meysam amirahmadi

This article examines the challenges of energy management in microgrids, considering the uncertainties associated with renewable energy sources, dynamic demand, and the presence of various devices such as batteries, distributed generation sources, and electric vehicles. The article introduces a complex optimization model designed for microgrid operations. This model focuses on mitigating the challenges of integrating power electronic generation units, managing demand within microgrids, and incorporating small-scale renewable energy sources. The goal of this model is to minimize various costs associated with energy losses, electricity purchases, load reduction, distributed generation operations, and battery costs over a 24-hour period. Simulations conducted on a test system demonstrate that the proposed model is effective, achieving up to a 20% reduction in microgrid operational costs. This approach provides an effective framework for enhancing the flexibility and efficiency of microgrid energy management, and the findings indicate that it outperforms comparative methods by a margin of at least 8%, demonstrating its effectiveness in improving critical indices in the microgrid system.

Since the presence of an energy hub (EH) leads to change the expansion planning problem of electrical power system. Therefore, in this study, the nature of optimal generation and transmission expansion planning in the presence of EH is studied. Also, the effect of applying the proposed hub with and without considering energy storages (ESs) as well as the short and long-term corrective actions to reduce the losses and costs are investigated. In addition, demand response and line transmission switching are considered as effective approaches to improve resilience in the proposed dynamic multi-level model. This nonlinear problem is solved sequentially considering the random approach and using differential evolution algorithm (DEA) and the symphony orchestra search algorithm (SOSA). In this paper, the proposed objective functions are studied in five-level and the results show the efficiency of this model in solving the planning problem. The findings show that the proposed planning model decreased capital costs of transmission switches as much as 26%, the capital cost of the transmission as much as 2.29%, the congestion cost as much as 1.8%, The capital cost of generation units as much as 3.75%, the payment capacity paid to generation units as much as 1.8%. Also, the expected profit of the generation units has increased as much as 3.75%. To show the competence of the proposed algorithms, the 400-kV test system with 52 buses in Iran is simulated in MATLAB environment.

In restructured power systems and in a wholesale power market, a distribution company as a market player intends to maximize its profit by utilizing its options. Hence determining an optimal energy acquisition strategy for a distribution company is vital, for attaining to this goal. However an important challenge for determining these strategies is forecasting other competitors and Generation companie's strategies and competitor's incomplete information must be considered as uncertainties in the problem. In this paper, an energy acquisition model for a distribution company with considering distributed generations, interruptible loads and information's uncertainties in a day-ahead electricity market has been presented. In the proposed method, distribution company energy acquisition strategy has been modeled as a two-level multi-objective optimization problem and has been solved by using nonlinear complementarities and L-P metric methods and then, the uncertainties in the competitor's information, has been applied to the model by using Monte Carlo method. An 8-bus system is employed to illustrate the proposed model and algorithm.