Dynamic Appliances Scheduling in Collaborative MicroGrids System

Bilil, Hasnae; Aniba, Ghassane; Gharavi, Hamid

doi:10.1109/tpwrs.2016.2613479

Cited by 26 publications

(20 citation statements)

References 29 publications

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“…To set the operation time of each shiftable load optimally, the load shifts are controlled by OSs. OSs in [46,47] schedule the operation time of loads to reduce PS's peak power consumption and to flatten the load profile. The OS in [47] optimally schedules the group of household appliances connected to a microgrid.…”

Section: Ps Oss Regulating the Power Consumption During The Optimizatmentioning

confidence: 99%

“…OSs in [46,47] schedule the operation time of loads to reduce PS's peak power consumption and to flatten the load profile. The OS in [47] optimally schedules the group of household appliances connected to a microgrid. To achieve optimal scheduling, the OS categorizes the appliances into flexible and non-flexible deferrable loads, according to their electrical components.…”

Section: Ps Oss Regulating the Power Consumption During The Optimizatmentioning

confidence: 99%

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Review of Trends and Targets of Complex Systems for Power System Optimization

Vysocky

Mišák

2020

Energies

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Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.

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Section: Ps Oss Regulating the Power Consumption During The Optimizatmentioning

confidence: 99%

Section: Ps Oss Regulating the Power Consumption During The Optimizatmentioning

confidence: 99%

Review of Trends and Targets of Complex Systems for Power System Optimization

Vysocky

Mišák

2020

Energies

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“…Equations (16) and (17) set the charge/discharge modes when the power consumed by the household loads is lower/higher than the power generated by the renewable generation units; Equation (18) maintains the power of the unit between prespecified limits; Equation (19) guarantees the charge and discharge modes are not simultaneously activated; Equations (20) and (21) account for the unit capacity to charge and discharge; finally, Equation (22) represents the active power of the unit at interval t + 1. It is important to mention that the multi-objective nature of GAs allows the inclusion of new objective functions and constraints without modifying the algorithm structure.…”

Section: Energy Storage Unitmentioning

confidence: 99%

“…A drawback of the former is that it cannot be applied to household appliances with fixed operating times, limiting its applicability, whereas the latter does not consider renewable energy generation and uses simplistic models of the household appliances. The NSGA-II algorithm is also used in [16], where a set of microgrids sent their corresponding load curve to an operating center for flattening purposes, then, dynamic appliance scheduling was carried out by sequentially solving two optimization problems: one for the non-flexible loads and one for the flexible loads; a Pareto front is generated based on the minimization of a load flattening function and the delay to supply the household appliances; a PV generation is also considered using a simplified model of negative load power. Just recently, fuzzy logic combined with optimization techniques and supervisory control strategies have been applied for cost, energy consumption, and peak-to-average ratio reduction [17,18].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Home Electric Energy Management System Considering Renewable Energy Availability

et al. 2019

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This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches.

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“…Therefore, space-dependency is another factor that needs to be incorporated into the optimization process. Solving problems in both space and time requires a suitable multi-objective optimization solution, such as NSGA-II [20, 21]. In this paper, and for the sake of simplicity, we extend the K-mean solution into two successive multi-objective clustering processes.…”

Section: Introductionmentioning

confidence: 99%

Space-Time Approach for Disturbance Detection and Classification

Gharavi

2018

IEEE Trans. Smart Grid

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A major challenge facing future grid systems is to identify the source of abnormal behaviors caused by faults or voltage instability. In this paper Phasor Measurement Units (PMUs) have been considered for detecting disturbances and degradation in the grid. Considering that the source of voltage instability mainly impacts neighboring areas, we present a simple and yet efficient algorithm that can identify affected areas. The algorithm is based on K-Mean optimization that classifies PMUs into different classes of power quality. We introduce the concept of a space-time solution to identify multiple faults. Since this is a multi-objective problem, we extend the K-mean algorithm to achieve space-time optimization in two successive stages (i.e., time and space). As an alternative approach to the space-time solution, we also present a hierarchical network that is based on a distributed mapping technique. This approach may only require time-based clustering as it limits the PMU population search to a small local region. We used our real-time Virtual PMU (VPMU) embedded into our software in the loop testbed to verify the performance of the proposed schemes. For the IEEE 39-bus transmission system, it is shown that the proposed space-time synchrophasor data classification scheme is capable of detecting and isolating areas in the grid that suffer from multiple disturbances, such as faults.

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Dynamic Appliances Scheduling in Collaborative MicroGrids System

Cited by 26 publications

References 29 publications

Review of Trends and Targets of Complex Systems for Power System Optimization

Review of Trends and Targets of Complex Systems for Power System Optimization

A Heuristic Home Electric Energy Management System Considering Renewable Energy Availability

Space-Time Approach for Disturbance Detection and Classification

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