Showing 12 results for Uncertainty
M. Zarif, M. H. Javidi, M. S. Ghazizadeh,
Volume 8, Issue 2 (6-2012)
Abstract
This paper presents a decision making approach for mid-term scheduling of large
industrial consumers based on the recently introduced class of Stochastic Dominance (SD)-
constrained stochastic programming. In this study, the electricity price in the pool as well as
the rate of availability (unavailability) of the generating unit (forced outage rate) is
considered as uncertain parameters. The self-scheduling problem is formulated as a
stochastic programming problem with SSD constraints by generating appropriate scenarios
for pool price and self-generation unit's forced outage rate. Furthermore, while most
approaches optimize the cost subject to an assumed demand profile, our method enforces
the electricity consumption to follow an optimum profile for mid-term time scheduling, i.e.
three months (12 weeks), so that the total production will remain constant.
V. Behnamgol, A. R. Vali,
Volume 11, Issue 2 (6-2015)
Abstract
In this paper, we extend the sliding mode idea to a class of unmatched uncertain variable structure systems. This method is achieved with introducing a new terminal sliding variable and the finite time stability of proposed method is proved using a new particular finite time condition in both reaching and sliding phases. In reaching phase new sliding mode controller is derived to guarantee the finite time stability of sliding surface with considering matched uncertainty. Also in sliding phase, because of introducing a new terminal sliding variable, the finite time stability of state variables with considering unmatched uncertainty has been guarantee. Therefore in proposed algorithm we are able to adjust reaching and sliding times in the presences of both matched and unmatched uncertainty. This algorithm is applied to designing control law for a moving cart system with bounded matched and unmatched uncertainties. Simulation results show the effectiveness and robustness of the proposed algorithm.
F. Amini, R. Kazemzadeh,
Volume 13, Issue 1 (3-2017)
Abstract
Development of distributed generations’ technology, trends in the use of these sources to improve some of the problems such as high losses, low reliability, low power quality and high costs in distributed networks. Choose the correct location to install and proper capacity of these sources, such as important things that must be considered in their use. Since distribution networks are actually unbalanced and asymmetric consumption loads are different, so in this paper with optimal placement and sizing of distributed generation sources that dependent on the load model and type of load connection and the uncertainties which caused by the generated power of wind turbines and solar panels, the positive effects of these sources have been examined on unbalanced distribution network. Hence with linear three-phase unbalanced load flow method and IPSO algorithm, allocation of distributed generation sources in IEEE standard of 37 bus unbalanced network have been done. Obtained results show improvement of voltage profile in each phase and reduction of network power losses and buses’ voltage unbalance factor.
V. Behnamgol, A. R. Vali, A. Mohammadi,
Volume 14, Issue 3 (9-2018)
Abstract
In this paper, a new guidance law is designed to improve the performance of a homing missiles guidance system in terminal phase. For this purpose first of all, the two dimensions equations of motion are formulated, then the approximation dynamic of missile control loop is added to these equations which are nonlinear whit unmatched uncertainty. Then, a new adaptive back-stepping method is developed in order to control this system. An adaptive term is used in the control law that is converged to the uncertainty. This convergence is proved based on Lyapunov stability theorem. Therefore using this adaptive term in the control law can be eliminated the uncertainty. Based on this algorithm, a new guidance law is designed. Then its performance is compared with common guidance laws in a guidance loop simulation in the presence of control loop dynamics.
H. Kiani Rad, Z. Moravej,
Volume 15, Issue 3 (9-2019)
Abstract
In this paper, a new method is conducted for incorporating the forecasted load uncertainty into the Substation Expansion Planning (SEP) problem. This method is based on the fuzzy clustering, where the location and value of each forecasted load center is modeled by employing the probability density function according to the percentage of uncertainty. After discretization of these functions, the location and value of each of the new load centers are determined based on the presented fuzzy clustering based algorithm. A Genetic Algorithm (GA) is used to solve the presented optimization problem in which the allocations and capacities of new substations as well as the expansion requirements for the existing ones are determined. With the innovative presented method, the impact of uncertainty of the power and location of the predicted loads on the results of SEP is measured, and finally, it is possible to make a proper decision for the SEP. The significant features of this method can be outlined as its applicability to large-scale networks, robustness to load changes, the comprehensiveness and also, the simplicity of applying this method to various problems. The effectiveness of proposed method is demonstrated by application on a real sub-transmission system.
M. Khajevand, A. Fakharian, M. Sedighizadeh,
Volume 16, Issue 3 (9-2020)
Abstract
Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used. The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied.
M. Ahmadi Jirdehi, V. Sohrabi-Tabar,
Volume 17, Issue 3 (9-2021)
Abstract
Control center of modern power system utilizes state estimation as an important function. In such structures, voltage phasor of buses is known as state variables that should be determined during operation. To specify the optimal operation of all components, an accurate estimation is required. Hence, various mathematical and heuristic methods can be applied for the mentioned goal. In this paper, an advanced power system state estimator is presented based on the adaptive neuro-fuzzy interface system. Indeed, this estimator uses advantages of both artificial neural networks and fuzzy method simultaneously. To analyze the operation of estimator, various scenarios are proposed including impact of load uncertainty and probability of false data injection as the important issues in the electrical energy networks. In this regard, the capability of false data detection and correction are also evaluated. Moreover, the operation of presented estimator is compared with artificial neural networks and weighted least square estimators. The results show that the adaptive neuro-fuzzy estimator overcomes the main drawbacks of the conventional methods such as accuracy and complexity as well as it is able to detect and correct the false data more precisely. Simulations are carried out on IEEE 14-bus and 30-bus test systems to demonstrate the effectiveness of the approach.
F. Amiri, M. H. Moradi,
Volume 17, Issue 4 (12-2021)
Abstract
In this paper, a coordinated control method for LFC and SMES systems based on a new robust controller is designed. The proposed controller is used to compensate for frequency deviations related to the power system, to prevent excessive power generation in conventional generators during load disturbances, and to reduce power fluctuations from wind power plants. The new robust controller does not require the measurement of all the power system states and it only uses the output feedback. It also has a higher degree of freedom than the conventional robust controllers (conventional output feedback) and thus it helps improve the system control. The proposed control method is highly robust against load and distributed generation resources (wind turbine) disturbances and it is also robust against the uncertainty of the power system parameters. The proposed method is compared under several scenarios with the coordinated control method for LFC and SMES systems based on Moth Swarm Algorithm-optimized PID controller, the LFC system based on Moth Swarm Algorithm-optimized PID controller with SMES, the coordinated control method for LFC and SMES systems based on Robust Model Predictive Control, and the LFC system based on optimized PID controller without SMES and it puts on satisfactory performance. The simulation was performed in MATLAB.
S. Rajamand,
Volume 18, Issue 2 (6-2022)
Abstract
Fair distribution of generated power has a significant impact on the performance of the power system. Many methods have been proposed for the safe and secure operation of power systems under the uncertainties of distributed generators and system load. In this paper, we present an optimal power distribution algorithm for distributed generators against uncertainties and load changes of direct-current and alternating-current transmission systems. In this optimal algorithm, considering the stable-state constraints for all uncertainties is performed. In order to establish these constraints at the lowest cost, the adaptive droop coefficients are employed to optimize the power sharing, reloading and modifying the power coefficient of each distributed generator in the power system. Simulation results show the efficiency of the proposed method to improve the performance of the system and reduce the total cost. The voltage/power deviation from reference value in the proposed method is about 1-1.5% where in the conventional droop control, it is more than 2-3%. In addition, in the same uncertainty of the load/distributed generator power in the test system, proposed method requires 20% less power redistribution compared to the conventional droop method. Also, total cost increasing (due to uncertainty increasing) in the conventional droop method is higher than the proposed method (about 10-15%) which shows the robustness of the suggested method against uncertainty changes.
M. Ehsani, A. Oraee, B. Abdi, V. Behnamgol, S. M. Hakimi,
Volume 19, Issue 1 (3-2023)
Abstract
A novel nonlinear controller is proposed to track active and reactive power for a Brushless Doubly-Fed Induction Generator (BDFIG) wind turbine. Due to nonlinear dynamics and the presence of parametric uncertainties and perturbations in this system, sliding mode control is employed. To generate a smooth control signal, dynamic sliding mode method is used. Uncertainties bound is not required in the suggested algorithm, since the adaptive gain in the controller relation is used in this study. Convergence of the sliding variable to zero and adaptive gain to the uncertainty bound are verified using Lyapunov stability theorem. The proposed controller is evaluated in a comprehensive simulation on the BDFIG model. Moreover, output performance of the proposed control algorithm is compared to the conventional and second-order sliding mode and proportional-integral-derivative (PID) controllers.
A. Ghanuni, R. Sharifi, H. Feshki Farahani,
Volume 19, Issue 3 (9-2023)
Abstract
Operation scheduling of a Virtual Power Plant (VPP) includes several challenges for the system according to the uncertain parameters, and security requirements, which intensify the need for more efficient models for energy scheduling and power trading strategies. Making suitable decisions under uncertainties, related to Renewable Energy Resources (RES), loads, and market prices impose extra considerations for the problem to make a clearer insight for the system operators to participate in local markets. This paper proposes a new risk-based hybrid stochastic model to investigate the effects of wind turbine power fluctuations on profit function, energy scheduling, and market participating strategies. Also, an incentivized Demand Response Program (DRP) is used, to enhance the system’s efficiency. The results of the study indicate that the proposed model based on Information Gap Decision Theory (IGDT) approach makes a clearer environment for the decision-maker to be aware of the effects of risk-taking or a risk-averse strategy on financial profits. The results show that a 30% of robustness and opportunity consideration would change the profit function from -12.5% up to 14.5%, respectively. A modified IEEE 33 bus test system is used to simulate a technical VPP considering the voltage stability and thermal capacity of line requirements.
Majid Najjarpour, Behrouz Tousi, Shahaboddin Yazdandoust Moghanlou,
Volume 20, Issue 1 (3-2024)
Abstract
In recent decades, because of the rapid population growth of the world, considerable changes in climate, the reduction of fossil fuel sources to consume the traditional power plants and their high depreciation, and the increase in fuel prices. Due to the increased penetration of DG units which have a random nature into the power system, the ordinary equations of power flow must be changed. For the power system to operate in a stable condition estimating future demand and calculating the important and operational indexes such as losses of the power system is an important duty that must be done precisely and rapidly. In this paper, the Improved Taguchi method and phasor measurement unit are used to model the uncertainties of DGs and estimate the error of voltage, respectively. The results show that the magnitude error and the angle error of voltage are decreased using PMU. The applied optimal power flow and state estimations are analyzed and verified using standard IEEE 30-bus and 14-bus test power systems by MATLAB, and MINITAB softwares. The Made Strides Taguchi strategy appears to have modeled the DG units precisely and successfully, and using the PMU, the mistake of the point and greatness estimation is exceptionally moot. The values that were evaluated are very close to the values that were done by the Newton-Raphson stack stream.
