Demand Response Strategies and Variability in Response (Research at Lawrence Berkeley Na3onal Labs) Saima Aman Agenda!
Paper 1: Introduction to Commercial Building Control Strategies and Techniques for Demand Response Naoya Motegi, et. al., LBNL Report, 2007 Paper 2: Variability in Automated Responses of Commercial Buildings and Industrial Facilities to Dynamic Electricity Prices Johanna L. Mathieu, et. al., Energy & Buildings, 2011 (Note: Focus on commercial buildings and industrial facilities) 2 3 Introduction to Commercial Building Control Strategies and Techniques for Demand Response Naoya Motegi, et. al., LBNL Report, 2007 Control Strategies and Techniques for DR !
Types •  Control strategies for HVAC and Lighting •  Strategies for miscellaneous building end-­‐use systems -­‐ fountain pumps, electric vehicle chargers, industrial process loads, cold storage, irrigation water pumps Energy Efficiency Vs DR •  Energy efficiency measures are widely understood; •  Limited understanding of DR strategies •  Energy Efficiency lowers energy use while providing the same level of service; permanently reduces peak demand •  Demand Response is dynamic and event-­‐driven short-­‐term modification in customer end-­‐use load •  Energy Conservation reduces unnecessary energy use. 4 Demand Side Management !
5 DR opera=on !
•  Dynamic pricing and tariffs •  Contractually obligated/Voluntary curtailment •  Direct load control •  Equipment cycling Need for Automation •  Needed for reliable and repeatable control •  Reduces the labor required to implement DR operational modes •  Automation needs pre-­‐planning Challenge – Balancing conflicting needs •  meeting electricity demand-­‐savings targets •  minimizing negative impacts on the occupants 6 Levels of automa=on for DR !
Manual DR •  Manually turning off switches •  Manually changing comfort set points at each switch/controller Semi-­‐automated DR •  Pre-­‐programmed load shedding strategy initiated by a person Fully automated DR •  No human intervention •  Initiated by EMCS after an external communication signal is received 7 HVAC Systems •  Excellent resource for DR savings •  Major portion of the electric load in commercial buildings •  Can be temporarily unloaded without immediate impact on the occupants •  Can be automated with energy management and control systems (EMCS) DR strategies •  Global Temperature Adjustment of Zone – yields best results •  Systemic Adjustments to the Air Distribution and/or Cooling Systems – can be disruptive to occupants Challenges: •  HVAC electric load is dynamic and sensitive to weather conditions, occupancy, and other factors. •  Difficult to estimate the demand savings achieved by HVAC strategies 8 Ligh=ng Systems • 
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9 Day-­‐lit and over-­‐lit areas – candidates for demand savings. Lighting produces heat, so reducing lighting levels also reduces cooling load Strategies – simple and provide constant and predictable demand savings Generally not automated with EMCS DR strategies •  Zone switching •  Fixture switching •  Lamp switching •  Stepped dimming •  Continuous dimming Challenges •  Lighting has safety implications and effects are noticeable -­‐ need to be done selectively and carefully 10 Variability in Automated Responses of Commercial Buildings and Industrial Facilities to Dynamic Electricity Prices Johanna L. Mathieu, et. al., Energy & Buildings, 2011 Variability in Response to DR signals 11 Introduction •  DR parameters are measured relative to an estimate of how much electricity a facility would have consumed in the absence of the DR event. •  DR parameters exhibit variability due to •  baseline model errors •  real event-­‐to-­‐event variability in the facility’s response •  Important to understand variability in response to DR signals: •  To predict how aggregations of facilities will respond on DR days •  To evaluate DR programs on the basis of the reliability of their response Variability in Automated Responses of Buildings Variable response to DR signals We would like to know if observed shed variability is a result of real shed variability (i.e. a facility curtails a different amount from event-­‐to-­‐event) or if it results from the baseline model error (un-­‐modeled load variability) 12 DR Parameters 13 DR parameters are computed by subtrac3ng the baseline from the actual power consump3on DR Program set-­‐up 14 •  PG&E’s Automated Critical Peak Pricing (CPP) Program (2006 – 2009) •  DR events called on up to 12 summer business days (non-­‐holiday, weekdays) •  On DR days, electricity prices were raised: •  moderate price period: 3X the normal price from 12 to 3 pm •  high price period: 5X the normal price from 3 to 6 pm •  In exchange for participating in the program, facilities paid lower energy prices on non-­‐DR days. •  OpenADR Communication Specification used for DR event notifications •  DR event notifications were provided by 3 pm the business day before the event. Data: 15-­‐minute interval whole building electric load data from 38 large C&I facilities (peak demand >200 kW) DR Program set-­‐up (Contd.) •  Each facility implemented a different set of pre-­‐programmed DR strategies and executed the same strategies from event-­‐to-­‐event. •  Strategies: changes to the HVAC system, light dimming/switching, and industrial process shedding/shifting. 15 Counterfactual baseline models •  Changes in electricity consumption during DR events are estimated using counterfactual baseline models •  Counterfactual – when there is no DR event •  Utilities use simple models to determine basic electric load: •  averaging the daily demand over several days before the DR day •  regression-­‐based baseline model •  other methods (e.g., ANN) have been proposed, but seldom used •  Advantage of using better baseline models: •  allows computation of more accurate DR parameter estimates •  Allows to better determine if a facility exhibits real variability in its responses to DR events. •  Regression Model used in the experiments (parameters: time of week and temperature) 16 17 Error Analysis •  Regression-­‐based models under-­‐estimate the true error: •  regression parameters are correlated. (time-­‐of-­‐week is correlated to temperature: the highest temperatures tend to occur in the afternoon and the lowest temperatures occur overnight) •  the regression residuals are auto-­‐correlated (from ACF and PACF plots) •  the regression residuals are heteroscedastic – variance of the residuals is a function of time-­‐of-­‐week. (typically, error variance tends to be lower at night and higher during the day when occupancy is fluctuating.) Residual error time of week Error Es=mates for DR Parameters 18 Introduce error metrics: •  Average Demand Shed Variability Metric (SVM) – to discern between baseline model error and real DR parameter variation. •  SVM is the difference in the variance of observed shed and the variance of un-­‐modeled load (baseline) Similarly define: •  Rebound Variability Metric (RVM) •  Daily Peak Demand Variability Metric (PVM) •  Daily Energy Variability Metric (EVM) Conclusions 19 •  The error associated with DR parameter estimates is often large and so DR parameter estimates reported without error estimates may be misleading. •  Often, the observed DR variability is driven, in large part, by baseline model error, not real DR variability. •  Future Research – to discern relationship between response variability and facility attributes 20 Thanks!