Portfolio optimization of energy communities to meet reductions in costs and emissions

Fleischhacker, Andreas; Lettner, Georg; Schwabeneder, Daniel; Auer, Hans

doi:10.1016/j.energy.2019.02.104

Cited by 65 publications

(28 citation statements)

References 21 publications

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“…Seasonal storage is included in [4] and [1]. Voltage constraints and power flow are included in [5] while [3] focuses on combining open source models. [8] simplifies the MILP optimization problem by separating it in parts and finds near optimal solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal investment in the energy system of Zero Emission Neighborhoods considering the refurbishment of the building stock

Pinel

Kopås

2021

E3S Web Conf.

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To increase the impact that Zero Emission Neighbourhoods (ZEN) can have in the effort to decrease CO2 emissions, the refurbishment of the existing building stock is a parameter that should be considered. The existing literature contains work on optimization models for the energy system of neighbourhoods taking into account emissions but fails to account for the refurbishment of buildings. This paper addresses this option and presents an optimization model for designing a cost-optimal energy system of a ZEN in the context of existing buildings. The model is presented and used in a case study in Norway and compared to a case with linearized binaries. A sensitivity analysis is performed on the cost of refurbishment. With the original refurbishment cost assumptions, it is not chosen by the optimization, contrary to the hydronic. The system relies mainly on PV, solar thermal collectors (ST), a biogas engine, a battery and heat pumps (HP) and heat storage. From 50% of the original refurbishment cost, it is chosen, and the system does not have a biogas engine and a heating grid anymore, but a much bigger battery and more heating technologies inside the buildings. With linearized binaries, the investments are similar to the case with 50% refurbishment cost, but the value of the linearized binaries cannot be used to indicate the share of building to refurbish.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal investment in the energy system of Zero Emission Neighborhoods considering the refurbishment of the building stock

Pinel

Kopås

2021

E3S Web Conf.

View full text Add to dashboard Cite

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“…Further literature evaluates the impact of shared battery energy storage systems (BESS) on PV self-consumption and their profitability [13] or may involve multi-objective optimization, e.g., focusing on retrofitting PV on apartment buildings [14]. Other approaches include multi-objective optimization in reducing costs and emissions [15]. The management of many distributed energy resources (DER) and its storages in neighborly communities is analyzed in [16] with a dynamic pricing approach.…”

Section: State Of the Artmentioning

confidence: 99%

“…The optimization model HERO community is based on the framework HERO [15], which allows portfolio-and investment optimization for individual consumers, as well as for communities. The original framework was extended by the capability of applying local grid tariffs within a community and community utilization of commonly used spaces.…”

Section: Optimization Problem and Nomenclaturementioning

confidence: 99%

Comparison of Profitability of PV Electricity Sharing in Renewable Energy Communities in Selected European Countries

et al. 2020

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The economic value of photovoltaic (PV) systems depends on country-specific conditions. This study investigates the impact of grid fees, solar irradiance and local consumption on the profitability and penetration of PV systems and batteries in renewable energy communities. The linear optimization model calculates the optimal investments into PV and storages applied on a test community, which represents the European housing situation. The comparison of eight countries considers individual heat and cooling demands as well as sector coupling. Results show that renewable energy communities have the potential to reduce electricity costs due to community investments and load aggregation but do not necessarily lead to more distributed PV. Besides full-load hours, the energy component of electricity tariffs has the highest impact on PV distribution. Under current market conditions, battery energy storage systems are rarely profitable for increasing PV self-consumption but there is potential with power pricing. Renewable energy communities enable individuals to be a prosumer without the necessity of owning a PV system. This could lead to more (community) PV investments in the short term. Hence, it hinders investments in a saturated PV market.

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“…The other time series are reconstructed from the clusters after the clustering. K-means is also used in [25], where two models are coupled, for providing representative weeks and for providing representative hours. The hours clustering is preceded by the removal of peaks from the time series and followed by their re-introduction.…”

Section: State Of the Art And Contributionmentioning

confidence: 99%

Clustering methods assessment for investment in zero emission neighborhoods’ energy system

Pinel¹

2020

International Journal of Electrical Power & Energy Systems

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This paper investigates the use of clustering in the context of designing the energy system of Zero Emission Neighborhoods (ZEN). ZENs are neighborhoods who aim to have net zero emissions during their lifetime. While previous work has used and studied clustering for designing the energy system of neighborhoods, no article dealt with neighborhoods such as ZEN, which have high requirements for the solar irradiance time series, include a CO2 factor time series and have a zero emission balance limiting the possibilities. To this end several methods are used and their results compared. The results are on the one hand the performances of the clustering itself and on the other hand, the performances of each method in the optimization model where the data is used. Various aspects related to the clustering methods are tested. The different aspects studied are: the goal (clustering to obtain days or hours), the algorithm (k-means or k-medoids), the normalization method (based on the standard deviation or range of values) and the use of heuristic. The results highlight that k-means offers better results than k-medoids and that k-means was systematically underestimating the objective value while k-medoids was constantly overestimating it. When the choice between clustering days and hours is possible, it appears that clustering days offers the best precision and solving time. The choice depends on the formulation used for the optimization model and the need to model seasonal storage. The choice of the normalization method has the least impact, but the range of values method show some advantages in terms of solving time. When a good representation of the solar irradiance time series is needed, a higher number of days or using hours is necessary. The choice depends on what solving time is acceptable.

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Portfolio optimization of energy communities to meet reductions in costs and emissions

Cited by 65 publications

References 21 publications

Optimal investment in the energy system of Zero Emission Neighborhoods considering the refurbishment of the building stock

Optimal investment in the energy system of Zero Emission Neighborhoods considering the refurbishment of the building stock

Comparison of Profitability of PV Electricity Sharing in Renewable Energy Communities in Selected European Countries

Clustering methods assessment for investment in zero emission neighborhoods’ energy system

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