Presenting a Multi-objective Mathematical Model for Smart Grids Considering Load Response Programs

The availability of energy is a vital aspect of a nation’s economic and social development, with energy consumption serving as a telling metric of the level of prosperity that can be achieved. However, the conventional systems of electricity production that rely on large, centralized power plants have become inadequate in recent years due to the high expenses of production, air pollution, and poor energy quality. In response to these challenges, smart grids have emerged and offer several advantages. Effective management of electricity demand is critical in the context of smart grids, and the implementation of demand-response techniques plays an instrumental role in achieving this objective. These programs enhance energy consumption patterns during peak load times, resulting in appropriate pricing and grid reliability. There are two distinct categories of load response programs: price-oriented and incentive-oriented. In the scope of this research, we focused on the former, which relies on real-time pricing. Our objective was to develop a multi-objective mathematical model that considers load response programs for smart energy grids.

The study employed a scenario-based approach and classified the parameters into two distinct categories: deterministic and non-deterministic. Wind speed, solar radiation, energy demand, and local electricity prices were marked as non-deterministic due to their nature. As each non-deterministic parameter adheres to a specific probability distribution, a scenario was created for each parameter based on its corresponding distribution. Subsequently, a mathematical multi-objective model was developed that aimed to minimize operating costs, reduce pollution emissions, and minimize peak load, along with the related constraints. After collecting the required data, the model was run using the GAMS programming language. In addition, the study evaluated the impact of load response programs on enhancing objective functions.

The study findings demonstrate that the implementation of smart grids, accompanied by active consumer participation in load response programs, can result in a significant reduction in operating costs, pollution emissions, and peak load. Additionally, the study indicates that a higher level of consumer participation in load response programs can enhance the overall effectiveness of the programs. Specifically, the study shows that a 20% increase in consumer participation resulted in a 15%, 17%, and 13% improvement in operating costs, pollution emissions, and peak load reduction, respectively.

Smart grids represent a modern digital solution that streamlines the transfer of electricity between suppliers and consumers in the realm of energy transmission. This advanced system enables the regulation of home appliances, promoting energy conservation and cost-effectiveness, while simultaneously enhancing the reliability of the energy transmission network. Governments may opt to implement smart grids as a strategic solution to address complex issues such as energy independence, global warming, and pollution emissions.