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• Establishing a realistic unbalanced harmonic power flow calculation model. • Establishing DG line loss optimization model with harmonic constraint. • Analyzing several DG line loss optimization schemes in distribution network with multi-type harmonic loads. • The harmonic contents have been decreased but also increased the line loss. • Under the same line loss, the optimal capacity of double DG is less than that of single DG, • The optimization of double DG is better without considering the cost. With the large penetration of power electronic devices in distribution networks, power quality problems such as line loss and harmonics have become the main factors affecting the access of distributed generation (DG). At present, the research on DG integration, such as the optimization of the DG capacity using particle swarm optimization algorithm and ant colony algorithm, is mainly focused on the traditional distribution network. In contrast, the research on the power electronic distribution networks, which include a large number of harmonic sources, is not sufficient. This paper presents a novel optimization method for connecting DG to power electronic distribution network to reduce line loss. Firstly, based on the frequency domain model of the distribution network, this paper establishes the harmonic model of distribution network components, including generators, transformers, lines, harmonic sources and non-harmonic sources. Then the location and capacity of DG are optimized using genetic algorithm with network loss reduction as a target and voltage and harmonic as constraints. Finally, the power electronic distribution network is modelled based on the IEEE 34 - node standard model. The obtained results confirmed that the optimization model with harmonic constraints can effectively reduce the line loss by 108.26 kW and the line loss rate by 4.67 % using single DG. Also, when using the double DG, the line loss and the line loss rate are decreased by 118.53 kW and 5.14 %, respectively. In addition, the optimization effect of dual DG is better than that of single DG without considering the cost. [ABSTRACT FROM AUTHOR]

This paper presents the forecasting of Turkey’s electricity domestic consumption using hybrid algorithm, which bases on Ant Colony Optimization (ACO) and Iterated Local Search (ILS) algorithms. The approximation forecasting of an important energy source like electric for countries such as Turkey, which greatly imported energy sources, is so vital. In this paper, electricity domestic consumption model is first proposed by hybrid approach of ACO and ILS. Both techniques are quite simple and powerful stochastic local search method. ACO uses pheromone update mechanism to escape the local optimum. ILS applies iteratively local search to an initial solution until finding a local optimum; then it perturbs the solution and restarts a local search. This paper aims combining the advantages of these two algorithms to perform successful estimations. We use some economic indicators such as population, gross domestic product (GDP), import and export when Hybrid Electricity Domestic Consumption Estimation (HEDCE) model is developed. HEDCE equations proposed here are linear and quadratic. Results show that Quadratic_HEDCE find better solution because it can reflect better the variations of used indicators. The HEDCE models forecast Turkey’s electricity domestic consumption until 2030 under dissimilar scenarios. [ABSTRACT FROM AUTHOR]

Highlights • Generalized and simplified probabilistic models of traction load are proposed. • Number of trains, train power, and load allocation are used for NSC simulation. • An improved ACO algorithm is developed for parameter identification. Abstract To analyze the negative effects caused by unbalanced traction loads, the key is to build an efficient load model. This paper proposes a probabilistic traction load model in both generalized and simplified forms, where two decisive factors, number of trains (TN) and train power (TP), are selected as the components. Based on data analysis, Poisson and normal distributions are selected to respectively describe TN and TP. With the flexibility of stochastic approach, the model can fit various load profiles by parameter match rather than structure modification, thus it has good applicability and low complexity. Moreover, a load allocation proportion (LP) is employed to represent the unbalanced level between two traction phases, hence negative-sequence current (NSC) can be computed according to the traction transformer type, and the detailed equations and steps are given as well. Considering the correlation of TN, TP, and LP, an improved ant colony optimization is developed for parameter identification. Case studies have been implemented for model verification, parameter identification, and NSC evaluation. The simulations show that the proposed model can well describe the characteristics of both traction load and NSC. [ABSTRACT FROM AUTHOR]

• A robust fault section location method for active distribution network is proposed. • A self-adaptive switching function is built based on current path information. • The FTU state is encoded considering FTUs' underreport and misreport. • Hierarchical strategy and ant colony optimization are used for optimal solution. Fault location is a key technology for active distribution network. Due to the injection of distributed generations and the flexible topology of active distribution network, traditional fault location method faces big challenges in accuracy and adaptability. This paper proposes a robust fault section location method for active distribution network. In the fault location model, to reduce calculation redundancy and improve adaptability to changed topology, a self-adaptive switching function is constructed based on the current path information of topology. The underreport and misreport of feeder terminal unit are taken into consideration by encoding the feeder terminal unit's state. Hierarchical strategy and ant colony optimization are used for rapidly obtaining optimal solution. Different cases and conditions of fault are simulated. The location accuracy and the location time are compared. The proposed fault section location method is able to give accurate location results in around 5 s. The adaptability to topology changes is also verified. It proves the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In distribution systems, feeder reconfiguration (FR) can lead to loss reduction, reliability improvement and some other economic savings. These advantages can be intensified by proper control and switching of Capacitor Banks (CBs). In this paper, using Ant Colony Optimization (ACO) technique, a novel method is proposed for simultaneous dynamic scheduling of FR and CB switching in the presence of DG units having uncertain and variant generations over time. This method is applicable to both smart and classic distribution systems. While for the latter, state estimation method should be used to estimate the loads at different buses by employing a limited number of measurements. The objective of this method is to minimize the total operational cost of the grid, including the cost of power purchase from the sub-transmission substation, cost of customer interruption penalties, Transformers Loss of Life (TLoL) expenses, and the switching costs (CBs and disconnecting switches). To perform this study, the planning period is divided into several intervals for each of them the network topology and CBs reactive power are determined to satisfy the objective function. Additionally, due to the curse of dimensionality, a Case Reduction Technique (CRT) is proposed in order to decrease the computational burden of the proposed method. Finally, the efficiency of the proposed method is verified through its application on the IEEE 118-bus distribution test system, and evaluating its economic and operational characteristics. [ABSTRACT FROM AUTHOR]

• Considering the contradiction between PVMFGCI harmonic, reactive power compensation and active grid connection, the dynamic optimization method of power quality control under PVMFGCI working state (considering meteorological conditions) and non working state (at night) is constructed. • Make full use of the remaining capacity of PVMFGCI to optimize harmonic and reactive power, greatly improve the utilization rate of inverter, and reduce the investment and operation cost of power quality control devices (such as APF) of PV grid connected system. • The adaptive ant colony algorithm is used to solve the objective function of the three-level optimization method. The adaptive value of the objective function is the largest and the convergence speed is faster. In this paper, a new harmonic suppression and reactive power compensation strategy based on photovoltaic multi-functional grid connected inverter (PVMFGCI) and a three-layer optimization model based on adaptive ant colony algorithm are proposed for optimal control of power quality of grid connected photovoltaic (PV) power generation system. Here, an improved CPT (Conservative Power Theory) current detection method is used to extract harmonic and reactive current components from the load current, and a new PVMFGCI hierarchical optimization model is developed to optimize the power quality at the common connection point (CCP). The proposed three-layer optimization model is an improved form of the two-layer optimization model, which successfully solves the contradiction between the active power grid connection of the PV grid connected inverter and the power quality control. The main objective of the proposed three-layer optimization model is to meet the requirements of active power output of PV power generation, and at the same time, to utilize the remaining capacity of the inverter to control harmonics and compensate reactive power. However, when the remaining capacity of PVMFGCI is sufficient, PVMFGCI can reasonably allocate the remaining capacity through the three-layer optimization model to meet the optimal power quality at the CCP. In this process, the power quality of the microgrid is improved. PVMFGCI provides reactive power compensation, harmonic control and other power quality problems. In addition, when the remaining capacity of PVMFGCI is insufficient, it only needs to maximize the active power and make the harmonic distortion rate and power factor meet the grid connection requirements, which improves the utilization rate of PVMFGCI. The proposed techniques are modeled and the IEEE 33 bus systems are analyzed and their performances are verified experimentally on a developed prototype, in PV output active power variation condition, imbalance loading condition for linear/nonlinear loads, where it has shown a very good performance. The effect of harmonic control and reactive power compensation at each node of the system is obvious. [ABSTRACT FROM AUTHOR]

This paper presents a newly developed hybrid optimization algorithm for solving the problem of Economic Dispatch (ED) for a multi-generator system. The hybrid algorithm named ACO–ABC–HS combines the framework of Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Harmonic Search (HS) algorithms to find the optimized solution for the system. The ACO algorithm is used to find the initial solution set, the ABC algorithm is employed to test and improve each of the probable solutions provided by the ACO module, while the HS module is used to discard the inferior solutions from the solution set and replace them with better ones. The performance of this hybrid algorithm is compared with those of conventional ED solving techniques like Gradient Search as well as other evolutionary algorithms namely ABC, ACO, HS and Particle Swarm Optimization (PSO). Valve point loading, environmental emissions, line losses and ramping rate constraints have been included in the ED analysis to provide more practical results. The algorithm’s performance is also tested for Multi-Area Economic Dispatch (MAED) with tie-line constraints. The results obtained clearly point out the superiority of the hybrid algorithm in finding out the optimum results, while satisfying the constraints of minimizing the generation costs, reducing the emissions as well as tie-line costs and transmission losses. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm for energy management system (EMS) based on multi-layer ant colony optimization (EMS-MACO) is presented to find energy scheduling in Microgrid (MG). The aim of study is to figure out the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real time scheduling. The proposed algorithm is based on ant colony optimization (ACO) method and is able to analyze the technical and economic time dependent constraints. This algorithm attempts to meet the required load demand with minimum energy cost in a local energy market (LEM) structure. Performance of MACO is compared with modified conventional EMS (MCEMS) and particle swarm optimization (PSO) based EMS. Analysis of obtained results demonstrates that the system performance is improved also the energy cost is reduced about 20% and 5% by applying MACO in comparison with MCEMS and PSO, respectively. Furthermore, the plug and play capability in real time applications is investigated by using different scenarios and the system adequate performance is validated experimentally too. [ABSTRACT FROM AUTHOR]

This paper introduces an Improved Artificial Bee Colony algorithm for modeling and managing Micro Grid (MG) connected system. IABC differs from ABC because of its inclusion of Gravitational search algorithm (GSA) in the scout bee phase. Hence, the scout bee phase is substantially improved as the gravitational constant of GSA increases searching accuracy. As already ABC works with memory, IABC tackles drawbacks occur due to memory-less search entertained by GSA. In the proposed technique, optimal MG’s configuration is determined based on load demand by reducing the fuel cost, emission factors, operating and maintenance cost. By using the input of MG’s configuration such as Wind Turbine (WT), Photovoltaic array (PV), Fuel Cell (FC), Micro Turbine (MT), Diesel Generator (DG) and battery storage and the corresponding cost functions, the proposed method achieves the required multi-objective function. The performance of the proposed method is examined by comparing with other techniques that are recently reported in the literature. The comparison results demonstrate the superiority of the proposed technique and confirm its potential to solve the problem. [ABSTRACT FROM AUTHOR]

This paper proposes a speed control of Switched Reluctance Motor (SRM) supplied by Photovoltaic (PV) system. The proposed design of the speed controller is formulated as an optimization problem. Ant Colony Optimization (ACO) algorithm is employed to search for the optimal Proportional Integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of Genetic Algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature, and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents a comparison study among three bio-inspired optimization algorithms applied to solve the power system stabilizers tuning problem: Ant Colony Optimization, Bat Algorithm and Genetic Algorithm. The tuning procedure is formulated as an optimization problem which aims at minimizing the system damping taking into account a set of pre-specified operating scenarios. The proposed methodologies are applied to stabilizers tuning of the well-known New England test system. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper presents a multi-objective artificial bee colony (MOABC) algorithm. [•] We propose a local search strategy based on progressive optimality algorithm. [•] The performances of MOABC in three different cases are analyzed. [•] Results demonstrate the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: Locating the most suitable locations in power system for any corrective and preventive actions is not an easy task especially for large scale network. The recent methods for determining the suitable generators for power scheduling and locations for shunt elements placement such as capacitor bank and Flexible Alternating Current Transmission System (FACTS) devices are sensitivity analysis, optimization approach, and stability index calculation. Meanwhile, there are also various power tracing techniques have been developed, but only applicable for transmission service pricing in deregulated environment. For a new approach, this paper proposes a technique to determine the suitability of locations in power system for any preventive and corrective actions via Fast Voltage Stability Index Tracing (FVSI-T) with the assistance of a new hybrid algorithm, Blended Crossover Continuous Ant Colony Optimization (BX-CACO). After experimentation and comparative studies, the proposed technique reflects a potential performance for real system applications. [Copyright &y& Elsevier]

Abstract: In this paper, we present a novel adaptive artificial bee colony (AABC) algorithm and compare its efficiency with other existing algorithms for long-term dispatch of cascaded hydropower systems. We formulate the long-term economic dispatch of hydropower systems as a complicated nonlinear optimization problem with a group of complex constraints. We analyze the performances of three different values of the control parameter modification rate (MR) in the AABC. We modify the employed bee phase to improve the global optimal capability of the AABC algorithm, and utilize a novel probabilistic method to enhance the search ability of the onlooker bee phase. Furthermore, we change the scout bee phase to avoid local maxima. We demonstrate the performance of the AABC algorithm and compare it with other algorithms using the data from hydropower systems of Three Gorges in China. [Copyright &y& Elsevier]

Abstract: In this paper, a multi-objective directed bee colony optimization algorithm (MODBC) is comprehensively developed and successfully applied for solving a multi-objective problem of optimizing the conflicting economic dispatch and emission cost with both equality and inequality constraints is showcased. Classical optimization techniques like direct search and gradient methods fail to give the global optimum solution. The proposed algorithm is an integration of the deterministic search, the multi-agent system (MAS) environment and the bee decision-making process. Thus making use of deterministic search, multi-agent environment and bee swarms, the MODBC realizes the purpose of optimization. The hybridization makes MODBC to obtain a unique and fast solution and hence generate a better pareto front for multi-objective problems. The above mentioned multi-objective evolutionary algorithms have been applied to the standard IEEE 30 bus six generator test system. Results of the proposed algorithm have been compared with traditional methods like linear programming (LP) and multi-objective stochastic search technique (MOSST). The performance of the introduced algorithm is also compared with other evolutionary algorithms like Non-dominated Sorting Genetic Algorithm (NSGA), Niched Pareto Genetic Algorithm (NPGA) and Strength Pareto Evolutionary Algorithm (SPEA) and Particle Swarm Optimization (PSO). The results show the robustness and accuracy of the proposed algorithm over the traditional methods and its other multi-objective evolutionary algorithm (MOEA) counterparts. [Copyright &y& Elsevier]

Abstract: Due to the high intermittent power generations from wind and photovoltaic in the microgrid (MG) system, these result in the severe power fluctuation. When the fuel cell (FC) equipped with the aqua electrolyzer (AE) has been installed in the MG, in addition to hydrogen production for FC, the absorbed power by AE can be controlled to alleviate the power fluctuation. This paper proposes the coordinated control of AE and FC to solve the power fluctuation problem in the MG. By control of the power absorption by AE and the power production by FC, the power fluctuation in the MG can be suppressed. The optimal fuzzy logic based-proportional-integral-derivative (FLPID) is used to design the controllers of AE and FC. Without trial and error as in the conventional FLPID controller design, scale factors, membership functions and control rules of the optimal FLPID controller are automatically and simultaneously tuned by a bee colony optimization. Simulation results confirm the superior stabilizing effect of the proposed optimal FLPID controller in comparison with the conventional FLPID controller under several system disturbances. [Copyright &y& Elsevier]

Abstract: This paper presents a dynamic multi objective model for distribution network expansion, considering the distributed generators (DGs) and network reinforcements. The proposed model simultaneously optimizes three objective functions namely, total cost, emission cost and technical satisfaction (voltage profile) by finding the optimal schemes of timing, sizing, placement and DG technologies in a long term planning period (dynamic planning). The importance of each objective function can be changed in the interactive steps. The calculation algorithm is based on Chaotic Local Search with Modified Honey Bee Mating Optimization (CLSMHBMO). The effectiveness of the proposed model and the calculation method are demonstrated through different studies and comparative analysis on an actual distribution network. [Copyright &y& Elsevier]

Abstract: This paper developed a fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm for solving the economic dispatch (ED) problems of thermal generators in power systems. The FCASO algorithm introduces a fuzzy system to dynamically tune the characteristic parameters ψd and ri of chaotic swarm optimization (CASO). The proposed method was applied to two cases of power systems. The simulation results demonstrate the applicability and effectiveness of the proposed algorithm to the practical ED problem. [Copyright &y& Elsevier]

Abstract: In this paper, chaotic ant swarm optimization (CASO) is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). This algorithm explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. While comparing CASO with either particle swarm optimization or genetic algorithm, it is revealed that CASO is more effective than the others in finding the optimal transient performance of a PSS and automatic voltage regulator equipped single-machine-infinite-bus system. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. Takagi Sugeno fuzzy logic (SFL) based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer variables. [Copyright &y& Elsevier]

• The concepts of feeder block and power supply unit are redefined based on the load complementary characteristics of power consumption area, and the practical technical framework of medium voltage distribution network planning is proposed based on the power supply unit division. • The evaluation indexes of feeder block division and power supply unit division are constructed considering the load complementary characteristics, and the division method of feeder block and power supply unit is proposed. • Aiming at the minimum cost of line investment, operation cost and outage loss cost considering reliability, each power supply unit is wired independently based on single tie line connection mode. In view of the problems of large network scale and low equipment utilization efficiency caused by the lack of consideration of load complementary characteristics in the current distribution network planning, a new practical automatic routing method of medium voltage distribution network considering load complementary characteristics and power supply unit division is proposed. Firstly, based on the existing conclusions of load clustering and gridding planning, the concepts of feeder block and power supply unit of medium voltage distribution network are redefined, and the framework of medium voltage distribution network planning considering the power supply unit division is proposed. Secondly, the evaluation indexes of feeder block division and power supply unit division considering the load complementary characteristics are proposed, and a centerline swing distance weighted clustering algorithm is proposed, which is used to realize the feeder block division. Thirdly, considering the annual comprehensive cost of line planning and the outage loss cost based on reliability index, the medium voltage distribution network planning model is established based on the power supply unit division. The automatic routing method is proposed based on ant colony algorithm, which is used to realize the combinatorial solution of the trunk line and branch line. Finally, an example is given to verify the scientificity and practicability of the model and method proposed in this paper. [ABSTRACT FROM AUTHOR]