Multi-objective optimization algorithm coupled to EnergyPLAN software: The EPLANopt model

Prina, Matteo Giacomo; Cozzini, Marco; Garegnani, Giulia; Manzolini, Giampaolo; Moser, David; Oberegger, Ulrich Filippi; Pernetti, Roberta; Vaccaro, Roberto; Sparber, Wolfram

doi:10.1016/j.energy.2018.02.050

Cited by 110 publications

(40 citation statements)

References 28 publications

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“…To properly analyze dependencies and synergies of the different energy sectors in future renewable energy systems, Smart Energy System approaches, as defined in [3], are needed. Therefore, holistic models containing all parts of the energy system with a focus on the national energy system, like the energy system model EnergyPLAN, have been developed [3,4]. With such models, optimal solutions for the complex, sector-coupled energy system considering all synergies can be found.…”

Section: Importance Of a Dynamic Emission Factor In Sector-coupled Enmentioning

confidence: 99%

“…Some approaches consider a flexible energy system design due to uncertainties, like [8] focusing on long term uncertainties, and [9] concentrating on demand uncertainties. Others use a multi-objective optimization, which often considers costs, emission and in some cases a third objective, like security, share of renewable energies within the system (e.g., [4]), exergy efficiency (e.g., [10]), or just costs and exergy [11]. There are approaches focusing on the flexible design of a special technology within district energy systems, such as the sizing of combined heat and power (CHP), or co-generation plants (e.g., [12][13][14]), respectively.…”

Section: Importance Of a Dynamic Emission Factor In Sector-coupled Enmentioning

confidence: 99%

See 1 more Smart Citation

Design of Renewable and System-Beneficial District Heating Systems Using a Dynamic Emission Factor for Grid-Sourced Electricity

et al. 2020

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In future energy scenarios with a high share of renewable energies within the electricity system, power-to-heat technologies could play a crucial role for achieving the climate goals in the heating sector. District heating systems can integrate volatile wind and photovoltaic energy sources and resolve congestions within the electricity grid, leading to curtailment of renewable electricity generation. This paper presents a design approach for setting up system-beneficial power-to-heat-based district energy systems. Within the scope of the project QUARREE100 an existing district in the provincial town Heide in Northern Germany is examined. A linear investment and unit commitment optimization model is applied. By considering local dynamic emission factors for grid-sourced electricity, which contain information on local wind energy curtailment as well as the emission intensity of the overall electricity generation, a renewable and system-beneficial design can be derived. With this method, the minimal rated power and capacity of energy converter and storage units can be determined to achieve emission reductions with respect to minimum costs. The approach of using different methods for the consideration of the emissions of grid-sourced electricity is analyzed based on different scenarios. By using a dynamic emission factor for grid-sourced electricity, lower emissions with fewer costs can be achieved. It is shown that a dynamic assessment leads to different design decisions and far-reaching deviations in the unit commitment. The results clearly show that a constant emission factor is no longer an option for grid-sourced electricity in urban energy system models.

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Section: Importance Of a Dynamic Emission Factor In Sector-coupled Enmentioning

confidence: 99%

Section: Importance Of a Dynamic Emission Factor In Sector-coupled Enmentioning

confidence: 99%

Design of Renewable and System-Beneficial District Heating Systems Using a Dynamic Emission Factor for Grid-Sourced Electricity

et al. 2020

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“…The present energy system simulation model can also be applied to energy system optimization, and examples of combining energy system simulation with external optimization can be found in [52][53][54]. This kind of simulation-based optimization, where the target value of the optimization is calculated with a separate simulation model, also helps to bridge the gap between the two archetypes of energy system models, i.e.…”

Section: System Optimizationmentioning

confidence: 99%

Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways

Pilpola

Lund

2020

Energy

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Decarbonization is an important goal of the future energy transition, but its modelling is also subject to several uncertainties. Here we investigate the impacts of such uncertainties through analyzing the overall performance and operation of a modelled national energy system undergoing deep decarbonization. Finland was chosen as a case, as it intends to become carbon-neutral already by 2035. Uncertainties in costs, energy consumption, and renewable resource potential and how they affect the operation of a modelled energy system is analyzed using a Monte Carlo method linked to a national energy system model with hourly resolution. The importance of the different uncertainties for the overall system indicators such as annual cost, CO2 emissions, and reliability are assessed. The impacts on different modelled low-carbon pathways are compared. For the Finnish case study, the projected energy consumption seems to be the most important uncertainty factor for the future energy system scenarios (e.g. for the CO2 emissions), followed by the production of wind power and the potential of biomass. The results indicate that addressing input uncertainties will be highly relevant for energy system modelling when pursuing decarbonization. None of the modelled cost-optimal decarbonization pathways stands out as fully resilient in this respect. Highlights • The impacts of uncertainties on low-carbon energy system modelling are analyzed. • Monte Carlo analysis is used to examine uncertainties' effects on modelled systems.

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“…One tool largely used for short-term modelling is PLEXOS [10]. Another example is EPLANopt [11]. One recent application of the latter by Prina et al includes combined cycle power plant flexibility constraints and assesses their role in balancing renewables with a one-year time domain and hourly resolution [12].…”

Section: Literature Backgroundmentioning

confidence: 99%

Representation of Balancing Options for Variable Renewables in Long-Term Energy System Models: An Application to OSeMOSYS

et al. 2019

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The growing complexity and the many challenges related to fast-changing and highly de-carbonised electricity systems require reliable and robust open source energy modelling frameworks. Their reliability may be tested on a series of well-posed benchmarks that can be used and shared by the modelling community. This paper describes and integrates stand-alone, independent modules to compute the costs and benefits of flexible generation options in the open source energy investment modelling framework OSeMOSYS. The modules are applied to a case study that may work as a benchmark. The whole documentation of the modules and the test case study are retrievable, reproducible, reusable, interoperable, and auditable. They create a case to help establish a FAIR-compliant, user-friendly, and low-threshold model and data standards in modelling practices. As is well known, one of the options for balancing high shares of variable renewables is flexible power generation by dispatchable units. The associated costs need to be considered for short-term operational analyses and for long-term investment plans. The added modules contribute to extending the modelling capacity by introducing (a) costs of ramping, (b) non-linear decrease of efficiency at partial load operation, and (c) refurbishment of existing units in the cost minimisation objective function of OSeMOSYS. From application to the test case study, two main insights are drawn: costs of ramping and decreased partial load efficiency may influence the competitiveness of generation technologies in the provision of reserve capacity; and refurbishment of existing units may represent attractive investment options for increasing flexibility. Both effects are also seen in the long-term and may impact infrastructure investment decisions to meet decarbonisation targets. These effects would not be captured without the introduction of the modules.

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Multi-objective optimization algorithm coupled to EnergyPLAN software: The EPLANopt model

Cited by 110 publications

References 28 publications

Design of Renewable and System-Beneficial District Heating Systems Using a Dynamic Emission Factor for Grid-Sourced Electricity

Design of Renewable and System-Beneficial District Heating Systems Using a Dynamic Emission Factor for Grid-Sourced Electricity

Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways

Representation of Balancing Options for Variable Renewables in Long-Term Energy System Models: An Application to OSeMOSYS

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