A Robust Optimization Approach for Smart Energy Market Revenue Management

We propose a network optimization model for smart energy market management in the context of an uncertain environment. The network optimization considers the stochastic programming approach to capture the randomness of the unknown demands. We utilize the particle swarm optimization technique in the proposed model to solve the proposed optimization problem. The present research is based on the inclusion of stochastic demands and uncertain energy prices. Optimizing produced energy is crucial for efficient usage and meeting the targets. The proposed model also focuses on addressing sustainability concerns by minimizing energy consumption in the scheduling process. An improved particle swarm optimization technique is implemented for energy-efficient production. Parameters such as number of particles, iterations, and energy usage specification are customized. A fitness function is taken that considers both completion time and energy consumption. The optimal of energy consumption is also visualized. The decision makers employ risk aversion in the objective function of the optimization problem to measure the risk deviation of the expected energy management.