In the ever-evolving landscape of energy management, a groundbreaking model is set to revolutionize how smart distribution networks operate. Developed by Gaurav Gangil, an electrical engineering expert from Manipal University Jaipur, this innovative approach promises to optimize both economic and technical operations, even in the face of uncertainty.
Gangil’s model, dubbed the Stochastic Multi-Objective Optimal Energy Management (SMO-OEM) model, tackles the complexities of modern smart distribution networks (SDNs). These networks, integrated with various generating resources like wind turbines (WTs), solar photovoltaics (PVs), diesel generators (DGs), battery energy storage systems (BESS), and the utility grid, must meet ever-increasing and unpredictable energy demands. The challenge lies in managing these diverse resources efficiently and cost-effectively.
The SMO-OEM model addresses this challenge head-on. It begins by generating initial scenarios based on day-ahead forecasts of PV and WT generation, load demand, and grid prices using Monte-Carlo simulations. These scenarios are then reduced to finalize input test scenarios for the next phase. Here, the model simultaneously optimizes two conflicting objectives: the expected total operational cost and the expected total active power loss. “The key is to balance these objectives,” Gangil explains, “to achieve both economic and technical efficiency.”
But the model doesn’t stop at optimization. It also recommends further reactive support from WTs, PVs, and BESS, along with a demand response program (DRP). This DRP shifts peak loads to other times, further reducing both operational costs and power losses. The results are impressive. Significant reductions in both objectives were achieved, along with improved bus voltage profiles and economic operations.
The model’s potential is evident in its application to two distinct sized networks: the modified IEEE-33 and IEEE-69 bus distribution networks. It was tested under different uncertainty ranges, proving its robustness and adaptability. “This model can significantly improve the way we manage energy in smart distribution networks,” Gangil asserts, “making them more reliable and cost-effective.”
The implications for the energy sector are profound. As variable renewable energy (VRE) sources like wind and solar become more prevalent, managing their intermittency and uncertainty becomes crucial. The SMO-OEM model provides a solution, offering a path to more stable and efficient energy management. It could shape future developments in the field, driving the adoption of VRE and energy storage systems, and paving the way for a more sustainable energy future.
The research, published in the IEEE Access journal, translates to English as “IEEE Open Access Publishing.” It opens up new possibilities for energy management, promising a future where smart distribution networks operate at peak efficiency, even in the face of uncertainty. As the energy sector continues to evolve, models like SMO-OEM will be instrumental in shaping its future, driving innovation, and promoting sustainability.