Analysis and Accurate Prediction of User’s Response Behavior in Incentive-Based Demand Response

Liu, Di; Yi, Sun; Yao, Qin; Li, Bin; Xu, Yanchun

doi:10.1109/access.2018.2889500

Cited by 44 publications

(24 citation statements)

References 37 publications

(44 reference statements)

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“…In addition, this paper only considers price-based DR programs [38], [39] such as TOU [40]. Actually as another important DR program, incentive-based DR program [41] is gradually applied into the residential sector [42]. The optimal scheduling of appliance under incentive-based DR programs will be incorporated into the HEMS in the future.…”

Section: Discussionmentioning

confidence: 99%

“…In order to consider customers' preferences for different types of energy when optimizing the cooperation of electric appliances and natural gas appliances, a novel model for calculating the dissatisfaction level caused by the alternative use of different kind of appliances is proposed as shown in Eq. (41), where U denotes the customers' preference parameters. A lower value of U means a higher preference level.…”

Section: ) Model Of Irreplaceable Reducible Appliancesmentioning

confidence: 99%

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Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Zhang

et al. 2019

IEEE Access

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Home energy management systems (HEMSs) enable residential customers to efficiently participate in demand response programs in order to obtain optimal benefits. Traditional HEMSs only manage household electric appliances to reduce the electricity consumption cost while the optimal scheduling of natural gas appliances has been overlooked. Due to the increasing popularity of natural gas appliances in modern smart homes, the electricity consumption of residential customers connected to the natural gas network is significantly affected by the use of natural gas appliances. To consider the interaction between electric and natural gas appliances in households, a day-ahead optimal joint scheduling model of electric and natural gas appliances for HEMS is proposed. Firstly, all household appliances are classified into several categories and the mathematical model of each appliance is presented. Then, a day-ahead optimal joint scheduling model of both electric and natural gas appliances for HEMS is formulated, in which the objective function is to minimize the household's energy cost and the dissatisfaction level caused by the shifting, reduction and replacement of loads in response to the time varying prices. Case studies using realistic data indicate that the proposed model can save the total energy costs up to 30% for customers whilst ensuring their satisfaction levels. INDEX TERMS Home energy management system, natural gas, demand response, joint scheduling, dissatisfaction. A. SUPERSCRIPTS bch Battery charging bdch Battery discharging dw Dishwasher eac Electric air conditioner el Electric light es Electric stove ev Electric vehicle ewh Electric water heater gahs Gas air heating system The associate editor coordinating the review of this manuscript and approving it for publication was Alexander Micallef. gs Gas stove gwh Gas water heater ua Uncontrollable appliance s Stove wh Water heater wm Washing machine B. INDEXES AND SETS

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Section: Discussionmentioning

confidence: 99%

Section: ) Model Of Irreplaceable Reducible Appliancesmentioning

confidence: 99%

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Zhang

et al. 2019

IEEE Access

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“…The ever-increasing demand for electricity and the growing penetration of intermittent renewable energy sources pose severe challenges to power grids in maintaining real-time balance between demand and supply [1]. In recent decades, demand response (DR), which aims to explore the inherent flexibility of the demand side, has been widely regarded as an effective and powerful means to meet those challenges [2,3]. In general, DR is envisaged to both help improve the overall operation efficiency and reliability of smart grids [4], and benefit consumers in certain ways.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Feasibility of Game-Theory-Based Demand Response Management by Practical Implementation

Zhang

Jiang

et al. 2021

IEEE Access

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Demand response (DR) has been widely recognized as an effective solution to help mitigate the stresses imposed on power grids. As new concepts evolve, DR induces various interactions among multiple emerging entities, which further complicates the decision-making processes in grid operations. Recently, game theory (GT) has received great attention in DR management, due to its ability to handle complex decision-making problems. Numerous theoretical GT-based approaches have been proposed for addressing various DR issues, but the feasibility of these theoretical approaches in practical implementation remains in doubt. To bridge the gap between theoretical studies and practical implementations, we first provide specific guidelines regarding how to construct a DR-oriented facility, and then investigate the effectiveness of deploying a Stackelberg game theory-based DR algorithm to manage the energy consumption of the facility, wherein the energy management center (EMC) serves as the leader and multiple devices act as the followers. The experimental evaluation results show that the GT-based DR algorithm achieved great performance in practical DR management, including optimal load control in responding to real-time price (RTP), and peak load reduction with a peak-to-average ratio (PAR) of 1.59.

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“…Other researchers have moved towards understanding and predicting customer behavior in demand response [15]- [18]. This behavior influences the customer's consumption/load profile and thus load forecasting [16], [17]. In [15] the nearest neighbor algorithm and Markov chain algorithm was used to predict user behavior and energy management.…”

Section: Introductionmentioning

confidence: 99%

Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms

2019

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Load forecasting is useful for various applications, including maintenance planning. The study of load forecasting using recent state-of-the-art hybrid artificial intelligence (AI) and deep learning (DL) techniques is limited in South Africa (SA) and South African power distribution networks. This paper proposes a novel hybrid AI and DL South African distribution network load forecasting system. The system comprises of modules that handle the collection of the loading data from the field, analysis of data integrity using fuzzy logic, data preprocessing, consolidation of the loading and the temperature data, and load forecasting. The load forecasting results are then used to inform maintenance planning. The load forecasting is conducted using a hybrid AI/DL load forecasting module. A novel comparative study of recent state-of-theart AI techniques is also presented to determine the best technique to deploy in this module when forecasting South African power redistributing customers' loads. The impact of the inclusion of weather parameters and loading data clean up on the load forecasting performance of a hybrid AI technique, optimally pruned extreme learning machines (OP-ELM), and a deep learning technique, long short-term memory (LSTM), is also investigated. These techniques are compared with each other and also with a commonly used powerful hybrid AI technique, adaptive neuro-fuzzy inference system (ANFIS). LSTM was found to achieve higher load forecasting accuracies than ANFIS and OP-ELM in forecasting the two distribution customers' loads in this paper. Only the LSTM models' performance improved with the inclusion of temperature in their development.INDEX TERMS Adaptive neuro-fuzzy inference systems, artificial intelligence, deep learning, distribution networks, extreme learning machines, load forecasting, recurrent neural networks, long short-term memory.

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Analysis and Accurate Prediction of User’s Response Behavior in Incentive-Based Demand Response

Cited by 44 publications

References 37 publications

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Assessing the Feasibility of Game-Theory-Based Demand Response Management by Practical Implementation

Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms

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