Learning the LMP-load coupling from data: A support vector machine based approach

Abstract

This paper investigates the fundamental coupling between loads and locational marginal prices (LMPs) in security-constrained economic dispatch (SCED). Theoretical analysis based on multi-parametric programming theory points out the unique one-to-one mapping between load and LMP vectors. Such one-to-one mapping is depicted by the concept of system pattern region (SPR) and identifying SPRs is the key to understanding the LMP-load coupling. Built upon the characteristics of SPRs, the SPR identification problem is modeled as a classification problem from a market participant's viewpoint, and a Support Vector Machine based datadriven approach is proposed. It is shown that even without the knowledge of system topology and parameters, the SPRs can be estimated by learning from historical load and price data. Visualization and illustration of the proposed data-driven approach are performed on a 3-bus system as well as the IEEE 118-bus system.

Intraday Load Forecasts with Uncertainty

“…Each percentage point reduction in the mean absolute percentage error (MAPE) is estimated to reduce the cost of supplying electricity by $1.6 million annually for a 10 GW utility [6]. The expansion of deregulated electricity markets [7,8], renewable generation [9,10] and demand response programs [11] have increased the importance of accurate intraday load forecasts. Load forecasts are particularly useful as an input to energy price forecasting and as a means for identifying which days may have similar price structure [12].…”

“…Table 1. The parameters of the composite kernel described by Equation (8). Figure 8 shows that the daily and weekly periodicity capture a substantial portion of the variation in load.…”

Intraday Load Forecasts with Uncertainty

“…Each percentage point reduction in the mean absolute percentage error (MAPE) 1 is estimated to reduce the cost of supplying electricity by $1.6 million annually for a 10 GW utility [5]. The expansion of deregulated electricity markets [6,7], renewable generation [8,9] and demand response programs [10] have increased the importance of accurate intraday load forecasts.…”

“…Studying the structure of the forecasted and actual data in Figure 8 suggests that a decay away from exact periodicity is desirable. One way to achieve this is via the kernel described in (7). In particular, we want to allow consecutive days to co-vary more strongly than nonconsecutive days, and similarly for weeks.…”

“…The details of the algorithm, including how we measure the informativeness of data to a model, are available in the Appendix B. 7 There is a trade-off to model combination. On the one hand it is straightforward to implement, provides empirically and provably better estimates (see e.g.…”