Impact of different time series aggregation methods on optimal energy system design

Modelling renewable energy systems is a computationally-demanding task due to the high fluctuation of supply and demand time series. To reduce the scale of these, this paper discusses different methods for their aggregation into typical periods. Each aggregation method is applied to a different type of energy system model, making the methods fairly incomparable. To overcome this, the different aggregation methods are first extended so that they can be applied to all types of multidimensional time series and then compared by applying them to different energy system configurations and analyzing their impact on the cost optimal design. It was found that regardless of the method, time series aggregation allows for significantly reduced computational resources. Nevertheless, averaged values lead to underestimation of the real system cost in comparison to the use of representative periods from the original time series. The aggregation method itself, e.g. k means clustering, plays a minor role. More significant is the system considered: Energy systems utilizing centralized resources require fewer typical periods for a feasible system design in comparison to systems with a higher share of renewable feed-in. Furthermore, for energy systems based on seasonal storage, currently existing models integration of typical periods is not suitable

Levelised cost of hydrogen

LEVELISED COST OF HYDROGEN Levelised cost of hydrogen / Nigbur, Florian (CC BY-NC-SA) ( -

Hydrogen Road Transport Analysis in the Energy System: A Case Study for Germany through 2050

Carbon-free transportation is envisaged by means of fuel cell electric vehicles (FCEV) propelled by hydrogen that originates from renewably electricity. However, there is a spatial and temporal gap in the production and demand of hydrogen. Therefore, hydrogen storage and transport remain key challenges for sustainable transportation with FCEVs. In this study, we propose a method for calculating a spatially resolved highway routing model for Germany to transport hydrogen by truck from the 15 production locations (source) to the 9683 fueling stations (sink) required by 2050. We consider herein three different storage modes, namely compressed gaseous hydrogen (CGH2), liquid hydrogen (LH2) and liquid organic hydrogen carriers (LOHC). The model applies Dijkstra’s shortest path algorithm for all available source-sink connections prior to optimizing the supply. By creating a detailed routing result for each source-sink connection, a detour factor is introduced for “first and last mile” transportation. The average detour factor of 1.32 is shown to be necessary for the German highway grid. Thereafter, the related costs, transportation time and travelled distances are calculated and compared for the examined storage modes. The overall transportation cost result for compressed gaseous hydrogen is 2.69 €/kgH2, 0.73 €/kgH2 for liquid hydrogen, and 0.99 €/kgH2 for LOHCs. While liquid hydrogen appears to be the most cost-efficient mode, with the integration of the supply chain costs, compressed gaseous hydrogen is more convenient for minimal source-sink distances, while liquid hydrogen would be suitable for distances greater than 130 km

Modeling Hydrogen Networks for Future Energy Systems: A Comparison of Linear and Nonlinear Approaches

Common energy system models that integrate hydrogen transport in pipelines typically simplify fluid flow models and reduce the network size in order to achieve solutions quickly. This contribution analyzes two different types of pipeline network topologies (namely, star and tree networks) and two different fluid flow models (linear and nonlinear) for a given hydrogen capacity scenario of electrical reconversion in Germany to analyze the impact of these simplifications. For each network topology, robust demand and supply scenarios are generated. The results show that a simplified topology, as well as the consideration of detailed fluid flow, could heavily influence the total pipeline investment costs. For the given capacity scenario, an overall cost reduction of the pipeline costs of 37% is observed for the star network with linear cost compared to the tree network with nonlinear fluid flow. The impact of these improvements regarding the total electricity reconversion costs has led to a cost reduction of 1.4%, which is fairly small. Therefore, the integration of nonlinearities into energy system optimization models is not recommended due to their high computational burden. However, the applied method for generating robust demand and supply scenarios improved the credibility and robustness of the network topology, while the simplified fluid flow consideration can lead to infeasibilities. Thus, we suggest the utilization of the nonlinear model for post-processing to prove the feasibility of the results and strengthen their credibility, while retaining the computational performance of linear modeling

Extreme events in time series aggregation: A case study for optimal residential energy supply systems

To account for volatile renewable energy supply, energy systems optimization problems require high temporal resolution. Many models use time-series clustering to find representative periods to reduce the amount of time-series input data and make the optimization problem computationally tractable. However, clustering methods remove peaks and other extreme events, which are important to achieve robust system designs. We present a general decision framework to include extreme events in a set of representative periods. We introduce a method to find extreme periods based on the slack variables of the optimization problem itself. Our method is evaluated and benchmarked with other extreme period inclusion methods from the literature for a design and operations optimization problem: a residential energy supply system. Our method ensures feasibility over the full input data of the residential energy supply system although the design optimization is performed on the reduced data set. We show that using extreme periods as part of representative periods improves the accuracy of the optimization results by 3% to more than 75% depending on system constraints compared to results with clustering only, and thus reduces system cost and enhances system reliability

Optimal Configuration of Wind-to-Ammonia with the Electric Network and Hydrogen Supply Chain: A Case Study of Inner Mongolia

Converting wind energy into ammonia (WtA) has been recognized as a promising pathway to enhance the usage of wind generation. This paper proposes a generic optimal configuration model of WtA at the network level to minimize the ammonia production cost by optimizing capacities and locations of WtA facilities including wind turbines, electrolyzers, hydrogen tanks and optimizing supply modes among regions. Specifically, the temporal fluctuation characteristics of wind resources, the operation flexibility of the ammonia synthesis reactor and the transport distances are considered. Three typical supply modes, i.e., the Local WtA, the EN (electric network)-based WtA and the HSC (hydrogen supply chain)-based WtA, combined with two energy transport modes including EN and HT (Hydrogen truck trailers) are included with the consideration of the maximal energy transport capacity of EN and transport distance per day of HT (500km). Real data of Inner Mongolia (a typical province in China with rich wind resources and existing ammonia industries) is employed to verify the effectiveness and significance of proposed model. The effect of above significant factors on optimal planning capacity of WtA facilities and optimal energy transport modes is analyzed, which provides guidelines for WtA configuration. The economic analysis shows that the average LCOA (levelized cost of ammonia) for WtA is approximately 0.57 euro/kg in Inner Mongolia and comparable to that for CtA (coal-to-ammonia, 0.41 euro/kg) with a reduction of 30% in capacity cost of the facilities

Agricultural innovation platform dynamics: A conceptual framework to analyze knowledge production.

Innovation platforms (IPs) appear to be one of the most appropriate tools to operationalize research for development. Increasingly, agricultural research initiatives for development set up innovation platforms to facilitate the management and support of innovation processes. Yet, the mechanisms by which they operate are not well understood. This paper seeks to open the "black-box" and proposes a framework to analyze processes that occur in innovation platforms from inception to maturity. Firstly, we use a New Institutional Economics (NIE) based analytical framework for the M&E of IP performance. Secondly, from a review of the literature, we identify three ways through which research could be done within IPs: 1) soft transfer, when research has readily available results that could help solve jointly identified problems; 2) co-creation, when researchers and IP members develop research objectives and protocols together; and 3) community-based research, when IP members set up experiments on their own. We propose that both frameworks should be used to improve the monitoring of IP dynamics. (Résumé d'auteur

Architectural Concept and Evaluation of a Framework for the Efficient Automation of Computational Scientific Workflows: An Energy Systems Analysis Example

Scientists and engineers involved in the design of complex system solutions use computational workflows for their evaluations. Along with growing system complexity, the complexity of these workflows also increases. Without integration tools, scientists and engineers are often highly concerned with how to integrate software tools and model sets, which hinders their original research or engineering aims. Therefore, a new framework for streamlining the creation and usage of automated computational workflows is introduced in the present article. It uses state-of-the-art technologies for automation (e.g., container-automation) and coordination (e.g., distributed message oriented middleware), and a microservice-based architecture for novel distributed process execution and coordination. It also supports co-simulations as part of larger workflows including additional auxiliary computational tasks, e.g., forecasting or data transformation. Using Apache NiFi, an easy-to-use web interface is provided to create, run and control workflows without the need to be concerned with the underlying computing infrastructure. Initial framework testing via the implementation of a real-world workflow underpins promising performance in the realms of parallelizability, low overheads and reliable coordination