• Energy Research

Abstract Due to the electricity systems’ increasing need for flexibility, demand side flexibility aggregation becomes more important. An issue is how to make such activities profitable, which may be obtained by selling flexibility in multiple markets. A challenge is to allocate volumes to the different markets in an optimal way, which motivates the need for advanced decision support models. In this paper, we propose a methodology for optimal bidding for a flexibility aggregator participating in three sequential markets. We demonstrate the approach in a generalized market design that includes an options market for flexibility reservation, a spot market for day-ahead or shorter and a flexibility market for near real-time dispatch. Since the bidding decisions are made sequentially and the price information is gradually revealed, we formulate the decision models as multi-stage stochastic programs and generate scenarios for the possible realizations of prices. We illustrate the application of the models in a realistic case study in cooperation with four industrial companies and one aggregator. We quantify and discuss the value of flexibility and find that our proposed models are able to capture most of the potential value, except for some extreme cases. The value of aggregation is quantified to 3%.

Energy and power system models represent important insights on the technical operations of energy technologies that supply the energy consumption in time steps with hourly resolution. This paper presents the European Model for Power system Investments with Renewable Energy (EMPIRE) that combines short-term operations with the representation of long-term planning decisions including infrastructure expansion. The EMPIRE model has an unique mathematical modelling structure based on multi-horizon stochastic programming, which means investment decisions are subject to short-term uncertainty represented by different realizations of operational scenarios. The model is open source and ready to use to analyse energy transition scenarios towards 2050 and beyond. This paper outlines the building blocks of the model and its software structure. We also present an illustrative example of results from using the software.

With the transition towards a decarbonized society, energy system integration is becoming ever more essential. In this transition, the energy vector hydrogen is expected to play a key role as it can be produced from (renewable) power and utilized in a plethora of applications and processes across sectors. To date, however, there is no infrastructure for the production, storage, and transport of renewable hydrogen, nor is there a demand for it on a larger scale. In order to link production and demand sites, it is planned to re-purpose and expand the existing European gas pipeline network in the future. During the early stages of ramping up the hydrogen sector (2020s and early 2030s), however, blending natural gas with hydrogen for joint pipeline transmission has been suggested. Against this background, this paper studies hydrogen blending from a modeling perspective, both in terms of the implications of considering (or omitting) technical modeling details and in terms of the potential impact on the ramp-up of the hydrogen sector. To this end, we present a highly modular and flexible integrated sector-coupled energy system optimization model of the power, natural gas, and hydrogen sectors with a novel gas flow formulation for modeling blending in the context of steady-state gas flows. A stylized case study illustrates that hydrogen blending has the potential to initiate and to facilitate the ramp-up of the hydrogen sector, while omitting the technical realities of gas flows -- particularly in the context of blending -- can result in suboptimal expansion planning not only in the hydrogen, but also in the power sector, as well as in an operationally infeasible system. 66 pages

Abstract The ability of pipelines to store gas by increasing their operating pressure, or linepacking, is a common operational practice used to mitigate future operational uncertainty. The optimal operation of a gas pipeline network considering linepacking is determined by weighing the trade-off between storing linepack and compressor power consumption. Existing compressor performance models do not accurately capture the rigorous nonlinear operating relationships, and the more accurate widely-used models are computationally complex. This paper develops a novel integer-linear data-driven compressor performance model which is shown to be both more accurate than the best existing model, and less computationally complex. An integer-linear gas transportation model that captures future operational uncertainty using a two-stage multi-period stochastic framework is introduced and solved in a case study on a subnetwork of the Norwegian natural gas network. The case study demonstrates the novel model is highly accurate and can be optimized quickly enough for real-time decision support.

When wind power constitutes a larger share of the electricity production mix, credible and reliable modelling of its operation in long-term investment models becomes increasingly important. In this paper the intermittent characteristics of wind power are modelled as a stochastic parameter in a long-term TIMES model of the Danish heat and electricity sector. To our knowledge, this is not a common approach in long-term investment models, and has not been done previously in TIMES, where the short-term uncertainty of wind power is normally taken into account by a deterministic constraint that ensures excess back-up capacity. In our model, the stochasticity gives lower total energy system costs, significant lower investments in wind power, less expected electricity export and higher expected biomass consumption compared to using the traditional deterministic approach. Also, the deterministic investment strategy can be insufficient in periods with poor wind conditions. Based on our findings, we recommend using a stochastic representation of intermittent renewables in long-term investment models to provide more solid results for decision makers.

The increasing demand for Electric Vehicle (EV) charging is putting pressure on the power grids and capacities of charging stations. This work focuses on how to use indirect control through price signals to level out the load curve in order to avoid the power consumption from exceeding these capacities. We propose mathematical programming models for the indirect control of EV charging that aim at finding an optimal set of price signals to be sent to the drivers based on price elasticities. The objective is to satisfy the demand for a given price structure, or minimize the curtailment of loads, when there is a shortage of capacity. The key contribution is the use of elasticity matrices through which it is possible to estimate the EV drivers’ reactions to the price signals. As real-world data on relating the elasticity values to the EV driver’s behaviour are currently non-existent, we concentrate on sensitivity analysis to test how different assumptions on elasticities affect the optimal price structure. In particular, we study how market segments of drivers with different elasticities may affect the ability of the operator to both handle a capacity problem and properly satisfy the charging needs.

Abstract The increasing demand of electric vehicles creates challenges for the electric grid both on the transmission level and distribution level. Charging sites in particular will have to face strong challenges especially in those countries where a massive penetration of electric vehicles happened in the last years and even more is expected in the forthcoming future. Such an increased forecast demand will lead to a capacity lack within the existing charging sites, therefore new investments in design and expansion have to be planned. We propose the so called SMACS MODEL that stands for Stochastic Multihorizon Approach for Charging Sites Management, Operations, Design and Expansion under Limited capacity conditions. The model is built to analyse critical decisions in terms of transformer expansion, grid reinforcements, renewable installation and storage integration, over a time horizon of 10 years, with a particular focus on the long term uncertainty in the price variations of the available resources. Long term investment decisions and short term operational decisions are addressed simultaneously in a holistic approach that includes also battery degradation issues and is able to tackle the optimal trade off between battery replacements, grid reinforcements and renewable installations throughout the chosen time horizon. Compared to traditional decision approaches the model is able to take more precise decisions due to its higher insight on the long term costs projections, the inclusion of battery degradation issues and the inclusion of grid rules and regulations limits that affect the final decisions.

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. Impact of energy communities on the European electricity and heating system decarbonization pathway: Comparing local and global flexibility responses