2018-08-18T12:56:12Z
http://joape.uma.ac.ir/?_action=export&rf=summon&issue=61
Journal of Operation and Automation in Power Engineering
J. Oper. Autom. Power Eng.
2322-4576
2322-4576
2015
3
1
Robust Agent Based Distribution System Restoration with Uncertainty in Loads in Smart Grids
N.
Zendehdel
This paper presents a comprehensive robust distributed intelligent control for optimum self-healing activities in smart distribution systems considering the uncertainty in loads. The presented agent based framework obviates the requirements for a central control method and improves the reliability of the self-healing mechanism. Agents possess three characteristics including local views, decentralizations and autonomy. The message, exchanged among neighboring agents, is used to develop a global information discovery algorithm and updates the topology information of out-of-service areas, available supply capacity and routing information. Fuzzy description is employed to take into account the uncertainties of measurements in which are exchanged between agents. Moreover, to find the optimal restoration plan, incorporating the discovered data, a routing problem is developed as a fuzzy binary linear optimization problem. This problem is approached by a novel method using a specific ranking function. Finally, robustness and applicability of the proposed self-healing method is tested on two standard case studies. The obtained results emphasize that ignoring the uncertainties may lead to non-realistic solutions.
Agent based self-healing framework
Fuzzy binary linear optimization
Smart grid
Uncertain load
2015
06
06
1
22
http://joape.uma.ac.ir/article_291_4adddb47b5ba8e299f3f84df21ad83a0.pdf
Journal of Operation and Automation in Power Engineering
J. Oper. Autom. Power Eng.
2322-4576
2322-4576
2015
3
1
Combined Economic Dispatch and Reliability in Power System by Using PSO-SIF Algorithm
E.
Babaei
N.
Ghorbani
Reliability investigation has always been one of the most important issues in power systems planning. The outages rate in power system reflects the fact that more attentions should be paid on reliability indices to supply consumers with uninterrupted power. Using reliability indices in economic dispatch problem may lead to the system load demand with high reliability and low probability of power's outage rate. In this paper, the Economic Dispatch (ED) problem is optimized using the reliability indices. That is, ED problem and system reliability are proposed as Combined Economic Dispatch and Reliability (CEDR) problem. In CEDR problem, it is tried to utilize generating units in a way that we have high reliability in supplying the system load demand as well as the minimum fuel costs. Due to multi-objective and non-convex characteristics of this problem, Particle Swarm Optimization with Smart Inertia Factor (PSO-SIF) is used to solve the problem. In this research, the ED of power plants is successfully implemented in two systems with 6 and 26 generating units considering emission and system reliability.
Economic dispatch
reliability
Particle Swarm Optimization
Smart inertia
Non-convex
2015
06
06
23
33
http://joape.uma.ac.ir/article_292_387f45490d8f288cf4cd96f90fa8fc60.pdf
Journal of Operation and Automation in Power Engineering
J. Oper. Autom. Power Eng.
2322-4576
2322-4576
2015
3
1
Stochastic Multiperiod Decision Making Framework of an Electricity Retailer Considering Aggregated Optimal Charging and Discharging of Electric Vehicles
A.
Badri
K.
Hoseinpour Lonbar
This paper proposes a novel decision making framework for an electricity retailer to procure its electric demand in a bilateral-pool market in presence of charging and discharging of electric vehicles (EVs). The operational framework is a two-stage programming model in which at the first stage, the retailer and EV aggregator do their medium-term planning. Determination of retailer's optimum selling price and the amount of energy that should be purchased from bilateral contracts are medium-term decisions that are made one month prior to real-time market. At the second stage, market agents deal with their activities in the short-term period. In this stage the retailer may modify its preliminary strategy by means of pool market option, interruptible loads (ILs), self-scheduling and EVs charging and discharging (V2G). Thus, a bi-level programming is introduced in which the upper sub-problem maximizes retailer profit, whereas the lower sub-problem minimizes the aggregated EVs charging and discharging costs. Final decision making is obtained in this stage that may be considered as a day-ahead market, keeping in mind the medium-term decisions. Due to the volatility of pool price and uncertainties associated with the consumers and EVs demand, the proposed framework is a mixed integer nonlinear stochastic optimization problem; therefore, Monte Carlo Simulation (MCS) is applied to solve it. Furthermore, a market quota curve is utilized to model the uncertainty of the rivals and obtaining retailer's actual market share. Finally, a case study is presented in order to show the capability and accuracy of the proposed framework.
Aggregator
Bilateral
Decision making
Electric vehicle
2015
06
06
34
46
http://joape.uma.ac.ir/article_298_39fe9062dc0300ac42e66cf449c4f252.pdf
Journal of Operation and Automation in Power Engineering
J. Oper. Autom. Power Eng.
2322-4576
2322-4576
2015
3
1
An Intelligent Approach Based on Meta-Heuristic Algorithm for Non-Convex Economic Dispatch
R.
Sedaghati
F.
Namdari
One of the significant strategies of the power systems is Economic Dispatch (ED) problem, which is defined as the optimal generation of power units to produce energy at the lowest cost by fulfilling the demand within several limits. The undeniable impacts of ramp rate limits, valve loading, prohibited operating zone, spinning reserve and multi-fuel option on the economic dispatch of practical power systems are scrutinized in this paper. Thus, the proposed nonlinear non-convex formulation is solved by a new modified version of bio-inspired bat algorithm. Due to the complexities associated with the large-scale optimization problem of economic dispatch, adaptive modifications are added to the original bat algorithm. The modification methods are applied at two separate stages and pledge augmentation in convergence rate of the algorithm as well as extricating the algorithm from local optima. Veracity of the proposed methodology are corroborated by performing simulations on three IEEE test systems.
Non-convex economic dispatch
Modification mechanism
Meta-heuristic algorithm
Nonlinear constrained optimization
2015
06
06
47
55
http://joape.uma.ac.ir/article_294_942ac5fc153ceb34d00d6fe5380987d7.pdf
Journal of Operation and Automation in Power Engineering
J. Oper. Autom. Power Eng.
2322-4576
2322-4576
2015
3
1
Optimal Reconfiguration and Capacitor Allocation in Radial Distribution Systems Using the Hybrid Shuffled Frog Leaping Algorithm in the Fuzzy Framework
Mostafa
Sedighizadeh
Mahdi
mahmoodi
In distribution systems, in order to diminish power losses and keep voltage profiles within acceptable limits, network reconfiguration and capacitor placement are commonly used. In this paper, the Hybrid Shuffled Frog Leaping Algorithm (HSFLA) is used to optimize balanced and unbalanced radial distribution systems by means of a network reconfiguration and capacitor placement. High accuracy and fast convergence are the highlighted points of the proposed approach because of solving the multi-objective reconfiguration and capacitor placement in fuzzy frame work. These objectives are the minimization of total network real power losses, the minimization of buses voltage violation, and load balancing in the feeders. Each objective is transferred into fuzzy domain using membership function and fuzzified separately. Then, the overall fuzzy satisfaction function is formed and considered as a fitness function. To gain the optimal solution, the value of this function will be maximized. In the literature, several reconfiguration and capacitor placement methods have been investigated, which are implemented separately. However, there are few studies which simultaneously apply these two strategies. The proposed algorithm has been implemented in three IEEE test systems (two balanced and one unbalanced systems). Numerical results obtained by simulation show that the performance of the HSFLA algorithm is much higher than several other meta-heuristic algorithms.
Artificial Intelligence
Optimal Reconfiguration and Capacitor Placement
Shuffled Frog Leaping Algorithm (SFLA)
Multi-objective optimization
Distribution Systems
2015
06
06
56
70
http://joape.uma.ac.ir/article_295_116ff0b2ad2377b2fe7878419e10b594.pdf
Journal of Operation and Automation in Power Engineering
J. Oper. Autom. Power Eng.
2322-4576
2322-4576
2015
3
1
Condition Monitoring Techniques of Power Transformers: A Review
Z.
Moravej
S.
Bagheri
Power transformers provide a vital link between the generation and distribution of produced energy. Such static equipment is subjected to abuse during operation in generation and distribution stations and leads to catastrophic failures. This paper reviewed the techniques in the field of condition monitoring of power transformers in recent years. Transformer monitoring and diagnosis are the effective techniques for preventing the eventual failures and contributing to ensure the planâ€™s reliability. This paper provided a survey on the existing techniques for monitoring, diagnosis, condition evaluation, maintenance, life assessment and possibility of extending the life of the existing assets of power transformers with be appropriate classifications. Thus, this survey could help researchers through providing better techniques for condition monitoring of power transformers.
Ageing
Maintenance plans
Condition monitoring techniques
Power transformers
2015
06
06
71
82
http://joape.uma.ac.ir/article_296_b806f25e611b4700b7ad55a7d4a665f9.pdf
Journal of Operation and Automation in Power Engineering
J. Oper. Autom. Power Eng.
2322-4576
2322-4576
2015
3
1
Probabilistic Multi Objective Optimal Reactive Power Dispatch Considering Load Uncertainties Using Monte Carlo Simulations
S.M.
Mohseni-Bonab
A.
Rabiee
S.
Jalilzadeh
B.
Mohammadi-Ivatloo
S.
Nojavan
Optimal Reactive Power Dispatch (ORPD) is a multi-variable problem with nonlinear constraints and continuous/discrete decision variables. Due to the stochastic behavior of loads, the ORPD requires a probabilistic mathematical model. In this paper, Monte Carlo Simulation (MCS) is used for modeling of load uncertainties in the ORPD problem. The problem is formulated as a nonlinear constrained multi objective (MO) optimization problem considering two objectives, i.e., minimization of active power losses and voltage deviations from the corresponding desired values, subject to full AC load flow constraints and operational limits. The control variables utilized in the proposed MO-ORPD problem are generator bus voltages, transformersâ€™ tap ratios and shunt reactive power compensation at the weak buses. The proposed probabilistic MO-ORPD problem is implemented on the IEEE 30-bus and IEEE 118-bus tests systems. The obtained numerical results substantiate the effectiveness and applicability of the proposed probabilistic MO-ORPD problem.
Monte Carlo simulation
Multi objective optimal reactive power dispatch
Real power loss
Voltage deviation
2015
06
06
83
93
http://joape.uma.ac.ir/article_297_24132e6e4adb189bffd1678113c9d456.pdf