Hybrid Big Bang-Big Crunch Research Articles

Hybrid Big Bang-Big Crunch based resource scheduling to improve QoS in cloud infrastructure

The service-oriented computing paradigm changes the way of computing. Emerging technologies like grid computing, cloud computing, and smart health care application have changed the way we compute and communicate. Cloud computing has made computing huge data on the fly and uses flexible resources according to the requirement for real-time applications. Cloud computing comes with pay per use model to pay for only those resources that you have used. Inside the cloud there lie many issues related to efficient and cost-effective models to improve cloud performance and complete the client task with the least cost and high performance. E-Health care services are one of the most computational intensive services in the cloud, they require real-time computing which can only be achieved if the computational resources can compute it in the least time. Cloud can accomplish this using an efficient scheduling algorithm. This manuscript focuses on the task scheduling technique which enhances the performance in real-time with the least execution time, network cost, and execution cost. The presented model is inspired by Big Bang-Big Crunch algorithm in astronomy. The presented algorithm enhances the quality of service by reducing the scheduling delay, network delay with the least resource cost to complete the task in the least cost to the user with high quality of service.

Day-ahead energy management and feeder reconfiguration for microgrids with CCHP and energy storage systems

Abstract Energy management featuring distribution feeder reconfiguration (DFR) and reactive power control, improves the technical and economic efficiency of microgrids. The present work proposes a framework that leverages scenarios to jointly manage the real and reactive power dispatches of the controllable generation resources as well as the topology of the distribution feeder. Multiple operation measures are optimized including the operation cost, real power loss, the voltage stability index (VSI), and the greenhouse gas emissions of the microgrid. Hybrid Big Bang- Big Crunch (HBB-BC) algorithm is used to solve the formulated optimization problem. Non-dispatchable and dispatchable distributed generation units (DGs), as well as the battery and thermal energy storage systems (BESS and TESS), are considered as a hybrid energy system. Combined cooling, heating, and power (CCHP) units are considered as dispatchable DGs and wind and solar photovoltaic generations are considered as non-dispatchable DGs. The efficiency of the proposed model and solution algorithm is investigated using a 33-bus microgrid, and the simulation outcomes are discussed.

Hydrothermal Economic Dispatch Using Hybrid Big Bang-Big Crunch (HBB-BC) Algorithm

In power generation, it is expected to generate required amount of power based on the load system with minimum cost which is generally called as economic dispatch (ED). Therefore, providing an approach which can be used to solve ED problem in electric systems for effective operation is important. In this paper, hybrid big bang-big crunch (HBB-BC) algorithm was proposed to solve hydrothermal ED problem. Basically, HBB-BC is an algorithm which combined between BB-BC and PSO that use the PSO to improve ability of BB-BC algorithm. To examine the performance of HBB-BC algorithm for hydrothermal ED, IEEE 26 bus was utilized as test system with 6 generators (3 thermal and 3 hydro generator units). Next, simulation output was compared with improved PSO, and modified improved PSO algorithm. From results, HBB-BC algorithm shown the best result under tested case. It confirmed by obtained total operating cost for HBB-BC algorithm was the lowest (11,344.593 $/h) compared to IPSO (11,451.001 $/h) and MIPSO algorithms (11,387.000 $/h).

Enhancing FRT performance and smoothing output power of DFIG wind farm equipped by SFCL and SMES in a fuzzy framework

Wind farms equipped by Doubly-Fed Induction Generators (DFIG) should have two capabilities so that system operators can efficiently utilizes them. These capabilities include Fault-Ride-Through (FRT) and smoothing the output active power fluctuations, especially when these generators provide significant electrical power. The Superconducting Fault Current Limiter (SFCL) and also the Superconducting Magnetic Energy Storage (SMES) are supplementary devices which are used to enhance these capabilities and reduce electrical power swings. In the present work, improvement of FRT capability and smoothing the output power of wind farms are formulated as a multi-objective problem in a fuzzy framework. The variables of optimization are the Proportional-Integral (PI) gains of DFIG and SMES controllers and also SMES and SFCL parameters. Minimization of the initial energy stored in the SMES unit, the energy losses of SFCL, deviations of the DC-link voltage of DFIG, deviations of output active power of DFIG, deviations of output voltage of DFIG, and DFIG speed deviations are six objective functions of the problem. These objective functions are scaled by a fuzzy operator, then the scaled objective functions are aggregated by the “max-geometric mean” operator to obtain the multi-objective function. For optimizing this multi-objective function, the Hybrid Big Bang Big Crunch (HBB-BC) as a meta-heuristic optimization algorithm is used. The proposed algorithm is implemented on a case study to numerically evaluate its efficiency. Simulation results show that the proposed algorithm is more effective to enhance FRT capability and smooth output power of wind farm in comparison with the other algorithms.

A two-stage optimal energy management by using ADP and HBB-BC algorithms for microgrids with renewable energy sources and storages

Abstract Energy management system (EMS) plays a key role to control and maintain efficiency and reliability of micro-grids (MGs) at satisfactory levels. In this paper, a two-stage optimal EMS is designed for MGs with renewable energy sources and storages, and residential and industrial loads, with the capability to predict stochastic electrical demands and electricity prices with respect to operational and model uncertainties. A two-stage optimization method, based on Approximate Dynamic Programing (ADP) and Hybrid Big Bang Big Crunch (HBB-BC) algorithm, is developed to optimally manage power generation, distribution and saving within MGs. Conditions of the main grid, operational cost functions are fully addressed into the design. In particular, Time of Use (ToU) tariffs and curtailable loads are considered to implement a Demand Response (DR) program to efficiently enhance the optimization performance. The proposed method is applied to a residential MG benchmark system, and simulation results are discussed and compared in various scenarios.

A daytime optimal stochastic energy management for EV commercial parking lots by using approximate dynamic programming and hybrid big bang big crunch algorithm

The design of optimal energy management systems has been an important problem, in particular for daytime plug-in electric vehicles’ (PEVs’) parking lots at workplaces and commercial buildings, where the number of vehicles, their arrival and departure times are typically unknown and time-varying. The paper addresses this problem by introducing a two-stage optimization based on Approximate Dynamic Programing (ADP) and Hybrid Big Bang Big Crunch (HBB-BC) algorithm considering a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) which predicts the electricity price. The proposed optimal energy management minimizes the cost of parking lot owner with respect to the Time of Use (TOU) Demand Response (DR) program without reducing the welfare of EV owners. The two-stage optimization manages the charge scheduling subject to short and long term data coming from the MLP-ANN and intelligent transportation system. The stochastic features of the commercial parking are fully addressed into the problem. The effectiveness of the proposed charging technology is assessed and discussed by using real electricity data from ERCOT, under various stochastic conditions. The results demonstrate optimal energy management during peak and off time periods at the minimum cost.

Coordinated optimization and control of SFCL and SMES for mitigation of SSR using HBB-BC algorithm in a fuzzy framework

Abstract Increasing demand for electrical power and expensive expansion of power systems to meet this demand leads the planners to find techno-economical solutions to overcome these challenges. Using series capacitors as reactive power compensation devices in a long transmission lines is a well-known and suitable way to face these challenges in power systems. Transmitted electrical power and the stability margin of power systems are economically improved by the proposed series devices. However, employing the series capacitors in long transmission lines causes a well-known problem in power systems as Sub Synchronous Resonance (SSR). In the present work, the mitigation of SSR is formulated as a Multi Objective Problem (MOP) in a fuzzy framework by using optimal coordinated control of Superconducting Fault Current Limiter (SFCL) and Superconducting Magnetic Energy Storage (SMES). Objective functions of MOP includes the minimization of the initial energy stored in the SMES unit, the energy loss of SFCL, the kinetic energy in generator rotor, the active power deviations in faulted bus, and the rotor speed deviations. These five objective functions are scaled by a fuzzy operator, then the scaled objective functions are aggregated by the “max-geometric mean” operator to generate the multi objective function. To solve the proposed multi-objective problem, the Hybrid Big Bang-Big Crunch (HBB-BC) as a meta-heuristic optimization algorithm is used. In order to evaluate the efficiency of the proposed algorithm, it is implemented on the IEEE First Benchmark Model (FBM) for SSR studies. According to the simulation results of the present study, the proposed algorithm is more effective in reducing the SSR problem in comparison with other algorithms.

Optimal Placement of Distributed Generators with Regard to Reliability ‎Assessment using Virus Colony Search Algorithm

The integration of distributed generation (DG) resources is growing day by day in electricity grids. There are many reasons which persuade the operators to utilize DGs such as restrictions on the construction and development of transmission lines and distribution network, transition of traditional power systems to restructured electricity markets, the competitive conditions in wholesale and retail markets, the implication of economic and environmental issues in the production of electrical energy, increasing the system reliability and customer satisfactory level. The reliability improvement capability of power systems by utilization of these units has attracted the attention of many electrical engineering experts and power system planners and operators. Reliability plays a prominent role in the satisfaction of all power industry participants, especially the consumers of the electricity. In this paper, the effect of distributed generation units on the power system reliability has been investigated. Therefore, the virus colony search (VCS) algorithm is employed to determine the optimal placement and size of distributed generators subject to improve the reliability indices. The simulation is carried out on a 34-bus IEEE test network. The optimization results are also compared with the results of both genetic algorithm (GA), particle swarm optimization (PSO) algorithm, differential evolution (DE) algorithm, multi-objective particle swarm optimization (MOPSO) algorithm, modified shuffled frog leaping algorithm (MSFLA), gravitational search algorithm (GSA), biogeography-based optimization (BBO) algorithm, hybrid big bang-big crunch (HBB-BC) algorithm and glowworm swarm optimization (GSO) algorithm.

A hybrid Big Bang-Big Crunch algorithm for energy optimization on heterogeneous distributed system

A hybrid algorithm is presented for global optimization of the energy consumption rather than makespan of the system. The cost profile is optimized by extending the execution time of the tasks on dynamic voltage scalable processing elements in embedded environment with meeting the execution constraints. The modified Big Bang Big Crunch (BBBC) method improves the scheduling efficiency of the tasks by simulating one of the theories of the evolution of the universe. BBBC algorithm generates disorder data points inspired by energy dissipation procedure of the Big Bang phase, and moves those data points to a single representative data point inspired by a center of mass cost approach in the Big Crunch phase. The Logistic and Sinusoidal chaotic maps are investigated and utilized to improve the movement step of the BBBC algorithm. The proposed hybrid algorithm is tested on several benchmark data sets and its performance is compared with those of Ant Colony Optimization and HEFT strategies. The simulation experiment shows that the presented evolutionary optimization algorithm is robust and suitable for energy saving.

Voltage and frequency regulation in autonomous microgrids using Hybrid Big Bang-Big Crunch algorithm

This paper proposes an optimal power control strategy for inverter-based Distributed Generation (DG) units in autonomous microgrids. It consists of power, voltage, and current controllers with Proportional-Integral (PI) regulators. The droop concept is used for the power control strategy. Static parameters in PI regulators may not ensure the most optimal solution due to inevitable changes happening in microgrid configuration and loads. In the proposed method, after occurring a load change in a standalone microgrid, parameters of the PI controller are dynamically adjusted to get the most optimal operating point that satisfies objective functions. The optimization problem is formulated as a multi-objective programming with objective functions of minimizing overshoot/undershoot, settling time, rise time, and Integral Time Absolute Error (ITAE) in the output voltage. These objective functions are combined using fuzzy memberships. The Hybrid Big Bang-Big Crunch algorithm (HBB-BC) is used to solve the optimization problem. The proposed methodology is simulated on a case study and according to obtained results, the suggested tuning of PI parameters leads to a better voltage response than previous methods. The case study is also solved using the Particle Swarm Optimization (PSO) and Big Bang-Big Crunch (BB-BC) algorithms and it is found that the HBB-BC gives a better solution than the PSO and BB-BC.

The effect of elite pool in hybrid population-based meta-heuristics for solving combinatorial optimization problems

This work investigates the effect of elite pool that has high-quality and diverse solutions in three hybrid population-based meta-heuristics with an elite pool of a hybrid Elitist-Ant System, a hybrid Big Bang-Big Crunch optimization, and a hybrid scatter search. The purpose of incorporating an elite pool in population-based meta-heuristics is to maintain the diversity of the search while exploiting the solution space as in the reference set of the scatter search. This may guarantee the effectiveness and efficiency of the search, which could enhance the performance of the algorithms and generalized well across different datasets. To test the generality of these meta-heuristics via their consistency and efficiency, we use three classes of well-known combinatorial optimization problems as follows: symmetric traveling salesman problem, 0–1 multidimensional knapsack problem, and capacitated vehicle routing problem. Experimental results showed that the performance of our hybrid population-based meta-heuristics, compared to the best known results, is competitive in many instances. This finding indicates the effectiveness of utilizing an elite pool in our hybrid meta-heuristics in diversifying the search and subsequently enhances their performance over different instances and problems.

Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty

In this paper, a multi-objective framework is proposed for simultaneous network reconfiguration and power allocation of DGs (Distributed Generations) in distribution networks. The optimization problem has objective functions of minimizing power losses, operation cost, and pollutant gas emissions as well as maximizing the voltage stability index subject to different power system constraints. The uncertainty of loads is modeled using the TFN (Triangular Fuzzy Number) technique. A novel solution method called MOHBB-BC (Multi-objective Hybrid Big Bang-Big Crunch) is implemented to solve the optimization problem. The MOHBB-BC derives a set of non-dominated Pareto solutions and accumulates them in a retention called Archive. The diversity of Pareto solutions conserved by applying a crowding distance operator and afterwards, the ‘best compromised’ Pareto solution is selected using a fuzzy decision maker. The proposed method is tested on two test systems of 33-bus and 25-bus in different cases including unbalanced three-phase loads. Results obtained from test cases elaborate that the MOHBB-BC results in more diversified Pareto solutions implying a better exploration capability even with a higher fitness. In addition, considering load uncertainty leads to a more realistic solution than deterministic loads but with higher level of power losses.

Simultaneous Coordinated Design of Power System Stabilizer 3 Band (PSS3B) and SVC by using Hybrid Big Bang Big Crunch Algorithm in Multi-Machine Power System

Power System Stabilizers (PSS) are appropriate for nominal conditions of networks and can induce the required damping; but, with changes in the working condition, systems will encounter some difficulties. Power System Stabilizers 3 Band (PSS3B) as another type of stabilizers shows better results with changes in the working conditions of systems. On the other hand, after determining the remarkable result of Flexible AC Transmission System (FACTS) equipment in oscillation damping, nowadays, this equipment is increasingly used in power systems. Regarding the nonlinearity of power systems, it is not appropriate to use linear methods for calculating their parameters. In this paper, Hybrid Big Bang-Big Crunch (HBB-BC) algorithm was applied for consistently designing PSS3B parameters in four generators of a two-area 11 and control parameters of static VAR compensator considered in this network. Then, to investigate the effect of changing system conditions on stability, working conditions of generators were changed in two steps and a comparison was made between PSS and PSS3b and the calculated parameters in the initial mode.

Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms

Metal oxide surge arrester accurate modeling and its parameter identification are very important for insulation coordination studies, arrester allocation, and system reliability since quality and reliability of lightning performance studies can be improved with the more efficient representation of the arresters´ dynamic behavior. In this work, the Big Bang - Big Crunch (BB-BC) and Hybrid Big Bang - Big Crunch (HBB-BC) optimization algorithms were used to select the optimum surge arrester model equivalent circuit parameter values, minimizing the error between the simulated peak residual voltage value and that given by the manufacturer. The proposed algorithms were applied to 63 kV and 230 kV metal oxide surge arresters. The results obtained showed that by using this method, the maximum percentage error was below 1.5%.

Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems

In this paper, a multi-objective framework is proposed for simultaneous optimal network reconfiguration and DG (distributed generation) power allocation. The proposed method encompasses objective functions of power losses, voltage stability, DG cost, and greenhouse gas emissions and it is optimized subject to power system operational and technical constraints. In order to solve the optimization problem, the HBB-BC (Hybrid Big Bang-Big Crunch) algorithm as one of the most recent heuristic tools is modified and employed here by introducing a mutation operator to enhance its exploration capability. To resolve the scaling problem of differently-scaled objective functions, a fuzzy membership is used to bring them into a same scale and then, the fuzzy fitness of the final objective function is utilized to measure the satisfaction level of the obtained solution. The proposed method is tested on balanced and unbalanced test systems and its results are comprehensively compared with previous methods considering different scenarios. According to results, the proposed method not only offers an enhanced exploration capability but also has a better converge rate compared with previous methods. In addition, the simultaneous network reconfiguration and DG power allocation leads to a more optimal result than separately doing tasks of reconfiguration and DG power allocation.

Congestion Management Using Real and Reactive Power Rescheduling Based on Big Bang-Big Crunch Optimization Algorithm

With appearance of deregulated electricity markets, line congestions in transmission systems is considered as critical problem for power system which may prevent dispatching all of the contracted power transactions. Many studies have been carried out to express techniques for congestion management (CM). In this paper real and reactive power rescheduling of generators approach is utilized. This method includes two steps. First one is optimum selection of generators on the basis of sensitivities of generator to power flow on congested line/lines. Next step is optimal rescheduling of generators and identification of capacitors required to provide real and reactive power support for CM. Optimization process in this paper is carried out using big bang-big crunch (BB-BC) algorithm which is improved by particle swarm optimization (PSO) method as hybrid BB-BC (HBB-BC) optimization for the first time. Effectiveness of proposed method has been tested on IEEE 30-bus system, the 39-bus New England system, and 75-bus Indian system and compared with the other proposed methodologies.

CO 2 and cost optimization of reinforced concrete footings using a hybrid big bang-big crunch algorithm

A procedure is developed for the design of reinforced concrete footings subjected to vertical, concentric column loads that satisfies both structural requirements and geotechnical limit states using a hybrid Big Bang-Big Crunch (BB-BC) algorithm. The objectives of the optimization are to minimize cost, CO $_{2}$ emissions, and the weighted aggregate of cost and CO $_{2}$ . Cost is based on the materials and labor required for the construction of reinforced concrete footings and CO $_{2}$ emissions are associated with the extraction and transportation of raw materials; processing, manufacturing, and fabrication of products; and the emissions of equipment involved in the construction process. The cost and CO $_{2}$ objective functions are based on weighted values and are subjected to bending moment, shear force, and reinforcing details specified by the American Concrete Institute (ACI 318-11), as well as soil bearing and displacement limits. Two sets of design examples are presented: low-cost and low-CO $_{2}$ emission designs based solely on geotechnical considerations; and designs that also satisfy the ACI 318-11 code for structural concrete. A multi-objective optimization is applied to cost and CO $_{2}$ emissions. Results are presented that demonstrate the effects of applied load, soil properties, allowable settlement, and concrete strength on designs.

CO2 and cost optimization of reinforced concrete frames using a big bang-big crunch algorithm

A hybrid Big Bang-Big Crunch (BB-BC) optimization algorithm is applied to the design of reinforced concrete frames. The objective of the optimization is to minimize the total cost or the CO2 emissions associated with construction of reinforced concrete frames subjected to constraints based on the specifications and guidelines prescribed by the American Concrete Institute (ACI 318-08).Designs are presented for several reinforced concrete frames that minimize the cost and the CO2 emissions associated with construction. In the first frame example, low-cost designs developed using BB-BC optimization are compared to designs developed using a genetic algorithm. In the second set of frame designs, both low-cost designs using BB-BC optimization are compared to designs developed using simulated annealing. The BB-BC algorithm generated designs that reduced the cost and the CO2 emissions of construction for example frames.