Building Energy Modeling and Studies of Electric Power Distribution Systems with Distributed Energy Resources

There is significant opportunity for savings in energy and investment from improved performance of electric Power Distribution Systems (PDSs) through optimal planning and operation of conventional voltage-controlling devices. Novel multi-step model conversion and optimal capacitor planning (OCP) procedures are proposed for large-scale utility PDSs and are exemplified with an existing utility circuit of approximately 4,000 buses. Simulated optimal control and operation is achieved with a cluster-based approach that utilizes load-forecasting to minimize equipment degradation by intelligently dispersing device setting adjustments over time such that they remain most applicable. Improved performance may also be achieved through smart building technologies and Virtual Power Plant (VPP) control of increasingly more prevalent Distributed Energy Resources (DERs). The established simulation test bed for PDSs incorporates DERs to evaluate VPP implementations and an optimization process for control timing is proposed that minimizes targeted peak power and possible resulting increase in total daily energy. The advanced VPP controls incorporate the Consumer Technology Association (CTA) 2045 standard and EnergyStar performance characterizations to leverage HVAC systems as Generalized Energy Storage (GES) for load manipulation and to support the integration of demand-side generating DERs, such as local solar Photo-Voltaic (PV) systems

Co-Simulation of Electric Power Distribution and Buildings with EnergyPlus and OpenDSS

Problem Formulation Need for accurate load modeling that incorporates human comfort into community-level demand response (DR) studies Achievable through co-simulation of advanced digital twins for buildings with EnergyPlus and distribution systems with OpenDS

An Aggregated and Equivalent Home Model for Power System Studies with Examples of Building Insulation and HVAC Control Improvements

Introducing new technologies into existing residences provides opportunities for the utility to enhance a community\u27s power system performance. To validate the benefits of such technologies, an energy model is required in which their integration with a community\u27s power system may be simulated. It can be difficult or impossible to properly model a power system without sufficient sample data. This paper proposes a method that uses Gaussian kernel density estimation (GKDE) to calculate the aggregated net power flow of a community\u27s distribution system with only limited sample points at each time step. Example case studies that confirm the usefulness of the GKDE method are presented in which the power system benefits of improved insulation and heating, ventilation, and air conditioning (HVAC) system control are analyzed. This analysis was performed using an EnergyPlus (EP) house model that was created and calibrated based upon an individual house. This power representation of the individual house was determined by accurately estimating the aggregated net power flow of a community in Glasgow, KY with GKDE. Simulation results based on the equivalent house energy model show that improved insulation reduces the energy consumption and the peak power at the power system level. Simulation results also show that the HVAC control reduces the peak power for the entire community

Optimal Combinations of Utility Level Renewable Generators for a Net Zero Energy Microgrid Considering Different Utility Charge Rates

High initial investment and the intermittent nature of resources are major challenges for large scale renewable generation. The size of photovoltaic (PV) and wind turbine (WT) farms in the microgrid needs optimized to avoid curtailment and to efficiently meet the demand of a power system. Battery energy storage systems (BESSs) may also be used to improve flexibility. This paper explores the optimal sizing for PV and wind generators, as well as a BESS at the utility level for a large grid-connected net zero energy (NZE) hybrid microgrid considering characteristics such as initial investment, levelized cost of energy (LCOE), operating costs, net present cost (NPC), and renewable fraction. Multi-objective formal optimizations were formulated as single objective problems with constraints and solved using the HOMER Pro computational engine. Ten optimizations with different utility charge rates are performed using actual data for the load profile, weather, and utility buy-back rates of Glasgow, KY. Simulation results demonstrated that various utility charge rates result in different optimal sizes for the solar PV and the WT farms, as well as for the BESS capacity

Electric Power System Studies for a Multi-MW PV Farm and Large Rural Community with Net Zero Energy and Microgrid Capabilities

Solar photovoltaic (PV) systems are currently being deployed at an accelerated rate because of their cost-competitiveness and environmental benefits, which make them a prime candidate for local renewable energy generation in communities. Microgrids can help accommodate for the problems that accompany PV systems, such as intermittency due to weather, by coordinating different distributed energy resources (DERs), while islanded or by drawing power from the utility while in grid-connected mode. An islanding option is also important for resilience and grid fault mitigation, even if other DERs are not present within the system. This paper studies the potential benefits that a multi-MW utility-scale PV farm may yield for a large rural community when installed within a grid-connected microgrid structure. The PV system was optimally sized based on net present cost (NPC) with a net zero energy (NZE) goal. With local solar PV generation and a connection to the grid to transmit overgeneration through, the community can be NZE by having a PV farm power rating that is much greater than the peak load demand. This may lead to cases of increased transient severity during mode transitions and may require substantial curtailment of PV. A control scheme is proposed to smooth system transients that result from the switching between the two modes of operation in order to avoid system damage or unreliable load service

ZIP Load Modeling for Single and Aggregate Loads and CVR Factor Estimation

ZIP load modeling has been used in various power system applications. The aggregate load modeling is common practice in utility companies. However, little research has been done on the theoretical formulation of the aggregate load. This paper formulates the aggregate ZIP load model using the single ZIP load model. The factors that may affect aggregate ZIP load estimation are studied. Common ZIP parameter estimation methods including least squares method, optimization method and neural network method have been used in this paper to estimate ZIP parameters. The case studies are based on the IEEE 13-bus and 34-bus system built in OpenDSS. The ZIP parameter estimation is also performed using field data, and the conservation voltage reduction (CVR) factor is computed based on the estimated ZIP load model

Power Factor and Reactive Power in US Residences – Survey and EnergyPlus Modeling

Electric power systems are experiencing a growing number of electronic loads in the residential sector as modern appliance technology progresses, and it has become increasingly more important to consider the total power factor (PF) of residential communities. This paper provides a survey based on literature and publicly available information of typical appliance PF values and effects at the residential level as well as a discussion on appliance energy use and corresponding operation schedules. A procedure for the calculation of equivalent PF is proposed and exemplified with minutely experimental data at 15-minute and hourly time intervals, which correspond to smart metering and traditional practices, respectively. The application of the proposed equivalent PF procedure in coordination with building energy modeling, may, in principle, be employed to determine PF for entire communities at an aggregated level. The paper includes a proposal to simulate reactive power through an approach that utilizes EnergyPlus, a building energy modeling software. Such simulation capability could facilitate improved planning for compensation implementation both in electric power distribution networks and in individual residences, which offers significant opportunity for energy savings

Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability

Building modeling, specifically heating, ventilation, and air conditioning (HVAC) load and equivalent energy storage calculations, represent a key focus for decarbonization of buildings and smart grid controls. Widely used white box models, due to their complexity, are too computationally intensive to be employed in high resolution distributed energy resources (DER) platforms without simulation time delays. In this paper, an ultra-fast one-minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel procedure to replicate white box models as an alternative to widespread experimental big data collection. Synthetic output data from experimentally calibrated EnergyPlus models for three existing smart homes managed by the Tennessee Valley Authority is used. The HMLM employs combined k-means clustering and multiple linear regression (MLR) models to predict minutely HVAC power with satisfactory nRMSE error of less than 10% across an entire year test set. An approach is provided to characterize HVAC systems through the newly proposed hybrid model as a generalized storage (GES) device suitable for DER control and event types in accordance with the Communication Technology Association (CTA) 2045 standard and Energy Star metrics such as “energy take”, currently developed by industry, to unify household appliance controls

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads with Application to Energy Management of Smart and NZE Homes

Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In addition to being used in the proposed HVAC separation method, the LSTM models are employed also for accurate day-ahead HVAC and solar photovoltaic (PV) energy forecasts. To test and validate the proposed method, the SHINES dataset, a publicly available dataset spanning three years at 15-minute time resolution from a large-scale DOE experimental project, is used. Two computational case studies are constructed with a test HEMS to investigate the power regulating capability of smart home virtual operation as dispatchable loads or generators. Prediction results obtained with the proposed method show hourly and daily CV(RMSE) of 29.4% and 11.1%, respectively. These results are well within the bounds of error established by academia and the ASHRAE building model and calibration standards

Large-Scale Modeling and DR Control of Electric Water Heaters With Energy Star and CTA-2045 Control Types in Distribution Power Systems

The paper proposes a generalized energy storage (GES) model for battery energy storage systems (BESS), electric water heaters (EWH) and heating, ventilation, and air-conditioning (HVAC) systems to enable demand response control complying to Energy Star and CTA-2045 standards. The demand response control has been implemented in the DER integration testbed, which was originally developed by EPRI, to demonstrate that the “energy content” and “energy take” for BESS and EWH with mixing valve technology are comparable for typical residential ratings. A distribution power system was modeled using the modified IEEE 123-bus feeder system, measured residential loads, and EWH power simulated based on realistic hot water draws from CBECC-Res software. The demand response control, which complies to CTA-2045 standards was implemented to the EWHs considering the energy take values. Results demonstrate that the EWHs can be controlled to postpone the peak power at the distribution system level and provide a large amount of energy storage, while maintaining system robustness. The impact on occupant comfort was also analyzed