No abstract

Expansion planning models are often used to support investment decisions in the power sector. Towards the massive insertion of renewable energy sources, expansion planning of energy storage systems (SEP-Storage Expansion Planning) is becoming more popular. However, to date, there is no clear overview of the available SEP models in the literature. To shed light on the existing approaches, this review paper presents a broad classification of SEP, which is used to analyze a database of about 90 publications to identify trends and challenges. The trends we found are that while SEP was born more than four decades ago, only in the last five years increasing research efforts were put into the topic. The planning has evolved from adequacy criteria to broader targets, such as direct costs, mitigation of CO2 emissions, and renewable integration. The modeling of the network, power system, energy storage systems (ESS), and time resolution are becoming more detailed. Uncertainty is often considered and the solution methods are still very diverse. As outstanding challenges, we found that (1) the large diversity of ESS, in contrast to conventional generation technologies, and (2) the complex lifetime and efficiency functions need to be addressed in the models. (3) Only a high temporal and spatial resolution will allow for dimensioning the challenge of integrating renewables and the role of ESS. (4) Although the value of ESS lies beyond shifting energy in time, current SEP is mostly blind to other system services. (5) Today, many flexibility options are available, but they are often assessed separately. In the same line, although cross-sectorial (power, heat, transport, water) SEP is becoming more frequent, there are many open tasks towards an integrated coordination. The planning of future energy systems will be multi-sectorial and multi-objective, consider the multi-services of ESS, and will inherently require interdisciplinary efforts.

Background: The focus of the paper is on scenario studies that examine energy systems. This type of studies is usually based on formal energy models, from which energy policy recommendations are derived. In order to be valuable for strategic decision-making, the comprehensibility of these complex scenario studies is necessary. We aim at highlighting and mitigating the problematic issue of lacking transparency in such model-based scenario studies.

Wind Powered Thermal Energy Systems (WTES) are the entirety of all conceivable combinations that consist of wind energy converters and thermal energy storage facilities. Although there is still a pressing demand for innovative technological solutions that allow the decarbonization of power and especially heat supply, comparative costs assessments that include the direct conversion of wind energy into heat are pending. In this paper, we conduct such an analysis for the first time. In particular, a techno-economic analysis based on the calculation of levelized costs of heat supply (LCOE) is presented. The novelty of this study is the comparison of five specific WTES concepts which either make use of electric boilers, hydro-dynamic retarders or heat pumps. The spectrum of applications considered ranges from heat supply for individual buildings to small villages and cities. The results show that LCOE below 5 c€/kWh can be reached. This indicates already competitiveness compared to conventional space heating technologies. In this means, we provide a systematic framework for future studies to evaluate the particular economic potentials of WTES in the energy market.

Energy system optimization models used for capacity expansion and dispatch planning are established tools for decision-making support in both energy industry and energy politics. The ever-increasing complexity of the systems under consideration leads to an increase in mathematical problem size of the models. This implies limitations of today’s common solution approaches especially with regard to required computing times. To tackle this challenge many model-based speed-up approaches exist which, however, are typically only demonstrated on small generic test cases. In addition, in applied energy systems analysis the effects of such approaches are often not well understood. The novelty of this study is the systematic evaluation of several model reduction and heuristic decomposition techniques for a large applied energy system model using real data and particularly focusing on reachable speed-up. The applied model is typically used for examining German energy scenarios and allows expansion of storage and electricity transmission capacities. We find that initial computing times of more than two days can be reduced up to a factor of ten while having acceptable loss of accuracy. Moreover, we explain what we mean by “effectiveness of model reduction” which limits the possible speed-up with shared memory computers used in this study.

Today's energy system models calculate power flows between simplified nodes representing transmission and distribution grid of a region or a countryso called copper plates. Such nodes are often restricted to a few tens thus the grid is not well represented or totally neglected in the whole energy system analysis due to limited computational performance using such models. Here we introduce our new methodology of node-internal grid calculation representing the electricity grid in cost values based on strong correlations between peak load, grid cost and feed-in share of wind and photovoltaic capacity. We validate in our case study this approach using a 491 node model for Germany. This examination area is modelled as enclosed energy system to calculate the grid in a 100% renewable energy system in 2050 enabling maximum grid expansion. Our grid model facilitates grid expansion cost and reduces computational effort. The quantification of the German electricity grid show that the grid makes up to 12% of total system cost equivalent up to 12 billion € per year.

Summary Many power plants in Germany and Europe are approaching the end of their technical lifetime. Moreover, the increasing wind and solar power generation reduces the operation times of thermal power plants, making future investments in new generation capacity uncertain under current market conditions. Consequently, the future development of security of power supply is unclear. In this paper, we assess the impact of stochastic fluctuations in power plant availability, renewable generation, and grid load on the future security of supply in Germany. We model variations in power plant availability by application of a combined Mean‐reversion Jump‐diffusion approach. On the basis of that and using Monte‐Carlo methods, we simulate 300 different time series of availability. These profiles are fed into the fundamental power system model REMix, applied to evaluate the appearance of supply shortfalls in hourly resolution. We assess 6 scenarios for the year 2025, differing in renewable generation and demand profiles, as well as grid infrastructure. Geographical focus of the analysis is Germany, but the electricity exchange with its European neighbours is modelled as well. Our results show that the choice of the power plant availability profile can change the loss of load expectation and loss of load hours by up to 50%. However, the influence of load and renewable generation profiles is found to be significantly higher. Assuming that no new conventional power plants are built and existing plants are decommissioned at the end of their empirical lifetime, we identify supply gaps of up to 2.7 GW in Germany.

Abstract:Energy scenario analyses are able to provide insights into the future and possible strategies for coping with challenges such as the integration of renewable energy sources. The models used for analyzing and developing future energy systems must be simplified, e.g., due to computational constraints. Therefore, grid-related effects and regional differences are often ignored. We tackle this issue by presenting a new methodology for aggregating spatially highly resolved transmission grid information for energy system models. In particular, such approaches are required in studies that evaluate the demand for spatially balancing power generation and consumption in future energy systems. Electricity transmission between regions is crucial, especially for scenarios that rely on high shares of renewable energy sources. The presented methodology estimates transmission line congestions by evaluating the nodal price differences and then applies a spectral clustering on these particular link attributes. The objective of the proposed approach is to derive aggregated model instances that preserve information regarding electricity transmission bottlenecks. The resulting models are evaluated against observables such as the annual amount of redispatched power generation. For a selection of defined performance indicators, we find a significantly higher accuracy compared to the commonly used, spatially aggregated models applied in the field of energy scenario analysis.