oriented integration reform and theoretical innovation. Hence, this paper investigates whether P2P strategy has a profitable and attractive point for power companies if non-utility-owned DGs are integrated into distribution networks. An investment planning scheme of the distribution network is presented to improve the profit of the power company. Network usage charges (NUC) is introduced to provide charge service for both parties of power transaction and optimize the distribution network planning and operation strategy. The profit of power companies is maximized by NUC while the economic benefit of non-utility-owned DGs is also taken into account. The effectiveness and availability of non-utility-owned DGs planning with P2P strategy on investment planning profit are illustrated by the numerical case studies.

Hosting Capacity Maximization of Wind and Solar DGs in Distribution Networks Using Demand Response and Renewables Curtailment

The wind and solar Distributed Generations (DGs) are currently the most used Renewable Energy Sources (RES) based DGs worldwide. The integration of RES in distribution networks has attracted great interest for several objectives; one of the main objectives is to maximize the RES hosting capacity of distribution networks. The wind speed and solar irradiance along with their associated generated power are stochastic in nature and uncertain, and thus their uncertainty should be considered. In this study, a general frame work is presented for the integration of renewable wind and solar DGs, Demand Response (DR), and renewable energy curtailment for hosting capacity maximization of distribution networks, considering renewables uncertainty. The proposed framework is implemented and tested on a standard IEEE 33-radial bus system. The obtained results demonstrate that using different types of renewables is more effective than using only one type for hosting capacity maximization of distribution networks. Moreover, using DR and renewable energy curtailment have proved their effectiveness as active network management (ANM) tools for enhancing the hosting capacity by controlling the load variation and renewables generation, respectively.

Developing a Hybrid Optimization Algorithm for Optimal Allocation of Renewable DGs in Distribution Network

Distributed generation (DG) is becoming a prominent key spot for research in recent years because it can be utilized in emergency/reserve plans for power systems and power quality improvement issues, besides its drastic impact on the environment as a greenhouse gas (GHG) reducer. For maximizing the benefits from such technology, it is crucial to identify the best size and location for DG that achieves the required goal of installing it. This paper presents an investigation of the optimized allocation of DG in different modes using a proposed hybrid technique, the tunicate swarm algorithm/sine-cosine algorithm (TSA/SCA). This investigation is performed on an IEEE-69 Radial Distribution System (RDS), where the impact of such allocation on the system is evaluated by NEPLAN software.

Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm

In today’s world, distributed generation (DG) is an outstanding solution to tackle the challenges in power grids such as the power loss of the system that is intensified by the exponential increase in demand for electricity. Numerous optimization algorithms have been used by several researchers to establish the optimal placement and sizing of DGs to alleviate this power loss of the system. However, in terms of the reduction of active power loss, the performance of these algorithms is weaker. Furthermore, the premature convergence, the precision of the output, and the complexity are a few major drawbacks of these optimization techniques. Thus, this paper proposes the multileader particle swarm optimization (MLPSO) for the determination of the optimal locations and sizes of DGs with the objective of active power loss minimization while surmounting the drawbacks in previous algorithms. A comprehensive performance analysis is carried out utilizing the suggested approach on the standard IEEE 33 bus system and a real radial bus system in the Malaysian context. The findings reveal a 67.40% and an 80.32% reduction of losses in the two systems by integrating three DGs with a unity power factor, respectively. The comparison of the results with other optimization techniques demonstrated the effectiveness of the proposed MLPSO algorithm in optimal placement and sizing of DGs.

Hybrid Optimization Strategy for Optimal Location and Sizing of DG in Distribution Networks

Objective: In this paper, is present a hybrid optimization methodology for the optimal location and sizing of distributed generators (DGs) in electrical distribution networks. We propose a mixed-integer nonlinear problem (MINLP) model for the mathematical formulation, whose objective function is the minimization of power losses due to the Joule effect in conductors. The constraints we considered include active and reactive power balance, voltage regulation, percentage of penetration of DGs into the distribution network, and total DGs allowed in such network.
 Methodology: To solve the MINLP model, we employed a master–slave strategy that uses the Chu-Beasley genetic algorithm (CBGA) and the optimal power flow (OPF) model as the master and slave algorithms, respectively. This hybrid technique helps to reduce the complexity of the MINLP model by eliminating binary variables through the master algorithm and then solving the resulting Nonlinear problem (NLP), which corresponds to the OPF model, using a classical interior-point method available in MATLAB’s fmincon toolbox.
 Results: We tested the efficiency and robustness of the proposed methodology in 33- and 69-node radial distribution networks. The results show its high performance in terms of power loss reduction and final sizing of DGs
 Conclusions: The results obtained in the test systems under analysis reveal that there is a direct and proportional relationship between technical losses, the percentage of distributed generation penetration, and the number of generators available.

ABC algorithm based optimal sizing and placement of DGs in distribution networks considering multiple objectives

In recent years, due to increase in load demand, distribution networks have got substantial attention to get optimized. It is significant to optimally place the distributed generators (DGs) in distribution networks to minimize power losses and voltage drops. However, the DGs incur certain investment and operational costs, and their placement is only viable if these costs overcome energy losses. Therefore, current paper investigates the optimal sizing and placement of DGs in distribution networks with a novel concept to simultaneously minimize total energy cost along with total power loss and average voltage drop. Artificial bee colony (ABC) algorithm is proposed to solve the considered multi-objective problem. The performance of the proposed ABC algorithm is tested with standard algorithms. Newton Raphson load flow (NRLF) analysis is conducted on IEEE 33 and 69-bus radial networks and on CIGRE medium voltage (MV) benchmark grid. Two test cases have been formed and investigated. The results prove that proposed ABC algorithm mostly outperforms other algorithms.

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.

Optimal Sizing and Sitting of Distributed Generations in Power Distribution Networks Using Firefly Algorithm

Compared to centralized generation, distributed generations (DGs) have numerous advantages including real power loss reduction, voltage deviation reduction, network stability enhancement, emission reduction, capacity increase of transmission lines and congestion reduction in distribution networks. Optimum placement of DGs plays a crucial role in this regard. In this paper, Firefly Algorithm (FA) was employed for sizing / sitting of various DGs in distribution networks. The aim of this paper was to minimize power loss by taking into account power factor, active power, and reactive power of DGs. Furthermore, different active and/or reactive generating/consuming DGs were also considered. The performance analysis of the proposed method was validated on standard IEEE 33- and 69-bus test systems.

Analysis of Optimal Deployment of Several DGs in Distribution Networks Using Plant Propagation Algorithm

In recent years, the substantial upsurge of electricity demand has directly impacted the performance of the distribution networks concerning the active power losses and voltage drops. In such circumstances, the distributed generators (DGs) could uphold these concerns if they are optimally deployed in terms of sizing and placement. For this reason, in current research, the optimal deployment of DGs has been proposed with the plant propagation algorithm (PPA) to simultaneously maximize the total active power loss reduction and to upgrade the magnitude of the minimum bus voltage. Alongside, the authors have examined four rounds of DGs. In that context, the optimal deployment of one DG is investigated in the first round. In each succeeding round, the number of DGs is increased: in the second round, this investigation is carried out for two DGs, for three DGs in the third round, and finally, for four DGs in the fourth round of the investigation. The effectiveness of the proposed PPA has been tested on IEEE 33 and 69-bus test networks in the load flow analysis, and results are compared with the standard optimization algorithms. Thereafter, a post deployment economic assessment based on loss calculation has been undertaken out as well. The ANOVA test has also been performed for statistical evaluation of standard algorithms. The simulation results exhibit that the proposed algorithm outdo other algorithms both technically and economically. It has been seen that as the deployment of DGs is increased, the total active power losses and voltage drops are also reduced. In terms of economic assessments, the total cost decreases with the increased deployment of DGs in IEEE 33-bus test network, whereas, the total cost increases with the increased deployment of DGs in IEEE 69-bus test network.