Performance evaluation of power demand scheduling scenarios in a smart grid environment
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TLDR
This paper presents and analyzes four power-demand scheduling scenarios that aim to reduce the peak demand in a smart grid infrastructure and unveils the consistency and necessity of the proposed scenarios and corresponding analytical models.About:
This article is published in Applied Energy.The article was published on 2015-03-15 and is currently open access. It has received 46 citations till now. The article focuses on the topics: Smart grid & Demand response.read more
Citations
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Journal ArticleDOI
Demand side management in a smart micro-grid in the presence of renewable generation and demand response
TL;DR: In this paper, a stochastic programming model is proposed to optimize the performance of a smart micro-grid in a short term to minimize operating costs and emissions with renewable sources.
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Multi-objective cost-load optimization for demand side management of a residential area in smart grids
G Hamed Shakouri,Aliyeh Kazemi +1 more
TL;DR: An intelligent energy management framework that can be used to minimize both electrical peak load and electricity cost is proposed and a multi-objective mixed integer linear programming (MOMILP) is proposed.
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Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties
TL;DR: In this article, the simultaneous scheduling of electrical vehicles and responsive loads to reduce operation cost and emission in presence of wind and PV powers in microgrid was proposed to reduce the system operation costs and emissions.
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Compression of smart meter big data: A survey
TL;DR: This study presents a comprehensive study on the compression techniques for smart meter big data to reduce the transmission pressure and storage overhead, improve data mining efficiency, and thus fulfil the potential of smart meterbig data.
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Optimizing the management of smart home energy resources under different power cost scenarios
TL;DR: The results suggest that contracted power scenarios may be phased out and safely replaced by variable power charges scenarios with the deployment of AHEMS and the feasibility of deploying the AHEMS under study in a low-cost embedded system is assessed.
References
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Journal ArticleDOI
Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid
Amir-Hamed Mohsenian-Rad,Vincent W. S. Wong,Juri Jatskevich,Robert Schober,Alberto Leon-Garcia +4 more
TL;DR: This paper presents an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid.
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Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments
TL;DR: Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios.
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A summary of demand response in electricity markets
TL;DR: In this article, the authors present a summary of demand response in deregulated electricity markets and highlight the most common indices used for DR measurement and evaluation, and some utilities' experiences with different demand response programs are discussed.
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Achieving Controllability of Electric Loads
TL;DR: Conceptual frameworks for actively involving highly distributed loads in power system control actions and some of the challenges to achieving a load control scheme that balances device- level objectives with power system-level objectives are discussed.
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Blocking in a Shared Resource Environment
TL;DR: It is shown that, for the important and commonly implemented policy of complete sharing, a simple one-dimensional recursion can be developed which eliminates all difficulty in computing quantities of interest-regardless of both the size and dimensionality of the underlying model.
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