Integration of machine learning with economic energy scheduling

Abstract

The aim of economic load dispatch (ELD) is to deliver required electrical power for a specified period at the lowest possible generation cost using available generating units (GUs). It is imperative to lower the generation costs in order to reduce the consumer costs and to generate adequate revenue from large capital investments in the power sector. There are several optimization algorithms (OAs) to solve this issue. In this study, a new method that combines machine learning (ML) with an OA is used to come up with a high-precision, best solution for ELD issues in the quickest time possible. The 'Lagrange Multiplier' (LM) method is used as the OA, while the 'Decision Tree' (DT) algorithm is used as the ML algorithm. ML algorithms require data to train themselves. A data generation algorithm (DGA) is used to generate data considering constraints such as the power balance constraint, transmission loss (TL), generating capacity, and prohibited operating zones (POZs). The DGA is based on the LM method with constraint handling techniques. Without considering ramp rate limits (RRLs), the optimal load sharing data is generated over the whole power capacity range of the committed GUs. The power capacity ranges from the sum of the minimum power capacity to the maximum power capacity of the committed GUs. This range is divided into several discrete data points with a step size of 0.01. Optimal load sharing among the GUs has been calculated for each of the data points using DGA. Then the DT model was trained with the generated data that could have been used further to predict the load sharing among the GUs. To impose RRLs, we have developed a search method using the trained DT model. We have validated our proposed method through three case studies: Case 1: 6 GUs with a 1263 MW power demand

Case 2: 15 GUs with a 2630 MW power demand

and Case 3: 140 GUs with a 49342 MW power demand. Finally, the optimal solution for all the case studies using the proposed method was compared with the existing methods. The proposed method was found to be better than the existing methods in terms of time, precision, and cost. This opens up a new way to help with the ELD issue by combining ML with OA. 2022 Elsevier Ltd