Advanced Control Technologies and Strategies Linking Demand Response and Energy Efficiency

This paper presents a preliminary framework to describe how advanced controls can support multiple modes of operations including both energy efficiency and demand response (DR). A general description of DR, its benefits, and nationwide status is outlined. The role of energy management and control systems for DR is described. Building systems such as HVAC and lighting that utilize control technologies and strategies for energy efficiency are mapped on to DR and demand shedding strategies are developed. Past research projects are presented to provide a context for the current projects. The economic case for implementing DR from a building owner perspective is also explored

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

Summary of the 2006 Automated Demand Response Pilot

This paper discusses the specific concept for, design of, and results from a pilot program to automate demand response with critical peak pricing. California utilities have been exploring the use of critical peak pricing (CPP) to help reduce peak day summer time electric loads. CPP is a form of price-responsive demand response. This Automated Critical Peak Pricing (Auto-CPP) project from 2006 draws upon three years of previous research and demonstrations from the years of 2003, 2004, and 2005. The purpose of automated demand response (DR) is to improve the responsiveness and participation of electricity customers in DR programs and lower overall costs to achieve DR. Auto-CPP is a form of automated demand response (Auto-DR)

Chilled Water Thermal Storage System and Demand Response at the University of California at Merced

University of California at Merced is a unique campus that has benefited from intensive efforts to maximize energy efficiency, and has participated in a demand response program for the past two years. Campus demand response evaluations are often difficult because of the complexities introduced by central heating and cooling, non-coincident and diverse building loads, and existence of a single electrical meter for the entire campus. At the University of California at Merced, a two million gallon chilled water storage system is charged daily during off-peak price periods and used to flatten the load profile during peak demand periods, further complicating demand response scenarios. The goal of this research is to study demand response savings in the presence of storage systems in a campus setting. First, University of California at Merced is described and its participation in a demand response event during 2008 is detailed. Second, a set of demand response strategies were pre-programmed into the campus control system to enable semi-automated demand response during a 2009 event, which is also evaluated. Finally, demand savings results are applied to the utility’s DR incentives structure to calculate the financial savings under various DR programs and tariffs

Variability in automated responses of commercial buildings and industrial facilities to dynamic electricity prices

Changes in the electricity consumption of commercial buildings and industrial facilities (C&I facilities) during Demand Response (DR) events are usually estimated using counterfactual baseline models. Model error makes it difficult to precisely quantify these changes in consumption and understand if C&I facilities exhibit event-to-event variability in their response to DR signals. This paper seeks to understand baseline model error and DR variability in C&I facilities facing dynamic electricity prices. Using a regression-based baseline model, we present a method to compute the error associated with estimates of several DR parameters. We also develop a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We analyze 38 C&I facilities participating in an automated DR program and find that DR parameter errors are large. Though some facilities exhibit real DR variability, most observed variability results from baseline model error. Therefore, facilities with variable DR parameters may actually respond consistently from event to event. Consequently, in DR programs in which repeatability is valued, individual buildings may be performing better than previously thought. In some cases, however, aggregations of C&I facilities exhibit real DR variability, which could create challenges for power system operation

Introduction to Commercial Building Control Strategies and Techniques for Demand Response -- Appendices

There are 3 appendices listed: (A) DR strategies for HVAC systems; (B) Summary of DR strategies; and (C) Case study of advanced demand response

Dynamic Controls for Energy Efficiency and Demand Response:Framework Concepts and a New Construction Study Case in New York

Many of today's advanced building control systems are designed to improve granularity of control for energy efficiency. Examples include direct digital controls for building heating, ventilation, and cooling systems (HVAC), and dimmable ballasts for continuous dimming for daylighting applications. This paper discusses recent research on the use of new and existing controls in commercial buildings for integrated energy efficiency and demand response (DR). The paper discusses the use of DR controls strategies in commercial buildings and provides specific details on DR control strategy design concepts for a new building in New York. We present preliminary results from EnergyPlus simulations of the DR strategies at the New York Times Headquarters building currently under construction. The DR strategies at the Times building involve unique state of the art systems with dimmable ballasts, movable shades on the glass facade, and underfloor air HVAC. The simulation efforts at this building are novel, with an innovative building owner considering DR and future DR program participation strategies during the design phase. This paper also discusses commissioning plans for the DR strategies. The trends in integration of various systems through the EMCS, master versus supervisory controls and dynamic operational modes concepts are presented and future research directions are outlined

Data Predictive Control for Peak Power Reduction

Decisions on how best to optimize today\u27s energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs