Volatile Renewable Energy Research Articles

Green light for bidirectional charging? Unveiling grid repercussions and life cycle impacts

Bidirectional charging, such as Vehicle-to-Grid, is increasingly seen as a way to integrate the growing number of battery electric vehicles into the energy system. The electrical storage capacity in the system can be enhanced by using electric vehicles as flexible storage units. However, large-scale applications of Vehicle-to-Grid may require significant expansion of distribution grids. Previous studies lack a comprehensive environmental assessment of related impacts. Contributing to this research gap, this article combines techno-economic grid simulations with scenario-based Life Cycle Assessments. The case study focuses on rural distribution grids in Southern Germany, projecting the repercussions of different charging scenarios by 2040. Besides a Vehicle-to-Grid scenario, a mixed scenario of Vehicle-to-Home, Vehicle-to-Grid, and direct charging is investigated. Results indicate that Vehicle-to-Grid charging increases grid impacts due to higher charging simultaneities and power losses, especially when following spot market prices. Despite these challenges, the secondary use of battery electric vehicles as storage units can offset adverse environmental effects. Bidirectional charging allows for higher use of volatile renewable energies and can accelerate their integration into the power system. When considering these diverse environmental effects, bidirectional charging scenarios show overall lower impacts on climate change per battery electric vehicle compared to direct charging. The insights provided are valuable for researchers, industry, utilities, and policymakers to understand the potential positive and negative impacts of large-scale battery electric vehicle integration. The article highlights the most influential parameters that should be considered before large-scale penetration.

System friendliness of distributed resources in sustainable energy systems

The increasing share of volatile renewable energy generation in current and future energy systems which does not follow the demand, makes it necessary to shift the synchronization tasks to the system and demand level. The intended demand side consumption management is usually incentivized via financial mechanisms. However, the optimum design of related incentives is still unclear. It is usually discussed under the term “system friendliness” which in turn still lacks a broadly accepted definition. In this article we introduce a new technical definition of “system friendliness” and develop comprehensive and holistic related indicators. For that, we introduce the burden concept for energy systems which represents the amount of technical infrastructure required for stable operation. We use the concept of a hypothetical system energy storage to derive indicators that are capable of quantifying the system friendliness of a given point-of-interest. The developed indicators are independent of regulations or market environments which enables comparability of results between studies and different systems. In a case study we demonstrate our newly developed methodology and exemplary examine decentral district energy storages. Our findings demonstrate that today’s regulatory environments that usually support residential self-consumption maximization are counterproductive. Instead, we show that dynamic end-user prices coupled with dynamic feed in tariffs are able to incentivize an almost 100% system friendly behavior of distributed prosumers.

Assessment of green hydrogen production by volatile renewable energy under different SSPs scenarios in China

In the context of increasing global concerns about climate change and sustainable energy, the production of green hydrogen (GH) from volatile renewable energy (VRE) sources, such as solar and wind, is a natural choice for China to meet its climate change mitigation goals. However, there are a number of challenges in current research on the assessment of green hydrogen potential. This study evaluates the potential of GH production through VRE at the regional level in China, under the various scenarios of Shared Socioeconomic Pathways (SSPs). For this purpose, a power generation prediction model suitable for VRE is proposed to predict the energy supply capacity for GH production in six regions of China, which combined Multilevel Discrete Wavelet Decomposition and Bi-directional Long Short-Term Memory with attention mechanism (MDWD - AMBiLSTM). The total amount of GH could potentially reach up to 428.01 Mt under the SSP1, with the northern region leading at the regional level at 104.21 Mt. Furthermore, the study evaluated the potential for GH promotion in six regions of China between 2023 and 2035 b y combining the three dimensions of GH production potential, economic evaluation, and environmental contribution. This study tentative identify priority areas and development scenarios for promoting GH production in China, and made policy recommendations for the promotion of green hydrogen in disparate areas.

How wind forecast updates are balanced in coupled European intraday markets

AbstractThe market coupling of adjacent market zones is a key element in terms of coping with rising levels of uncertainty in the European energy system due to the adoption of more volatile renewable energy sources. An adequate analysis of this issue yet requires an appropriate consideration of uncertainty along with a detailed modelling of market coupling. Thus, we present a novel approach combining a comprehensive unit commitment and dispatch model with a sophisticated methodology to reflect the uncertainty of wind power generation by considering forecast updates for the German market region in the market clearing process. Subsequently, we investigate the impact of uncertainty on cross‐border electricity flows in a European case study. Our results show that all adjacent market zones and all relevant technologies significantly contribute to balancing forecast updates in Germany, underlining the benefits of intraday market coupling. Further, we observe that a joint and unrestricted intraday market is the most cost‐efficient market solution. Finally, our analyses show that more extensive market coupling is indispensable for the further integration of renewable energy sources in the future.

Will Hydrogen Be a New Natural Gas? Hydrogen Integration in Natural Gas Grids.

Hydrogen is similar to natural gas in terms of its physical and chemical properties but does not release carbon dioxide when burnt. This makes hydrogen an energy carrier of great importance in climate policy, especially as an enabler of increasing integration of volatile renewable energy, progressive electrification, and effective emission reductions in the hard-to-decarbonize sectors. Leaving aside the problems of transporting hydrogen as a liquid, technological challenges along the entire supply chain can be considered as solved in principle, as shown in the experimental findings of the Hydrogen Innovation Program of the German Technical and Scientific Association for Gas and Water. By scaling up production and end-use capacities and, most importantly, producing hydrogen in regions with abundant renewable energy, hydrogen and its applications can displace natural gas at affordable prices in the medium term. However, this substitution will take place at different rates in different regions and with different levels of added value, all of which must be understood for hydrogen uptake to be successful.

Enhancing the economic viability and reliability of renewables based electricity supply through Power-to-Gas-to-Power with green hydrogen

This study explores the role of green hydrogen in the Power-to-Gas-to-Power process as a solution to grid instability caused by the rise of volatile renewable energy sources. A comprehensive numerical model is developed to evaluate the economic viability and reliability of electricity supply, using key performance indicators such as system levelized cost of electricity and loss of power supply probability. Through simulations based on hourly renewable energy production profiles for a scenario with an average power demand of 100 MW, the effectiveness of batteries and green hydrogen is compared. Findings indicate that while batteries are suited to photovoltaic systems, green hydrogen excels in wind energy applications, particularly in wind-solar hybrid setups. Here, green hydrogen substantially enhances the power supply reliability (loss of probability reduced from 0.23 to 0.06) and achieves a competitive system levelized cost of electricity at 0.157 USD/kWh. Sensitivity analysis regarding system size and cost variations further highlights green hydrogen's potential, providing strategic insights for improving grid stability and economic efficiency.

Flexibility Potential in the Austrian Building Sector

What would happen if 15% of the Austrian building stock in 2030 – both refurbished and newly-built – would use flexible heating/cooling systems to offer their flexibility on a market? How much annual residual loads from volatile renewable energy sources (RES) could be absorbed? Our simple agent-market model says: ~5% of RES surpluses and around 2% of RES residual loads. The cost of this operation is additional energy demand of about 30%, indicating an efficiency of approx. 70% in using these volatile supply peaks in flexible building operation. In this model, buildings are agents offering additional electricity consumption from pre-emptive HVAC operation, effectively using the building mass as storage, their energy demand is modelled in a simple thermal RC model. The grid flexibility demand is derived from future residual load scenarios and the offered flexibility depends both on signal parameters, mainly signal frequency and duration, and on key building parameters indoor temperature comfort bounds, building mass and thermal envelope quality.

Planning Tools for Decentralized Heat Supply: Modeling the Effects of Volatile Renewable Energies

An increasing share of decentralized feed-in is necessary for the transition of district heating networks but it comes with various challenges. Software tools for modelling, simulation and optimization allow a theoretical examination of those challenges and possible solutions. Some of these have been used and further developed in the research project ZellFlex. One selected examination is the flexibility of supply temperatures by varying setpoint temperatures for a decentral solar thermal system (500 m²) integrated into an existing district heating network. The analysis of the supply temperature distribution in the network showed: A decrease of the solar thermal feed-in set point temperature by 5 K compared to the supply temperature of the central heat producer seemed acceptable in terms of security of supply, while a 10 K reduction comes with a high risk of undersupply. Furthermore, it was discovered that the points of time with the lowest supply temperatures at the consumers were just before and after they were provided with solar heat due to specific effects concerning temperature loss of the fluid.

A data-driven operating improvement method for the thermal power unit with frequent load changes

In the near and medium term, thermal power generation will still play an important peak-shaving role in providing grid connection to volatile renewable energy. Thermal power units need to adjust from the current load to the given load within a given time period, which provides space for operational improvement. In this paper, we propose an operating improvement method for the thermal power unit based on real-time monitoring data by observation dividing, feature construction and selection, clustering, and machine learning. The unit operation data is categorized into three feature subsets based on domain knowledge, which is used to distinguish different functions of historical data. Subsequently, the optimal feature subset and observations are selected for building regression models, including linear regression (LR), ensemble tree regression (ETR), neural network regression (NNR), and support vector regression (SVR). R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE) are adopted to test the performance of the proposed regression model on the real-time monitoring data of a thermal unit, which has 80,000 observations of 36 different variables. Compared with benchmark methods, the proposed method has lower regression error in the numerical experiment. Thus, we can thereby improve the efficiency of operational management based on the built learning model. Furthermore, in response to peak shaving requirements, the proposed method in this article considers operational optimization over time periods containing multiple time points compared to the traditional operational optimization perspective. With the continuous arrival of monitoring data, the above method can be updated in a timely manner based on the new database to address model adjustments caused by unit aging and other factors.

Challenges of Large Converter-Fed Synchronous Machines for Variable-Speed Pumped Hydro Storage

The green energy transition of electrical energy production is leading to an increasing share of total energy production for volatile renewable energy sources, mainly wind and solar power. To handle this volatile production, flexible and efficient energy storage is required. The development of high-power converters has enabled the generation of variable-speed pumped hydro storage power plants, combining the so-far-unequalled energy storage capacity of classical pumped-storage hydro power plants and the recently increased operation requirements. The introduction of large-scale converters has led to new challenges in the overall design of power plant systems. This paper intended to take a closer look at a large-scale converter-fed synchronous generator, especially the distribution of the current and voltage harmonics caused by the converter in the implemented generation system. Thereby, holistic design considerations for an ideal loss distribution as well as possible measures to limit the effects of harmonic coupling at the generator shaft and bearings are discussed. Furthermore, basic considerations of harmonic emission to the connected network are described. These topics are addressed by analyzing on-site measurements at an 85 MVA converter-fed synchronous generator with a voltage source inverter, underpinned with the theoretical background.

RAMS assessment approach of self-consistent energy system in highway service areas

Self-consistent energy system (SCES) that integrates volatile renewable energy, challenges power system operation of highway service areas. How to evaluate its resilience and energy efficiency is a key issue for the entire SCES. In this paper, we investigate the assessment approach of SCES. Based on the electricity consumption model consisting of different functional sections of SCES in service areas, by deriving the corresponding three-level indicators from the different functional sections, we propose a new SCES assessment approach from four aspects: reliability, availability, maintainability, and safety (RAMS). Simulation results show that the proposed approach can comprehensively and accurately assess the RAMS of SCES, provides data support for improving the resilience and energy efficiency of SCES, and gives a guide for the development of SCES.

Comparison of Different Coupling Modes between the Power System and the Hydrogen System Based on a Power–Hydrogen Coordinated Planning Optimization Model

Hydrogen is receiving unprecedented momentum and is projected to meet a sizable share of the final energy demand in the future. The coupling between the hydrogen and power systems can help integrate volatile renewable energy, reduce curtailment, and realize long-term energy storage. Different coupling modes are being proposed, yet quantitative comparisons are absent. To fill this gap, this paper firstly summarizes the system composition of potential power–hydrogen coupling modes and analyzes their advantages and disadvantages. Then, a model for power–hydrogen coordinated planning optimization is proposed for quantitative analysis. Southern Xinjiang is chosen as a representative of the northwestern area in China, which has plentiful renewable resources and a relatively small local load at present, for a case study. Through result analysis, it is found that the mode of power–hydrogen coupling at the source side, either for in situ utilization or for long-distance transport via pipelines, shows better economic competitiveness. The discussion provides suggestions and a reference to policymakers for formulating infrastructure or industry plans in advance to better accommodate the rapidly developing renewable energy.

Post-Lithium Batteries with Zinc for the Energy Transition

The energy transition is only feasible by using household or large photovoltaic powerplants. However, efficient use of photovoltaic power independently of other energy sources can only be accomplished employing batteries. The ever-growing demand for the stationary storage of volatile renewable energy poses new challenges in terms of cost, resource availability and safety. The development of Lithium-Ion Batteries (LIB) has been tremendously pushed by the mobile phone industry and the current need for high-voltage traction batteries. This path of global success is primarily based on its high energy density. Due to changing requirements, other aspects come to the fore that require a rebalancing of different technologies in the “Battery Ecosystem”. In this paper we discuss the evolution of zinc and manganese dioxide-based aqueous battery technologies and identify why recent findings in the field of the reaction mechanism and the electrolyte make rechargeable Zn-MnO2 batteries (ZMB), commonly known as so-called Zinc-Ion batteries (ZIB), competitive for stationary applications. Finally, a perspective on current challenges for practical application and concepts for future research is provided. This work is intended to classify the current state of research on ZMB and to highlight the further potential on its way to the market within the “Battery Ecosystem”, discussing key parameters such as safety, cost, cycle life, energy and power density, material abundancy, sustainability, modelling and cell/module development.

The role of hydrogen for a greenhouse gas-neutral Germany by 2045

This paper aims to provide a holistic analysis of the role of hydrogen for achieving greenhouse gas neutrality in Germany. For that purpose, we apply an integrated energy system model which includes all demand sectors of the German energy system and optimizes the transformation pathway from today's energy system to a future cost-optimal energy system. We show that 412 TWh of hydrogen are needed in the year 2045, mostly in the industry and transport sector. Particularly, the use of about 267 TWh of hydrogen in industry is essential as there are no cost-effective alternatives for the required emission reduction in the chemical industry or in steel production. Furthermore, we illustrate that the German hydrogen supply in the year 2045 requires both an expansion of domestic electrolyzer capacity to 71 GWH2 and hydrogen imports from other European countries and Northern Africa of about 196 TWh. Moreover, flexible operation of electrolyzers is cost-optimal and crucial for balancing the intermittent nature of volatile renewable energy sources. Additionally, a conducted sensitivity analysis shows that full domestic hydrogen supply in Germany is possible but requires an electrolyzer capacity of 111 GWH2.

Regression model-based adaptive receding horizon control of soft open points for loss minimization in distribution networks

This study proposes a method of using a soft open point (SOP) to flexibly connect different distribution networks. A novel operation strategy of SOP using a model predictive control (MPC) framework that adheres to the adaptive receding horizon control rule is developed to effectively respond to volatile renewable energy resources. Notably, the proposed method models the plant corresponding to voltage and network losses as a linear time-variant system, which provides better performance than the conventional MPC method in reducing network losses and improving voltage profile. To minimize the need for network observation of distribution system operators, we propose a process of deriving voltage-to-power and network loss-to-power sensitivity using a polynomial regression model. The effectiveness of the proposed strategy is verified on the modified IEEE 33-bus test systems, and its superiority is demonstrated through performance comparison with the existing general MPC method.

Impact of forecast uncertainty and electricity markets on the flexibility provision and economic performance of highly-decarbonized multi-energy systems

To enable the widespread integration of volatile renewable energy for decarbonization, electricity systems will need to become significantly more flexible to absorb power fluctuations. Therefore, current fossil-fueled flexible power generation will have to be complemented and partially replaced by decentralized flexibility. To address flexibility challenges, multi-energy system (MES) is a promising concept. In this paper, a MES design and operation optimization model is developed considering flexibility provision. MES technologies are harnessed to provide flexibility to the electricity grid in the form of frequency control reserves after mitigating the impact of load and generation forecast errors within the MES. The developed optimization model is applied to a neighborhood MES in Switzerland, participating in the day ahead and intraday electricity spot markets and the daily and weekly tenders for frequency control reserves via the ancillary services market. Results show that the MES designed to achieve 80% decarbonization offers up to 15–45% of its available capacity as flexible reserves to the surrounding electricity grid after reserving 2–10% of its available capacity to mitigate the impact of forecast errors. Battery storage systems (BSS) and heat pumps (HPs) offer flexibility and revenues from flexible reserves provide economic benefits to operations. However, the revenues offered by current electricity markets are not attractive enough to incentivize the MES design. Short ancillary service delivery periods and attractive ancillary service remuneration rates combined with large BSS capacity and high decarbonization targets foster flexible reserves.

Improving Wind Speed Uncertainty Forecasts Using Recurrent Neural Networks

For integration of growing amounts of volatile renewable energy in the European electricity system, reliable weather prognosis gains importance. But, depending on weather conditions, forecast reliability of wind speed for predicting wind power can vary drastically with time. Thus, relevance of risk-aware system operation strategies is increasing based on wind speed uncertainty a measure of which is provided by the standard deviations of ensemble forecasts of the German Weather Service. However, lacking validity of this measure is known as a long-standing problem.Therefore, this work investigates how machine learning based on a suitably selected set of physical quantities of weather ensemble data as well as historic wind data allows for a more realistic uncertainty quantification. A recurrent neural network (RNN) based sequence-to-sequence architecture is implemented and probabilistic wind speed forecasts are generated for a region in northern Germany.The results are evaluated and compared with the forecasts of the German Weather Service thereby revealing improved validity of such deep-learning based uncertainty measures.

Two-Timescale Dynamic Energy and Reserve Dispatch With Wind Power and Energy Storage

The integration of volatile renewable resources and energy storage entails making dispatch decisions for conventional coal-fired units and fast-response devices in different timescales. This paper studies intraday dynamic energy-reserve dispatch following a two-timescale setting. The coarse timescale determines the hourly reference output and reserve allocation, which offers a sufficient backup for the fine-timescale operation; the fine timescale determines the adjustment of gas-fired units and energy storage system every 15 minutes in response to the actual wind power. A stochastic dynamic programming method is proposed to make decisions at the coarse timescale while guaranteeing the robust feasibility of the fast process via vertex scenarios of uncertainty set and bounding the state-of-charge intervals for energy storage; the fast-response actions at the fine timescale are updated using a truncated rolling-horizon optimization, which incorporates the cost-to-go functions calculated at the coarse timescale to prevent the fast decisions from being myopic. Case studies on the modified IEEE 5-bus system and 118-bus system validate the proposed framework.

Make or buy: IT-based decision support for grid imbalance settlement in smarter electricity networks

Decision (support) systems are a particularly important type of information system to energy informatics. A key challenge in energy informatics is that electricity supply must be in balance with demand at all times. More volatile renewable energy sources increase the relevance of electricity network balancing, i.e., imbalance settlement. Typically, electricity distribution network operators bought balancing power from external service providers (Buy option). Interestingly, however, more local energy resources help smarter electricity networks develop a Make option, as in our real-world evaluation. Choosing the better decision alternative within the relevant timeframes challenges human decision-making capabilities. Therefore, this research proposes a model-based decision system to improve the operators’ decisions concerning Make or Buy under various levels of data quality represented by availability, granularity, and timeliness. The study reports savings up to 40% of costs for imbalance settlement supporting ambitious development efforts by the municipality we study in our real-world evaluation.

High-Frequency Mode Shape Dependent Flame-Acoustic Interactions in Reheat Flames

Abstract Gas turbines featuring sequentially staged combustion systems offer excellent performance in terms of fuel flexibility, part load performance and combined-cycle efficiency. These reheat combustion systems are therefore a key technology for meeting fluctuating power demand in energy infrastructures with increasing proportions of volatile renewable energy sources. To allow the high operational flexibility required to operate in this role, it is essential that the impact of thermoacoustic instabilities is minimized at all engine load conditions. In this case, high-frequency thermoacoustic instabilities in the second “reheat” combustion stage are investigated. Reheat flames are stabilized by both auto-ignition and propagation and, as a result, additional thermoacoustic driving mechanisms are present compared with more conventional swirl-stabilized combustors. Two self-excited thermoacoustic modes have been observed in a 1 MW reheat test rig at atmospheric pressure, one which exhibits limit-cycle behavior while the other is only intermittently unstable. The underlying driving mechanisms for each individual mode have been investigated previously and, in this paper, the two modes are directly compared to understand why these instabilities are each associated with different driving phenomena. It is shown that, due to the different flame regimes present in the reheat combustor, the potential for flame-acoustic coupling is highly dependent on the thermoacoustic mode shape. Different interactions between the flame and acoustics are possible depending on the orientation of the acoustic pressure nodes and antinodes relative to the auto-ignition- and propagation-stabilized flame regions, with the strongest coupling occurring when an antinode is located close to the auto-ignition zone. This provides insight into the significance of the different driving mechanisms and contributes to the ongoing development of models to allow prediction and mitigation of thermoacoustic instabilities in reheat combustion systems, which are crucial for reliable combustor designs in the future.