An Incentive-Based Optimization Approach for Load Scheduling Problem in Smart Building Communities

The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart building communities. This model’s objectives are three-fold: minimizing total electricity costs, maximizing assigned incentives for each customer, and minimizing inconvenience level. In this model, two groups of assets are considered: time-shiftable assets, including electronic appliances and plug-in electric vehicle (PEV) charging facilities, and thermal assets such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters. For each group, specific energy consumption and inconvenience level models were developed. The designed model assigned the incentives to the participants based on their willingness to reschedule their assets. The LSP is a discrete–continuous problem and is formulated based on a mixed-integer nonlinear programming approach. Zoutendijk’s method is used to solve the nonlinear optimization model. This formulation helps capture the building collaboration to achieve the objectives. Illustrative case studies are demonstrated to assess the proposed model’s effect on building communities consisting of residential and commercial buildings. The results show the efficiency of the proposed model in reducing the total energy cost as well as increasing the participants’ satisfaction. The findings also reveal that we can shave the peak demand by 53% and have a smooth aggregate load profile in a large-scale building community containing 500 residential and commercial buildings.

[1]  Stéphane Ploix,et al.  Managing Energy Smart Homes according to Energy Prices: Analysis of a Building Energy Management System , 2014 .

[2]  Xiaohua Xia,et al.  Optimal scheduling of household appliances with a battery storage system and coordination , 2015 .

[3]  Zhen Ni,et al.  Smart home energy optimization with incentives compensation from inconvenience for shifting electric appliances , 2019, International Journal of Electrical Power & Energy Systems.

[4]  Multiobjective demand side management solutions for utilities with peak demand deficit , 2014 .

[5]  Mohsen A. Jafari,et al.  HVAC load synchronization in smart building communities , 2019, Sustainable Cities and Society.

[6]  Mohsen A. Jafari,et al.  Prediction of building indoor temperature response in variable air volume systems , 2020 .

[7]  Jiawei Zhu,et al.  Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm , 2019, Energy.

[8]  Abidin Kaya,et al.  An analysis of load reduction and load shifting techniques in commercial and industrial buildings under dynamic electricity pricing schedules , 2015 .

[9]  Kittisak Jermsittiparsert,et al.  Modeling and prioritizing dynamic demand response programs in the electricity markets , 2020 .

[10]  Anup Pradhan,et al.  Multi-objective optimization of household appliance scheduling problem considering consumer preference and peak load reduction , 2020 .

[11]  Yonghong Kuang,et al.  Smart home energy management systems: Concept, configurations, and scheduling strategies , 2016 .

[12]  Jason W. Black,et al.  Integrating demand into the U.S. electric power system : technical, economic, and regulatory frameworks for responsive load , 2005 .

[13]  Shahram Jadid,et al.  Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS , 2015 .

[14]  M.H. Nehrir,et al.  Power Management of Aggregate Electric Water Heater Loads by Voltage Control , 2007, 2007 IEEE Power Engineering Society General Meeting.

[15]  Kelum A. A. Gamage,et al.  Demand side management in smart grid: A review and proposals for future direction , 2014 .

[16]  Hanife Apaydin Özkan,et al.  A home power management system using mixed integer linear programming for scheduling appliances and power resources , 2016, 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[17]  X. Xia,et al.  Combined residential demand side management strategies with coordination and economic analysis , 2016 .

[18]  Mohsen A. Jafari,et al.  A multi-scale adaptive model of residential energy demand , 2015 .

[19]  Tongquan Wei,et al.  Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home , 2013, IEEE Transactions on Smart Grid.

[20]  Jiangfeng Zhang,et al.  Optimal scheduling of household appliances for demand response , 2014 .

[21]  Fred Schweppe,et al.  Space Conditioning Load Under Spot or Time of Day Pricing , 1983, IEEE Transactions on Power Apparatus and Systems.

[22]  Xiaohua Xia,et al.  Optimal Scheduling of Household Appliances Incorporating Appliance Coordination , 2014 .

[23]  Karl Henrik Johansson,et al.  Scheduling smart home appliances using mixed integer linear programming , 2011, IEEE Conference on Decision and Control and European Control Conference.

[24]  Tamer Khatib,et al.  Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods , 2019, Electronics.

[25]  Phani Chavali,et al.  A Distributed Algorithm of Appliance Scheduling for Home Energy Management System , 2014, IEEE Transactions on Smart Grid.

[26]  Jianhui Wang,et al.  MPC-Based Appliance Scheduling for Residential Building Energy Management Controller , 2013, IEEE Transactions on Smart Grid.

[27]  Marco L. Della Vedova,et al.  Peak shaving through real-time scheduling of household appliances , 2014 .

[28]  Seyyed Danial Nazemi,et al.  The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings , 2021, Energies.

[29]  Chi-Cheng Chuang,et al.  Multi-Objective Air-Conditioning Control Considering Fuzzy Parameters Using Immune Clonal Selection Programming , 2012, IEEE Transactions on Smart Grid.

[30]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[31]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[32]  Marco L. Della Vedova,et al.  Electric load management approaches for peak load reduction: A systematic literature review and state of the art , 2016 .

[33]  Bing Dong,et al.  Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network , 2011 .

[34]  Hanife Apaydin Ozkan.  Appliance based control for Home Power Management Systems , 2016 .

[35]  Anup Pradhan,et al.  Optimal load scheduling of household appliances considering consumer preferences: An experimental analysis , 2018, Energy.

[36]  Mohsen A. Jafari,et al.  An Automated Cluster-Based Approach for Asset Rescheduling in Building Communities , 2020, 2020 IEEE Texas Power and Energy Conference (TPEC).

[37]  Joel J. P. C. Rodrigues,et al.  A preference-based demand response mechanism for energy management in a microgrid , 2020 .

[38]  Wei-Jen Lee,et al.  A Residential Consumer-Centered Load Control Strategy in Real-Time Electricity Pricing Environment , 2007, 2007 39th North American Power Symposium.

[39]  Babak Rezaee,et al.  Utilizing renewable energy sources efficiently in hospitals using demand dispatch , 2020 .