• Energy Research

This paper presents applying the grey wolf optimization (GWO) to find an optimal solution for the combined economic and emission dispatch problem which aims to minimize the generation costs and keeping emission reduction. Six mutation operators are applied to the GWO to enhance its performance. The effect of a weight factor between generation cost and emission is also studied in this paper. A test system that consists of 10 units is simulated, the results show the effect of applying the mutation operators to the GWO.

This paper presents applying the grey wolf optimization (GWO) to find an optimal solution for the combined economic and emission dispatch problem which aims to minimize the generation costs and keeping emission reduction. Six mutation operators are applied to the GWO to enhance its performance. The effect of a weight factor between generation cost and emission is also studied in this paper. A test system that consists of 10 units is simulated, the results show the effect of applying the mutation operators to the GWO.

Economic dispatch aims to determine the optimal generated power from the generation units to meet the required load at the lowest fuel cost. In this paper, a stochastic whale optimization (SWO) method is proposed to solve the economic dispatch problem. Whale optimization algorithm is enhanced using mutation and crossover operators. To test the proposed method two systems (3 and 10 generating units) are tested. We compare the proposed SWO algorithm with whale optimization algorithm, artificial bee colony algorithm, dragonfly algorithm, ant lion algorithm, gray wolf optimization, and whale optimization algorithm with mutation only. The obtained results demonstrate the high efficiency of the proposed method compared with the other methods.

Economic dispatch aims to determine the optimal generated power from the generation units to meet the required load at the lowest fuel cost. In this paper, a stochastic whale optimization (SWO) method is proposed to solve the economic dispatch problem. Whale optimization algorithm is enhanced using mutation and crossover operators. To test the proposed method two systems (3 and 10 generating units) are tested. We compare the proposed SWO algorithm with whale optimization algorithm, artificial bee colony algorithm, dragonfly algorithm, ant lion algorithm, gray wolf optimization, and whale optimization algorithm with mutation only. The obtained results demonstrate the high efficiency of the proposed method compared with the other methods.

This paper solves the combined economic and emission dispatch (CEED) problem which aims to achieve minimum generating costs with emission reduction using different optimization methods. Six methods are discussed: moth-flame optimization (MFO), moth swarm algorithm (MSA), grey wolf optimization (GWO), antlion optimization (ALO), sine cosine algorithm (SCA), and multi-verse optimization (MVO). Different mutation operators are integrated to these methods to improve their performance. Two test systems are simulated, and the results are compared to see the effectiveness of applying mutation operators to the optimization methods.

This paper solves the combined economic and emission dispatch (CEED) problem which aims to achieve minimum generating costs with emission reduction using different optimization methods. Six methods are discussed: moth-flame optimization (MFO), moth swarm algorithm (MSA), grey wolf optimization (GWO), antlion optimization (ALO), sine cosine algorithm (SCA), and multi-verse optimization (MVO). Different mutation operators are integrated to these methods to improve their performance. Two test systems are simulated, and the results are compared to see the effectiveness of applying mutation operators to the optimization methods.

The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles.

The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles.

The penetration of photovoltaic (PV) has obviously been increased in distribution systems throughout the world. To sufficiently assess the energy losses with PV, comprehensive simulations with high time-resolution data are required. These simulations have a heavy computational burden, which makes it difficult to analyze distribution systems and evaluate PV impacts with fine resolutions. To cope with this issue, most related works down-sample, cluster, or quantize the full data to reduce the computational time on the expense of the accuracy. In this paper, we propose a fast yet accurate energy-loss assessment approach in distribution systems using machine learning. The unique feature of the proposed approach is that it uses all data to estimate losses, which yields accurate results close to the exact solutions in a very short time. The simulation results demonstrate that the proposed approach extremely reduces the computational time of energy-loss estimation with high accuracy rates. The speedup of the proposed approach with respect to power flow simulations for a yearlong at a 30-s time resolution is 28 691 (99.9965 $\%$ reduction in computational time). The effectiveness of the proposed approach is also illustrated by applying it to optimize the PV size for minimizing energy losses.

The penetration of photovoltaic (PV) has obviously been increased in distribution systems throughout the world. To sufficiently assess the energy losses with PV, comprehensive simulations with high time-resolution data are required. These simulations have a heavy computational burden, which makes it difficult to analyze distribution systems and evaluate PV impacts with fine resolutions. To cope with this issue, most related works down-sample, cluster, or quantize the full data to reduce the computational time on the expense of the accuracy. In this paper, we propose a fast yet accurate energy-loss assessment approach in distribution systems using machine learning. The unique feature of the proposed approach is that it uses all data to estimate losses, which yields accurate results close to the exact solutions in a very short time. The simulation results demonstrate that the proposed approach extremely reduces the computational time of energy-loss estimation with high accuracy rates. The speedup of the proposed approach with respect to power flow simulations for a yearlong at a 30-s time resolution is 28 691 (99.9965 $\%$ reduction in computational time). The effectiveness of the proposed approach is also illustrated by applying it to optimize the PV size for minimizing energy losses.