نشریه فناوری های نوین مهندسی برق در سیستم انرژی سبز
سال دوم شماره 4 (پیاپی 8، زمستان 1402)

In this paper, a new non-isolated soft switched DC-DC converter with high step-down conversion ratio is proposed. The proposed converter consists of two switch cells that their inputs and outputs are cascaded. This converter provides ultra-high step-down conversion ratio and due to all semiconductor devices are soft switched, switching losses are reduced, also reverse recovery losses of diodes are reduced because of ZCS turn off condition. The duty cycle of proposed converter is much larger than step-down converter and therefore does not have the problems of the converter with a narrow duty cycle and since no auxiliary switch is used to achieve soft switching, the control circuit remains simple. In this paper, the convertor operating modes are discussed and to verify the performance of the converter a 50W prototype with 100 kHz switching frequency, 100V input and 16V output is built. The converter efficiency is measured and the peak efficiency is 94% also the conducted electromagnetic interference peak in the proposed converter is reduced by 6dBµv.

Due to the ever-increasing price of fossil fuels and growing concerns about environmental pollution, the utilization of renewable resources, such as photovoltaic (PV) systems, has witnessed significant growth. However, the lack of an optimal structure and control strategy for PV systems poses a crucial challenge in fully exploiting their potential capabilities. This article proposes a suitable structure and adaptive control strategy for PV systems, enabling the maximum utilization of PV system capabilities in island microgrids. The proposed control structure and strategy are based on a two-stage converter, facilitating maximum power point tracking in PV, injecting the generated PV power into the microgrid with minimal harmonic levels, and improving the power quality of the microgrid by compensating for harmonic components. In this method, the tasks of the DC/AC converter, including the injection of PV active power into the microgrid, provision of reactive power, and harmonic compensation, are prioritized and managed by considering the current peak limitation to prevent inverter overcurrent. Additionally, an adaptive controller is designed to enhance the accuracy and speed of power control. The proposed structure and strategy have been evaluated by simulating a sample microgrid in MATLAB/Simulink. The simulation results demonstrate that the proposed method enables the PV system to operate at maximum power with minimum harmonic levels, leading to a significant improvement in the speed and accuracy of the control system and enhancing the power quality of the islanded microgrid.

In this article, a comprehensive two-stage framework for conducting competitive energy and ancillary services markets in transmission and distribution networks is presented. In the first and second stages of the proposed framework, energy and ancillary services markets are held, respectively. In the proposed framework, the suppliers of spinning reserve market capacities are conventional thermal units, while the suppliers of regulation market capacities are fast response generators, energy storage systems, electric vehicles, and demand response aggregators. A linear AC power flow program is included in the proposed framework to verify the applicability of the simulation results in real operating conditions. The introduced framework is modeled as a linear optimization problem in which the objective function of each stage is solved separately. This framework is implemented on a test system that includes a 30-bus transmission network connected to four 8-bus distribution networks, and the CPLEX solver in GAMS software is used to simulate it. The simulation outputs clearly confirm that the participation of resources within the distribution networks in providing spinning reserve capacities significantly reduces the share of expensive thermal units in the market and thereby lowers the daily costs of the system. Moreover, the simulation outputs indicate that the participation of demand response aggregators, energy storage systems and electric vehicles in providing regulation market capacities, not only lowers the costs of this market but also significantly improves technical indicators such as voltage characteristics.

In this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controller (LOC) and the particle swarm algorithm (PSO). The control bank in the multi-model controller includes three LOC controllers that generate optimal signals through the linearization of the nonlinear equations of the system and the minimization of an energy function to be combined by the Bayes recursive algorithm simultaneously to the generator excitation system and SVC. In order to generate an optimal linear signal, Riccati's equation must be solved; Riccati's equation includes two weight matrices Rric and Qric. These matrices elements are optimized by PSO algorithm. The PSO algorithm has calculated the optimal Rric and Qric with two different objective functions of maximizing the eigenvalues and minimizing the area under the speed curve. To evaluate the MMC-LOC-PSO control strategy, the symmetrical three-phase error is applied to the worst bus and the results of these two objective functions are compared. The simulation of the single machine power system has been done by MATLAB. The proposed control strategy, while maintaining stability, also effectively damps the LFOs, in addition, the permanent rotor speed and rotor angle error have also been favorably pushed to zero.

This paper presents a tri-level model for the simultaneous management of energy and ancillary services markets between transmission and distribution networks integrated with renewable energy sources, smart homes based on the Internet of Things, and electric vehicles. In the first level of the proposed model, smart homes plan their participation in the energy and regulation markets and send it to the distribution network operator. In the second level, the operators of the distribution networks plan their area according to the programs received from the smart homes and determine their strategy for participation in the energy, reservation and adjustment markets. In the third level, the strategy of distribution networks is sent to the operator of the transmission system so that the final planning of the energy, reservation and adjustment markets can be done according to them. The proposed model is formulated as a mixed integer linear programming problem and solved by GUROBI solver in GAMS. The implementation of the proposed model showed that this model was able to significantly use the potential of subscribers based on the Internet of Things, electric vehicles, storage systems and demand response programs to improve the technical aspects of transmission and distribution networks as well as to improve the economic aspects of energy markets and ancillary services.

In recent years, the interest in conducting research on non-intrusive load monitoring is increasing strongly due to the increase in electrical energy consumption. Numerous studies have underscored that the implementation of non-intrusive load monitoring methods, apart from various advantages such as load response, increasing the accuracy of load prediction, etc., will increase the level of cost savings for occupants of residential structures. Recently, with the adoption of techniques grounded in deep learning, the use of these methods has also increased in order to load disaggregation. However, the most important problem with these methods is the need for complex hardware in order to train and examine the techniques. For this reason, it is necessary to transfer the power signal sampled from the smart meter to data processing centers and be analyzed. In addition to the need for high-speed communication networks, this also endangers data security. Accordingly, in this article, a non-intrusive load monitoring method is presented based on extracting the feature matrix from the instantaneous frequency signal obtained from the power signal of household appliances. The most important feature of the presented method is to increase the accuracy of the classical KNN model. The presented method has been analyzed using EMBED open-access data, which includes the consumption dataset from three different apartments. The results show that the KNN model attains significantly enhanced accuracy when using the feature matrix data introduced in this article compared to other feature extraction methods.