نشریه تحقیقات نوین در سیستم های قدرت هوشمند
سال دوازدهم شماره 3 (پیاپی 35، پاییز 1402)

Economic Dispatch for power systems with the aim of optimizing the total production cost for active units in the power network is one of the most important topics for today's modern systems. In other words, the purpose of the economic Dispatch problem is optimal and appropriate planning for production units, taking into account the nonlinear factors and limitations in the power network and production units. And it plays a very important role in the economic exploitation of power systems Solving the problem of economic Dispatch optimization is very complex due to the huge dimensions of the nonlinear objective function, various restrictions and limitations. However, the use of optimization algorithms and artificial intelligence has been used to solve this problem. In this research, based on the use of a new algorithm called search and rescue, which is modeled based on the individual and group behavior of humans in searching for an individual or a group that has had an accident or is lost. This algorithm has great power in solving problems with high constraints, which can ignore local optima to confirm the proposed method in minimizing the cost and pollution of simulation in the software environment (MATLAB) on test networks (3, 6, 10) units done and the simulation results obtained from the proposed algorithm show that the accuracy and speed of finding the answers have been improved with this method.

One of the achievements of the restructuring of the electricity industry is the creation of a competitive environment in the electricity generation sector, in which a set of small-scale generation units with loads and a covered network, which managed by a specific institution and is called a virtual power plant, can Have an active presence in the wholesale market of energy and rotating storage. This study presents a new framework for planning the optimal bidding strategy for the participation of virtual power plants in the energy and reserve markets, in which wholesale electricity prices, retail market, required storage, and period are the main parameters. A genetic algorithm is used to solve the resulting optimization problem. Uncertainties governing wholesale prices and forecasting consumption needs in the area covered by the virtual power plant are considered. The logarithmic and normal probability distribution functions were used, respectively. The method of Monte Carlo simulation is used to considering uncertain parameters. The simulation results of this paper show that the proposed method is a powerful and appropriate tool to develop an optimal strategy for virtual power plant bidding in the electricity market, taking into account uncertainties and its proper interaction with consumers with consideration of demand response.

Economic dispoatch in the operation of modern power systems is of great importance. The economic load dispatch, taking into account all constraints, needs to be allocated among different multi-area generators. Economically distributing the load in multi-area economic dispatch (MAED) can simultaneously satisfy multiple constraints. In this regard, the use of algorithms can be helpful. This study aimed to determine the decentralized economic power dispatch in the transmission system using Grey Wolf Optimization (GWO) and Wild Horse Optimization (WHO) algorithms. In this study, a combination of GWO and WHO algorithms, aided by a fuzzy logic system, was employed in MATLAB software to obtain the optimal power generation schedule for a 24-hour period in the presence of HVDC and HVAC transmission lines. The results of this study on the IEEE 118 BUS network demonstrated that the GWO and WHO algorithms, considering point load, achieved satisfactory results in reducing losses and generation costs. Therefore, the use of these algorithms is recommended for improving decentralized economic power dispatch in the transmission system.

In this paper, the resilience-oriented energy management of the multiple microgrids is followed. For this purpose, a three levelgame theory based model is developed for energy management of microgrids. In this model, the coalition between the microgrids is formed to achieve optimal cumulative energy management considering the resilience concept. In this model, at first, a bi-level problem is developed from the perspective of the central operator to determine the optimal energy trading between the upstream network and multiple microgrids. The proposed bi-level model is formulated as a mathematical program with equilibrium constraints (MPEC) problem. In the bi-level problem, an optimization problem is formulated in the upper level from the perspective of the whole coalition. Therefore, minimizing the operating cost of the entire coalition is considered as the objective function. The lower level of the problem is also modeled from the point of view of each microgrid. In order to resilience-oriented model the energy management problem, the possibility of disconnecting the coalition connection line with the upstream network is probabilistically modeled based on the scenarios. After solving the MPEC problem and determining the energy exchanges, the local microgrids are operated separately from the perspective of the local operator in the third level.In this way, the operational statues of the electrical units as well as the energy storage systems are determined.The results of the simulations show that by using the method proposed in this article, with the presence of energy storage systems, the interrupted load and also the operating cost in the resilient mode will be equal to the normal mode.Also, the studies demonstrate using the proposed three-level model, the total cost is reduced by about 5.7 for the normal mode and about 7% for the resilience mode compared to the conventional two-level model.

To evaluate the reliability of active distribution networks, there is a need to model distributed generation sources, mainly renewable ones. The traditional (common) reliability models are not able to take into account the intermittent and random nature of these energy sources, so the need for a new reliability model is necessary to model the production sources of this type of distribution network. In analytical methods, an analytical model of the network is prepared and the problem is solved mathematically; but with the complexity of the network, solving problems becomes very difficult and practically impossible. Therefore, in terms of the complexity of the structure and the variety of operating modes of active distribution networks, a simulation [Monte Carlo] method has been proposed to evaluate their reliability. In this method, all failure modes of network components are randomly classified and specified. On the other hand, by using the proposed model of reliability of DGs, the nature of randomness of these sources has been accurately considered in the simulation process. The simulation results of a sample active distribution network indicate that, with the introduction of microgrids, the reliability indicators of the network and its loads have improved, but the microgrid has had the greatest impact on the load indicators within the microgrid itself.

To deal with the challenge of the level of penetration of renewable energy sources against uncertainties and in order to develop the secondary load frequency control loop, with the aim of reducing the impact of disturbances and adjusting the resistant gain coefficient to uncertainties, using a virtual inertial controller based on the resistant control of the Mu-synthesis method. For the lack of inertia in the islanded microgrid clusters, alternating current has been done. The stability and performance of this method is proved based on the structured singular value. The experimental results of the hardware in the loop by the TMS320F2812 digital signal processor and the simulation in the MATLAB/SIMULINK software environment confirm the performance of the proposed controller for the development of traditional load frequency control in comparison with the enhanced virtual inertial control and inertialess mode, under various disturbances, that the application of the controller The virtual inertia based on Mu-synthesis improves the stability of islanded microgrid clusters and damping of power fluctuations, and also reduces frequency deviations to a significant extent.