@article{hafiz_awal_queiroz_husain_2020, title={Real-Time Stochastic Optimization of Energy Storage Management Using Deep Learning-Based Forecasts for Residential PV Applications}, volume={56}, ISSN={["1939-9367"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85084192067&partnerID=MN8TOARS}, DOI={10.1109/TIA.2020.2968534}, abstractNote={A computationally proficient real-time energy management method with stochastic optimization is presented for a residential photovoltaic (PV)-storage hybrid system comprised of a solar PV generation and a battery energy storage (BES). Existing offline energy management approaches for day-ahead scheduling of BES suffer from energy loss in real time due to the stochastic nature of load and solar generation. On the other hand, typical online algorithms do not offer optimal solutions for minimizing electricity purchase costs to the owners. To overcome these limitations, we propose an integrated energy management framework consisting of an offline optimization model concurrent with a real-time rule-based controller. The optimization is performed in receding horizon with load and solar generation forecast profiles using deep learning-based long short term memory method in rolling horizon to reduce the daily electricity purchase costs. The optimization model is formulated as a multistage stochastic program where we use the stochastic dual dynamic programming algorithm in the receding horizon to update the optimal set point for BES dispatch at a fixed interval. To prevent loss of energy during optimal solution update intervals, we introduce a rule-based controller underneath the optimization layer in finer time resolution at the power electronics converter control level. The proposed framework is evaluated using a real-time controller-hardware-in-the-loop test platform in an OPAL-RT simulator. The proposed real-time method is effective in reducing the net electricity purchase cost compared to other existing energy management methods.}, number={3}, journal={IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS}, author={Hafiz, Faeza and Awal, M. A. and Queiroz, Anderson Rodrigo and Husain, Iqbal}, year={2020}, pages={2216–2226} }
 @article{hafiz_queiroz_husain_2019, title={Coordinated Control of PEV and PV-Based Storages in Residential Systems Under Generation and Load Uncertainties}, volume={55}, ISSN={["1939-9367"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85075497159&partnerID=MN8TOARS}, DOI={10.1109/TIA.2019.2929711}, abstractNote={Energy storage deployment in residential and commercial applications is an attractive proposition for ensuring proper utilization of solar photovoltaic (PV) power generation. Energy storage can be controlled and coordinated with PV generation to satisfy electricity demand and minimize electricity purchases from the grid. For optimal energy management, PV generation and load demand uncertainties need to be considered when designing a control method for the PV-based storage system. Another resource available at the residential level is the plug-in electric vehicle (PEV) which also has bi-directional power flow capability. The charging and discharging routines of the PEV can be controlled to help reduce the energy drawn from the power grid during peak hours. In this paper, a method of coordinated optimal control between PV-based storage and PEV storage is proposed considering the stochastic nature of solar PV generation and load demand. The stochastic dual dynamic programming algorithm is employed to optimize the charge/discharge profiles of PV-based storage and PEV storage to minimize the daily household electricity purchase cost from the grid. Simulation analysis shows the advantage of the coordinated control compared to other control strategies.}, number={6}, journal={IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS}, author={Hafiz, Faeza and Queiroz, Anderson Rodrigo and Husain, Iqbal}, year={2019}, pages={5524–5532} }
 @article{hafiz_queiroz_fajri_husain_2019, title={Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach}, volume={236}, ISSN={["1872-9118"]}, DOI={10.1016/j.apenergy.2018.11.080}, abstractNote={The aim of this paper is to propose a new energy management framework and storage sizing for a community composed of multiple houses and distributed solar generation. Uncertainties associated with solar generation and electricity demand are included to make the mathematical models more realistic, and as a result, provide more accurate control strategies to manage storage devices utilization. To evaluate that, a multi-stage stochastic program model designed to minimize community electricity purchase cost per day is used to support decision-making by creating control policies for energy management. Two different strategies are created to represent the interest of a single household (the individual energy management - IEM) and households that share their assets with the community (shared energy management - SEM). Our strategies consider time-of-use rates (ToU), load and resource variation during different seasons, with their distinct days of the year, to calculate net present value (NPV) associated with the energy savings. IEM and SEM are then used in a framework designed to establish the requirement of optimal energy storage size for each house of the community based on NPV values. The results of this study for an analysis considering a community with five houses show that the proposed SEM strategy reduces the overall electricity purchase costs for a summer day up to 11% and 3% compared with heuristic and IEM control respectively. Moreover, our results suggest that the application of the methodology increases peak energy savings up to 17%, scales up solar generation usage up to 23%, and the optimal storage size obtained in the shared community case reduces up to 50%.}, journal={APPLIED ENERGY}, author={Hafiz, Faeza and Queiroz, Anderson Rodrigo and Fajri, Poria and Husain, Iqbal}, year={2019}, month={Feb}, pages={42–54} }
 @article{doubleday_hafiz_parker_elgindy_florita_henze_salvalai_pless_hodge_2019, title={Integrated distribution system and urban district planning with high renewable penetrations}, volume={8}, ISSN={["2041-840X"]}, DOI={10.1002/wene.339}, abstractNote={Recent efforts to reduce energy consumption and greenhouse gas emissions have resulted in the development of sustainable, smart districts with highly energy efficient buildings, renewable distributed energy resources (DERs), and support for alternative modes of transportation. However, there is typically little if any coordination between the district developers and the local utility. Most attention is paid to the district's annual net load and generation without considering their instantaneous imbalance or the connecting network's state. This presents an opportunity to learn lessons from the design of distribution feeders for districts characterized by low loads and high penetrations of DERs that can be applied to the distribution grid at large. The aim of this overview is to summarize current practices in sustainable district planning as well as advances in modeling and design tools for incorporating the power distribution system into the district planning process. Recent developments in the modeling and optimization of district power systems, including their coordination with multi‐energy systems and the impact of high penetration levels of renewable energy, are introduced. Sustainable districts in England and Japan are reviewed as case studies to illustrate the extent to which distribution system planning has been considered in practice. Finally, newly developed building‐to‐grid modeling tools that can facilitate coordinated district and power system design with utility involvement are introduced, along with suggestions for future research directions.}, number={5}, journal={WILEY INTERDISCIPLINARY REVIEWS-ENERGY AND ENVIRONMENT}, author={Doubleday, Kate and Hafiz, Faeza and Parker, Andrew and Elgindy, Tarek and Florita, Anthony and Henze, Gregor and Salvalai, Graziano and Pless, Shand and Hodge, Bri-Mathias}, year={2019}, month={Sep} }
 @article{doubleday_parker_hafiz_irwin_hancock_pless_hodge_2019, title={Toward a subhourly net zero energy district design through integrated building and distribution system modeling}, volume={11}, ISSN={["1941-7012"]}, DOI={10.1063/1.5093917}, abstractNote={A modeling framework integrating both building energy modeling and power system modeling is introduced for the design of net zero energy (NZE) districts for the simultaneous selection of both demand-side efficiency measures and supply-side generation technologies. A novel district control scheme is proposed for pursuing NZE on a subhourly basis while mitigating potential grid impacts such as power backfeeding and voltage violations. As a case study, Peña Station NEXT, a new 100-building, mixed-use district on a 1200-node distribution feeder in Denver, Colorado, is modeled in the integrated framework. An exhaustive scenario analysis is conducted for sizing the district's distributed energy resources, considering multiple objectives such as capital cost, net energy import, and equipment violations. When trying to achieve annual NZE, the district incurs frequent operating violations, and achieving NZE on a 15-min basis is also limited by seasonal fluctuations in photovoltaic output, illustrating the need for diverse generation or seasonal storage. As a practical compromise, both annual and 15-min district import can be reduced by ∼78% without significant violations.}, number={3}, journal={JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY}, author={Doubleday, Kate and Parker, Andrew and Hafiz, Faeza and Irwin, Benjamin and Hancock, Samuel and Pless, Shanti and Hodge, Bri-Mathias}, year={2019}, month={May} }
 @article{hafiz_chen_chen_queiroz_husain_2019, title={Utilising demand response for distribution service restoration to achieve grid resiliency against natural disasters}, volume={13}, ISSN={["1751-8695"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85069476402&partnerID=MN8TOARS}, DOI={10.1049/iet-gtd.2018.6866}, abstractNote={The increased frequency of power outages due to natural disasters in recent years has highlighted the urgency of enhancing distribution grid resilience. The effective distribution service restoration (DSR) is an important measure for a resilient distribution grid. In this work, the authors demonstrate that DSR can be significantly improved by leveraging the flexibility provided by the inclusion of demand response (DR). The authors propose a framework for this by considering integrated control of household-level flexible appliances to vary the load demand at the distribution-grid level to improve DSR. The overall framework of the proposed system is modelled as a three-step method considering three optimization problems to (i) calculate feasible controllable aggregated load range for each bus, (ii) determine candidate buses to perform DR and their target load demand, and (iii) maintain the load level in each house through home energy management during the DSR, considering uncertainties in load and solar generation sequentially. The optimization problems are formulated as linear programming, mixed-integer linear programming, and multistage stochastic programming (solved using the stochastic dual dynamic programming) models. Case studies performed in the IEEE 123-node test feeder show improvements in resilience in terms of energy restored compared to the restoration process without DR.}, number={14}, journal={IET GENERATION TRANSMISSION & DISTRIBUTION}, publisher={Institution of Engineering and Technology (IET)}, author={Hafiz, Faeza and Chen, Bo and Chen, Chen and Queiroz, Anderson Rodrigo and Husain, Iqbal}, year={2019}, month={Jul}, pages={2942–2950} }
 @article{hafiz_queiroz_husain_2018, title={Solar Generation Storage, and Electric Vehicles in Power Grids}, volume={6}, ISSN={["2325-5897"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85057761435&partnerID=MN8TOARS}, DOI={10.1109/MELE.2018.2871319}, abstractNote={Solar energy is an abundant renewable energy source that is available all around the world every day. Each hour, the solar rays that reach our Earth (if properly converted to electricity and other forms of energy) represent more than the total energy consumption of the entire human race over the course of one year. Wind energy is another important renewable resource available in large amounts every day. These two renewable energy sources are attracting significant investment as countries seek technology cost reductions to aid sustainability.}, number={4}, journal={IEEE ELECTRIFICATION MAGAZINE}, author={Hafiz, Faeza and Queiroz, Anderson Rodrigo and Husain, Iqbal}, year={2018}, month={Dec}, pages={83–90} }
 @inproceedings{hafiz_de queiroz_husain_2017, title={Multi-stage stochastic optimization for a PV-storage hybrid unit in a household}, volume={2017-January}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044105670&partnerID=MN8TOARS}, DOI={10.1109/ias.2017.8101704}, abstractNote={In the face of increasing global energy supply challenges, renewable energy sources provide a cleaner and environmentally friendly energy alternative. To address the intermittency in PV power generation, battery storage can be used to store energy during lower demand periods. This requires the charging and discharging routine of the storage system to be controlled to achieve optimal economic benefits. In this paper, coordinated control between PV component and an accompanying storage unit is presented considering the stochastic nature of PV generation. The stochastic dual dynamic programming (SDDP) algorithm is employed to optimize the charge/discharge profiles with the goal to minimize the overall cost of satisfying the daily household load demand. The PV-storage hybrid unit can jointly contribute in reducing the consumer costs as shown through simulation analysis.}, booktitle={2017 IEEE Industry Applications Society Annual Meeting, IAS 2017}, author={Hafiz, F. and De Queiroz, A.R. and Husain, I.}, year={2017}, pages={1–7} }
 @inproceedings{alkuhayli_hafiz_husain_2017, title={Volt/VAR control in distribution networks with high penetration of PV considering inverter utilization}, DOI={10.1109/pesgm.2017.8273823}, abstractNote={In this paper, a Volt/Var control strategy in distribution networks with high PV penetration considering inverter thermal model is presented. This strategy considers a central distribution control to schedule active power and reactive power to deal with solar power generation intermittency. The objective is to provide voltage regulation control while solving for optimal power flow to minimize both distribution system losses and inverter losses. A radial distribution feeder with high PV penetration is considered for the analysis. Simulation results verify the effectiveness of the proposed strategy in regulating voltage within limits while allowing optimal reactive power allocation among distributed generation (DG) units to relieve the stress on some inverters especially those at the end of the feeder.}, booktitle={2017 ieee power & energy society general meeting}, author={Alkuhayli, A. and Hafiz, F. and Husain, I.}, year={2017} }
 @inproceedings{hafiz_fajri_husain_2016, title={Load regulation of a smart household with PV-storage and electric vehicle by dynamic programming successive algorithm technique}, DOI={10.1109/pesgm.2016.7741717}, abstractNote={With increasing concerns on gasoline demand and for a cleaner environment, plug-in hybrid electric vehicles (PHEVs) and zero emission electric vehicles (EVs) have recently received great attention. However, the integration of these vehicles into the power grid has a significant impact on the household load profile. In this paper, load leveling of a residential household by means of coordinated control between solar PV system storage and vehicle battery storage is discussed. Dynamic programming successive algorithm (DPSA) is employed to obtain the optimal charging strategies by minimizing the overall load variance of daily household load demand. The PHEV battery and PV system energy storage can be charged during low demand and the stored power can provide power during high demand period. Thus, both energy storage systems can participate effectively in balancing the demand of the consumer. The simulation results show that the proposed optimal charging strategy reduces the load variance compared to uncontrolled cases of smart household.}, booktitle={2016 ieee power and energy society general meeting (pesgm)}, author={Hafiz, F. and Fajri, P. and Husain, I.}, year={2016} }
 @inproceedings{hafiz_fajri_husain_2015, title={Effect of brake power distribution on dynamic programming technique in plug-in series hybrid electric vehicle control strategy}, DOI={10.1109/ecce.2015.7309675}, abstractNote={Plug-in Hybrid Electric Vehicle (PHEV) control strategies have received much attention in recent years for their significant impact in reducing the overall fuel cost. Dynamic programming (DP) is a control method which calculates every possible outcome at each step to find out the optimal supervisory control trajectory. In this work, DP is applied to a PHEV control strategy using a backward looking powertrain model while demonstrating the effect of considering the regenerative braking power distribution. A case study with a Series PHEV model is considered using DP based powertrain control strategy with different drive cycles to demonstrate the importance of considering brake power distribution on the cost-to-go function of these vehicles. The simulation results show that there is significant deviation from the optimal trajectory especially in heavy stop and go traffic situations while brake power distribution is considered.}, booktitle={2015 ieee energy conversion congress and exposition (ecce)}, author={Hafiz, F. and Fajri, P. and Husain, I.}, year={2015}, pages={100–105} }