Interval Linear Programming For Economic Operation Optimization Of Microgrid

The field of energy easily changes depending on various factors, such as climaticvariation, power supply safety, the opening of the electricity market, the aging of hairpower transmission and distribution infrastructure, the concern for the environmentand so on. Moreover, with the global increasing dependency of fossil energyresources, developing local new energy and using distributed generation have been anincreasingly important way for government’s all over the world to save energy, reduceemission and develop green energy. The gradually standardized batch using of newenergy, such as wind energy, solar energy and oceanic energy and so on, gives aninfluence on structure of modern power system. As the main path of distributedpower’s access to grid integration, the economy of micro grid in the grid connectiondirectly influences the benefit of users and the grid.The renewable energy power and load power in the micro-grid both have theuncertainty characteristics, so it is worth to in-depth study how to consider the effectbrought by these uncertainty factors in the economic optimization of a micro-grid. Inorder to characterize these uncertainties in the micro-grid, the randomness of the windspeed and light intensity were described by the interval and then the output predictioninterval value of the wind turbine and photovoltaic were obtained, combined with theinterval description of load uncertainty, a micro-grid economic optimization modelbased on Interval Linear Programming (ILP) is proposed. Then combined with thetime-of-use, question of the power exchange with the external grid been considered;the CPLEX optimization software combined with the C++programming was appliedto the above question. Finally, take the effects analysis of storage on the economicoperation of the system as an example; this article has discussed the impact ofuncertainty of renewable energy power and load power on the optimization result, aswell as the degree of load uncertainty or called the size of the load fluctuation on thescheduling results. The results verify the robustness of the proposed method andmodel, and show the effectiveness in dealing with uncertainty optimization problem.