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This analysis aims at the investigation of the techno-economic RES potentials and to minimize GHG emissions in the ENTSO-E. The ENTSO-E represents one of the world’s largest economic regions with a GDP of 19,835 billion US dollar (23%) and a primary energy consumption of 29,705 TWh (18%). ENTSO-E is coined with high degrees of fossil energy dependencies (49.9%) from third-party countries and contributes 3,517 Mt CO2eq (9%) to global GHG emissions. The Primary Sources onshore wind, offshore wind, utility-scale PV, and CSP techno-economic potentials are assessed via GIS-based georeferenced spatial analysis. Solar rooftop potentials are calculated based on a per capita approach. The Secondary Sources bioenergy, hydropower, geothermal, and hydrokinetic energy potentials are approximated based on meta-analysis. Primary and Secondary Sources potentials are input into the IES linear optimization model to simulate an energy system with high shares of RES and PTX. It is found that RES-based primary energy production potentials account for 13,492 TWh and energy dependency could be reduced by 6,482 TWh to 20.5%. Compared to the base year 1990, the direct GHG emission reduction amounts to 3,246 Mt CO2eq (70.7%). This reduction does not correspond with the EU reduction target of 80–95%. Hence, RES-based fuel imports, such as synthetic fuels and hydrogen, from third-party countries become necessary, to substitute fossil fuels and reduce domestic emissions. Additional GHG reduction potentials are suspected in the indirect emission sectors agriculture and waste management. In reference to the Sankey methodology, the analysis entails comprehensive energy flow balances for the ENTSO-E 2016 and 2050 energy systems. The ENTSO-E member countries exhibit heterogeneous properties regarding RES potentials, GHG emissions, and energy dependencies, and, hence, must be assessed individually. Integrated energy systems with high shares of RES and PTX represent a promising means to decrease direct energy use and non-energy use emissions as well as energy dependencies significantly.
A cellular approach to optimize the integration of renewable generation into distribution networks
(2022)
The steady growth of the renewable-based technologies in the last twenty years has changed the character of the power systems significantly. Until today, there are more than 112 GW installed photovoltaic and wind parks in Germany and around 90% of the installed renewable generators are integrated into distribution networks. As a result, distribution networks are often facing congestion problems and more investments are needed for the required network development plans.
The political decisions in Germany for increasing the share of renewables in electricity consumption up to 65% until 2030 and the nuclear phase out until 2022 and further shutdowns of the coal power plants raised serious concerns about the reliability of power supply and feasibility of the transition plan.
The present dissertation has a look over the recent developments and offers a methodology for reduction of the resulted costs from further integration of renewable generators into the distribution networks. The suggested methodology is based on a cellular approach and helps also to postpone the unnecessary costly network expansions. Furthermore, it helps to integrate the renewable generators in an optimized way which has an added value to move towards the defined sustainability goals.
The proposed methodology has two steps. The first step is made up of the cellular approach and grey wolf optimization in MATLAB environment. In this step, the optimal combination of technologies for fulfillment of the defined goals are found out. The second step consists of the quasi dynamic simulations in PowerFactory environment. In this step, the suggested results from MATLAB optimization are investigated in semi-real situations. With the quasidynamic simulations, it is checked whether the results are tolerable from the point of view of network operation and whether it is possible to facilitate the network operation with certain strategies.