dr. mohsen simab

In recent years, the local feeding of the required loads in the micro grids has received much attention comparing to the extension of the large fuel-based power plants, which require the development of costly transmission lines. On the other hand, environmental constraints have led to the increasing development of renewable energy sources that can generate electricity in the form of small-scale generation units in micro grids. In this study, an appropriate mixture of renewable sources incorporating the wind turbines, current type tidal generation units and the photovoltaic systems is integrated to the micro grid connected to the energy storage systems. The proposed micro grid can be customized in the coastal regions and islands for supplying required loads. To optimally determine capacity and size of renewable power plants, different metaheuristic algorithms are applied, and among them, the particle swarm optimization methodology is used to minimize cost function of the system including the investment, operation and the reliability costs. To calculate reliability cost of micro grid, variable hazard rate of the assembled elements influenced by change in air and water temperature, wind velocity, tidal stream velocity and sun irradiance is taken into account. Load curtailment of the micro grid is occurred due to failure of the assembled elements and the change in renewable sources that both are addressed in the paper. For examining effectiveness of proposed approach, numerical results associated to the planning of a micro grid incorporating renewable sources considering the reliability cost are given.

This paper explores the optimization of distribution network reconfiguration to minimize the time dial setting of overcurrent relays, essential for improving protection and reliability in power systems. The study focuses on employing advanced optimization techniques to enhance network efficiency. Specifically, the proposed modified sine cosine algorithm (MSCA) is investigated and compared against conventional methods. Results demonstrate that MSCA consistently achieves superior performance across critical metrics including significant reductions in power losses, improved system stability, and optimized voltage profiles. For instance, MSCA achieves a notable 16% reduction in power losses and enhances system stability by 22%, demonstrating its effectiveness in optimizing network operation. Simulations conducted on both 33-bus and 119-bus IEEE network configurations validate the robustness and versatility of MSCA in diverse network environments. These findings underscore the practical applicability of MSCA in modern power systems, offering tangible benefits in terms of efficiency, reliability, and operational flexibility. The research provides valuable insights for power system engineers and researchers seeking to implement advanced optimization strategies to address challenges in network reconfiguration and enhance overall system performance.

In this article, the control method of the economic predictive model for the use of the efficiency tariff of the photovoltaic backup system, diesel generator and microgrid, connected to the grid using the closed loop control system, the optimal open loop control, and also through the control and strengthening of the primary open loop has been The main goal of this study is to minimize the power grid energy and fuel costs by evaluating the limits related to the level of fuel level in diesel fuel tanks. In addition to complying with the restrictions among the controllable variables, this control method also meets the load requirements. In order to obtain the benefits of feedback and predict the optimal power timing as a back-up energy system control problem, as well as the diesel generator connected to the microgrid, it is modeled based on the linear programming structure. Specifically, analysis is divided into two groups. The first case in the alternative model is when: outage occurs between 7 AM and 6 PM and the other in the grid energy state occurs when the grid is available for more than 24 hours. Energy performance shows, cost savings and income, in the control of daily economic forecasting model has improved. As long as, daily energy saving is up to 52%, while diesel energy is up to 85%. Optimum operation control can be well associated with uncertainty and disturbance in the result.

The capability of a neuro-fuzzy control approach for frequency fluctuation damping in an isolated hybrid microgrid (IHMG) system (DEG/WTG /PV/FC/ESSs) is investigated in this paper. Due to the intermittent behavior of renewable energy sources (RESs) like wind turbines and photovoltaic arrays and the time-varying nature of demands, frequency fluctuation is more likely, specifically in the grid-connected mode. Model parametric uncertainties as well as load changes, wind power, and solar irradiation variations are the main uncertainty sources of the IHMG system. In the suggested approach, a neuro-fuzzy output feedback controller with three inputs that are inspired by PID control is designed considering the power balance between demands and generations, by optimizing fuzzy membership functions’ locations. The proposed controller is compared with two popular other methods on the investigated IHMG system in terms of time-domain characteristics. The outcome illustrates remarkable merit compared to the state-of-the-art methods in the presence of simultaneous disturbances and the model parametric uncertainties.

In this paper, the Forward-Backward (FB) strategy has been developed to solve the load flow (LF) problem of a large-scale multi-carrier network. The Teaching–Learning-Based Optimization (TLBO), as a powerful heuristic tool, has been used for the optimization of the proposed LF. For this purpose, a large-scale unbalanced multi-carrier energy system (MCES) including an IEEE 33-bus system, a natural gas network with 25 nodes, and a heat network with 20 nodes have been considered to ascertain the applicability of the proposed FB-based machine learning strategy. By minimizing an objective function of the MCES system under unequal constraints, the TLBO tries to solve the optimization problem of MCES. The unbalanced current and voltage performance of the MCES system has been investigated by employing the current unbalance index (CUI) and voltage unbalance index (VUI). The numerical analysis with the application of the current unbalance index and voltage unbalance index has been studied to appraise the efficiency of the proposed optimal strategy to solve the LF problem of the MCES system.

This paper presents the optimal participation of strategic electricity and gas producers (SEPs and SGPs) in the coupled electricity and gas market of the day-ahead (DA) reserve regulation and energy type in accordance with the Energy Management Strategy (EMS). The deterministic model of this scheme has two bi-level problems, which refer to modelling of the participation of SEPs/SGPs in the DA energy and reserve markets. In any bi-level problem, the upper-level formulation minimizes the difference between the cost and revenue of SEPs or SGPs in these markets subjected to the operational model of these producers. In the lower-level formulation, the market clearing price (MCP) model is expressed in the problem such that the minimization of the energy cost of non-SEPs / non-SGPs are subjected to the electrical / gas network, electricity / gas generation units, and the reserve requirements model. In this scheme, load, renewable generation power, and reserve demand are uncertain. Hence, this paper proposes the robust modelling to achieve an optimal point in the worst-case scenario. Furthermore, the master-slave decomposition (MSD) method is suggested to reach the optimal solution at a low calculation time. Finally, the proposed scheme is simulated on the standard test system. The numerical results confirm its capability to achieve benefits for SEPs / SGPs along with the extraction of economic operation for energy networks.

With an increasing penetration rate of electric vehicles in distribution networks, it is becoming vital to schedule their battery charging/discharging to maintain the network balance and increase the vehicle owners’ profit. Electric vehicles are now considered one of the most important and accessible sources of revenue for their owners since they can be connected to the grid (V2G) as a power source during peak hours. As such, while flattening the power profile, they can improve the voltage drop across the grid buses. If charging/discharging of the vehicles is scheduled irregularly, the power drawn from the phases will become unbalanced, which can cause global outages and impair system stability in addition to increasing the harmonic volume and decreasing power quality. The present paper uses dynamic programming to reduce operating costs and enhance the profits of vehicle owners who participate in the V2G program. This optimization algorithm eliminates the undesirable paths leading to unconventional responses in the search space, which will greatly increase the speed and accuracy by which the optimal response is achieved. This model, along with multi-part tariffs on electricity prices, can lead to the more active participation of vehicle owners and help improve the power quality indices of the electrical parameters of the grid. The proposed method is simulated on a sample distribution network, and the case studies conducted prove the validity of the proposed algorithm.

Using bidirectional parallel converters improves system reliability as they can bypass faults. Therefore, the bidirectional parallel converters structure is adopted for the DC micr ogrid connected to the grid. There is a circulating current between the bidirectional parallel converters that limits the overall capacity of the system and can damage switching devices. To solve this problem, in this paper, the mechanism of generating circulating current on the AC side when several bidirectional parallel converters work together was taken into consideration with the effect of constant power loads by the droop strategy Pdc-vdc2-f to share DC power and network frequency adaptation. The PI coefficients of the bidirectional parallel converters controller were optimized by online genetic algorithm and neural networks and updated with system conditions. Both the stable and dynamic relationships between Pdc and vdc2 are linear and did not create constant power loads of unstable poles for the system. The results indicated that compared with conventional idc-vdc droop control, the proposed Pdc-vdc2-f strategy can increase dc power control and system stability, consequently increasing the dc voltage regulation dynamics.

The issue of unit commitment (UC) is one of the most important issues in the electricity market, and with the smartening and restructuring of networks which its goals and variables have undergone changes. In this paper, solving and modeling the problem of unit commitment by considering smart networks is proposed. As we know, in smart grids, there are new issues such as demand side management and energy storage systems (batteries) as well as new goals such as reducing the environmental pollution of the units that must be considered. In this paper, the issue of unit commitment with demand side management modeling along with optimal charging and discharging of battery storage system with the aim of minimizing the economic costs of units and batteries and environmental pollution is presented. The proposed model is a Mixed Integer Linear Programming (MILP) model that is solved using powerful commercial software such as Gurobi and guarantees optimal global solutions. The proposed model is implemented on two standard IEEE 6 and 118 bus networks, the results of which show the efficiency of the proposed model.

This paper studies a new method for reconfiguration of the distribution network that considers access to energy storage devices. Initially, a new distribution network reconfiguration model is being created that takes into account access to energy storage devices. This model takes as an objective function the minimum of network losses and takes into account the current power and voltage constraints induced by energy storage devices. In reconfiguration schemes, the dual power flow directions are also deliberated. To solve the proposed model, the Binary Particle Swarm Optimization (BPSO) is implemented to get the optimal reconfiguration scheme. Finally, to check the correctness and efficacy of the proposed process, two distribution networks with admission to energy storage systems are being checked (Initial topology of 12-bus system - Optimal topology without considering energy storage - Optimal topology considering energy storage), Then proposed more suitable method for solving the proposed reconfiguration models in this manuscript.

Today, the use of distributed generation resources connected to the network has remarkable growth therefore network-connected to the converters must be able to provide services beyond the injection of power into network, including maintaining network stability. In this paper the control and management of energy for battery wind, photovoltaic and diesel connected to a three-phase network by activating the current limitation under unbalanced fault has been introduced. In this method injected currents are limited to a certain amount along the faults Also, the operation mode without tracking the maximum power point for the converter is included. This state is activated during severe faults, when the converter cannot control Maximum power of the system. Therefore, in a microgrid with, the operation of a fuzzy logic-based controller the transformation operation of DC to DC converter control is performed sparsely by two-way battery and distributed generation resources management. Also proportional resonance controller is used due to its proper performance. In fact, with the use of reference current generation, sinusoidal current with a total harmonic distortion (THD) value of less than 5% is injected. The results show that the control strategy along with energy management with multiple energy generation resources is able to limit and maintain DC link voltage within an acceptable range in addition to The existence of an asymmetric fault. Therefore, the fault crossing operation has been performed correctly without the operation of the protection relays and secondly in comparison with microgridsolely with the presence of one photovoltaic production source the process of injecting active and reactive power in the upstream network is more appropriate.

A reconfiguration model for the distribution network with the optimization objective of reducing three -phase disequilibrium is suggested in order to cope with an increasingly serious three -phase unbalance in distribution network. Second, the distribution network reconfiguration problem is transformed into a problem of constructing the spanning tree of the graph by evaluating the distribution network in topology, which is solved by the process of breaking -cycle -basis. Then an improved Binary Particle Swarm Optimization (BPSO) algorithm is suggested to solve the reconfiguration problem by randomly choosing the first-branch and canceling the heuristic value of the network, which will extend the search scope and prevent search stagnation. Relevant examples of verification show that, relative to traditional approaches, the proposed algorithm can achieve the best global solution with less computational time and greater probability. The proposed algorithm is tested in 33 bus system and the results show the load balancing in distribution network.

The problem of unit commitment (UC) or in other words, the issue of placing the units in orbit is a very important optimization problem, the exact solution of which can lead to a significant reduction in operation costs. In general, UCchr('39')s goal is to minimize the total cost of production units by considering the constraints of the units and the system during the intended period. In fact, UC is divided into two issues. First, there is the issue of determining the on or off mode of production units for each hour, which is usually 24 hours, and the second is the distribution of economic dispatch between production units. Considering both problems at the same time will complicate the solution. It should also be noted that the difficulty of the problem also increases in proportion to the number of production units as well as the constraints considered. As mentioned, unit cost is typically the primary goal of minimizing UC issues. This paper considers smart grids, one of whose goals is reducing environmental pollution in addition to reducing costs. Therefore, this paper considers UC problem-solving in smart grids by considering the pollution of production units, so a multi-objective function is selected for minimization. On the other hand, with the introduction of smart grids, the existence of energy storage systems (ESS) in the network is also considered. This paper suggests optimal charging and discharging of energy storage systems, too. Another issue modeled in this paper is the issue of demand response in smart grids. Demand response program modeling allows grid demands to be shifted within the specified range at different times of the day. The proposed model is a mixed integer linear programming model (MILP) that has been tested to validate the performance of the proposed model, i.e., 4-unit and 10-unit systems with storage resources. To solve the proposed model, a powerful Gurobi solver is used. This solver also guarantees optimal global solutions and can get optimal solutions in the shortest time. In the end, the simulation results showed that modeling the problem of optimizing the charge and discharge of the energy storage system, along with the problem of demand-response management in the issue of unit participation, is very effective in reducing operating costs and reducing pollution. On the other hand, due to the linear nature of the problem, the proposed model can be solved in larger dimensions of the power systems.

The topic of distribution network reconfiguration has been recognized as a common problem in the operation of distribution networks. Up to now, reconfiguration of the distribution network has been used to reduce power loss, issues with reliability and emergency problems. As a result of the increasing installation rate of Distributed Generations (DGs) and the increasingly increasing number of Electric Vehicles (EVs), distribution network operators have faced new problems as a result of high sensitive loads. These new facets of the problem of network operation consist of the problem of congestion and enable the business mechanism to be executed by an existing distribution market. With the application of Distribution Locational Marginal Price (DLMP), using Particle Swarm Optimization (PSO), this paper introduces a new methodology to solve this problem. As a suitable case study, the standard IEEE 33-bus network has been selected and simulation results show the compatibility of the proposed system. In the case of not considering DGs and the problem of congestion, comparisons with other papers were also discussed. The participation of DGs in the electricity distribution industry, the issue of congestion and the numerical results have been shown and discussed below.

One of the common problems of power quality is the occurrence of voltage sags due to different types of balanced and unbalanced short-circuit faults in electrical distribution systems. Dynamic Voltage Restorer (DVR) is the most effective equipment used for voltage recovery in power distribution systems and it injects voltage in series with line voltage for voltage recovery. In this paper, the structure and general components of this equipment are presented. In addition, by applying a control scheme based on Synchronous Reference Frame Theory (SRFT) in its control system, the effective role of this equipment in compensating and maintaining the voltage of a power distribution system under the occurrence of balanced three-phase short-circuit fault and unbalanced single-phase to ground short-circuit fault is investigated and analyzed using Simulink/Matlab software.

Nowadays renewable energy resources have been experiencing rapid progresses in electric power networks all over the world. As a result, it is important that these clean electrical power resources be utilized for power generation in the modern power systems in the planning and operation studies. However, the uncertainty related to these energy resources and their intermittent nature result in some challenges when integrating to the power networks. The reliability of power systems is affected when the penetration level of these intermittent renewable power generations is significant. In this regard, this paper introduces an analytical technique to study the adequacy of a power system containing significant tidal power generation. For this purpose, a multi-state reliability model of a tidal plant considering both failure rate of composed components and variation in the generated power arisen from variation in the tidal current speed is developed. At first, an equivalent reliability model considering failure effects of composed components is obtained. Then Fuzzy-C-means clustering method is applied for reducing numerous values of the output power of the plant. For determining the optimum number of states in the model, XB index is calculated. The proposed multi-state model is then used for adequacy studies of RBTS and IEEE-RTS and the related indices of these systems in presence of tidal units is evaluated.

Contingency ranking is one of the most important stages in the analysis of power system security. In this paper, an integrated algorithm has been proposed to address this issue. This algorithm employs neural networks method to fast-estimate the power system parameters and stochastic frontier analysis (SFA) in order to calculate the efficiency of each contingency. Network security indices (voltage violation and line flow violation indices) and economic indices (locational marginal price and congestion cost indices) have been simultaneously considered to rank the contingencies. The efficiency of each contingency shows its severity and indicates that it affects network security and economic indices concurrently. The proposed algorithm has been tested on IEEE 14-bus and 30-bus test power systems. Simulation results show the high efficiency of the algorithm.Test results indicate that predicted quantities are estimated accurately and quickly. The proposed method is capable of producing fast and accurate network security indices and economic indices, so it can be used for online ranking.

In this paper, a multi-level benchmarking method is presented for assessing efficiency of electrical distribution companies’ expenditures. Proposed method considers mutual cooperation of capital expenditure (CAPEX) and operational expenditure (OPEX) for improving quality and decreasing energy losses. Firstly, weighted fuzzy c-means clustering (WFCM) is used by proposed method to find similar companies. Then, proposed method utilizes data envelopment analysis (DEA) and slack analysis in multi levels and through creating pseudo competition between companies, estimates companies’ efficiency scores and slacks in their expenditures. Finally, proposed IBRS is applied on Iranian distribution companies and its results are discussed.

In trading electrical energy in deregulated power systems, violation of operation constraints such as transmission lines flow limits is possible. These conditions have been called “congestion”. One of the most important responsibility of Independent System Operator (ISO) is congestion management in a manner that all the system equipments be operated in their nominal ranges. In this paper, using topological generation distribution factor, participation of each generator in power flow in each line is calculated. Then, by means of decomposition of nodal price and mentioned factor, the effect of generation in each bus on the locational marginal price in all buses of the system are calculated. And the congestion price is calculated based on re-dispatch in constant demand. In this paper a new method is introduced to dedicate congestion price to each generator and by this method a fair congestion cost allocation is implemented. The method has been tested on IEEE 24-bus test system and the results have been demonstrated.