Xiangqi Zhu

A Graphical Performance-Based Energy Storage Capacity Sizing Method for High Solar Penetration Residential Feeders

This paper presents a graphical, performance-based energy storage capacity sizing method for residential feeders with high solar penetration levels. The rated power and storage capacity of an energy storage device (ESD) are calculated to fulfill a specified operational requirement. Three locations for installing ESDs are investigated: 1) consumer-owned ESDs inside single-family households; 2) utility-owned distribution transformer-level ESDs; and 3) third-party owned ESDs in a community. First, historical solar radiation data, residential household load data, and residential load models are used for creating the net load (load minus solar generation) ensembles at the house level with resolution of 15 min. Then, a novel graphical capacity selection method using equal probability lines on compressed, composite cumulative distribution function curves is developed for sizing the energy storage needs at the house, distribution transformer, and community levels. Demand-side management methods are investigated for further reducing the need of energy storage. Simulation results demonstrate that the proposed method avoids over- or under-sizing ESDs and allows the users to compare the marginal benefit of increasing the capacity of the ESD.

A probabilistic-based PV and energy storage sizing tool for residential loads

This paper presents a probabilistic-based sizing tool for residential home owners, load serving entities, and utilities to select energy storage (ES) and photovoltaic (PV) based on historical load characteristics and load management options. The inputs of the tool include historical residential load profiles and solar radiation data. The outputs of the tool include ensembles of the net load profiles (load minus solar), with and without applying load energy management for different PV and ES installation capacities. The operation statistics of the ES is used to determine the confidence levels of meeting selected performance criterion. In the simulation, a set of 1-year, 15-minute data collected from 50 actual residential homes is used as the load inputs. A set of 1-year, 5-minute actual solar radiation data is used as the solar inputs. Managing load consumptions for reducing the size of ES is investigated by controlling air conditioning loads. Simulation results show that the probabilistic-based sizing method can give the users a clear comparison of the tradeoffs among different options and assist them make more informed decisions.

Control strategies for residential microgrids during islanded situation

Microgrids can serve to integrate distributed energy resources (DERs) and controllable loads in a smarter and more reliable fashion. The operation of residential microgrids with DER during islanded situation is of great significance to both customers and utility providers. This paper proposes two control strategies for a residential microgrid that has a shared energy storage (ES) in islanded mode. With ES being the only energy resource, these strategies rationally allocate energy over the islanded time period and also regulate loads to coordinate with ES. A simulation testbed based on household load models for typical residential devices is utilized. Test cases for islanded-situation days using these control strategies are simulated for validation. Finally, simulation results of control signals and resulting load profiles are shown and analyzed. These two control strategies are proved to be effective for the energy management of a residential microgrid during islanded situation.

Smart hybrid house test systems in a solid-state transformer supplied microgrid

This paper presents the design and setup, considerations, and preliminary results of an AC/DC hybrid smart house test system. Rapid growth of rooftop photovoltaic (PV) in distribution systems makes it viable to supply a residential home with both AC and DC power sources. Solid-state transformers (SST) used as energy routers for rerouting power during normal and emergency situations are being developed in the FREEDM center at North Carolina State University. To provide a load test system for studying the control and monitoring of such an SST supplied AC/DC mixed power supply system, an 1-SST AC/DC hybrid smart house test system is built. The test system consists of a solid state transformer (SST), on-site photovoltaic panels, energy storage devices and three AC/DC smart houses with a home energy management system (HEMS). A demonstration case is presented to illustrate the operation of the test system for algorithm development and validation.

Continuation power flow analysis for PV integration studies at distribution feeders

This paper presents a method for conducting continuation power flow simulation on high-solar penetration distribution feeders. A load disaggregation method is developed to disaggregate the daily feeder load profiles collected in substations down to each load node, where the electricity consumption of residential houses and commercial buildings are modeled using actual data collected from single family houses and commercial buildings. This allows the modeling of power flow and voltage profile along a distribution feeder on a continuing fashion for a 24-hour period at minute-by-minute resolution. By separating the feeder into load zones based on the distance between the load node and the feeder head, we studied the impact of PV penetration on distribution grid operation in different seasons and under different weather conditions for different PV placements.

A Matlab-based home energy management algorithm development toolbox

This paper presents the architecture and modeling approach of a Matlab-based toolbox for developing and testing home energy management (HEM) algorithms under a number of typical operation conditions. This toolbox serves as a developer platform that includes a graphical user interface, a model database, a computational engine, and an input-output database. The model database consists of home appliance models, energy storage models, baseload models, roof-top photovoltaic models, as well as typical weather profiles. The graphical interface allows the users to select different inputs such as utility rate structure, customer comfort settings, solar profiles, and outdoor temperature profiles. The HEM algorithms serve as the computational engines to power the virtual house so that the performance of those algorithms can be evaluated fairly using the same set of inputs and models. Simulation results have demonstrated the effectiveness of the modeling platform when developing HEM algorithms.