Abstract
Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.
Highlights
Forecasting of locational marginal price (LMP) in electricity markets provide valuable information for decisionmaking
In order to test the proposed tool based on machine learning approach through an SVM-based multi-output regressor, a test IEEE power system is used to generate different operational scenarios with variations through a stochastic process simulation of bus loads, solve the optimal power flow (OPF) to obtain the corresponding LMPs
All-Bus Approach The LMP forecasting with the proposed tool considering all power system buses for only one test operating scenario, as that presented in Figure 7, is close to the real LMP values, with a trend to be lower than the real ones
Summary
Forecasting of LMPs in electricity markets provide valuable information for decisionmaking. The LMPs reflect the restrictions in the transmission system according to the availability of supply resources and demand. This value tends to fluctuate over an operating horizon [4]. LMPs have been a widely used mechanism by many countries due to their already proven incentive schemes compatibility and cost tracking. It can facilitate decision-making for different scenarios and tasks [5]
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