Analiza potencijala proizvodnje biomase iz brzorastućih nasada s neobrađenih poljoprivrednih zemljišta za korištenje u energetskim postrojenjima u Republici Hrvatskoj

U ovom radu je obrađena mogućnost uzgoja energetskih nasada na neiskorištenom poljoprivrednom zemljištu u Republici Hrvatskoj. Hrvatska raspolaže s gotovo 3 000 000 ha poljoprivrednog zemljišta, ali se koristi samo oko 1 000 000 ha. Stoga je očito da postoji značajan potencijal koji se može koristiti mnogo efikasnije nego što je to danas slučaj. Izložene su vrste kultura kratkih ophodnji i energetskih usjeva koji se trenutno koriste u Europi za energetsku eksploataciju te su opisane njihove karakteristike koje su važne za primjenu na zemljištima koja su uzeta u obzir i za energetsku eksploataciju. Također je analiziran potencijal tako kultiviranih zemljišta po županijama u Hrvatskoj i elaboriran energetski i tehnički potencijal. Potom se pomoću optimizacijskog modela odredilo 5 makrolokacija u Hrvatskoj za energetska postrojenja instalirane snage do 15 MWe koja bi bila pogonjena na biomasu pridobivenu iz energetskih nasada. Za ta postrojenja, provedena je tehno-ekonomska analiza i analiza osjetljivosti s obzirom na promjene vrijednosti značajnih parametara. Potom je dan zaključak o ovom načinu korištenja neiskorištenog potencijala poljoprivrednog zemljišta za energetsku upotrebu koristeći pritom energetske nasade koji su već u Europi uspostavljeni i korišteni, a čija se korist ogleda, ne samo u pridobivenoj biomasi, nego i u povoljnim utjecajima na zemljište, biološku raznolikost i korištenje kao ponor za stakleničke plinove – naročito CO2

Flexibility index and decreasing the costs in energy systems with high share of renewable energy

Recent European Green Deal includes decision to become carbon neutral and even carbon negative region in order to tackle the climate crisis. Main technical challenge and a key factor in techno-economic analysis of the energy system of the future, based on variable renewable energy sources, is their variable production and its integration. In order to deal with this problem in long-term energy planning, different approaches have been tried, focusing on overcapacity, storage capacities and sectors coupling with heating and transport. In this research, different flexibility options, storage and demand response technologies are modelled on a national energy systems level. With the case study area modelled in EnergyPLAN model, the goal of the research is to show how each flexibility option influences the economically feasible generation capacities of renewable energy sources, storage technologies and demand response in order to reach a certain share of renewable energy in final energy consumed. To follow the numerous possible configurations of the system, flexibility index for each option and a flexibility vector for each scenario are introduced. Results show which flexibility options play key role in important steps of energy transition to 70%, 80%, 90% and 100% RES energy system

Flexibility Options in 100% Renewable Energy World Regions

Designing the energy systems for high variable renewable energy penetrations, one should look for the flexibility of an energy systems that may be provided from various sources at supply, demand or network level. The flexibility can be provided from different options including, but not limiting to: (1) electricity demand (household and industry), (2) thermal (power, CHP) plants, (3) power to heat (CHP, heat pump district/individual), (4) transport (V2G + smart charge, synthetic fuels), (5) interconnection and (6) storage (batteries, pumped hydro, rockbed, compressed air, hydrogen…). The flexibility might be provided according to different criteria: economics, technical complexity, utilization, acceptability, feasibility, material use. Further, different constraints regarding percentages, shares, emission reductions… might be set according to proclaimed sustainable energy policy in certain region. Therefore, we will simulate various flexibility options according to their availability and priority assumed by authors for each region of nine World regions (USMCA, Latin America, United Kingdom, China, Russia, South-East Asia and Oceania, Rest of the world, ) using EnergyPLAN-Python permutation framework . This way number of synthetically generated temporary scenarios before finding optimal one and the execution time is increased in comparison to optimization approach

Modelling the long-term energy transition in different regions of the world, consideration of different region-specific data

This research presents the results of energy transition pathways in the different world regions. These regions are differentiated in the aspects of the availability of resources, different demand levels and distributions as well the different hourly distributions of the availability of renewable energy. Also, the differences in the regions are presented throughout different energy transition policies that are implemented. In order to model the specifics of interactions between the renewable energy generation and power to X technologies, the process of interlinking the outputs from EnergyPLAN simulations with the WILIAM (Within Limit Integrated Assessment Model) was developed. The ranges of simulations are run in EnergyPLAN and the method of regression is used to create simple relations between input and output values. Furthermore, an additional step of input data normalization was developed to speed-up the process of conducting the simulations. The research focusses on the differences in the resulting curves for relations of the flexibility technology application levels and capacity factors of variable generation. Also, the curves resulting from regression approach are compared as well as the results of implementation in WILIAM. Preliminary results show the differences in transition pathways with the regions predominantly located in the north or south focussing more on the implementation of wind and offshore wind energy, while the regions located closer to the equator such as India opting more for solar based energy systems

Grid operation and investment limitations for variable renewable energy in integrated assessment modelling

In order to integrate more and more variable renewable energy sources (VRES) electric grids play a significant role. There is an obvious need for more interconnections and more enhanced grids with more VRES. On the other side, the monetary, material, social and spatial constraints (free corridors) impose limits on grid expansions. Robust modelling of electric grids (distribution, transmission and interconnection) at World level in integrated assessment models (IAMs) is beyond the state of the art of current computation capabilities and data availability. A number of relevant input data like length of the grids, voltage levels, rated power, and after that material prices, population density, VRES shares of generation and flexibility technologies, hourly curtailments in the energy system are not sufficient to calculate explicit limitations imposed by power flows. Therefore, the idea of this paper is that in the absence of sufficient data and computability, for the first time some expert assumptions in the form of proportions are used to represent interconnection grids as an important constraint and flexibility option for variable renewable energy production, modelled at hourly level during one year using EnergyPLAN. These assumptions are about proportions of newly integrated renewable energy between transmission and distribution grids, proportions of grids to the generation plants and their utilization. Suggested method for application in WILIAM will be illustrated

Modelling of 100% Renewable Energy Systems in Integrated Assessment Models by multi-timeframe regression analysis

Working on holistic approaches that aim to capture a wide range of knowledge, researchers are usually faced with phenomena characterized by different time and geographical scales. This is the case of energy systems and Integrated Assessment Models (IAMs). More specifically, the nature of the variable renewable energy supply (VRES) has traditionally posed a barrier to accurately capturing the effects inflicted by VRES in the energy system. This research provides a soft link between an energy system model running with an hourly time step, on the one hand, and a yearly-based IAM, on the other hand, by the implementation of an emulator. The proposal here presented is a bridge, based on different types of knowledge, which successfully allows the flow of information between time scales. Results achieve a 100% renewable energy system on a case of Bulgaria. After a brief literature review on the topic, the method is explained in detail, including some results between EnergyPLAN (energy system model) and MEDEAS (Integrated Assessment Model, IAM) for Bulgaria. Results show that the ability of assessment is notably increased from the previous MEDEAS version. Finally, both results and limitations of this method are discussed. The authors hope this article captures interest in the field of IAMs, especially those which address with energy transition studies

Modelling the long-term dynamics of the energy transition accounting for socioeconomic behaviour and biophysical constraints: overview of the Wiliam Energy Module

WILIAM (Within Limit Integrated Assessment Model) is a global multiregional IAM that combines economic, social, demographic, environmental, energy and material related aspects into one system dynamics model. It aims to provide stakeholders with an open source, welldocumented model to assess the feasibility, effectiveness, costs and impacts of different sustainability policy options. The adequate representation of energy production is key to assess future sustainability pathways. The main function of the developed energy module is to estimate the primary energy requirements and related GHG emissions for satisfying the economic demand. This goal was achieved by 7 major sub-modules: (1) End-use: translates the economic demand into final energy demand through a hybrid approach combining bottom-up with energy intensities for different sectors. (2) Energy transformation: maps the entire energy conversion chain from final to primary energy, including intermediary energy commodities and an allocation function for power plant utilization. (3) Energy capacity: keeps track of the current power plant capacity stock, decommissioning of expired capacities, as well as the build-up of new capacities. An allocation function for choosing the suitable technology types for new capacities stands at the core of this sub-module. (4) Computation of the EROI of green technologies (5) Variability and storage: keeps track of sub-annual time scale effects on annual energy balances depending on the current power system setup (DSM, Storage, sector coupling). (6) Consideration of techno-sustainable potentials of RES considering geographical, resource and Energy Return on Energy Investment (EROI) constraints. (7) Computation of the energy-related GHG emissions