Assessment of Stress in Active Distribution Networks with Asset Dynamic Ratings

Active distribution networks are vulnerable to random disturbances and the severity of stress of disturbances can be increased with dynamic rating of network assets, level of penetration of intermittent distributed generation (DG), and rise in customer demand. Increased stress in a distribution network can lead to major system disturbances including blackouts. This paper investigates this problem to assess how vulnerable the active networks to stresses arisen through random outages, dynamic variation of network asset ratings, demand rise, and high penetration of intermittent DG. The Monte Carlo simulation is the main driver of the assessment which incorporates dynamic rating of network assets through probabilistic modeling. The stress of the active network is recognizes as the product of network stress and the customer stress of not supplying the energy. A case study is performed and the results suggest that the active network stress can be buffered by the increased penetration of wind through strategic stations. The buffer is more effective at stressed operating conditions than the less stressed operating conditions

Innovative Prepositioning and Dispatching Schemes of Electric Vehicles for Smart Distribution Network Resiliency and Restoration

Mobile power sources (MPS), such as electric vehicles (EVs), potentially improve distribution network (DN) restoration under extreme event conditions. However, employing EVs as a major power source is under researched, as is the prepositioning, routing, and dispatch of large numbers of EVs. This study proposes a three-stage optimization approach to achieve proactive prepositioning, dynamic routing, and dynamic power scheduling for effective assessment of resilience and needy restoration. First, EVs are prepositioned in the DN to enable swift pre-restoration and improve the survival of loads. Second, following an extreme event, EVs are dynamically routed in the DN and transportation system (TS) to improve system recovery. This stage also proposes a novel EV travelling model, bridging consumption rate and distance to study the efficacy of EV's state of charge (SOC) and the participation decision of the EV's user. Third, dynamic power scheduling of EVs is addressed, based on decisions made in the previous two stages. A mixed-integer programming model that addresses matters such as various timeframes of EV dispatch and DS operation, and the connection of road and power networks, is tested via case studies of a three-phase AC IEEE 123-node test system to demonstrate the effectiveness of the proposal

Probabilistic Optimisation of Generation Scheduling Considering Wind Power Output and Stochastic Line Capacity

Optimising the power flow in a system has been a challenge for decades. Due to the complexities that are introduced by new technologies, this problem is evolving. Lately, the effect of integrating wind turbines into the system has been taken into account when solving optimal power flow. However, transmission system constraints are usually modeled as fixed constraints using deterministic methods. Deterministic transmission line ratings have been shown to significantly underestimate the capability of the network. However, probabilistic line ratings are not used in optimization studies. In this paper, stochastic optimisation is used to consider the integration of wind turbines as well as probabilistic real time line capacities. It is shown that optimization considering probabilistic line ratings that lead to dynamic constraints in the OPF problem, represents the operational situation more accurately. This approach further reduces the optimum cost of system operation