الفهرس 
Environmental protection of integrating Renewable Energy Resources (RERs) in the smart grid
CONCEPT OF APPLIED ALGORITHMS ON THE CEED PROBLEMOnly 14 pages are availabe for public view
 |  | from | 131 |  |  |
| | | from | 131 | | |
Abstract
A technically handling of several types of emission profiles lead to successful pathways for deep decarbonization. By 2050, 2.1 ton CO2 per capita will be reduced across the many European countries for an optimistic consideration, almost 87% with average emissions relative to 2010. A pathway of replacing the uncontrolled fossil fuel-fired with renewable energy resources such as wind, solar, nuclear, natural gas... etc., in addition to, electrifi¬cation of space, water heating and cooling; and adoption of electric vehicles will help in generating electricity with low- or zero-carbon emissions. It is a necessary condition from a decarbonization point of view to make some fuel shifting for example in electrical, industrial and transport sections.
Integrating Renewable Energy Resources (RERs) are showed a challenge in order to solve the uncertain and intermittent behavior by merging them with the batteries storage in the smart grid. Governments encourage the investments in establishing renewable energy plants due to lowering the running costs and attaining significant reduction in CO2 emissions, however the highest capital cost of establishing these renewable energy plants.
One of the most important switching technology and a famous application of battery storage systems in the transport and electricity sectors is Electric Vehicles (EVs). These types of vehicles are used for fuel shifting from fossil-burnt fuels to electric motors to drive vehicles. This type is called Battery Electric Vehicles (BEVs); another type of Electric Vehicles (EVs) is called Plug-in Hybrid Electric Vehicles (PHEVs) where electric motors are combined with Internal Combustion Engine (ICE). Gridable Vehicles (GVs) are considered in the smart grid which act as bidirectional way to be charged and discharged according to a predetermined schedule. The schedule is declared from the deregulated market in response with the announcing prices in the wholesale market. Demand Response (DR) program can be deemed by contributing owners of EVs to share their electricity in any bad situations such as on-peak periods, high prices of electricity or rush hours of loads.
Integrating several types of generating units are required a priority list in order to get the optimum solution in this study. A Combined Economic Emission Dispatch problem (CEED) is considered to minimize both the operational costs including emissions from PHEVs and conventional generating units in addition to reducing the losses of the network. Robust and intelligent techniques are needed due to the large number of the committed units under various different constraints. Water Cycle Optimization Algorithm (WCOA), Genetic Algorithm (GA) and Dynamic Programing (DP) are used for intelligently scheduling the committed units and obtaining the optimum solution for this system.
The suggested scenarios will be done as follows: the proposed system with hourly demand, Dispatchable Distributed Generators (DDGs), Stochastic Generators (SGs) and Plug-in Hybrid Electric Vehicles (PHEVs). Hourly prices and hourly bidding power will be obtained for certain winter-day from Ontario Market. Four scenarios are executed in two cases (considering losses of transmission line of the network and without considering losses) in the Combined Economic Emission Dispatch problem (CEED). Hourly distribution of the committed generating units and contributing the demand and PHEVs in the Demand Response program (DR) in order to minimize both total production costs and emissions and maximize the profit and the revenue. The results are compared by using the three techniques WCOA, GA, DP for four scenarios;
• Scenario 1: integrating only the three thermal Dispatchable Distributed Generators (DDGs).
• Scenario 2: integrating 5000 PHEVs with the thermal Dispatchable Distributed Generators (DDGs).
• Scenario 3: integrating two stochastic plants (RERs) with the thermal Dispatchable Distributed Generators (DDGs).
• Scenario 4: integrating 5000 PHEVs with two stochastic plants (RERs) with the thermal Dispatchable Distributed Generators (DDGs).