Grid-Interactive Efficient Buildings Research Articles

Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial Buildings

Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial Buildings

Online Distributed Coordination Operation for Grid-Interactive Efficient Residential Buildings

Online Distributed Coordination Operation for Grid-Interactive Efficient Residential Buildings

Architecting sustainability performances and enablers for grid-interactive efficient buildings

Today, grid-interactive, efficient buildings are gaining popularity due to their potential sustainability performances through their ability to learn, adapt, and evolve at different scales to improve the quality of life of their users while optimizing resource usage and service availability. This is realized through various practices such as management and control measures enabled by smart grid technologies, interoperability, and human-cyber-physical security. However, despite their great potential, the research of those technologies still faces various challenges. These include a lack of communication and control infrastructure to address interpretability, security, cost barriers, and difficulties balancing occupant needs with grid benefits. Initially, system modelling and simulation are promising approaches to address those challenges ahead of time. It involves consideration of complex systems made up of components from various research domains. This paper addresses the above practices, highlighting the value of integrating technology and intelligence in the planning and operation of buildings, both new and old. It provides a way to educate architects and engineers about this emerging field and demonstrates how these practices can help in creating efficient, resilient, and secure buildings that contribute to occupant comfort and decarbonization.

Large-scale simulation-based parametric analysis of an optimal precooling strategy for demand flexibility in a commercial office building

Grid-interactive efficient buildings (GEBs) optimize their operations by using flexible load techniques, such as precooling, to reduce peak electricity demand. However, precooling effectiveness depends on factors like building properties, weather, and utility rates. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. On top of that, we investigated the impact of building thermal mass, outdoor air conditions, and energy price profiles on the optimal precooling strategy. Our results show substantial cost savings and peak load reduction potential in buildings with heavy thermal mass, with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings, peak load reduction, and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.

Hierarchical-stochastic model predictive control for a grid-interactive multi-zone residential building with distributed energy resources

Using Model Predictive Control (MPC) is a promising method for enabling grid-interactive efficient buildings. Since MPC relies on a building model and the forecasts of external disturbances to derive optimal inputs, the uncertainties due to forecast errors and model inaccuracies can deteriorate the control performance. Most existing MPC studies for the built environment are deterministic MPC without consideration of uncertainties even though several different methods (e.g., stochastic MPC) are available in the literature to deal with them. Even studies that consider forecast uncertainties often neglect model inaccuracies. Hence, in the present paper, a novel hierarchical-stochastic MPC is proposed considering forecast uncertainties and model inaccuracies, and its performance is compared with deterministic, stochastic, and hierarchical MPC for power management in a residential building with distributed energy resources. The control objective is the cost-optimal scheduling of a heat pump, a battery for energy storage, and a rooftop photovoltaic system. Measurement data is used to identify the building model. The results for one-week simulation in winter show that (1) the deterministic MPC results in an unacceptable level of temperature constraint violations in two out of five rooms; (2) the hierarchical MPC can reduce the temperature constraint violations to an acceptable level at the expense of increased cost; (3) the stochastic MPC achieves the same reduction in temperature constraint violations as the hierarchical MPC but at slightly lower costs; and (4) the new proposed hierarchical-stochastic MPC results in both lower temperature constraint violations and lower financial expenses than the use of stochastic or hierarchical MPC individually.

Model predictive control for demand flexibility of a residential building with multiple distributed energy resources

One of the major challenges with increasing penetration of renewable energy sources (e.g., solar and wind) is to maintain grid stability. Grid-interactive efficient buildings are regarded as a promising approach to advance the role buildings can play in energy system operations and planning. This paper evaluates the demand flexibility in a residential building located in Karlsruhe, Germany, incorporating multiple distributed energy resources, including a photovoltaic and battery system, an electric vehicle, an electric water heater and a heat pump. A multi-criteria optimization problem is formulated with model predictive control (MPC) for five cases of demand flexibility. The simulation results of five winter days show that the peak power during the peak periods can be reduced by 92%, 69%, 100%, and 100% for real-time pricing, demand limiting, load shedding, and load shifting, respectively. For the power tracking scenario, the MPC tracked the reference power profile successfully for 70% of the simulation time. Achieving demand flexibility does not necessarily cause an increase in energy costs.

MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities

Building and power generation decarbonization present new challenges in electric grid reliability as a result of renewable energy source intermittency and increase in grid load caused by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide grid flexibility services through demand response. Reinforcement learning is well-suited for energy management in grid-interactive efficient buildings as it is able to adapt to unique building characteristics compared to rule-based control and model predictive control. Yet, factors hindering the adoption of reinforcement learning in real-world applications include its sample inefficiency during training, control security and generalizability. Here we address these challenges by proposing the MERLIN framework for the training, evaluation, deployment and transfer of control policies for distributed energy resources in grid-interactive communities for different levels of data availability. We utilize a real-world community smart meter dataset to show that while independently trained battery control policies can learn unique occupant behavior and provide up to 60% performance improvement at the district level, transfer learning provides comparable building and district level performance while reducing training costs.

Two-Level Decentralized-Centralized Control of Distributed Energy Resources in Grid-Interactive Efficient Buildings

The flexible, efficient, and reliable operation of grid-interactive efficient buildings (GEBs) is increasingly impacted by the growing penetration of distributed energy resources (DERs). Besides, the optimization and control of DERs, buildings, and distribution networks are further complicated by their interconnections. In this letter, we exploit load-side flexibility and clean energy resources to develop a novel two-level hybrid decentralized-centralized (HDC) algorithm to control DER-connected GEBs. The proposed HDC 1) achieves scalability w.r.t. a large number of grid-connected buildings and devices, 2) incorporates a two-level design where aggregators control buildings centrally and the system operator coordinates the distribution network in a decentralized fashion, and 3) improves the computing efficiency and enhances communicating compatibility with heterogeneous temporal scales. Simulations are conducted based on the prototype of an office building at the Oak Ridge National Laboratory to show the efficiency and efficacy of the proposed approach.

A semantic ontology for representing and quantifying energy flexibility of buildings

• EFOnt, a semantic ontology for building energy flexibility applications, is introduced. • EFOnt is designed to work with other building energy data tools as part of an ecosystem • EFOnt standardizes energy flexibility definition, characterization, and quantification. • One measurement-based and one simulation-based use case of EFOnt are given. • The future development and potential use cases of EFOnt are discussed. Energy flexibility of buildings can be an essential resource for a sustainable and reliable power grid with the growing variable renewable energy shares and the trend to electrify and decarbonize buildings. Traditional demand-side management technologies, advanced building controls, and emerging distributed energy resources (including electric vehicle, energy storage, and on-site power generation) enable the transition of the building stock to grid-interactive efficient buildings (GEBs) that operate efficiently to meet service needs and are responsive to grid pricing or carbon signals to achieve energy and carbon neutrality. Although energy flexibility has received growing attention from industry and the research community, there remains a lack of common ground for energy flexibility terminologies, characterization, and quantification methods. This paper presents a semantic ontology—EFOnt (Energy Flexibility Ontology)—that extends existing terminologies, ontologies, and schemas for building energy flexibility applications. EFOnt aims to serve as a standardized tool for knowledge co-development and streamlining energy flexibility related applications. We demonstrate potential use cases of EFOnt via two examples: ( 1 ) energy flexibility analytics with measured data from a residential smart thermostat dataset and a commercial building, and ( 2 ) modeling and simulation to evaluate energy flexibility of buildings. The compatibility of EFOnt with existing ontologies and the outlook of EFOnt's role in the building energy data tool ecosystem are discussed.

Defining and applying an electricity demand flexibility benchmarking metrics framework for grid-interactive efficient commercial buildings

• 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.

Enhancement of phase change material hysteresis model: A case study of modeling building envelope in EnergyPlus

Nowadays, buildings are expected to offer demand side services to the power grid to enhance the electrical load flexibility, which leads to the concepts of grid-interactive efficient buildings (GEBs). Phase change material (PCM)-based thermal energy storage has seen increasing attention in recent years for peak load shifting of grid-interactive efficient buildings (GEBs). Numerical models are critical tools for design and evaluation of PCM-integrated systems. Most industrial-grade PCMs are reported to melt/freeze over a temperature range instead of at a unique temperature. Such thermal hysteresis effect significantly affects the reliability of simulation results because not only the heat transfer process depends on melting and freezing temperatures, the PCM thermal properties change significantly during the phase change process as well. This study is aimed to develop a model for the PCMs used in the building envelope with the capability to accurately simulate hysteretic behaviors. This model is based on a two-phase assumption and is implemented in a whole building energy performance simulation program (i.e., EnergyPlus). A comparison between numerical results and experimental data shows that during a complete phase transition, the two-phase model could achieve a good agreement with the experimental data. During a partial phase transition, the two-phase model could lead to significant improvements compared to other alternative PCM models, including the existing PCM model in EnergyPlus. Last, whole building simulations were performed to study this model's performance regarding heating/cooling loads and zone mean air temperature of a given building. The results show that the difference in hourly heating/cooling loads introduced by the models was less than 1% in design conditions, while significant changes were observed in both hourly heating/cooling loads and zone mean air temperature when the PCM envelope underwent partial phase transition processes.

IoT Cloud-Based Power Quality Extended Functionality for Grid-Interactive Appliance Controllers

Due to the myriad of loads that are collected into commercial grid-interactive efficient buildings (GEBs) focused on the industry 4.0 paradigm, it is important to ensure their proper electrical operation. The power quality (PQ) here requires a granular monitoring approach, reaching a point where each device connected to the microgrid can diagnose whether its power supply is optimal. Otherwise, it can participate cooperatively in decision-making to avoid anomalies or faults in the microgrid. In this work, we present cloud-based extended functionality to make smart appliances responsive to the grid, either autonomously or managed under the open automated demand response standard. Further to acting as a switch, the main strength lies in its PQ monitoring via the FIWARE Internet of Things (IoT) platform with data-driven analytics capabilities. It identifies and even predicts a broad spectrum of electrical disturbances, far exceeding the capabilities of previous solutions such as the grid-friendly appliance controller, so it is possible to customize a battery of alarms at will (e.g., according to IEEE 1547 standard). Moreover, although it can act autonomously, its main mission will be to act in a coordinated manner, either cooperatively or under the supervision of the GEB Energy Management System. Finally, different case studies are presented to show their capabilities. With the integration of these distributed submetering systems, under standards IoT wireless communication protocols, a further step will be taken in the advent of the digital utility paradigm.

Peak Power Minimization for Commercial Thermostatically Controlled Loads in Multi-Unit Grid-Interactive Efficient Buildings

The load profiles of most commercial and industrial consumers are characterized by brief periods of very high power consumption followed by intervals of lower demand. To encourage such consumers to flatten their load profiles, power utilities around the world often levy a monthly demand charge (DC) on the peak demand measured over brief intervals. In this work, we consider the joint optimization of energy costs (EC) and the instantaneous peak power of a multi-unit building which uses a hydronic heating, ventilation and air-conditioning (HVAC) system and responds to a demand response (DR) program. Despite the apparent non-convexity of the control framework, we show how it may be transformed into a convex optimization problem. Next, we study the power demand patterns resulting from our proposed strategy for thermostatically controlled loads (TCLs), and evaluate the strategy’s performance for various climate zones in the US under both typical and atypical weather conditions. The results show that, depending on the ambient conditions and the tariff structure, our strategy can result in utility bill savings of up to nearly 19% compared to the baseline. The results also indicate that our power control strategy can significantly reduce the instantaneous peak power consumption in commercial TCLs.

Assessing the performance of global thermostat adjustment in commercial buildings for load shifting demand response

Abstract Efficiently leveraging new sources of flexibility is critical to mitigating load balancing challenges posed by variable renewable resources. The thermal inertia of commercial buildings allows us to shift their power consumption on minute to hourly timescales to provide demand response (DR) to the grid while maintaining occupant comfort. Global thermostat adjustment (GTA) provides a readily available and scalable approach for implementing load shifting DR using commercial heating, ventilation, and air conditioning (HVAC) systems, since it leverages the inherent sophistication of modern building automation systems. However, there is an incomplete understanding of GTA’s performance for this purpose and its impact on building systems and occupant comfort. In this paper, we explore the performance of GTA by analyzing results from nearly nine hundred experiments on eight university campus buildings in Michigan and North Carolina. Using GTA, we manipulate each building’s thermostat setpoints causing the building to shift its power consumption with respect to its baseline. We quantify the magnitude of HVAC power response, energy use of HVAC subsystems, and impact on occupant comfort. Finally, we connect our experimental results with power system operation using an optimization model that coordinates GTA actions across a large collection of grid-interactive efficient buildings to reduce high ramp rates on the grid and mitigate renewable energy curtailment. Overall, our work finds that the impacts on HVAC subsystems are often complex, and may result in additional energy being consumed by fans and terminal reheat. These effects must be considered when using GTA for load shifting. Additionally, we demonstrate that occupant comfort, as assessed by indoor temperature and humidity, can be maintained during GTA events. From a societal perspective, our modeling work finds that the additional renewable energy that can be integrated through the use of GTA strategies eclipses any additional energy consumed by buildings.

Tri-Level Scheduling Model Considering Residential Demand Flexibility of Aggregated HVACs and EVs Under Distribution LMP

Residential loads, especially heating, ventilation and air conditioners (HVACs) and electric vehicles (EVs), have great potentials to provide demand flexibility which is an attribute of grid-interactive efficient buildings (GEB). Under this new paradigm, EV and HVAC aggregator models are first developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least squares parameter estimation (DG-LSPE), which can deal with heterogeneous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). The first two levels form a bilevel model to optimize the aggregators' payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, and the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons of payment reduction due to demand flexibility: load shifts, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.

Modeling and evaluation of cyber-attacks on grid-interactive efficient buildings

Grid-interactive efficient buildings (GEBs) are not only exposed to passive threats (e.g., physical faults) but also active threats such as cyber-attacks launched on the network-based control systems. The impact of cyber-attacks on GEB operation are not yet fully understood, especially as regards the performance of grid services. To quantify the consequences of cyber-attacks on GEBs, this paper proposes a modeling and simulation framework that includes different cyber-attack models and key performance indexes to quantify the performance of GEB operation under cyber-attacks. The framework is numerically demonstrated to model and evaluate cyber-attacks such as data intrusion attacks and Denial-of-Service attacks on a typical medium-sized office building that uses the BACnet/IP protocol for communication networks. Simulation results show that, while different types of attacks could compromise the building systems to different extents, attacks via the remote control of a chiller yield the most significant consequences on a building system’s operation, including both the building service and the grid service. It is also noted that a cyber-attack impacts the building systems during the attack period as well as the post-attack period, which suggests that both periods should be considered to fully evaluate the consequences of a cyber-attack.

Evaluation of phase change plaster/paste composites for building envelopes

Thermal energy storage (TES) materials increase a building’s thermal mass and provide the flexibility to shave and time-shift energy use. These materials are needed to enable grid-interactive efficient buildings, allowing building occupants to lower costs by leveraging free ambient heating/cooling as well as shifting consumption to cheaper electricity periods. Phase change materials (PCMs) that store heat through a solid–liquid/solid–solid transformation are of particular interest for TES because of their ability to store large amounts of energy across a constant temperature or very small temperature range. Previous studies on PCM building envelopes evaluated products that may only be incorporated as part of new construction and are not easily applicable to existing, energy-intensive buildings. In this work, we leverage laboratory PCM characterization and whole-building energy simulation to study the energy and cost savings of PCM plaster/paste coatings, which can be applied post construction to both new builds and retrofits of existing buildings (about 40% of residential buildings in the United States fall into the retrofit category). We evaluated the energy impact of PCM plaster/paste composites for a single-family residential building in the United States, including layer design (location and thickness), composite properties (phase change temperature, energy density, and shape of the enthalpy curve), and U.S. climate zone. The enthalpy curve shape of PCM shows that phase change temperature and energy density are not the only two parameters that must be considered for PCM selection. Maximizing the energy density amid the effective TES zone is key to boosting energy savings. We also studied PCM price targets with the aim of achieving reasonable payback periods based on both block and time-of-use electricity rates. Moreover, we developed an empirical equation to provide a rough estimate of the energy savings potential of incorporating PCM without the need to perform detailed simulations. These results will help guide the selection and design of PCM plaster/paste composite elements for residential building envelopes.

A flexible and generic functional mock-up unit based threat injection framework for grid-interactive efficient buildings: A case study in Modelica

Grid-interactive efficient buildings (GEBs) have been considered as an important asset to support the power grid reliability by utilizing the demand flexibility offered by GEBs. GEBs are enabled by advances in sensors and controls, and the communication between building equipment, whole buildings, and the grid. The integration of different building technologies and network-based communication system makes GEBs vulnerable to passive threats such as equipment failure and active threats such as cyber-attacks. Modeling and simulation is an effective way to evaluate the impact of threats on the system performance. This paper proposes a generic and flexible threat injection framework for commonly-used building energy simulators such as EnergyPlus and Modelica to support threat modeling and evaluation. This framework leverages functional mock-up unit (FMU) to develop a general modeling interface for threat injection and simulation. A numerical case study using Modelica as a building energy simulator is conducted to demonstrate the capability of the framework for supporting single/multiple-order threat modeling and simulation of a GEB. Four threats and their combinations are injected on a Modelica-based threat-free building energy and control system, including operating supply fan at its full speed, remotely cycling the chiller on and off, blocking the chiller from receiving the chilled water supply temperature setpoints, and hijacking the global zone air temperature setpoint. Simulation results show that the cyber-attack that leads to short-term signal blocking has small effects on the system operation due to the “self-healing” feature of the heating, ventilation, and air-conditioning (HVAC) interactive control system. The threat that takes control of resetting the global zone air temperature setpoints has the most adverse impact on the system energy use, peak power demand, thermal comfort and the provision of demand flexibility. The combination of four threats have aggregative effects on the system but the effects are less than the additive effects of the individual threat.

Performance of Existing Methods in Baselining Demand Response From Commercial Building HVAC Fans

Abstract Commercial buildings generally have large thermal inertia and thus can provide services to power grids (e.g., demand response (DR)) by modulating their heating, ventilation, and air conditioning (HVAC) systems. Shifting consumption on timescales of minutes to an hour can be accomplished through temperature setpoint adjustments that affect HVAC fan consumption. Estimating the counterfactual baseline power consumption of HVAC fans is challenging but is critical for assessing the capacity and participation of DR from HVAC fans in grid-interactive efficient buildings (GEBs). DR baseline methods have been developed for whole-building power profiles. This study evaluates those methods on total HVAC fan power profiles, which have different characteristics than whole-building power profiles. Specifically, we assess averaging methods (e.g., Y-day average, HighXofY, and MidXofY, with and without additive adjustments), which are the most commonly used in practice, and a least squares-based linear interpolation method recently developed for baselining HVAC fan power. We use empirical submetering data from HVAC fans in three University of Michigan buildings in our assessment. We find that the linear interpolation method has a low bias and by far the highest accuracy, indicating that it is potentially the most effective existing baseline method for quantifying the effects of short-term load shifting of HVAC fans. Overall, our results provide new insights on the applicability of existing DR baseline methods to baselining fan power and enable the more widespread contribution of GEBs to DR and other grid services.

Power allocation by load aggregator with heterogeneous loads using weighted projection

With the recently increasing adoption of transactive energy markets, the role of a load aggregator is becoming increasingly important. Among the many tasks of an aggregator, the task of managing the electricity consumption among a group of grid-interactive efficient buildings according to the demand on the grid is taken up. Specifically, the task of allocating a certain amount of power among the participating customers while respecting their preferences and comfort is considered. Modeling this as an aggregate flexibility, the virtual battery conditions that are employed to verify the feasibility of a candidate power profile are derived. A feasible power profile, when allocated, satisfies specified comfort bands. To determine the ideal power requirement of the loads, the model-free control method is employed. The feasible power profile is allocated by embedding the customers’ preferences through weights, posing the power constraint as a hyperplane, and employing a weighted projection from the ideal power requirements onto the constraint plane to solve the considered task. The proposed method is computationally efficient and scalable to any number of heterogeneous thermostatically controlled loads in buildings.