6. Simualation Results

The current system has variety of units, including wind unit, photovoltaic unit, fuel cell unit, diesel generator, used in order to storage electrical power in batteries. A diesel generator has been used to control the load oscillations and microgrid frequency that its cost function is of a quadratic equation:
(16)
To make the micro-grid load and power generation units balanced, upstream grid hourly sell tariff is presented at the point which it possess the delivery of power from micro-grid to upstream grid. Both Weibull and normal curves can be applied so as to model the generated power of wind and solar units and hence normal curve has been used in this section for power description of solar unit. Uncertainty of wind power unit generation can be the consequence of disregarding effective parameters such as moisture and temperature in prediction of wind velocity. At first, to model this uncertainty, standard deviation of main scenario is calculated and for obtaining 2 other scenarios, this amount of standard deviation is added to the main scenario hourly (scenario1) and by minimizing (scenario3) using the presented method, three individual scenarios for each solar and wind power generation units are obtained which defines occurrence probability for the main scenarios in each unit with upper probability and two generation scenarios with lower probability where sum of each scenario probability is equal to one. Nine final states are generated by the combination of these scenarios. Optimal scheduling for generation costs of units from the least to the most is precisely performed and in all scenarios, charge of batteries has been done with accuracy for the least cost and low load grid at night hours. Sale of micro-grid extra energy to upstream grid at hours which the tariff is in the highest level shows that optimal scheduling has been done accurately. In order to guarantee the micro-grid load at peak hours, the maximum battery discharge is used and all the constraints of the micro-grid have been also taken into account. Best scheduling of micro-grid can be done for one day or one month period, and in this regard the best scheduling for one day and nine scenarios have been done. Figures 5 to 13 show optimal scheduling of the nine scenarios where planning and determining the optimal strategy of micro-grid energy resources by predicting uncertainties indicates the generation schedule and possible generation intervals of the system. It can be observed that the battery can be charged in all scenarios while the discharge can be occurred at specific hours. As the wind and solar power usually experience fluctuations in their power outputs, their scheduling has been determined according to the uncertainties. The intervals for photovoltaic power are predicted to be between hours 6th to 19th. Furthermore, utility grid can sell power to the main grid in order to meet the required load demand at hours 8th to 12th and 19th to 22th. By considering scenarios which are created through standard deviation, the system operational cost is less compared to GA, and by applying the presented method, 169 euro is saved in comparison with GA which emphasizes that the methodology is more profitable and efficient for cost optimizing.