Matching supply and demand of electricity network-supportive flexibility: A case study with three comprehensible matching algorithms

Due to an ongoing energy transition, electricity networks are increasingly challenged by situations where local electrical power demands are high but local generation is low and vice versa. This finally leads to a growing number of technical problems. To solve these problems in the short-term, the electrical power of load and generation must be adjusted as available flexibility. In zonal electricity systems, one often discussed concept to utilize flexibility is local flexibility markets. Based on auction theory, we provide a comprehensible framework for the use of network-supportive flexibility in general. In this context, we discuss the problem of matching supply and demand. We introduce three matching approaches that can be applied and adapted for different network situations. In addition to a qualitative description of the three approaches, we present a case study of an exemplary distribution network and explore different scenarios to demonstrate the utility of the algorithms. We compare the three approaches on a qualitative level with quantitative inputs from the case study. The comparison considers the specific cost, flexible energy, ensured demand coverage, data minimization, computational effort and the transferability of the three approaches.