Online Algorithms for Dynamic Matching Markets in Power Distribution Systems

This letter proposes online algorithms for dynamic matching markets in power distribution systems. These address the problem of flexible loads with renewable generation, objective maximizing social welfare exchange system. More specifically, two are proposed generation-load scenarios: (i) when mean generation is greater than load, and (ii) condition reversed. With intuition that performance such degrades increasing randomness supply demand, properties assessing algorithms. First property convergence to optimality (CO) as underlying customer goes zero. The second deviation from optimality, which measured a function standard loads. algorithm first scenario shown satisfy CO varies linearly variation deviation. We then show satisfies plus an offset under certain condition.