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At least 181 records · Page 10

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DOE OSTI2019

An Optimized Input/Output-Constrained Control Design With Application to Microgrid Operation

In this paper, a nonlinear control design is presented for systems whose input and output vectors are saturated. The proposed synthesis includes an optimal tracker as the seed controller and explicitly imposes all the relevant constraints on system input, on the rate of change in the input, and on system output by using the tools of barrier functions, input-output feedback linearization, comparison argument, Lyapunov argument, and a real-time constrained optimization. The proposed design is applied to microgrid operation with high penetration of renewable generation, where net load and variable generation must be balanced and the microgrid should be operated at an optimal or near-optimal performance level. Since traditional generation has a limited ramping rate, battery storage devices and demand responses become necessary to effectively deal with variability of renewable generation and to maintain frequency stability, but their capacities are also limited. Finally,it is shown that the proposed design is effective for coordinated control of traditional generation, storage and demand response so that the power system frequency is guaranteed to be within the required operational limits and that renewable curtailment is eliminated or minimized.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Open-Source High-Fidelity Aggregate Composite Load Models of Emerging Load Behaviors for Large-Sale Analysis

The goal of this project is to accurately model the dynamic loads for power system planning and operation and to address the challenges inherent in such modeling. Existing load models are inadequate to handle the increasing proliferation of residential air conditioner loads and distributed energy resources, the emerging trends of price-responsive demand, and the growing importance of protective devices in equipment and buildings. A need has emerged to develop a fundamentally new class of load models and next-generation data tools. This work proposes to develop a set of regional–level, scalable open source load models and tools, including large-scale aggregate load protection, concept of price responsive demand, advanced load composition data, next-generation load model data tools, and advanced composite load model calibration and validation frameworks to address new challenges in the representation of next-generation load models.

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DOE OSTI2020

Commercial building loads providing ancillary services In PJM

The adoption of low carbon energy technologies such as variable renewable energy and electric vehicles, coupled with the efficacy of energy efficiency to reduce traditional base load has increased the uncertainty inherent in the net load shape. Handling this variability with slower, traditional resources leads to inefficient system dispatch, and in some cases may compromise reliability. Grid operators are looking to future energy technologies, such as automated demand response (DR), to provide capacity-based reliability services as the need for these services increase. While DR resources are expected to have the flexibility characteristics operators are looking for, demonstrations are necessary to build confidence in their capabilities. Additionally, building owners are uncertain of the monetary value and operational burden of providing these services. To address this, the present study demonstrates the ability of demand response resources providing two ancillary services in the PJM territory, synchronous reserve and regulation, using an OpenADR 2.0b signaling architecture. The loads under control include HVAC and lighting at a big box retail store and variable frequency fan loads. The study examines performance characteristics of the resource: the speed of response, communications latencies in the architecture, and accuracy of response. It also examines the frequency and duration of events and the value in the marketplace which can be used to examine if the opportunity is sufficient to entice building owners to participate.

MacDonald, Jason↗

DOE OSTI2022

Hierarchical Transactive Control of Flexible Building Loads Under Distribution LMP

With grid modernization efforts, future distribution networks, which consist of various distributed generators and flexible loads, will be more flexible and active. All new network components of distributed energy resources (DERs) drive and enable the transition towards a market-based distribution net-work that seeks the optimal allocation of all DERs. To address challenges associated with DERs, one promising solution is to utilize demand-side flexibility of building loads facilitated by demand response (DR) programs and provide ancillary grid services through distribution-level markets. Under this new paradigm, this paper proposes an efficient DR management strategy incorporating emerging price signals of distribution markets, i.e., distribution locational marginal price (DLMP), based on a hierarchical transactive control approach. The proposed approach establishes a two-layer decision-making framework; the upper layer formulates a bilevel model to obtain an optimal demand response (ODR) under DLMP, and the lower layer employs model-free control to dispatch the (aggregated) ODR to individual end users. Numerical case studies using a modified IEEE 33 test network are performed to verify the effectiveness of the proposed approach; load shifting and peak shaving for the distribution system operator and payments’ reduction for end users while maintaining their comfort.

Park, Byungkwon↗

DOE OSTI2024

Proactive and Reactive Thermal Comfort Behaviors

The expansion of renewable electricity generation, growing demands due to electrification, greater prevalence of working from home, and increasing frequency and severity of extreme weather events, will place new demands on the electric supply and distribution grid. Broader adoption of demand response programs (DRPs) for the residential sector may help meet these challenges; however, experience shows that occupant overrides in DRPs compromises their effectiveness. There is a lack of formal understanding of how discomfort, routines, and other motivations affect DRP overrides and other related human building interactions (HBI). This paper reports preliminary findings from a study of 20 households in Colorado and Massachusetts, US over three months. Participants responded to ecological momentary assessments (EMA) triggered by thermostat interactions and at random times throughout the day. EMAs included Likert-scale questions of thermal preference, preference intensity, and changes to 7 different activity types that could affect thermal comfort, and an opened ended question about motivations of such actions. Twelve tags were developed to categorize motivation responses and analyzed statistically to identify associations between motivations, preferences, and HBI actions. Reactions to changes in the thermal environment were the most frequently observed motivation (118 of 220 responses). On the other hand, almost half (47%) responses were at least partially motivated by non-thermal factors, suggesting limited utility for occupant behavior models founded solely on thermal comfort. Changes in activity level and clothing were less likely to be reported when EMAs were triggered by thermostat interactions, while fan interactions were more likely. Windows, shades, and portable heater interactions had no significant dependence on how the EMA was triggered. These results suggest that better understanding of motivations for HBI may improve effectiveness of demand response programs.

Pathak, Maharshi↗

DOE OSTI2024

Field Demonstration of Residential DER Service-Oriented Load Participation

Amidst a concerning surge in power consumption during peak hours, coupled with heightened power grid instability, and driven by a growing demand for electricity, aggregations of Distributed Energy Resource are becoming a viable means for providing essential reliability services.Electric utility companies have proactively implemented Demand Response programs for decades. These programs employ Direct Load Control methods to enhance power grid stability, achieved by controlling customers’ Distributed Energy Resource during peak hours to reduce power consumption. However, a notable drawback of Direct Load Control has been high unenrollment rates of DR program participants due to customer discomfort.Therefore, the underlying issue of over-consumption persists. To address these concerns, this paper introduces a Service-Oriented Load Participation approach to providing grid services such as Demand Response. Leveraging a Service-Oriented Architecture, this method offers the advantage of efficient service management and provisioning within the system. The SOLP approach not only aims to reduce power consumption but also to maintain customer satisfaction by ensuring a comfortable grid service experience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

DATA-DRIVEN ALGORITHMS FOR DISTRIBUTION SYSTEM OPERATION AND CONTROL

Transactive energy systems are an alternative solution for the problems that distribution system operators face due to an increase in the use of distributed energy resources(DERs) and resulting much required scalability in managing active distribution system. To meet the reliability, sustainability and security requirement, ecient management and control of DERs including distributed generators, energy storage systems, and demand response is necessary. DERs can provide services such as reactive power compensation, voltage control, energy balancing, peak load reduction. These edge resources can also be used for system restoration after the extreme events within regulatory bound and enabling resiliency of the electric grid. While there are numbers of related work focused on solving these problems, availability of additional data resources has not been utilized to its potential to enable the reliable, sustainable and resilient electric grid. In order to address these challenges, data-driven algorithms have been proposed in this dissertation, including a) Volt/Var control, b) activity aware demand response and c) estimation of behind the meter distributed energy resources. Results indicate satisfactory performance of the developed algorithms.

ZHANG, YUE↗

DOE OSTI2019

How Much Are We Saving after All? Characterizing the Effects of Commonly Varying Assumptions on Emissions and Damage Estimates in PJM

In recent years, several methods have emerged to estimate the emissions and health, environmental, and climate change damages avoided by interventions such as energy efficiency, demand response, and the integration of renewables. However, differing assumptions employed in these analyses could yield contradicting recommendations regarding intervention implementation. We test the magnitude of the effect of using different key assumptions—average vs marginal emissions, year of calculation, temporal and regional scope, and inclusion of nonemitting generation—to estimate Mid-Atlantic region power pool (PJM) emissions and damage factors. We further highlight the importance of factor selection by evaluating three illustrative 2017 power system examples in PJM. We find that for a simple building lighting intervention, using average emissions factors incorporating nonemitting generation underestimates avoided damages by 45% compared to marginal factors. For PJM demand response, outdated marginal emissions factors from 2016 overestimate avoided damages by 25% compared to 2017 factors. Our assessment of PJM summer load further suggests that fossil-only average emissions factors overestimate damages by 63% compared to average factors incorporating nonemitting generation. Here, we recommend that energy modelers carefully select appropriate emissions metrics when performing their analyses. Furthermore, since the U.S. electric grid is rapidly changing, we urge decision-makers to frequently update (and consider forecasting) grid emissions factors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Estimating the value of jointly optimized electric power generation and end use: a study of ISO-scale load shaping applied to the residential building stock

A generation-to-load simulation estimated the impact, in terms of production costs and CO2 emissions, attributable to the joint optimization of electric power generation and flexible end uses to support increasing penetrations of renewable energy. Newly conceived, evaluated, and foundational in developing a U.S. National Standard was a transaction-less yet continuous demand response system based on a day-ahead optimum load shape (OLS) designed to encourage Internet-connected devices to autonomously and voluntarily explore options to favour lowest cost generators - without requiring two-way communications, personally identifiable information, or customer opt-in. Boundary conditions used for model calibration included historical weather, residential building stock construction attributes, home appliance and device empirical operating schedules, prototypical power distribution feeder models, thermal generator heat rates, startup and ramping constraints, and fuel costs. Results of an hourly-based annual case study of Texas indicate a 1/3 reduction in production costs and a 1/5 reduction in CO2 emissions are possible.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Two-Stage Reinforcement Learning Policy Search for Grid-Interactive Building Control

This paper develops an intelligent grid-interactive building controller, which optimizes building operation during both normal hours and demand response (DR) events. To avoid costly on-demand computation and to adapt to non-linear building models, the controller utilizes reinforcement learning (RL) and makes real-time decisions based on a near-optimal control policy. Learning such a policy typically amounts to solving a hard non-convex optimization problem. We propose to address this problem with a novel global-local policy search method. In the first stage, an RL algorithm based on zero-order gradient estimation is leveraged to search for the optimal policy globally, due to its scalability and the potential to escape some poor performing local optima. The obtained policy is then fine-tuned locally to bring the first-stage solution closer to that of the original unsmoothed problem. Experiments on a simulated five-zone commercial building demonstrate the advantages of the proposed method over existing learning approaches. They also show that the learned control policy outperforms a pragmatic linear model predictive controller (MPC) and approaches the performance of an oracle MPC in testing scenarios. Using a state-of-the-art advanced computing system, we demonstrate that the controller can be learned and deployed within hours of training.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Management of Risk and Uncertainty Through Optimized Co-Operation of Transmission Systems and Microgrids With Responsive Loads (Final Report)

The evolution of the power system to the reliable, efficient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. Ambitious national and state-level goals around the decarbonization of electricity relies on the integration of very high levels of renewable resources, most of which are variable and intermittent. The use of demand response is an ideal approach to counterbalance the intermittency of renewable generation and brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distribution system, these resources are typically modeled as variables at the transmission network level. Demand-side participation cannot be leveraged effectively without explicitly including the distribution system dynamics in the optimization-based wholesale market operations. This project grew from a vision for co-optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system. In this framework, microgrids encompass consumers, distributed renewables and storage. The energy management system of the lower voltage system (distribution or microgrid) can also sell (buy) excess (necessary) energy from the transmission system. Until recently, very little research had been conducted on the co-optimization of these two systems due to computational limitations. However, advances in computational capabilities, and the judicious use of decomposition methods and innovative approximation methods for high-dimension dynamic programming made this goal a viable objective for this project, leading to a fundamental shift in the ability to integrate and fully utilize demand-side resources. To this end, the modeling framework developed introduces a novel co-optimization framework, to include the operations of both the transmission and distribution systems (or microgrids) in operational decision making. This framework was used to analyze renewable and distributed generation along with responsive demand and to compare the capability of co-optimized systems to perform with higher levels of variable renewables. An ideal microgrid is defined as an electric entity capable of operating in both interconnected (with the high-voltage grid) and islanded mode. As such, the microgrid should incorporate generating units (traditional units and intermittent) and if needed, exchange power with the high-voltage grid. The interplay between the microgrid and high-voltage grid motivated the development of the co-optimization approach to ensure efficient performance of the interconnected network. Results show that the use of a bi-level optimization approach is an appropriate structure, capable of co-optimizing a transmission system with multiple distribution systems and microgrids. While increasing the number of connected systems provides increasing flexibility for renewables integration this can also the economic benefits to the low-voltage subsystems with each additional system connected. Comparison of a traditional single-level decision structure with the co-optimization approach illustrates a reduction in overall system cost under co-optimization, while specific cost allocations to transmission and distribution systems are changed.

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DOE OSTI2020

Management of Risk and Uncertainty Through Optimized Co-Operation of Transmission Systems and Microgrids with Responsive Loads (Final Report)

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DOE OSTI2021

A Study of Cost-Saving Potential of Load Flexibility Measures in Grid-Interactive Multifamily Buildings

With recent advances in smart technologies, more and more smart devices are penetrating the residential and commercial buildings market. The introduction of these smart devices is also helping IoT companies emerge with load aggregator roles in the sector. With more utility companies on the track of supporting OpenADR protocols, the aggregators could play a significant role in providing load flexibilities by automatically responding to demand response (DR) events and coordinating load flexibility measures between customers. This would benefit utility companies by reducing stress on the grid during critical peak demand hours as well as customers by allowing them to utilize utility rate structures advantageous to those able to reduce electric usage during high-demand hours. This study evaluates cost and energy savings from adopting multiple load flexibility measures in multifamily buildings. Combinations of different load flexibility measures, including space temperature floating, light dimming, automatic window shading, and water heater temperature floating, are considered. The simulations are performed using OpenStudio®, an open-source U.S. Department of Energy (DOE) simulation platform. For the case study, we used a midrise apartment building with weather conditions from Denver, Colorado. To compare climate zone differences, simulations were also performed for Los Angeles, California, and Chicago, Illinois. Initial results indicate that the application of automated load flexibility measures without careful consideration of dispatching strategies and DR program enrollments could significantly affect the savings. To get meaningful cost savings, aggregators need to encourage tenant awareness to curtail energy usage through occupant behavior in addition to dispatching automatic load flexibility measures. The outcomes from this study are believed to help load aggregators understand the risks and benefits of load flexibility opportunities.

building energy modeling↗

DOE OSTI2021

Modeling Savings for ENERGY STAR Smart Home Energy Management Systems

The objective of this study was to develop a repeatable and defensible methodology to analyze the energy savings for Home Energy Management Systems (HEMS) that meets the minimum requirements for certification under ENERGY STAR ® Smart Home Energy Management System (SHEMS) Version 1. Mandatory connected loads include a smart thermostat, two smart lights, and one smart power strip or smart outlet. Control strategies must include feedback to occupants through an in-home display, user programming, occupancy sensor-based controls, and responsiveness to utility signals such as demand response programs. Several occupant behavior patterns were selected to quantify the range of energy savings potential for a HEMS with this basic functionality. A literature review was conducted to establish realistic room-by-room occupancy levels and usage patterns for connected devices. A series of event-driven hourly profiles were created, followed by adjustments based on application of HEMS control strategies to thermostats, interior lighting, and plug load schedules. EnergyPlus modeling was performed using these hourly schedules in three locations (Boston, Houston, and Phoenix) to examine climate dependence of energy savings. Total site energy savings ranged from 4.3 to 27.1 MBtu/year (7%-35%), and utility bill savings ranged from $\$$123 to $\$$670/year (6%-29%). The highest predicted savings was realized by occupants that were not energy conscious prior to HEMS installation, but highly engaged with the HEMS controls once the system was installed. The smart thermostat accounted for most of the savings, followed by the smart power strip. Smart lighting did not save a significant amount of energy in our analysis, based on an assumption that efficient LEDs with no standby power would normally be installed anyway.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Demonstration of Intelligent HVAC Load Management With Deep Reinforcement Learning: Real-World Experience of Machine Learning in Demand Control

We report that buildings account for 40% of total primary energy consumption and 30% of all CO 2 emissions worldwide. A large portion of building energy consumption is due to heating, ventilation, and air-conditioning (HVAC) systems. In the summer, for example, more than 50% of a building’s electricity consumption is used for cooling. With proper energy management, buildings can provide load shifting, peak shaving, frequency regulation, and many other demand response services.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

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 in and 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 cooling (HVAC) system and responds to a demand response (DR) program. Despite the non-linear structure of the problem, we show how optimal solutions can be obtained efficiently using linear programming. 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. Finally, 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.

HVAC↗

DOE OSTI2020

Dirty dishes or dirty laundry? Comparing two methods for quantifying American consumers' preferences for load management in a smart home

One challenge of transitioning to renewable energy is that household electricity use and renewable generation are often misaligned. Smart home energy management systems hold promise for shifting usage to match generation, but these systems need to be designed with the occupants’ preferences in mind. The purpose of the present research is to compare two approaches for collecting and modeling consumers’ load management preferences, both of which are amenable to use in a home energy management system. Specifically, we examine the performance of Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) and Analytic Hierarchy Process (AHP) in quantifying consumers’ preferences regarding air temperature (air conditioning and heating), water heating, dishwashing, clothes washing and drying, monetary costs, environmental impacts, and comfort/convenience. Two studies are presented: Study 1 examines the SMARTER approach, and Study 2 focuses on the AHP approach. In both studies, online surveys (N SMARTER = 956 and N AHP = 1023) were conducted to elicit preferences from participants across the United States. The preferences modeled by both approaches were validated based on (a) their ability to predict participants’ choices in a Discrete Choice Experiment and (b) their convergence with previous research on load-shifting behavior. The validation procedure suggests that the SMARTER approach is superior in modeling consumers’ preferences for load management. Overall, this research lays the groundwork for designing a smart home interface capable of collecting occupants’ preferences and using those preferences to deliver improved occupant comfort, lower operating costs, reduced environmental impact, and more significant demand response than exists today.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Clean Grid Vision: A U.S. Perspective - Chapter 4. Demand-Side Development

This chapter focuses on recent developments on the demand side of the electricity system in the United States and beyond. After decades of slow-moving change in power system technology, business models, regulatory structures, and even financing schemes, changes on the demand side are accelerating rapidly. These changes are also more tightly linking the supply side with the demand side. This chapter provides high-level summaries of traditional subjects like demand-side management, which includes energy efficiency and demand response, and then transition to a subject that has gathered increasing attention as the most dynamic change likely to impact power systems in the coming decades: beneficial electrification. In each case, we attempt to address the impacts on system flexibility to the degree possible. These demand-side subjects are widely considered essential elements of a clean grid strategy.

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