Renewable resources and storage systems stochastic multi-objective optimal energy scheduling considering load and generation uncertainties

Average household electricity use and industrial branches electrical energy consumptions are on a rising trend these days. Actually, utilization of fossil fuels for electricity generation purposes, results in contamination of the environment. Besides, loss of resources may be encountered owning to the persistent consumption of the energy produced through this process. Hence, the mentioned concerns necessitate the existent generations substitution by renewable resources obtained energies, for instance wind and solar, which can be accounted as a reasonable approach. However, one of the complications ahead is modeling of the uncertainty for these resources, accompanied by their random nature aggravating the resultant planning and prediction. Accordingly, micro-grid optimal scheduling in presence of renewable resources for energy production and storage systems is assessed in this study, assuming the case in which micro-grid is connected to the main grid. The simulations are devoted to mixed-integer linear programming (MILP), which are performed via GAMS software. With the help of scheduling a central control system in the studied micro-grid, a virtual power producer (VPP) is capable of controlling the load as well as managing optimal production of the energy. According to the load variable and unpredictable behavior, in addition to the diesel generator, some other renewable resources are employed for load management and control. Again, for uncertainty modeling associated with solar and wind resources, probabilistic schemes are evaluated, and besides, stochastic programming is designated. Moreover, the generated pollution of the units is also modeled. As a final step, a comparison is made between the operation cost results achieved by the proposed technique and those derived from genetic algorithm (GA) in the studied micro-grid, validating the correctness of the scheme.

Automated summary

The increasing use of electricity in households and industries, along with the environmental pollution caused by fossil fuels, emphasizes the need for renewable energy sources. This study evaluates optimal scheduling and load management of renewable resources in micro-grids, using mixed-integer linear programming and stochastic programming to address uncertainties. Results are compared with genetic algorithm, validating the effectiveness of the proposed technique.