Eco-Sim: A Parametric Tool to Evaluate the Environmental and Economic Feasibility of Decentralized Energy Systems

Due to climate change and the need to decrease the carbon footprint of urban areas, there is an increasing pressure to integrate renewable energy and other components in urban energy systems. Most of the models or available tools do not provide both an economic and environmental assessment of the energy systems and thus lead to the design of systems that are sub-optimal. A flexible and modular simulation tool, Eco-Sim, is thus developed in the current study to conduct a comprehensive techno-economic and environmental assessment of a distributed energy system considering different configuration scenarios. Subsequently, an intermodel comparison is conducted with the Hybrid Optimization Model for Electric Renewable (HOMER) Pro as well as with a state-of-the-art industrial tool. Eco-Sim is then extended by including the heating demand, thermal conversion (by using heat pumps and solar thermal) methods and thermal storage. A parametric analysis is conducted by considering different capacities of solar photovoltaics (PV), solar thermal panels and energy storage technologies. The levelized cost of electricity, the autonomy level and the CO2 emissions are used as the key performance indicators. Based on the analysis of a study case conducted in a neighbourhood in Geneva, Switzerland, the study reveals that, with the present market prices for batteries and seasonal changes in solar energy potential, the combination of solar PV with battery storage doesn’t bring a significant autonomy to the system and increases the CO2 emissions of the system. However, the integration of thermal storage and solar thermal generation is shown to considerably increase the autonomy of the neighbourhood. Finally, multiple scenarios are also run in order to evaluate the sensitivity of economic parameters on the performance indicators of the system. Under the assumptions of the model, to foster investments in solar PV and battery installations, falling installation costs or stronger policies in favor of renewable energy seem necessary for the future.

Evaluating the need for energy storage to enhance autonomy of neighborhoods

Dasaraden Mauree, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini, Karni Siraganyan

Energy storage is generally considered as a means to bridge a period between when/where energy is available and when/where it is in demand. Storage plays an important role by providing flexibility to energy systems, increasing the potential to accommodate variable renewables generation and improving management of electricity networks. However, currently it remains unclear when and under which conditions energy storage can be profitably operated at a district level. The present study aims to quantify the level of integration of solar energy and storage in the Junction district of Geneva. A simulation tool is developed to investigate the techno-economical and environmental assessment under different scenarios. For a given investment over 20 years, the model calculates the levelized cost of electricity (LCOE), the autonomy level as well as the CO2 emissions. Given the assumptions of the model, four scenarios are analysed based on the combination of solar PV, storage, solar thermal and heat pump to find out an economically optimal configuration in terms of system size. A comparison with the Homer software is performed to test the robustness of the solar PV and battery model. The economic profitability of solar PV and battery system is in very good agreement with Homer and the autonomy level is validated by using a simulation tool created by SI-REN (Services Industriels des Energies Renouvelables de Lausanne). However, combining solar PV with battery system doesn’t bring additional autonomy to the model for Geneva study case. Under the assumptions of the model, to foster investments in solar PV and battery installations, falling investments costs seem necessary for the future. A reduction gap between buying and selling price in grid for solar panel is recommended to increase solar installations. A validated simulation tool has been developed in this work and provide a reliable based that will be extended in the future to include the thermal demand and production. The availability of thermal storage at a large scale as well as the production over a district should further increase the autonomy of the district.

Elsevier2017

Spatio-Temporal Estimation of Renewable Energy Potential in Built Environments using Big Data

Alina Ursula Rosa Walch

To achieve the goals of the Paris Agreement on climate change, governments around the world must shift to renewable energy (RE) to drastically reduce their CO2 emissions within the next 10-30 years. The development of effective strategies for RE integration into national energy systems requires assessing the spatial and temporal availability of these resources at country scale. This spatio-temporal availability is described by the technical RE potential, defined as the maximum electrical or thermal energy that can be obtained from a given technology. While most previous technical potential assessments have used empirical models and geospatial tools to estimate RE potential, the increasing opportunities provided by Big Data and Machine Learning approaches, which extract additional information from large building and environmental datasets, are largely under-exploited. Integrating such data-driven methods with existing models enables national-scale RE resource assessments with higher precision and at higher spatial and temporal resolution than ever before.This work proposes Big Data approaches to estimate renewable energy potentials at national scale, with application to Switzerland. Focusing on the built environment, the technical potentials of Rooftop Solar Photovoltaics (RPV) and shallow Ground-Source Heat Pumps (GSHPs) are assessed at the spatial resolution of individual buildings and at hourly (RPV) or monthly (GSHP) temporal resolution. The proposed approaches combine large-scale data processing and Machine Learning with novel computational methods and state-of-the-art models of RE technologies. Furthermore, the quantification and propagation of uncertainties is addressed for RPV. The results are publicly available datasets of RE potentials, which enable spatio-temporal assessments of energy systems with a high share of renewables. At national level, the estimated RPV potential for Switzerland is 25+/-9 TWh, which may increase up to 39 TWh for more optimistic scenarios for solar PV panel installation, or beyond 60 TWh if rooftop-integrated PV is used. The shallow GSHP potential is estimated at up to 97 TWh, of which 20% is located in urban areas. I show that this potential may increase by over 50% through seasonal regeneration from space cooling. Spatial mismatches between supply and demand may be reduced by district heating networks (DHN).These findings have several implications for the Swiss Energy Transition for 2050: (i) To achieve the national target of 34 TWh from PV systems, 50-55% of all nearly flat (< 20° tilt) or south-facing surfaces must be exploited, whereby roofs with a high annual yield should be prioritised; (ii) GSHPs may deliver 30-70% of the heat demand of the targeted 1.5 million heat pumps and contribute significantly to the expansion of DHN in urban areas; (iii) these fractions may be increased through seasonal regeneration, for example from space cooling or excess solar thermal generation. The proposed approaches are transferable to RE potential assessments in other regions or countries, in particular by adapting them to open and cross-country input data, as well as to related RE technologies. The proposed data-driven methods and the publicly available datasets can be used by researchers, practitioners and policy makers to assess future decarbonisation pathways. I hope that this work will contribute to achieving national CO2 emission targets, moving one step closer to the goals of the Paris Agreement.

EPFL2022

Thermo-Economic Optimisation of Solar Tower Thermal Power Plants

James Spelling

Amidst a backdrop of rapidly increasing world-wide electricity demand, solar thermal power generation shows great potential for supplying the electricity needs of numerous countries in the Sun-belt regions of the world. In these regions, the absence of significant biomass, hydrological or geothermal reserves makes solar thermal power the most promising solution for meeting that most fundamental of demands: energy. Amidst the different options available for harnessing the Sun’s power, solar thermal technologies have been shown capable of producing electricity at the most economically viable rates [32]. However, all currently existing solar thermal power plants have been based around the use of Rankine, or steam turbine, cycles which are limited in the efficiencies they can achieve. In order to make better use of the Sun’s energy, higher efficiency cycles should be used. With their high concentration ratios, solar thermal power tower systems are amongst the best options for making use of the Sun’s potential. As the energy delivery temperature of central receiver systems is higher, they harness the solar radiation at a higher exergy level [4]. This higher temperature also opens the road to the use of more advanced thermodynamic cycles. Hybrid fossil-fuel – solar systems are also imaginable [13], as the energy delivery temperature is compatible with standard gas turbine cycles. Hybrid systems can help mitigate the requirements of thermal energy storage, as well as reducing the perceived risk when investing in new solar technology, which stimulates research and development, as well as providing employment. Recent developments in the field of high temperature volumetric receivers [10] along with rock- based packed-bed storage systems have opened up an interesting possibility. High temperature receivers allow the use of higher-efficiency combined-cycle setups, whereas packed-bed units offer the possibility of cheap storage. Larger storage volumes allow pure-solar systems to extend their power production into the night, making hybrid options less attractive. This is compounded by the common practice of guaranteeing a preferred tariff for electricity sales from pure-solar sources With this in mind, a complete dynamic model of a power plant based on the pure-solar concept has been elaborated. The performance of the setup is evaluated over a range of days, using solar insolation profiles obtained from satellite data. In order to examine different design options and their impact on the performance of the power plant, a multi-objective, thermo-economic optimisation of both the power plant superstructure and operating conditions was performed using the new, dynamic models. By means of an evolutionary algorithm [17], a family of Pareto-optimal points were obtained, representing the trade-off between increased power plant efficiency, and lower levelised energy costs. Through use of optimisation, it was shown that exergetic efficiencies in the region of 21-27% can be achieved, for relatively low power outputs of between 3 and 11 MWe. These configurations correspond with levelised electricity costs in the region of 14-21 UScts/kWhe. In most regions, the use of solar power is encouraged by the guarantee of a preferential tariff for electricity sales from renewable sources. Sale of electricity at a price of around 24 UScts/kWhe [32] should therefore be imaginable, ensuring the economic viability of solar thermal power tower systems. It can be concluded that, when properly designed, solar thermal power plants based on combined cycles are both economically and thermodynamically promising. By raising the efficiency of the plant, the size of the solar collector field is diminished, increasing the otherwise low energy densities of solar power systems. By reducing the levelised electricity costs, solar thermal power plants become more economically viable, accelerating their construction and thus, hopefully, reducing our dependence on fossil-fuels.

2009