Design and Simulation of the Autonomous Decentralized Dispatching System of Generalized Demand Side Resources

Pan, Feng; Liu, Shengyuan; Lin, Jia Wei; Fan, Shangchun; He, Guangyu; Jia, Kunqi

doi:10.1109/isgt-asia.2018.8467786

Cited by 2 publications

(2 citation statements)

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“…The automatic recognition algorithm was revealed in our previous work [26], and it is not the contribution of this paper, so here we introduce it in a simple form. First, based on HEMS [8], which include smart plugs and gateways, various experiments can be used to collect and store different kinds of electric appliances' operation data. Then, we can use the K-Means algorithm to cluster the power data to several centers [27], in which each center corresponds to a typical operating state.…”

Section: Automatic Recognition Of Appliance Statusmentioning

confidence: 99%

An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality

et al. 2019

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With the development of techniques, such as the Internet of Things (IoT) and edge computing, home energy management systems (HEMS) have been widely implemented to improve the electric energy efficiency of customers. In order to automatically optimize electric appliances’ operation schedules, this paper considers how to quantitatively evaluate a customer’s comfort satisfaction in energy-saving programs, and how to formulate the optimal energy-saving model based on this satisfaction evaluation. First, the paper categorizes the utility functions of current electric appliances into two types; time-sensitive utilities and temperature-sensitive utilities, which cover nearly all kinds of electric appliances in HEMS. Furthermore, considering the bounded rationality of customers, a novel concept called the energy-saving cost is defined by incorporating prospect theory in behavioral economics into general utility functions. The proposed energy-saving cost depicts the comfort loss risk for customers when their HEMS schedules the operation status of appliances, which is able to be set by residents as a coefficient in the automatic energy-saving program. An optimization model is formulated based on minimizing energy consumption. Because the energy-saving cost has already been evaluated in the context of the satisfaction of customers, the formulation of the optimization program is very simple and has high computational efficiency. The case study included in this paper is first performed on a general simulation system. Then, a case study is set up based on real field tests from a pilot project in Guangdong province, China, in which air-conditioners, lighting, and some other popular electric appliances were included. The total energy-saving rate reached 65.5% after the proposed energy-saving program was deployed in our project. The benchmark test shows our optimal strategy is able to considerably save electrical energy for residents while ensuring customers’ comfort satisfaction is maintained.

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Section: Automatic Recognition Of Appliance Statusmentioning

confidence: 99%

An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality

et al. 2019

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“…In our previous works, an MQTT/MQTT-SN-based user energy management system (UEMS) was established and verified. [13][14][15] In the UEMS, all the end-user's electric appliances can be integrated into the system, such as air conditioners and water heaters. Based on these works, it is possible to realize appliance-level energy management and optimization.…”

Section: Introductionmentioning

confidence: 99%

Data-driven demand-side energy management approaches based on the smart energy network

Pan

Liu

Yang

et al. 2019

Journal of Algorithms & Computational Technology

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The energy shortage problem cannot be ignored in the development of economics. A demand-side smart energy network is introduced in this paper, which integrates renewable energy resources, energy storage devices, and various types of load into an autonomous distributed architecture. Our approach employs the internet, Internet of Things (IoT), data mining, and other advanced technologies. The network aims to share energy information to realize distributed smart energy management, which can, for example, save energy while ensuring the reliability and quality of electricity for customers. Based on the network, a series of smart energy management theories are proposed to support the smart energy network. The core idea of these theories is to allow the network to teach itself in order to learn from the massive amounts of collected energy data, by using machine learning algorithms. The concept of power utility is proposed to quantitatively assess load energy efficiency. Then, a data-driven consumer energy activity recognition method is proposed based on a hidden Markov model (HMM). A test system is generated using field data from a pilot project in Guangdong Province, China. The energy saving rate in our test is 37.9%, which means that the smart energy network and the proposed algorithms perform well for automatic and intelligent energy efficiency management.Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us. sagepub.com/en-us/nam/open-access-at-sage).

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Design and Simulation of the Autonomous Decentralized Dispatching System of Generalized Demand Side Resources

Cited by 2 publications

References 11 publications

An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality

An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality

Data-driven demand-side energy management approaches based on the smart energy network

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