A new approach for power system black-start decision-making with vague set theory

Power system restoration after a large area outage involves many factors, and the procedure is usually very complicated. A decision-making support system could then be developed so as to find the optimal black-start strategy. In order to evaluate candidate black-start strategies, some indices, usually both qualitative and quantitative, are employed. However, it may not be possible to directly synthesize these indices, and different extents of interactions may exist among these indices. In the existing black-start decision-making methods, qualitative and quantitative indices cannot be well synthesized, and the interactions among different indices are not taken into account. The vague set, an extended version of the well-developed fuzzy set, could be employed to deal with decision-making problems with interacting attributes. Given this background, the vague set is first employed in this work to represent the indices for facilitating the comparisons among them. Then, a concept of the vague-valued fuzzy measure is presented, and on that basis a mathematical model for black-start decision-making developed. Compared with the existing methods, the proposed method could deal with the interactions among indices and more reasonably represent the fuzzy information. Finally, an actual power system is served for demonstrating the basic features of the developed model and method

A Policy optimization-based Deep Reinforcement Learning method for data-driven output voltage control of grid connected solid oxide fuel cell considering operation constraints

Solid oxide fuel cells (SOFCs) have many applications in microgrids, but they often face the challenge of maintaining the fuel utilization within a safe range, which affects their lifespan. To address this issue, we propose a Data-driven Output voltage control (DDD-OVC) approach that treats the SOFC output voltage controller as an intelligent agent. The agent can design a reward function that incorporates the fuel utilization constraint and learn the optimal control policy through offline training. The objective is to optimize both the SOFC output voltage control performance and lifetime. Furthermore, we develop a policy optimization-based Deep Reinforcement Learning (PO-DRL) algorithm that adopts the idea of proximal policy optimization to enhance the learning speed and convergence of the agent, as well as the policy stability and quality of DDD-OVC. We validate the effectiveness of our method on a 5kW SOFC model