A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential

Jia, Ning; Tang, Yi; Chen, Qian; Wang, Jianming; Zhou, Jian; Gao, Bingtuan

doi:10.3390/en10040525

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…Each community contains many residential users with a rich flexible load, such as air condition and an electrical vehicle. The dispatching center of the smart grid is mainly responsible for DR transaction with the residential communities in group N [24,25]. In the day-ahead DR market, since only the bidding price and amount are exchanged between the dispatching center and community, each community does not reveal the details about the energy consumption of native users' appliances.…”

Section: System Modelmentioning

confidence: 99%

Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior

Liu

Wang

2019

Energies

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Demand response (DR) has been recognized as a powerful tool to relieve energy imbalance in the smart grid. Most previous works have ignored the irrational behavior of energy consumers in DR project implementation. Accordingly, in this paper, we focus on solving two questions during the execution of DR. Firstly, considering the bounded rationality of residential users, a population dynamic model is proposed to describe the decision behavior on whether to participate in the DR project, and then the evolutionary process of consumers participating in DR is analyzed. Secondly, for the DR participants, they have to compete dispatching amounts for maximal profit in a day-ahead bidding market, hence, a non-cooperative game model is proposed to describe the competition behavior, and the uniqueness of the Nash equilibrium is analyzed with mathematical proof. Then, the distributed algorithm is designed to search the evolutionary result and the Nash equilibrium. Finally, a case study is performed to show the effectiveness of the formulated models.

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Section: System Modelmentioning

confidence: 99%

Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior

Liu

Wang

2019

Energies

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“…As for the diversity of cluster users, they are often in different states when dispatch instructions are issued, making it difficult to cut off all the load agreed at the designated moment. Therefore, such IL protocols need to be set to be more relaxed, enabling users not to cut off all the load immediately [38]. This will lead to the position that the time characteristics of their response are very important for the accuracy of grid operation and dispatching, however there is little relevant literature to study or discuss that.…”

Section: User Response Model and Compensation Strategy Of Interruptibmentioning

confidence: 99%

Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads

et al. 2018

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Abstract:In order to better handle the new features that emerge at both ends of supply and demand, new measures are constantly being introduced, such as demand-side management (DSM) and prediction of uncertain output and load. However, the existing DSM strategies, like real-time price (RTP), and dispatch methods are optimized separately, and response models of active loads, such as the interruptible load (IL), are still imperfect, which make it difficult for the active distribution network (ADN) to achieve global optimal operation. Therefore, to better manage active loads, the response characteristics including both the response time and the responsibility and compensation model of IL for cluster users, and the real-time demand response model for price based load, were analyzed and established. Then, a collaborative optimization strategy of RTP and optimal dispatch of ADN was proposed, which can realize an economical operation based on mutual benefit and win-win mode of supply and demand sides. Finally, the day-ahead and intra-day integrative dispatch model using different time-scale prediction data was established, which can achieve longer-term optimization while reducing the impact of prediction errors on the dispatch results. With numerical simulations, the effectiveness and superiority of the proposed strategy were verified.

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“…Similar logic is suitable for WH. For the EV, when it is connected to the charging station and can charge to the minimum SOC before the desired finish time, it has DRP [29].…”

Section: Aggregated Drpmentioning

confidence: 99%

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

2017

Self Cite

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Abstract:The uncertainty of wind power results in wind power forecasting errors (WPFE) which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR) is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC) units to smooth the system's imbalance, considering the real-time DR potential (DRP) and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC), water heater (WH), electric vehicle (EV) loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers' comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts.

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A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential

Cited by 17 publications

References 17 publications

Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior

Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior

Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

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