Techno-Economic Analysis Of Counter Strategies To Control The Overloading Of Distribution Networks In The Context Of Ev’s Charging Infrastructures

Abstract

The most significant issue that we are currently dealing with is climate change. We are observing its extensive destructions and it has been harming all of humanity. Worldwide efforts are being made to lessen the severity of the effects of climate change, and steps are being taken to decarbonise the environment. Reduction of CO2 emissions from road transport sector is increasingly acknowledged as a vital component in order to achieve the global and national targets set forth in the Paris Agreement. The transport sector is recognized as one of the biggest barriers to decarbonisation, as it is very expensive to replace energy-rich liquid fossil fuels. One possible choice is to switch to electro-mobility, more specifically to electric vehicles (EVs) instead of gasoline and diesel ones. Therefore, substituting electric vehicles (EVs) for conventional internal combustion engines (ICE) in vehicles can be a feasible solution and assist reduce the negative environmental effects of the transportation industry. Alongside benefits of EVs, a massive increase in the number of EVs will cause a significant load on the electrical network and can cause overloading peaks. In the next few years, the number of electric vehicles is expected to increase significantly due to government incentives and falling battery prices. Understanding how these vehicles will be charged, how that will impact the transmission and distribution level networks, and how smart charging might ease this strain are all important. Conventional solution to the overloading in the network is very uneconomical. Hence some smart charging strategies need to be adopted. The demand response (DR) strategies are used in this study. The CIGRE low voltage distribution system is taken into consideration and is implemented. This is done by the help of DIgSILENT GmbH Software. The load flow and the techno economic analysis are also done. After that the DR techniques including Price-based and Incentive-based are implemented and results are deduced. The Photovoltaic Energy (PV) and Vehicle to Grid (V2G) is also integrated into the system and results are deduced. Some improvements are made in the DR strategy based algorithms, we will compare the load flow and techno economic analysis results with the previous results and we can see a significant improvement in the voltage peaks, distribution of load in the network and its economical benefits. This analysis will aid in determining the effects of EVs on the system as well as their overall costs to the network. Moreover, the substantial economic gains made and the resulting reduction in network demand.