Journal of Operation and Automation in Power Engineering
Volume:8 Issue: 2, Summer 2020

Recently, economic and environmental problems have created a strong attitude toward utilizing renewable energy sources (RESs). Nevertheless, uncertainty of wind and solar power leads to a more complicated energy management (EM) of RESs in microgrids. This paper models and solves the EM problem of microgrid from the generation point of view. To do this, mathematical formulation of a grid- connected microgrid including wind turbine (WT), photovoltaic (PV), micro turbine (MT), fuel cell (FC) and energy storage system (ESS) is presented. Furthermore an improved incentive-based demand response program (DRP) is applied in microgrid EM problem to flatten the load pattern. Comprehensive studying of EM in both intra-day and day-ahead markets is another contribution of this paper. However, the main novelty of this paper is proposing a new uncertainty modeling technique which is based on copula function and scenario generation. This paper tries to optimize operational cost and environmental pollution as the objective functions and solve them using group search optimization (GSO) algorithm. Numerical results approve the efficiency of the proposed method in solving microgrid EM problem.

Flexible AC Transmission Systems (FACTS) devices have shown satisfactory performance in alleviating the problems of electrical transmission systems. Optimal FACTS allocation problem, which includes finding optimal type and location of these devices, have been widely studied by researchers for improving variety of power system technical parameters. In this paper, a DIgSILENT-based Discrete Particle Swarm Optimization (DPSO) algorithm is employed to manage the power flow, alleviate the congestion, and improve the voltage profile in a real case study. The DPSO have been programmed in DPL environment of DIgSILENT software and applied to the power grid of Gilan Regional Electric Company (GilREC), located in north of Iran. The conducted approach is a user-friendly decision making tool for the engineers of power networks as it is executed in DIgSILENT software which is widely used in electric companies for the power system studies. The simulation results demonstrate the effectiveness of the presented method in improving technical parameters of the test system through several case studies.

In this paper, a new transformerless buck-boost converter based on ZETA converter is introduced. The proposed converter has the ZETA converter advantages such as, buck-boost capability and input to output DC insulation. The suggested converter voltage gain is higher than the classic ZETA converter. In the presented converter, only one main switch is utilized. The proposed converter offers low voltage stress of the switch; therefore, the low on-state resistance of the main switch can be selected to decrease losses of the switch. The presented converter topology is simple; hence, the control of the converter is simple. The mathematical analyses of the proposed converter are given. The experimental results confirm the correctness of the analysis.

Brushless doubly fed reluctance generator (BDFRG) has been recently suggested as a wind generator. Different control methods are presented in literature for the BDFRG, but there is a gap on control under unbalanced grid voltage condition (UGVC). This paper presents a predictive direct power control (PDPC) method for the BDFRG under UGVC. The proposed PDPC method is based on power compensation strategy, and aims to balance the BDFRG current (strategy I), and to remove the electrical torque pulsation (strategy II). The control objectives are defined using the BDFRG positive sequence (PS) and negative sequence (NS) equations. Then, the active power and reactive power variations are predicted to compute the required voltage for the BDFRG control winding. Finally, the BDFRG is controlled by applying the calculated voltage to the control winding. Simulink toolbox of MATLAB software is used to simulate the system model. Both the proposed PDPC method (with strategies I & II) and the original PDPC method (without a compensation strategy) are applied to control of the BDFRG under UGVC, and the results are compared. The results show the good performance of the proposed PDPC method.

This paper deals with day-ahead programming under uncertainties in microgrids (MGs). A two-stage stochastic programming with the fixed recourse approach was adopted. The studied MG was considered in the grid-connected mode with the capability of power exchange with the upstream network. Uncertain electricity market prices, unpredictable load demand, and uncertain wind and solar power values, due to intrinsically stochastic weather changes, were also considered in the proposed method. To cope with uncertainties, the scenario-based stochastic approach was utilized, and the reduction of the environmental emissions generated by the power resources was regarded as the second objective, besides the cost of units’ operation. The ɛ-constraint method was employed to deal with the presented multi-objective optimization problem, and the simulations were performed on a sample MG with one month of real data. The results demonstrated the applicability and effectiveness of the proposed techniques in real-world conditions.

After extreme events such as floods, thunderstorms, blizzards and hurricanes there will be devastating effects in the distribution networks which may cause a partial or complete blackout. Then, the major concern for the system operators is to restore the maximum critical loads as soon as possible by available generation units.  In order to solve this problem, this paper provides a restoration strategy by using Distributed Generations (DGs).  In this strategy, first, the shortest paths between DGs and critical loads are identified. Then, the best paths are determined by using a decision-making method, named PROMOTHEE-II to achieve the goals. The uncertainties for the output power of DGs are also considered in different scenarios. The IEEE 123-node distribution network is used to show the performance of the suggested method. The simulation results clearly show the efficiency of the proposed strategy for critical loads restoration in distribution networks.

In this paper, a distributed method for reactive power management in a distribution system has been presented. The proposed method focuses on the voltage rise where the distribution systems are equipped with a considerable number of photovoltaic units. This paper proposes the alternating direction method of multipliers (ADMMs) approach for solving the optimal voltage control problem in a distributed manner in a distribution system with high penetration of PVs. Also, the proposed method uses a clustering approach to divide the network into partitions based on the coupling degrees among different nodes. The optimal reactive power control strategy is conducted in each partition and integrated using ADMM. The proposed method is tested on a 33 bus IEEE distribution test system and a modified IEEE 123-node system. The result evidence that the proposed method has used the lower reactive power if compared to the conventional method.

Model predictive control (MPC) based methods are gaining more and more attention in power converters and electrical drives. Nevertheless, high computational burden of MPC is an obstacle for its application, especially when the prediction horizon increases extends. At the same time, increasing the prediction horizon leads to a superior response. In this paper, a long horizon MPC is proposed to control the power converter employed in the rotor side of DFIG. The main contribution of this paper is to propose a new comparative algorithm to speed up the optimization of the objective function. The proposed algorithm prevents examining all inputs in each prediction step to saving the computational time. Additionally, the proposed method along with the use of an incremental algorithm applies a sequence of weighting factors in the cost function over the prediction horizon to maximize the impact of primary samples on the optimal vector selection. Therefore, the proposed MPC strategy can predict a longer horizon with relatively low computational burden. Finally, results show that the proposed controller has the fastest dynamic response with lower overshoots compared to direct torque control and vector control method. In addition, the proposed strategy with more accurate response reduces the calculation time by up to 48% compared to classical MPC, for the prediction horizon of three.