A new approach to economic-emission load dispatch using NSGA II. Case study

Moraes, Nadime Mustafa; Bezerra, Ubiratan Holanda; Rodríguez, Jorge Laureano Moya; Nascimento, Manoel Henrique Reis; Leite, Jandecy Cabral

doi:10.1002/etep.2626

Cited by 12 publications

(11 citation statements)

References 47 publications

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“…The ED optimization problem has a preponderant role in the modern energy generation system [37], [4]. It consists of correctly programming the electrical generation to reduce operating costs [38], [39]. The proposed model consists of distributing the demand for power generation between renewable and nonrenewable sources (hybrid system) among the plants more economically, reducing costs and emissions of polluting gases and maintaining the stability of the electricity grid after the penetration of electricity photovoltaic solar energy [40], [41].…”

Section: Introductionmentioning

confidence: 99%

Efficient use of the Generators for the Environmental Economic Dispatch from the energy system, including solar photovoltaic generation

Santos

Nunes

Júnior

et al. 2021

Int J Innov Educ Res

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The classic Economic Dispatch (ED) problem considers only the cost of power generation by thermal generators, often disregarding the safety parameters of the electrical network, environmental costs and especially the importance of predictive maintenance of the generators, when considering environmental costs in the optimization of ED this becomes a multi-objective problem Environmental Economic Dispatch (EED). Considering the global pressure to reduce emissions of pollutants in the atmosphere and environmental sustainability, incorporating the generation of Renewable Energies (RE) or Green Energy in the electricity grid is indispensable. Solar energy is becoming an important part of the power generation portfolio in many regions due to the fast decline in its costs and political incentives that favor the generation of clean energy sources. This article uses the Ant Lion Optimizer (ALO) method to solve the problem and EED restricted to the grid in a hybrid system (thermoelectric and photovoltaic). The results of the optimization problem were simulated in MATLAB. This research included 01 thermoelectric with 06 generators and 13 solar plants.

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Section: Introductionmentioning

confidence: 99%

Efficient use of the Generators for the Environmental Economic Dispatch from the energy system, including solar photovoltaic generation

Santos

Nunes

Júnior

et al. 2021

Int J Innov Educ Res

Self Cite

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“…In literature MO in the sense of Pareto optimality is applied to the problems in the various disciplines from economics to the engineering. Typical works can be given for economic emission dispatch [20], job shop [21], virtualization network functions (VNF) [22], tuition fees [23], on the power distribution [24], the microwave filter design [25], device modelling and LNA design [26]- [30], and antennas [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Pareto Optimal Characterization of a Microwave Transistor

2020

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Herein, noise, gain and port mismatchings of a microwave small-signal transistor are expressed as all the set of acceptable Pareto optimal solutions and trade-off relations within the device operation (V DS , I DS , f) domain without any need of expert knowledge of microwave device. In this multi-objective optimization problem, non-dominated sorting genetic algorithm (NSGA)-III is applied to an ultra-low noise amplifier (LNA) transistor NE3511S02 (HJ-FET) where the noise F req ≥ F min and output mismatching V outreq ≥ 1 are preferred as the reference points, while the input mismatching V inopt ≥ 1 and gain G Tmax are optimized with respect to source Z S and load Z L within the unconditionally stable working area. Thus, diverse set of the Pareto optimal (the required noise F req , the optimum input V inopt , the required output V outreq , the maximum transducer gain G Tmax) quadruples are resulted from a fast search of the solution space. Furthermore, the optimum bias condition (V DS , I DS) and sensitivities of the terminations to fabrication tolerances are also determined using the cost analysis in the operation domain for the required P max , I DSmax and performance quadruple. Finally, this work is expected to enable a designer to provide the feasible design target space (FDTS) consisting of all trade-off relations among all the transistor's performance ingredients to be used in the challenging LNA designs. INDEX TERMS Non-dominated sorting genetic algorithm, Pareto optimal solutions, optimization, impedance mismatching, transducer gain, noise figure.

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“…In this regard, references [37] and [38] proposed a cat swarm optimization (CSO) algorithm and modified gray wolf optimization (MGWO) algorithm to improve the GA and PSO algorithms, respectively. In addition, J. Moshtagh proposed a nondominated sorting genetic algorithm II (NSGA-II) to solve the multiobjective solution in the reconfiguration of the distribution network [39][40]. However, these algorithms are easily affected by the parameters, and the solution is unstable.…”

Section: Introductionmentioning

confidence: 99%

An Improved Beetle Swarm Algorithm Based on Social Learning for a Game Model of Multiobjective Distribution Network Reconfiguration

Chen

Wang

et al. 2020

IEEE Access

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With the increased distributed generation (DG) and electric vehicle (EV) load penetration in distribution networks, it is more difficult to ensure the safe and economic operation of the distribution networks because of the great volatility and randomness of DG and EV loads. In this paper, the uncertainties of wind power, photovoltaics, conventional loads, and EV loads are considered. Photovoltaics and conventional loads are related to solar radiation, and they are subtracted to form the net load. Then, the Wasserstein distance is used to divide the scene, and K-means clustering is used to reduce the scene, so the reconstruction analysis is carried out in the limited typical scene. In addition, to minimize network loss, load balance and maximum voltage deviation, a multiobjective game reconstruction model of the distribution network is established under the condition of satisfying network constraints. Moreover, the social beetle swarm optimization algorithm considering two social behaviors is adopted to solve the complex problem. Finally, the simulation results are verified on the standard IEEE-33 system and IEEE-118 system. The results show that the proposed strategy and algorithm can effectively reduce the network loss, improve the node voltage level and ensure that the load is not overloaded.

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A new approach to economic-emission load dispatch using NSGA II. Case study

Cited by 12 publications

References 47 publications

Efficient use of the Generators for the Environmental Economic Dispatch from the energy system, including solar photovoltaic generation

Efficient use of the Generators for the Environmental Economic Dispatch from the energy system, including solar photovoltaic generation

Pareto Optimal Characterization of a Microwave Transistor

An Improved Beetle Swarm Algorithm Based on Social Learning for a Game Model of Multiobjective Distribution Network Reconfiguration

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