{'fraction_non_alphanumeric': 0.02930728241563055, 'fraction_numerical': 0.0017761989342806395, 'mean_word_length': 5.590643274853801, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Security-and time-constrained OPF applications

Optimal power flow (OPF) and security-constrained OPF (SCOPF) studies are important tools for both planning and operation of power supply systems. Conventional gradient-based SCOPF methods are commonly used for that purpose, but when analysed system is overstressed with severe multiple contingencies, conventional SCOPF methods may fail to converge. In such cases, meta-heuristic SCOPF methods can be used to identify “critical constraints” and help the convergency of conventional methods. As the time available for devising emergency control actions is typically limited, the paper first evaluates computational performance of used meta-heuristic SCOPF method (particle swarm optimization method) and then demonstrates how a suitable handling of constraints, i.e. penalization of objective function, can improve efficiency and practical value of the solutions found by the meta-heuristic SCOPF method. This approach is denoted as “time-constrained SCOPF analysis” and is illustrated by considering thermal limits of overloaded lines in a widely-used IEEE 14-bus test network.