Including Smart Loads for Optimal Demand Response in Integrated Energy Management Systems for Isolated Microgrids

Solanki, Bharatkumar V.; Akash, R.; Bhattacharya, Kankar; Cañizares, Claudio A.

doi:10.1109/tsg.2015.2506152

Cited by 143 publications

(59 citation statements)

References 16 publications

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“…Solanki et al [97] presented a mathematical model of the smart loads and energy management of a stand-alone MG. Loads are modeled using neural networks. Energy management is realized with the predictive control method, which performs an optimal power dispatch taking the elements and controllable loads into consideration.…”

Section: Energy Management Using Predictive Control Methodsmentioning

confidence: 99%

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

2019

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Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

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Section: Energy Management Using Predictive Control Methodsmentioning

confidence: 99%

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

2019

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“…However, demand-side management does not consider the characteristics of every load demand and the available power, so it cannot give a good power service for users. Paper [17] presents a mathematical model of smart loads in demand-response schemes that is integrated into centralized unit commitment. The goal is to obtain an optimal power flow coupled with an energy management system for isolated microgrids for optimal generation and peak load dispatch.…”

Section: Introductionmentioning

confidence: 99%

DC Microgrid System Modeling and Simulation Based on a Specific Algorithm for Grid-Connected and Islanded Modes with Real-Time Demand-Side Management Optimization

2020

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This paper presents an algorithm considering both power control and power management for a full direct current (DC) microgrid, which combines grid-connected and islanded operational modes, with real-time demand-side management optimization. The full microgrid is a hybrid dynamic system model consisting of two interacting parts: continuous-time dynamics and discrete-event dynamics. Such a full microgrid consists of photovoltaic sources, a DC load, battery storage systems, supercapacitor storage, a diesel generator, and a public grid connection, all connected on a DC common bus. This full microgrid is more reliable than a microgrid with only renewable sources or with only traditional energy sources, considering the power constraints imposed by the public grid as well as the sluggish dynamic of the diesel generator, self-discharging characteristic of the supercapacitor, and load shedding optimization. Meanwhile, this algorithm can automatically switch between grid-connected and islanded operational modes to optimize the power of the load shedding, take advantage of renewable energy, and keep the power balance in the full DC microgrid. The results under MATLAB/Simulink verify that the real-time control algorithm can maintain the power balance in real-time for the whole day and satisfy the power management strategy.

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“…In [34], a rolling horizon strategy is defined for a specify case study while in [35], an online MG-EMS focused on the optimization process presented. In [36], [37], centralized MG-EMSs for isolated microgrids are designed by decomposing the unit commitment and optimal power flow in order to avoid a mixed-integer non-linear formulation. However, most of these proposals are not tested experimentally and also the optimization model is not defined to be used in both grid-connected and islanded operation modes.…”

Section: Introductionmentioning

confidence: 99%

Online Energy Management Systems for Microgrids: Experimental Validation and Assessment Framework

Luna

Meng

Díaz

et al. 2018

IEEE Trans. Power Electron.

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Abstract-Microgrids are energy systems that can work independently from the main grid in a stable and self-sustainable way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, the main research in this field is focused on scheduling problems applicable for specific case studies rather than in generic architectures that can deal with the uncertainties of the renewable energy sources. This paper contributes a design and experimental validation of an adaptable energy management system implemented in an online scheme, as well as an evaluation framework for quantitatively assess the enhancement attained by different online energy management strategies. The proposed architecture allows the interaction of measurement, forecasting and optimization modules, in which a generic generation-side mathematical problem is modeled, aiming to minimize operating costs and load disconnections. The whole energy management system has been tested experimentally in a test bench under both grid-connected and islanded mode. Also, its performance has been proved considering severe mismatches in forecast generation and load. Several experimental results have demonstrated the effectiveness of the proposed EMS, assessed by the corresponding average gap with respect to a selected benchmark strategy and ideal boundaries of the best and worst known solutions.

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Including Smart Loads for Optimal Demand Response in Integrated Energy Management Systems for Isolated Microgrids

Cited by 143 publications

References 16 publications

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

DC Microgrid System Modeling and Simulation Based on a Specific Algorithm for Grid-Connected and Islanded Modes with Real-Time Demand-Side Management Optimization

Online Energy Management Systems for Microgrids: Experimental Validation and Assessment Framework

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