Qinran Hu

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

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.

Probabilistic Model of Payment Cost Minimization Considering Wind Power and Its Uncertainty

The penetration of wind energy sources to power systems has significantly increased in recent years. With variable and uncertain wind power output, the payment and market-clearing price (MCP) may vary in different cases. In this paper, a methodology to quantitatively model the payment cost minimization (PCM) considering the effects of wind power from a probabilistic viewpoint is presented. The autoregressive moving average (ARMA) method with normal distribution of wind forecast error is used to model a time series of wind speed. Based on the wind turbine power curve, the probability distribution of wind power output can be obtained. Then, Monte Carlo simulation (MCS) is used to produce random samples of wind speed, and the genetic algorithm is applied to solve PCM for each sample. The proposed methodology and its solution are verified with simulation studies of two sample systems. The probabilistic distribution results can give consumers an overview of how much they should pay in a probabilistic sense. Further, the simulation results can serve as a lookup table to provide useful input for more refined unit commitment, and also provide a benchmark for future research works on PCM considering wind power.

A Smart Home Test Bed for Undergraduate Education to Bridge the Curriculum Gap From Traditional Power Systems to Modernized Smart Grids

There is a worldwide trend to modernize old power grid infrastructures to form future smart grids, which will achieve efficient, flexible energy consumption by using the latest technologies in communication, computing, and control. Smart grid initiatives are moving power systems curricula toward smart grids. Although the components of smart grids fall within the broader discipline of electrical and computer engineering, undergraduate students are rarely assigned single design projects that require classic power systems knowledge combined with communication, computing, and control. Therefore, as a significant step toward potential curriculum changes, this paper presents such a project, a smart home test bed based on the pedagogical model of project-based learning (PBL) for undergraduate education. The proposed test bed allows undergraduates to gain key knowledge in smart grid topics, such as flattening demand peaks, real-time price response, wireless sensor networks, machine learning, pattern recognition, embedded system programming, user interface design, circuit design, and databases. This is well aligned with smart grid initiatives and provides a platform for students to develop their creativity in engineering design. It also offers real-life examples to be used for raising general public awareness of energy conservation.

Robust mean-variance optimization model for grid-connected microgrids

This paper proposes a mean-variance optimization model for the grid-connected microgrid energy management system (MG-EMS). In the proposed method, both the expected system operating cost and the tie-line power fluctuation variance are taken as the objective functions to provide a trade-off between operating benefit and risk assessment for decision makers. Further, robust optimization (RO) has been applied to eliminate the potential reliability issue due to wind power uncertainty. Therefore, the proposed method can effectively generate a robust optimal day-ahead operating schedule for wind power, energy storage (ES), and load management under worst-case scenarios in the microgrid. Finally, the case study on a revised IEEE 14-bus system has been implemented to demonstrate the validity and effectiveness of the proposed method.

The impact of FTR on LSE's strategic bidding considering coupon based demand response

Financial transmission rights (FTR) is a financial instrument for the electricity market participant to hedge the transmission congestion cost. With growing development in demand response, load serving entities (LSEs) may participate in the electricity market as a strategic bidder by offering coupon-based demand response (C-DR) programs to customers. In the LSEs bidding process, the impact of FTR which the LSE holds should be considered. To address this challenge, a new strategic bidding model is proposed in which the primary objective is to maximize the LSEs profit including the benefit from FTR by providing C-DR to customers. The proposed strategic bidding is a bi-level optimization problem with the LSEs net revenue maximization as the upper level problem and the ISOs economic dispatch (ED) for generation cost minimization as the lower level problem. This bi-level model is then converted to a mathematic problem with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush-Kuhn-Tucher (KKT) optimality conditions. Further, this MPEC is transformed to a mixed-integer linear programming (MILP) problem based on the strong duality theory, which is solvable using available optimization software. In addition, the validity of the proposed method has been verified with case studies.

A Framework of Residential Demand Aggregation With Financial Incentives

Due to the development of intelligent demand-side management with automatic control, distributed populations of large residential loads, such as air conditioners (ACs) and electrical water heaters (EWHs), have the opportunities to provide effective demand-side ancillary services for load serving entities (LSEs) to reduce the emissions and network operating costs. Most present approaches are restricted to 1) the scenarios involving with efficiently scheduling the large number of appliances in real time; 2) the issues about evaluating the individual residents’ contributions towards participating demand response (DR) program, and fairly distributing the rewards; and 3) the concerns on preforming cost-effective LSEs’ demand reduction request (DRR) with minimal rewards costs while not effecting residents’ living comfortableness. Therefore, this paper presents an optimal framework for residential load aggregators (RLAs) which helps solve the problems mentioned above. Under this framework, RLAs are able to realize the DRR for LSEs to generate optimal control strategies over residential appliances quickly and efficiently. To residents, the framework is designed with probabilistic model of comfortableness, which minimizes the impact of DR program to their daily life. To LSEs, the framework helps minimize the total reward costs of performing DRRs. Moreover, the framework fairly and strategically distributes the financial rewards to residents, which may stimulate the potential capability of loads optimized and controlled by RLAs in demand side management. The proposed framework has been validated on several numerical case studies.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.

Application of battery-supercapacitor energy storage system for smoothing wind power output: An optimal coordinated control strategy

In the application of energy storage for smoothing wind power output, the combination of battery and supercapacitor (SC) is considered as an effective alternative to improve the battery lifetime and enhance the system economy. In this paper, third-order Butterworth low-pass filter and high-pass filter are adopted to smooth the wind power and allocate power between battery and SC. Then, an optimal coordinated control strategy is proposed to determine the cut-off frequencies of the two filters and the power sharing between battery and SC. With this strategy, the total deprecation cost of the system caused by charge/discharge is minimized while satisfying the requirements of wind power integration. Finally, the effectiveness of the proposed method is demonstrated with a simulation study.

A comprehensive user interactive simulation tool for smart home application

With the development of the smart grid combined with the fluctuating nature of electricity market, demand-side management with controllable loads has grown. This paper presents a comprehensive user interactive simulation tool based on previously proposed hardware designs of a smart home energy management system (SHEMS). The simulation focuses on implementing optimal control strategies onto predominant loads including electrical water heater (EWH), electric vehicle (EV) charging station, etc. Most importantly, this simulation provides an easy and user-friendly user interface, which allows users to customize the simulation as the situation in their daily lives. This way, the simulation results will not only show the significant amount electricity payment reduced by SHEMS, but also increase the awareness of SHEMS to the public.

Mitigate overestimation of voltage stability margin by coupled single-port circuit models

Wide-area measurement-based voltage stability assessment (VSA) by coupled single-port circuit models has been widely discussed recently. This method models the coupling effects of load buses within a meshed network into extra impedance of a single-port model for each load bus. In simulation studies, overestimations of voltage stability margin using this approach have been observed when critical load bus or buses are decoupled from other load buses. In this paper, the overestimations are reported for the first time through examples and are further analyzed in details. Moreover, to mitigate such overestimations, two methods are proposed: one method uses a mitigation factor based on actual system reactive power response; the other method changes the types of certain weak generation buses when forming the coupled impedance. Both approaches are applied to a sample 4-bus system as well as the IEEE 118-bus system and successfully mitigate the overestimations.

Heuristic optimal restoration based on constructive algorithms for future smart grids

This paper proposes a heuristic optimization algorithm for online restoration for future smart grids based on constructive approaches to reinstate supply to the loads following a fault. The algorithm tries to minimize the total switching operations for the post-fault restoration strategy. This algorithm is also compared with the one in a previous work which tends to minimize the loading imbalance among all available substations. The proposed algorithm is based on the constructive network tracing approach that comes up with the final network restoration strategy. This is achieved by determining the switching sequences of switchable components such as circuit breakers, reclosers, sectionalizers, or intelligently controlled switches. Sample systems are studied to demonstrate the method. The results obtained from the constructive methods are compared with the ones from the enumeration method. The comparison verifies the effectiveness of the proposed method.