30 publications from this institution
In modern engineering, optimizing energy hub-based microgrids that incorporate renewable energy resources to meet both electrical and thermal demands presents a significant challenge. This study focuses on the stochastic optimization of a multi-carrier energy microgrid, integrating renewable energy sources to enhance efficiency and reduce operational costs. A demand response program is employed to optimize the allocation of costs and improve the load profiles for both electricity and thermal energy. To address the uncertainty of renewable resources, a scenario-based planning approach is implemented to reduce the impact of variability. The model schedules energy production and consumption for a 24-hour period, with objective functions targeting energy purchase costs, fuel costs, profits from energy sales, and greenhouse gas emission reduction. The proposed methodology is tested on a sample microgrid system using Python solvers for optimization. Results, analyzed under various scenarios, show a significant reduction in costs when compared to conventional systems. Specifically, the total cost for meeting electrical and thermal demands through the traditional electricity and gas network is 279,910 cents, while the optimized system reduces the cost to 164,682 cents, yielding a savings of approximately 41%. These findings highlight the effectiveness of the proposed optimization model in reducing both costs and environmental impact.
The electricity market has become a competitive model due to generation, transmission, distribution, and large-scale investments in microgrids where no electricity supplier intends to set the price. However, the competition itself provides opportunities for the buyer to be able to experience more profit. Based on the available optimal bidding model, the supplier can only offer the final bid. Hence, active participation on the demand side creates an efficient and reasonable electricity market, and also promotes additional optimal allocation of economic resources. This article has concentrated over the final marginal bidding price and creating an optimal bidding strategy for consumers. It has specifically focused and proposed a new optimized model for electricity market bidding to improve and obtain a maximum turnover and profit for players. Since the solution of the aforementioned issue is inadequate due to multiple decision variables in mathematical and classical methods; a new enhanced Harris Hawks optimization (HHO) is proposed. Trapping into local optimum point can be occurred in different optimization algorithms, while the proposed model has simple and efficient structure, without derivative operator, which is integrated with mathematical techniques to increase local and global search abilities. The standard HHO algorithm is utilized to increase the probability of entrapment in local optima through increasing the number of optimization variables; however, the challenge of large number of variables is resolved through proposing a novel local/global operators. The proposed model is examined with a standard IEEE system in 2-scenarios, with symmetric and asymmetric information through finding the optimal solution desirably.
In recent years, many countries have set specific goals to replace fossil fuel vehicles with the electric ones due to environmental concerns and issues related to energy supply security; it is predicted that using these vehicles will increase rapidly in the upcoming years. Therefore, in addition to home chargers, fast charging stations are needed to accelerate the charging speed and to save the costs of the consumed energy by the owner, thus lowering the disruptive effects of the home chargers on the power quality of the electricity grid. The price of the electric vehicle, independence, charging process and charging infrastructures are the main factors that have major effects on the progress and development of electricity. During the last few years, numerous concepts and topics such as energy management, infrastructure and the best charging plan with integrated energy and developed technologies are introduced for modeling charging stations. Therefore, the most important requirements in this field are improving the efficiency of charging stations in terms of charging speed, managing between charging and discharging, existence of renewable sources and Energy Storage System (ESS). Recognizing and studying these components and their development are the important parts of this research, which has not been studied before. In other words, this paper review the state-of-the-art aspects for different levels of designing a fast-charging station with complete coverage of the research work done related to the upcoming challenges. Considering the advantages and disadvantages of electric vehicles (EVs), some challenges in this concept and ideas for the future expansion of EVs charging station and its communications are introduced. Results from different surveys show that along with mutual communications and people's increasing desire for EVs, participation in planning will be beneficial for both the society and the government, which will result in the desired social welfare. Also, the presence of renewable resources due to technology development has become far more impressive. Finally, the various aspects of fast-charging stations along with an overview of probable areas for future study in this field are also presented.
With increasing the energy demand, the optimal and safe operation of power systems is the main challenge for engineers. Thus, a technique for the optimal implementation of demand response programs (DRPs), installation of distributed generation (DG) with power transmission distribution factors, and DC dynamic load flow is presented in this paper. In fact, finding the optimal time execution of DRPs and the bus for installing wind units with its probabilistic effects is considered. In this model, the congestion is decreased and the available transfer capability (ATC) rates are significantly improved. According to various types of price-based DRPs, the customers motivate to change their utilization models by shifting the price of electricity at different times. Finally, the proposed model is evaluated on the well-known IEEE 39-bus New England power system. The numerical results show the efficiency of the proposed method because, after its application, the available transmissibility values in the critical buses have significantly increased. At the same time, system peak loads, total system costs, and losses are reduced, and the voltage profile also shows a significant improvement. Totally, numerical results demonstrate that using the recommended algorithm, system loss and cost decrease by 9 percent and $4472.
The restructuring of the interconnected power systems under the Independent System Operator (ISO) managing to get an unforced and independent electricity market is the ultimate goal for the federal energy regulatory commission (FERC). Therefore, it is converted a common topic of general studies for the period of last years. Therefore, FERC presents various ancillary services such as frequency control based on load following under automatic generation control (AGC) in the deregulated environment. This paper presents a multi-area multi-source AGC in a deregulated power system that operates under deregulation platform on the bilateral policy having the thermal power plants with reheat turbine, gas, diesel and hydro power units in the form of a four-area interconnected power system with various of the possible contracts. Since damping of frequency oscillation under unexpected faults is very important to grantee system stability, therefore, the optimal tuning of proportional-integral-derivative controller with low pass filter is converted a single objective function optimization problem considering system parameter’s uncertainties and eigenvalues. The important benefit of this approach is its high insensitivity to large load changes and disturbances in the existence of unit parameter discrepancy and model’s nonlinearities. To cope with the disadvantage of the conventional controllers in the AGC power system, the proposed optimization problem is solved by a new modified virus colony search (MVCS). Simulations are carried out for a large-scale AGC power system under the deregulated regime with the various possible contracts and load distributions.
Wind power generation forecasting is a crucial aspect in renewable energy industry since accurate predictions of wind power generation can support the power grid operators and power plant owners to optimize their energy resources management. This will potentially reduce the purchase of extra electricity from other stakeholders or neighboring countries and will consequence significant cost savings and further reliability. With the increasing importance of wind power utilization in modern power systems, employing accurate forecasting model cannot be understated. In this study, a new probabilistic forecasting model based on the utilization of quantile functions is discussed. The proposed model integrates an adaptive optimal weighted continuous ranked probability score (CRPS) is presented to improve the computational efficiency of the prediction process and enabling more accurate forecast output. Also, an adaptive Adam optimization algorithm is presented for the CRPS loss minimization. A convolutional neural network based long short-term memory (CNN-LSTM) is employed to evaluate the quantile function parameters. To validate the effectiveness of our approach, the specific forecasting models have been compared across a wide range of scenarios. The obtained results unequivocally reveal the superiority and improved accuracy of the proposed forecasting model.
Increasing technology developments and economic considerations have also made the use of renewable energy sources (RESs) and energy storage systems (ESSs) inevitable. To achieve the holistic planning of a microgrid consisting of several energy resources, this paper proposes a novel multi-vector energy system based on electricity, heating, and water generation sources. To supply electrical energy from heat generation units, a water-power nexus (WPN), an ESS, photovoltaic (PV) sources, and a combined heat and power (CHP) system are used. Considering the importance of Hydro generation in planning, combined energy-water and water-only systems are used to supply water demand, while heat-power and heating generation systems are used to supply heating. To make the model more realistic, the effects of the valve point, maximum and minimum generation constraints, increasing/decreasing rate, energy, water, and heating demand balance were taken into account. The main objective function of this study was to minimize multi-vector energy system costs in 24 h. To promote demand-side performance, the price-based demand response program was used to reduce the final costs in the entire study period. The proposed models have been formulated as a mixed-integer linear problem solved by the CPLEX solver in GAMS. Simulation results show that the use of RES and their proper management can reduce the generation of a thermal generator, which reduces the costs considerably. With the demand response program, the costs were reduced by 1.03%. This paper presents a systematic method for optimal system control that can provide a regulatory basis for the use of integrated generation sources.
Demand-side management (DSM) is the modification of consumer utilization manner for energy through various methods to smooth their power consumption curve and increase their energy efficiency. Although the DSM improves the electricity grid stability and protects the environment, it allows customers to reduce their costs. The available literature classifies DSM into two different areas: (i) energy efficiency (EE) and (ii) demand response (DR). In other words, cement plants are the largest customers of electricity, so the implementation of DSM programs in these heavy industries is very important for electricity companies. Past studies have often examined the impact of DR programs in cement factories on solving peak problems and changing load profiles but have not studied the effects of DR implementation on the energy efficiency index in the short and long term in these industries. In this paper, while examining the effect of implementing three types of common DR programs in smart grids including demand bidding (DB), ancillary services (A/S), and time of use (TOU) on the load profile of cement plants, based on the mathematical model of the load and the real data of the case study, it is shown that the implementation of the TOU program has created the highest energy efficiency by reducing specific energy consumption (SEC) in cement plants.
This study focuses on optimizing hybrid energy storage systems for improved energy management in power networks. Combining batteries and supercapacitors, these systems offer a promising solution for addressing various network challenges, such as power quality enhancement and voltage stabilization. However, effective control remains a critical aspect. Conventional control methods are reviewed, highlighting their limitations. To overcome these challenges, a novel approach integrating fuzzy logic and rule-based systems is proposed. This hybrid method enables adaptable operation in both network-connected and independent modes, catering to diverse network conditions and operational requirements. The proposed control strategy aims to maintain DC bus voltage within acceptable limits, regulate battery and supercapacitor charge levels, and maximize supercapacitor utilization to prolong battery lifespan. Simulation studies conducted in Matlab/Simulink validate the efficacy of the proposed approach. Results demonstrate rapid voltage recovery with minimal overshoot and undershoot, ensuring stable network operation. Additionally, the proposed method significantly increases supercapacitor utilization compared to traditional methods, indicating enhanced system performance and energy storage capacity.
The increased number of Electric Vehicles (EVs) in smart grids has highlighted the need for fast charging stations to provide services in the minimum time duration. This duration for charging at the station can be regarded as a challenge due to increasing the grid load. To mitigate the effects of these disadvantages, Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) can be employed. Thus, the present paper aims to design a fast-charging station while considering parameters such as the solar panel capacity, storage systems, wind turbine, Demand Response (DR) program, and stochastic model of RESs. Accordingly, two models of wind power plant ownership are proposed for the station and the grid. The correct estimation of the wind-generated power can reduce the uncertainties in programming; thus, a forecasting method based on the fuzzy-neural network and improved Particle Swarm Optimization (PSO) algorithm with time-varying coefficients is proposed. In the first ownership, based on the forecast wind-generated power, the station signs a contract with the grid, and by using EV, RES, and ESS load management, try to reduce the imbalance and costs. In the second ownership, the wind power plant is at the service of the grid and the station owner makes revenues by servicing the grid. The objective function of the problem is based on the current net value over a 10-year time horizon, including the costs of performance and maintenance. The findings revealed that when the charging station uses load management, it increases profitability and reduces the initial capital investment in an acceptable manner. In the first and second ownerships, the total 10-year cost in the presence of Demand Response (DR) is reduced by 17.85% and 3.31, respectively. Based on the findings, the initial capital cost for supplying internal loads and providing flexible services to the grid is slightly higher in the second than the first ownership. The simulation results also indicate that the proposed hybrid algorithm forecasts wind speed changes with proper precision.
<title>Abstract</title> In contemporary engineering, devising an optimal plan for an energy hub-based microgrid, which incorporates renewable resources and meets both electrical and thermal demands, presents a substantial challenge. In this study, the stochastic optimization of a multi-carrier energy microgrid system with the presence of renewable energy sources is addressed. To control and optimally allocate costs and create a better profile for electrical and thermal loads, a demand response program is utilized. Given the uncertainty of renewable resources, scenario-based planning and reduction are proposed to address this issue. For realistic planning, this study models a 24-hour ahead scheduling, with objective functions based on energy purchase costs, fuel costs, profits from energy sales, and the reduction of greenhouse gas emissions. The proposed model and method are then applied to a sample system in a Python software environment using solvers. The results are analyzed under various scenarios, considering different parameters, including uncertainty and demand response programs. According to the obtained results, the total cost of meeting the electrical and thermal demand of consumers through the electricity and gas network is 279,910 cents, whereas the cost of meeting the same demand by the optimized system is 164,682 cents, achieving approximately 41% savings.
Optimal reactive power dispatch (ORPD) is a main issue for actual power transfer and network stability. This is why ORPD in power systems has been examined as a key problem. Solving such problems is an optimization problem due to the multitude of solutions. Reactive power control reduces grid losses and improves its voltage level. ORPD in power systems is performed by regulating the generators' voltage, transformer taps changing under load and switched capacitors. In this study, the goal of ORPD in power systems was to recognition rein variable to minimize the target function based on system constraints, reduce power losses, betterment the system's voltage profile and reduction the costs of operation. Control variables in this study were the power generated in power stations, generator terminal voltage size, parallel installed capacitor size and transformer tap size. Due to its unique complexity, this problem was solved by proposing a developed salp swarm algorithm (SSA). The proposed algorithm and method are evaluated on a well-known power system. The findings demonstrated the appropriate efficiency and superiority of this method compared to others.
Nowadays, power systems are in direct contact with sources such as renewable energy sources, Electric Vehicles (EVs), distributed generation resources, etc. In other words, these sources impose additional imbalances on the system and disturb the balance between generation and consumption. Hence, this study aims to model multi-area multi-source systems with the presence of units such as thermal, hydro, diesel, and gas units, wind farms, EVs, and energy storage sources. Without a properly coordinated controller, they can expose the system to severe stress under unwanted disturbances and cause it to deviate from normal operating conditions. Therefore, after modeling the proposed system, a Proportional–Integral–Derivative (PID) controller based on a low-pass filter is presented as a widely used device in the industry. Since tuning coefficients of this controller based on trial and error were irrational, this issue turned to a frequency-based optimization problem with different operating points in unwanted operating conditions in order to have a robust design. In the previous articles, a fixed working point employed to design the controller. To cover this issue, modeling towards reality, and a wider range of unexpected conditions and events, ± 30% design uncertainty and different loading conditions are considered in this study. Then, the developed Grasshopper Optimization Algorithm (GOA) with decreasing coefficients is applied to solve it. The time-varying coefficients can properly improve the local and global search capabilities. Different scenarios based on unwanted disturbances, uncertainties, and random load changes are considered to evaluate the system and algorithm performance. Numerical results obtained by analyzing comparative criteria on time and frequency domains indicated the proposed method had over 25% better performance compared to other methods, on average. The results obtained from the frequency and time domains properly show that the settlement and overshoot and undershoot times of the system equipped with the proposed controller are less compared to other designs in the literature, and better stability has been provided in the s-plane. Also, Nyquist and Bode analyses have shown that the system can ensure optimal performance in a wide range of working points.
Wind energy is one of the most important resources for clean power generation. However, due to its periodic and irregular nature, prediction of its output power is very challenging for power system operation and planning. Therefore, in this work, a multi-level model for its power generation is proposed. First, wind speed is considered as an input signal with nonlinear behavior through multi-level model based on support vector machine (SVM), wavelet transform (WT) and entropy-based feature selection (FS). In this model, the wind signal is applied to the wavelet transform and after decomposition, will be considered as the input of feature selection. Finally, the proposed SVM is used to predict the best pattern. The proposed method is evaluated on real world engineering test case; the results verifies the accuracy and shows high speed of suggested method.
Surely, electricity market participants need an accurate estimate for the price and load signals to properly manage their programs to increase their profit
Owing to the fast growth of the energy demand, the current power system may reach the marginal operating design resulting in unfavourable frequency/voltages conditions, the timeframe of stability and unexpected faults. Therefore, it is necessary to use all capacity without any additional development costs and having a secure and safe system. Hereby, this study addresses a novel coordination strategy for improving low-frequency oscillations in multi-machine models under various operating conditions. To achieve this goal, the proposed solution is divided into three main parts, the first part presents eigenvalues and time-domain analyzes to extract the unstable modes for a nonlinear model of multi-machine to capture a real-world problem. The second part proposes two controllers based on fuzzy theory and thyristor controlled series compensator (TCSC) with power oscillation damper (POD) structure to reach considerable damping of low-frequency oscillations. Since an uncoordinated design between two controllers in the power system aggravated instability, therefore, the last part proposes a modified virus colony search (VCS) to optimally adjust the decision variables with two conflicting objectives based on time and frequency domains. To demonstrate the proposed coordinated strategy, the well-known two-area and four-machine test system have been selected and the obtained results are compared in several loading conditions such as ± 20 load changing, with other available controllers through several analysing indices. According to numerical results, the proposed design can improve the statical indices such as integral of time multiplied absolute value of the error (ITAE) and figure of demerit (FD) about 12 and 11%, respectively. Also, the eigenvalues can be collected between the damping ratio 0.2 and real part -1.0.
The novelty of the article should be better expressed. Review the articles to be more up-to-date. Modeling the problem should be explained in better detail. Discuss and review the results and limitations of the proposed methodology. The abstract shou...
