Efficient energy dispatching in smart microgrids using an integration of fuzzy AHP and TOPSIS assisted by linear programming

Energy dispatching in smart (micro)grids must take into account more conflicting objectives (or criteria), such as power reliability and quality, proper handling of the electricity demand, and cost decrease. The choice of the best alternative in energy dispatching decisions can be dealt with as a multi-criteria optimization and decision making problem. To this aim, we propose the use of linear programming to generate the possible alternatives, and the integration of fuzzy AHP and TOPSIS to select the best alternative. In particular, fuzzy AHP and TOPSIS are used, respectively, to prioritize the criteria and to evaluate the alternatives with respect to four conflicting criteria, namely, environmental impact, cost of the energy, distance of supply, and load level of power lines

Using multi-criteria decision-making for selecting a smart metering infrastructure

In this research a methodology based on multi-criteria decision analysis for the evaluation and selection of infrastructure energy smart metering in the Colombian context is presented. The selection process of these measurement infrastructures covers additional technical and financial criteria, becoming a complex problem. The methodology used in this work is the technique called Analytic Hierarchy Process (AHP) that considers seven assessment criteria (Technology, Finance, Environmental, Regulatory, Political, Infrastructure and SocioCultural). Of these assessment criteria, emerge 25 sub-criteria, which are integrated in a hierarchical structure to evaluate three energy smart metering alternatives. Nine experts were consulted to obtain the results. The results show the versatility of AHP method for making complex decisions with respect to the implementation of energy smart metering infrastructure, and provide a useful guide for assessing Smart Grid projects through multi-criteria analysis.In this research a methodology based on multi-criteria decision analysis for the evaluation and selection of infrastructure energy smart metering in the Colombian context is presented. The selection process of these measurement infrastructures covers additional technical and financial criteria, becoming a complex problem. The methodology used in this work is the technique called Analytic Hierarchy Process (AHP) that considers seven assessment criteria (Technology, Finance, Environmental, Regulatory, Political, Infrastructure and SocioCultural). Of these assessment criteria, emerge 25 sub-criteria, which are integrated in a hierarchical structure to evaluate three energy smart metering alternatives. Nine experts were consulted to obtain the results. The results show the versatility of AHP method for making complex decisions with respect to the implementation of energy smart metering infrastructure, and provide a useful guide for assessing Smart Grid projects through multi-criteria analysi

Using multi-criteria decision-making for selecting a smart metering infrastructure

In this research a methodology based on multi-criteria decision analysis for the evaluation and selection of infrastructure energy smart metering in the Colombian context is presented. The selection process of these measurement infrastructures covers additional technical and financial criteria, becoming a complex problem. The methodology used in this work is the technique called Analytic Hierarchy Process (AHP) that considers seven assessment criteria (Technology, Finance, Environmental, Regulatory, Political, Infrastructure and SocioCultural). Of these assessment criteria, emerge 25 sub-criteria, which are integrated in a hierarchical structure to evaluate three energy smart metering alternatives. Nine experts were consulted to obtain the results. The results show the versatility of AHP method for making complex decisions with respect to the implementation of energy smart metering infrastructure, and provide a useful guide for assessing Smart Grid projects through multi-criteria analysis.In this research a methodology based on multi-criteria decision analysis for the evaluation and selection of infrastructure energy smart metering in the Colombian context is presented. The selection process of these measurement infrastructures covers additional technical and financial criteria, becoming a complex problem. The methodology used in this work is the technique called Analytic Hierarchy Process (AHP) that considers seven assessment criteria (Technology, Finance, Environmental, Regulatory, Political, Infrastructure and SocioCultural). Of these assessment criteria, emerge 25 sub-criteria, which are integrated in a hierarchical structure to evaluate three energy smart metering alternatives. Nine experts were consulted to obtain the results. The results show the versatility of AHP method for making complex decisions with respect to the implementation of energy smart metering infrastructure, and provide a useful guide for assessing Smart Grid projects through multi-criteria analysi

Neural Network-Based Objectives Prioritization for Multi-Objective Economic Dispatch in Microgrids

In this paper we describe a neural network- based approach for automatic prioritization of objectives to solve the multi-objective economic dispatch (MOED) problem in the framework of smart microgrids. Four objectives are considered: energy cost, distance of supply, load balancing, and environmental impact. The proposed system tries to reproduce the preference function used by an expert to prioritize the objectives by assigning weights to the objectives themselves. To this aim, we use a multi-layer perceptron neural network whose inputs are four operating condition indicators sensed, with a regular time frequency, by the information network of the microgrid. Such indicators represent the current state of the microgrid. Learning has been performed by using a dataset composed of 150 samples, each one composed by a combination of the operating condition indicators, associated with a configuration of weights assigned to the objectives by an expert. Accuracies of 99.203% and 98.547% on the training and test sets, respectively, were achieved, with mean squared errors of 3.24 · 10^-4 and 6.59 · 10^-4 on the training and test sets, respectively