جستجوی مقالات مرتبط با کلیدواژه « Microgrid » در نشریات گروه « فنی و مهندسی »

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.

Microgrid is a system of generating and distributing electrical energy. Microgrids include various power systems, each of which has different power capacity and produces different oscillations. By producing reliable and flexible power, microgrids can ensure the safety and electrical reliability of the network and reduce energy costs. A microgrid is a small power system with distributed energy sources and loads, which can work in two modes connected to the grid and island. The controller in a microgrid must provide a seamless transition between different modes of operation. By reviewing the studies, the application of microgrids has expanded to respond to the growing energy demand and solve environmental pollution problems. In this paper, a brief overview of the effectiveness of direct current microgrids in the power system has been provided. Direct current microgrids do not need frequency synchronization and reactive power management, and in terms of stability, flexibility, and complexity, they are superior to alternating current microgrids. The source voltages are the variables that control the direct current microgrid power, so they must be well managed to achieve the desired voltage regulation. Various classifications have been stated for microgrids based on control methods, structures, performance, and other parameters. This review can be considered as an initial platform for research study in the development of direct current microgrids based on centralized and intelligent control.

Fault location is always one of the protection requirements in direct current microgrids. The variable characteristics of currents, bi-direction load flow, and output power fluctuations in renewable sources, which cause problems for protection devices with fixed regulation, are among the challenges of these methods. Today, real-time data access in microgrids and recent developments in high-precision measurement units have become a new research milestone. In this paper, a fault location method in direct current microgrids in grid-connected mode is proposed using voltage and current measurements at the beginning and end of the section and the presence of photovoltaic and energy storage systems. In this method, using the direct current components of voltage and current and considering the π line model, in addition to the fault distance, the fault section has also been determined. Different resistances and fault locations have little effect on the performance of this algorithm. In addition, changes in line parameters and resource productions do not affect the accuracy of this method. The performance of this method was investigated using a microgrid with 8 nodes in MATLAB software and the results show the acceptable accuracy of this method.

In this paper, an optimization model based on stochastic quadratic mixed integer programming to provide an integrated energy management of electricity and heat in electrical and thermal microgrids, taking into account the uncertainty of renewable energy sources, location of electricity generation sources and combined heat and power (CHP) along with energy and thermal storage systems and demand side management are provided in island operation and connected to the grid. A multi-objective function including minimization of energy loss, voltage deviation, cost of resource utilization, as well as reduction of renewable energy sources and reduction of installation cost is considered. The IEEE 69 bus distribution network was selected for analysis and coding was done in MATLAB software and CVX optimization package. The proposed model is also solved by the most powerful existing solver called Gurobi. The obtained results show the performance and accuracy of the proposed model.

This article introduces a two-stage linear model designed for the coordination of networked microgrids, aimed at optimizing energy management and enhancing profitability through proactive and corrective strategies. Initially, the first stage involves day-ahead hourly planning for microgrids, executed in a deterministic environment without accounting for uncertainties. Subsequently, the second stage addresses these uncertainties in real-time network operation through a stochastic programming approach. The model's objective function quantifies and incorporates the variations resulting from both the proactive and corrective phases. To handle uncertainties in wind and solar energy production as well as load demand, probability distribution functions derived from Monte Carlo simulations are utilized. From these, representative scenarios are chosen using a scenario reduction technique. Specifically, the K-means algorithm is employed for scenario clustering, with the Davies-Bouldin (DB) index facilitating automatic clustering. Additionally, load management is conducted via a demand response program. The proposed model stipulates that, within microgrids, only non-critical load levels can be modulated based on the network's economic benefit. This optimization model, formulated as mixed integer programming, is simulated and resolved in the GAMS software environment. The primary goal of this two-stage model is to achieve optimal energy management by balancing economic efficiency with robust network performance. The results obtained validate the model's effectiveness.

Today, with the growing demand for hybrid electric vehicles in microgrids, electricity supply, environmental issues, and rescheduling are among the challenges of microgrids that must be solved and suitable solutions provided. To overcome these challenges, this paper introduces a new multi-objective optimization model, which in the first objective, minimizes the total operation cost of the microgrid, and in the second objective, improves the reliability index by reducing the amount of energy not supplied. Due to these two objectives, a multi-objective evolutionary seagull optimization algorithm is used to find the optimal global solutions. In this regard, hybrid electric vehicles and demand response programs are used to smooth out distribution nodal prices and reduce CO2 emissions. The 69-bus distribution network has been used to evaluate the efficiency of the proposed method.

With the increasing penetration of renewable energy sources such as wind and photovoltaic generation in future microgrids, challenges arise due to variable weather conditions. In this paper, a model is proposed to optimize the performance of microgrids under the worst-case scenario of renewable energy source failures using a bi-level optimization. In the upper-level problem, optimization is carried out in terms of energy loss reduction, load shedding in the load management program, as well as optimal charging and discharging of energy storage systems. The lower-level problem considers maximizing the utilization of renewable energy. A bi-level optimization solution method is proposed, which involves binary variables at both levels and is solved using a non-dominated sorting genetic algorithm (NSGA-II). The proposed model and algorithm are implemented using the Julia programming language. The performance of the model is examined under various conditions using a 33-bus microgrid, and the optimization results demonstrate the optimal performance of the microgrid under the worst-case scenario of renewable energy source failures.

In this paper, a two-level optimization model of mixed quadratic integer programming (MIQP) is presented in order to optimally operate microgrids under worst-case output conditions of renewable energy sources. This two-level model is divided into two high-level and low-level problems. In the high-level problem, the goal is to reduce energy loss and load shedding in the demand response program, optimal charging and discharging of electric vehicles and energy storage systems. In the low-level problem, the objective is to maximize the power outage of renewable energy sources. In this model, a new method for solving the optimization problem is proposed, which is based on the reformulation of the problem to Karush-Cohen-Tucker (KKT) optimality conditions. For the analysis, a 33 bus microgrid is considered. The simulation results show that the proposed model maintains the flexibility of the network in the worst output conditions of renewable energy sources and no load interruption occurs in the network.