TY - GEN
T1 - Measuring the potential value of demand response using historical market data
AU - Abrate, Graziano
AU - Benintendi, Daniele
PY - 2009
Y1 - 2009
N2 - One of the main barriers to the deployment of Demand Response (DR) and Distributed Generation (DG) is the ex-ante assessment of their benefits. The aim of this study is to develop a general framework to evaluate this potential using day-ahead historical market data. We observe that profitability of DR-based strategies can be linked to several market characteristics. Among them are the price levels and the frequency of high levels of prices, the price differentials between consecutive hours and the frequency of high price spikes, the duration of peaks, the variables affecting prices (e.g. load, seasonality, weather, temperature, etc.). Although a single indicator to take into account all the above dimensions is not readily available, we propose to compare different markets in terms of "interesting events" referred to one or more of the above listed dimensions. The empirical analysis is based on five European markets, which present interesting differences in price patterns and are analyzed in order to give some insights on the different technologies and business models which may better exploit the potential of DR. This work shows a methodology which has to be refined in order to draw conclusions for specific sets of customers and technology combinations in order to understand the right price differentials and the possible DR time intervals.
AB - One of the main barriers to the deployment of Demand Response (DR) and Distributed Generation (DG) is the ex-ante assessment of their benefits. The aim of this study is to develop a general framework to evaluate this potential using day-ahead historical market data. We observe that profitability of DR-based strategies can be linked to several market characteristics. Among them are the price levels and the frequency of high levels of prices, the price differentials between consecutive hours and the frequency of high price spikes, the duration of peaks, the variables affecting prices (e.g. load, seasonality, weather, temperature, etc.). Although a single indicator to take into account all the above dimensions is not readily available, we propose to compare different markets in terms of "interesting events" referred to one or more of the above listed dimensions. The empirical analysis is based on five European markets, which present interesting differences in price patterns and are analyzed in order to give some insights on the different technologies and business models which may better exploit the potential of DR. This work shows a methodology which has to be refined in order to draw conclusions for specific sets of customers and technology combinations in order to understand the right price differentials and the possible DR time intervals.
KW - Demand response
KW - Peak prices
KW - Volatility
UR - http://www.scopus.com/inward/record.url?scp=70449333275&partnerID=8YFLogxK
U2 - 10.1109/EEM.2009.5207127
DO - 10.1109/EEM.2009.5207127
M3 - Conference contribution
AN - SCOPUS:70449333275
SN - 9781424444557
T3 - 2009 6th International Conference on the European Energy Market, EEM 2009
BT - 2009 6th International Conference on the European Energy Market, EEM 2009
T2 - 2009 6th International Conference on the European Energy Market, EEM 2009
Y2 - 27 May 2009 through 29 May 2009
ER -