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DOE OSTI2023

Data-driven method for electric vehicle charging demand analysis: Case study in Virginia

Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55% home, 9% work, and 36% public) in September and 45,920,358 kWh (61% home, 9% work, and 30% public) in February.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Supporting mobile WEEE collection on demand: A method for multi-criteria vehicle routing, loading and cost optimisation

Highlights: • Multi-criteria method of WEEE mobile collection on demand. • Vehicle routing and loading problem included in the model and algorithm. • Optimisation of collecting vehicles routes including genetic algorithms and fuzzy logic. • Case study for heterogeneous fleet of collection vehicles for medium size city in Poland. • Possible application in a software supporting waste collection on demand. - Abstract: The task of increasing collection rates of waste electrical and electronic equipment (WEEE) is an important challenge in the global economy, and especially in the European Union where stiffer collection targets set out in a new WEEE directive are to be effective by 2019. As the circular economy approach replaces the linear model, resource recycling activities become a priority in waste management policy. As new techniques and possibilities of waste collection systems emerge, opportunities are created for improving efficiency for collection companies and affording benefits for the environment. A model proposed for mobile WEEE collection in this study considers a multi-criteria approach in developing a cost efficient method for pick up on demand from residents or electrical and electronic equipment (EEE) stores. The algorithm used in this model optimises vehicle routes and helps in selecting a number of vehicles from a heterogeneous fleet, incorporating the WEEE loading problem. Using genetic algorithm and fuzzy logic, this model optimises costs and resources required to complete the WEEE collection assuring timely pick up of the waste equipment. The numerical model is verified in a case study in Opole, a city in the south of Poland. The results show that the proposed model can handle the multiple parameter optimisation problem including operational costs, efficient use of vehicles from a fleet, efficient waste loading in vehicles and residents' satisfaction with timely pick up of the waste equipment from a household. Such system can be successfully applied even for large cities. The algorithm provides an opportunity for writing software or mobile apps design to be used by WEEE collection companies.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Heterogeneous climate change impacts on electricity demand in world cities circa mid-century

Rising ambient temperatures due to climate change will increase urban populations’ exposures to extreme heat. During hot hours, a key protective adaptation is increased air conditioning and associated consumption of electricity for cooling. But during cold hours, milder temperatures have the offsetting effect of reducing consumption of electricity and other fuels for heating. We elucidate the net consequences of these opposing effects in 36 cities in different world regions. We couple reduced-form statistical models of cities’ hourly responses of electric load to temperature with temporally downscaled projections of temperatures simulated by 21 global climate models (GCMs), projecting the effects of warming on the demand for electricity circa 2050. Cities' responses, temperature exposures and impacts are heterogeneous, with changes in total annual consumption ranging from –2.7 to 5.7%, and peak power demand increasing by as much as 9.5% at the multi-GCM median. The largest increases are concentrated in more economically developed mid-latitude cities, with less developed urban areas in the tropics exhibiting relatively small changes. The results highlight the important role of the structure of electricity demand: large temperature increases in tropical cities are offset by their inelastic responses, which can be attributed to lower air-conditioning penetration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Sustainability, Scalability, and Resiliency of the Town of Innisfil Mobility-on-Demand Experiment: Preliminary Results, Analyses, and Lessons Learned

In 2017, the town of Innisfil, Ontario, launched Innisfil Transit in partnership with Uber, a transportation network company, to provide a subsidized on-demand public mobility service as an alternative to investing in a new fixed-route bus service. The performance of Innisfil Transit is documented in a 2021 Ryerson University report by Sweet, Mitra, and Benaroya, which shows greater cost effectiveness of the mobility provided over the proposed bus alternative. This paper expands on those findings by assessing Innisfil Transit with respect to sustainability, scalability, and resiliency. First, we quantify the energy and emissions of this program relative to traditional transit and driving alone across varying powertrains. We then characterize a conservative first-order estimate of the percentage of US communities that fall within a similar spatial-demographic tier as Innisfil. Replicability also hinges on service cost and performance in comparison to average values for low-density transit in the US. Lastly, most transit agencies experienced a significant drop in demand (as much as 90%) with slowly rebounding ridership since the onset of the COVID-19 pandemic. The resiliency of the Innisfil program to the pressures induced by the pandemic is examined in comparison to other transit operations. The lessons learned across these three dimensions complement prior work to better understand the efficiency and sustainability of on-demand public mobility service for low-density communities like Innisfil.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2014

Efficient Customer Selection for Sustainable Demand Response in Smart Grids

Regulating the power consumption to avoid peaks in demand is a common practice. Demand Response(DR) is being used by utility providers to minimize costs or ensure system reliability. Although it has been used extensively there is a shortage of solutions dealing with dynamic DR. Past attempts focus on minimizing the load demand without considering the sustainability of the reduced energy. In this paper an efficient algorithm is presented which solves the problem of dynamic DR scheduling. Data from the USC campus micro grid were used to evaluate the efficiency as well as the robustness of the proposed solution. The targeted energy reduction is achieved with a maximum average approximation error of ≈ 0.7%. Sustainability of the reduced energy is achieved with respect to the optimal available solution providing a maximum average error less than 0.6%. It is also shown that a solution is provided with a low computational cost fulfilling the requirements of dynamic DR.

change making↗

DOE OSTI2012

Electric Water Heater Modeling and Control Strategies for Demand Response

Abstract— Demand response (DR) has a great potential to provide balancing services at normal operating conditions and emergency support when a power system is subject to disturbances. Effective control strategies can significantly relieve the balancing burden of conventional generators and reduce investment on generation and transmission expansion. This paper is aimed at modeling electric water heaters (EWH) in households and tests their response to control strategies to implement DR. The open-loop response of EWH to a centralized signal is studied by adjusting temperature settings to provide regulation services; and two types of decentralized controllers are tested to provide frequency support following generator trips. EWH models are included in a simulation platform in DIgSILENT to perform electromechanical simulation, which contains 147 households in a distribution feeder. Simulation results show the dependence of EWH response on water heater usage . These results provide insight suggestions on the need of control strategies to achieve better performance for demand response implementation. Index Terms— Centralized control, decentralized control, demand response, electrical water heater, smart grid

Control↗

DOE OSTI2018

Real-Time Procurement Strategies of a Proactive Distribution Company With Aggregator-Based Demand Response

In this paper, we present a real-time trading framework for distribution networks where a rational aggregator is identified as a broker by contracting with individual demands and dealing with the distribution company. Demand response capability is characterized by the coexistence of elastic and inelastic demand components. A one-leader multi-follower bi-level model is proposed to derive the procurement strategies, i.e., the upperlevel problem intends to maximize the profit of the proactive distribution company, while the lower-level expresses the profit maximization per rational aggregator. The proposed model is then transformed into a solvable mathematical program with equilibrium constraints through a primal-dual approach. A modified 33-bus distribution network is utilized to demonstrate the effectiveness of the proposed model.

demand response (DR)↗

DOE OSTI2017

Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations

A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

14 SOLAR ENERGY↗

DOE OSTI2011

Projections of highway vehicle population, energy demand, and CO{sub 2} emissions in India through 2040.

This paper presents projections of motor vehicles, oil demand, and carbon dioxide (CO{sub 2}) emissions for India through the year 2040. The populations of highway vehicles and two-wheelers are projected under three different scenarios on the basis of economic growth and average household size in India. The results show that by 2040, the number of highway vehicles in India would be 206-309 million. The oil demand projections for the Indian transportation sector are based on a set of nine scenarios arising out of three vehicle-growth and three fuel-economy scenarios. The combined effects of vehicle-growth and fuel-economy scenarios, together with the change in annual vehicle usage, result in a projected demand in 2040 by the transportation sector in India of 404-719 million metric tons (8.5-15.1 million barrels per day). The corresponding annual CO{sub 2} emissions are projected to be 1.2-2.2 billion metric tons.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Demand Response and Smart Buildings: A Survey of Control, Communication, and Cyber-Physical Security

In this paper, we perform a comprehensive survey of the technical aspects related to the implementation of demand response and smart buildings. Specifically, we discuss various smart loads such as heating, ventilating, and air-conditioning (HVAC) systems and plug-in electric vehicles (PEVs); the power architecture with multibus characteristics; different control algorithms such as the hybrid centralized and decentralized control and the distributed coordination among buildings; the communication technologies and network architectures; and the potential cyber-physical security issues and possible mechanisms for enhancing the system security at both cyber and physical layers. Finally, the current status of the demand response in United States, Europe, Japan, and China is reviewed, and the benefits, costs, and challenges of implementing and operating demand response and smart buildings are also discussed.

42 ENGINEERING↗

DOE OSTI2012

Assessing the Control Systems Capacity for Demand Response in California Industries

California's electricity markets are moving toward dynamic pricing models, such as real-time pricing, within the next few years, which could have a significant impact on an industrial facility's cost of energy use during the times of peak use. Adequate controls and automated systems that provide industrial facility managers real-time energy use and cost information are necessary for successful implementation of a comprehensive electricity strategy; however, little is known about the current control capacity of California industries. To address this gap, Lawrence Berkeley National Laboratory, in close collaboration with California industrial trade associations, conducted a survey to determine the current state of controls technologies in California industries. This study identifies sectors that have the technical capability to implement Demand Response (DR) and Automated Demand Response (Auto-DR). In an effort to assist policy makers and industry in meeting the challenges of real-time pricing, facility operational and organizational factors were taken into consideration to generate recommendations on which sectors Demand Response efforts should be focused.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2013

Laboratory Testing of Demand-Response Enabled Household Appliances

With the advent of the Advanced Metering Infrastructure (AMI) systems capable of two-way communications between the utility's grid and the building, there has been significant effort in the Automated Home Energy Management (AHEM) industry to develop capabilities that allow residential building systems to respond to utility demand events by temporarily reducing their electricity usage. Major appliance manufacturers are following suit by developing Home Area Network (HAN)-tied appliance suites that can take signals from the home's 'smart meter,' a.k.a. AMI meter, and adjust their run cycles accordingly. There are numerous strategies that can be employed by household appliances to respond to demand-side management opportunities, and they could result in substantial reductions in electricity bills for the residents depending on the pricing structures used by the utilities to incent these types of responses.The first step to quantifying these end effects is to test these systems and their responses in simulated demand-response (DR) conditions while monitoring energy use and overall system performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2013

Grid Integration of Aggregated Demand Response, Part 1: Load Availability Profiles and Constraints for the Western Interconnection

Demand response (DR) has the potential to improve electric grid reliability and reduce system operation costs. However, including DR in grid modeling can be difficult due to its variable and non-traditional response characteristics, compared to traditional generation. Therefore, efforts to value the participation of DR in procurement of grid services have been limited. In this report, we present methods and tools for predicting demand response availability profiles, representing their capability to participate in capacity, energy, and ancillary services. With the addition of response characteristics mimicking those of generation, the resulting profiles will help in the valuation of the participation of demand response through production cost modeling, which informs infrastructure and investment planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2014

Utility Sector Impacts of Reduced Electricity Demand

This report presents a new approach to estimating the marginal utility sector impacts associated with electricity demand reductions. The method uses publicly available data and provides results in the form of time series of impact factors. The input data are taken from the Energy Information Agency's Annual Energy Outlook (AEO) projections of how the electric system might evolve in the reference case, and in a number of side cases that incorporate different effciency and other policy assumptions. The data published with the AEO are used to define quantitative relationships between demand-side electricity reductions by end use and supply-side changes to capacity by plant type, generation by fuel type and emissions of CO2, Hg, NOx and SO2. The impact factors define the change in each of these quantities per unit reduction in site electricity demand. We find that the relative variation in these impacts by end use is small, but the time variation can be significant.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2013

Laboratory Testing of Demand-Response Enabled Household Appliances

With the advent of the Advanced Metering Infrastructure (AMI) systems capable of two-way communications between the utility's grid and the building, there has been significant effort in the Automated Home Energy Management (AHEM) industry to develop capabilities that allow residential building systems to respond to utility demand events by temporarily reducing their electricity usage. Major appliance manufacturers are following suit by developing Home Area Network (HAN)-tied appliance suites that can take signals from the home's 'smart meter,' a.k.a. AMI meter, and adjust their run cycles accordingly. There are numerous strategies that can be employed by household appliances to respond to demand-side management opportunities, and they could result in substantial reductions in electricity bills for the residents depending on the pricing structures used by the utilities to incent these types of responses. The first step to quantifying these end effects is to test these systems and their responses in simulated demand-response (DR) conditions while monitoring energy use and overall system performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Solar + Storage Synergies for Managing Commercial-Customer Demand Charges

We study the synergies between behind-the-meter solar and storage in reducing commercial-customer demand charges. This follows two previous studies that examined demand charge savings for stand-alone solar in both the residential and commercial sectors. In this study we show that solar and storage show consistent synergies for demand charge management, that the magnitude of reductions are highly customer-specific, and that the magnitude of savings is influenced by the design of the electricity tariff.

14 SOLAR ENERGY↗

DOE OSTI2018

Field Testing of Telemetry for Demand Response Control of Small Loads

The electricity system in California, from generation through loads, must be prepared for high renewable penetration and increased electrification of end uses while providing increased resilience and lower operating cost. California has an aggressive renewable portfolio standard that is complemented by world-leading greenhouse gas goals. The goal of this project was to evaluate methods of enabling fast demand response (DR) signaling to small loads for low-cost site enablement. We used OpenADR 2.0 to meet telemetry requirements for providing ancillary services, and we used a variety of low-cost devices coupled with open-source software to enable an end-to-end fast DR. The devices, architecture, implementation, and testing of the system is discussed in this report. We demonstrate that the emerging Internet of Things (IoT) and Smart Home movements provide an opportunity for diverse small loads to provide fast, low-cost demand response. We used Internet-connected lights, thermostats, load interruption devices, and water heaters to demonstrate an ecosystem of controllable devices. The system demonstrated is capable of providing fast load shed for between 20 dollars and $300 per kilowatt (kW) of available load. The wide range results from some loads may have very low cost but also very little shed capability (a 10 watt [W] LED light can only shed a maximum of 10 W) while some loads (e.g., water heaters or air conditioners) can shed several kilowatts but have a higher initial cost. These costs, however, compare well with other fast demand response costs, with typically are over $100/kilowatt of shed. We contend these loads are even more attractive than their price suggests because many of them will be installed for energy efficiency or non-energy benefits (e.g., improved lighting quality or controllability), and the ability to use them for fast DR is a secondary benefit. Therefore the cost of enabling them for DR may approach zero if a software-only solution can be deployed to enable fast DR after devices are installed for other reasons. We recommend that the DR research community continue to engage with the IoT community to encourage the use of documented and open development interfaces. A library of device drivers and machine-readable interface specifications would significantly reduce the burden on users or system integrators for deploying systems in large numbers of buildings in California.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Demand Response Optimization and Management System for Real-TIme (DROMS-RT)

To design and demonstrate DROMS-RT, a highly distributed Demand Response Optimization and Management System for Real-Time (DROMS-RT) power flow control to support large scale integration of distributed renewable generation into the grid. AutoGrid developed a novel control and communications platform to allow highly dispatchable demand response (DR) services in time frames suitable for providing ancillary services to the transmission grid. These services will be substantially less expensive and more efficient than other forms of ancillary services options currently available to manage the intermittency associated with large-scale renewable integration. DROMSRT successfully leveraged Automated Demand Response (ADR) by fundamentally re-thinking the architecture of the DR platform from the ground up and by developing innovative new technologies in a number of areas related to DR. DROMS-RT leveraged the low-cost, open, interoperable DR signaling technology, OpenADR, and low-cost, internet-protocol based telemetry solutions to reduce the cost of hardware. This allowed DROMS-RT to provide dynamic price signals to millions of OpenADR clients. Statistically rigorous signal processing techniques were developed to reliably detect even small load reductions in the presence of noisy baseline profiles. Novel forecasting engines based on modern online machine learning algorithms enabled accurate individualized forecasts for customer loads in the presence of dynamic pricing signals, and a real-time decision engines enabled continuous optimization and optimal dispatch of DR resources across a large portfolio of heterogeneous loads that respond at varying time-scales. Moreover, the real-time optimization conducted by the decision engine can utilize grid physics to maximize load reduction at the transmission system in addition to the distribution sites, for more efficient grid operation. Finally, the Software-as-a-Service (SaaS) availability of the DROMS-RT platform has reduced the cost of deployment and enable participation of small commercial and residential customers in DR who otherwise would not be able to do so.

24 POWER TRANSMISSION AND DISTRIBUTION↗