Grid-tied solar is governed by a variety of complex regulations. Since a higher solar penetration imposes indirect costs on the grid, these regulations generally limit the aggregate amount of grid-tied solar, as well as the compensation its owners receive. These regulations are also increasingly limiting solar's natural growth by preventing users from connecting it to the grid. One way to address the problem is to partially deregulate solar by allowing some solar generators to participate in the electricity market. However, day-ahead electricity markets require participants to commit to selling energy one day in advance to ensure system stability and avoid price volatility. Thus, to operate in the day-ahead market, solar generators must solve a solar commitment problem by determining how much solar energy to commit to sell each hour of the next day that maximizes their revenue despite the uncertainty in next-day solar generation. We present a probabilistic approach to addressing the solar commitment problem that combines a solar performance model with an analysis of weather measurement and forecast data to determine a conditional probability distribution over next-day solar generation outcomes, which we use to determine solar energy commitments each hour that maximize expected revenue. We show that, as the deviation penalty for over-committing solar increases, our probabilistic approach enables increasingly more savings than a deterministic approach that simply trusts weather measurements and forecasts.

并网太阳能受各种复杂法规的约束。由于较高的太阳能普及率会给电网带来间接成本，因此这些法规通常会限制并网太阳能的总量以及所有者获得的补偿。这些法规还通过阻止用户将太阳能连接到电网来日益限制太阳能的自然​​增长。解决该问题的一种方法是通过允许一些太阳能发电机参与电力市场来部分放松对太阳能的管制。但是，日前的电力市场要求参与者承诺提前一天出售能源，以确保系统稳定并避免价格波动。因此，要在日前市场上运营，太阳能发电机必须解决太阳能承诺问题通过确定第二天的每个小时要出售多少太阳能来最大化他们的收入，尽管第二天的太阳能发电存在不确定性。我们提出了一种解决太阳能承诺问题的概率方法，该方法将太阳能性能模型与天气测量和预测数据的分析相结合，以确定第二天太阳能发电结果的条件概率分布，我们将用来确定每小时的太阳能承诺使预期收入最大化。我们表明，随着过度使用太阳能的偏差损失增加，与仅信任天气测量和预报的确定性方法相比，我们的概率方法可以节省更多费用。