• Energy Research

The modeling of energy systems with high penetration of renewables is becoming more relevant due to environmental and security issues. Researchers need to support policy makers in the development of energy policies through results from simulating tools able to guide them. The EPLANopt model couples a multi-objective evolutionary algorithm to EnergyPLAN simulation software to study the future best energy mix. In this study, EPLANopt is applied at country level to the Italian case study to assess the best configurations of the energy system in 2030. A scenario, the result of the optimization, is selected and compared to the Italian integrated energy and climate action plan scenario. It allows a further reduction of CO2 emissions equal to 10% at the same annual costs of the Italian integrated energy and climate action plan scenario. Both these results are then compared to climate change scenarios through the carbon budget indicator. This comparison shows the difficulties to meet the Paris Agreement target of limiting the temperature increase to 1.5 °C. The results also show that this target can only be met through an increase in the total annual costs in the order of 25% with respect to the integrated energy and climate action plan scenario. However, the study also shows how the shift in expenditure from fossil fuels, external expenses, to investment on the national territory represents an opportunity to enhance the national economy.

Abstract The scope of energy system modelling is to support policy-makers in the definition of an energy strategy. Energy system models typically provide one single optimal solution. On the contrary, presenting the results of energy system modelling in the form of a set of optimal or sub-optimal alternatives improves the transparency towards the policy makers. A method to achieve this is marginal abatement cost curve. It estimates the relationship between potential reduction of CO2 emissions and relative costs. Model based methods to obtain marginal abatement cost curve lack of simultaneous high resolution in time and in sector coupling. Moreover, model based methods obtain smooth curves which can be transformed in step-wise only through a decomposition analysis. This latter shape is particularly important for providing the explicit technological detail in the graphical representation. The paper aims at developing a method to address these two issues in marginal abatement cost curves. The method, called EPLANoptMAC, is based on the EnergyPLAN software, developed by Aalborg university, and a hill climbing algorithm for expansion capacity optimisation. It is presented by applying it to the Italian energy system in 2030. The results show how in the initial phase of the decarbonisation process it is cheaper to generate overgeneration and curtailments from variable renewable energy sources than save these curtailments through balancing and storage solutions. This is driven by the low cost of generation of VRES and the high cost of balancing and storage solutions.

Energy system modeling has a central role in assessing the future energy system and helping policy makers to set targets and subsidizing mechanisms. In the years, different energy system models have been developed with the aim of supporting political decision-makers in the design of energy policies. It is relevant to compare and cross-validate these models in order to add robustness to the final results and technical feasibility of the identified transition pathways. Two main methods to compare these models exist i) comparison of the technical features and characteristics of the models and ii) comparison of the final results. While there are several reviews focusing on the comparison of models based on their technical characteristics and features, to the authors’ knowledge, no review article exists on comparison techniques based on final results. The scope of this paper is to carry out a review of existing methods and techniques to compare energy system frameworks, models, and scenario results regarding their final results.

Abstract Increasing electrification of final uses can be a viable solution towards low-carbon energy systems, when coupled with local renewable power generation. Mountain areas can already benefit from high shares of hydro-power generation, but, at the same time, rely on oil products for transport and for the heating sector in remote areas where natural gas infrastructures are not available. This research work evaluates potential scenarios for the electrification of transport and heating sectors, by coupling the simulation tool EnergyPLAN with a multi-objective optimization algorithm to analyse economic and environmental aspects. Results show that the largest benefits are expected from the electrification of the heating sector. Indeed, a CO2 emissions reduction up to 30% can be reached by acting on the transport sector alone, while up to 65% combining it with measures on heating, industry and agriculture sectors and additional electricity generation from photovoltaic systems. Moreover, the use of heat pumps can lead to significant CO2 emissions decrease with only to a slight increase in the overall annual costs thanks to lower variable costs that partly compensate the higher required initial investment and electricity storage deployment. The optimization analyses also highlight the effect of progressive penetration of electric vehicles in the private cars fleet and hydrogen trucks in the light-duty vehicles one.

Abstract This paper reviews the classification schemes used for bottom-up energy system modelling and proposes a novel one as re-elaboration of the previous schemes. Moreover, this paper identifies that the main challenges of this research field rotate around the concept of resolution. A matrix of challenges in which four main fields are identified: resolution in time, in space, in techno-economic detail and in sector-coupling. These main fields are divided into different levels of resolution: low, medium and high. The use of a low resolution introduces errors in the modelling as demonstrated by different studies. Several existing bottom-up energy system models are reviewed in order to classify them according to the proposed approach and map them through the proposed matrix. 13 different models are analyzed in the category of bottom-up short-term and 9 as bottom-up long-term energy system models. The following mapping shows how several models reach a high level of resolution in one or more than one area. However, the ultimate challenge is the simultaneous achievement of high resolution in all these fields. The literature review has shown how this final aim is not reached by any model at the current stage and it highlights the gap and weaknesses of this branch of research and the direction versus which is important to work to improve this type of modelling.

Abstract The planning of an energy system with high penetration of renewables is becoming increasingly important to face environmental and energy security issues. Within bottom-up energy system modelling, two different approaches exist: one optimizes the energy mix of a selected future year, while a second optimizes the transition pathway between the current baseline and a future year. Markal/TIMES and OSeMOSYS are examples of valid modelling tools for both approaches. Due to computational issues, these models usually adopt low time resolutions and follow a time slice approach. The latter approximation is questionable given that renewables are intermittent and storage, stationary and in electric vehicles, is needed. To overcome the accuracy issue, a modelling approach based on a multi-objective evolutionary algorithm and the EnergyPLAN software, which allows for year by year simulations with an hourly time-step, was introduced. The method that includes cost decrease of technologies year by year and decommissioning of old plants is applied to the Italian energy system. The results show the importance of timing in renewables capacity expansion planning. The deepest cumulated CO2 emission reduction of the energy system operating only on residential photovoltaic, wind power and batteries is 24% while introducing electric mobility lower this value at almost 30%.

This paper investigates the impact of applying optimisation and simulation approaches for energy system modelling and scenario design in a municipal context. In doing so, the previous understanding of what constitutes “optimisation” and “simulation” is expanded with further distinctions and it is documented how the outlined modelling approaches are applied in the existing literature. In a practical comparison, it is tested how the choice of modelling approach influences the design of future energy system scenarios for a municipal case study. The energy system scenarios and results obtained from a proposed stepwise simulation approach are compared to an established multi-objective optimisation approach. The results show that for the investigated municipal case it is possible to obtain results with a simulation-based approach that are comparable to the results obtained from multi-objective optimisation. Ultimately, the choice of modelling approach is a complex issue in which modellers must consider the necessary degree of modelling freedom, stakeholder involvement, and available system knowledge. Modellers need to consider not only what tool to use, but also how it is used; a tool can be used for both optimisation and simulation, and both can be valid approaches for developing future energy system scenarios.