The sum-utility maximization problem is known to be important in the energy systems literature. The conventional assumption to address this problem is that the utility is concave. But for some key applications, such an assumption is not reasonable and does not reflect well the actual behavior of the consumer. To address this issue, the authors pose and address a more general optimization problem, namely by assuming the consumer's utility to be sigmoidal and in a given class of functions. The considered class of functions is very attractive for at least two reasons. First, the classical NP-hardness issue associated with sum-utility maximization is circumvented. Second, the considered class of functions encompasses well-known performance metrics used to analyze the problems of pricing and energy-efficiency. This allows one to design a new and optimal inclining block rates (IBR) pricing policy which also has the virtue of flattening the power consumption and reducing the peak power. We also show how to maximize the energy-efficiency by a low-complexity algorithm. When compared to existing policies, simulations fully support the benefit from using the proposed approach.
翻译:在能源系统文献中,已知利用最大化的总问题很重要。解决该问题的传统假设是,公用事业是串通的。但对于某些关键应用来说,这种假设是不合理的,不能很好地反映消费者的实际行为。为了解决这一问题,作者提出并解决了一个更普遍的优化问题,即假定消费者的效用是悬浮性的,并且属于某一类功能。据认为的功能类别由于至少两个原因非常吸引人。首先,与利用总量最大化有关的典型的NP-硬性问题被绕过。第二,考虑的功能类别包括用来分析定价和能源效率问题的众所周知的绩效指标。这样可以设计出一个新的和最佳的区块定价政策,其好处也是稳定电力消费和降低峰值。我们还表明如何通过低兼容性算法实现能源效率最大化。与现行政策相比,模拟完全支持使用拟议方法的好处。