فهرست مطالب azita azarfar

In this paper, an optimization model based on stochastic quadratic mixed integer programming to provide an integrated energy management of electricity and heat in electrical and thermal microgrids, taking into account the uncertainty of renewable energy sources, location of electricity generation sources and combined heat and power (CHP) along with energy and thermal storage systems and demand side management are provided in island operation and connected to the grid. A multi-objective function including minimization of energy loss, voltage deviation, cost of resource utilization, as well as reduction of renewable energy sources and reduction of installation cost is considered. The IEEE 69 bus distribution network was selected for analysis and coding was done in MATLAB software and CVX optimization package. The proposed model is also solved by the most powerful existing solver called Gurobi. The obtained results show the performance and accuracy of the proposed model.

In the optimization problems of intelligent distribution systems where there are many variables and parameters, providing an efficient algorithm that has the ability to converge and solve in large networks is one of the main challenges of researchers. In this paper, a compound integer quadratic optimization model for improving the performance of large-scale distribution network using demand-side management problem, energy storage system, battery-to-metro system, optimal control of OLTC and SVR tap-trans and distributed generation resources. Fossil and renewable are offered alongside capacitor and shunt reactors. The considered multi-objective function is a scenario-based stochastic model, which accurately models the uncertainties in renewable energy sources. According to the considered problems and also the model of large networks of 118 and 874 buses, most of the algorithms are not able to converge due to the existence of many variables. In this article, the optimal performance of the proposed hybrid evolutionary algorithm is shown on large distribution networks, which is able to reach global optimal solutions compared to similar algorithms.

In this paper, a bi-level optimization model is proposed for the coordinated management of integrated transmission and distribution networks. The problem of the security-constrained unit commitment as an upper-level problem to reduce operating costs, startup/shutdown costs, and no-load along with load shedding as a mixed integer linear programming model and the problem of optimal operation in independent distribution networks by considering renewable and non-renewable resources along with charging stations for electric vehicles as a lower-level problem to reduce the costs of purchasing power from the upstream network and reduce the costs of power outages. The resources and charging power of electric vehicle charging stations are considered a linear model. To solve the bi-level problem, the proposed lower-level model is modeled as Karush-Kahn-Tucker optimality conditions. Several different networks have been considered for validating the model and the proposed method, and the results obtained from the simulation prove the efficiency of the model and the proposed method in considering the coordinated operation of intelligent transmission and distribution networks. To show the superiority of the proposed method over other algorithms for solving multilevel models, the proposed method has been compared with decomposition algorithms, and the results show the superiority of the proposed method in terms of execution time and faster convergence.

Nowadays, with the use of devices such as fossil distributed generation and renewable energy resources and energy storage systems that are operated at the level of distribution networks, the problem of optimal reconfiguration has faced major challenges, so any change in the power of this resources can have different results in reconfiguration. Similarly, load changes during the day can lead to different results in reconfiguration, so it is necessary to provide a model that dynamically considers the daily load and models the uncertainties in renewable energy resources. The main issue is the reconfiguration that the distribution system operator must consider in the next 24 hours. This paper presents a mixed-integer second-order cone programming model to solve the problem of stochastic dynamic reconfiguration in distribution networks by including fossil and renewable energy resources, battery, electric vehicle parking lots, and demand-side management programs. The objective is to reduce the cost of load curtailment, reduce the cost of losses, reduce the cost of purchasing energy and reduce the cost of cutting off the power of renewable energy resources. The 33-bus distribution network is presented by defining different cases for the analysis of the proposed model, the results of which show the efficiency of the proposed model.

This paper presents a model for distribution network optimization considering a high penetration of photovoltaic (PV) sources and electric vehicle charging stations (EVCSs) based on on-load tap changing transformers (OLTC) and step voltage regulator (SVR), shunt capacitor (SC), and shunt reactor (ShR). The purpose is to prevent overvoltage due to power injection by PV sources and voltage drop due to EV charging in distribution networks. The proposed model is solved using a new hybrid algorithm called PSO-GA. Relevant studies show that with the increasing number of PSO replications, particle population variability is easily eliminated and placed in local optimization. The idea of ​​combining GA is based on the PSO introduced in this study. Crossover and mutations of GA are performed on the PSO population, which is useful for improving the overall optimal ability of particles and causing the algorithm to deviate from the local optimal point. Two different IEEE standard test networks are tested under different load scenarios to analyze the proposed model. The results reveal the performance of the proposed model.

Due to the high penetration of renewable energy resources and the direct impact on the power system, the issue of energy management has received more attention than researchers. Power-to-gas (P2G) system causes the surplus electricity generated from renewable energy resources in the network to be converted to gas and sold to the gas network, so energy management and profitability are a matter of particular importance, considering the two grids as a joint optimization of integrated energy systems. This paper presents a scenario-based stochastic mixed-integer linear programming (MILP) model to optimize integrated gas and electricity integrated systems considering natural gas distributed generation resources, P2G systems, energy storage systems, and electric vehicles. It aims to reduce the cost of purchasing energy and cut off the power of renewable energy resources. The 33-bus power distribution network and the 7-node natural gas network are considered for the analysis of the proposed model, and the proposed model is solved using the powerful Gurobi solver, considering various cases. The results of different cases show the performance of the proposed model.

Demand-side response in residential homes is responsible for significant changes in their electricity consumption patterns. Such systems are implemented to shift the load from peak hours to off-peak hours. This approach not only reduces the costs of consumer’s energy bills but also brings about many benefits such as postponing power system planning investments, improving network reliability, reducing unexpected outages, and so on. This paper introduces a new structure for managing the electrical energy consumption of residential homes via power aggregation by considering the consumers’ priorities in a microgrid. In this situation, the performance priorities of the controllable types of equipment are first sent along with the consumption information of the total electrical equipment to the power aggregator unit through a smart meter. After gathering all information from customers, scheduling is done by a power aggregator in which network constraints are considered. Finally, management programs are sent as a series of binary codes directly from the aggregator to the smart sockets with the help of the Internet of things (IoT) infrastructure. In fact, in this project, there is no need to use home energy management systems (HEMSs) for residential homes, and only smart meters are employed to send information. In this method, information is sent directly from the central control unit to the smart sockets using the IoT technology and the process of information by another separate unit is not needed. In other words, consumption planning for all consumer’s controllable devices is coordinated from the power aggregation’s point of view to minimize the energy cost of all consumers by taking the constraints of the distribution network and all consumption priorities into account. In this project, an IEEE standard 15-bus microgrid with 50 households (with the average consumption pattern of a 4-person household for 3 months) is used. For each family, there are 12 electrical devices, in which two of them (dishwasher and washing machine) are considered as controllable appliances, and all planning programs are done for scheduling their operating time. The time horizon is considered 24 hours consisting of 15-min time-steps. To better understand the results in different working conditions, six different scenarios are defined in this regard and the results are compared with each other. Finally, according to the simulation results based on the time of use (TOU) tariff defined for 2019 in Iran, it can be realized that by planning the consumption of controllable types of equipment, 42.1% of peak-load duration cost and 21.8% of the total cost of electricity consumption is saved.

Summary :Mining production planning is a very vital subject of mine design process. One of the most important issues in mine production planning is the cutoff grade which is simply a grade used to distinguish between ore and waste. Waste materials may either be left in place or sent to waste dump. Ore is sent to the mill for further processing. Lower cutoff grade causes higher amounts of ore to be processed and subsequently lower amounts of waste materials to be dumped resulted in fluctuations in the cash flow of a mining project. The main goal of the long-term production planning is to determine strategies to implement the cutoff grade and short-term production planning. One of the most important aspects of mine design is to determine the optimum cutoff grade. The optimum cutoff grade leads to maximize the profit or the Net Present Value (NPV). Maximizing of NPV is a non-liner programing problem that has been considered in the recent decades. The main factors involved in the Lane algorithm are the capacities of each part of the mine (e.g. extraction capacity of mine, refinery plant, and market), time value of currency and distribution grad of deposit. Since Lane algorithm calculation steps are very time consuming, In this study a novel technique namely Imperialism Competitive Algorithm (ICA) is used to determine the optimum cut-off grade. The results show that optimum cut-off grade obtained by ICA is more accurate and faster than other simulation algorithms. In this paper a novel optimization algorithm based on imperialist competitive algorithm (ICA) is used to determine the optimum cut-off grade in the open-pit mines.
One of the important aspects of open-pit mine design is determination of cutoff grade, by definition cutoff grade is the grade at which the mineral reserve ca no longer be mined and processed at profit. A cutoff grade is used to assign the destination of material exploited from the mine. This destinations are: (1) to mill, (2) to the waste dump and (3) to the stockpiles. In this paper, determining the optimal cutoff grade of ore to maximize the NPV due to mining limitations, concentration and refining is described by using ICA algorithm.
Methodology and Approaches :The ICA algorithm starts with an initial population. Each population in ICA is called country. Countries are divided in two groups: imperialists and colonies. In this algorithm the more powerful imperialist, have the more colonies. When the competition starts, imperialists attempt to achieve more colonies and the colonies start to move toward their imperialists. So during the competition the powerful imperialists will be improved and the weak ones will be collapsed. At the end just one imperialist will remain. In this stage the position of imperialist and its colonies will be the same. In this paper the cutoff grade of hypothetical deposit is calculated using ICA algorithm. This algorithm has 40 country, 6 imperialist and 34 colony. Finally the results is validated by dichotomous method.
Results and One of the important parameters of open-pit mine design is determination of cutoff grade. In this paper imperialist competitive algorithm is used to optimize the cutoff grade. Since the roulette wheel mechanism is not used In the ICA algorithm and only a probability density function (PDF) is needed to reach the answer, the ICA algorithm converges faster and better to the optimum point compare with other algorithms.

Singular systems behave more powerfully in termsof dynamical system modeling than ordinary state space systems. Since thealgebraic equations in singular models can describe the systems constraints,nonlinear singular systems can present a general method for modeling andcontrol of constrained dynamical systems. This paper discusses an adaptivecontrol for nonlinear singular systems which satisfy Lipschitz condition. Adaptivemethods for singular systems are hardly ever investigated in literatures;however they are very useful methods in practice because the adaptive mechanismduring the adaptive control can adjust the controller for a system with unknownstructures and parameters to improve the system performance. The presentedcontroller is composed of a state feedback approach with adaptive gains and amechanism to adjust the gains based on the Lyapunov stability theorem. Firstthe controller is designed to stabilize the system then it is extended for the trackingproblem. A simulation on a mobile robot singular model is provided toillustrate the effectiveness of the proposed control approach

In recent years the analysis of the constrained mechanical system by using singular models has been established. Singular models consist of both differential and algebraic equation so they are very useful for modeling constraint mechanical systems where the restrictions are usually introduced by algebraic equations. Due to the importance of singular systems، control approaches of such systems have attracted many interests. In this paper، we address an adaptive state feedback control approach for MIMO singular systems that makes the closed loop dynamics of this class of systems regular، impulse free and stable. The proposed controller has been implemented on a 3-link constrained robot manipulator which is modeled by singular systems. The control objective is that the Robot end-effector moves on a special surface with determined force applying to that. The wall surface limit the robot manipulator moves. The stability of proposed controller has been proved using Lyapunov theorem. Simulation results illustrate the effectiveness of presented controller