Qinran Hu

Bio: Qinran Hu is an academic researcher from Harvard University. The author has contributed to research in topics: Computer science & Demand response. The author has an hindex of 15, co-authored 33 publications receiving 1155 citations. Previous affiliations of Qinran Hu include University of Tennessee.

Hardware Design of Smart Home Energy Management System With Dynamic Price Response

Abstract: The smart grid initiative and electricity market operation drive the development known as demand-side management or controllable load. Home energy management has received increasing interest due to the significant amount of loads in the residential sector. This paper presents a hardware design of smart home energy management system (SHEMS) with the applications of communication, sensing technology, and machine learning algorithm. With the proposed design, consumers can easily achieve a real-time, price-responsive control strategy for residential home loads such as electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer. Also, consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. Further, SHEMS is designed with sensors to detect human activities and then a machine learning algorithm is applied to intelligently help consumers reduce total payment on electricity without or with little consumer involvement. Finally, simulation and experiment results are presented based on an actual SHEMS prototype to verify the hardware system.

Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities

Abstract: With the growing development in demand response, load serving entities (LSEs) may participate in electricity market as strategic bidders by offering coupon-based demand response (C-DR) programs to customers. However, due to customers' versatile electricity consumption patterns under C-DR programs as well as the increasing penetration of wind power generation, obtaining the deterministic bidding decision becomes unprecedented complex for LSEs. To address this challenge, a new strategic bidding model for an LSE is proposed in which the primary objective is to maximize the LSE's profit by providing optimal C-DR considering high wind power penetration. The proposed strategic bidding is a bi-level optimization problem with the LSE's net revenue maximization as the upper level problem and the ISO's economic dispatch (ED) for generation cost minimization as the lower level problem. This bi-level model is converted to a stochastic mathematic program with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush-Kuhn-Tucher (KKT) optimality conditions. Then, the stochastic MPEC is transformed to a mixed-integer linear programming (MILP) problem, which is solvable using available optimization software, based on strong duality theory. In addition, the effectiveness of the proposed method has been verified with simulation studies of two sample systems.

Participation of an Energy Hub in Electricity and Heat Distribution Markets: An MPEC Approach

Abstract: Integration of electricity and heat distribution networks offers extra flexibility to system operation and improves energy efficiency. The energy hub (EH) plays an important role in energy production, conversion and storage in such coupled infrastructures. This paper provides a new outlook and thorough mathematical tool for studying the integrated energy system from a deregulated market perspective. A mathematic program with equilibrium constraints (MPEC) model is proposed to study the strategic behaviors of a profit-driven EH in the electricity and heating markets under the background of energy system integration. In the upper level, the EH submits bids of prices and quantities to a distribution power market and a heating market. In the lower level, these two markets are cleared and energy contracts between the EH and two energy markets are determined. Network constraints of physical systems are explicitly represented by an optimal power flow problem and an optimal thermal flow problem. The proposed MPEC formulation is approximated by a mixed-integer linear program via performing integer disjunctions on the complementarity and slackness conditions and binary expansion technique on the bilinear product terms. Case studies demonstrate the effectiveness of the proposed model and method.

A Framework of Residential Demand Aggregation With Financial Incentives

Abstract: The development of intelligent demand-side management with automatic control enables a large amount of residential demands to provide efficient demand-side ancillary services for load serving entities. In this paper, we introduce the concept of a comfort indicator, present an advanced reward system, and finally propose a framework for aggregating residential demands enrolled in incentive-based demand response (DR) programs. The proposed framework not only allocates load serving entities’ demand reduction requests among residential appliances quickly and efficiently without affecting residents’ comfort levels but also rewards residential consumers based on their actual participation. Also, since the framework is designed with the practical considerations of simplicity and efficiency, it can be utilized as a quick implementation for existing pilot development works. The effectiveness and merit of this framework are demonstrated and discussed in the comparison studies with conventional incentive-based DR.

Smart home energy management systems: Concept, configurations, and scheduling strategies

Abstract: With the arrival of smart grid era and the advent of advanced communication and information infrastructures, bidirectional communication, advanced metering infrastructure, energy storage systems and home area networks would revolutionize the patterns of electricity usage and energy conservation at the consumption premises. Coupled with the emergence of vehicle-to-grid technologies and massive distributed renewable energy, there is a profound transition for the energy management pattern from the conventional centralized infrastructure towards the autonomous responsive demand and cyber-physical energy systems with renewable and stored energy sources. Under the sustainable smart grid paradigm, the smart house with its home energy management system (HEMS) plays an important role to improve the efficiency, economics, reliability, and energy conservation for distribution systems. In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented. Then, the advanced HEMS infrastructures and home appliances in smart houses are thoroughly analyzed and reviewed. Furthermore, the utilization of various building renewable energy resources in HEMS, including solar, wind, biomass and geothermal energies, is surveyed. Lastly, various home appliance scheduling strategies to reduce the residential electricity cost and improve the energy efficiency from power generation utilities are also investigated.

Tackling Climate Change with Machine Learning

Abstract: Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Smart Household Operation Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR

Abstract: As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest in the literature recently, especially for residential areas. As a new type of consumer load in the electric power system, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options instead of peak power procurement from the grid. In this paper, as the main contribution to the literature, a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized. A mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided for this purpose. A distributed small-scale renewable energy generation system, the V2H and V2G capabilities of an EV together with two-way energy trading of ESS, and different DR strategies are all combined in a single HEM system for the first time in the literature. The impacts of different EV owner consumer preferences together with the availability of ESS and two-way energy trading capabilities on the reduction of total electricity prices are examined with case studies.