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Huang, Sen

Publications and source records attributed to Huang, Sen.

At least 55 records · Page 3
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DOE OSTI2020

Flexible Reinforcement Learning Framework for Building Control using EnergyPlus-Modelica Energy Models

In recent years, reinforcement learning (RL) methods have been greatly enhanced by leveraging deep learning approaches. RL methods applied to building control have shown potential in many applications due to their ability to complement or replace conventional methods such as model-based or rule-based controls. However, RL-based building control software is likely tailored either to one target building system or to a specific RL method so that significant additional effort would be required to customize the RL-based controller for use in other building systems or with other RL approaches. Also, RL-based building controls usually depend on building energy simulations to train controllers, so emulating building dynamics (i.e., thermal dynamics and control dynamics) and capturing sub-hourly dynamic profiles are crucial to further the development of effective RL-based building control methods. To address these challenges, we present an open source RL-based control software employing a high-fidelity hybrid EnergyPlus-Modelica building energy model which emulates building dynamics at 1-minute resolution. This software consists of decoupled components (environment, building emulator, control agent, and RL algorithm), which allows for quick prototyping and benchmarking of standard RL algorithms in different systems; for example, a single component can be replaced without revising all of the software. To demonstrate this software framework, we conducted a benchmark study using an EnergyPlus-Modelica building energy model for a Chicago office building with an RL-based controller to dynamically control the chilled water temperature setpoint and the air handling unit supply air temperature setpoint on selected floors.

Lee, Joon-Yong↗
DOE OSTI2020

Characterization of Connected Lighting System Potential for Grid Services Under Real-Time Pricing

Increasingly over the last decade, the management of end-use electricity demand has been examined as a potential source of services for the electric power grid beyond contracts with large commercial and industrial customers. Lighting represents about 17\% of U.S. commercial building annual electricity consumption; however, its potential rapid speed of response may position it to uniquely contribute to services that improve the reliability and resilience of the grid. Connected lighting systems (CLS), which build upon solid-state lamp technology, can change state by adjusting power demand more quickly than most other building electricity end uses and could be used as a platform to monitor lighting electricity use and space conditions. But the potential of CLS to provide grid services has not been extensively studied. In this paper, we describe initial research to evaluate the potential of CLS for grid services. We develop a model for CLS using a set of parameters to represent operation behaviors and constraints: maximal power, minimal power, nominal power, ramp rate, and time delay. Parameter values are generated for representative building types. CLS demand curves are constructed, indirectly capturing building occupant preferences for lighting as functions of electricity price. The CLS model and the demand curves are incorporated into a Transactive Control and Coordination (TCC) Platform to simulate CLS providing grid services. Previous research has shown TCC to be a powerful tool to enable end uses to provide grid services through a hybrid economic-control approach. Initial quantitative results will be provided for CLS grid services potential.

Wang, Peng↗
DOE OSTI2020

Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

model predictive control↗
DOE OSTI2020

Optimal Energy Dispatch Controller for Fuel Cell-Integrated Multi-Zone Building

Stationary fuel cells provide potential opportunities for energy savings when integrated with buildings. Through smart dispatch of both electrical power and heat generated by the fuel cells and managing the building loads, the buildings can achieve more efficient operation. In this paper, we develop an optimal energy dispatch controller to operate a fuel cell-integrated building. The controller leverages the inherent thermal storage and the dispatchable fuel cell to reduce its operating cost and to allow the building to participate in grid services. The proposed controller is implemented on two types of commercial buildings, a large office building and a large hotel, and the effectiveness of the controller is demonstrated through simulations. The results also indicate that the potential saving varies significantly with different system parameters, including season, fuel prices, and equipment sizing, which provide helpful insights for building operators and other stake holders.

39 EE - Hydrogen and Fuel Cell Technologies (EE-3F↗
DOE OSTI2020

Power-to-Gas System Valuation. Final Report

To provide a framework for evaluating the technical and financial benefits of a power-to-gas (P2G) system deployed in the Holyoke Gas and Electric (HG&E) system, Pacific Northwest National Laboratory modified its energy storage valuation tools to run a one-year simulation of P2G operations. The simulation was used to evaluate the benefits of P2G for multiple grid and industrial applications over the economic life of the unit, or units, depending on the asset configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Forecasting Commercial Building Electricity Consumption, Zone Airflow and Zone Temperature: Update - Development of a Generalized Machine Learning Approach

The U.S. power grid is being transformed to make it smarter, more efficient, and cleaner. This transformation is leading to the addition of a significant of energy generated by distributed, variable, and renewable resources. Because of the variable nature of renewable generation, the short- and long-term supply and demand imbalances are less predictable, and conventional approaches to mitigating the imbalances will be less efficient or cost effective. To address this challenge and to support the mission and the vision of the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office has developed a Grid-Interactive Efficient Building Strategy. The strategy focuses on simultaneously improving building energy efficiency and supporting reliability and resilience of the electric grid more efficiently and at a lower cost. In addition, EERE and DOE’s Office of Electricity created an initiative led by DOE and supported by the national laboratories under the Grid Modernization Lab Consortium structure to enhance grid modernization. The work reported in this document is part of the first set of projects funded under the initiative to design, develop, and validate scalable transactive control technologies for the commercial buildings sector. Transactive controls requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical grey- and black-box models have been widely used to express flexibility. Although this approach is generally easy to construct and simple to use, it does not capture non-linear behavior that some end-use loads represent. Therefore, Pacific Northwest National Laboratory (PNNL) with support from Western Washington University conducted this research to explore the use of deep machine learning (ML) techniques. The work reported in this document is limited to forecasting whole building electricity consumption, the zone airflow and the zone temperature predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A Hierarchical Charging Control of Plug-in Electric Vehicles with Simple Flexibility Model

The demand for plug-in electric vehicles (PEVs) is increasing exponentially, which could have significant effects on the power grid, because charging one PEV is nearly equivalent to adding three houses to the power system. Even though PEVs increase demand on the electric grid, their capability to actively interact with the grid presents excellent opportunities for load management, demand response, and various other grid services. This paper proposes an innovative scheduling and control framework to enable smart PEV charging for grid services while meeting PEV owners’ travel needs. In the proposed framework, each set of electric vehicle supply equipment (EVSE) is equipped with a controller that estimates charging power and energy flexibility based on vehicle characteristics, EVSE power rating, battery energy state, and upcoming trip information. With the charging flexibility model received from each EVSE controller, the central coordinator determines the optimal power allocation for a look-ahead time window for the given grid services. The proposed charging coordination strategy can help reduce the computational complexity and communication requirement compared with existing methods. It is also scalable to the expanding PEV fleet and robust to uncertainties in upcoming vehicle trips and future system condition. The proposed method is studied using a prototypical feeder developed at Pacific Northwest National Laboratory and detailed trip information extracted from the National Household Travel Survey.

Wu, Di↗
DOE OSTI2019

Validation on Aggregate Flexibility from Residential Air Conditioning Systems for Building-to-Grid Integration

To facilitate the integration of buildings to the power grid, battery-equivalent models have been proposed to characterize and quantify aggregate flexibility from building loads. An analytical method has been proposed to construct such a flexibility model for thermostatically controlled loads. Since this analytical method is derived from a simplified representation of building thermal dynamics, it is necessary to evaluate the resulting flexibility model in a more realistic context. This paper focuses on the validation of this analytical method for estimating aggregate flexibility from residential air conditioning (ACs) systems. A high-fidelity model is first developed to mimic thermal behaviors of a residential AC system through a co-simulation between Modelica and EnergyPlus. A population of virtual residential AC systems is then generated by randomizing parameters of the high-fidelity model. Finally, an aggregate flexibility model is constructed for the residential AC population using the analytical method and validated using results from simulation of the high-fidelity model. It is found that the flexibility estimated using the analytical method is with reasonable accuracy, but can be improved by explicitly incorporating heat gains into the method.

Huang, Sen↗
DOE OSTI2019

Enhancing the Implementation of a First-order Equivalent Thermal Parameter Model to Enable Accurate and Robust Building Thermal Response Prediction

Predicting indoor temperature conditions are often necessary for advanced building controls. Gray-box models are preferable to do so because they can over- come limitations of white-box and black-box models; however, their current implementation methods can be problematic due to their difficulties in capturing non-linear behaviors and time-varying disturbances. This paper proposes a novel method to implement a popular gray-box model, the 1st-order equivalent thermal parameter (ETP) model. To evaluate performance, we applied the proposed method to predict in- door temperatures for a medium-office building, over the course of one day. Predictions are compared against two existing methods: the ETP model implemented through a common approach and a black-box model. Results suggest that the proposed method can yield more accurate results compared to the other methods; additionally, the proposed method demonstrated similar capabilities in capturing non-linear behaviors as the black-box model.

Hinkelman, Kathryn↗
DOE OSTI2019

Prototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings

Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.

Blum, David↗
DOE OSTI2019

A Data-driven Control Method for Operating the Commercial HVAC Load as a Virtual Battery

Building loads have a great potential to provide ancillary services because of its large power consumption. In order to quantify their flexibility, methods of modeling building loads as virtual battery (VB) have been proposed. The VB model provides an effective way to coordinate building loads and energy storage systems together when providing service to grid. However, after grid send a power signal to VB, the control strategy has not been fully studied to let building consumes this desired amount of power yet. This paper introduces a method to control commercial heating, ventilation, and air conditioning (HVAC) system. First, the thermal model of commercial building is introduced. We then used several data-driven models to approximate this thermal dynamic and the operation of different HVAC components. Base on these data-driven models, we developed a centralized control algorithm to utilize HVAC system consuming the desired amount of power. Finally, we modeled a realistic commercial building in EnergyPlus to test the performance of this proposed control algorithm

Wang, Jiyu↗
DOE OSTI2019

Coordinated PEV Charging for Distribution System Management

Plug-in electric vehicles (PEVs) can help to reduce worldwide dependence on petroleum and carbon emissions. In the past a few years, PEVs have received considerable attention as an eco-friendly and cost-effective alternative to conventional gasoline vehicles. PEVs consume higher power from the grid during charging compared to conventional residential loads. Therefore, the emerging fleet of PEVs will introduce a considerable amount of additional load on power system. On the other hand, majority of PEVs are parked for more than 90% of the time, making them ideal for providing various services through Electric Vehicle Supply Equipment (EVSE), a.k.a. charging stations which are PEVs’ connection points to the power system. In this project, coordinated PEV charging are developed to provide distribution system services, considering PEV characteristics, realistic travel pattern, charging behaviors, and EVSE power rating and availability. The main efforts and contributions are summarized as follows. • Mobility models and EVSE charging capability/availability are indispensable for evaluating PEV charging coordination strategies and developing PEV charging control. Many existing studies utilize simplified mobility models assuming that the entire vehicle fleet returns home in the evening and is parked at home until the next morning. Some other PEV models better represent diversified home arrival and departure time, but cannot capture varying charging flexibility and capability at different locations, and therefore are not appropriate to study the impacts of public EVSE on PEV load and utilization. In this work, a mobility and charging flexibility model is proposed to better represent the temporal availability and varying charging capability from PEV onboard charger and EVSE. • Based on the mobility model, a generalized optimization method is proposed to evaluate different PEV charging coordination strategies. In existing literature, different algorithms and methods need to be designed to evaluate each charging strategy from both vehicle owners’ and system’s perspective. With the optimization method proposed in this work, different charging control strategies can be studied and compared by only updating objective functions. Moreover, optimization tricks are provided to convert the optimization problems to equivalent linear programming problems, which can be efficiently solved with existing solvers. • An innovative scheduling and control framework is proposed to enable smart PEV charging for grid services while meeting PEV owners’ travel needs. In the proposed framework, each set of EVSE is equipped with a controller that estimates charging power and energy flexibility based on vehicle characteristics, EVSE power rating, battery energy state, and upcoming trip information. With the simplified charging flexibility model received from each EVSE controller, the central coordinator determines the optimal power allocation for a look-ahead time window for the given grid services. The proposed charging control can help reduce the computational complexity and communication requirement compared with existing methods. It is also scalable to the expanding PEV fleet and robust to uncertainties in upcoming vehicle trips and future system condition. • The proposed charging strategy evaluation and smart charging control methods are applied to one of the prototypical feeders developed by the Pacific Northwest National Laboratory (PNNL) for case studies. Detailed trip information extracted from the National Household Travel Survey (NHTS) is used to represent travel and parking patterns. Different scenarios are simulated to evaluate the performance of the proposed methods and understand how PEV mobility and public EVSE availability affect PEV load and potential grid services.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Online Learning for Commercial Buildings

There has been an increased interest in designing optimization based techniques for the control of building heating, ventilation, and air-conditioning (HVAC) systems for either improving the energy efficiency of buildings or providing ancillary services to the electric grid. The performance of such prediction-based control techniques heavily rely on the model of a building’s thermal dynamics. However, the development of high fidelity building thermal dynamic models is challenging given the presence of large uncertainties that affect thermal loads in buildings such as building envelope performance, thermal mass, internal heat gains as well as occupants' behavior. In this paper, we propose a method to identify both a resistive-capacitive parametric model, and non -parametric load uncertainties using measured input-output data. The parametric model is obtained using semi-parametric regression, whereas the non-parametric part is based on Random Forest, where regression trees are used to derive the dependency of non-parametric terms on both building operation parameters and ambient temperature. The effectiveness of the method is evaluated using experimental data collected from an office building at the Pacific Northwest National Laboratory (PNNL) campus. The proposed methodology was observed to provide improved accuracy over appropriate baseline strategies, when predicting indoor air temperatures.

Dong, Jin↗
DOE OSTI2019

Coupling Power System Dynamics and Building Dynamics to Enable Building-to-Grid Integration

The interactions between power system dynamics and building dynamics are usually ignored or over-simplified in existing power systems and building modeling and simulation tools, which limits how system modeling can sup-port Building-to-Grid integration. This paper discusses a new approach to consider those interactions by model-ing motor-driven building devices or systems. The motor-driven model is based on simplified mechanical rotation equations and allows us to study the coupling relationship between frequency/voltage in the power system and motor-driven device operation. This model is validated by performing one proof-of-concept case study with Modelica. The simulation results suggest that the proposed model can yield better representations of these interactions than the existing simplified models, especially the ones with the fast transient dynamics.

Buildings-to-grid integration, motor, coupling sim↗
DOE OSTI2019

Transactive Control Design for Commercial Buildings to Provide Demand Response

Transactive coordination and control is a new type of distributed strategy that is designed to engage various self-interested distributed energy resources such as controllable loads, distributed generators, and energy storage for providing demand response. In this paper, a novel transactive control design is proposed for commercial buildings to coordinate the electricity demand of the package rooftop air conditioning units (RTUs) for demand response. Such a coordination is realized by introducing a local relationship between the temperature setpoint and the market clearing price. With the proposed market-based control, the demand of individual RTUs is adjusted by changing their temperature setpoints as a response to the market clearing prices. Two dierent scenarios of demand response are considered herein. One is to limit the peak demand of commercial buildings, in which a new market clearing strategy based on proportionalintegral- derivative control with respect to the demand limit is proposed. The other one is to perform price responsive control, in which the total demand of commercial buildings will respond to the real-time pricing. The proposed transactive strategy is also tested and demonstrated on a real occupied building located at Pacic Northwest National Laboratory. The eld testing results conrm the eectiveness of the proposed transactive coordination and control for commercial building with RTUs to provide demand response.

Huang, Sen↗
DOE OSTI2019

Development and Calibration of an Online Energy Model for AHU Fan

The model development is necessary for the study of the energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems. To predict the HVAC energy consumption accurately, one needs to model the individual HVAC components either from the measured data or based on the knowledge of the underlying physical phenomenon. Online model characterization is critical for improving the performance of real-time model-based fault detection and diagnosis (FDD) strategies. For HVAC control, models can be used to optimize the supervisory and local feedback control strategies to improve the energy consumption efficiency, or for providing ancillary services to the grid. It has been reported that, fans in HVAC systems of commercial buildings alone can provide substantial frequency regulation service, with little change in the indoor environment. In this paper, a real-time data-driven Air Handling Unit (AHU) fan model was developed based on recursive multi regression model. A generic nonlinear polynomial model has been studied to cover scenarios with different combinations of measurement variables, variable orders as well as different training and prediction horizons. Typical measurements including static pressure, mass flow rate, and damper positions are utilized as inputs to model the power consumption of the fan. The developed models have been validated both with simulation data from EnergyPlus-Dymola co-simulation model and with field measurement data for small to medium commercial buildings. The validation results show that the online model proposed can provide an effective prediction of the AHU fan power consumption.

Dong, Jin↗
DOE OSTI2018

VOLTTRON/volttron-GS

PNNL grid related applications Object oriented design intended to facilitate reuse and extensibility of system features. The template enforces that needed structure and behaviors are available. Users may inherit and adapt these structures and behaviors to meet their specific requirements. Recognizes separation of the transactive system calculations into (1) scheduling of assets, (2) balancing of supply and demand, and (3) coordination among independent agents. This separation facilitates useful computation boundaries in which computations can be completed without requiring end-to-end communication for each update. The code provides fundamental market solutions for balancing. The code provides mechanisms for inter-agent coordination. Allows event-driven agent interactions; not limited to prescribed time intervals. Aggregates value and electricity at nodes and conveys opportunities up and down through the network.

Hammerstrom, Don↗