Sharif Digital Repository

Available transfer capability (ATC) provides important information for power marketers and planning sectors in restructured power systems. The stochastic nature of power system behaviour, however, made ATC determination a difficult and complicated task. A stochastic framework has been established for ATC calculation with implication of uncertainties in transmission failures, hourly peak loads and system cascading collapse. The proposed ATC calculation framework is based on parametric bootstrap technique which enables generating random samples of system operating condition with uncertainty at a predefined confidence interval. The IEEE RTS-96 network is employed to demonstrate the... 

The substantial presence of different uncertainties in energy systems highlights the need for probabilistic analysis of operational and planning studies. Motivated by this fact, a new stochastic planning framework for energy hubs (EHs) is presented in this study based on the probability transformation concept. In the proposed framework, a measure of relative-likelihood impact is developed to estimate the importance of uncertainty sources in the studies, which, in turn, can remarkably reduce the overall complexity of stochastic problems. Step-by-step algorithm for implementation of the proposed framework on planning studies of an EH is also addressed in this study. Three different case... 

Time-varying electricity rates enable demand-side potentials, which provide an opportunity for distribution companies (DisCos) to hedge against the financial risk imposed by volatile spot market prices and uncertain customers' load. In particular, time-varying rates can be effective alternatives for at least a portion of costly forward contracts. This paper establishes a stochastic framework to determine optimal forward market purchases under time-varying rates. Various electricity rating strategies with different time intervals covering flat, time-of-use, and real-time pricing schemes are considered. The objective of the framework is to maximize DisCo's expected profit while the exposure... 

This paper presents a stochastic framework for short-term operation of a distribution company (disco). The proposed framework optimizes disco's operational decisions in two hierarchical stages. The first stage, called day-ahead operation stage (DAOS), deals with the operational decisions on purchases from the day-ahead market and commitment of distributed generation (DG) units. The objective of this stage is to minimize the expected operating cost while the financial risk exposed by uncertain real-time prices and loads is restricted to a given level. The model associated with this stage is based on the mixed-integer programming (MIP) format. The second stage, named real-time operation stage... 

This paper presents a stochastic framework to properly involve the uncertainties associated with demand in distribution system restoration (DSR) problem. To reach this goal, these uncertainties are represented as probabilistic scenarios corresponding to different load levels. Subsequently, the associated stochastic optimization problem is formulated such that it can be readily solved using dynamic programming approach. Moreover, a clustering technique is presented that enables the dynamic programming approach to find near-optimal solutions for the stochastic load restoration problem with reasonable computational effort. The proposed framework is implemented on a real-world test system, and... 

This article proposes a new three-stage stochastic framework for dealing with predictable two-phase natural disasters in distribution systems. This framework is a multiobjective optimization, in which the amount of curtailed energy, the number of switching actions, and the vulnerability of operational components are selected as the main criteria for decision-making process. The optimization problem is formulated in the form of a stochastic mixed-integer linear programming (MILP) problem. In this article, a windstorm event followed by flooding is analyzed as a two-phase natural disaster. In this regard, the uncertainties associated with gust-wind speed, floodwater depths, and load demands are... 

This article proposes a new three-stage stochastic framework for dealing with predictable two-phase natural disasters in distribution systems. This framework is a multiobjective optimization, in which the amount of curtailed energy, the number of switching actions, and the vulnerability of operational components are selected as the main criteria for decision-making process. The optimization problem is formulated in the form of a stochastic mixed-integer linear programming (MILP) problem. In this article, a windstorm event followed by flooding is analyzed as a two-phase natural disaster. In this regard, the uncertainties associated with gust-wind speed, floodwater depths, and load demands are... 

This paper investigates the optimal operation of distribution feeder reconfiguration (DFR) strategy in the smart grids with high penetration of plug-in electric vehicles (PEVs) and correlated wind power generation. The increased utilization of PEVs in the system with stochastic volatile behavior along with the high penetration of renewable power sources such as wind turbines (WTs) can create new challenges in the system that will affect the DFR strategy greatly. In order to reach the most efficiency from the PEVs, the idea of vehicle-to-grid (V2G) is employed in this paper to make a bidirectional power flow (either charging/discharging or idle mode) strategy when providing the main charging... 

Distribution feeder reconfiguration (DFR) is a precious operation strategy that can improve the system from different aspects including total cost, reliability, and power quality. Nevertheless, the high complexity of the new smart grids has resulted in much uncertainty in the DFR problem that necessities the use of a sufficient stochastic framework to deal with them. In this way, this paper proposes a new stochastic framework based on cloud theory to account the uncertainties associated with multiobjective DFR problem from the reliability point of view. Cloud theory is constructed based on fuzzy theory and probability idea. In comparison with the Monte Carlo simulation method, cloud models... 

The objective of this paper is to provide a stochastic framework to optimally avoid collision between a maneuverable spacecraft and a space object or debris. The satellite collision can be caused through a cyber-attack on a satellite by colliding it with a considered strategic satellite. Consequently, it is highly imperative that critical operational space assets be provided with autonomous collision avoidance systems. The collision avoidance methodology proposed in this paper will reduce the collision probability to an acceptable level and protect the satellite against indirect kinetic cyber-attacks initiated by designing optimal collision avoidance maneuvers using a stochastic model...