Grid-to-prosumer (G2P) interactions: Using bi-directional LSTM techniques to enhance the smart grid network through a demand response scheme

Abstract

To solve the issues in the electric power distribution network, oscillations in Electric Power Consumption (EPC) and Renewable Energy Generation (REG) must be considered. EPC and renewable energy resources (RES) are mostly used by prosumers integrated with smart grid. An incentive and dynamic pricing-based Demand Response (DR) can control the supply and demand balance. Uncertainty issues include supply and demand imbalances, EV charging, and natural REG fluctuations. This research study proposes an incentive and dynamic pricing-based DR technique for Distributed Generation and Demand Management (DGDM). This DGDM method considers the two uncertainties: demand and prosumer generation. The DGDM scheme, as proposed in this research article, has a dynamic incentive and penalty scheme. The policy applicability has been enhanced by the Bi-directional Long Short-Term Memory (B-LSTM) model’s predictive capabilities and ability to restrict the prosumers who participated in the DGDM program. The results demonstrate that the proposed DR policy benefits all parties involved, minimizes the electricity tariff and imbalance in supply and demand, and improves system stability while addressing prosumer issues. The proposed DR for prosumers to get a daily incentive of 89.4088 cents and 425.7844 cents reduce the daily electricity tariff.