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1
Yasin, Z. M., N. F. A. Aziz, N. A. Salim, N. A. Wahab und N. A. Rahmat. „Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm“. Indonesian Journal of Electrical Engineering and Computer Science 12, Nr. 1 (01.10.2018): 168. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp168-174.
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In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to test the effectiveness of the proposed technique which are fuel cost minimization, carbon emission minimization and multiobjective function with fixed weighted sum. The effectiveness of the MOCSA’s performances are illustrated through comparative study with other techniques such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Particle Swarm Optimization (MOPSO) in terms of fitness functions. The proposed study was conducted on three generating unit system at various loading condition. The result proved that MOCSA provide better solution in minimizing fuel cost and carbon emission usage as compared to other techniques.
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., N. Ramyasri. „FUZZIFIED PSO FOR MULTIOBJECTIVE ECONOMIC LOAD DISPATCH PROBLEM“. International Journal of Research in Engineering and Technology 02, Nr. 08 (25.08.2013): 157–62. http://dx.doi.org/10.15623/ijret.2013.0208026.
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3
Zhang, Guoping, Weijun Wang, Jie Du und Haoyun Sheng. „Multiobjective Economic Optimal Dispatch for the Island Isolated Microgrid under Uncertainty Based on Interval Optimization“. Mathematical Problems in Engineering 2021 (11.10.2021): 1–14. http://dx.doi.org/10.1155/2021/9983104.
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In order to analyse the impact of renewable generation and load uncertainties on the economic operation optimization of the island microgrid, a multiobjective economic optimal dispatch model under uncertainty based on interval optimization is proposed in this paper. The mathematical model of distributed generation and the prediction model of wind speed and wave generation are established. The uncertainties of renewable generation and load are described by the interval mathematical method. On this basis, the interval multiobjective optimal dispatch model is presented. For the “battery disgusting” users on the island, the battery cost is regarded as a separate optimization objective, and a multiobjective optimization objective function to minimize the economic cost, battery cost, and pollution emission of the island microgrid is discussed. An island microgrid, composed of wind turbine, photovoltaic, wave energy generation, diesel generator, and energy storage system, is chosen as a case study. The NSGA-II algorithm is applied to solve the multiobjective optimal problem. The results for deterministic forecast data and load are analysed, and the optimal operation scheme is obtained by the improved multiobjective grey target decision-making method. The influence of renewable generation fluctuations ±10%, ±20%, and ±30% and the load fluctuations ±10% and ±20% on island microgrid operation optimization is discussed in detail, respectively. The relevant research results can provide a reference for formulating the operating scheme of the island microgrid.
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Singh, Nagendra, und Yogendra Kumar. „Multiobjective Economic Load Dispatch Problem Solved by New PSO“. Advances in Electrical Engineering 2015 (19.02.2015): 1–6. http://dx.doi.org/10.1155/2015/536040.
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Proposed in this paper is a new particle swarm optimization technique for the solution of economic load dispatch as well as environmental emission of the thermal power plant with power balance and generation limit constraints. Economic load dispatch is an online problem to minimize the total generating cost of the thermal power plant and satisfy the equality and inequality constraints. Thermal power plants use fossil fuels for the generation of power; fossil fuel emits many toxic gases which pollute the environment. This paper not only considers the economic load dispatch problem to reduce the total generation cost of the thermal power plant but also deals with environmental emission minimization. In this paper, fuel cost and the environmental emission functions are considered and formulated as a multiobjective economic load dispatch problem. For obtaining the solution of multiobjective economic load dispatch problem a new PSO called moderate random search PSO was used. MRPSO enhances the ability of particles to explore in the search spaces more effectively and increases their convergence rates. The proposed algorithm is tested for the IEEE 30 bus test systems. The results obtained by MRPSO algorithm show that it is effective and efficient.
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Hou, Hui, Mengya Xue, Yan Xu, Jinrui Tang, Guorong Zhu, Peng Liu und Tao Xu. „Multiobjective Joint Economic Dispatching of a Microgrid with Multiple Distributed Generation“. Energies 11, Nr. 12 (23.11.2018): 3264. http://dx.doi.org/10.3390/en11123264.
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Based on the operation characteristics of each dispatch unit, a multi-objective hierarchical Microgrid (MG) economic dispatch strategy with load level, source-load level, and source-grid-load level is proposed in this paper. The objective functions considered are to minimize each dispatching unit’s comprehensive operating cost (COC), reduce the power fluctuation between the MG and the main grid connect line, and decrease the remaining net load of the MG after dispatch by way of energy storage (ES) and clean energy. Firstly, the load level takes electric vehicles (EVs) as a means of controlling load to regulate the MG’s load fluctuation using its energy storage characteristics under time-of-use (TOU) price. Then, in order to minimize the remaining net load of the MG and the COC of the ES unit through Multiobjective Particle Swarm Optimization (MPSO), the source-load level adopts clean energy and ES units to absorb the optimized load from the load level. Finally, the remaining net load is absorbed by the main grid and diesel engines (DE), and the remaining clean energy is sold to the main grid to gain benefits at the source-grid-load level. Ultimately, the proposed strategy is simulated and analyzed with a specific example and compared with the EVs’ disorderly charging operation and MG isolated grid operation, which verifies the strategy’s scientificity and effectiveness.
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Farag, A., S. Al-Baiyat und T. C. Cheng. „Economic load dispatch multiobjective optimization procedures using linear programming techniques“. IEEE Transactions on Power Systems 10, Nr. 2 (Mai 1995): 731–38. http://dx.doi.org/10.1109/59.387910.
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Swain, Rajkishore, Pallab Sarkar, Krishna Chandra Meher und Chandan Kumar Chanda. „Population variant differential evolution-based multiobjective economic emission load dispatch“. International Transactions on Electrical Energy Systems 27, Nr. 10 (09.08.2017): e2378. http://dx.doi.org/10.1002/etep.2378.
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Jain, N. K., Uma Nangia und Iqbal Singh. „Multiobjective Economic Load Dispatch in 3-D Space by Genetic Algorithm“. Journal of The Institution of Engineers (India): Series B 98, Nr. 5 (05.08.2017): 495–501. http://dx.doi.org/10.1007/s40031-017-0280-x.
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Rajesh, Kummari, und N. Visali. „Hybrid method for achieving Pareto front on economic emission dispatch“. International Journal of Electrical and Computer Engineering (IJECE) 10, Nr. 4 (01.08.2020): 3358. http://dx.doi.org/10.11591/ijece.v10i4.pp3358-3366.
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In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multi-objective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.
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Singh, Nagendra, Tulika Chakrabarti, Prasun Chakrabarti, Martin Margala, Amit Gupta, Sivaneasan Bala Krishnan und Bhuvan Unhelkar. „A New PSO Technique Used for the Optimization of Multiobjective Economic Emission Dispatch“. Electronics 12, Nr. 13 (05.07.2023): 2960. http://dx.doi.org/10.3390/electronics12132960.
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Most power is generated using fossil fuels like coal, natural gas, and diesel. The contribution of coal to power generation is very high compared to other sources. Almost all thermal power plants use coal as a fuel for power generation. Such sources of fossil fuels are limited and thus the cost of power generation increases. At the same time, the induced toxic gases due to these fossil fuels pollute the environment. The objective of this work is to solve the economic emission dispatch problem. Economic emission dispatch helps to find out how to operate power plants at the minimum cost and induce the minimum emissions at a thermal power plant. Economic emission dispatch with constraints is a nonlinear optimization problem. For the solution of such nonlinear economic emission load dispatch problems, this work considers a new particle swarm optimization technique. The proposed new PSO gives the best solution for economic emission load dispatch and handles the constraints. For the testing of the proposed new PSO algorithm, this work considered a case study of a system of six generating units, and it was tested for load demands of 700 MW, 800 MW, and 1000 MW. The results of the new PSO for the three load demands considered give the minimum generation cost, minimum emission, and minimum total cost compared to other optimization algorithms. The proposed techniques are effective, and they can help obtain the minimum generation cost and minimize emissions.
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Mousa, A., Kotb Kotb und Adel Elmekawy. „Hybrid Multiobjective evolutionary Algorithm Based Technique for Economic Emission Load Dispatch Optimization Problem“. International Conference on Electrical Engineering 7, Nr. 7 (01.05.2010): 1–12. http://dx.doi.org/10.21608/iceeng.2010.33021.
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Yang, Le, Dakuo He und Bo Li. „A Selection Hyper-Heuristic Algorithm for Multiobjective Dynamic Economic and Environmental Load Dispatch“. Complexity 2020 (20.01.2020): 1–18. http://dx.doi.org/10.1155/2020/4939268.
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Dynamic economic and environmental load dispatch (DEED) aims to determine the amount of electricity generated from power plants during the planning period to meet load demand while minimizing energy consumption costs and environmental pollution emission indicators subject to the operation requirements. This planning problem is usually expressed using a nonsmooth cost function, taking into account various equality and inequality constraints such as valve-point effects, operational limits, power balance, and ramp rate limits. This paper presents DEED models developed for a system consisting of thermal units, wind power generators, photovoltaic (PV) generators, and energy storage (ES). A selection hyper-heuristic algorithm is proposed to solve the problems. Three heuristic mutation operators formed a low-level operator pool to direct search the solution space of DEED. The high level of SHHA evaluates the performances of the low-level operators and dynamically adjusts the chosen probability of each operator. Simulation experiments were carried out on four systems of different types or sizes. The results verified the effectiveness of the proposed method.
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Mousa, Abd Allah A. „Hybrid ant optimization system for multiobjective economic emission load dispatch problem under fuzziness“. Swarm and Evolutionary Computation 18 (Oktober 2014): 11–21. http://dx.doi.org/10.1016/j.swevo.2014.06.002.
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Wan, Xiaofeng, Hai Lian, Xiaohua Ding, Jin Peng, Yining Wu und Xin Li. „Hierarchical Multiobjective Dispatching Strategy for the Microgrid System Using Modified MOEA/D“. Complexity 2020 (28.09.2020): 1–19. http://dx.doi.org/10.1155/2020/4725808.
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The large-scale electric vehicles connected to the microgrid have brought various challenges to the safe and economic operation of the microgrid. In this paper, a hierarchical microgrid dispatching strategy considering the user-side demand is proposed. According to the operation characteristics of each dispatch unit, the strategy divides the microgrid system into two levels: source-load level and source-grid-load level. At the source-load level, priority should be given to the use of the renewable energy output. On the basis of considering the user demand, energy storage, electric vehicles, and dispatchable loads should be utilized to maximize the consumption of the renewable energy and minimize the user’s electricity cost. The source-grid-load level can smooth the tie-line power fluctuation through dispatching of the power grid and diesel generators. Furthermore, the study presents a modified MOEA/D algorithm to solve the hierarchical scheduling problem. In the proposed algorithm, a modified Tchebycheff decomposition method is introduced to obtain uniformly distributed solutions. In addition, initialization and replacement strategies are introduced to enhance the convergence and diversity. A wind-photovoltaic-diesel-storage hybrid power system is considered to verify the performance of the proposed dispatching strategy and the modified algorithm. The obtained results are compared with other dispatching approaches, and the comparisons confirm the effectiveness and scientificity of the proposed strategy and algorithm.
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Yamaguti, Lucas do Carmo, Juan Manuel Home-Ortiz, Mahdi Pourakbari-Kasmaei und José Roberto Sanches Mantovani. „Economic/Environmental Optimal Power Flow Using a Multiobjective Convex Formulation“. Energies 16, Nr. 12 (12.06.2023): 4651. http://dx.doi.org/10.3390/en16124651.
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This paper addresses the problem of economic/environmental optimal power flow with a multiobjective formulation using a second-order conic programming (SOCP) optimization model. This problem formulation considers renewable energy sources (RES), fossil-fuel-based power generation units, and voltage control. The proposed SOCP model is a stochastic scenario-based approach to deal with RES and load behavior uncertainties. An ε-constrained algorithm is used to handle the following three objective functions: (1) the costs of power generation, (2) active power losses in the branches, and (3) the emission of pollutant gases produced by fossil-fuel-based power generation units. For comparative purposes, the SOCP model is also presented using a linearized formulation, and numerical results are presented using a 118-bus system. The results confirm that changing the energy matrices directly affects the cost of objective functions. Additionally, using a linearized SOCP model significantly reduces reactive power violation in the generation units when compared to the nonlinearized SOCP model, but also increases the computational time consumed.
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Gao, Jinjin, Yuan Zheng, Jianming Li, Xiaoming Zhu und Kan Kan. „Optimal Model for Complementary Operation of a Photovoltaic-Wind-Pumped Storage System“. Mathematical Problems in Engineering 2018 (26.12.2018): 1–9. http://dx.doi.org/10.1155/2018/5346253.
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An optimization model for the complementary operation of a photovoltaic-wind-pumped storage system is built to make full use of solar and wind energy. Apart from ensuring the maximum economic benefit which is normally used as the only objective, the stable objectives of minimizing the output fluctuation and variation of load and output difference are added to form the multiobjective problems because of lack of study on access capacity of photovoltaic and wind power. The model aims to increase the power benefit and reduce the output fluctuation and variation of load and output difference under the constraints of station, output balance, and transmission limitation. In a case study, four schemes including single-objective independent operation, single-objective complementary operation, and multiobjective complementary operation are compared to discuss the effect of pumped storage station on economic objective and stable objectives. Furthermore, the opposite trend of the two objectives is proved and a compromise optimal solution is given. The results indicate that the pumped storage station can effectively increase power benefit and access capacity of photovoltaic and wind power. The study can provide references to the complementary optimization of the pumped storage station and the intermittent renewable energy.
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Mousa, A., und I. El-Desoky. „A Novel Multiobjective Genetic Algorithm Based Technique for Economic Emission Load Dispatch Optimization Problem“. International Conference on Electrical Engineering 6, Nr. 6 (01.05.2008): 1–20. http://dx.doi.org/10.21608/iceeng.2008.34524.
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Jain, Narender Kumar, Uma Nangia und Aishwary Jain. „PSO for Multiobjective Economic Load Dispatch (MELD) for Minimizing Generation Cost and Transmission Losses“. Journal of The Institution of Engineers (India): Series B 97, Nr. 2 (07.02.2015): 185–91. http://dx.doi.org/10.1007/s40031-014-0184-y.
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Osman, M. S., M. A. Abo-Sinna und A. A. Mousa. „An ɛ-dominance-based multiobjective genetic algorithm for economic emission load dispatch optimization problem“. Electric Power Systems Research 79, Nr. 11 (November 2009): 1561–67. http://dx.doi.org/10.1016/j.epsr.2009.06.003.
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Bao, Man, Hongqi Zhang, Hao Wu, Chao Zhang, Zixu Wang und Xiaohui Zhang. „Multiobjective Optimal Dispatching of Smart Grid Based on PSO and SVM“. Mobile Information Systems 2022 (18.01.2022): 1–9. http://dx.doi.org/10.1155/2022/2051773.
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The optimization of microgrid is an important part of smart grid. The global energy consumption is seriously greater than the energy it has, and the environmental pollution brought by it should not be underestimated. If we want to reduce their impact, introducing the optimization of microgrid is a good solution. Short-term load forecasting is a very important prerequisite for microgrid optimization, which lays a solid foundation for the realization of the development goal of environmental protection and the improvement of the economic benefits of microgrid. In this paper, a Multi-PSO-SVM forecasting model is proposed to forecast the actual load. By simulating four prediction models with three different samples, we can see that the average predicted value and actual load value of Multi-PSO-SVM algorithm in the three different samples are almost less than 10 MV. Compared with the other three algorithms, Multi-PSO-SVM is superior in accurately predicting the load value at each time point, which provides important conditions for the success of microgrid optimization.
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Almalaq, Abdulaziz, Tawfik Guesmi und Saleh Albadran. „A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem“. Energies 16, Nr. 12 (06.06.2023): 4554. http://dx.doi.org/10.3390/en16124554.
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The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD).
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Dash, Subrat Kumar, Sivkumar Mishra und Almoataz Y. Abdelaziz. „A Critical Analysis of Modeling Aspects of D-STATCOMs for Optimal Reactive Power Compensation in Power Distribution Networks“. Energies 15, Nr. 19 (21.09.2022): 6908. http://dx.doi.org/10.3390/en15196908.
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Distribution static compensators (D-STATCOMs) can enhance the technical performance of the power distribution network by providing rapid and continuous reactive power support to the connected bus. Accurate modeling and efficient utilization of D-STATCOMs can maximize their utility. In this regard, this article offers a novel current-injection-based D-STATCOM model under the power control mode of operation for the reactive power compensation of the power distribution network. The versatility of the proposed D-STATCOM model is demonstrated by combining it with two of the most established distribution load flow techniques, viz., the forward–backward sweep load flow and the BIBC–BCBV-matrix-based direct load flow. Further, the allocation of the proposed D-STATCOM model is carried out under a multiobjective mathematical formulation consisting of various technical and economic indices such as the active power loss reduction index, voltage variation minimization index, voltage stability improvement index and annual expenditure index. A novel parameter-free metaheuristic algorithm, namely a student-psychology-based optimization algorithm, is proposed to determine the optimal assignment of the different number of D-STATCOM units under the multiobjective framework. The proposed allocation scheme is implemented on a standard 33-bus test system and on a practical 51-bus rural distribution feeder. The obtained results demonstrate that the proposed D-STATCOM model can be efficiently integrated into the distribution load flow algorithms. The student-psychology-based optimization algorithm is found to be robust and efficient in solving the optimal allocation of D-STATCOMs as it yields minimum power loss compared to other established approaches for 33-bus PDNs. Further, the economic analysis carried out in this work can guide network operators in deciding on the number of D-STATCOMs to be augmented depending on the investment costs and the resulting savings.
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Liu, Xiaojuan, und Jian’an Fang. „Long-Term Load Forecasting Based on a Time-Variant Ratio Multiobjective Optimization Fuzzy Time Series Model“. Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/781043.
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Load forecasting problem is a complex nonlinear problem linked with economic and weather factors. Long-term load forecasting provides useful information for maintenance scheduling, adequacy assessment, and limited energy resources for electrical power systems. Fuzzy time series forecasting models can be used for long-term load forecasting. However, the interval length has been chosen arbitrarily in the implementations of known fuzzy time series forecasting models, which has an important impact on the performance of these models. In this paper, a time-variant ratio multiobjective optimization fuzzy time series model (TV-RMOP) is proposed, and its performance is tested on the prediction of enrollment at the University of Alabama. Results clearly promote the forecasting accuracy as compared to the conventional models. A genetic algorithm is used to search for the length of intervals based on the training data while Pareto optimality theory provides the necessary conditions to identify an optimal one. The TV-RMOP model is applied for the long-term load forecasting in Shanghai of China.
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Ma, Li, Chao Zhang, TaoFeng Liu, DingRong Tian, Wei Zhang und ShuPing Gao. „Research on Optimal Access Point and Capacity of Multiscenario Wind Turbine Based on Voltage Sag“. Mathematical Problems in Engineering 2022 (28.02.2022): 1–13. http://dx.doi.org/10.1155/2022/2368924.
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Aiming at reducing the impact on sensitive loads and the economic losses caused by voltage sags in the distribution network containing wind turbines, a multiobjective mathematical model of the optimal access point and access capacity is established from the three perspectives of the basic costs of wind turbines, the economic losses of voltage sags, and the economic losses of the power grid. A scene generation method based on the similarity of wind curve changes and a scene reduction algorithm based on DPC & k-means are proposed to solve the mathematical problem of wind turbine uncertainty. Based on the analysis of the voltage sag of the distribution network containing wind turbines, the evaluation indicator of the voltage sag depth of the bus and the evaluation indicator of the economic loss of the voltage sag of the sensitive load are proposed. The simulation results of the IEEE 33-bus system show that the reasonable access of wind turbines can help reduce the power loss and voltage sag of the distribution network, thereby ensuring the safe and economical operation of the system.
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Li, Xin, und Yanjun Fang. „Dynamic Environmental/Economic Scheduling for Microgrid Using Improved MOEA/D-M2M“. Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/2167153.
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The environmental/economic dynamic scheduling for microgrids (MGs) is a complex multiobjective optimization problem, which usually has dynamic system parameters and constraints. In this paper, a biobjective optimization model of MG scheduling is established. And various types of microsources (like the conventional sources, various types of renewable sources, etc.), electricity markets, and dynamic constraints are considered. A recently proposed MOEA/D-M2M framework is improved (I-MOEA/D-M2M) to solve the real-world MG scheduling problems. In order to deal with the constraints, the processes of solutions sorting and selecting in the original MOEA/D-M2M are revised. In addition, a self-adaptive decomposition strategy and a modified allocation method of individuals are introduced to enhance the capability of dealing with uncertainties, as well as reduce unnecessary computational work in practice and meet the time requirements for the dynamic optimization tasks. Thereafter, the proposed I-MOEA/D-M2M is applied to the independent MG scheduling problems, taking into account the load demand variation and the electricity price changes. The simulation results by MATLAB show that the proposed method can achieve better distributed fronts in much less running time than the typical multiobjective evolutionary algorithms (MOEAs) like the improved strength Pareto evolutionary algorithm (SPEA2) and the nondominated sorting genetic algorithm II (NSGAII). Finally, I-MOEA/D-M2M is used to solve a 24-hour MG dynamic operation scheduling problem and obtains satisfactory results.
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SIDDIQUE, Nazmul, und Hojjat ADELI. „APPLICATIONS OF GRAVITATIONAL SEARCH ALGORITHM IN ENGINEERING“. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 22, Nr. 8 (25.11.2016): 981–90. http://dx.doi.org/10.3846/13923730.2016.1232306.
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Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In a recent article the authors reviewed the principles of GSA. This article presents a review of applications of GSA in engineering including combinatorial optimization problems, economic load dispatch problem, economic and emission dispatch problem, optimal power flow problem, optimal reactive power dispatch problem, energy management system problem, clustering and classification problem, feature subset selection problem, parameter identification, training neural networks, traveling salesman problem, filter design and communication systems, unit commitment problem and multiobjective optimization problems.
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Tan, Mao, Hua-li Yang, Bin Duan, Yong-xin Su und Feng He. „Optimizing Production Scheduling of Steel Plate Hot Rolling for Economic Load Dispatch under Time-of-Use Electricity Pricing“. Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1048081.
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Time-of-Use (TOU) electricity pricing provides an opportunity for industrial users to cut electricity costs. Although many methods for Economic Load Dispatch (ELD) under TOU pricing in continuous industrial processing have been proposed, there are still difficulties in batch-type processing, since power load units are not directly adjustable and nonlinearly depend on production planning and scheduling. In this paper, for hot rolling, a typical batch-type and energy intensive process in steel industry, a production scheduling optimization model for ELD is proposed under TOU pricing, in which the objective is to minimize electricity costs while considering penalties caused by jumps between adjacent slabs. A NSGA-II based multiobjective production scheduling algorithm is developed to obtain Pareto optimal solutions, and then TOPSIS based multicriteria decision-making is performed to recommend an optimal solution to facilitate field operation. Experimental results and analyses show that the proposed method cuts electricity costs in production, especially in case of allowance for penalty score increase in a certain range. Further analyses show that the proposed method has effect on peak load regulation of power grid.
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Cao, Lixia, Guoliang Feng, Xingong Cheng und Luhao Wang. „Configuration of smart phase-swapping switches in low-voltage distribution systems based on sequenced participation indices“. Measurement and Control 53, Nr. 7-8 (27.05.2020): 1159–70. http://dx.doi.org/10.1177/0020294020920899.
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The smart phase-swapping switches are used to rapidly change the phases of single-phase loads online in low-voltage distribution systems. They can reduce the three-phase imbalance indices. However, the effectiveness of phase-swapping operations is determined by not only the control strategy but also by the quantity and locations of smart phase-swapping switches. In this paper, a configuration method is proposed to determine the preferable quantity and locations of smart phase-swapping switches with considerations of economic benefits and operational requirements. Based on historical load information, the active and reactive powers of the loads are used to formulate the current imbalance index. The configuration problem is modeled as a multiobjective optimization that minimizes the current imbalance indices of all nodes and phase-swapping operations. The problem is solved by the particle swarm optimization algorithm to obtain the phase-swapping participation index of each single-phase load. The loads with high phase-swapping participation indices are preferably equipped with smart phase-swapping switches. The simulation results verify that the proposed method is effective and easy to be implemented in practical applications.
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Jain, N. K., Uma Nangia und Jyoti Jain. „Multiobjective Economic Load Dispatch Studies in 2-D and 3-D Space by Particle Swarm Optimization Technique“. Journal of The Institution of Engineers (India): Series B 100, Nr. 3 (07.02.2019): 233–47. http://dx.doi.org/10.1007/s40031-019-00386-z.
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Lihui, Zhang, Xin He und Ju Liwei. „A Multiobjective Scheduling Optimization Model for Multienergy Complementary System Integrated by Wind-Photovoltaic-Convention Gas Turbines considering Demand Response“. Mathematical Problems in Engineering 2018 (31.07.2018): 1–16. http://dx.doi.org/10.1155/2018/3208934.
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To utilize the complementary feature of different power sources, wind power plant (WPP), and solar photovoltaic power (PV), convention gas turbines (CGT) and incentive-based demand response (IBDR) are integrated into a multienergy complementary system (MECS) with the implementation of price-based demand response (PBDR). Firstly, the power output model of WPP, PV, and CGT is constructed and the mathematical model of DR is presented. Then, a multiobjective scheduling model is proposed for MECS operation under the objective functions of the maximum economic benefit, the minimum abandoned energy, and the minimum risk level. Thirdly, the payoff table of objective functions is put forward for converting the multiobjective model into a single objective model by using entropy weight method to calculate weighting coefficients of different objective functions. Finally, the improved IEEE 30 bus system is taken as the simulation system with four simulation scenarios for comparatively analyzing the influence of PBDR and IBDR on MECS operation. The simulation results show the following: (1) The MECS fully utilized the complementarity of different power sources; CGT and IBDR can provide peaking service for WPP and PV to optimize overall system operation. (2) The proposed algorithm can solve the MECS multiobjective scheduling optimization model, and the system scheduling results in the comprehensive optimal mode can take into account different appeal. And the total revenue, abandoned energy capacity, and load fluctuation are, respectively, 108009.30¥, 11.62 MW h, and 9.74 MW. (3) PBDR and IBDR have significant synergistic optimization effects, which can promote the grid connection of WPP and PV. When they are both introduced, the peak-to-valley ratio of the load curve is 1.19, and the abandoned energy is 5.85 MW h. Therefore, the proposed MECS scheduling model and solution algorithm could provide the decision basis for decision makers based on their actual situation.
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Zheng, Tao, Jing Cao, Yufeng Yang und Hui Gao. „Considering Source-Charge-Storage Multiple Time Scale Cooperative Control Strategy“. Mathematical Problems in Engineering 2021 (27.05.2021): 1–10. http://dx.doi.org/10.1155/2021/5526783.
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In order to achieve the optimization of power consumption mode, improve user power efficiency, and realize the coordination of power supply and demand, considering the advantages of distributed energy and energy storage, a source-charge-storage multi-time-scale coordinated control strategy is proposed. According to the needs of the hybrid energy system, we analyze the complementary potential of the hybrid energy system from the output side and analyze the response priority mechanism of the integrated energy system equipment, taking the economic cost and pollutant gas emissions as the objective function, economy, environment, and system. This is a constrained source-load-storage multiobjective joint optimization and adjustment model, which is solved by a multi-time-scale cooperative control strategy. Finally, a calculation example is used to verify the feasibility of the proposed method and provide technical support for the coordinated and optimized operation of “source-load-storage.”
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Xu, Ye, Na Meng, Xu Wang, Junyuan Tan und Wei Li. „A Multiobjective Fractional Programming for a CHP System Operation Optimization Based on Energy Intensity“. Energies 15, Nr. 6 (08.03.2022): 1965. http://dx.doi.org/10.3390/en15061965.
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The objective of this research is to establish a multiobjective fractional programming (MOFP) model for supporting the operational management of a combined heat and power (CHP) system. Compared with the traditional operational optimization model of the CHP system, the importance of the energy intensity (i.e., the ratio of energy consumption and energy production) was emphasized in the MOFP model, which is considered as the system objective for replacing the common objective of minimizing the economic cost. This innovative transformation effectively reduces excessive energy consumption, accompanied by improvement in the system revenue. The CHP system of an industrial park in the City of Jinan, China, was used as a study case for demonstration. The obtained results reflected that the combination of two gas turbines (GTs) ensured safe, efficient, and stable output for meeting daily power requirements in various seasons. As for the steam load, during the summer, two heat recovery steam generators (HRSGs) play a major role, where the insufficient part is supplemented by two gas-fired boilers (SBs); conversely, the steam load in winter is mainly satisfied by the aid of two SBs. The successful application of the MOFP model in the park could provide a good demonstration for CHP management in many other districts and cities.
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Tang, Qingfeng, Nian Liu und Jianhua Zhang. „Optimal Operation Method for Microgrid with Wind/PV/Diesel Generator/Battery and Desalination“. Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/857541.
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The power supply mode of island microgrid with a variety of complementary energy resources is one of the most effective ways to solve the problem of future island power supply. Based on the characteristics of seawater desalination system and water demand of island residents, a power allocation strategy for seawater desalination load, storage batteries, and diesel generators is proposed with the overall consideration of the economic and environmental benefits of system operation. Furthermore, a multiobjective optimal operation model for the island microgrid with wind/photovoltaic/diesel/storage and seawater desalination load is also proposed. It first establishes the objective functions which include the life loss of storage batteries and the fuel cost of diesel generators. Finally, the model is solved by the nondominated sorting genetic algorithm (NSGA-II). The island microgrid in a certain district is taken as an example to verify the effectiveness of the proposed optimal method. The results provide the theoretical and technical basis for the optimal operation of island microgrid.
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Chang, Chen-Yu, und Pei-Fang Tsai. „Multiobjective Decision-Making Model for Power Scheduling Problem in Smart Homes“. Sustainability 14, Nr. 19 (21.09.2022): 11867. http://dx.doi.org/10.3390/su141911867.
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The aim of this study was to solve power scheduling issues in smart homes to enable demand response in smart grids. The objective of demand response is to match demand with supply by reflecting supply expectations through consumer price signals, and especially to avoid peak demand during times of high prices and when supply is limited. Three objectives were considered: first, economic rationing by minimizing the total costs for consumers with the given hourly prices; second, to achieve better efficiency in terms of supply and greater stability in a power system by reducing peaks in usage or load, which is defined by minimizing the percentage of power rate; third, related to consumer comfort levels, by reducing variance in the schedule of appliances to actual usage periods requested. This multiobjective power scheduling problem for smart homes (PHPSH) was explored using a nondominated sorting genetic algorithm, called NSGA-II. The results showed that the Pareto-optimal solutions from NSGA-II are compatible with the weighted-sum-based model from the literature, and viable alternatives are available for end users with different weighted objectives.
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Fina, Bernadette, Andreas Fleischhacker, Hans Auer und Georg Lettner. „Economic Assessment and Business Models of Rooftop Photovoltaic Systems in Multiapartment Buildings: Case Studies for Austria and Germany“. Journal of Renewable Energy 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/9759680.
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This paper analyses the profitability and business models of shared, nonsubsidized PV systems’ usage in multiapartment buildings in Austria in the context of legislative amendments which came into force in July 2017. In addition, it compares the Austrian results with those of Germany, where significantly higher retail electricity prices determine the profitability benchmark. To that end, a multiobjective optimization model is developed for the optimal dimensioning of PV systems and energy storage facilities in keeping with different end user objectives, ranging from minimizing annual electricity costs to maximizing self-consumption. The results show that the profitability of shared use of nonsubsidized PV systems is marginal in Austria. This means that, based on individual apartment load profiles, the profitability gap ranges between 0 and 40 euros per apartment, whereas the consideration of the building as total load leads to a small cost-saving potential of about 90 euros for the whole building in the best case and thus profitability. In contrast, significant profitability of shared PV systems in multiapartment buildings can be achieved in Germany, where the renewable energy surcharge results in high retail electricity prices. At present, different business models, accounting and billing concepts, are being tested in these countries to learn about the best-practice concepts.
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Zhang, Lihui, He Xin, Jing Wu, Liwei Ju und Zhongfu Tan. „A Multiobjective Robust Scheduling Optimization Mode for Multienergy Hybrid System Integrated by Wind Power, Solar Photovoltaic Power, and Pumped Storage Power“. Mathematical Problems in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/9485127.
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Wind power plant (WPP), photovoltaic generators (PV), cell-gas turbine (CGT), and pumped storage power station (PHSP) are integrated into multienergy hybrid system (MEHS). Firstly, this paper presents MEHS structure and constructs a scheduling model with the objective functions of maximum economic benefit and minimum power output fluctuation. Secondly, in order to relieve the uncertainty influence of WPP and PV on system, robust stochastic theory is introduced to describe uncertainty and propose a multiobjective stochastic scheduling optimization mode by transforming constraint conditions with uncertain variables. Finally, a 9.6 MW WPP, a 6.5 MW PV, three CGT units, and an upper reservoir with 10 MW·h equivalent capacity are chosen as simulation system. The results show MEHS system can achieve the best operation result by using the multienergy hybrid generation characteristic. PHSP could shave peak and fill valley of load curve by optimizing pumping storage and inflowing generating behaviors based on the load supply and demand status and the available power of WPP and PV. Robust coefficients can relieve the uncertainty of WPP and PV and provide flexible scheduling decision tools for decision-makers with different risk attitudes by setting different robust coefficients, which could maximize economic benefits and minimize operation risks at the same time.
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Chen, Guang, Bin Chen, Pan Dai und Hao Zhou. „A Sustainability-Oriented Multiobjective Optimization Model for Siting and Sizing Distributed Generation Plants in Distribution Systems“. Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/291930.
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This paper proposes a sustainability-oriented multiobjective optimization model for siting and sizing DG plants in distribution systems. Life cycle exergy (LCE) is used as a unified indicator of the entire system’s environmental sustainability, and it is optimized as an objective function in the model. Other two objective functions include economic cost and expected power loss. Chance constraints are used to control the operation risks caused by the uncertain power loads and renewable energies. A semilinearized simulation method is proposed and combined with the Latin hypercube sampling (LHS) method to improve the efficiency of probabilistic load flow (PLF) analysis which is repeatedly performed to verify the chance constraints. A numerical study based on the modified IEEE 33-node system is performed to verify the proposed method. Numerical results show that the proposed semilinearized simulation method reduces about 93.3% of the calculation time of PLF analysis and guarantees satisfying accuracy. The results also indicate that benefits for environmental sustainability of using DG plants can be effectively reflected by the proposed model which helps the planner to make rational decision towards sustainable development of the distribution system.
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Ahmadi Kamarposhti, Mehrdad, Hassan Shokouhandeh, Yahya Gholami Omali, Ilhami Colak, Phatiphat Thounthong und William Holderbaum. „Optimal Coordination of TCSC and PSS2B Controllers in Electric Power Systems Using MOPSO Multiobjective Algorithm“. International Transactions on Electrical Energy Systems 2022 (28.11.2022): 1–18. http://dx.doi.org/10.1155/2022/5233620.
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Oscillations are an intrinsic phenomenon in interconnected power systems, leading to steady-state stability, safety decline, transmission power limitation, and electric power systems’ ineffective exploitation by developing power systems, particularly by connecting these systems to low-load lines. In addition, they affect the economic performance of the systems. In this study, PSS2B power system stabilizers and TCSC compensators are used to improve the stability margin of power systems. In order to coordinate TCSC compensators, the MOPSO multiobjective algorithm with integral of the time-weighted absolute error (ITAE) and figure of demerit (FD) objective functions was used. The MOPSO algorithm optimization results are compared with nondominated sorting genetic algorithm (NSGAII) and multiobjective differential evolution (MODE) algorithms. The optimization results indicated a better performance of the proposed MOPSO algorithm than NSGAII and MODE. The simulations were iterated in two scenarios by creating different loading conditions in generators. The results indicated that the proposed control system, where the coordination between PSS2B power system stabilizers and TCSC compensators using the MOPSO algorithm, is better than power systems in which PSS2B Stabilizers or TCSC compensators are utilized solely. All criteria, e.g., ITAE, FD, maximum deviation range, and the required time for power oscillation damping in hybrid control systems, have been obtained. This means more stability and accurate and proper performance.
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Xu, Chunyan, und Cai Song. „Optimization of Innovation and Entrepreneurship Education and Training System in Colleges and Universities Based on OpenStack Cloud Computing“. Scientific Programming 2022 (30.08.2022): 1–12. http://dx.doi.org/10.1155/2022/2868499.
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With economic globalization and rapid development of science and technology, many colleges and universities pay more and more attention to the cultivation of students’ innovative thinking and creativity, and innovation and entrepreneurship education has also become an important part of the education system. Due to the current unevenness of teachers in innovation and entrepreneurship education in colleges and universities, high training cost, and lack of strong atmosphere, this paper optimizes the innovation and entrepreneurship education and training system in colleges and universities through OpenStack cloud computing. This paper optimizes the cloud computing platform according to the OpenStack virtual machine and the multiobjective ant colony improvement algorithm and then designs the innovation and entrepreneurship education and training system. The multiobjective ant colony improvement algorithm uses the way ants find food to find the best information resource route from the traces left by the information trend in the cloud platform. In order to test the effectiveness of these methods, this paper uses the simulation method to test. The results show that, sometimes, the load utilization rate of the innovation and entrepreneurship education and training system in colleges and universities exceeds 80%, which is in line with the expected settings. Through the OpenStack cloud computing platform, it can provide a good innovation and entrepreneurship training environment for more users at low cost and low risk and promote the development of innovation and entrepreneurship education.
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Karandikar, H. M., und F. Mistree. „Tailoring Composite Materials Through Optimal Selection of Their Constituents“. Journal of Mechanical Design 114, Nr. 3 (01.09.1992): 451–58. http://dx.doi.org/10.1115/1.2926573.
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The use of composite materials has provided designers with increased opportunities for tailoring structures and materials to meet load requirements and changing and demanding environments. This has led to their increased use in structural applications. As with traditional materials the selection of an appropriate material for a design is important. In case of design using composite materials the selection of a material consists of selecting a fiber-resin combination which meets all design requirements. This involves choosing the fiber, the resin, and the proportion of these two constituents in the composite material. The phrase “material selection” refers to the problem of laminate selection. This corresponds to the task of choosing a fiber and resin combination based on technical and economic factors. Materials tailoring, on the other hand, involves manipulating the composition of the composite material to achieve desired properties and it is the selection of a fiber and resin simultaneously but separately. In this paper we present, through an example, a multiobjective optimization-based method for assisting a designer in tailoring composite materials for specific technical and economic objectives.
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Chu, Xiaolin, Peng Wang und Dong Yang. „Multiobjective Model for Microgrids Integrating Electric Vehicles to Grid and Building Based on Interest Balance“. Mathematical Problems in Engineering 2022 (27.12.2022): 1–18. http://dx.doi.org/10.1155/2022/4578868.
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Microgrids allow energy exchange among multiple interconnected microgrids for greater energy efficiency and collective economic interest. However, in some cases, the benefit of some microgrids within the network may not be uncertain. In view of the increasing development of electric vehicles (EVs), a multiobjective model is proposed to improve the performance of microgrids by integrating electric vehicles-to-grid (V2G) and vehicles-to-building (V2B) based on global and individual benefit balance. Two reference models are built to verify the validity of the proposed method, and models are formulated as mixed integer linear programming formats solved by the weighting method. A set of parameters of microgrids are adopted to model the driver behaviors (e.g., available hours of EV), energy transactions (e.g., electricity), performance factor (e.g., emission factor), distributed energy (e.g., solar panel), and energy load of five commercial buildings (e.g., hotel) located in Shanghai. Simulation results demonstrate the effectiveness of the operation decision models in the energy management of microgrids under neutral, proeconomic, proenvironmental, and proenergy weighting scenarios. The case study results specify that the proposed method can achieve operational cost, CO2 emission, and primary energy consumption reductions for each microgrid, with total benefits declining slightly.
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Ramadan, Ashraf, Mohamed Ebeed, Salah Kamel, Ahmed M. Agwa und Marcos Tostado-Véliz. „The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer“. Energies 15, Nr. 4 (11.02.2022): 1302. http://dx.doi.org/10.3390/en15041302.
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Renewable distributed generators (RDGs) are widely embedded in electrical distribution networks due to their economic, technological, and environmental benefits. However, the main problem with RDGs, photovoltaic generators, and wind turbines, in particular, is that their output powers are constantly changing due to variations in sun irradiation and wind speed, leading to power system uncertainty. Such uncertainties should be taken into account when selecting the optimal allocation of RDGs. The main innovation of this paper is a proposed efficient metaheuristic optimization technique for the sizing and placement of RDGs in radial distribution systems considering the uncertainties of the loading and RDG output power. A Monte Carlo simulation method, along with the backward reduction algorithm, is utilized to create a set of scenarios to model these uncertainties. To find the positions and ratings of the RDGs, the artificial gorilla troops optimizer (GTO), a new efficient strategy that minimizes the total cost, is used to optimize a multiobjective function, total emissions, and total voltage deviations, as well as the total voltage stability boosting. The proposed technique is tested on an IEEE 69-bus network and a real Egyptian distribution grid (East Delta Network (EDN) 30-bus network). The results indicate that the proposed GTO can optimally assign the positions and ratings of RDGs. Moreover, the integration of RDGs into an IEEE 69-bus system can reduce the expected costs, emissions, and voltage deviations by 28.3%, 52.34%, and 66.95%, respectively, and improve voltage stability by 5.6%; in the EDN 30-bus system, these values are enhanced by 25.97%, 51.1%, 67.25%, and 7.7%, respectively.
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Liu, Cong, Xianghua Li, Jian Liang, Kun Sheng, Lingzhao Kong, Xiaoyan Peng und Wenxin Zhao. „A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid“. International Transactions on Electrical Energy Systems 2023 (22.04.2023): 1–10. http://dx.doi.org/10.1155/2023/1358099.
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Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule.
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Li, Fei, Guang Zhang und Shaohua Hu. „An Algorithm for Optimal Allocation of Water Resources in Receiving Areas Based on Adaptive Decreasing Inertia Weights“. Journal of Advanced Transportation 2022 (16.04.2022): 1–12. http://dx.doi.org/10.1155/2022/3329628.
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As the biggest rigid constraint for high-quality economic and social development, how to improve the carrying capacity of water resources, realize the stable and coordinated development of water resources-ecological environment-economic and social integrated system, and provide water resources guarantee for regional transformation and upgrading development is a major issue in the current social development. This paper firstly selects the minimum loss of water resources allocation as the objective function for mathematical modelling, chooses the particle swarm algorithm as the objective algorithm, and proposes a particle swarm algorithm based on the standard particle swarm algorithm with improved adaptive decreasing inertia weights. It is a time-varying process for the inertia weights and acceleration factors of the standard particle swarm algorithm so that they change nonlinearly with the continuous advancement of the iterative optimization seeking process, thus improving the convergence accuracy and speed of the algorithm and reducing the risk of falling into local optimum solutions at a later stage. Finally, based on the actual installation of the current water distribution reactive power compensation device, the shunt distributor set is selected as the reactive power compensation device, sensitivity analysis is applied to the load nodes for sensitivity calculation, and the nodes requiring compensation are connected to the shunt distributor set for flexible and optimal configuration. The stronger local search capability of the inertia weight adaptive decreasing algorithm is utilized in the generation process of new particles to perform a local search operation for particles, which avoids premature convergence and improves the search performance of the algorithm. To realize the rational allocation of water resources, a multiobjective receiving area water resources optimization allocation model with maximum water supply benefit, minimum regional water shortage, and minimum pollutant emission is established.
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Tomczewski, Andrzej, Stanisław Mikulski, Adam Piotrowski, Sławomir Sowa und Krzysztof Wróbel. „Multicriteria Optimisation of the Structure of a Hybrid Power Supply System for a Single-Family Housing Estate in Poland, Taking into Account Different Electromobility Development Scenarios“. Energies 16, Nr. 10 (16.05.2023): 4132. http://dx.doi.org/10.3390/en16104132.
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This article focuses on determining the optimum structure for a hybrid generation and storage system designed to power a single-family housing estate, taking into account the different number of electric vehicles in use and an assumed level of self-consumption of the generated energy. In terms of generation, two generation sections—wind and solar—and a lithium-ion container storage system will be taken into account. With regards to energy consumption, household load curves, determined on the basis of the tariff for residential consumers and modified by a random disturbance, will be taken into account, as well as the processes for charging electric cars with AC chargers, with power outputs ranging between 3.6 and 22 kW. Analyses were carried out for three locations in Poland—the Baltic Sea coast (good wind conditions), the Lublin Uplands (the best insolation in Poland) and the Carpathian foothills (poor wind and insolation conditions). The mathematical and numerical model of the system and the MOPSO (multiobjective particle swarm optimisation) algorithm were implemented in the Matlab environment. The results include Pareto fronts (three optimisation criteria: minimisation of energy storage capacity, minimisation of energy exchanged with the power grid and maximisation of the self-consumption rate) for the indicated locations and three electromobility development scenarios with determined NPVs (net present values) for a 20-year lifetime. The detailed results relate to the inclusion of an additional expert criterion in the form of a coupled payback period of no more than 10 years, a maximum NPV in the last year of operation and a self-consumption rate of at least 80%. The economic calculations take into account the decrease in PV installation capacity as a function of the year of operation, as well as changes in electricity and petrol prices and variations in energy prices at purchase and sale.
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Khodadadi, Ali, Taher Abedinzadeh, Hasan Alipour und Jaber Pouladi. „Optimal Operation of Energy Hub Systems under Resiliency Response Options“. Journal of Electrical and Computer Engineering 2023 (10.01.2023): 1–13. http://dx.doi.org/10.1155/2023/2590362.
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The economic and resilient operation of power systems has always been one of the main priorities of energy systems. In spite of improvements in various fields of energy systems, especially power systems, the issue of resilience has become more important. For this purpose, this paper proposes a multiobjective optimization model to improve the economic performance of energy hub systems and improve the resilience of electrical consumers. Also, consumer welfare, which is a function of the energy not supplied index, is maximized over a 24-hour period by considering extreme weather conditions. The ε-constraint method is applied to solve the proposed model by transforming the multiobjective optimization problem into several single-objective optimization problems. The max-min fuzzy method is also used to select the optimal solution among the Pareto solutions. A sample hub system is made up of electrical, thermal, and gas loads, electrical and thermal energy sources, and storage systems employed as a test system. A group of actions is applied to improve the resilience of the system, which may be affected by outages caused by storms under the resilience response program (RRP). The results proved the efficiency of the proposed RRP in improving economics and resilience.
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Xue, Wuxia. „Application of Multiobjective Particle Swarm Optimization in Rural Credit System“. Journal of Sensors 2021 (23.12.2021): 1–9. http://dx.doi.org/10.1155/2021/3468479.
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In China’s rural credit system, the problem of credit constraints is prominent. Due to the imperfect credit market, a large number of rural residents have credit constraints. Rural credit constraint is a serious problem restricting China’s rural economic development. Aimed at solving the rural credit constraints, this paper makes an optimization analysis on the rural credit system and loan decision-making. To more reasonably evaluate customers’ borrowing ability, the credit risk based on farmers’ data on the big data platform is evaluated in this paper. The stacked denoising autoencoder network is improved by adopting the deep learning framework to improve the accuracy of credit evaluation. For improving the loan decision-making ability of rural credit system, a loan optimization strategy based on multiobjective particle swarm optimization algorithm is proposed. The simulation results show that the optimization ability, speed, and stability of the proposed algorithm have achieved good results in dealing with the loan portfolio decision-making problem.
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Miracle, D. Blandina, R. K. Viral, P. M. Tiwari und Mohit Bansal. „Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System“. International Transactions on Electrical Energy Systems 2023 (06.04.2023): 1–25. http://dx.doi.org/10.1155/2023/5395658.
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Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.
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Sheng, Wanxing, Qing Duan, Haoqing Wang, Guanglin Sha und Chunyan Ma. „Archive-Based Multiobjective Evolutionary Algorithm for Large-Scale EV Charging Station Energy Management“. Discrete Dynamics in Nature and Society 2021 (02.09.2021): 1–6. http://dx.doi.org/10.1155/2021/2000041.
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With the increase in renewable energy, improving the utilization rate of renewable energy is of great practical significance. The microgrid has been proved effective in addressing this issue. As a flexible load, electric vehicles are connected to the grid on a large scale, which will have an impact on the grid. In order to solve this problem, this paper proposes a microgrid energy management model for electric vehicle charging stations, which takes into account the economics of microgrid operation and the stability of grid operation. Subsequently, this paper proposes an evolutionary multiobjective optimization algorithm to deal with constraints. Finally, this paper verifies the effectiveness of the proposed model and algorithm through experiments.
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Ricco, Juan Tamassia, Rogerio Frauendorf Faria Coimbra und Guilherme Ferreira Gomes. „Multiobjective optimization of the LASER aircraft wing’s composite structural design“. Aircraft Engineering and Aerospace Technology 93, Nr. 6 (17.06.2021): 995–1010. http://dx.doi.org/10.1108/aeat-06-2020-0113.
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Purpose Aircraft wings, one of the most important parts of an aircraft, have seen changes in its topological and design arrangement of both the internal structures and external shape during the past decades. This study, a numerical, aims to minimize the weight of multilaminate composite aerospace structures using multiobjective optimization. Design/methodology/approach The methodology started with the determination of the requirements, both imposed by the certifying authority and those inherent to the light, aerobatic, simple, economic and robust (LASER) project. After defining the requirements, the loads that the aircraft would be subjected to during its operation were defined from the flight envelope considering finite element analysis. The design vector consists of material choice for each laminate of the structure (20 in total), ply number and lay-up sequence (respecting the manufacturing rules) and main spar position to obtain a lightweight and cheap structure, respecting the restrictions of stress, margins of safety, displacements and buckling. Findings The results obtained indicated a predominance of the use of carbon fiber. The predominant orientation found on the main spar flange was 0° with its location at 28% of the local chord, in the secondary and main web were ±45°, the skins also had the main orientation at ±45°. Originality/value The key innovations in this paper include the evaluation, development and optimization of a laminated composite structure applied to a LASER aircraft wings considering both structural performance and manufacturing costs in multiobjetive optimization. This paper is one of the most advanced investigations performed to composite LASER aircraft.