JOURNAL OF IRANIAN ASSOCIATION OF ELECTRICAL AND ELECTRONICS ENGINEERS
Year:2015 | Volume:12 | Issue:1|Papers Count:13

In this paper, a model for power management of an isolated balanced microgrid considering technical constraint such as: small signal stability and dynamic response is presented. In power management of an isolated balanced microgrid, it is necessary to consider technical and economical constraints. The proposed model consists of two stages. First, operation cost is minimized, then at the second stage, considering economical operation while load variation, small signal linearized model of a microgrid and technical constraints such as: rise time, settling time, damping coefficient modes, optimal value of droops are evaluated. Simulation has been done using GAMS, MATLAB software and the results show the effectiveness of proposed the model.

Incorporating the demand response program (DRP) is one of the best approaches to increase the efficiency level and improving the competitive electricity market performance. In a competitive market, consumers can response to the wholesale market price variations during different time scale. The DRP allows the independent system operator to decrease the price volatility in peak hours. Different references present various models for consumers’ response to the energy price but generally DRP can be categorized into two main groups which are: time based program and incentive based program. To determine the DR model, predicted values of energy price are largely uncertain, so it is desired to determine the optimal behavior of demand in a way that can be used to lessen the risk resulting from exposure to fluctuating price. In this paper a comprehensive model of DR is presented considering incentives and penalties set by policymaker and underprices uncertainty. The risk management method which is used in this paper is a conditional value at risk. In order to evaluate the performance of the proposed model, numerical studies are conducted on the Iranian interconnected network load profile on the annual peak day of the year 2012.

Distributed generations that are connected to the network via a converter, employ dq current control method to control their active and reactive power components in grid-connected mode. In this paper a simple lead-lag control strategy is proposed for a distributed generation (DG) unit in island mode. When it is connected to the utility grid, the DG is controlled by a conventional dq-current control strategy for active and reactive power components. Once the islanding occurs, the dq-current controller is disabled and the proposed controller is used in order to for control of the DG unit operating with its local load. The problem of tuning of controller parameters is converted to an optimization problem with a time-domain objective function which is solved by a genetic algorithm. To achieve a robust performance ITAE criterion is used as objective function. The robustness of controller is proved by zero-pole diagram and frequency domain analysis. Simulations results under different operating conditions verify robustness of controller in comparison with a classical d-q based controller. The results reveal that the proposed control strategy has an excellent capability in achieving good robust performance and greatly enhances the dynamic stability of the system against load parameter uncertainties.

The micro-turbines can be used to supply both thermal and electrical energy demands of islanded microgrid. Depleting fossil fuels and ever-increasing environmental concerns have attracted much attention to the renewable energy resources. Meanwhile, the stochastic nature, non-compliance with the load, inability to meet the thermal load and high cost of the wind and solar resources are disadvantages for them. In this paper, proposed microgrid includes a storage energy system, heater, micro-turbines and boiler, and is designed so that not only the mentioned problems could be solved but also the optimal size of renewable and non-renewable resources in the microgrid, for supplying electrical and thermal load, is determined. The renewable resources development strategies and optimal size for other resources is determined with using sensitivity analysis for fuel prices, pollution penalties and for government assistance in financing renewable resources. Modified Particles Swarm Optimization (MPSO) Algorithm is used to obtain global optimal solution. IEEE load as well as wind speed and solar radiation data, from the Ardabil region, have been used for our case study.

In this paper, the effect of incentive-based support schemes on promoting renewable energy resources is investigated in the generation expansion planning (GEP) framework, while the environmental issues are considered. Hence, a comprehensive GEP model incorporated with Feed-in-Tariff (FIT) mechanism is proposed. By considering a restructured environment, gravitational search algorithm (GSA) is employed to find the optimal GEP strategy from a generation company point of view. The numerical studies confirm the fact that sufficient incentive policies intervention is treated as a necessity in the renewable expansion planning resulting in more environmental protection.

Recently, with growth in utilization of renewable resources engineers and researchers have been motivated to offer new methods for their application in distribution networks. In this regard, optimal sitting and sizing of renewable resources as two main parameters in designing distribution network can reduce loss, operation costs and voltage deviations. In this paper, a multi-objective evolutionary algorithm based on fuzzy methods has been proposed for sitting and sizing of renewable resources. The provided algorithm minimizes economic, environmental and technical objective functions on 70-bus distribution test system. The results show that with optimal sitting and sizing of renewable resources in different scenarios costs, pollution and loss can be reduced significantly.

This paper proposes a dynamic game approach to distributed energy resource (DER) expansion planning from investors’ viewpoint with incomplete information. An innovative framework is proposed here to encounter different aspects of DER planning. Wind turbines, gas engines and combined heat and power (CHP) are considered as DERs in this study. For reducing the risk of investment, some support schemes are proposed for wind and CHP. Furthermore, strategic uncertainty of rival behavior as one of the uncertainties has been modeled in electricity market using incomplete information game theory. By the proposed model, the dynamic behaviors of DERs investors and the effects of regulatory intervention on expansion planning are analyzed. The proposed model is capable of evaluating the mutual effects of supporting policies on penetration rate of multi-resources simultaneously. The effectiveness of the proposed model is illustrated through implementing on a test system.

One of the most important goals of restructuring and privatization in electricity industry has been competition of firms to improve productivity. In this new competitive environment, electricity sellers try to select the best strategy for maximizing their profits through participating in energy market.Hence, competitive electricity market can be considered as the arena of a strategic game in which, generation companies-as players of this game- compete to gain maximum benefit. This paper first describes the concepts of game theory and electricity markets precisely and then develops an algorithm for direct application of Nash equilibrium method to analyze strategic behavior of electricity sellers in a Bertrand Game. For better understanding of GENCOs' strategic gaming and for studying the consequences of their decisions, several case studies are carried out over a well-known test system and the results are discussed.

Despite the fact that transmission network has a vital role in electricity markets and cost of transmission expansion is considerable, but transmission charges represent a small percentage of power system operating costs. Thus, transmission cost allocation should be a reasonable economic indicator to make decisions on facility allocation, transmission expansion and other similar market tasks.This paper, focusing on value-based transmission pricing, includes commercial and reliability benefits. The capacity of each transmission facility is divided into two parts: commercial capacity and reliability capacity. Commercial capacity represents the economic role of transmission capacity as well as reliability capacity that represents the security role of transmission. The cost allocation of commercial capacity is determined by calculation of beneficiaries of each transmission facility. For reliability capacity of each facility, the contribution of each transmission user is determined by estimation of reliability benefits of each facility by taking into account transmission forced outage rates as well as the outage impact factors. With this transmission pricing method, we allocate the transmission cost to the users in a more reasonable way as compared to conventional transmission pricing. Finally, the proposed method is applied to a 3-bus and 24-bus (IEEE - RTS) test system.

In this paper, solving optimal power flow problem has been investigated by using hybrid particle swarm optimization and Nelder Mead Algorithms. The goal of combining Nelder-Mead (NM) simplex method and particle swarm optimization (PSO) is to integrate their advantages and avoid their disadvantages. NM simplex method is a very efficient local search procedure but its convergence is extremely sensitive to the selected starting point. In addition, PSO belongs to the class of global search procedures but requires much computational effort. PSO-NM algorithm is proposed in order to solve the optimal power flow and find optimal settings of OPF control variables. The proposed method is examined on IEEE 30-bus standard test system by considering fuel cost minimization, voltage profile improvement, and voltage stability enhancement in normal and contingency conditions. It is also possible to apply the mentioned algorithm for optimal settings of OPF control variables considering non-smooth piecewise quadratic and non-convex cost functions. Simulation results are presented and compared with other intelligent algorithms in the previous literature. The results indicate that PSO-NM algorithm is effective in solving OPF problem.

The application of Flexible AC Transmission System (FACTS) devices shows that they can control the power system technical parameters effectively. However, optimal placement and sizing of them are difficult problems and analytical methods cannot be used to solve these problems. To solve such problems, some evolutionary algorithms are employed. The efficiency of these algorithms is related to their objective function. In this paper, two multi objective optimization algorithms, namely: Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) are used to solve the congestion problem in power system. To verify our proposed method, simulations are performed to modify IEEE 14-bus and modified IEEE 30-bus test systems. Firstly, load of the system is increased uniformly until congestion occurs. Then, optimization algorithms are used to find out the optimal location and size of FACTS devices, as well as the optimal amount of changes in active power generation. Two types of FACTS devices namely: Static VAr Compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC) are used for congestion elimination. The obtained results show the effectiveness of the proposed objective functions.

The nonlinear nature and model uncertainties in electric power system show the importance of designing a suitable controller to operate under different operating conditions. In this paper, the problem of designing controller for UPFC is carried out with two objectives of easy implementation and operation under different loading conditions. In this regard, model reference adaptive system is proposed for UPFC control and this MRAS is improved by using PSO method. Besides, in order to limitation of input signal and also robustness, the normalization technique is used. The performance of the proposed algorithm is studied under nominal and heavy loading conditions and simulation results are presented to show robust performance of the proposed method. Also, the results of the proposed algorithm are compared with a PI type controller which is tuned by using PSO. Simulation results show the ability and effectiveness of the proposed method in comparison with the classical method.