Micro-grids are practical solution for combining distributed energy resources and combined heat and power units in order to satisfy the system power and heat demands. Nowadays, in order to integrate both renewable and non-renewable energy resources like photovoltaic, wind turbine, combined heat and power systems and fuel-cell unit; micro-grid seems to be a good idea. The aim of this paper is to obtain optimal scheduling of proposed generating units and to reduce the total operational cost and net emission of the system through economic/environmental power dispatch, while considering the impact of grid tied and autonomous mode of operation and satisfying the operational constraints. In this paper, a novel whale optimization algorithm is employed to solve this multi-objective problem. The obtained optimal results through this proposed whale optimization algorithm represents the efficiency, feasibility and capability of handling non-linear optimization problems in an efficient way compared to other optimization techniques. The proposed system is studied in a 24-h time horizon. The results obtained from this proposed technique are compared with other techniques which are recently employed.

with increasing issues about global warming and the depletion of fossil fuel sources and there is a need for alternative clean energy, the electrification of vehicles in micro grids has emerged as an undeniable alternative to meet current difficulties. As part of micro grid energy management, the system aims to reduce system purchasing costs while meeting all technological limits and meeting environmental requirements. As a result, this study presents a unique approach for effective charging/discharging schedules of electric vehicles in micro grid in order to reduce network purchasing costs while also lowering Carbon dioxide emission by taking into consideration of different kinds of Generating units (i.e., PV, WT, MT, FC and DG). Under the realistic restrictions of EVs and DERs, the influence of electric vehicle aggregation on operating cost, procuring electricity from main networks and air pollution (CO 2 ) has been analyzed by using a novel improved whale optimization algorithm. A typical test case is being used to assess the suggested model under two different scenarios. In the assessments, simulation results indicate that the presented approach reduces total cost and carbon dioxide emission significantly. Finally, the obtained results are compared with other optimization techniques.

The main aim of Distribution Companies (DISCOs) is to satisfy end user demand with quality and reliable supply of power at all possible locations in the distribution network. Since majority of loads connected to the distribution network are inductive in nature, there exists possibility of higher energy loss and lower reliability in the distribution feeder sections. In this study optimal planning of Distributed Generation (DG) and capacitor is investigated considering maximization of total cost benefit as main objective. Here, the cost benefit due to DG and capacitor installation is attained by minimizing energy purchased from the substation including energy loss and by reducing Expected Interruption Cost (ECOST) of the system. Moreover, a detailed analysis is carried out in the DG and capacitor planning problem considering different compensation coefficients in feeder’s failure rate evaluation so that the compensation coefficient resulting in enhanced net cost benefit is identified. Furthermore, a hybrid combination of Weight Improved Particle Swarm Optimization (WIPSO) and Gravitational Search Algorithm (GSA) called hybrid WIPSO-GSA algorithm is proposed to solve the optimal DG and capacitor planning problem in the distribution network. The proposed methodology is tested on standard 33-bus and Indian 85-bus distribution systems. The superiority of the proposed hybrid algorithm is also illustrated by comparing the results with other optimization techniques.

This paper proposes distributed generation-integrated unified power quality conditioner (UPQC-DG) with adaptive fuzzy proportional–integral (AFPI) controller to improve the power quality (PQ) of a distribution network. In the proposed system, the DG units are integrated at dc-link of the UPQC to provide additional functionalities. The additional functionalities which are unique to the proposed system is to (i) export the available active power from renewable energy sources to the grid, (ii) compensate long term PQ problems, (iii) compensate voltage interruption. Additionally, the dc-link voltage of the system is controlled by using the proposed AFPI controller to effectively improve the dynamic performance of the system during disturbances. Unlike conventional PI controller, in the proposed controller, the gains are not fixed. It is dynamically adjusted by the fuzzy logic-based intelligent supervisory control system according to system operating conditions. The effectiveness of the proposed system is verified using extensive simulation studies and necessary results are compared with the existing literature.

Purpose This paper aims to optimally plan distributed generation (DG) and capacitor in distribution network by optimizing multiple conflicting operational objectives simultaneously so as to achieve enhanced operation of distribution system. The multi-objective optimization problem comprises three important objective functions such as minimization of total active power loss (Plosstotal), reduction of voltage deviation and balancing of current through feeder sections. Design/methodology/approach In this study, a hybrid configuration of weight improved particle swarm optimization (WIPSO) and gravitational search algorithm (GSA) called hybrid WIPSO-GSA algorithm is proposed in multi-objective problem domain. To solve multi-objective optimization problem, the proposed hybrid WIPSO-GSA algorithm is integrated with two components. The first component is fixed-sized archive that is responsible for storing a set of non-dominated pareto optimal solutions and the second component is a leader selection strategy that helps to update and identify the best compromised solution from the archive. Findings The proposed methodology is tested on standard 33-bus and Indian 85-bus distribution systems. The results attained using proposed multi-objective hybrid WIPSO-GSA algorithm provides potential technical and economic benefits and its best compromised solution outperforms other commonly used multi-objective techniques, thereby making it highly suitable for solving multi-objective problems. Originality/value A novel multi-objective hybrid WIPSO-GSA algorithm is proposed for optimal DG and capacitor planning in radial distribution network. The results demonstrate the usefulness of the proposed technique in improved distribution system planning and operation and also in achieving better optimized results than other existing multi-objective optimization techniques.

This paper presents the dynamic modeling and simulation of microgrid consisting of photovoltaic (PV) system and solid oxide fuel cell (SOFC) system. The microgrid system is considered to be operating in grid-connected mode. A bidirectional voltage source converter (VSC) is used to connect the microgrid to the utility grid. The main objective of this work is to smoothly control the power flow between the power sources (PV and SOFC) and utility grid. To achieve this objective, real-reactive power (PQ) control technique based on fuzzy logic controller (FLC) is proposed to control the VSC. The proposed controller provides better response than the traditional proportional-integral (PI) controller by smoothly regulating the active and reactive power flow with reduced overshoot and oscillations. The performance of the proposed FLC is verified through extensive simulation studies carried under MATLAB/Simulink environment.

In this developed modern dynamic world the atmospheric pollution focus is now changing the power generation from conventional method into Non-conventional method. Distributed generations have been consecutively assimilated with distribution systems. Optimal size and site of Distributed generation (DG) and Distribution Static Compensator (DSTATCOM) have significant impacts on the system real power loss and also in addition improvement of voltage magnitude in distribution system. In this study, lightning search algorithm (LSA) is used to find out the optimum placement of DG and DSTATCOM in distribution system. Reduction of real power losses in the distribution system is taken as the main objective function. The proposed technique is examined by using IEEE-33 bus standard test system. The overall optimized results show the performance of the proposed technique.

Insufficient reactive power generation has major impact on power loss, voltage stability margin (VSM) and maximum loadability of electric distribution system. In this paper optimal separate as well as simultaneous multiple installation of DG and capacitor are presented to solve these issues, which alters system reactive power flow, thereby having positive influence on system parameters. Minimization of total reactive power loss (QL) is taken as the main objective and the optimization problem is solved using a swarm intelligence based optimization algorithm called dragonfly algorithm (DA). The proposed methodology is studied using standard 33-bus distribution systems. Results demonstrate the effectiveness of DG and capacitor in minimizing QL, thereby enhancing VSM and loadability of distribution system. The efficiency of the proposed technique is also demonstrated by comparing the results with other optimization techniques.

In this study a hybrid configuration of weight improved particle swarm optimization (WIPSO) algorithm and gravitational search algorithm termed hybrid WIPSO-GSA algorithm is proposed for separate and simultaneous planning of distributed generation (DG) and capacitor in radial distribution network. The optimal DG and capacitor planning problem is examined from local distribution company's viewpoint considering minimization of total active power loss (P loss total ) as main objective. In addition, apart from constant power load modeling, the voltage dependent realistic mixed customer load modeling is also considered at different load levels such as light, medium and peak. Moreover, the total cost benefit achieved through DG and capacitor planning is established by considering necessary economic factors for the total planning period. The proposed methodology is tested on standard 33-bus radial distribution system. The superiority of the proposed technique is also illustrated by comparing the results with other optimization techniques.

In this study, a hybrid configuration of weight improved particle swarm optimization (WIPSO) and gravitational search algorithm (GSA) termed hybrid WIPSO-GSA algorithm is proposed in multi-objective problem domain. In order to solve multi-objective optimization problem, the proposed hybrid WIPSO-GSA algorithm is integrated with two components. The first component is fixed-sized archive that is responsible for storing a set of non-dominated pareto optimal solutions and the second component is a leader selection strategy that helps to update and identify the best compromised solution from the archive. Here, in order to minimize power losses and to enhance voltage stability and maximum loadability, multiple objective functions such as minimization of total active power loss \pmb(Ploss total ), minimization of total reactive power loss \pmb(Qloss total ) and reduction of voltage deviation (VD) are optimized simultaneously to determine best compromised installation of distributed generation (DG) and capacitor in distribution network. The proposed technique is tested on standard 33-bus radial distribution system and its superiority is illustrated by comparing the results with other commonly used multi-objective techniques.

In this study, the optimal allocation of distributed generation (DG) and capacitor is presented by optimizing active power loss (P-loss(total)), expected interruption cost (ECOST) and voltage deviation (VD) objective functions. Here, the local search strength of gravitational search algorithm (GSA) is hybridized with the global search strength of weight improved particle swarm optimization (WIPSO) algorithm to form a hybrid WIPSO-GSA algorithm. The multi-objective optimization problem is solved using a robust multi-objective hybrid WIPSO-GSA (MO-WIPSO-GSA) algorithm integrated with a fixed-size archive and a leader selection mechanism. A standard 33-bus radial distribution system is used to validate the proposed methodology. The supremacy of the proposed technique is illustrated through comparison with existing approaches.

In the electric power system, the majority of loads connected to the distribution side are inductive loads, thereby resulting in enhanced energy loss and reduced voltage stability and maximum loadability. In this study, in order to overcome these issues, optimal multiple installation of distributed generation (DG) and capacitor are presented with minimisation of total apparent power loss () taken as the main objective function. The optimal DG and capacitor planning problem are solved using a hybrid configuration of the weight improved particle swarm optimisation (WIPSO) algorithm and gravitational search algorithm (GSA) called the hybrid WIPSO-GSA algorithm. Moreover, the total economic benefit due to optimal DG and capacitor installation are also established by considering essential cost parameters for the total planning period. The proposed methodology is tested on standard 33-bus and Indian 85-bus radial distribution systems. The superiority of the proposed technique is also illustrated by comparing the results with other optimisation techniques.

This paper investigates optimal location and sizing of single as well as multiple distributed generation (DG) units in radial distribution network using social learning particle swarm optimization (SLPSO) algorithm. The SLPSO is basically developed from particle swarm optimization (PSO) by introducing social learning mechanism concepts into PSO. Moreover, so as to reduce the burden in parameter settings, a parameter control method based on problem dimension is adopted in SLPSO algorithm for solving lower as well as higher dimension problems efficiently. Minimization of total real power loss (Ploss) is used as main objective to solve optimal DG location and sizing problem. Results show the efficiency of the proposed algorithm in minimizing total Ploss. The supremacy of the proposed algorithm is also illustrated by comparing the results with other optimization approaches.

Due to the increase in load demand and raise in the energy crises micro-grids (MG) have been more concentrated. The MGs have the better utilization of Renewable energy sources (RES). In this study distributed generation (DG) like wind turbine (WT), microturbine (MT), photo-voltaic (PV) and fuel cells (FC) are attached to the MG. The main objective is to minimize the operation cost of the MG. The operation cost is minimized through optimal operation of different DG units. The combination of DG units has been chosen based on bid rate and load demand at that particular time. The power is injected or absorbed from the utility grid depends upon the energy available in the MG. Here, Firefly algorithm (FA) is used as an efficient tool to solve the required objectives. The proposed method is tested on the typical MG and the simulation results obtained from FA is compared with other optimization techniques.

In this paper, an adaptive fuzzy proportional-integral (AFPI) controller is proposed to improve the dynamic performance of a microgrid power quality (PQ) conditioner. The voltage source converters (VSCs) associated with the distributed generation (DG) units and a distribution static compensator (DSTATCOM) together form an effective microgrid PQ conditioner. The dc-link voltage of the DSTATCOM is controlled using the proposed AFPI controller to improve the dynamic performance of the system during system uncertainties. Unlike the conventional PI controller, in the proposed controller the gains are no longer fixed. It is dynamically adjusted by the fuzzy logic-based intelligent supervisory control system according to system operating conditions. Thus, the intelligent supervisory control system forces the controller to work in a linear region for a wide range of operating conditions and improves the dynamic performance. Simultaneously, the microgrid PQ conditioner effectively compensates the current-based PQ problems and keeps the grid current balanced and sinusoidal with low total harmonic distortion under all conditions.

In power distribution network, the gradual increase in system load is a natural process, and it results in increased real and reactive power losses and reduced voltage profile. In this paper, optimal single and multiple installations of different types of distributed generation (DG) units are used to handle annual growth in system load, while satisfying system operational constraints. For load growth study, a predetermined growth in system annual load is considered. Minimization of system total real power loss is taken as the main objective, and optimal location and sizing of different DG types are determined using a hybrid configuration of weight-improved particle swarm optimization (WIPSO) with gravitational search algorithm (GSA) called hybrid WIPSO-GSA algorithm. The effect of load growth is studied using standard 33-bus radial distribution system, and the results illustrate significant reduction in system real and reactive power losses, enhancement in system voltage profile, and improvement in load carrying capacity of distribution feeder sections. Moreover, the economic benefits of DG on system annual load growth are also established. Also, the effectiveness of the proposed algorithm is demonstrated by comparing the results with other evolutionary optimization techniques.

In this paper, an adaptive fuzzy proportional-integral (AFPI) controller is proposed to improve the dynamic performance of the inverter interfaced autonomous microgrid. For this purpose, an accurate small signal state space model of the microgrid with electronically interfaced distributed generation (DG) units is developed. The developed model includes the network dynamics, controller dynamics, dynamics of LCL filter and load dynamics. In addition, the effect of damping resistor is also included in the proposed model to show its effect on damping high frequency oscillatory modes. After that, eigenvalue analysis is carried out to determine the optimal ranges of critical control parameters, which greatly affects the small signal stability of the microgrid. Finally based on the analysis, an AFPI controller is designed here to enhance the dynamic performance of the autonomous microgrid during disturbances. To validate the effectiveness of the proposed controller, non-linear time domain simulation is carried out using Matlab/Simulink and results are compared with conventional proportional integral (PI) controller and also with other existing intelligent controllers to show the superiority of the proposed controller.

This paper proposes a multi-objective control strategy using adaptive fuzzy PI (AFPI) controller for grid interactive converter (GIC). The proposed controller utilizes the robust and adaptive nature of fuzzy logic control (FLC) and simple structure of PI controller to effectively improve the dynamic performance of the GIC during uncertainties. In the proposed method, the gains of the PI controller are dynamically adjusted by the fuzzy logic based supervisory control system according to the system operating conditions. Hence, it provides fast dynamic response with reduced overshoot and settling time during disturbances. In addition, the proposed Takagi-Sugeno (TS) fuzzy model is computationally more effective when compared to mamdani type fuzzy models. In the proposed multi-objective control scheme, the GIC is utilized to provide various ancillary services in addition to its primary function of injecting active power to the grid. Computer simulation shows that the dynamic performance of the proposed controller is robust than the conventional PI controller during disturbances. Additionally, the results are compared with the existing literature to validate the performance of the proposed controller. (C) 2017 Elsevier B.V. All rights reserved.

This paper proposes a novel intelligent control technique based on interval type-2 fuzzy logic controller (IT-2 FLC) for three-phase photovoltaic (PV) power generation system connected to a weak utility grid. In the proposed control technique, the grid-connected PV system is effectively controlled to inject high quality sinusoidal current into the utility grid despite various uncertainties in system operating conditions. The presence of third dimension in the membership function of IT-2 FLC provides an additional degree of freedom and hence it offers excellent control performance than the conventional PI controller and type-1 FLC (T-1 FLC) during system uncertainties. In addition, the proposed control strategy does not require any mathematical model of the system and hence the controller design task is simple. The effectiveness of the proposed controller in reducing the total harmonic distortion of the grid current is verified using extensive simulation studies carried out under different scenarios. Additionally, the results are also compared with existing controller reported in the literature to show the superior performance of the proposed controller under disturbances.

Recent days, the concept of microgrid and distributed generation (DG) based on renewable energies such as wind, solar energy, small hydro, micro turbine and biomass has greater attraction. In the proposed work, the operation and control of wind and photo voltaic (PV) system based hybrid AC/DC microgrid in grid connected mode is discussed. The hybrid microgrid proposed in this work contains both AC and DC grids and they are linked with each other with the help of bidirectional Z-Source converter. With the proposed structure, it is possible to reduce the multiple conversion losses and to improve the overall efficiency of the microgrid. All the converters are coordinately controlled to smoothly transfer the real and reactive power between the AC and DC grid under all conditions. To verify the effectiveness of the proposed system, simulations are carried out using Matlab/Simulink and necessary results are presented.