Two‐stage stochastic demand response in smart grid considering random appliance usage patterns

Wang, Yue; Liang, Hao; Dinavahi, Venkata

doi:10.1049/iet-gtd.2018.5943

Cited by 28 publications

(18 citation statements)

References 32 publications

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“…It is essential to study the response of stochastic demand in the smart grid considering the use patterns of random devices. An advance in this type of research will undoubtedly improve the quality of life of users [25]. User interactions, household appliances, and human activity recognition data and their relationship with household equipment are analysed.…”

Section: Related Researchmentioning

confidence: 99%

Smart Grids and Their Role in Transforming Human Activities—A Systematic Literature Review

2020

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In this work, a systematic review of the literature has been carried out to analyse the design of intelligent networks in environments inhabited by people and the applications of sensors to improve quality of life and aid human activities. This study aims to answer three research questions. The first question is whether the design of smart grids is made with people in mind. The second question focuses on whether intelligent networks are being taken account of in the research on human activity recognition, the Internet of Things, and the recognition of activities of daily living. The third question looks at whether there are synergies and multidisciplinary teams studying state-of-the-art technologies applied to environments inhabited by elderly or disabled people. Installations with sensors deployed for the improvement of the quality of human life will also help to improve the quality of the intelligent network, thus integrating the Human–Technology binomial. This study concludes with an analysis of the results of the sources examined, putting forward a protocol of seven proposals to guide future work.

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Section: Related Researchmentioning

confidence: 99%

Smart Grids and Their Role in Transforming Human Activities—A Systematic Literature Review

2020

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“…Few of such approaches are by Nilotpal et al [6,11,12], wherein multiple AC scheduling problem has been modelled as a graph and solved using a fast greedy approach. Subsequently, a genetic algorithm-driven system for optimal load scheduling was proposed in [13][14][15], which describes their system's performance without and with local renewable generation, respectively. Li et al [16] have proposed an energy management system, which has dependency on solar PV power prediction.…”

Section: Related Literaturementioning

confidence: 99%

Agent‐based system to control the air‐conditioner and EV charging for residents in smart cities

2019

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Air-conditioner (AC) accounts for a significant share of residential energy consumption. Considering the widespread rise in electric vehicle (EV) usage, its charging would also contribute a considerable percentage of consumer's total energy consumption. Consequently, the concurrent operation of AC and EV charging would result in peaky load curves. Hence, this study proposes a system of agents for AC and EV charging applications, which incorporates load-management strategies to flatten the load curve. Thereby, the presented system includes two agents, namely: a smart load node for a thermostatically controlled load (SLN-TCL) and a smart battery charge controller. Subsequently, a subagent, namely micro-node, has been introduced to support SLN-TCL and to implement the concept of distributed temperature sensing (DTS). The implementation of DTS subdues the conventional temperature sensing mechanism of AC and ensures a more flexible operation. This study includes the design, development, and features of agents and subagents for AC and EV applications. Furthermore, this study also demonstrates the agent-based control actions for peak-shaving under real conditions to showcase the performance of this system.

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“…PV power generation is assumed to be the same due to the same area. By combining the power level using (21) and (22), we can reduce the total scenario to shorten the execution time, while maintaining the accuracy of the proposed method, details can be found in [46]. Therefore, the results for execution time for a different number of scenarios are shown in Table 5.…”

Section: Ieee 33-bus and 119-bus Test Distribution Systemmentioning

confidence: 99%

Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

Wang¹,

Liang²,

Dinavahi³

2019

IET Renewable Power Generation

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Renewable power generation combined with energy storage (ES) is expected to bring enormous economical and environmental benefits to the future smart grid. However, the ES management in smart grid is facing significant technical challenges due to the volatile nature of renewable energy sources and the buffering effect of ES units. The challenges are further complicated by the increasing size and complexity of the system, as well as the consideration of random usage patterns of electrical appliances by customers. To address these challenges, this study proposes a parallel decomposition method for large-scale stochastic programming in a distribution system with renewable energy sources and ES units. By leveraging nested decomposition, the problem can be converted into independent sub-problems with a series of time periods. In addition, the reformulated problem is fully parallel for speed up in execution. The performance of the proposed method is evaluated based on the IEEE 4-bus and 33-bus test distribution systems with real photovoltaic generation and electrical appliance usage data. The case study demonstrates that the proposed scheme can substantially reduce the system operation cost, with low computational complexity.

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Two‐stage stochastic demand response in smart grid considering random appliance usage patterns

Cited by 28 publications

References 32 publications

Smart Grids and Their Role in Transforming Human Activities—A Systematic Literature Review

Smart Grids and Their Role in Transforming Human Activities—A Systematic Literature Review

Agent‐based system to control the air‐conditioner and EV charging for residents in smart cities

Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

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