1 Improvement of Classification of Electricity Customers by Demand Response Nobuyuki Yamaguchi ＊ , Member, Hiroshi Asano, Senior Member (CRIEPI), Junqiao Han, Girish Ghatikar, Sila Kilicotte, Mary Ann Piette, Non-Member (Lawrence Berkeley National Laboratory) 1. Introduction According to the 2008 Federal Energy Regulatory Commission (FERC) Survey on Demand Response (DR) in the U.S. [1], the number of utilities and ISOs that offered DR programs has increased since 2006. These circumstances would provide informative experiences of DR deployment for utilities and independent systems operators which are considering the introduction of DR programs and expecting to conduct better ex ante evaluation of programs. For estimate of demand reduction by DR programs, load profiles of each customer have been recently available because of a spread of interval meters. Since the load profile represents each customer’s load characteristic, acceptable estimates of the demand reduction would be achieved by using the load profile. The purpose of this study is for ex ante evaluation of the demand reduction by DR programs to examine whether the demand reduction can be explained by regression models, which employ the actual load profile and outside air temperature (OAT). In cluster analysis, each load profile is classified according to its load shape. Then, the index of the cluster is used as one of the explanatory variables of the regression models. Another explanatory variable is load sensitivity to OAT. In order to verify the proposed method, we apply the regression model to demand reduction performed in 2008 summer by customers who participated in Critical Peak Pricing (CPP) program, one of Pacific Gas and Electric (PG&E) ’s price-based DR tariffs. 2. Demand Response Program 2.1 PG&E’s CPP Program In PG&E’s CPP program [2], customers sign up on a tariff where all electric usage during summer on-peak and partial-peak hours is discounted on days when no CPP events are called. Contrarily, on CPP event days, maximum of 12 times during the summer season, higher “critical peak” energy charges are imposed for all electric usage that occurs weekdays, excluding holidays, as follows: -Moderate Price Period (MPP): Noon to 3 p.m. customers are charged approximately three times the partial-peak energy rate shown on their otherwise applicable rate schedule. -High Price Period (HPP): 3 p.m. to 6 p.m. customers are charged approximately five times the on-peak energy rate shown on their otherwise applicable rate schedule. Commercial and industrial customers must have peak demand of 200 kW or greater and equipped with an interval meter provided free of charge by PG&E. There are several ways to control the demand on the customer’s side. The most primitive way is a manual control. The second semi-automated control involves a pre-programmed demand reduction strategy initiated by a person via centralized control system. One of the most advanced measures is “Automated Demand Response (Auto-DR).” Demand Response Research Center at the Lawrence Berkeley National Laboratory (LBNL) has developed the Auto-DR and analyzed effectiveness of its deployment in PG&E’s service territory [3], [4]. Auto-DR does not involve human intervention and pre-programmed DR strategies are executed by energy management control systems in a building or facility through receipt of an external communications signal. LBNL has recently published “Open Auto-DR Communications Specification (OpenADR)” [5]. OpenADR specification is being donated to standards development organizations for formal standards. The National Institute of Standards and Technology, in proposal to U.S. Department of Energy, has considered it for Smart Grid standards. 2.2 Calculation of Demand Reduction Demand reduction is the difference between an actual load and a customer baseline load (CBL). The CBL L i,d,t is defined as follows: d i t d i t d i L L , , , , , Δ + ′ = ............................................................ (1) t d i t d i t i t i t d i T C L , , , , , , , , ε ′ + ⋅ Α + ′ = ′ .......................................... (2) Where, L’ i,d,t : the CBL i at period t on event day d before a morning adjustment [6], C’ i , t : a constant, A i,t : a load sensitivity to OAT at period t, T i,d,t : OAT, t d i , , ε ′ : an error term, and d i , Δ : a shift term for the morning adjustment. The morning adjustment is to mitigate a difference between actual demand and the CBL before the morning adjustment. The shift term is calculated from the data from 9am to noon as follows: ∑ ∑ ∈ ∈ − = Δ noon to am t t d i noon to am t t d i d i L AL 12 9 , , 12 9 , , , .................................... (3) Where, AL i,d,t represents the actual demand on CPP event day d. In order to estimate A i,t and C’ i, t in (2), we used 10 previous business days’ data of event day d. We use OAT T i,d,t which is measured at the nearest weather station chose from 15 weather stations of National Oceanic and Atmospheric Administration. The demand reduction rate R i,d,xPP is defined as follows: ( ) ∑ ∈ − ⋅ ⋅ = xPP t t d i t d i i xPP xPP d i AL L PL N R , , , , , , 1 1 ......................... (4) Where, xPP represents HPP or MPP. N xPP means the number of period t in the xPP. Table 1 illustrates the descriptive statistics of demand reduction rate of sample participants in the CPP program. The number of sample participants is 99 accounts which include 27 Auto-DR accounts. The load sensitivity to OAT i α in table 1 is the mean value of A i,t and defined as follows: