Towards Structuring Smart Grid: Energy Scheduling, Parking Lot Allocation, and Charging Management

Nowadays, the conventional power systems are being restructured and changed into smart grids to improve their reliability and efficiency, which brings about better social, economic, and environmental benefits. To build a smart grid, energy scheduling, energy management, parking lot allocation, and charging management of plug-in electric vehicles (PEVs) are important subjects that must be considered. Accordingly, in this dissertation, three problems in structuring a smart grid are investigated. The first problem investigates energy scheduling of smart homes (SHs) to minimize daily energy consumption cost. The challenges of the problem include modeling the technical and economic constraints of the sources and dealing with the variability and uncertainties concerned with the power of the photovoltaic (PV) panels that make the problem a mixed-integer nonlinear programming (MINLP), dynamic (time-varying), and stochastic optimization problem. In order to handle the variability and uncertainties of power of PV panels, we propose a multi-time scale stochastic model predictive control (MPC). We use multi-time scale approach in the stochastic MPC to simultaneously have vast vision for the optimization time horizon and precise resolution for the problem variables. In addition, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool. Further, we propose cooperative distributed energy scheduling to enable SHs to share their energy resources in a distributed way. The simulation results demonstrate remarkable cost saving due to cooperation of SHs with one another and the effectiveness of multi-time scale MPC over single-time scale MPC. Compared to the previous studies, this work is the first study that proposes cooperative distributed energy scheduling for SHs and applies multi-time scale optimization. In the second problem, the price-based energy management of SHs for maximizing the daily profit of GENCO is investigated. The goal of GENCO is to design an optimal energy management scheme (optimal prices of electricity) that will maximize its daily profit based on the demand of active customers (SHs) that try to minimize their daily operation cost. In this study, a scenario-based stochastic approach is applied in the energy scheduling problem of each SH to address the variability and uncertainty issues of PV panels. Also, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool for the energy scheduling problem of a SH. Moreover, Lambda-Iteration Economic Dispatch and GA approaches are applied to solve the generation scheduling and unit commitment (UC) problems of the GENCO, respectively. The numerical study shows the potential benefit of energy management for both GENCO and SH. Moreover, it is proven that the GENCO needs to implement the optimal scheme of energy management; otherwise, it will not be effective. Compared to the previous studies, the presented study in this paper is the first study that considers the interaction between a GENCO and SHs through the price-controlled energy management to maximize the daily profit of the GENCO and minimize the operation cost of each SH. In the third problem, traffic and grid-based parking lots allocation and charging management of PEVs is investigated from a DISCO’s and a GENCO’s viewpoints. Herein, the DISCO allocates the parking lots to each electrical feeder to minimize the overall cost of planning problem over the planning time horizon (30 years) and the GENCO manages the charging time of PEVs to maximize its daily profit by deferring the most expensive and pollutant generation units. In both planning and operation problems, the driving patterns of the PEVs’ drivers and their reaction respect to the value of incentive (discount on charging fee) and the average daily distance from the parking lot are modeled. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing (SA) algorithm (QSA algorithm) and genetic algorithm (GA), respectively. We demonstrate that the behavioral model of drivers and their driving patterns can remarkably affect the outcomes of planning and operation problems. We show that optimal allocation of parking lots can minimize every DISCO’s planning cost and increase the GENCO’s daily profit. Compared to the previous works, the presented study in this paper is the first study that investigates the optimal parking lot placement problem (from every DISCO’s view point) and the problem of optimal charging management of PEVs (from a GENCO’s point of view) considering the characteristics of electrical distribution network, driving pattern of PEVs, and the behavior of drivers respect to value of introduced incentive and their daily distance from the suggested parking lots. In our future work, we will develop a more efficient smart grid. Specifically, we will investigate the effects of inaccessibility of SHs to the grid and disconnection of SHs in the first problem, model the reaction of other end users (in addition to SHs) based on the price elasticity of demand and their social welfare in the second problem, and propose methods for energy management of end users (in addition to charging management of PEVs) and model the load of end users in the third problem

Design of liquid-liquid extraction system

The unit operation of liquid-liquid extraction has developed enormously in the last 20 years and is now recognized as one of the most important techniques for the physical separation and refining of industrial liquids. For years the conventional apparatus used for liquid-liquid extraction has been towers employing countercurrent flow of the liquids and equipped with various means to increase the interfacial area. The countercurrent column consists of a tower through which flows a continuous phase while countercurrent to this flow is a stream of droplets (the dispersed phase). Provision is made for dispersing one phase with a settling region at each end of the tower. This arrangement corresponds to the single spray tower in absorption but in practice such a simple apparatus has a very low efficiency due to incomplete mixing and coalescing of the original droplets. A recent design for the improvement of liquid-liquid extraction equipment involves a column utilizing countercurrent flow, mechanical agitation, and baffled settling zones. In one case, the column is fitted with sieve plates that may be moved up and down relative to the liquid producing the agitation, while the liquids flow countercurrently through the plates. Settling takes place during the pauses between the strokes. The operation and construction or this column can be simplified by using stationary plates and agitating by imparting a reciprocating motion to the liquids relative to the plates. Another possible design is a column which consists of a number of compartments formed by a series of stator rings, with a rotating disc centered in each compartment and supported by a rotating shaft. This apparatus has been proved to be highly efficient, simple, and cheap to operate, and easy to maintain. The purpose of this thesis was to design a liquid-liquid extraction system containing (1) a pulse extraction column and (2) a rotating-disc contactor column for non-hazardous, simulated, nuclear fuel processing. The system was designed such that the two columns may be operated (1) separately, (2) as a countercurrent system, and (3) as a multiple contact system --Introduction, pages 1-2

Two-level Robust State Estimation for Multi-Area Power Systems Under Bounded Uncertainties

This paper introduces a two-level robust approach to estimate the unknown states of a large-scale power system while the measurements and network parameters are subjected to uncertainties. The bounded data uncertainty (BDU) considered in the power network is a structured uncertainty which is inevitable in practical systems due to error in transmission lines, inaccurate modelling, unmodeled dynamics, parameter variations, and other various reasons. In the proposed approach, the corresponding network is first decomposed into smaller subsystems (areas), and then a two-level algorithm is presented for state estimation. In this algorithm, at the first level, each area uses a weighted least squares (WLS) technique to estimate its own states based on a robust hybrid estimation utilizing phasor measurement units (PMUs), and at the second level, the central coordinator processes all the results from the subareas and gives a robust estimation of the entire system. The simulation results for IEEE 30-bus test system verifies the accuracy and performance of the proposed multi-area robust estimator

The Phase Transition of Non-minimal Yang-Mills AdS Black Brane

In this paper, we shall study the phase transition of non-minimal Einstein-Hilbert gravity and Yang-Mills term in AdS space-time. We couple the Ricci scalar to the Yang-Mills invariant to obtain a modified theory of gravity. A Black brane solution is introduced up to the first order of the term $RF^{(a)}_{\mu \alpha }F^{(a)\mu \alpha}$ in this model. Then, the phase transition of this solution will be investigated. Our investigations show that both the first and second order phase transitions exist in this model.Comment: 17 Pages, 12 figure

The phase transition of Rastall AdS black hole with cloud of strings and quintessence

In this paper, we introduce the black hole solution in Rastall theory of gravity in the presence of quintessence and the cloud of strings. Our investigations show that this model meets only second-order phase transition in four dimensions. While both the first and second order phase transitions are seen in five dimensions. Therefore, according to the AdS/CFT duality, the confinement-deconfinement phase transition only occurs in five dimensions for this model.Comment: 15 pages, 11 figures, , remove typos, references added, minor modifications, to appear in IJMP