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Energy trading and demand are key components of the electricity market, with accurate load forecasting essential for predicting consumption and optimizing costs. This research aims to enhance peer-to-peer energy trading (P2PET) through accurate short-term load forecasting (STLF). It addresses the challenge of forecasting future electricity load to facilitate P2PET, using the supply-to-demand ratio (SDR) method with stochastic integrated STLF. Machine learning-based regression methods are evaluated for STLF. The electricity load data used for STLF was collected from residential buildings in Brunei Darussalam. Comparative analysis identifies among several methods, LightGBM as the best-performing method, with an RMSE of 0.114, R2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${R}^{2}$$\end{document} of 0.74, MAE of 0.081, and a training time of 2.34 s. Hyperparameter-tuned STLF is used for effective P2PET, with energy trading prices determined via the SDR approach. Implemented in Python and MATLAB, the model demonstrates a 23.55% reduction in total energy costs, validating the impact of STLF on P2PET efficiency. [ABSTRACT FROM AUTHOR]

The integration of demand response (DR) programs into the optimal scheduling algorithms offers numerous advantages for both the distribution system operator (DSO) and customers. However, practical implementation of any DR program cannot be realized by just acknowledging its benefits. In this article, a new DR-based optimal scheduling (DROS) framework that incorporates renewable sources and energy storage has been presented to optimize the operation of the distribution network while ensuring financial benefits for both customers and the DSO. The primary idea of this article is to determine the optimal schedules for responsive loads, power drawn from the grid, and the charging/discharging cycles of the energy storage system (ESS). In the existing literature, DR-based optimal scheduling algorithms have been developed aiming to maximize customer benefits, DSO profits, and minimize distribution power loss, overall operating costs, carbon emissions, etc., however, little attention has been devoted to power quality, network security, and reliability. Therefore, the proposed scheduling framework addresses this gap by taking into account the critical objectives such as the voltage stability index (VSI), energy not supplied (ENS), and voltage deviation factor (VDF), alongside DSO profit. This approach enhances network security, reliability, and power quality. The scheduling problem is modeled as a deterministic optimization problem, and solved using a meta-heuristic algorithm, and its performance is evaluated on the modified 33 bus system. To ensure the accuracy of the results, the result of the proposed scheduling problem is verified using Python optimization modeling objects (Pyomo) with the interior point optimizer (IPOPT). [ABSTRACT FROM AUTHOR]