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For over a decade, distributed generations (DGs) have sufficiently convinced the researchers that they are the economic and environment-friendly solution that can be integrated with the centralized generations. The optimal planning of distributed generations requires the appropriate location and sizing and their corresponding control with various power network types to obtain the best of the technical, economical, commercial, and regulatory objectives. Most of these objectives are conflicting in nature and require multi-objective solutions. Therefore, this paper brings a comprehensive literature review and a critical analysis of the state of the art of the optimal multi-objective planning of DG installation in the power network with different objective functions and their constraints. The paper considers the adoption of optimization techniques for distributed generation planning in radial distribution systems from different power system performance viewpoints; it considers the use of different DG types, distribution models, DG variables, and mathematical formulations; and it considers the participation of different countries in the stated DG placement and sizing problem. Moreover, the summary of the literature review and critical analysis of this article helps the researchers and engineers to explore the research gap and to find the future recommendations for the robust optimal planning of the DGs working with various objectives and algorithms. The paper considers the adoption of uncertainties on the load and generation side, the introduction of DGs with energy storage backups, and the testing of DG placement and sizing on large and complex distribution networks. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed. [ABSTRACT FROM AUTHOR]

Energy is very important in daily life. The smart power system provides an energy management system using various techniques. Among other load types, campus microgrids are very important, and they consume large amounts of energy. Energy management systems in campus prosumer microgrids have been addressed in different works. A comprehensive study of previous works has not reviewed the architecture, tools, and energy storage systems of campus microgrids. In this paper, a survey of campus prosumer microgrids is presented considering their energy management schemes, optimization techniques, architectures, storage types, and design tools. The survey is comprised of one decade of past works for a true analysis. In the optimization techniques, deterministic and metaheuristic methods are reviewed considering their pros and cons. Smart grids are being installed in different campuses all over the world, and these are considered the best alternatives to conventional power systems. However, efficient energy management techniques and tools are required to make these grids more economical and stable. [ABSTRACT FROM AUTHOR]

Summary: The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem. [ABSTRACT FROM AUTHOR]

The integration of distributed generations (DGs) to the distribution networks is increasing day by day to reduce the pressure of rising load demand. The excessive penetration of the DGs at improper locations leads to performance deterioration in terms of higher losses, over-voltages, poor voltage stability and changes in fault levels. Thus, in this paper, the DGs' optimal location, size and power factor, capable of satisfying maximum load demand, are obtained in distribution networks using performance indices related to power loss, voltage stability, short-circuit current deviation and substation power intake. The optimisation is performed using particle swarm optimization and chaotic particle swarm optimisation algorithms considering the voltage and current constraints. A new voltage stability index that remains unaffected by radial or mesh flow of current in the network is proposed to quantify the voltage stability of the system. The short-circuit current deviation index is formulated to decrease the deviations in fault level, so the existing protective devices, with minimum or no changes in their settings, protect the system having DGs. The minimisation of the substation power index causes an increase in penetration of the DGs. The optimal power factor of the DGs gives better loss reduction. The proposed methodology is also applied to obtain optimal penetration of the DGs at their optimal locations for each hour of 96-h load profile representing the load curve of four seasons of a year. The methodology is tested on 33-node and 69-node distribution systems in both radial and mesh configurations. [ABSTRACT FROM AUTHOR]

nature inspired Harris Hawk optimization algorithm for position and rating of distributed generating units in distribution system is proposed in this paper. By installing DGs nearby consumer load centers, line losses can be reduced and voltage profile improves. But penetration of DGs at unsuitable location with unmatched rating will have large impact on distribution systems. This will affect reliability of distribution network, will lessen voltage quality and rises harmonics. Hence there is a requirement of optimization of position and rating DGs before installing in distribution systems. Voltage profile and DG capacity limitation are considered while optimizing DGs. IEEE 33 bus radial distribution system is considered for allocation. Harris Hawk Optimization Algorithm is implemented to optimize position and rating because of its global convergence and reduced calculation liability. Minimization of main objective function is carried out by considering total losses, voltage profile and capacity of DGs. The recommended process is verified on IEEE 33 bus radial distribution system in MATLAB environment and results show the effectiveness of proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is treated with optimum energy management in a DC/AC Microgrid (MG) containing hybrid power sources to supply the load within cost minimization. In this hybrid electrical networks, energy sources are exploited as DC and AC manner, in which the Independent System Operator (ISO) should provide a practical coordination between them in order to procure the demand load optimally. This paper presents a framework that all available resources are formulated mathematically in hybrid microgrid with full constraints along with Demand Response (DR) programs implementation. The network under consideration can operate both in grid-tied and autonomous modes to manage power exchanging. Uncertainty and intermittent of Photovoltaic (PV) with Maximum Power Point Tracker (MPPT) equipped, Wind Turbine (WT), Energy Storage Systems (ESS) and DR programs are also considered to achieve the optimal control and operation. The ESSs are capable to connect both DC and AC links and the State of Charge (SOC) is maintained within permissible range. The proposed DG control framework and operation scheduling has facilitated the energy management of renewables using dynamic programing approach. A 24-hour time horizon simulation and discussion through three scenarios verified on a IEEE 33 bus distribution network, is done to represent the effectiveness of proposed energy management strategy to keep the whole hybrid grid stable. [ABSTRACT FROM AUTHOR]

The rapid growth of consumers, industrial development and global economic expansion inspired a lot of improvement in reliable power supplies. The integration of distributed generations (DGs) in unbalanced radial distribution systems (RDS) is one of the solutions to handle all above issues. This paper proposes an optimal planning for unbalanced RDS integrated with DGs. The objective function is designed as a non-linear function, which is framed as the combination of active power losses, voltage deviation and load balancing index of RDS. The Crow Search Optimization Algorithm (CSOA) is used for identifying the optimal size, location, power factor for DG and distribution system phase in order to reduce the proposed objective function by satisfying all system constraints. The proposed model is tested on 33-bus unbalanced RDS. There is reduction of annual cost of energy loss (ACEL) due to proper integration of DG. The obtained results prove that the proposed CSOA can effectively solve the non-linear objective function and can help to obtain optimized results for unbalanced RDS. [ABSTRACT FROM AUTHOR]

This paper propounds a new metaheuristic optimization algorithm to obtain the optimum site and size of distributed generation (DG) in radial distribution system (RDS). This problem is concomitant with reducing the active power loss, the total voltage deviation and the voltage stability index of the RDS considering different types of load models (such as constant power (CP), industrial (IL), residential (RES) as well as commercial (COM)). To solve this weighting factor-based multi-objective DG allocation problem, a novel metaheuristic optimization algorithm, student psychology-based optimization (SPBO), is suggested in this article. A multi-criteria approach (such as the analytic hierarchy process) is employed to optimize the weighting factors involved. To the best of the authors' knowledge, this is the first time that this novel SPBO algorithm is being used for optimal siting and sizing of DGs in the RDS for different types of load models (like CP, IL, RES and COM) for IEEE 33-bus, IEEE 69-bus and practical Brazil 136-bus RDS. The simulation results attained by the proposed SPBO algorithm are compared with the results provided by recently surfaced Harris hawks optimization (HHO) algorithm and other state-of-the-art algorithms. The outcomes prove that the suggested SPBO algorithm is more efficient to solve the optimal multiple DG allocation problem with minimum real power loss, less computational time and prominent convergence rate. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a multi‐objective coyote optimization algorithm (MOCOA) is proposed for techno‐economic benefits of simultaneous placement of fault current limiters (FCLs) and distributed generation (DGs) units in distribution grids. The proposed MOCOA is employed for mitigating the loss of power in distribution lines, minimizing the fault current levels, minimizing the economic costs of FCL size, enhancing the total voltage stability index, and reducing the total voltage deviation. The MOCOA is enhanced by emerging the concept of pareto‐optimal dominance to store the distinguished solutions in each generation. Finally, TOPSIS, a technique is implemented to find the compromise solution to fulfill the operator requirements. Applications are carried out in three distribution systems. The simulation results show the great effectiveness of the proposed MOCOA in acquiring several alternatives with appropriate technical improvements in power losses, voltage stability, fault level reduction, and economical FCL size. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a two‐stage planning approach for the efficient utilization of distributed generation (DG) and capacitor banks (CBs) for the simultaneous grid‐connected and islanded operations of the balanced distribution networks. The first stage determines the optimal installation locations and capacities of DGs and CBs using an improved variant of the Jaya optimization algorithm (IJaya) to minimize the power losses and voltage deviation during the grid‐connected operation of the distribution network. The second stage identifies the optimal operating point of the DG and CB combination to carry the maximum load during the islanded network operation under power supply‐demand imbalance conditions. For the second stage, an analytical approach is proposed to identify the best operating power factor for the DG‐CB combination, calculating the power loss, and under‐utilization of the installed real‐reactive power sources. Simulation results on the IEEE 33‐bus grid‐dependent distribution network demonstrate that the proposed IJaya significantly improved the network performance and outperforms various existing optimization methods. In addition, during the network's islanded operation, maintaining the network power factor equal to the source power factor (pfsource) makes the DG‐CB combination operate at their rated capacities. [ABSTRACT FROM AUTHOR]

In this paper, heuristic optimization techniques, such as integrated particle swarm optimization (IPSO), teaching-learning-based optimization (TLBO), and Jaya optimization, were applied effectively for the first time to optimize the radial distribution network (RDN) by simultaneously considering reconfiguration of the network and allocation and sizing of the distributed generations (DG). The objectives were to maximize the voltage stability and to minimize the power loss of the network without violating the system constraints. In standard PSO technique, the movement of current particle depends upon global best position and its own best position up to current step. However, if the particle lies too close to any of these positions, the guiding role highly decreases and even vanishes. To resolve this problem and to find the global best solution, IPSO was utilized to optimize the network reconfiguration and DG allocation and sizing problem in the RDN. Also, the optimization techniques, such as TLBO and Jaya optimization, which do not require any tuning of parameters, unlike other heuristic optimization techniques, were implemented successfully in this paper. Seven test cases were generated from different combinations of network reconfiguration and DG allocation and sizing. Moreover, for comparison, the optimization techniques, such as particle swarm optimization (PSO), adaptive cuckoo search algorithm (ACSA), harmony search algorithm (HSA), and fireworks algorithm (FWA), were also applied to IEEE 33- and 69-bus distribution test networks. The comparison results prove overall superiority of Jaya optimization when applied on the two IEEE bus systems with seven test cases undertaken. [ABSTRACT FROM AUTHOR]

Highlights • A performance-based resilience assessment method is proposed for engineered systems. • System resilience quantified through functionality loss and associated monetary costs. • Resilience matrix is introduced where elements are resilience indices in different failure scenarios. • Methodology was applied to a large integrated energy system for an office building. • Methodology illustrated to evaluate two resilience enhancement measures. Abstract One of the key elements of any community or facility is the integrated energy system (IES) which consists of utility power plants, distributed generation systems, and building heating and cooling systems. Assessing the sustainability of an IES would be of great value to decision-making relevant to design, future growth planning, and operation of such systems. This paper addresses one of the basic issues in this regard, i.e. resilience assessment and quantification of IES. A new performance-based method for characterizing and assessing resilience of multi-functional demand-side engineered systems is proposed in this study. Through modeling of system response to potential internal and external failures (called failure modes) during different operational temporal periods (such as different diurnal and seasonal periods of the year), the proposed methodology quantifies resilience of the system based upon loss in the services which the system is designed to deliver. A three-dimensional matrix, called Loss Matrix, is introduced whose elements represent the undelivered system services under different scenarios, i.e. combinations of failure modes and different operational temporal periods. Assigning monetary penalty costs to such losses and including them in the objective function of an optimization model of the entire system allows the three-dimension loss matrix to be reframed into a two-dimensional Consequence Matrix where individual elements represent the imposed penalty costs to the system stakeholders due to undelivered services and/or non-optimal system performance. Normalizing the individual elements results in the Resilience Matrix of the system for different scenarios. The developed methodology is illustrated for IES of a large office building serves to satisfy critical and noncritical electrical, heating, and cooling loads. The resilience assessment framework proposed in this paper would serve as a mean to identify critical components of a particular IES, thereby facilitating resilient design and operation, and also to evaluate cost-effective resilience enhancement strategies. [ABSTRACT FROM AUTHOR]

This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. Thus, the proposed method is a favorable method in solving the optimal determination of DGs in radial distribution networks. [ABSTRACT FROM AUTHOR]

To mitigate climate change, households are increasingly incentivized to install PV systems in combination with battery energy storage systems to increase their self-sufficiency and flexibility as well as to relieve the stress caused by the high penetration of distributed generation on the grid. This paper aims to assess the need for non-linear loss models as opposed to linear loss models found in the literature when optimizing the sizing and operation of PV-battery systems. Therefore, an optimization model is presented which implements non-linear, convex and linear converter and battery loss model formulations. The relaxed convex formulation is equivalent to the original non-linear formulation and can be solved more efficiently, decreasing the run time by a factor of 4. The impact of non-linear as opposed to linear loss models on the optimal solution is illustrated for a residential DC-coupled PV-battery system. The linear loss model is shown to result in an underestimation of the cost by 14.7% and the battery size by 12.4%. Further, the battery utilization is underestimated by a third. The proposed method is useful to accurately model the losses when optimizing the sizing and operation of a PV-battery system in exchange for a slightly higher computational time compared to linear loss models, though far below that of solving the non-relaxed non-linear problem. • Measurement-based quadratic loss models were implemented using convex relaxations • Up to four times faster run times compared to non-linear optimization • Optimization results using linear and convex loss models were compared • Simultaneous optimization of PV-battery system sizing and operation • Optimal sizing of the battery, PV and individual converters of the DC-coupled system [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to the distributed generation system protection coordination based on directional overcurrent protections with inverse-time characteristics. The key question of protection coordination is the determination of correct values of all inverse-time characteristics coefficients. The coefficients must be correctly chosen considering the sufficiently short tripping times and the sufficiently long selectivity times. In the paper a new approach to protection coordination is designed, in which not only some, but all the required types of short-circuit contributions are taken into account. In radial systems, if the pickup currents are correctly chosen, protection coordination for maximum contributions is enough to ensure selectivity times for all the required short-circuit types. In distributed generation systems, due to different contributions flowing through the primary and selective protections, coordination for maximum contributions is not enough, but all the short-circuit types must be taken into account, and the protection coordination becomes a complex problem. A possible solution to the problem, based on an appropriately designed optimization, has been proposed in the paper. By repeating a simple optimization considering only one short-circuit type, the protection coordination considering all the required short-circuit types has been achieved. To show the importance of considering all the types of short-circuit contributions, setting optimizations with one (the highest) and all the types of short-circuit contributions have been performed. Finally, selectivity time values are explored throughout the entire protected section, and both the settings are compared. [ABSTRACT FROM AUTHOR]

The traditional service restoration methods are experiencing a significant challenge because the performance of such methods is often dominated by very large processing time. Therefore, it is important to develop a fast and an accurate method to solve a complex non-linear optimization problem representing the service restoration problem in a distribution system. In this paper, due to a binary nature of the service restoration problem, the Binary Water Cycle Algorithm (BWCA) is proposed to reduce out-of-service loads and system losses considering operational constraints, the radial topology of the distribution systems, and the influence of the presence of distributed generations (DGs) on the service restoration problem. The proposed method is a modified form of the Water Cycle Algorithm (WCA) that is mainly used for discrete and continuous optimization problems. The effectiveness of the proposed method is validated by comparing with other well-known meta-heuristic algorithms using a modified IEEE 33-node radial distribution system. The simulation results demonstrate the robustness of this method to provide a precise decision with short convergence time for solving service restoration problems. [ABSTRACT FROM AUTHOR]

At times, emergency load shedding is required based on predicted load in next interval. Under such situations, the load shedding strategy must be planned in anticipation and may be implemented after the load increase or in previous interval itself. In view of this, the paper presents a methodology for optimal load shedding in advance such that operating constraints are satisfied in the present condition and in the next interval predicted load condition. The effect of distribution generation has been considered. Sine-cosine optimization technique has been employed to get minimum load shedding at minimum buses. The algorithm has been implemented on a 14-bus standard test system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High penetration of photovoltaic and wind turbine-based distributed generators (DGs) can help reduce carbon emissions which is an important goal for the whole world. DG can be used to improve the voltage stability, present generation reserve/emergency, and consequently, the system power quality can be improved. However, it is very important to select the right size and location of a DG so that the power system can increase the gained benefits of such an installation to the maximum. In this paper, a hybrid optimization technique is proposed to determine the optimal allocation of DG in the standard IEEE 33-bus radial distribution system in order to improve the voltage stability and minimize the total power loss. The proposed hyprid technique is based on the gray wolf optimizer algorithm with loss sensitivity factor. The performance of the system is analyzed without DG installation, then it is compared with the performance of the system when DGs are installed with the predefined optimal sizes and locations. The study is performed by MATLAB M-Files and NEPLAN software. [ABSTRACT FROM AUTHOR]

In a deregulated power system, incentive regulations for network owners are designed to direct the network investment. An innovative method that assesses the incentive regulation in distribution networks is proposed in this paper. The method quantifies the interplay between incentive regulation, network investment, and network performances. It allows regulators and the distribution system operators (DSOs) evaluating the economic effects of investments within the incentive regulation framework. Considered network investments include the investment in network infrastructure and performance improving. The assessment is based on a network investment optimization model considering multi-period optimal power flow and regulatory constraints. The main contributions of this paper include the modeling of the incentive regulations and the quantification of the impacts of incentive regulation on network infrastructure investment and performance improving investment. [ABSTRACT FROM AUTHOR]

The presence of power electronic-based wind turbines and photovoltaic systems in distribution networks has provided distribution companies an opportunity to implement voltage control through using the reactive power of these systems. In this paper, a decentralized method based on fuzzy logic is proposed to control the reactive power of distributed generations (DGs) regarding the technical constraints. The fuzzy system is optimized by gradient descent algorithm (GDA) and then implemented on various DG technologies including a photovoltaic (PV) system, permanent magnet synchronous generator (PMSG) wind turbine and also a doubly fed induction generator (DFIG) wind turbine. The system under study is tied to a real distribution network. Having simulated the system, the paper shows that the fuzzy system can appropriately determine the desired reactive power that should be produced by each DG based on the voltage variation of the bus at which the DG is connected. Furthermore, a centralized voltage control is also applied to the same network to verify the performance of the method proposed. The verification indicates that the method is capable of finding the near-optimal solution. A scenario in which an unwanted conflict appears in the DGs’ function is defined in detail and then a strategy is presented to resolve the situation. In addition to this, the coordination between the stator of the wind turbines and grid side converter (GSC) is examined. To investigate the robustness of the proposed method in different distribution networks, simulation results are also presented for IEEE 33-bus distribution test system. The numerical results show that the fuzzy system can effectively control the voltage of the DG connection bus. [ABSTRACT FROM AUTHOR]

This paper presents the application of heuristic optimization techniques for determining optimal placement and sizing of photovoltaic-based distributed generation (PVDG) in a distribution system. The objective functions of the optimization problem, consider a real power loss, voltage deviation, average voltage total harmonic distortion (THD) and system average voltage dip magnitude (SAVDM). Various heuristic optimization techniques were applied and compared in order to determine the optimum placements and sizing of PVDG in IEEE 69-bus radial distribution system. This paper proposed the improved gravitational search algorithm (IGSA) and its performances are compared with two other algorithms such as the gravitational search algorithm (GSA) and particle swarm optimization (PSO). A comparison of the performances is also made using optimization techniques when PVDGs are fixed at critical buses. The test results showed that IGSA outperformed GSA and PSO in finding the optimal PVDG locations and sizes. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems (MOP) emerging in smart grid, such as optimal operation of distributed generation (DG) and microgrid, are very complex because of conflicting objectives, high dimension variables, and numerous operational or security constraints, and difficult to be solved. Multi-objective particle swarm optimization (MOPSO) has powerful potential for obtaining Pareto optimal solutions of these MOPs in a run because it has advantages of parallel computation, faster convergence, and easier implementation. This paper summarizes general procedure of MOPSO at first and then well categorizes MOPSO improvements according to parameter adjusting method, archive update scheme, flying guidance selection, diversity preservation approach, and hybridization with other algorithms. Moreover, it also provides a comprehensive survey on MOPSO applications in smart grid, and gives valuable MOPSO design suggestions to solve MOP in smart grid. This paper can serve a very useful purpose by providing a good reference source of MOPSO design to those interested in Multi-objective optimization issues in smart grid. [ABSTRACT FROM AUTHOR]

To solve the coordinated dispatch and time-space coupling problem in the distribution network with distributed generations (DGs), a novel two-stage (Heuristic search and Variable correction) dynamic reactive power dispatch strategy is proposed in this paper. Meanwhile, according to the analysis of Gauss-type and Z-type membership function, a new objective function is put forward to achieve the balance between network loss and voltage deviation. First, the pre-optimization is made in Niche genetic algorithm to coordinate on-load tap changer (OLTC), capacitor banks (CBs), and DGs. Second, the sequential fuzzy c-means (SFCM) based on artificial bee colony (ABC) algorithm is proposed and then the Heuristic search based on this method is used to formulate the day-ahead plans of OLTC and CBs. Finally, the dispatch plans of OLTC and CBs are applied to recalibrate the day-ahead dispatch plans of DGs. Comparing with existing schemes, strategies and optimization approaches based on diverse evaluation indicators, optimization results on IEEE 33-bus, IEEE 14-bus, and PG&E 69-bus test systems verify the practicality and efficiency of the proposed method. In addition, the effects on the optimization results with different adjustment step and action time of the discrete adjust devices are discussed by the IEEE 33-bus test system. [ABSTRACT FROM AUTHOR]

Highlights • A scalable optimization method is proposed to maximize a DG hosting capacity. • The method enumerates all possible optimal configurations while simultaneously considering optimal DG placement. • A compressed data structure is utilized to represent the solution space compactly. • The method obtains 1.49 × 1018 optimal configurations in 17.1 h for a network containing 235 switches. Abstract At the stage of planning distributed generation (DG) for a distribution network, the network configuration is a key factor in increasing the DG hosting capacity. The determination of a configuration that maximizes the hosting capacity is a highly complex, nonlinear combinatorial optimization problem. No existing method can yield the global optimal solution for practical-scale networks. Therefore, this paper proposes a scalable optimization method. Specifically, the proposed method enumerates all optimal configurations while simultaneously considering optimal DG placement. The proposed method first optimizes the DG placement for possible partial networks using a second-order cone programming technique. Next, it enumerates possible combinations of the partial networks while avoiding a combinatorial explosion using a highly compressed data structure. Finally, it finds the optimal configurations by exploring solutions over the data structure. In experiments involving a large-scale network containing 235 switches, our enumeration method obtained 1.49 × 10 18 global optimal configurations in 17.1 h. Another powerful feature of our method is that it enables distribution system operators to select the preferred optimal configuration interactively. [ABSTRACT FROM AUTHOR]

Highlights • A method is proposed to calculate the optimal oversize of the DG inverters. • The reactive power capability and active power curtailment are considered. • Different control schemes of the inverter are considered in the proposed method. • The effect of OLTC operation on the total costs and inverter oversize is studied. • IEEE 69-bus and IEEE 33-bus distribution systems are used for simulation. Abstract The penetration of renewable distributed generations (DG) is recently being increased in distribution systems. Due to the intermittent nature of these generation units, several technical problems are expected based on the environmental conditions and load profiles, such as voltage rise and increasing losses. A traditional way to alleviate these technical problems is to oversize their interfacing inverters for releasing their capacities to inject/absorb further reactive power. In the literature, the DG inverter is normally oversized by a certain percentage (e.g., 10%) for this purpose. In turn, an optimization-based method is proposed in this paper to calculate the optimal oversize of the interfaced inverter employed in various utility-owned DG types to regulate voltages and to reduce losses with minimum total costs. The proposed method considers the active power curtailment (APC) feature in the DG inverter and the transformer taps. Different control schemes of the interfaced inverter are considered and incorporated in the proposed optimization model. The simulation results demonstrate the effectiveness of the proposed method compared with existing methods. [ABSTRACT FROM AUTHOR]

Abstract High energy losses and voltage instability issues in distribution system are prevalent for energy planners and utilities due to high penetration of Electric Vehicles (EVs). Battery Swapping Stations (BSS) are important component for energizing EVs equipped with battery charging mode. In this paper, energy loss reduction and voltage stability factor are enhanced by optimal allocation of Distributed Generation (DG) and BSS, using new Grasshopper Optimizer Algorithm (GOA). The zone based strategy with dispersed DG ensures sufficient distance among BSS's to adequately serve the motorists and enhance system performance. The operational constraints of BSS, DG and loads in each zone for 33-and 69-bus networks are considered. GOA method is compared with established and well-known techniques, namely Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Particle Swarm Optimization (PSO). Using GOA, energy losses are reduced to 82% and 92%, while loadability is increased to 13% and 19% in 33- and 69-bus systems, respectively. The results have shown superiority of GOA in terms of system performance and convergence characteristics. Optimal sites and sizes for DG-BSS placement in the system are beneficial to EV users, BSS developers, electricity grid and utility. Highlights • Simultaneous placement of DG-BSS units using newly proposed GOA. • Optimal allocation of multiple DG-BSS in predefined zones for facilitating motorist. • 82% and 92% energy losses reduced for 33- & 69-bus system, respectively. • Improvement in loadability up to 13% and 19% in 33- & 69-bus systems, respectively. • Results indicate that GOA has superiority among other applied techniques. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology based on multiobjective optimization techniques to perform an optimized, coordinated, and selective allocation of control and protection devices in distribution networks with distributed generation (DG). The proposed mathematical model consists of two objective functions that consider economic issues and the network continuity index. Physical and operational constraints are taken into account, with emphasis on the set of constraints based on practical rules of distribution companies and international technical standards, which require the specification, coordination, and selectivity of the protection devices installed in the network. The possibility of load transfer from neighboring feeders and islanded DG operation is also considered. The proposed model is a mixed integer nonlinear programming, and we use NSGA-II (Nondominated Sorting Genetic Algorithm) to solve it. The proposed methodology is applied in a real 135-bus system found in the literature. [ABSTRACT FROM AUTHOR]

This paper presents the optimal location and sizing of distributed generators and capacitor banks in radial distribution networks to minimize the active and reactive power losses. A mixed-integer nonlinear mathematical model is proposed to formulate the problem. This mathematical model is solved through general algebraic modeling system (GAMS) and the CONOPT solver. GAMS implementation is presented with 10 nodes test system classical used in the specialized literature. As test system, the 33 nodes system is used under three simulation scenarios. The first simulating scenario consists only in the location of distributed generators, while the second scenario evaluates only the location of capacitor banks and the third scenario combines both elements. The simulation results obtained show that the integration of both elements improve significantly the operating features of the system by comparison with the first two scenarios analyzed. [ABSTRACT FROM AUTHOR]

Despite the fact that microgrids are growing in popularity, their integration in distribution networks is problematic because of the omnipresent variability of renewable energy sources. New research focuses on viewing microgrids as having an active role in the system and providing additional services, such as the concept of low-carbon microgrid nodes. The paper presents a mixed integer linear programming optimization algorithm for determining the optimal size of the distributed generation unit and battery storage system based on operational savings and investment costs, as well as estimation of environmental benefits. The flexibility of the analyzed power node comes not only from its capability to store electricity in the battery banks but to optimally schedule flexible loads such as electric vehicles (EVs). Stochasticity of EV arrival and their state of charge are modeled daily and seasonally. The results show that active market signal-driven integration of such nodes is still not feasible without incentives. But as additional services start creating profits, the gap to feasibility will become smaller. [ABSTRACT FROM AUTHOR]

Distributed generation can be defined as power generation by small scale generating units that are installed at distribution systems. The penetration of distributed generation (DG) units in electric distribution systems is continually increasing. The process of finding optimal type, location and size of DG units is called “DG allocation”. DG allocation is a hot area of research and represents a difficult problem in electrical power engineering. In this paper, the existing research works on DG allocation problem are reviewed from viewpoint of their used optimisation algorithms, objectives, decision variables, DG type, applied constraints and kind of uncertainty modelling. Based on the review of existing research works, the research gaps are identified and some helpful recommendations for future research on DG allocation will be provided. The author strongly believes that this paper can be helpful for researchers and engineers in the related field. [ABSTRACT FROM AUTHOR]

In this paper, the authors present a neural-network-based distributed secondary control to regulate the output voltage and frequency of a smart autonomous microgrid system. Generally, the secondary controller is implemented in a centralized manner using a fixed-gain proportional-plus-integral controller which may perform well under certain operating conditions only. Also the failure of centralized controller implies no secondary control action for the entire system. The control technique proposed in this paper is a distributed one and makes use of neural network (NN) concept to improve the performance of system. A well-trained NN supplies the controller with suitable gains according to each operating point. Before training the NN, evolutionary optimization technique, differential evolution, is employed to obtain the optimal gains of controller at each operating load condition which forms the training set for NN. Simulation results show that the proposed controller damps the oscillations caused by load changes, restores the output voltage and frequency of the system to their nominal values, and maintains proper load sharing property of the baseline controller. The performance of the controller is also compared with fixed-gain controller. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper deals with the subject of finding the site and size of distributed generation in distribution systems. This problem corresponds to a mixed-integer nonlinear problem which is difficult and hard to solve with classical optimization techniques. Many approaches and with different objective functions have been applied to solve it. In this paper, the problem to solve comprises multiple distributed generation sources and the objective of minimizing power losses and generation costs, both for the distributed and conventional generators. Since it is highly combinatorial, a search space reduction is needed. So, an approximate model is used to reduce the search space of the possible buses where the distributed generation has to be located. Then, for each combination of the reduced space search, a nonlinear equations system is solved by a numerical method to get the size of the distributed generation, checking the voltage limits and the lines’ capacities. To test the method, a comparison between the proposed algorithm and a force brute algorithm is performed on a 69 and 118 bus test systems. The obtained results indicate that this method find the optimal or near optimal solution in a reasonable computation time. [ABSTRACT FROM AUTHOR]

It is often necessary to investigate the output power against load demand in a system having distributed generation (DG) resources connected to the existing conventional power system. In this paper, the load frequency control (LFC) problem is presented using different optimization algorithms for two types of power system configurations: (i) hybrid configuration of thermal power system (TPS) integrated with DG, comprising wind turbine generators (WTGs), diesel engine generators (DEGs), fuel cells (FCs), aqua-electrolyzer (AE) and battery energy storage system (BESS); (ii) two area interconnected power system with DG connected in area-1. The inclusion of wind energy system in DG, having high variability in its output power, results into a challenging task for the realization of an effective controller design. This difficulty is further enhanced with random variation of load demand. The control scheme proposed in this paper is based on linear matrix inequalities (LMI) with its parameters tuned by particle swarm optimization (PSO), as a new contribution to earlier studies. The robustness of this controller is thoroughly demonstrated in the above hybrid power systems with different conditions of load disturbances, wind power and parameter variations. [ABSTRACT FROM AUTHOR]

The interest in optimization problems involving distributed generation units has grown among undergraduate and graduate students since distributed generation unit connections have been encouraged around the world, requiring efficient power system analysis tools to solve them. The available open access tools are commonly focused on transmission network analysis. On the other hand, commercial tools adapted to radial networks do not allow the students to follow the intermediary calculations and learn with them. In many cases, the students have to develop their own computational tools, which is a time-consuming activity. This paper aims to fill the gap encountered by undergraduate and graduate students when they are performing their optimization problems involving distributed generation units, presenting the advantages of a new power flow tool focused on radial electrical distribution networks with high levels of unbalanced loads which require a three-phase power flow tool to be analyzed. The use of the proposed tool allows the students focus on their research, reducing the time spent on programming, and learning about power flow analysis applied to radial networks. [ABSTRACT FROM AUTHOR]

DC microgrids will be an important part of the future electrical power systems, i.e., smart grids. Similar to the other power systems, one of the important issues of dc microgrids is the fulfillment of the optimal power sharing. In this paper, a practical, modular, robust, highly reliable, autonomous, distributed, optimal power sharing method, namely the ODCPS (optimal dc power sharing) method, is proposed for radial dc microgrids with different numbers of distributed energy sources. The presented power sharing method inherently considers the power loss in its optimization process. Furthermore, the ODCPS method necessitates the least telecommunication. Here, to determine the proper amounts of the parameters of the proposed power sharing method, a novel technique calculating the nodal prices in radial dc microgrids is also presented. Although the success of the ODCPS method is demonstrated through the analytical analyses carried out in this paper, finally, the remarkable performance of the proposed power sharing method is described by the simulation results too. [ABSTRACT FROM AUTHOR]

This paper is a research article for finding the optimal control of smart power substations for improving the network parameters and reliability. The included papers are the most essential and main studies in the field, which propose a different approach to reach the best performance in electrical power systems. The parameters for improvement are the ability for tracking of the reference signal, stabilizing the system, reducing the error in steady state and controlling the behavior in transient state. The research focuses with the reaching a better transient stability considering voltage and frequency dynamic parameters. The optimal model for the control is focused on minimizing energy consumption but maintaining the controllable parameters, exploring some optimization techniques to find the optimal control, with of aim of minimizing the response time, the energy consumption, and maximizing the reliability by means of improving the controller to be more robust. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a method to determine the optimum capacity of distributed generation technologies for buildings. The Combined Heat and Power (CHP) unit has both absorption and electric chiller for cooling loads, and a supplementary boiler for heating loads. The demand is uncertain in the model. The underlying uncertainty is modeled using probabilistic theory. In this method, the expected value of the objective function is evaluated using numerical approaches. A simulation based optimization method is presented which can estimate the cost of system operation and find the appropriate design by means of stochastic programming. The effect of minimum economic turn-down of the facilities is also discussed in this paper. The optimum capacity of distributed generation technologies has a great effect on the cost of the building. Therefore, the proposed method is applied for a hospital in Iran as a case study. The results show that the optimum capacities of CHP and absorption chiller decreases and the capacities of the electric chiller and boiler rise by an increase in demand uncertainty. In addition, the appropriate minimum economic turn-down of facilities has a considerable effect on the overall system cost. [Copyright &y& Elsevier]

Based on Game Theory and Multi-objective optimization problems (MOP), Game Optimization Theory (GOT) is discussed in this paper. Optimization Stability Analysis (OSA), Distance Entropy Multi-Objective Particle Swarm Optimization (DEMPSO) and Fuzzy Multi-weights Decision-making Method (FMW) are proposed as well. Game Optimization Theory, which is a comprehensive system, could not only handle multi-objective optimization problems effectively, but also could offset the disadvantages of traditional optimization theories, such as lack of framework and the insufficient consideration of relevant elements. In this paper GOT is used for the first time in solving the distribution systems planning (DSP) issue by implementing distributed generation. The proposed model integrates costs, losses, and voltage index to achieve optimal size and site of distributed generation. The model allows minimizing total system costs, system power losses and maximizing voltage improvement. A detailed DSP example is used for verifying the effectiveness and reasonableness of GOT in this context. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) is a new approach for solving some problems of older power networks. Due to the increasing power demand in recent power systems, the importance of power loss reduction and maintaining system voltages within an acceptable range has given rise to the wide use of DG units in power systems. On the other hand, unplanned and non-optimal application such as installation and operation of DG units might cause other technical problems. In addition, it is important to consider the load pattern in the network, and the best decision for DG unit's operation must be chosen accordingly. In this paper, a method is introduced in order to make the optimal placement and find an optimal operating point for the DG units, which means the power output of DG units, considering the load pattern of the network. This load pattern has an average load of 24 hr a day, four seasons a year. In the proposed method, optimization has two goals: first, is optimizing the DG unit's placement based on improvement of the voltage profile, and the second is operating DG units with optimum power factor, minimizing power loss, and improving voltage profile, with regard to the load pattern. In order to solve this problem, the gravitational search algorithm and genetic algorithm are used. The proposed method is applied on the IEEE 33-bus test system, and the result shows the effectiveness of the proposed method. In order to solve the optimization problem, MATLAB software is used. [ABSTRACT FROM AUTHOR]

Distributed generation, with respect to its ability in utilizing the alternative resources of energy, provides a promising future for power generation in electric networks. Distributed generators contribution to power systems include improvement in energy efficiency and power quality to reliability and security. These benefits are only achievable with optimal allocation of distributed resources that considers the objective function, constraints, and employs suitable optimization algorithm. In this paper, a comprehensive review on the optimal allocation of distributed generators was carried out for different objectives, constraints, and algorithms. Current review highlights how the methods and algorithms for optimal distributed generation allocation play an important role in improving the accuracy and efficiency of the results. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) is widely used to minimize total power losses in distribution networks. However, one of the problems of DG in a grid system is reverse current ow (RCF), which is when the DG output becomes greater than the connected load. Therefore, this paper proposes a multiobjective artificial bee colony (MOABC) algorithm to determine the optimal network reconfiguration for reducing total RCF in DG. The proposed algorithm is tested on 33-bus radial distribution systems in two different scenarios, i.e. base case and with 50% load. The proposed technique can reduce reverse current by up to 93%; however, the total power loss in the system will increase by 7%. Therefore, a suitable weight value is needed in MOABC for balancing the effect of RCF and the power loss value. [ABSTRACT FROM AUTHOR]

One of the effective mitigation solutions of the problems due to the penetration of distributed generation (DG) into the distribution network is a fault current limiter (FCL). Main attention is given to designs for resistive and inductive FCLs and others should meet the same requirements. At first in this paper network analysis is used to determine a suitable type of limiting impedance of a FCL in a distribution network in the presence of DG and then a comparative study of resistive, inductive, and complex FCLs is conducted to show the effect of size and type of FCL on the amplitude and phase of DG and network fault current. To illustrate the impact of FCLs on relay coordination restoration, an optimization algorithm is used to determine the minimum size of FCL and subsequently the role of FCL impedance type on the coordination time interval of the main and backup relays in a sample IEEE typical distributed network is investigated. Simulations are carried out by programming in MATLAB and the obtained results are reported and discussed. [ABSTRACT FROM AUTHOR]

In this paper, two-level decentralized optimization power dispatch control strategies are proposed in an islanded microgrid without communication network to ensure frequency security and economic benefit. First, the secondary-level decentralized optimization control strategy is designed only by using local measurement regarding frequency deviation. It is able to implement the optimal power dispatch for all dispatchable distributed generation units in a coordinated way in order to completely restore the system frequency with minimum operation cost. Then, the first-level decentralized droop compensation control strategy is designed to implement real-time optimal power sharing among all energy storage units in order to quickly compensate large supply-demand imbalance and ensure frequency security. Moreover, the proposed control strategies are proved to be able to meet the requirements of the 'plug and play' operation in the absence of communication network. Simulation results verify the validity of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Renewable sources can provide a clean and smart solution to the increased demands. Thus, Photovoltaic (PV) system and Wind Turbine (WT) are considered here as sources of Distributed Generation (DG). Allocation and sizing of DG have greatly affected on the system losses. This paper aims to propose Ant Lion Optimization Algorithm (ALOA) for optimal allocation and sizing of renewable DG sources in various distribution networks. First the most candidate buses for installing DG are suggested using Loss Sensitivity Factors (LSFs). Then the proposed ALOA is employed to deduce the locations of DG and their sizing from the elected buses. The proposed algorithm is tested on 33 and 69 bus radial distribution systems. The obtained results via the proposed algorithm are compared with others to highlight its benefits in reducing total power losses and consequently maximizing the net saving. Moreover, the results are introduced to verify the superiority of the proposed algorithm to improve the voltage profiles for various loading conditions. Also, the Wilcoxon test is applied to confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Research regarding the optimal planning of distributed generation is often based on coarse energy use and generation data, which does not accurately reflect real variations in energy profiles. This paper investigates the impact of this lack of temporal variation on the optimal planning of distributed generation. The problem of loss minimization in the residential setting is used as a guideline. The outcomes of a stochastic optimization model for energy profiles defined on different time aggregation levels are compared. At first glance, modeling on a finer time scale seems to affect optimal planning solutions, with a shift from variable stochastic sources to sources that provide constant generation. However, it turns out that the gains of using these new optimal solutions in terms of reducing energy losses are limited. The results suggest that for optimization purposes it is not necessary to use data at a resolution smaller than hourly time steps. If energy profiles are defined on time steps smaller than one hour it is important that the full range of the stochastic fluctuations is taken into account, rather than evaluating a couple of scenarios. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new approach to optimally determine the appropriate size and location of the distributed generator (DG) in a large mesh connected system. This paper presents a visual optimization approach in which the planner plays an important role in determining the optimal siting and sizing of the DG through the choice of the appropriate weight factors of the parameters included in the optimization technique according to the system deficiencies. Losses, voltage profile and short circuit level are used in the algorithm to determine the optimum sizes and locations of the DG. The short circuit level parameter is introduced to represent the protective device requirements in the selection of the size and location of the DG. The proposed technique has been tested on the IEEE 24—bus mesh connected test system. The obtained results showed clearly that the optimal size and location can be simply determined through the proposed approach. [Copyright &y& Elsevier]

Control strategies of distributed generation (DG) are investigated for different combination of DG and storage units in a microgrid. In this paper the authors proposed a microgrid structure which consists of a detailed photovoltaic (PV) array model, a solid oxide fuel cell (SOFC) and various loads. Real and reactive power (PQ) control and droop control are developed for microgrid operation. In grid-connected mode, PQ control is developed by controlling the active and reactive power output of DGs in accordance with assigned references. Two PI controllers were used in the PQ controller, and a novel heuristic method, artificial bee colony (ABC), was adopted to tune the PI parameters. DGs can be controlled by droop control both under grid-connected and islanded modes. Droop control implements power reallocation between DGs based on predefined droop characteristics whenever load changes or the microgrid is connected/disconnected to the grid, while the microgrid voltage and frequency is maintained at appropriate levels. Through voltage, frequency, and power characteristics in the simulation under different scenarios, the proposed control strategies have demonstrated to work properly and effectively. The simulation results also show the effectiveness of tuning PI parameters by the ABC. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid optimization algorithm for optimizing the Distributed Generation (DG) parameters in deregulated power system which improves the stability, reduces the losses and also increases the cost of generation. This Hybrid algorithm which includes Fuzzy-Genetic Algorithm (FGA) is used to optimize the various DG parameters simultaneously. The various parameters taken into consideration are their type, location and size of the DG devices. The simulation was performed on a distribution system and modeled for steady state studies. The optimization results are compared to the solution given by another search method like Genetic Algorithm (GA) and Micro Genetic Algorithm (MGA). The results reveal the benefits of the proposed method, for solving simultaneous combinorial problems of DG devices in a power system network. [ABSTRACT FROM AUTHOR]