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Summary In this paper, a proportional nucleolus game theory (PNGT)–based iterative method has been presented to compute locational marginal price (LMP) at buses where distributed generation (DG) units were installed in a distribution network. Proportional nucleolus theory is one of the solution concepts for a cooperative game theory problem. The PNGT‐based iterative method provides financial incentives in terms of LMP to DG owners as per their contribution in loss reduction and emission reduction at a particular loading on the distribution system. In this method, LMP values depend on the distribution company's decision maker's preference among loss reduction, emission reduction, and distribution company's additional benefit. This proposed PNGT‐based iterative method has been implemented on a Taiwan power company's distribution network consisting of 84 buses with 15 DG units using MATLAB. The computed LMP values have operated the network based on decision‐maker priority so as to enable fair competition among DG owners.

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Estimation of electric power load on electric power substation is an essential task for system operator in order to operate the system in a reliable and optimal manner. In this paper, machine learning with artificial neural network is used for forecasting the load at a particular hour of the day on an electric power substation. Historical load data at each hour of the day for the period from September-2018 to November-2018 is taken from 33/11 kV substation near Kakatiya University in Warangal. A new artificial neural network architecture is developed based on the approach used to forecast the load. The developed model is simulated in MATLAB with available historical data to forecast the load on 33/11 kV electric power substation. Based on the analysis it is observed that the proposed architecture forecasts the load with better accuracy.Keywords Artificial neural networks · Electric power load forecasting · Machine learning · Mean square error · Mean absolute percentage error List of symbols L(D, t) Load at Dth day and tth hour L(D, t − 1) Load at Dth day and (t − 1)th hour L(D, t − 2) Load at Dth day and (t − 2)th hour L(D, t − 3) Load at Dth day and (t − 3)th hour L(t, D − 1) Load at (D − 1)th day and tth hour L(t, D − 2) Load at (D − 2)th day and tth hour L(t, D − 3) Load at (D − 3)th day and tth hour L(t, D − 4) Load at (D − 4)th day and tth hour MAPE Mean absolute percentage error MSE Mean square error y Target i Actual output y Predicted i Predicted output m Number of samples R Regression coefficient * Venkataramana Veeramsetty,

Electric load estimation is an important activity for electrical power system operators to operate the system stably and optimally. This paper develops a machine learning model with a long short-term memory and a factor analysis to predict the load at a specific hour of the day on an electrical power substation. Historical load data from the 33-/11-kV substation near Kakatiya University in Warangal are taken at each hour of the day for the period from September 2018 to November 2018. A new long short-term memory architecture with factor analysis is being designed based on the approach used to predict substation loads by simulation in Microsoft Azure Notebooks. Based on the study, it was found that the proposed design predicts loads with good accuracy.