A methodology to design a stochastic cost efficient der scheduling considering environmental impact

Electric power generation and transportation sectors are considered as main sources of gas emission today. Renewable energy and Electric Vehicles (EV) show potential as promising solutions for emission reduction and energy cost saving. However, the integration of renewable energy generation into the electric grid can be difficult, because of the source intermittency and inconsistency with energy usage; uncontrolled EV charging can also impose more burdens on power systems. Those situations can be improved through coordinated charging of EVs and optimized operation of distributed generators (DG) that not only mitigates fluctuations in generation and supply, but also reduces energy cost and the emission of pollutants (CO₂, SO₂, and NO_x). Emerging smart grid also brings new options for Distribution System Operator (DSO) toward efficient and sustainable operation of the network. One of these options is the use of Distributed Energy Resources (DER) including DG, EV, and Demand Response (DR). Operating DER has several advantages for DSO such as having DER close to load centers which reduces total network power loss. Since DSO has several energy sources to satisfy the electric load demand in the network, it is necessary to deploy optimal scheduling for efficient usage of available energy resources. In this paper we discuss a stochastic scheduling in the distribution network considering uncertainty in renewable energy generation. The proposed model can be used to analyse the effect of using DGs and EVs on emission and operation costs of the network. Results clearly shows that cost saving could be achieved with proper planning and coordination of various DERs.