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This article proposes a robust inertial controller for converter-based distributed generators employed in low-inertia power systems like microgrids. The increasing penetration level of renewable energy sources based on power electronics converters in modern power systems reduces the inertial features of the system. It also increases concerns associated with the system uncertainty and sensitivity against disturbances. To cope with these challenges, by employing the proposed linear matrix inequality (LMI)-based mixed H 2 / H ∞ robust method, an optimal robust controller aided for inertial support as well as fast frequency restoration is provided. Using the proposed solution not only presents a better inertial response but also proposes a faster frequency restoration, by which the system’s frequency can be restored immediately following any disturbance, even in the presence of system uncertainties. Through in-detailed frequency response analysis and time-domain simulations for different scenarios, it is illustrated that the proposed mechanism can be successfully employed to address the inertial requirements in power electronic-based power systems. In addition, the proposed LMI-based mixed H 2 / H ∞ control solution is compared with a number of other solutions to illustrate its better performance against disturbances. Simulation results validate the merits and effectiveness of the proposed controller.

This article is treated with the protection coordination of bidirectional overcurrent relays (OCRs) in distribution power systems to obtain the best performance at a minimum total time dial setting (TDS). High penetration of distribution generation (DG) into distribution systems may significantly affect the protective system; since they increase the short-circuit level by injecting an extra share of fault current, it results in changes in the power/current flow direction. Accordingly, the fault current limiter (FCL) devices should be utilized to mitigate the short-circuit current. FCLs and DGs both will disturb the protection settings of protective equipment. The characteristics and the set point of inverse time relays depend on the fault clearing time (FCT) and the pick-up current. Therefore, an optimization approach is required to minimize the FCTs and TDSs of main relays and backup ones to protect all equipment. Moreover, this strategy selects the optimal placement of the FCL cooperatively according to the cost minimization. The optimization approach is implemented based on the particle swarm optimization (PSO) method within an extra parameter to improve the convergence speed and find the global solution, especially at initial iterations. The proposed optimization approach named developed PSO (DPSO) is tested on the IEEE 14-bus system and compared with some existing methods. Finally, the analysis and evaluations present the effectiveness of the best current transformer (CT) settings with

The needs of human communities for electrical energy is increasing every day, and as a result, the price of fossil fuels is steadily increasing. Considering the trend of advances in renewable energy technologies and the support of governments and energy policymakers to make more use of these clean and inexpensive resources. Limitations such as low capacity, the uncertainty of output power and stability issues have made it costly and difficult to use Distributed Energy Resources (DERs). To solve these problems, a new concept known as Virtual Power Plant (VPP) has been proposed to facilitate the exploitation of DERs. The VPP is a set of DERs that are put together for market participation. Also, efforts to reduce the amount of environmental pollution have replaced the use of Electric Vehicles (EV) instead of internal combustion engines. Regarding this paper, a new model for optimizing virtual power plant is presented. In this model, the Demand response (DR) has been used, whose modeling is based on Price-Based Demand Response for ordinary loads and incentive. To solve the problem a mixed integer linear programming model is proposed to maximize the profit of the virtual power plant. In order to see the effectiveness and satisfying performance of proposed model, a case study including DERs, EV, and loads is studied as test system. The simulation results using Matlab and GAMS show the efficiency of the proposed model and proves that simultaneous use of the price-based demand response program, smart charging, and the participation of electric vehicles in demand responses reduces operating costs.

In recent years, due to lack of sufficient quantity of fossil fuel, the need of Renewable Energy Sources (RESs) has become an important matter. In addition to the shortage of fossil fuel, global warming is another concern for many countries and companies. These issues have caused a large number of RESs to be added into modern distribution systems. Nevertheless, the high penetration of RESs beside the intermittent nature of some resources such as Wind Turbines (WT) and Photovoltaic (PV) cause the variable generation and uncertainty in power system. Under this condition, an idea to solve problems due to the variable outputs of these resources is to aggregate them together. A collection of Distributed Generators (DGs), Energy Storage Systems (ESSs) and controllable loads that are aggregated and then are managed by an Energy Management System (EMS) which is called Virtual Power Plant (VPP). The objective of the VPP in this paper is to minimize the total operating cost, considering energy loss cost in a 24 h time interval. To solve the problem, Imperialist Competitive Algorithm (ICA), a meta-heuristic optimization algorithm is proposed to determine optimal energy management of a VPP with RESs, Battery Energy Storage (BSS) and load control in a case study.

In this paper, Superconducting Fault Current Limiter has been used to restore the missing protection coordination between fuse and recloser in the distribution network in presence of distributed generation resources. In order to evaluate different SFCL models, various types of this equipment including resistive, inductive and hybrid are modelled and investigated for achieving protection coordination. The purpose of this paper is to limit the current injected by DGs connected to the radial distribution network with fuse and recloser protection in presence of DGs protection coordinate between fuses and reclosers confirmed. Since increasing the size of SFCL increases its price, using the Genetic and the PSO Algorithms to calculate the minimum size of different types of SFCL in order to maintain network protection coordination. Results of simulation performed on an IEEE-34 bus network in DIgSILENT® software show that using this optimization method, not only missing protection coordination of the fuse and recloser caused by presence of DG can be solved but also impedance of SFCL can be minimized.

This paper presents the optimal protection coordination (OPC) to grid-connected and islanded micro-grid (MG) consisting renewable energy sources (RESs), and energy storage system (ESS). The proposed approach considers objective function as minimizing the total operation time of the dual setting overcurrent relays (DSORs) in primary and backup protection mode. Problem constraints are the regulation parameters limits of the DSROs, sizing limit of FCLs and also the coordination time interval (CTI) inequality. MG includes generally RESs and ESS, hence, this problem needs to the daily optimal operation results of MG in both grid-connected and islanded modes to obtain the network variables before the fault occurrence. Therefore, it is minimized the MG operation and load shedding costs of islanded MG while it is constrained to MG optimal power flow equation in the presence of RESs and ESS. The proposed OPC includes non-linear model, hence, this paper uses the crow search algorithm (CSA) to obtain a reliable and optimal solution for this formulation. Finally, this strategy is implemented on the 9-bus and 32-bus MGs, thus, it can obtain the high quick protective solution for these MGs by selecting optimal regulation parameters and sizing to DSORs and FCLs, respectively, according to numerical results.

After restructuring the power systems, demand response programs form the main part of consumption management programs. This is because these programs are by nature particularly suitable for adapting to the new structure in power system management. These programs are considered as a suitable solution for solving some problems in power system restructuring. Unit commitment (UC) programs are used for power production with minimum cost. In this paper, after presenting an overview of demand response methods, a mechanism is proposed for simultaneous execution of the UC program, emergency demand response programs, and interruptible/curtailable loads. The proposed mechanism, in addition to decreasing the payment costs, ensures a flat load curve and increases system reliability.

In recent years, a large number of renewable energy sources (RESs) have been added into modern distribution systems because of their clean and renewable property. Nevertheless, the high penetration of RESs and intermittent nature of some resources such as wind power and photovoltaic (PV) cause the variable generation and uncertainty of power system. In this condition, one idea to solve problems due to the variable output of these resources is to aggregate them together. A collection of distributed generations (DGs) such as wind turbine (WT), PV panel, fuel cell (FC), and any other sources of power, energy storage systems, and controllable loads that are aggregated together and are managed by an energy management system (EMS) are called a virtual power plant (VPP). The objective of the VPP in this paper is to minimize the total operating cost for a 24-h period. To solve the problem, a metaheuristic optimization algorithm, teaching–learning based optimization (TLBO), is proposed to determine optimal management of RESs, storage battery, and load control in a real case study.

In the deregulated power system, it is necessary to develop an appropriate pricing scheme that can provide the useful economic information to market users, such as generation companies, transmission companies and customers. However, accurately assessment and allocating the transmission cost in the transmission pricing scheme is a challenge, although many methods have been proposed. The objective of this paper is to propose a simple transmission pricing scheme using tracing method based on the proportional tree, in which transmission fixed cost, congestion cost and loss cost are considered. A case study based on IEEE 24-bus test system is applied to assess the effectiveness of the cost allocation procedure.

In this paper, the transmission cost allocation problem is discussed in a deregulated electricity market. The proposed method is based on power flow equation. In this approach, first, the relation between the generator or load currents and the bus injection currents is defined using a power invariant matrix, then the active power flow through each line is expressed in terms of generator or load currents. A four-bus test system is used to explain how the proposed method allocates the cost of real power flow to generators or loads separately. The obtained results are compared with the conventionally adopted methodologies to defend easy implementation and effectiveness of the proposed method. The obtained results explicitly show that the proposed method is fitting and behaves in a physically fair manner. This method dominates the difficulties of conventionally used approaches, encouraging the economically optimal usage of the transmission facilities. A case study based on IEEE 24-bus test system is applied to assess the effectiveness of the cost allocation procedure.

Transmission pricing has become a major issue in the discussions about the deregulated electricity markets. Consequently, open access to the transmission system is one of the basic topics to allow competition among participants in the energy market. Transmission costs have an important impact on relative competition among participants in the energy market as well as on short- and long-term economic efficiencies of the whole electricity industry, although they represent only close to 10% of the energy market price. This paper deals with the design and tests of a transmission pricing method based on the optimal circuit prices derived from the economically adapted network (EAN). Prices derived from the EAN have the advantage of being in tune with the maximum revenue allowed to the owner of transmission assets and simplifying the optimal allocation of transmission costs among participants. Beginning from the conceptual design, the proposed method is tested on a three-bus network and on the IEEE 24-bus reliability test system.

The introduction of flexible AC transmission system (FACTS) in a power system improves the stability, reduces the losses, reduces the cost of generation and also improves the loadability of the system. In the proposed work, a non-traditional optimization technique, modified particle swarm optimization (MPSO) is used to optimize the various process parameters involved of FACTS devices in a power system. The various parameters taken into consideration were the location of the device, their type, and their rated value of the devices. The simulation was performed on a modified IEEE 30-bus power system with two types of FACTS controllers (SVC, TCSC), modeled for steady state studies. The optimization results clearly indicate that introduction of FACTS devices in a right location increases the loadability of the system and algorithm can be effectively used for this kind of optimization.