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Distribution networks are unbalanced three-phase networks due to one/two phase loads, distribution feeders, and distributed energy resources. Operating such DNs is challenging with constant network expansion/changes as a result of faults, maintenance, uncertain resources, etc. Distribution network operators (DSOs) may follow the spot pricing theory with distribution locational marginal pricing (DLMP) to maintain their commitment to flexible operation of DNs with higher social welfare. With critical response-time in DN operation, linear programming (LP) is the most practical model which must include the network losses. In this work, an LP model is proposed to calculate DLMPs in fully three-phase unbalanced DNs. Uncertainties in solar energy, wind power, energy storage systems, cables, overhead lines, shunt capacitors, voltage regulators, and transformers are addressed by scenarios in a detailed model by exploiting stochastic optimization. Through a proposed No U-Turn sampler (NUTS) based algorithm, probability density functions (PDFs) of DLMPs are calculated. These PDFs provide statistical information about the locational and temporal price risks. The numerical results showed promising performance of the proposed LP model and the NUTS-based algorithm in creating PDFs of DLMPs in a timely manner. DLMP price densities will be increasingly useful as DSOs seek flexible, low risk solutions from embedded generators and aggregators resources., Part of ISBN 978-1-6654-6441-3QC 20231130

The electrical grid is a safety-critical system, since incorrect actions taken by a power system operator can result in grid failure and cause harm. For this reason, it is desirable to have an automated power system operator that can reliably take actions that avoid grid failure while fulfilling some objective. Given the existing and growing complexity of power system operation, the choice has often fallen on deep reinforcement learning (DRL) agents for automation, but these are neither explainable nor provably safe. Therefore in this work, the effect of shielding on DRL agent survivability, validation computational time, and convergence are explored. To do this, shielded and unshielded DRL agents are evaluated on a standard IEEE 14-bus network. Agents are tasked with balancing generation and demand through redispatch and topology changing actions at a human timescale of 5 minutes. To test survivability under controlled conditions, varying degrees of scheduled unavailability events are introduced which could cause grid failure if unaddressed. Results show improved convergence and generally greater survivability of shielded agents compared with unshielded agents. However, the safety assurances provided by the shield increase computational time. This will require trade-offs or optimizations to make real-time deployment more feasible., Part of ISBN 9781665464413QC 20231106