جستجوی مقالات مرتبط با کلیدواژه "منابع انرژی تجدیدپذیر" در نشریات گروه "برق"

The development of electric vehicles in the transportation sector requires the establishment of charging infrastructures such as fast charging stations. Besides, in order to reduce the pollutants caused by fossil fuel power plants, renewable energy sources (RESs) and energy storage systems should be integrated with fast charging stations. In this article, a mixed-integer linear programming model is presented to determine the capacity of RESs and battery energy storage system in a charging station, considering two objective functions including the minimization of economic costs and emissions. The proposed model considers the possibility of using wind and solar resources and four types of battery technology including lead-acid, nickel-cadmium, lithium-ion, and sodium-sulfur. Regarding the two contradictory objectives in the proposed model, the epsilon constraint method has been employed to obtain the Pareto front for optimal solutions. Then, the fuzzy satisfying method has been used to determine the final solution. The results of the proposed model have been examined in four different planning horizons. The results show that with the increase in the importance of the objective function of reducing emissions, the installed capacity of renewable resources increases..

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.

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.

In modern power grid structure, solar power generation has grown extensively. Due to the output fluctuations and the inability to store energy, the power provided by RESs is variable; Therefore, using energy storage devices along with them is inevitable. Energy storage devices used for power management in systems based on renewable energy sources. In this paper, an off-grid DC system including photovoltaic unit and hybrid battery-supercapacitor energy storage was studied. Each of units is connected to the DC link through DC-DC converters. In such a system, it is essential to use a suitable control strategy in order to maintain the power balance between the photovoltaic, energy storages and load demand. For this purpose, a new control strategy for energy management and maintaining power balance in DC link is presented in this paper, which controls voltage fluctuations caused by load and source changes, using the high accuracy of delta sigma modulators. The effectiveness of the proposed strategy is validated through simulation under five different scenarios, taking into account all possible modes of changes, including stepwise increase or decrease in load or resource production. The results indicate the better performance of the proposed method in terms of maximum range of oscillation in all investigated modes.

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 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 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.

Due to renewable generation increment in power systems and the complexity of control and protection of them, the evaluation of   transient stability is necessary. This paper aims at analyzing the impact of wind power generation on the transient stability of power systems. To this end, doubly fed induction generator (DFIG) converters were first investigated in a closed-loop control mode. More detailed controls were used for the rotor side converter. Controls of the DC link converter on the network side were assumed to be ideal. Then, through using Gershgorin's theory, a new approach to adjust converter control parameters was presented. The simulation results in different operating modes confirmed the capability of this method in adjusting the converter’s control parameters. Also, by using performed simulations, the effect of increasing wind power production penetration and the changes of reactive power compensation by DFIG on the transient stability of power system were investigated. Finally, the effect of power system strength and DFIG parameters change, in the presence of DFIG-based wind power generation, on transient stability was analyzed. The simulation results showed that an increase in wind power generation initially led to an improvement and finally to the weakening of the transient stability of the system. In fact, this paper provides a professional and valuable perspective towards the investors and operators of the power systems with wind power generation from the viewpoint of transient stability.

Using security-constrained unit commitment as a preventative method of supplying power system frequency response can increase the penetration of renewable energy sources while maintaining frequency response parameters within their allowable range. Moreover, it can reduce the need for corrective actions such as under-frequency load shedding and can prevent power blackouts. So far, there have been two views about the virtual inertia of grid-connected energy storage units, pessimistic (lack of virtual inertia) and optimistic (failure-free frequency response). However, in reality, the frequency response supply system may experience malfunction and incorrect operation, which is modeled and analyzed in this paper from a techno-economic standpoint. False sensor detection, control system malfunction, wrong power injection of energy storage, inverter failure, improper parameter setting, etc., can all be considered disruptive factors. However, in order to have a more general model, the article avoids dealing with the reason behind the poor performance of the storage system and focuses only on its effects, i.e., the possibility of the storage system not participating correctly. The proposed model takes into account the system's frequency stability by linearly integrating the nadir frequency and the rate of change of frequency limits to the robust mixed-integer linear programming model with YALMIP and MOSEK solvers. The results of implementing this model in the IEEE-RTS96 network show that neglecting energy storage system malfunctions can lead to violations of frequency constraints that must be compensated by increasing costs and keeping more online units with more free headroom.

Simultaneous consumption of electrical and thermal energy, together with economic and technical advantages of close-to-consumption feeding of consumers, has demonstrated the importance of multi-carrier energy systems planning in recent years. Therefore, in this paper, the planning of an islanded hybrid system (IHS) with renewable energy sources (RESs), combined heat and power system (CHP), boiler, electric and thermal energy storage systems (ESSs) to provide clean energy for electric and thermal consumers is presented. The proposed design is responsible for minimizing the total weighted annual operating costs and pollution consumption of the system. The problem is then subjected to the planning-operation constraints and modeling the RESs and ESSs. The proposed problem is in the form of mixed-integer nonlinear programming. Therefore, in order to obtain the optimal solution with a low standard deviation error, the Grey Wolf Optimizer (GWO) is developed in this paper. The novelties of this paper include the extraction of the IHS with electric and thermal sources and ESSs, and provision of clean energy to the electrical and thermal consumers. Finally, by implementing the proposed design on the actual data of Rafsanjan city in Iran and investigating the numerical results, the ability of design to obtain the optimal hybrid combination of RESs and ESSs to supply electric and thermal consumers from an economic and environmental point of view is confirmed.

With the expansion of renewable energy resources and the reduction of the power system total inertia, the problem of providing the primary frequency response (PFR) has been raised. Other methods were welcomed. The issue of generation/transmission expansion planning must also adapt to these methods. Therefore, in this paper, a model of generation/transmission expansion planning is presented in which each of the renewable energy sources such as wind and solar power plants has virtual inertia. So, they can participate in the primary frequency response. Moreover, Frequency security margin constraints are also included in the issue. Frequency security margin is the maximum allowable and tolerable unbalance between the generation and consumption power of the network which can lead to more accurate expansion planning than other PFR-based method, if considered. Compared to the traditional method, the network's tolerance to sudden events has increased, which has certainly can be achieved at a higher cost than the traditional one. The IEEE-RTS96 network was used to verify the proposed model. The results show a percentage increase in cost compared to the traditional method, and an increase in network frequency security margin. The combination of YALMIP and MOSEK software has been used to solve the mixed integer linear programming model, which have shown its superiority in solving these models.

In the light of the emergence of smart grids, the functions associated with this type of grids in the blocks of the energy management system require the adoption of robust strategies in order to provide a higher level of control and protection. Under-frequency load shedding (UFLS) sheds load blocks when the frequency drop is below the threshold limit. In adaptive UFLS, in an advanced telecommunications platform, the main quantities of the system are measured at each hour and in real-time; besides, the optimal amount of load is shed according to frequency deviation. In this paper, an hourly framework using Mixed-Integer Linear Programming (MILP) is proposed for an optimal design of an adaptive UFLS in smart networks. The goal is to provide a more systematic adaptive UFLS that is less dependent on trial and error. Here, the under-frequency relay is set for each hour in regard to operating conditions and uncertainties resulting from contingencies, to renewable energy sourses (RES), and to load fluctuations at that hour. The simulations are performed on the IEEE 39-bus test system over a 24-hour time horizon.

In this paper, a three-level scenario-based framework for determining the optimal strategy and planning of microgrids located in a 118-bus distribution system is presented. This paper considers the uncertai nties of renewable energy resources, load demand, and the charge / discharge schedule of electric vehicles. In order to increase planning flexibility, the operator will be able to change the flow through the distribution feeder reconfiguration. Also in the proposed model, customers will be able to reduce their costs by participating in a demand response program. In the first level of the proposed model, the bidding strategy of microgrids is determined. In the second level, the market clearing price is determined by the independent system operator and according to the submitted bids. Finally, in the third stage, the problem of final microgrid programming is solved by a participatory game theory method. The proposed model is solved by the CPLEX solver in GAMS software and the results show that the dynamic topology improves the planning flexibility and thus reduces the total operating cost by about 10%. The results also show that the coordination of electric vehicles with scheduling, the presence of storage systems and the implementation of the demand response program leads to a significant reduction in the level of market-clearing price and thus reduce operating costs.

In this paper, a comprehensive energy management model is proposed in order to the operation of a ‎modified 33-radial bus distribution system, in the presence of smart homes. In the proposed model, ‎smart home customers are able to participate in a demand response (DR) program and their comfort ‎index is also considered as the main constraint. The model also considers uncertainties related to the ‎load demand, the generation of renewable energy resources and electricity price. The Monte Carlo ‎simulation method and the ScenRed tool are utilized to generate and reduce the scenarios, respectively. ‎In order to mimic the actual operating conditions, in the simulation, the seasonal variations of load ‎and generation are considered and the operation problem is solved for four seasons.‎‏ ‏A linear AC ‎power flow is also used in the model. Finally, the problem is modeled as a mixed-integer linear ‎programming (MILP) problem and solved by the CPLEX solver in GAMS software. The simulation ‎results demonstrate that the model proposed in this study is a comprehensive framework for the ‎operation of distribution systems in the presence of smart homes, which not only reduces operating ‎costs but also increases the consumers’ comfort index.‎

This paper presents the system stability-constrianted optimal protection coordination (SSCOPC) as stochastic problem in the microgrid with grid-connected and islanded operation modes, including renewable energy sources (RESs) and energy storage systems (ESSs). To regulate optimally the dual setting overcurrent relays (DSORs) and to selectin the reactance size of fault current limiter (FCL), the system minimizes the total relays operation time in the primary and backup protection modes. Also, it is subject to the limits of the coordination time interval (CTI), DSORs setting parameters, FCL size, and microgrid stability in the fault condition. Generally, the microgrid includes RESs and ESSs; thus, the proposed SSCOPC needs a daily operation of microgrid as stochastic model to calculate network variables before fault occurrence. Hence, s this situation minimizes the microgrid operation cost and load shedding cost of islanded microgid which are subjected to optimal power flow equations in the presence of RESs and ESSswhile considering the uncertainty of the load and its generation. Therefore, by applying the proposed problem on the 9 and 32 bus microgrid, solving the problem by the Crow Search Algorithm (CSA), and selecting optimal size of RESs and ESSs, based on operation and protection coordination indices, the proposed SSCOPC can obtain a fast protection solution that considers system stability in faulty conditions.

This study optimizes the consumption of electrical energy by monitoring the power consumption caused by the activities of residents at different time intervals during the day and night and stores their electricity consumption in a database to create predicted models based on machine learning methods. Modeling the energy consumption of smart buildings, and then by presenting an algorithm for machine learning based on energy efficiency management system for automatic operation of home appliances based on the previous behavior of residents, leads to the formation of automatic smart building without resident intervention. Managing and monitoring of energy supply and demand process and integration of home solar panels in the building to supply part of the energy consumption was the main advantage of implementing smart grid technology in the building under study. This study showed that 9 kWh of electricity is generated daily from home solar panels. Finally, by comparing each part of the building with a similar normal building in the presence scenario where residents have the highest energy consumption, optimization results were displayed. So that in the lighting system to 25%, the outlet system to 15%, and the cooling and heating system about 40% of energy consumption was saved without reducing the comfort level of residents.