Building Load Models Research Articles

TRNSYS 컴포넌트를 활용한 Grey-box 건물부하 모델 개발

TRNSYS 컴포넌트를 활용한 Grey-box 건물부하 모델 개발

Research on Load Modeling Method for Typical Low Carbon Energy Consumption Scenarios in Border and Cross border Regions Considering Seasonal Migration Characteristics

With the process of urbanization and the ‘the Belt and Road’ initiative, the cross-border energy demand in southwest China has grown rapidly, driving the development of the energy system. The accuracy of load forecasting directly affects the application of energy systems, so it is crucial to conduct research on load forecasting for energy terminals in border and cross-border areas. However, there is a seasonal shift in the diverse energy consumption loads in border and cross-border regions, and currently, research on load forecasting and simulation of typical low-carbon energy consumption scenarios under this feature is basically in a blank state. Based on existing problems, this article conducts research on load modeling methods under the significant ‘seasonal migration’ characteristics of border and cross-border loads, conducts research on characteristic industries in border and cross-border areas, establishes typical low-carbon energy consumption scenarios and simulation models in border and cross-border areas, and uses sensitivity analysis method of dynamic simulation to analyze the impact of different influencing factors on the load of various building types, The Monte Carlo simulation prediction method is used to obtain the sensitivity probability distribution of various influencing characteristic factors, and the typical energy consumption building load model is modified. Finally, by comparing the energy consumption simulation results with statistical results, the accuracy of simulation energy consumption prediction is verified to be higher than 90%.

Performance Analysis of Integrated Photovoltaic-Thermal and Air Source Heat Pump System through Energy Simulation

The concept of zero energy buildings (ZEBs) has recently been actively introduced in the building sector, globally, to reduce energy consumption and carbon emissions. For the implementation of ZEBs, renewable energy systems, such as solar collectors, photovoltaic (PV) systems, and ground source heat pump (GSHP) systems, have been used. The system performance of solar collectors and PV systems are dependent on the weather conditions. A GSHP system requires a large area for boring machines and mud pump machines. Therefore, inhabitants of an existing small-scale buildings hesitate to introduce GSHP systems due to the difficulties in installation and limited construction area. This study proposes an integrate photovoltaic-thermal (PVT) and air source heat pump (ASHP) system for realizing ZEB in an existing small-scale building. In order to evaluate the applicability of the integrated PVT-ASHP system, a dynamic simulation model that combines the PVT-ASHP system model and the building load model based on actual building conditions was constructed. The heating and cooling performances of the system for one year were analyzed using the dynamic simulation model. As the simulation analysis results, the average coefficient of performance (COP) for heating season was 5.3, and the average COP for cooling season was 16.3., respectively. From April to June, the electrical produced by the PVT module was higher than the power consumption of the system and could realize ZEB.

Analysis and determination of a seasonal performance evaluation for air source heat pumps

Performance evaluations of heat pumps play an important role in regulating energy markets and promoting energy conservation technologies. The development of air source heat pumps (ASHP) standards, especially seasonal performance factors including the annual performance factor (APF) method and integrated part-load value (IPLV) method, are reviewed for applications worldwide and in China. Based on the reviews, this study introduces and analyzes the methods of evaluating the seasonal performance of ASHPs by analyzing and proposing principles and key factors of seasonal performance evaluations, including building load models, operational period distributions (bin hours), and ASHP performance models. Then, The similarities and differences between the APF and IPLV approaches were analyzed and compared, indicating that the APF method should be applied as a seasonal performance evaluation method for ASHPs (for both air–water and air–air heat pumps). Therefore, the rules for three key factors of seasonal performance evaluations were determined, which take China as an example. The specific results and parameters were calculated according to meteorological data and simulation. Besides, the related energy efficiency limitations and comparisons of the APF index, which greatly improve the ASHP industry, were discussed. This paper provides a basic reference for other countries to formulate their own ASHP standards.

Framework to Develop Time- and Voltage-Dependent Building Load Profiles Using Polynomial Load Models

The power consumption of buildings over the course of each minute, hour, day and season plays a major role in how this load influences the Electric Power System voltage and frequency, and vice versa. This consumption is based on the building’s load component types, efficiencies, and how they consume power and react to changes in real time. Due to this complexity, standard full-building load models are typically voltage-invariant. This paper proposes a novel framework to transform these voltage-invariant building load models into fully time- and voltage-dependent load profiles using available data on the voltage sensitivity of individual load components. While a voltage-dependent building model could theoretically be generated from static load models of every component in a building, this approach faces two challenges: first, load models representing all load components are impractical to develop for all possible load component types; second, building energy consumption is never measured or modeled at the individual component level. The proposed framework compiles available component data in the form of static ZIP load model parameters, and maps them into the end use categories utilized by standard building modeling programs. The voltage sensitivity of each end use category is then bounded by the extrema of the component models within it. This framework is applied to a load profile case study representing the aggregate U.S. residential building stock. In addition to the minimum/ maximum conditions, a load profile based on typical load composition and weighted ZIP parameters is generated for the same building stock. The results show that for a 10% drop in voltage, using the least sensitive ZIP parameters, active power is expected to be 3% to 14% lower than nominal, depending on the season and time of day. Using the most sensitive ZIP parameters, the active power is expected to be 9% to 20% lower than nominal, also depending on the season and time of day.

Comparison between experiment and simulation for the development of a Tri-generation system using photovoltaic-thermal and ground source heat pump

In order to develop a more accurate photovoltaic–thermal collector and ground source heat pump integrated simulation model, it is necessary to consider the operation condition assumed in real situations, such as partial load of system, energy use situation, or energy production condition. Furthermore, the integrated simulation model should be verified by measurement data based on real-scale experiment. Therefore, in this study, we constructed a demonstration plant considering the real application for a residential house, and installed a monitoring system that could measure detailed operation situations, such as weather conditions, heat source temperatures, and individual system performance. The integrated simulation model was composed of a Photovoltaic–thermal collector model, a ground heat exchanger model, a heat pump model, a building load model, and a controller model. The error rate between the monitoring result and the integrated simulation model was determined. This was used to verify the prediction accuracy of the integrated simulation model. The coefficient of variation of root mean square error (CV(RMSE)) for the heat pump coefficient of performance (COP) and system coefficient of performance were calculated as 19.2% and 14.8%, respectively. In addition, the error rates of the PVT collector and the heat pump relative to the daily heat exchange rate (HER) were calculated to be less than 30%, respectively, while the error rate of the integrated simulation model satisfied ASHRAE Guideline 14-2014. Moreover, the long-term performance of the system was analyzed through the integrated simulation model considering the weather condition of Seoul. The annual COP of the system was calculated to be 4.03.

A proactive-adaptive monthly peak demand-limiting strategy for buildings with small-scale thermal storages considering load uncertainty

Peak demand limiting is an efficient means to reduce the monthly electricity cost in a billing period (typically a month) in cases where peak demand charge is applied. This article presents an online proactive-adaptive peak demand-limiting strategy for buildings with very small-scale thermal energy storages considering load uncertainty in a billing cycle. The proposed strategy involves three major functions, as follows. First, the probabilistic demand profiles are forecast using a building load model. Second, the adaptive optimal monthly limiting threshold is identified using an optimal threshold resetting scheme based on the forecasted probabilistic demand profiles. Third, a proactive-adaptive demand-limiting control scheme is developed to online update the limiting threshold and conduct online limiting control before using up the storage capacity. Real-time tests are conducted, and the results show that this strategy can effectively reduce the monthly peak demand cost for buildings with small-scale thermal storages under load uncertainty.

Efficient identification of critical load model parameters affecting transient stability

In this paper, an efficient sensitivity analysis (SA) technique is implemented to identify critical load model parameters affecting the transient stability of a power system. Transient stability is becoming more crucial in modern power systems due to the adoption of a deregulated market structure and an increasing penetration level of renewable generation. Research has revealed that power system loads have a significant influence on transient stability. Ranking of critical load model parameters ensures accurate power system simulation results with less effort and resources have been invested in building load models. The ranking has been made for different loading conditions in order to make the ranking applicable most of the time. Different types of load model have been applied to investigate their influence on transient stability. All simulations have been illustrated using a modified version of the 68 bus NETS-NYPS test system. The results revealed the important load model parameters and important loads, and the level of accuracy required for these critical parameters.

Development and evaluation of a generalized rule-based control strategy for residential ice storage systems

In recent years, variable electricity pricing has become available to residential consumers to incentivize demand reductions during midday peak hours. Thermal energy storage (TES) systems enable consumers to store cooling energy when demand is low and assist A/C operation during peak demand periods. However, the cost savings achievable using TES are highly dependent on how the system is operated for a given utility rate structure. This study investigates control strategies for a packaged chiller unit integrated with ice storage that leverage available residential utility rate structures in the U.S. to reduce consumer electricity cost. The present work describes the development and evaluation of a generalized rule-based control strategy inspired by the performance of an optimal controller that minimizes monthly electricity cost considering both time-of-use energy and demand charges. The generalized rule-based controller is compared against the optimal controller as well as to heuristic control strategies for TES that were originally developed for commercial buildings for a range of equipment cooling capacities, TES sizes, geographic locations, and residential utility rates. The total electricity cost is determined using a simulation model that includes models for the chiller unit, ice storage tank, and secondary loop components, along with a building load model. Results show that the generalized rule-based controller can approximate the performance of the optimal controller within 20% for all cases tested, and within 10% of the optimal cost in 53% of the cases tested. The controller also performs significantly better than the heuristic strategies for commercial buildings that were evaluated.

Development of load models and operation simulator for a building complex with mixtures of multi-type engines and renewable devices

A building load model for the electricity consumption of the KIER (Korea Institute of Energy Research) complex was developed by compiling the year-round hourly data of each member building. Heating and cooling load models for the complex were also estimated by building TRNSYS models for the member buildings. By matching measured data that are only available for a limited time on a building, suitable parameter values for the TRNSYS simulation were empirically determined. Then, an operational simulator that can accommodate arbitrary combinations of energy producing devices was developed. Our simulator allows for prioritization among the energy supplying devices, and cases where photovoltaic (PV) and solar thermal devices are mixed can be easily simulated. Several scenarios were composed for the KIER buildings. Tested components include small to medium-size conventional cogeneration systems and renewable energy production devices such as photovoltaic cells, solar collectors, fuel cells, and bio-ORC (organic Rankine cycle). Tests reveal that our tool produces reasonable results of physical quantities such as electricity production, fuel consumption, and the device operational characteristics. Scenario comparisons were made as a preliminary part of a feasibility study, and renewable energy contribution was estimated.

Physically-Based Building Load Model for Electric Grid Operation and Planning

This paper describes a building electrical-thermal load model that is both suitable for power system analysis as well as retains essential thermodynamic information. The model is developed using observations made over multiple time windows to elicit both steady state characteristics, for planning, as well as dynamic behavior, for demand side control applications. Consequently, multiple model evolutions based on data availability are presented. Physically-based parameters are used in the modeling process to describe the influence of building automation systems on the electrical load profile. The end goal is to provide an improved understanding of building temperature-load interactions to facilitate more efficient use of buildings as demand side resources.

Comparison of building energy demand for hotels, hospitals, and offices in Korea

Energy demand characteristics were surveyed and measured throughout Korea to establish load models for three types of buildings: hotels, hospitals, and offices. The consumption of electricity and fuel was collected and statistically analysed to establish four types of building loads: electricity, heating, hot water, and cooling energy. The annual, monthly, daily, and hourly patterns of the four load types were derived for the three building types. Key statistical values such as the annual mean, maximum, and minimum values are provided. A procedure is presented to compose building load models as a time series covering the 8760 h of a year by combining daily and hourly patterns. The key features of the load models were compared to better understand the energy consumption characteristics of the three building types. Substantial variations in the magnitudes and patterns were observed among the types of buildings and loads. The load models can be applied to a wide range of problems in building energy system design and planning, including simulations and optimizations of community energy systems.

A Simulink®-Based Building Load-Ground Source Heat Pump Model Used to Assess Short-and Long-Term Heat Pump and Ground Loop Performance

An integrated building load-ground source heat pump model is developed to capture short-term (30 s) and long-term (10–20 yr) performance of ground source heat pumps with vertical boreholes. The model takes advantage of the built-in computation and organization functions of the simulink®/matlab environment to couple the component building load, heat pump, and ground loop models at every time step. The building load model uses the HAMBASE thermal program and is applicable to residential and commercial buildings. The heat pump model uses manufacturer data and sensible heat corrections to accurately model heat pump operation across a wide range of input conditions. The ground loop model is a combination of Hellstrom's borehole tube model, Eskillson's long-term (>10 yr) g-function ground model and the one-dimensional, short-term (<5 min) numerical ground model by Xu. Fifteen year simulation results for a base case residential house are presented to illustrate the integrated model's ability to predict a wide range of time responses and to illustrate a limiting ground loop sizing criterion that reveals the slow degradation in system performance due ground heating effects. Simulations with varying borehole lengths also illustrate the sensitivity of ground loop sizing on the system's thermal and economic performances. The work emphasizes the importance of proper borehole sizing, design, and placement especially in cooling-dominated climates, where the unbalance of heat loads to the ground cause slowly rising ground temperatures.

Dynamic aggregated building electricity load modeling and simulation

In order to support the growing interest in demand response modeling and analysis, there is a need for physical-based building load models. This work presents a new approach for simulating electrical power flow in buildings. The new approach handles the power flow capacity shortage in existing building simulation programs, which have been used for the past few decades by building energy communities. The suggested approach represents the building as a group of electrical networks, organized in hierarchical levels. On the top level, the user defines key parameters such as rated power and power factor of existing loads. The power cables are modeled by their equivalent PI model. Accurate simulation models are developed for solving the building network equations where building loads are integrated into building network. Smart meters are implemented at different locations for power quality and energy auditing. Two case studies of residential and commercial buildings are investigated to prove the capability of the introduced approach. A comparison with EnergyPlus, as verified building energy software, is introduced to prove the ability of the proposed Matlab-based model to evaluate the annual energy consumption of the building. All results show the accuracy and ability of the proposed approach for simulating the electrical power flow of the building and can be integrated with renewable and storage energy.

Generic algorithm to select building-lighting technologies for minimizing total harmonic distortion

The proliferation of nonlinear loads has raised new concerns about the power quality in commercial buildings. Higher cost of repair, and the reduction in average life of equipment, both on the supply and demand sides, as a result of poor power quality cannot be overlooked or ignored. As lighting loads are the largest fraction of the load in most commercial buildings, a small increase in their harmonic distortion levels may jeopardize the other loads in the building or loads connected to the same utility bus. In this paper, we present a generic algorithm to select energy-efficient lighting technologies that will minimize harmonic distortion levels in a building. Results from the algorithm are validated on a building-load model to test its accuracy.