Simultaneous expansion of the electrical and thermal energies collected with conventional expansion options is scrutinised. A robust, bio-inspired evolutionary optimisation method is proposed, to ...handle the complex expansion planning of a system consisting of both electrical and thermal forms of energy. Rewiring, network reconfiguration, installation of new lines and also new electrical and thermal generation units are considered as the traditional alternatives in expansion planning. To solve the problem, overall generation requirements of a network are assigned along the planning horizon. The allocation problem is formulated as a mixed-integer non-linear programming problem that minimises the overall system cost owing to generation capacity among the grid nodes and the newly added or upgraded lines. The performance of the original shuffled frog leaping (SFL) optimisation algorithm is advanced to overcome the complexity of the proposed expansion planning problem. Two modification steps were added to the original SFL technique to enable the proposed modified SFL algorithm to extricate from local minima. The two modification phases pledge a fast convergence rate by achieving a rapid adaptive algorithm, besides a better diversification which is the key to extricate from local minima. The efficacy and robustness of the proposed methodology are verified by applying the method to two modified standard test systems.
The establishment of new multigeneration processes is comparable and assessable from the sustainability viewpoint. Indeed, the sustainability approach assists the economic analysis leading to ...appropriate combination methods. Concerning the energetic flow exhausting a solid oxide fuel cell system, this study proposes an innovative trigeneration process with high sustainability index utilizing three stages of sequential heat recovery. The process encompasses a two-stage Rankine cycle boosted by a thermoelectric generator and an ejector refrigeration unit, and a polymer electrolyte membrane electrolyzer. This system is simulated in engineering equation solver software and a comprehensive sensitivity study is accomplished. It is concluded that the most influential parameter in the whole framework is the operational temperature of the stack and in the integrated process is turbine 1 pressure. Afterward, an advanced evolutionary optimization method is employed using the MATLAB software, and the optimal point is designated from the sustainability and economic standpoints. The optimal state reveals the sustainability index of 2.61 and total unit cost of 35.93 $/GJ, improved by 12.5% and 3.8% against the base case, respectively. Besides, the electric power, cooling, and hydrogen are correspondingly produced at 389.4 kW, 112.4 kW, and 0.45 kg/h, resulting in exergy efficiency and exergoeconomic factor of 57.75% and 52.1%, respectively.
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•A novel sequential heat recovery for energetic stream exiting a SOFC.•Single and dual parametric sensitivity analyses from sustainability and cost aspects.•NSGA-II+TOPSIS method for multi-objective optimization.•Proposed sequential heat recovery is valuable compared to previous designs.•Optimum sustainability index and products’ unit cost equal 2.61 and 35.93 $/GJ.
With the increasing dependence of industry, agriculture and day-to-day household comfort upon the continuity of electric supply, the reliability of power systems has assumed great importance. ...Therefore, the objectives of the power system such as economy, gaseous emission, security etc. must be properly coordinated in arriving at operational optimal power dispatch. The methodology, which simultaneously satisfies multiple contradictory criteria/goals, is called multiobjective optimization. In this article, cost, NO
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emission, overloading of lines due to active and reactive power flow on transmission lines are undertaken as individual objectives to be minimized simultaneously. Normally, interactive multiobjective problems are solved to generate non-inferior solution surface because of their conflicting nature. Afterwards, the decision maker (DM) is provided with effective tools to resolve the conflict among participating objectives and arrives at a 'best' compromising solution. In this study, the weighting method is used to generate non-inferior solutions for the DM in which the problem is solved many times for different set of weights. The 'best' compromised solution has been obtained by searching for the optimal weight pattern using evolutionary optimization method. The weightage pattern that gives a non-inferior solution is chosen as the 'best' when it attains maximum satisfaction level from the membership function of the participating objectives. The proposed method circumvents the exhaustive evaluation of complete non-inferior surface needed. The validity of the proposed method has been demonstrated on a 25-bus sample system comprising five generators.
A hybrid population-based metaheuristic, Hybrid Canonical Differential Evolutionary Particle Swarm Optimization (hC-DEEPSO), is applied to solve Security Constrained Optimal Power Flow (SCOPF) ...problems. Despite the inherent difficulties of tackling these real-world problems, they must be solved several times a day taking into account operation and security conditions. A combination of the C-DEEPSO metaheuristic coupled with a multipoint search operator is proposed to better exploit the search space in the vicinity of the best solution found so far by the current population in the first stages of the search process. A simple diversity mechanism is also applied to avoid premature convergence and to escape from local optima. A experimental design is devised to fine-tune the parameters of the proposed algorithm for each instance of the SCOPF problem. The effectiveness of the proposed hC-DEEPSO is tested on the IEEE 57-bus, IEEE 118-bus and IEEE 300-bus standard systems. The numerical results obtained by hC-DEEPSO are compared with other evolutionary methods reported in the literature to prove the potential and capability of the proposed hC-DEEPSO for solving the SCOPF at acceptable economical and technical levels.
For precise modeling of electromagnetic devices, we have to model material hysteresis. A Genetic Algorithm, Differential Evolution with three different strategies, teaching–learning-based ...optimization and Artificial Bee Colony, were used for testing seven different modified mathematical expressions, and the best combination of mathematical expression and solving method was used for hysteresis modeling. The parameters of the hysteresis model were determined based on the measured major hysteresis loop and first-order reversal curves. The model offers a simple determination of the magnetization procedure in the areas between measured curves, with the only correction of two parameters based on only two known points in the magnetization process. It was tested on two very different magnetic materials, and results show good agreement between the measured and calculated curves. The calculated curves between the measured curves have correct shapes. The main difference between our model and other models is that, in our model, each measured curve, major and reversal, is described with different parameters. The magnetization process between measured curves is described according to the nearest measured curve, and this ensures the best fit for each measured curve. In other models, there is mainly only one curve, a major hysteresis or magnetization curve, used for the determination of the parameters, and all other curves are then dependent on this curve. Results confirm that the evolutionary optimization method offers a reliable procedure for precise determination of the parameters.
Today's intelligent computing environments, including the Internet of Things (IoT), Cloud Computing (CC), Fog Computing (FC), and Edge Computing (EC), allow many organizations worldwide to optimize ...their resource allocation regarding the quality of service and energy consumption. Due to the acute conditions of utilizing resources by users and the real-time nature of the data, a comprehensive and integrated computing environment has not yet provided a robust and reliable capability for proper resource allocation. Although traditional resource allocation approaches in a low-capacity hardware resource system are efficient for small-scale resource providers, for a complex system in the conditions of dynamic computing resources and fierce competition in obtaining resources, they cannot develop and adaptively manage the conditions optimally. To optimize the resource allocation with minimal delay, low energy consumption, minimum computational complexity, high scalability, and better resource utilization efficiency, CC/FC/EC/IoT-based computing architectures should be designed intelligently. Therefore, the objective of this research is a comprehensive survey on resource allocation problems using computational intelligence-based evolutionary optimization and mathematical game theory approaches in different computing environments according to the latest scientific research achievements.
This study introduces a newly developed method for optimized time-cost trade-off under uncertainty. It identifies the optimal execution mode for each project activity that results in minimizing the ...overall project cost and (or) duration while satisfying a specified joint confidence level of both time and cost. The method uses an evolutionary-based algorithm along with a design generator of experiments and blocking techniques. The developed method accounts for managerial flexibility towards the selection of execution modes. This accommodates experience-based judgement of project managers in this process. Hence, the second fold of the developed method is a completely randomized experiment module that depicts the main effect of changing an activity mode on the project total cost and overall duration. The method provides the decision-maker a guideline for making well-informed implementation strategies. The results obtained demonstrate benefits and accuracy of the developed method and its applicability for large-scale projects.
In this paper, a solution to the optimal power flow (OPF) problem in electrical power networks is presented considering high voltage direct current (HVDC) link. Furthermore, the effect of HVDC link ...converters on the active and reactive power is evaluated. An objective function is developed for minimizing power loss and improving voltage profile. Gradient-based optimization techniques are not viable due to high number of OPF equations, their complexity and equality and inequality constraints. Hence, an efficient global optimization method is used based on teaching–learning-based optimization (TLBO) algorithm. The performance of the suggested method is evaluated on a 5-bus PJM network and compared with other algorithms such as particle swarm optimization, shuffled frog-leaping algorithm and nonlinear programming. The results are promising and show the effectiveness and robustness of TLBO method.
Determination of the seven parameters of a Direct Current (DC) motor and drive is presented, based on the speed and current step responses. The method is extended for the motor and drive parameter ...determination in the case of a controlled drive. The influence of a speed controller on the responses is considered in the motor model with the use of the measured voltage. Current limitation of the supply unit is also considered in the DC motor model. For parameter determination, a motor model is used, which is determined with two coupled differential equations. Euler’s first-order and Runge–Kutta fourth-order methods are used for the motor model simulations. For parameter determination, evolutionary methods are used and compared to each other. Methods used are Genetic Algorithm, Differential Evolutions with two strategies, Teaching–Learning-Based Optimization, and Artificial Bee Colony. To improve results, deviation of the motor model simulation time is used and Memory Assistance with three different approaches is analyzed to shorten the calculation time. The tests showed that Differential Evolution (DE)/rand/1/exp is the most appropriate for the presented problem. The division of the motor model simulation time improves the results. For the presented problem, short-term memory assistance can be suggested for calculation time reduction.
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