The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation.

This paper presents a novel risk-based analysis framework designed to enhance the resilience of power distribution networks by leveraging a failure probability metric for assessing interruption likelihood. To simulate failure scenarios, Monte Carlo simulation is employed in conjunction with a decision tree approach. Additionally, the framework incorporates the calculation of the cost of energy not supplied. The effectiveness of two smart grid techniques – automatic fault location, isolation, and service restoration, along with demand side management – are evaluated within this framework. By implementing these techniques, the operational resilience of the distribution network can be substantially improved. Empirical validation using a modified IEEE 136-bus test system demonstrates the efficacy of the proposed framework in aiding distribution network operators in making informed investment decisions geared towards enhancing system resilience, with a strong emphasis on risk mitigation. Adopting this risk-based analysis framework enables operators to proactively identify vulnerable areas within the network and implement appropriate measures to mitigate potential disruptions. Ultimately, this approach leads to a more resilient and dependable power distribution infrastructure.