124 research outputs found
Estimating the Benefits of Electric Vehicle Smart Charging at Non-Residential Locations: A Data-Driven Approach
In this paper, we use data collected from over 2000 non-residential electric
vehicle supply equipments (EVSEs) located in Northern California for the year
of 2013 to estimate the potential benefits of smart electric vehicle (EV)
charging. We develop a smart charging framework to identify the benefits of
non-residential EV charging to the load aggregators and the distribution grid.
Using this extensive dataset, we aim to improve upon past studies focusing on
the benefits of smart EV charging by relaxing the assumptions made in these
studies regarding: (i) driving patterns, driver behavior and driver types; (ii)
the scalability of a limited number of simulated vehicles to represent
different load aggregation points in the power system with different customer
characteristics; and (iii) the charging profile of EVs. First, we study the
benefits of EV aggregations behind-the-meter, where a time-of-use pricing
schema is used to understand the benefits to the owner when EV aggregations
shift load from high cost periods to lower cost periods. For the year of 2013,
we show a reduction of up to 24.8% in the monthly bill is possible. Then,
following a similar aggregation strategy, we show that EV aggregations decrease
their contribution to the system peak load by approximately 40% when charging
is controlled within arrival and departure times. Our results also show that it
could be expected to shift approximately 0.25kWh (~2.8%) of energy per
non-residential EV charging session from peak periods (12PM-6PM) to off-peak
periods (after 6PM) in Northern California for the year of 2013.Comment: Pre-print, under review at Applied Energ
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Enhancing Price Response Programs through Auto-DR: California's 2007 Implementation Experience
This paper describes automated demand response (Auto-DR) activities, an innovative effort in California to ensure that DR programs produce effective and sustainable impacts. Through the application of automation and communication technologies coupled with well-designed incentives and DR programs such as Critical Peak Pricing (CPP) and Demand Bidding (DBP), Auto-DR is opening up the opportunity for many different types of buildings to effectively participate in DR programs. We present the results of Auto-DR implementation efforts by the three California investor-owned utilities for the Summer of 2007. The presentation emphasizes Pacific Gas and Electric Company's (PG&E) Auto-DR efforts, which represents the largest in the state. PG&E's goal was to recruit, install, test and operate 15 megawatts of Auto-DR system capability. We describe the unique delivery approaches, including optimizing the utility incentive structures designed to foster an Auto-DR service provider community. We also show how PG&E's Critical Peak Pricing (CPP) and Demand Bidding (DBP) options were called and executed under the automation platform. Finally, we show the results of the Auto-DR systems installed and operational during 2007, which surpassed PG&E's Auto-DR goals. Auto-DR is being implemented by a multi-disciplinary team including the California Investor Owned Utilities (IOUs), energy consultants, energy management control system vendors, the Lawrence Berkeley National Laboratory (LBNL), and the California Energy Commission (CEC)
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Field Test Results of Automated Demand Response in a Large Office Building
Demand response (DR) is an emerging research field and an effective tool that improves grid reliability and prevents the price of electricity from rising, especially in deregulated markets. This paper introduces the definition of DR and Automated Demand Response (Auto-DR). It describes the Auto-DR technology utilized at a commercial building in the summer of 2006 and the methodologies to evaluate associated demand savings. On the basis of field tests in a large office building, Auto-DR is proven to be a reliable and credible resource that ensures a stable and economical operation of the power grid
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Design and Implementation of an Open, Interoperable AutomatedDemand Response Infrastructure
This paper describes the concept for and lessons from the development and field-testing of an open, interoperable communications infrastructure to support automating demand response (DR). Automating DR allows greater levels of participation and improved reliability and repeatability of the demand response and customer facilities. Automated DR systems have been deployed for critical peak pricing and demand bidding and are being designed for real time pricing. The system is designed to generate, manage, and track DR signals between utilities and Independent System Operators (ISOs) to aggregators and end-use customers and their control systems
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Linking Continuous Energy Management and Open Automated Demand Response
Advances in communications and control technology, the strengthening of the Internet, and the growing appreciation of the urgency to reduce demand side energy use are motivating the development of improvements in both energy efficiency and demand response (DR) systems. This paper provides a framework linking continuous energy management and continuous communications for automated demand response (Auto-DR) in various times scales. We provide a set of concepts for monitoring and controls linked to standards and procedures such as Open Automation Demand Response Communication Standards (Open Auto-DR or OpenADR). Basic building energy science and control issues in this approach begin with key building components, systems, end-uses and whole building energy performance metrics. The paper presents a framework about when energy is used, levels of services by energy using systems, granularity of control, and speed of telemetry. DR, when defined as a discrete event, requires a different set of building service levels than daily operations. We provide examples of lessons from DR case studies and links to energy efficiency
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Installation and Commissioning Automated Demand Response Systems
Demand Response (DR) can be defined as actions taken to reduce electric loads when contingencies, such as emergencies and congestion, occur that threaten supply-demand balance, or market conditions raise supply costs. California utilities have offered price and reliability DR based programs to customers to help reduce electric peak demand. The lack of knowledge about the DR programs and how to develop and implement DR control strategies is a barrier to participation in DR programs, as is the lack of automation of DR systems. Most DR activities are manual and require people to first receive notifications, and then act on the information to execute DR strategies. Levels of automation in DR can be defined as follows. Manual Demand Response involves a labor-intensive approach such as manually turning off or changing comfort set points at each equipment switch or controller. Semi-Automated Demand Response involves a pre-programmed demand response strategy initiated by a person via centralized control system. Fully-Automated Demand Response does not involve human intervention, but is initiated at a home, building, or facility through receipt of an external communications signal. The receipt of the external signal initiates pre-programmed demand response strategies. We refer to this as Auto-DR (Piette et. al. 2005). Auto-DR for commercial and industrial facilities can be defined as fully automated DR initiated by a signal from a utility or other appropriate entity and that provides fully-automated connectivity to customer end-use control strategies. One important concept in Auto-DR is that a homeowner or facility manager should be able to 'opt out' or 'override' a DR event if the event comes at time when the reduction in end-use services is not desirable. Therefore, Auto-DR is not handing over total control of the equipment or the facility to the utility but simply allowing the utility to pass on grid related information which then triggers facility defined and programmed strategies if convenient to the facility. From 2003 through 2006 Lawrence Berkeley National Laboratory (LBNL) and the Demand Response Research Center (DRRC) developed and tested a series of demand response automation communications technologies known as Automated Demand Response (Auto-DR). In 2007, LBNL worked with three investor-owned utilities to commercialize and implement Auto-DR programs in their territories. This paper summarizes the history of technology development for Auto-DR, and describes the DR technologies and control strategies utilized at many of the facilities. It outlines early experience in commercializing Auto-DR systems within PG&E DR programs, including the steps to configure the automation technology. The paper also describes the DR sheds derived using three different baseline methodologies. Emphasis is given to the lessons learned from installation and commissioning of Auto-DR systems, with a detailed description of the technical coordination roles and responsibilities, and costs
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Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California
This paper reports on the potential impact of demand response (DR) strategies in commercial buildings in California based on the Demand Response Quick Assessment Tool (DRQAT), which uses EnergyPlus simulation prototypes for office and retail buildings. The study describes the potential impact of building size, thermal mass, climate, and DR strategies on demand savings in commercial buildings. Sensitivity analyses are performed to evaluate how these factors influence the demand shift and shed during the peak period. The whole-building peak demand of a commercial building with high thermal mass in a hot climate zone can be reduced by 30percent using an optimized demand response strategy. Results are summarized for various simulation scenarios designed to help owners and managers understand the potential savings for demand response deployment. Simulated demand savings under various scenarios were compared to field-measured data in numerous climate zones, allowing calibration of the prototype models. The simulation results are compared to the peak demand data from the Commercial End-Use Survey for commercial buildings in California. On the economic side, a set of electricity rates are used to evaluate the impact of the DR strategies on economic savings for different thermal mass and climate conditions. Our comparison of recent simulation to field test results provides an understanding of the DR potential in commercial buildings
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Statistical Analysis of Baseline Load Models for Non-Residential Buildings
Policymakers are encouraging the development of standardized and consistent methods to quantify the electric load impacts of demand response programs. For load impacts, an essential part of the analysis is the estimation of the baseline load profile. In this paper, we present a statistical evaluation of the performance of several different models used to calculate baselines for commercial buildings participating in a demand response program in California. In our approach, we use the model to estimate baseline loads for a large set of proxy event days for which the actual load data are also available. Measures of the accuracy and bias of different models, the importance of weather effects, and the effect of applying morning adjustment factors (which use data from the day of the event to adjust the estimated baseline) are presented. Our results suggest that (1) the accuracy of baseline load models can be improved substantially by applying a morning adjustment, (2) the characterization of building loads by variability and weather sensitivity is a useful indicator of which types of baseline models will perform well, and (3) models that incorporate temperature either improve the accuracy of the model fit or do not change it
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