Sharif Digital Repository

The increasing demand for clean and nonfossil energy has escalated the integration of renewable power sources into the distribution system. Renewable distributed generators (DGs) have the potential to reduce the environmental impact when the integration into the distribution system is carefully optimized. However, serious technical issues will be raised when the integration is not properly implemented. This chapter presents optimal siting and sizing of renewable DG units within distribution systems. Both deterministic and probabilistic models of renewable-based DG units will be discussed in details along with characteristics of well-known renewable generators. The effects of DGs on... 

This paper proposes a constrained stochastic successive convex approximation (CSSCA) algorithm to find a stationary point for a general non-convex stochastic optimization problem, whose objective and constraint functions are non-convex and involve expectations over random states. Most existing methods for non-convex stochastic optimization, such as the stochastic (average) gradient and stochastic majorization-minimization, only consider minimizing a stochastic non-convex objective over a deterministic convex set. The proposed CSSCA algorithm can also handle stochastic non-convex constraints in optimization problems, and it opens the way to solving more challenging optimization problems that... 

Consistent growth in the volume and dynamic behavior of traffic mandates new requirements for fast and adaptive resource allocation in metro networks. We propose a dynamic resource allocation technique for adaptive minimization of spectrum usage in metro elastic optical networks. We consider optical transmission as a service specified by its bandwidth profile parameters, which are minimum, average, and maximum required transmission rates. To consider random traffic events, we use a stochastic optimization technique to develop a novel formulation for dynamic resource allocation in which service level specifications and network stability constraints are addressed. Next, we employ the elegant... 

In this paper, the joint problem of project selection and project scheduling under uncertain environment is formulated, analyzed, and solved by multistage stochastic programs. First of all, a general mathematical formulation which can address several versions of the problem is presented. A multi-period project selection and scheduling problem is introduced and modeled by multistage stochastic programs, which are effective for solving long-term planning problems under uncertainty. A set of scenarios and corresponding probabilities is applied to model the multivariate random data process (costs or revenues, available budget, chance of success). Then, due to computational complexity, a scenario... 

Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time.... 

Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Multi-period project portfolio selection problems can be modeled by multistage stochastic programs. These models utilize a set of scenarios and corresponding probabilities to model the multivariate random data process (costs or revenues, available budget, chance of success). For most practical problems, the optimization problem that contains all possible scenarios is too large. Due to computational complexity, this program is often approximated by a model involving a (much) smaller number of scenarios. The scenario reduction algorithms determine a subset of the initial scenario set and... 

In this paper, an adaptive random search method, based on continuous action-set learning automata, is studied for solving stochastic optimization problems in which only the noisecorrupted value of a function at any chosen point in the parameter space is available. First, a new continuous action-set learning automaton is introduced and its convergence properties are studied. Then, applications of this new continuous action-set learning automata to the minimization of a penalized Shubert function and pattern classification are presented. © Sharif University of Technology  

This paper deals with energy management of microgrids during unscheduled islanding events, initiated by disturbances in the main grid. In these situations, the main challenge is uncertainty about duration of disconnection from the main grid. In order to tackle this issue, a stochastic framework is proposed for optimal scheduling of microgrid resources over this period. The presented framework addresses the prevailing uncertainties of islanding duration as well as prediction errors of demand and renewable power generation. According to this framework, the probability distribution of islanding duration needs to be estimated, instead of predicting its exact value. The objective is to minimize... 

This chapter investigates implementation of some parking lots for a plug-in electric vehicle (PEV) aggregator to participate in energy market. Herein, behaviors of the PEVs’ drivers regarding their cooperation with the aggregator with respect to the introduced incentive (value of discount on charging fee of PEVs) are modeled. The considered incentive includes the value of discount on the charging fee of PEVs’ batteries. In addition, the capability of parking lots for transacting electrical energy is modeled based on the hourly arrival/departure time of PEVs to/from the parking lots and the hourly state of charge (SOC) of PEVs’ batteries. Also, the degradation of PEVs’ batteries is modeled... 

In this paper, we consider the problem of cross-layer radio resource management in heterogeneous networks (HetNets) with flexible backhaul, which aims at minimizing the total transmit power of base stations (BSs) while guaranteeing the average end-To-end data rate requirement of each data flow. We formulate the problem as a two-Timescale stochastic optimisation, where the long-Timescale control variables are flow control, routing control and interference coordination, while the short-Timescale control variable is instantaneous beamforming within each cell. Using a stochastic cutting plane (SCP) method, we propose a cross-layer queue-state information (QSI) aware radio resource management... 

In this paper, a stochastic framework including two hierarchical stages is presented for the operation of distribution systems with high penetrations of wind power. In the first stage, termed Day Ahead Market Stage (DAMS), power purchases from day-ahead (DA) market and commitment of distributed generations (DGs) are determined. The DAMS model is formulated as a mixed integer linear programming (MILP) optimization problem. The uncertainty in predictions of wind generation, real time prices, and load profile are included in the optimization problem according to a scenario-based stochastic programming approach. The risk encountered due to the uncertainties is also taken into account. The... 

Penetration of renewable energy resources in power systems and their uncertain natures have caused some challenges in a Real-Time Markets (RTMs) such as price forecasting and balancing issues. Independent System Operator's (ISO's) introduced flexible ramp products in RTMs to overcome this problem. In this paper, a novel method is presented for scheduling flexible ramp requirements in RTM considering Demand Response (DR). In this method, net load uncertainty is modeled using robust optimization (RO) technique in which each period is defined by some scenarios and RO is used to model the uncertainty of each scenario. In order to verify the effectiveness of the method, a typical case study is... 

This paper presents a stochastic framework for day-ahead scheduling of microgrid energy storage systems in the context of multi-objective (MO) optimization. Operation cost of microgrid in normal conditions and load curtailment index in case of unscheduled islanding events (initiated by disturbances in the main grid) are chosen as main criteria of the proposed scheme. In practice, duration of disconnection from the upstream network is unknown in unscheduled islanding incidents and cannot be predicted with certainty. To properly handle the uncertainties associated with time and duration of such events as well as microgrid load and renewable power generation, stochastic models are involved in... 

In natural gas transmission networks, the efficiency of daily operation is strongly dependent on our knowledge about the customer future demands. Unavailability of accurate demand forecasts makes it more important to be able to characterize the loads and quantify the corresponding uncertainty. A planning strategy for natural gas transmission networks under future demand uncertainty is addressed, which involves coupling of a gas transmission network dynamic simulator with the stochastic optimization framework. Loads from a gas-fired power plant are studied where the loads are characterized by a number of uncertain parameters, and unscented transform is utilized for uncertainty propagation.... 

Providing backhaul connectivity for macro and pico base stations (BSs) constitutes a significant share of infrastructure costs in future heterogeneous networks (HetNets). To address this issue, the emerging idea of flexible backhaul is proposed. Under this architecture, not all the pico BSs are connected to the backhaul, resulting in a significant reduction in the infrastructure costs. In this regard, pico BSs without backhaul connectivity need to communicate with their nearby BSs in order to have indirect accessibility to the backhaul. This makes the radio resource management (RRM) in such networks more complex and challenging. In this paper, we address the problem of cross-layer RRM in... 

Nature, as a rich source of solutions, can be an inspirational guide to answer scientific expectations. Seed dispersal mechanism as one of the most common reproduction method among the plants is a unique technique with millions of years of evolutionary history. In this paper, inspired by plants survival, a novel method of optimization is presented, which is called Fertile Field Algorithm. One of the main challenges of stochastic optimization methods is related to the efficiency of the searching process for finding the global optimal solution. Seeding procedure is the most common reproduction method among all the plants. In the proposed method, the searching process is carried out through a... 

In this paper, we propose efficient wireless power transfer (WPT) policies for various practical scenarios in wirelessly powered communication networks (WPCNs). First, we consider WPT from an energy access point (E-AP) to multiple energy receivers (E-Rs). We formulate the problem of maximizing the total average received power of the E-Rs subject to power constraints of the E-AP, which is a non-convex stochastic optimization problem. Using eigenvalue decomposition techniques, we derive a closed-form expression for the optimal policy, which requires the distribution of the channel state information (CSI) in the network. We then propose a near-optimal policy that does not require this knowledge...