1. Introduction
To achieve climate goals, the transition to renewable energy is imperative, but the inherent volatility of wind and solar power poses a fundamental challenge to grid stability. This paper directly confronts the groundbreaking critique by H.-W. Sinn, who argued that to smooth out this volatility, the required pumped storage capacity would be "several orders of magnitude" greater than Germany's existing capacity, thereby relegating renewables to a secondary role dependent on conventional power plants for support. The authors present a counterargument, proposing a three-pronged strategy—excess capacity, smart meters, and optimization technologies—to drastically reduce storage requirements and enable a 100% wind and solar power system, potentially even expanding to meet broader energy demands.
2. The Volatility Problem and Sinn's Challenge
The core disadvantage of wind and solar energy lies in their dependence on variable weather conditions, leading to fluctuating power output. This creates a mismatch between power generation ($P_v$) and demand ($P_d$). Sinn's analysis highlights the enormous scale of energy storage required to buffer these fluctuations and concludes that this is economically and practically infeasible, thus necessitating fossil fuels as backup. The central argument of this paper is to challenge this conclusion by redefining the parameters of the problem.
2.1. Quantifying Volatility and Energy Storage Demand
Volatility is defined as fluctuations around the annual average. The required energy storage capacity $E_{sf}^{max}$ is defined as the difference between the maximum and minimum values of the integrated net fluctuating power $E_{sf}(t) = E_{vf}(t) - E_{df}(t)$, where $E_{vf}$ and $E_{df}$ are the fluctuating components of volatile generation and demand, respectively.
3. Proposed Solution Framework
The authors propose a synergistic three-pronged approach to reduce effective volatility, thereby decreasing the energy storage requirements calculated by Sinn.
3.1. Excess Capacity (Overbuilding)
部署超过平均需求所需的风能和太阳能容量（$P_{va} > P_{da}$），可确保即使在次优条件下也能产生足够的电力。这减少了发电短缺的深度和频率，平滑了 $E_{vf}(t)$ 曲线。
3.2. Smart Meter and Demand Side Management
Smart demand response enabled by smart meters allows consumption ($P_{df}$) to be shifted to align with periods of high generation. This "load shaping" proactively reduces the net fluctuation $P_{sf} = P_{vf} - P_{df}$, effectively using demand as a virtual energy storage resource.
3.3. Technical Optimization: Low-Wind Turbines and Low-Light Photovoltaics
Hardware that goes beyond standard efficiency optimization. Using turbines designed for lower wind speeds and photovoltaic panels efficient under diffuse light (e.g., perovskite or bifacial cells) can expand the power generation curve, reduce zero-output periods, making generation more predictable and less "peaky".
4. Mathematical Framework and Results
The analysis is based on a clear mathematical model and applied to actual grid data from Germany in 2019.
4.1. Power Balance Equation
The fundamental equations of the system are:
4.2. Proportional Analysis and Application of 2019 Data
Using 2019 data: $P_{da} = 56.4$ GW, measured $\hat{P}_{va} = 18.9$ GW. To meet demand solely with wind and solar generation, the generation must be scaled by a factor $s = P_{da} / \hat{P}_{va} \approx 3$. The key assumption is that the fluctuation patterns scale linearly. Applying the proposed three strategies within this scaled model, the calculated $E_{sf}^{max}$ is significantly reduced compared to Ziehn's baseline, indicating its feasibility.
Key data points (2019, Germany)
Average electricity demand ($P_{da}$): 56.4 GW
Average volatile generation ($\hat{P}_{va}$): 18.9 GW
Required scaling factor ($s$): ~3.0
5. Critical Analysis and Industry Perspective
Core Insights
Lustefeld's paper is not merely a technical rebuttal; it represents a pivotal shift in the strategic perspective on grid decarbonization, moving from a storage-centric approach to one grounded in systems engineering. The true breakthrough lies in recognizing that the problemis not merelyabout smoothing volatile supply, but about dynamically managing therelationship between supply and demand.. This aligns with modern grid architecture principles emphasizing "hybrid systems" and flexibility, as highlighted by institutions like the National Renewable Energy Laboratory (NREL).
Logical Thread and Advantages
Its logic is compelling: 1) Acknowledge the daunting energy storage calculations presented by Sinn. 2) Introduce three non-storage levers (overbuilding, smart demand, better technology). 3) Mathematically demonstrate how these levers directly reduce the storage gap. Its advantage lies in using Germany's real, granular (15-minute) data—a case of high renewable energy penetration—which lends credibility to the analysis. The focus on technology choices (low-wind turbines) is particularly astute, moving beyond financial models to touch on hardware innovation.
Defects and Shortcomings
However, the paper has significant blind spots. Firstly,Linear Scaling Assumptionis a major simplification. Deploying three times the capacity does not simply triple the output pattern; geographical diversification and grid congestion will produce nonlinear effects. Secondly, itunderestimates grid integration costs. Overbuilding will lead to large-scale wind and solar curtailment during peak generation periods, which will undermine asset economics unless paired with ultra-cheap energy storage or hydrogen production—a point emphasized in recent MIT and Princeton "Net-Zero America" studies. Third, the social and regulatory feasibility of widespread demand-side management is downplayed.
Insights that can be acted upon
For policymakers and investors, the conclusion is clear:Stop focusing solely on energy storage.The combination method is the key:
- Formulate regulations for flexibility: Mandate the promotion of smart meters and create a demand response market, similar to the models in the UK or California.
- Invest in niche technologies: Fund R&D for low-light photovoltaic and low-wind turbines, not just incremental efficiency improvements of standard models.
- Planning for Overbuilding and Curtailment: Use "green hydrogen" production facilities as strategic sinks for surplus renewable energy, turning costs into potential revenue streams.
6. Technical Details and Experimental Insights
The analysis relies on decomposing the power data into average and fluctuating components. Figure 1 in the paper (referenced but not shown here) typically plots the integrated fluctuation energy of demand over time, $E_{df}(t)$, showing its cumulative deviation from the average. The "required storage" $E_{sf}^{max}$ is visually the vertical distance between the peak and trough of the net fluctuation energy curve $E_{sf}(t)$ after applying scaling and strategic adjustments. The results show that with the proposed measures, this peak-to-trough distance—and thus the required storage capacity—is significantly smaller than in a simple fluctuation-matching scenario.
7. Analytical Framework: A Simplified Case Study
Scenario: A regional power grid with an average demand of 1 GW. Historically, variable generation averaged 0.4 GW and was highly volatile. Traditional (Sinn) method: Scale the power generation to 1 GW. The resulting net fluctuation $E_{sf}(t)$ is large, requiring large-scale energy storage. Integrated (Lustfeld) method: 1. Overbuilding: 安装2.5 GW的容量。平均发电量变为>1 GW，使 $E_{vf}$ 曲线趋于平缓。 2. Smart demand: Shift 0.2 GW of industrial load (e.g., electric vehicle charging, water heating) to peak generation periods, reducing $P_{df}$ during off-peak hours. Superior Technology: Utilize wind turbines with a capacity factor of 15% at low wind speeds (standard turbines are 5%) to eliminate part of the generation gap. Result: The amplitude of the modified $E_{sf}(t)$ curve is significantly reduced. The calculated $E_{sf}^{max}$ may be 60-70% lower than that from traditional methods, demonstrating this principle without the need for complex simulations.
8. Future Applications and Research Directions
The framework opens up several key pathways:
- Multi-energy systems: Applying this logic to sector coupling—using surplus electricity for heating (power-to-heat), transportation (electric vehicles), and hydrogen production (power-to-gas). This creates flexible demand sinks capable of absorbing excess power generation.
- AI-optimized scheduling: Integrates machine learning (similar to techniques used for optimizing computational physics and other complex systems) to predict power generation and implement real-time dynamic pricing for demand response.
- Geographic and Technological Portfolio Optimization: Extend the model to optimize the portfolio of onshore/offshore wind, PV, and CSP, as well as the siting of low-wind turbines across Europe, to minimize continent-scale variability.
- Long-Duration Energy Storage Integration: Combine this method with emerging long-duration energy storage (e.g., flow batteries, compressed air) to handle residual, multi-day fluctuation events.
9. References
- Sinn, H.-W. (2017). Buffering volatility: A study on the limits of Germany's energy revolution. European Economic Review, 99, 130-156.
- German Federal Ministry for Economic Affairs and Energy. (2020). Energy Storage Monitoring Report.
- Fraunhofer Institute for Solar Energy Systems (ISE). (2020). Energy Charts [Data set]. Retrieved from https://www.energy-charts.de
- International Energy Agency (IEA). (2020). World Energy Outlook 2020. Paris: IEA Publications.
- National Renewable Energy Laboratory (NREL). (2021). Hybrid Renewable Energy Systems. Retrieved from https://www.nrel.gov/research/hybrid-systems.html
- Jenkins, J. D., Luke, M., & Thermstrom, S. (2018). Getting to Zero Carbon Emissions in the Electric Power Sector. Joule, 2(12), 2498-2510.
- MIT Energy Initiative. (2019). The Future of Energy Storage.