Concentrating solar power (CSP) tower technologies capture thermal radiation from the sun utilizing a field of solar-tracking heliostats. When paired with inexpensive thermal energy storage (TES), CSP technologies can dispatch electricity during peak-market-priced hours, day or night. The cost of utility-scale photovoltaic (PV) systems has dropped significantly in the last decade, resulting in inexpensive energy production during daylight hours. The hybridization of PV and CSP with TES systems has the potential to provide continuous and stable energy production at a lower cost than a PV or CSP system alone. Hybrid systems are gaining popularity in international markets as a means to increase renewable energy portfolios across the world. Historically, CSP-PV hybrid systems have been evaluated using either monthly averages of hourly PV production or scheduling algorithms that neglect the time-of-production value of electricity in the market. To more accurately evaluate a CSP-PV-battery hybrid design, we develop a profit-maximizing mixed-integer linear program (\({\mathcal {H}}\)) that determines a dispatch schedule for the individual sub-systems with a sub-hourly time fidelity. We present the mathematical formulation of such a model and show that it is computationally expensive to solve. To improve model tractability and reduce solution times, we offer techniques that: (1) reduce the problem size, (2) tighten the linear programming relaxation of (\({\mathcal {H}}\)) via reformulation and the introduction of cuts, and (3) implement an optimization-based heuristic (that can yield initial feasible solutions for (\({\mathcal {H}}\)) and, at any rate, yields near-optimal solutions). Applying these solution techniques results in a 79% improvement in solve time, on average, for our 48-h instances of (\({\mathcal {H}}\)); corresponding solution times for an annual model run decrease by as much as 93%, where such a run consists of solving 365 instances of (\({\mathcal {H}}\)), retaining only the first 24 h’ worth of the solution, and sliding the time window forward 24 h. We present annual system metrics for two locations and two markets that inform design practices for hybrid systems and lay the groundwork for a more exhaustive policy analysis. A comparison of alternative hybrid systems to the CSP-only system demonstrates that hybrid models can almost double capacity factors while resulting in a 30% improvement related to various economic metrics.

中文翻译：

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利用公用事业规模的光伏发电优化太阳能发电的调度

聚光太阳能塔技术利用太阳跟踪定日镜领域捕获来自太阳的热辐射。与廉价的热能存储（TES）配合使用时，CSP技术可以在白天或晚上以高峰市场价格供电。在过去的十年中，公用事业规模的光伏（PV）系统的成本已大大降低，从而导致白天的能源生产成本低廉。与单独的PV或CSP系统相比，PV和CSP与TES系统的混合具有以较低的成本提供连续稳定的能源生产的潜力。混合动力系统作为在全球范围内增加可再生能源组合的一种手段，在国际市场上越来越受欢迎。历史上，CSP-PV混合系统已使用每小时光伏发电的月平均值或调度算法进行了评估，而这些算法忽略了市场上的发电时间价值。为了更准确地评估CSP-PV电池混合设计，我们开发了一个利润最大化的混合整数线性程序（\（{\ mathcal {H}} \））确定具有小时小时保真度的各个子系统的调度时间表。我们提出了这样一个模型的数学公式，并表明解决该问题在计算上是昂贵的。为了提高模型的可处理性并减少求解时间，我们提供以下技术：（1）减小问题的大小；（2 ）通过重新制定公式和引入来加强（\（{\ mathcal {H}} \））的线性编程松弛（3）实施基于优化的启发式算法（可以产生（\（{\ mathcal {H}} \））的初始可行解，并且无论如何都会产生接近最优的解）。对于我们的48小时实例（\（{\ mathcal {H}} \），应用这些求解技术可使解决时间平均缩短79％）; 年度模型运行的相应求解时间减少多达93％，其中这种运行包括求解（\（{\ mathcal {H}} \））的365个实例，仅保留了前24 h解决方案，并将时间窗口向前滑动24小时。我们提供了两个地区和两个市场的年度系统指标，这些指标可为混合系统的设计实践提供依据，并为更详尽的政策分析奠定基础。替代混合系统与仅CSP系统的比较表明，混合模型几乎可以将容量因子提高一倍，而与各种经济指标相关的结果却可以提高30％。