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In this paper, a multi-stage model for expansion co-planning of transmission lines, Battery Energy Storages (BESs), and Wind Farms (WFs) is presented considering resilience against extreme weather events. In addition to High Voltage Alternating Current (HVAC) lines, Multi-Terminal Voltage Source Converter (MTVSC) based High Voltage Direct Current (HVDC) lines are planned to reduce the impact of high-risk events. To evaluate the system resilience against hurricanes, probable hurricane speed (HS) scenarios are generated using Monte Carlo Simulation (MCS). The Fragility Curve (FC) concept is utilized for calculating the failure probability of lines due to extreme hurricanes. Based on each hurricane damage, the probable scenarios are incorporated in the proposed model. Renewable Portfolio Standard (RPS) policy is modeled to integrate high penetration of WFs. To deal with the wind power and load demand uncertainties, a Chronological Time-Period Clustering (CTPC) algorithm is introduced for extracting representative hours in each planning stage. A deep learning approach based on Bi-directional Long Short-Term Memory (B-LSTM) networks is presented to forecast the yearly peak loads. The Mixed-Integer Linear Programming (MILP) formulation of the proposed model is solved using a Benders Decomposition (BD) algorithm. A modified IEEE RTS test system is used to evaluate the proposed model effectiveness.

In this paper, a dynamic (i.e., multi-year) hybrid model is presented for Transmission Expansion Planning (TEP) utilizing the High Voltage Alternating Current (HVAC) and multi terminal Voltage Sourced Converter (VSC)-based High Voltage Direct Current (HVDC) alternatives. In addition to new HVAC and HVDC lines, the possibility of converting existing HVAC transmission lines to HVDC lines is considered in the proposed model. High shares of renewable resources are integrated into the proposed hybrid AC/DC TEP model. Due to the intermittency of renewable resources, the planning of large-scale Energy Storage (ES) devices is considered. In order to accurately estimate the total TEP costs and hence capturing the scenarios of load and renewable generation uncertainty, using a clustering approach, each year of the planning horizon is replaced with four representative days. The proposed model is formulated as a Mixed-Integer Linear Programming (MILP) problem. Using Benders Decomposition (BD) algorithm, the proposed model is decomposed into a Master investment problem to handle the decision variables, and Sub-problems to check the feasibility of master problem solution and optimize the operation and ES investment cost. Three test systems are used as case studies to demonstrate the effectiveness of the proposed hybrid AC/DC TEP model.

Improvement of project scheduling is a complicated task due to numerous activities and various resources. Accordingly, the resource-constrained project scheduling problem (RCPSP) is an NP-hard problem that minimizes the time and cost of the project. In this paper, the goal is to model the problem under uncertainty in the availability of the resources and activities duration simultaneously. Subcontractors of the construction projects are motivated to form a coalition to reduce the makespan, increase the availability of the resources and maximize their profits. Initially, the robust optimization (RO) model is developed for the RCPSP based on the multi-objective optimization method in which the uncertainty is modeled in a set of scenarios. The behavior of subcontractors is considered by supper-additivity in cooperation, satisfaction level and stability of coalitions. The results of the proposed model demonstrate that the subcontractors can work together in a coalition and obtain more profit rather than working individually. Then, game theory methods such as Shapley Value, Max-Min core, and Equal Profit Method are applied to fairly allocate excess profit of the coalition.

This paper develops a new model for optimal placement of a combination of single-phase and three-phase Micro Phasor Measurement Units (μPMUs) in smart distribution networks with radial asymmetrical structure for complete observability. The presented model is formulated as a Binary Integer Linear Programing (BILP) problem. In order to take the full advantage of the zero injection properties to make distribution network observable, two types of Zero Injection Buses (ZIBs) are taken into account including Fully Zero Injection Buses (FZIBs) and Partially Zero Injection Buses (PZIBs). With regard to the location of a FZIB or a PZIB in the radial distribution network, an innovative technique is applied to reduce the number of μPMUs. In addition, the single μPMU outage contingency is incorporated in the model. The suggested model is examined on asymmetrical IEEE 13-bus and 37-bus and 123-bus test feeders for different cases and the obtained results are analyzed to confirm the presented model.

In this paper, a game framework for defending the power system against intelligent physical attacks is modeled by investigating the impacts of Energy Storages (ESs). For this modeling, Optimal Placement of Energy Storage (OPES) with regard to two main cases is investigated. In the first case, it is assumed that just one transmission lines are attacked. This case is equal to making the power system N-1 secure using OPES. In the second case, it is assumed that two transmission line are attacked. Therefore, this case is equal to making the power system N-2 secure using OPES. Moreover, two proposed Algorithms are implemented for limited budget allocation to the power system protection and recovery. A five-bus system as a common case study system is considered for evaluation of the presented model. The obtained results justify the effectiveness of considering OPES for defending the power system against targeted physical attacks.

Intelligent (a.k.a. smart or ‘flow control completion’) well (i-well) technology is being successfully used in thousands of wells worldwide. Dozens of companies are involved in manufacturing, modelling, installing, and optimizing i-well equipment. I-wells have their completion intervals divided into zones with packers, followed by regulation of production or injection in each zone using flow control devices. In production wells this balances flow profile as well as reduces inflow of unwanted fluids, overall resulting in increased oil or gas recovery. Finding the optimal locations of any given number of packers that improve the long-term production performance is a complex problem with few solution attempts published to-date. This paper is the first to offer an easy-to-implement, comprehensive solution to this packer placement optimization problem. The complexity of this problem comes from the fact that ideally the i-well's designed flow control performance should be optimized together with its packer placement configuration, which makes it a largely multi-variable, mixed-integer optimization problem often requiring from thousands to millions of iterations to solve. Furthermore, where there is a long-term optimization objective, e.g. ultimate asset value or oil recovery, each evaluation of an i-well design requires a separate reservoir model run that takes time. This easily makes the i-well optimization problem insurmountable. The solution approach developed in this paper rests on two unique findings: the first one shows that optimality of a given i-well flow control performance reduces its subsequent, packer placement optimization problem to a simpler, perturbation problem which is much easier to solve. The second finding is a novel method relating an arbitrary, i-well flow performance to a mathematically equivalent non-linear electric circuit performance. This allows the well performance to be fast-solved semi-analytically using methodology from the circuit design theory. Firstly in this paper, reduction of the i-well optimization problem to the packer placement -caused perturbation one is carried out by evaluating the impact of the annular flow isolation disturbance on the well/reservoir flow state. The relatively small weight of such disturbance on the scale of a simulation time-step is explained and further demonstrated on a test model. Secondly, an i-well flow model is presented as a nodal system, equivalent to a non-linear electric circuit, and subsequently solved using an approach adopted from the circuit theory. This solution is verified by numerical modelling. Lastly, these two findings enabled development of a fast, packer placement optimization algorithm that is described, verified, and finally successfully applied to a basic, test model. The packer placement optimization algorithm presented in this paper, as well as the novel i-well nodal analysis approach, complement and complete the multiple i-well performance optimization workflows published to-date, all of which use the simplifying ‘infinite number of packers’ assumption for the lack of anything better. This algorithm will be of immediate use to the well and reservoir engineers involved in design, installation, and evaluation of i-wells.

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The sitting and sizing of Battery Energy Storage (BES) devices as flexible options is addressed to cover the intermittency of Renewable Energy Sources (RESs), mitigate lines congestion, and postpone the need for new transmission lines and power plants installation. For efficient handling of RESs uncertainties, and operational flexibility, the upward and downward Flexible Ramp Spinning Reserve (FRSR) are modeled. Besides, the Low-Carbon Policy (LCP) is considered in the objective function of the proposed Transmission, Generation, and Storage Expansion Planning (TGSEP) model. A hierarchical clustering method that can preserve the chronology of input time series throughout the planning horizon periods is developed to capture the short-term uncertainties of load demand and RESs. The short-term operational flexibility requirements make the joint long-term transmission and generation planning a high computational problem. Therefore, the Mixed-Integer Linear Programming (MILP) formulation of the model is solved using an accelerated Benders Dual Decomposition (BDD) method. The IEEE RTS test system is utilized to validate the effectiveness of the proposed joint expansion planning model.

A new autonomous outflow control device is developed to choke back the injection fluid into natural/induced fractures and mitigate the disproportional injection of fluid into the thief zone and potentially creating short-circuit to the nearby producer wells. This paper will present an overview of the flow loop performance testing, and demonstrates the design consideration and integration with completion design and its benefit by reservoir modelling. The bi-stable devices should be installed in several compartments in the wells and operate as normal outflow control valves initially. When the injected flowrate flowing through a bi-stable valve exceeds a designed threshold, the bi-stable valve will autonomously move to another position to choke back the injection of fluid at that specific compartment. This allows the denied fluid to be distributed among the valves installed at neighbouring compartments. This performance enables the operator to minimise the impacts of natural fractures on the injected fluid conformance and to control the growth of thermal fractures while improving the efficiency of the injection well systems. The flow performance of the bi-stable valve has been validated and the flow behaviour can be simulated in the reservoir model. Static flow modelling has been used to establish the valve setting and packer placement in the well section and to demonstrate an improved distribution of the water injection and the effect of restricting water to the thief zone on the nearby producer oil recovery. A reservoir modelling method has been established to evaluate the bi-stable device performance in reservoir environments and compared with outflow control devices (OCDs) and open hole completions. Due to the uncertainty of heterogeneous reservoirs and the potential for dynamic changes of injection properties, the simulation study showed that with a lower pressure drop compared to OCDs, the fluid front can be managed more efficiently to achieve the desired sweep and maximised ultimate recovery. The first autonomous injection valve that restricts water into dilated/propagated fractures is developed. This device removes most of the deficiencies of OCDs and eliminates the requirements of running PLT and the prescribed well interventions e.g. closing/opening of sliding sleeves. Instead, it provides operators with a tool that enables the optimised completion to deliver optimum water injection techniques autonomously.

In this study, we analyze three portfolio selection strategies for loss-averse investors: semi-variance, conditional value-at-risk, and a combination of both risk measures. Moreover, we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto evolutionary algorithm 2 to tackle this optimization problem. The effectiveness of these algorithms is compared with two alternatives from the literature from five publicly available datasets. The computational results indicate that the proposed algorithms in this study outperform the others for all the examined performance metrics. Moreover, they are able to approximate the Pareto front even in cases in which all the other approaches fail.

This article, focuses on optimal placement of phasor measurement units (PMU) in transmission and sub-transmission networks of Mazandaran province, as a practical network. An algorithm for optimal PMU placement by using genetic algorithm is presented. The objective function of presented model is minimizing the number of PMUs in a network and observability rules are assumed as the problem constraints. As the genetic algorithm is a stochastic optimization tool for optimizing problems, the algorithm is executed many times to find the optimal solutions. The effectiveness of the presented model is verified using standard networks including IEEE 14, 30, 39, 57 and 118 bus test systems. Finally, the result of applying the model on practical Mazandaran province network indicates that the presented model can be used in real networks and make the sub-transmission network observable with minimum number of PMUs.

Balancing plays a major role in performance improvement of robotic manipulators. From an optimization point of view, some balancing parameters can be modified to decrease motion cost. Recently introduced, this concept is called optimal balancing: an umbrella term for static balancing and other balancing methods. In this method, the best combination of balancing and trajectory planning is sought. In this note, repetitive full cycle motion of robot manipulators including different subtasks is considered. The basic idea arises from the fact that, upon changing dynamic equations of a robotic manipulator or cost functions in subtasks, the entire cycle of motion must be reconsidered in an optimal balancing problem. The possibility of cost reduction for a closed contour in potential fields is shown by some simulations done for a PUMA-like robot. Also, the obtained results show 34.8% cost reduction compared to that of static balancing.

The balancing of robotic systems is an important issue, because it allows for significant reduction of torques. However, the literature review shows that the balancing of robotic systems is performed without considering the traveling trajectory. Although in static balancing the gravity effects on the actuators are removed, and in complete balancing the Coriolis, centripetal, gravitational, and cross-inertia terms are eliminated, it does not mean that the required torque to move the manipulator from one point to another point is minimum. In this chapter, “optimal balancing” is presented for the open-chain robotic system based on the indirect solution of open-loop optimal control problem. Indeed, optimal balancing is an optimal trajectory planning problem in which states, controls, and all the unknown parameters associated with the counterweight masses or springs must be determined simultaneously to minimize the given performance index for a predefined point-to-point task. For this purpose, on the base of the fundamental theorem of calculus of variations, the necessary conditions for optimality are derived which lead to the optimality conditions associated with the Pontryagin’s minimum principle and an additional condition associated with the constant parameters. In this chapter, after presenting the formulation of the optimal balancing and static balancing, the obtained optimality conditions are developed for a two-link manipulator in details. Finally the efficiency of the suggested approach is illustrated by simulation for a two-link manipulator and a PUMA-like robot. The obtained results show that the proposed method has dominant superiority over the previous methods such as static balancing or complete balancing.

The optimal design of Advanced Well Completions (AWCs) is an important step in field development planning nowadays. However, determination of optimum design of (Autonomous) Inflow Control Devices [(A)ICDs] and their integration with Annular Flow Isolations (AFIs) in an advanced well completed across a heterogeneous reservoir is a challenge which has not been addressed properly yet. Most used, variable strength (A)ICD completion design, workflows by industry are based on trial and error evaluations of a static well-reservoir models. Several researchers also suggested relating the strengths of the devices to near wellbore static reservoir properties such as permeability of the encountering layers. Both of these approaches do not capture the dynamic interactions between the well and constantly changing reservoir conditions. Moreover, neglecting mutual dependency of AFI location and (A)ICD design in the traditionally step-by-step (A)ICD-AFI design workflows, may result in a design delivering low overall productivity throughout the field life. A novel, integrated (A)ICD-AFI completion design workflow is presented in this paper. The workflow simultaneously optimises (A)ICDs strengths and number plus locations of AFIs in the horizontal and/or multilateral wells based on field's production lifetime. The approach includes stochastic optimisation algorithm coupled with a reservoir simulator and an in-house completion design programming code. The code is developed to automatically generate various (A)ICD-AFI completion configurations required to determine the optimum completion design while honouring practical concerns and economics. A novel AFI modelling technique developed allows integration of AFIs in the optimisation process. The application of the new and the traditional workflows are illustrated using heterogeneous reservoir simulation models. A significant improvement in total oil production for the wells, designed by the new workflow, was achieved comparing with the wells designed by the traditional workflows. The study aims to help well engineers in designing optimal advanced well completion delivering maximum possible field's added-value.