Behnam Mohammadi-ivatloo

The need for effective technologies to deal with environmental issues is one of the basic approaches within the smart grid concept. Electrical vehicles (EV) are promising technology that provides multiple advantages for both utility and consumers. One of the main challenges of EVs is charging management, which effects on efficiency and popularity of EVs operation. For handling this issue, a new concept named battery swapping station (BSS) for more integration of EVs in microgrids is introduced in this paper. In addition to market participation, BSS as a large energy storage system can provide adequate reserve for microgrid in islanded operation. So, in this paper, a novel microgrid operation scheduling consisting of BSS is proposed. The problem is formulated as a bi-level problem: the upper-level minimizes microgrid operation cost including generation and purchasing cost, while BSS profit maximization is the target of lower-level. Participation of BSS in reserve market beside the local generation units, causes the microgrid capability in operating in islanding mode for multiple hours. The proposed model is implemented on the 10-bus microgrid test system where the results shows its effectiveness.

This paper presents a new scenario based method to prevent voltage instability under wind and load uncertainties considering correlation among wind turbines and loads. The correlated load and wind scenarios are generated based on the correlation matrix as well as Normal and Rayleigh probability density functions. Electrical distances are used to generate the correlation matrix among loads. Then, the preventive voltage instability problem is formulated two-stage stochastic programming problem. Control facilities include rescheduled active and reactive power of generation units, load shedding and demand response. The considered control facilities are classified into two different categories based on the stage of decision making. These categories are named here-and-now and wait-and-see. Demand response, load shedding and reactive power output of power plants are wait-and-see facilities, whereas active power of power plants is considered as here-and-now facility. The proposed method is tested on the standard IEEE 118-bus test system. Comprehensive analyses are carried out demonstrating the impact of uncertainties and correlations, as realistic load and wind modeling, on the problem.

ABSTRACT The objective of dynamic economic dispatch (DED) problem is to determine the generation schedule of the committed generation units, which minimizes the total operating cost over a dispatch period, while satisfying a set of constraints. The effect of valve-points and prohibited operating zones (POZs) in the generating units' cost functions makes the DED a highly non-linear and non-convex optimization problem with multiple local minima. Considering the ramp-rate limits and transmission losses, makes the DED problem even more complicated. Hence, proposing an effective solution method for this optimization problem is of great interest. This paper presents a novel heuristic algorithm to solve DED problem of generating units, by employing hybrid immune genetic algorithm (IGA). To illustrate the effectiveness of the proposed approach, four test systems consisting different number of generating units are studied. The valve-point effects, POZs and ramp-rate constraints along with transmission losses are also considered in simulation cases. The results obtained through the proposed method are compared with those reported in the literature. These results substantiate the applicability of the proposed method for solving the constrained DED problem with non-smooth cost functions. Index Terms—Dynamic economic dispatch , immune-genetic algorithm , Prohibited operation zone (POZ) , valve-point effect I. INTRODUCTION Generally, the economic dispatch of power system can be categorized into static economic dispatch (SED) and dynamic economic dispatch (DED). The SED optimizes the system ob-jective function (total fuel cost in general) in specified time and does not take into account the fundamental relation of system be-tween the different operating times. The DED takes into account the connection of different operating times by considering ramp rate constraints. The DED is one of the important optimization problems used in power systems to obtain the optimal operation schedule of the committed units over the entire dispatch period. Considering the dynamic constraints like ramp rate limits makes the DED problem more complicated. One way to simplify the solution of DED is to consider it as a sequential SED problems [1] and force the ramp rates between the sequential hours. It is shown that this method would lead into being trapped in a local optimal solution [2]. Generators are modeled using input-output curves in most of the power system operation studies.

The combined heat and power economic dispatch (CHPED) is a complicated optimization problem which determines the production of heat and power units to obtain the minimum production costs of the system, satisfying the heat and power demands and considering operational constraints. This paper presents a real coded genetic algorithm with improved Mühlenbein mutation (RCGA-IMM) for solving CHPED optimization task. Mühlenbein mutation is implemented on basic RCGA for speeding up the convergence and improving the optimization problem results. To evaluate the performance features, the proposed RCGA-IMM procedure is employed on six benchmark functions. The effect of valve-point and transmission losses is considered in cost function and four test systems are presented to demonstrate the effectiveness and superiority of the proposed method. In all test cases the obtained solutions utilizing RCGA-IMM optimization method are feasible and in most instances express a marked improvement over the provided results by recent works in this area.

Micro-grids (MGs) are introduced as a solution for distributed energy resource (DER) units and energy storage systems (ESSs) to participate in providing the required electricity demand of controllable and non-controllable loads. In this paper, the authors study the short-term scheduling of grid-connected industrial heat and power MG which contains a fuel cell (FC) unit, combined heat and power (CHP) generation units, power-only unit, boiler, battery storage system, and heat buffer tank. The paper is aimed to solve the multi-objective MG dispatch problem containing cost and emission minimization with the considerations of demand response program and uncertainties. A probabilistic framework based on a scenario method, which is considered for load demand and price signals, is employed to overcome the uncertainties in the optimal energy management of the MG. In order to reduce operational cost, time-of-use rates of demand response programs have been modeled, and the effects of such programs on the load profile have been discussed. To solve the multi-objective optimization problem, the e-constraint method is used and a fuzzy satisfying approach has been employed to select the best compromise solution. Three cases are studied in this research to confirm the performance of the proposed method: islanded mode, grid-connected mode, and the impact of time of the use-demand response program on MG scheduling.

The short-term hydrothermal scheduling (STHS) problem is providing a daily planning of hydro and thermal generations, aiming to minimize the total fuel cost of thermal plants. The minimization of total operation cost of hydrothermal power system is considered as a complex nonlinear hard optimization problem with a series of several equality and inequality constraints. This paper proposes real-coded genetic algorithm with an improved Mühlenbein mutation (RCGA-IMM) for the solution of STHS optimization problem, considering the minimization of operation cost which satisfies hydraulic and electrical constraints. The proposed optimization procedure is employed on two test systems in which different constraints have been taken into account including valve point loading effect of thermal units and transmission losses. The provided optimal solutions have been compared with recent studies in this area, which manifest superiority of the proposed method. It is found that the proposed RCGA-IMM has the capability of obtaining better solutions with respect to other optimization methods which are implemented on STHS problem.

Optimal generation scheduling of hydro-based power units has a significant place in electric power systems, which considerably has been dealt with as a subject of investigations for several years. Hydrothermal system is introduced as an important hydro-based power generation system. The objective of short-term hydrothermal scheduling (STHS) problem is obtaining the power generation schedule of the available hydro and thermal power units, which aims to minimize total fuel cost of thermal plants during a determined time period. Many conventional optimization procedures are first introduced for solving STHS problem. Recently, heuristic and meta-heuristic optimization methods, which are defined as an experience-based procedure, are implemented for obtaining optimal solution of generation planning of hydrothermal systems. This paper provides a comprehensive review on the application of heuristic methods to obtain optimal generation scheduling of hydrothermal systems, which compares the implemented procedures from different points of view. Optimal solutions obtained by employment of multiple heuristic and meta-heuristic optimization methods for different test instances are demonstrated and the introduced methods are compared in terms of convergence speed, attained optimal solutions, and constraints. Future research trends are discussed, which can be introduced as the subject of studies in the area of STHS problem.

The optimal energy management in energy hubs has recently attracted a great deal of attention around the world. The energy hub consists of several inputs (energy resources) and outputs (energy consumption) and also some energy conversion/storage devices. The energy hub can be a home, large consumer, power plant, etc. The objective is to minimize the energy procurement costs (fuel/elec- tricity/environmental aspects) subject to a set of technical constraints. One of the popular options to be served as the input resource is renewable energy like wind or solar power. Using the renewable energy has various benefits such as low marginal costs and zero environmental pollution. On the other hand, the uncertainties associated with them make the operation of the energy hub a difficult and risky task. Besides, there are other resources of uncertainties such as the hourly electricity prices and demand values. Hence, it is important to determine an economic schedule for energy hubs, with an acceptable level of energy procurement risk. Thus, in this chapter a comprehensive multi-objective model is proposed to minimize both the energy procurement cost and risk level in energy hub. For controlling the pernicious effects of the uncertainties, conditional value at risk (CVaR) is used as risk management tool. The proposed model is formulated as a mixed integer nonlinear programming (MINLP) problem and solved using GAMS.

This study optimises and compares the operation of a conventional gas-fired power generation company with its operation in combination with wind power and compressed air energy storage (CAES). A mixed integer non-linear programming (MINLP) formulation is developed for the optimisation problem. Limits in ramp rate, capacity, and minimum on/off time, as well as start-up cost constraints, are considered for the modelling of conventional units. Injected and produced power constraints, storage, air balance and CAES-operation ...

Phasor measurement units (PMUs), which provide time-synchronized measurements
of current and voltage phasors, are considered as an advanced tool for monitoring,
protection and management of modern power systems. In this paper, a novel
method for optimal placement of PMUs for complete observability of power network
is presented. However the installation cost of the PMUs in different places differ with
each other, which is related to some factors like as the number of branches connectedto
the placed bus, a big quantity of reported methods for optimal PMU placement problem
considered an equal cost for PMU installation in different places. An upgraded
binary harmony search algorithm is utilized in this paper as an optimization method
to attain the minimum number of PMUs and their relevant locations considering the
installation costs of the PMUs. The proposed method is applied to IEEE 14-bus, IEEE
30-bus, IEEE 39-bus and IEEE 118-bus standard test systems to obtain the optimal
PMU placement. The simulation results confirm that the proposed method is efficient
in optimal PMUs placement with minimum cost of configuration.

Phasor measurement unit (PMU) plays an important role in operation, protection, and control of modern power systems. PMU provides real time, synchronized measurements of bus voltage and branch current phasors. It is neither economical nor possible to place all the buses of the system with PMUs because of their high cost and communication facilities. Attaining the minimal number of PMUs to access an observable power system is the main objective of optimal PMU placement (OPP) problem, which is solved by utilizing different techniques. Graph theoretic and mathematical programming procedures have been first introduced to solve OPP problem, aiming to access power system observability. Heuristic method as an experience-based technique is defined as a quick method for obtaining solutions for optimization problems, in which optimal solutions are not achievable using mathematical methods in finite time. This paper provided the literature review on different heuristic optimization methods to solve the OPP problem. Then, the available methods were classified and compared with different points of views. Results from the tests of researches on heuristic algorithms with and without the consideration of zero-injection buses were compared and superiorities of the introduced heuristic concepts were demonstrated with relative to each other.