Day-ahead Probabilistic Forecasting of Smart Households’ Demand Response Capacity under Incentive-based Demand Response Program

The advancement in technology and development in electricity market make it possible for smart households (SHs) to participate in the incentive-based demand response (IBDR) programs. As the agent of SHs’ participation in the IBDR program, it is crucial for load aggregators (LAs) to understand the SHs’ demand response (DR) capacity when trading with the system operator in the day-ahead market. Therefore, this paper proposes a probabilistic forecasting model to forecast the aggregated SHs’ DR capacity and model its uncertainty in the day-ahead market in LAs’ point of view. Firstly, a home energy management system (HEMS) is adapted to perform an optimal scheduling for SHs and to model the customers’ responsive behavior in the IBDR program; secondly, several features which may have significant impacts on the aggregated DR capacity are extracted; finally, a quantile regression (QR) based probabilistic forecast model is proposed to provide a probabilistic forecasting for available aggregated SHs’ DR capacity in the day-ahead market.

[1]  Gianfranco Chicco,et al.  Definitions of Demand Flexibility for Aggregate Residential Loads , 2016, IEEE Transactions on Smart Grid.

[2]  Chi-Keung Woo,et al.  Relative kW Response to Residential Time-Varying Pricing in British Columbia , 2013, IEEE Transactions on Smart Grid.

[3]  Fei Wang,et al.  Synchronous Pattern Matching Principle-Based Residential Demand Response Baseline Estimation: Mechanism Analysis and Approach Description , 2018, IEEE Transactions on Smart Grid.

[4]  Joao P. S. Catalao,et al.  Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression , 2018 .

[5]  Ning Lu,et al.  Cooling Devices in Demand Response: A Comparison of Control Methods , 2015, IEEE Transactions on Smart Grid.

[6]  Fei Wang,et al.  A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features , 2018 .

[7]  Ning Lu,et al.  An Evaluation of the HVAC Load Potential for Providing Load Balancing Service , 2012, IEEE Transactions on Smart Grid.

[8]  Hanchen Xu,et al.  The values of market-based demand response on improving power system reliability under extreme circumstances , 2017 .

[9]  Fei Wang,et al.  Multi-objective optimization model of source-load-storage synergetic dispatch for building energy system based on TOU price demand response , 2017, 2017 IEEE Industry Applications Society Annual Meeting.

[10]  Joao P. S. Catalao,et al.  Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response , 2018 .

[11]  Henk Visscher,et al.  The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock , 2009 .

[12]  Fei Wang,et al.  Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting , 2019, Energy Conversion and Management.

[13]  Chi-Keung Woo,et al.  Residential winter kWh responsiveness under optional time-varying pricing in British Columbia , 2013 .

[14]  Neven Duić,et al.  Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns , 2018, Energy Conversion and Management.

[15]  Guang Yang,et al.  Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting , 2015 .

[16]  Miadreza Shafie-Khah,et al.  A Stochastic Home Energy Management System Considering Satisfaction Cost and Response Fatigue , 2018, IEEE Transactions on Industrial Informatics.

[17]  Fei Wang,et al.  Broadcast Gossip Algorithms for Distributed Peer-to-Peer Control in AC Microgrids , 2019, IEEE Transactions on Industry Applications.

[18]  Gang Ma,et al.  Accuracy analysis and improvement of the state-queuing model for the thermostatically controlled loads , 2017 .

[19]  Fei Wang,et al.  Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations , 2017, IEEE Transactions on Smart Grid.

[20]  João P. S. Catalão,et al.  Assessment of Demand-Response-Driven Load Pattern Elasticity Using a Combined Approach for Smart Households , 2016, IEEE Transactions on Industrial Informatics.

[21]  Tao Hong,et al.  Long Term Probabilistic Load Forecasting and Normalization With Hourly Information , 2014, IEEE Transactions on Smart Grid.

[22]  Miadreza Shafie-Khah,et al.  Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting , 2018, 2018 IEEE Industry Applications Society Annual Meeting (IAS).

[23]  Joao P. S. Catalao,et al.  Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting , 2019, International Journal of Electrical Power & Energy Systems.

[24]  Palle Andersen,et al.  An intuitive definition of demand flexibility in direct load control , 2013, 2013 IEEE International Conference on Control Applications (CCA).

[25]  Karl Aberer,et al.  When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers , 2014, IEEE Transactions on Smart Grid.

[26]  Fei Wang,et al.  Multi-Objective Optimization Model of Source–Load–Storage Synergetic Dispatch for a Building Energy Management System Based on TOU Price Demand Response , 2018, IEEE Transactions on Industry Applications.

[27]  Dipti Srinivasan,et al.  Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination , 2018, Energy.

[28]  J. Catalão,et al.  Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting , 2018 .

[29]  Miadreza Shafie-Khah,et al.  A Business Model Incorporating Harmonic Control as a Value-Added Service for Utility-Owned Electricity Retailers , 2019, IEEE Transactions on Industry Applications.

[30]  Tao Hong,et al.  Probabilistic electric load forecasting: A tutorial review , 2016 .

[31]  Lazaros Gkatzikis,et al.  The Role of Aggregators in Smart Grid Demand Response Markets , 2013, IEEE Journal on Selected Areas in Communications.