Data-driven demand response optimization with hybrid forecasting and AC power flow validation in renewable-integrated distribution systems

Partha Das, Milan Sasmal, Rituparna Mitra, Reshmi Chandra, Kamalika Banerjee, Maitrayee Chakrabarty, Biswamoy Pal

Abstract


The huge integration of renewable energy sources such as solar and wind power has introduced significant uncertainty and unpredictability in modern power systems, which creats challenges for reliable and sustainable grid operation. Exact short-term load forecasting combined with effective demand side elasticity is therefore essential to enhance renewable energy utilization and reduce dependence on conventional power generation. To support sustainable operation of renewable integrated power systems, this research paper proposes a hybrid data-driven framework that integrates machine learning(ML) based load forecasting with optimal demand response (DR). A hybrid forecasting model merging Long Short-Term Memory (LSTM) networks and a XGBoost regression is developed to exploit temporal dependencies & nonlinear relationships in electricity demand. For improving forecasting accuracy and robustness over individual models, a meta learning fusion strategy is employed. The forecasted demand is then utilized in a mixed integer linear programming (MILP) based demand response optimization model that reduces grid procurement cost, at same time it satisfying consumer comfort constraints and accounting for renewable generation availability. Simulation is done on a IEEE 33 bus distribution system using synthetically generated load, renewable, and price data that demonstrate up to 22% peak demand reduction, 21.9% grid energy reduction, & 21.9% CO₂ emission reduction compared to baseline operation. The usage of synthetic data does not affect the validity of the proposed methodology in this paper, as the objective is comparative and system level performance evaluation rather than specific site forecasting. The operational feasibility of the proposed study is authenticated through power flow analysis on the standard IEEE 33 bus distribution system. Simulation results establish that the hybrid forecasting approach achieves higher accuracy in terms of MAE, RMSE, and R², while the demand response strategy efficiently reduces peak demand, grid energy consumption, and related carbon emissions. The proposed framework offers a practical & scalable solution for advancing sustainable development in renewable rich power systems.


Received: 25 January 2026

Accepted: 25 May 2026

Published: 10 June 2026


Keywords


Demand Response, Distribution Systems, Hybrid Forecasting, Long Short-Term Memory (LSTM), Mixed-Integer Linear Programming (MILP), Renewable Energy Integration, SHapley Additive exPlanations (SHAP), Smart Grids, XGBoost

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DOI: https://dx.doi.org/10.21622/resd.2026.12.1.1913

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Copyright (c) 2026 Partha Das, Milan Sasmal, Rituparna Mitra, Reshmi Chandra, Kamalika Banerjee, Maitrayee Chakrabarty, Biswamoy Pal

Renewable Energy and Sustainable Development

E-ISSN: 2356-8569

P-ISSN: 2356-8518

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

resd@aast.edu