The rapid expansion of renewable energies has the potential to decarbonize the electricity supply. This is more challenging in difficult-to-electrify sectors. The use of hydrogen provides a massive ...potential for this issue. However, expanding hydrogen production increases electricity demand while providing additional flexibility to the electricity market. This paper mainly aims to analyze the economic effects of this sector coupling between the European electricity and national hydrogen markets. The developed energy market model jointly considers both markets to reach an overall welfare optimum. A novel modeling approach allows the interaction of these markets without the need for several iterative optimization runs. This allows for a detailed analysis of various market participants’ changes in consumer and producer surpluses. The optimization is conducted in 13 connected Central European countries to account for various power plant fleets, generation mixes, and electricity prices. Results show an overall welfare increase of EUR 4 to 28 billion in 2030 and an EUR 5 to 158 billion increase in 2040. However, there is a surplus shift from consumers to producers. The consumer surplus is reduced by up to EUR 44 billion in 2030 and EUR 60 billion while producers benefit to achieve the overall welfare benefits. The reduction of consumer surplus changes if significant price peaks occur. Fuel cell applications can avoid these price peaks, resulting in a surplus shift from thermal power plants to consumers. Hence, consumer surplus can increase by up to EUR 146 billion in the respective 2040 scenarios. Pink hydrogen accounts for a sizable portion of total hydrogen production, up to 58 percent in 2030 and up to 30 percent in 2040. As a result, nuclear power plants that are nearly entirely allocated in France stand to benefit greatly from this sector coupling. Additional efforts could be made to address the link between hydrogen and natural gas prices. Furthermore, the potential for cross-border hydrogen trade and the implementation of national legal and regulatory frameworks could be assessed.
Electric vehicles represent a necessary alternative for wheeled transportation to meet the global and national targets specified in the Paris Agreement of 2016. However, the high concentration of ...electric vehicles exposes their harmful effects on the power grid. This reflects negatively on electricity market prices, making the charging of electric vehicles less cost-effective. This study investigates the economic potential of different charging strategies for an existing office site in Austria with multiple charging infrastructures. For this purpose, a proper mathematical representation of the investigated case study is needed in order to define multiple optimization problems that are able to determine the financial potential of different charging strategies. This paper presents a method to implement electric vehicles and stationary battery storage in optimization problems with the exclusive use of linear relationships and applies it to a real-life use case with measured data to prove its effectiveness. Multiple aspects of four charging strategies are investigated, and sensitivity analyses are performed. The results show that the management of the electric vehicles charging processes leads to overall costs reduction of more than 30% and an increase in specific power-related grid prices makes the charging processes management more convenient.
Energy communities often lack authority for establishment and operation management. Municipal authorities could take the role of such community operators. Therefore, Local Sustainable Municipalities ...are introduced, providing a local energy market on the municipal level with inclusion of sustainable resource utilization. The analyses include examinations of the scope of local markets in a municipality, portfolio investigations on different waste treatment plants and greywater system installation analyses. Furthermore, different adopted municipality strategies and their impact on municipal portfolio and market operation are examined. A clustering-based optimization framework for portfolio and market optimization is developed to perform these analyses. The proposed modeling approach leads to a significant model size reduction compared with hourly data optimization while providing location determination and portfolio estimation. The results indicate energy-sharing differences between municipal markets and local markets in energy communities. Decentralized energy provision is similar to centralized energy provision but on the municipal level. Furthermore, results show that waste incineration energy recovery can provide dispatchable low-emission energy with a high level of energy security and should be supported until the energy transition is more advanced. Finally, results on local strategies show that specific municipal goals always lead to increased costs for the municipality.
•The paper introduces Local Sustainable Municipalities (LSM).•The methodology proposes a modeling framework for municipal technology.•The paper assesses the impacts of various municipal sustainability strategies.•LSM energy provision is similar to centralized provision on the municipal level.•Municipal strategies lead to intended targets but increased costs.
Sustainability indicators should implement the United Nations Sustainable Development Goals (UN SDGs). Indicators in literature often consider large sets of actions and are thus complex in ...application. Therefore, this work derives energy- and resource-related SDG indicators for communities and municipalities with low complexity. Moreover, this work analyzes three different policy paths to promote SDG contribution. The policy paths consider SDG target settings and two different incentive schemes in the form of penalties and investment subsidies. The indicators and policy actions are applied in two case studies for communities and municipalities in Austria. Therefore, an optimization model that considers the case study setups, SDG targets and policy actions is developed. The modeling approach shows applicability and positive contribution to sustainable development by indicators. Moreover, the results show the applicability of the three policy paths. Implementing the target-setting path directly leads to the desired SDG targets and provides insights into the costs for target achievement. The incentive scheme paths also lead to selected targets, but they require a cost assessment of the provided incentive schemes. A combination of both incentive schemes leads to the lowest costs. However, policymakers should implement a workflow that considers all three policy paths for policy action settings.
•The paper introduces an SDG indicator system for communities and municipalities.•The paper establishes three different policy paths for sustainable development.•The method applies indicators and policies in two Austrian case studies.•Policy setting workflows should consider a combination of the paths.•Combining penalties and investment subsidies leads to the lowest incentive costs.
There are many excellent plotting libraries. Each excels at a specific use case: one is particularly suited for creating printable 2D figures for publication, another for generating interactive 3D ...graphics, while a third may have excellent LaTeX integration or be ideal for creating dashboards on the web. The aim of Plots.jl is to enable the user to use the same syntax to interact with a range of different plotting libraries, making it possible to change the library that does the actual plotting (the backend) without needing to touch the code that creates the content - and without having to learn multiple application programming interfaces (API). This is achieved by separating the specification of the plot from the implementation of the graphical backend. This plot specification is extendable by a recipe system that allows package authors and users to create new types of plots, as well as to specify how to plot any type of object (e.g. a statistical model, a map, a phylogenetic tree or the solution to a system of differential equations) without depending on the Plots.jl package. This design supports a modular ecosystem structure for plotting and yields a high code reuse potential across the entire Julia package ecosystem. Plots.jl is publicly available at Keywords: visualization, julia, plotting, julia-language, user-extendable
Cities are expected to grow further, and energy communities are one promising approach to promote distributed energy resources and implement energy efficiency measures. To understand the motivation ...of those communities, this work improves two existing open source models with a Pareto Optimization and two objectives: costs and carbon emissions. Clustering algorithms support the improvement of the models' scalability and performance. The methods developed in this work gives stakeholders the tool to calculate the capabilities and restrictions of the local energy system. The models are applied to a case study using data from an Austrian city, Linz. Four scenarios help to understand aspects of the energy community, such as the lock-in effect of existing infrastructure and future developments. The results show that it is possible to reduce both objectives, but the solutions for minimum costs and minimum carbon emissions are contrary to each other. This work quantifies the highest effect of emission reduction by the electrification of the system. It may be concluded, that a steady transformation of the local energy systems is necessary to reach economically sustainable goals.
•Development of a framework for energy communities.•Improvement of two Python based open source models.•Spatial aggregation method based on characteristic city blocks.•Application of a case study in an Austrian city district.•Steady low-emission transformation of local energy systems to avoid sunk costs.
•A modular operational optimization framework for energy aggregators is presented.•Several flexibility options are considered using a generic component interface.•The operation on sequential ...balancing, day-ahead and intraday markets is simulated.•Consideration of multiple markets significantly improves economic efficiency.
Distributed flexible energy consumption, production and storage technologies are an option to increase the flexibility of electricity systems and foster the integration of variable renewable energy sources. Aggregation business models, providing residential customers access to different electricity markets, can activate and utilize this untapped flexibility potential. However, economic feasibility for both aggregator and customers is a prerequisite for the adoption of these business models. In a European electricity market design with sequential markets, participation on multiple markets is supposed to further increase the economic benefits of aggregated demand response. In this work, a modular and extensible operational optimization and simulation framework based on mixed interger linear programming is developed to investigate different business models for aggregation of residential flexibility options on multiple markets. Simulation results of a specific case study show that considering day-ahead, balancing and intraday markets with adequate risk management in the optimization can significantly improve economic benefits compared to single-market optimization. Battery storages contribute most to these benefits. Business models on multiple markets are complex in terms of business model design and optimization, but they are economical for both aggregator and customers. Moreover they provide additional flexibility options to electricity systems. Thus, barriers for their implementation should be mitigated.
Redispatch measures are a key instrument of the Central European electricity market design to prevent congestion of several transmission lines after the market clearing. In addition to generation ...rescheduling, demand-side flexibility can reduce this redispatch need. This paper mainly aims to analyse the flexibility demand application of electric vehicle fleets in the redispatch market. The developed European electricity market model minimises dispatch- and subsequently redispatch costs whilst using electric vehicle fleets as flexible demand. A novel modelling approach allows the integration of this flexibility into large-scale linear programming models without losing their essential parameters. This case study is examined in Austria with a remarkably high share of electricity from renewable energy sources and different market penetrations of electromobility. Results show that integrating this system as a redispatch measure leads to a reduced curtailment of renewable energies (up to 25%), whilst less additional thermal power plant usage is needed. Furthermore, redispatch cost and the associated CO2 emissions are reduced by 3.3% to 13.9%. By contrast, using this flexibility as a market-based charging strategy raises CO2 emissions and redispatch costs drastically by 186% if 2 million electric vehicles are considered. Especially with the high electrification rate of the transport sector, the provided flexibility potential significantly impacts the electricity market. Further efforts could address the influence of demand-side flexibility not only on redispatch within one control area but also on cross-border redispatch and counter-trading.
•Implementation of electric vehicle fleets as demand-side flexibility.•Integration of flexibility in the dispatch and redispatch of the electricity market.•Flexibility as redispatch measure reduces redispatch costs and CO2 emissions.•Flexibility as market-based charging strategy leads to an increase in congestion.
This paper investigates how the European electricity and heating system is impacted when medium-scale energy communities (ECs) are developed widely across Europe. We study the response on the ...capacity expansion of the cross-border transmission and national generation and storage within the European electricity and heating system with and without ECs in selected European countries. The representation of ECs has a special focus on flexibility, and we analyze the difference between flexibility responses by ECs towards local versus global cost minimization. Results show that EC development decreases total electricity and heating system costs on the transition towards a decarbonized European system in line with the 1.5 °C target, and less generation and storage capacity expansion is needed on a national scale to achieve climate targets. We also identify a conflict of interest between optimizing EC flexibility towards local cost minimization versus European cost minimization.
•We study investments in the European energy market with energy community roll-out.•We compare flexibility responses with local versus European perspective.•Energy communities partly replace centralized generation and storage capacity.•Energy community roll-out shifts investments from onshore to offshore wind.•There is conflicting interest between local and European flexibility utilization.
•Two unit commitment appraoches for a combined-cycle power plant are investigated.•State prediction is used with dynamic programming to reduce calculation time.•The benefit of the implementation is ...shown by several simulation cases.•The results are verified by comparing it with a conventional approach.
Recently, the increasing prevalence of renewable energies has faced the challenge of operating power supply systems to efficiently plan electricity generation on a daily basis, since renewable energies are generated intermittently and the decisions of the individual generation units are discrete. The Unit Commitment (UC) problem, which determines the dispatch of generation units, is one of the critical problems in the operation of power supply systems. A long list of formulation proposals have been made that claim to solve this problem. For this purpose, two established approaches, mixed-integer linear programming (MILP) and backward dynamic programming (DP), are used as basis for a deterministic single-generator unit with general convex cost function in this paper. The DP algorithm is enhanced by a so-called state prediction, which reduces the time to find the optimal solution. The proposed formulation is tested empirically on the basis of existing formulations at long-term profit based UC instance derived from real data. Finally, the calculation results show that the derived approach significantly shortens the computation time, which confirms the effectiveness of state prediction. The comparison of the approaches shows that the DP algorithm with state prediction delivers a satisfying solution in significantly less time than DP and MILP. Furthermore, the given linearity of the dependence of the computation time on number of steps is a superior advantage of the DP strategy. This superiority becomes even more evident when the planning horizon extends over a longer period of time.
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