概率框架效应下的无功补偿博弈
Reactive Power Compensation Game under Prospect-Theoretic Framing Effects
摘要：
无功补偿是当前和未来智能电力系统中的一个重要挑战。然而，在无功功率补偿的背景下，大多数现有的研究假设客户可以评估其补偿价值，即，无功功率单位，客观地说。在本文中，客户被假定为作出决定，属于无功功率协调。因此，这些客户评估其个人决策所产生的补偿价值的方式将影响整体网格性能。基于前景理论的框架效应，提出了一个行为分析框架，研究了客观价值和主观评价在无功补偿博弈中的作用。例如，这种效果允许客户优化其效用的主观值，其基本上相对于参考点框定客观效用。这个游戏使客户能够协调使用他们的电气设备来补偿无功功率。对于所提出的游戏，无论是使用预期效用理论（EUT）和PT考虑的客观情况下，通过一个学习算法，收敛到一个混合策略纳什均衡解决。此外，这个游戏的几个关键属性解析推导。仿真结果表明，在PT下，客户可能会做出不同于经典模型预测的决策。例如，使用说明性的两个客户的情况下，我们表明，PT客户将增加保守的策略（实现高功率因数）相比，传统的客户29%。对于具有三个客户的案例也观察到类似的见解。
英文摘要：
Reactive power compensation is an important challenge in current and future smart power systems. However, in the context of reactive power compensation, most existing studies assume that customers can assess their compensation value, i.e., Var unit, objectively. In this paper, customers are assumed to make decisions that pertain to reactive power coordination. In consequence, the way in which those customers evaluate the compensation value resulting from their individual decisions will impact the overall grid performance. In particular, a behavioral framework, based on the framing effect of prospect theory (PT), is developed to study the effect of both objective value and subjective evaluation in a reactive power compensation game. For example, such effect allows customers to optimize a subjective value of their utility which essentially frames the objective utility with respect to a reference point. This game enables customers to coordinate the use of their electrical devices to compensate reactive power. For the proposed game, both the objective case using expected utility theory (EUT) and the PT consideration are solved via a learning algorithm that converges to a mixed-strategy Nash equilibrium. In addition, several key properties of this game are derived analytically. Simulation results show that, under PT, customers are likely to make decisions that differ from those predicted by classical models. For instance, using an illustrative two-customer case, we show that a PT customer will increase the conservative strategy (achieving a high power factor) by 29% compared to a conventional customer. Similar insights are also observed for a case with three customers.
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