Pathway for decarbonizing residential building operations in the US and China beyond the mid-century

Data-driven prediction and optimization toward net-zero and positive-energy buildings: A systematic review

Modeling and management performances of distributed energy resource for demand flexibility in Japanese zero energy house

Technological advancement and supportive government policy have accelerated investments in distributed energy resources (DERs) in buildings, demand-side management has become essential to fully exploit their value, including demand flexibility and grid support. Using high-resolution smart-meter data, this work presents an optimization framework for assessing the flexible scheduling of coupling distributed resources in existing zero energy houses (ZEHs) in Kyushu, Japan. Two price-based energy management programs are investigated for different energy coupling models with associated variables and constraints. Specifically, demand sides are encouraged to adjust energy usage and grid feed-in photovoltaics (PV) generation following a fixed Feed-in Tariff (FiT) value or market-oriented Feed-in Premium (FiP). The scheduling results present the impacts of proposed dispatch strategies on PV consumption and grid interactions. Results show two price-following strategies result in significant differences in dynamic grid feed-in power flows, the trade-off between dynamic PV self-consumption and grid import helps reduce the annual energy cost to feed PV generation by matching real-world spot prices. The improvement of PV self-consumption through coupling strategies has a close relationship with the pattern of household demand profiles. The energy flexibility potential of the heat pump system is constrained by daily hot water demand, flexible scheduling of coupled PV/battery/heat pump systems achieves the highest PV self-consumption ratios. The flexible dispatch of battery storage systems presents reliable performances in increasing on-site PV self-consumption and reducing annual household energy costs, the effects of storage charging rates on cost savings are less, especially for larger capacity batteries. Findings may help design sustainable and equitable pathways for decentralized solar PV system deployment. 

Flexible coupling and grid-responsive scheduling assessments of distributed energy resources within existing zero energy houses

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types. 

Energy Flexibility Quantification of a Tropical Net-Zero Office Building using Physically Consistent Neural Network-based Model Predictive Control

• Introduces a novel demand flexibility (DF) benchmarking metrics framework • Allows benchmarking across geographic areas with different climates and grid needs • Enables empirical approaches to achieve understanding of key influential factors of DF • Framework tested with two benchmarking case studies of 132 office and retail buildings • Identified load shed intensity range and variability; influence of event duration and timing Building demand flexibility (DF) research has recently gained attention. To unlock building DF as a predictable grid resource, we must establish a quantitative understanding of the resource size, performance variability, and predictability based on large empirical datasets. Researchers have proposed various sets of theoretical metrics to measure this performance. Some metrics have been applied to simulation results, but most fall short of exploring the complexities in real building applications. There are practical metrics used in individual demand response field studies but they alone cannot fulfill the job of DF benchmarking across a diverse group of buildings. The electrical grid's geographically diverse and changing nature presents challenges to comparing building DF performance measured under different conditions (i.e., benchmarking DF ). To address this challenge, a novel DF benchmarking framework focused on load shedding and shifting is presented; the foundation is a set of simple, proven single-event metrics with attributes describing event conditions. These enable benchmarking and visualization in different dimensions for identifying trends that represent how these attributes influence DF. To test its feasibility and scalability, the DF framework was applied to two case studies of 11 office buildings and 121 big-box retail buildings with demand response participation data. These examples provided a pathway for using both building level benchmarking and aggregation to extract insights into building DF about magnitude, consistency, and influential factors. Potential applications of the framework and real-world values have been identified for grid and building stakeholders. 

Defining and Applying an Electricity Demand Flexibility Benchmarking Metrics Framework for Grid-Interactive Efficient Commercial Buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called "global temperature adjustment" using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m2), 10.0 W/m2, 11.1 W/m2, 7.1 W/m2, and 4.7 W/m2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. The comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility. 

Comparing simulated demand flexibility against actual performance in commercial office buildings

Factors Influencing Building Demand Flexibility

Distribution network is a noticeable part of Energy Internet construction. The operation, maintenance and control of low-voltage distribution network of Energy Internet is of great importance. However, the full collection and calculation of real-time information of equipment in low-voltage distribution network will bring tremendous pressure to communication and main station. Therefore, this paper presents an evaluation method of health status of low-voltage distribution network based on edge computing. In the evaluation method, five indexes are selected to establish the evaluation system of low-voltage distribution network. The indexes are processed consistently, and the indexes dimensionless is processed with extreme value. Then the subjective and objective weights of each evaluation index are obtained by the order relation analysis method and the variation coefficient method which simulate the expertise respectively. Finally, the comprehensive weight of each index is calculated by Lagrange optimal multiplier method, so the evaluation function of health status in low-voltage distribution network is obtained after sorting out. According to the function value, the health status of low-voltage distribution network is reasonably evaluated. The feasibility and efficiency of the evaluation method are verified by the case study of several low-voltage distribution networks in a certain area.

Evaluation of Health Status of Low-voltage Distribution Network Based on Edge Computing

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Papers

Defining and Applying an Electricity Demand Flexibility Benchmarking Metrics Framework for Grid-Interactive Efficient Commercial Buildings

Comparing simulated demand flexibility against actual performance in commercial office buildings

Factors Influencing Building Demand Flexibility

Evaluation of Health Status of Low-voltage Distribution Network Based on Edge Computing