Microgrid is a new concept for future energy distribution system that enables renewable energy integration. It generally consists of multiple distributed generators that are usually interfaced to the grid through power inverters. For the islanding operation of ac microgrids, two important tasks are to share the load demand among multiple parallel connected inverters proportionately, and maintain the voltage and frequency stabilities. This paper reviews and categorizes various approaches of power sharing control principles. Simultaneously, the control schemes are graphically illustrated. Moreover, various control approaches are compared in terms of their respective advantages and disadvantages. Finally, this paper presents the future trends.

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Review of Power Sharing Control Strategies for Islanding Operation of AC Microgrids

This paper proposes a secondary voltage control of microgrids based on the distributed cooperative control of multi-agent systems. The proposed secondary control is fully distributed; each distributed generator only requires its own information and the information of some neighbors. The distributed structure obviates the requirements for a central controller and complex communication network which, in turn, improves the system reliability. Input-output feedback linearization is used to convert the secondary voltage control to a linear second-order tracker synchronization problem. The control parameters can be tuned to obtain a desired response speed. The effectiveness of the proposed control methodology is verified by the simulation of a microgrid test system.

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Distributed Cooperative Secondary Control of Microgrids Using Feedback Linearization

During the last decade, a big progress has been achieved in the analysis and numerical treatment of Initial Value Problems (IVPs) in Differential Algebraic Equations (DAEs) and Ordinary Differential Equations (ODEs). In spite of the rich variety of results available in the literature, there are still many specific problems that require special attention. Two of such, which are considered in this work, are the optimization of order of accuracy and reduction of cost of functional evaluations of Explicit Runge - Kutta (ERK) methods.

Traditionally, the maximum attainable order p of an s-stage ERK method for advancing the solution of an IVP is such that
p(s)
 4
 
In 1999, Goeken presented an s-stage ERK Method of order p(s)=s +1,s>2.
However, this work focuses on the design of a new approximation algorithm that reduces the cost of functional evaluations and yet increases the attainable order higher 
U n and Jonhson [94]; and the classical ERK methods. The order p of 
the new scheme called Multiderivative Explicit Runge-Kutta (MERK) Methods is such that p(s) 2. The stability, convergence and implementation for the optimization of IVPs in DAEs and ODEs systems are also considered.

Numerical solution of initial value problems in differential - algebraic equations

Uneconomical extension of the grid has led to generation of electric power at the end user facility and has been proved to be cost effective and to an extent efficient. With augmented significance on eco-friendly technologies the use of renewable energy sources such as micro-hydro, wind, solar, biomass and biogas is being explored. This paper presents an extensive review on various issues related to Integrated Renewable Energy System (IRES) based power generation. Issues related to integration configurations, storage options, sizing methodologies and system control for energy flow management are discussed in detail. For stand-alone applications integration of renewable energy sources, performed through DC coupled, AC coupled or hybrid DC–AC coupled configurations, are studied in detail. Based on the requirement of storage duration in isolated areas, storage technology options can be selected for integrated systems. Uncertainties involved in designing an effective IRES based power generation system for isolated areas is accounted due to highly dynamic nature of availability of sources and the demand at site. Different methodologies adopted and reported in literature for sizing of the system components are presented. Distributed control, centralized and hybrid control schemes for energy flow management in IRES have also been discussed.

A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control

Transition to a sustainable energy environment results in aggregated generator and load dynamics in the distribution network. State estimation is a key function in building adequate network models for online monitoring and analyzes. The requirements of distribution system state estimation (DSSE) is becoming stringent because of the needs of new system modeling and operation practices associated with integration of distributed energy resources and the adoption of advanced technologies in distribution network. This paper summarizes the state-of-the-art technology, major hurdles, and challenges in DSSE development. The opportunities, paradigm shift, and future research directions that could facilitate the need of DSSE are discussed.

A Review on Distribution System State Estimation

Distribution systems are undergoing many enhancements and developments to enable the future smart grid, and distribution system state estimation (DSSE) provides the control centers with the information necessary for several of its applications and operational functions. However, the quality of DSSE typically suffers from a lack of adequate/accurate measurements. Recently, many electric utilities have started to install fairly accurate smart meters throughout their distribution networks, which create an opportunity to achieve higher quality DSSE. However, the signals provided by smart meters are generally not synchronized and the difference between the measurement times of smart meters can be significant. Therefore, a complete snapshot of the entire distribution system may not be available. This paper proposes a method to deal with the issue of nonsynchronized measurements coming from smart meters based on the credibility of each available measurement and appropriately adjusting the variance of the measurement devices. To illustrate the effectiveness of the proposed method, two IEEE benchmark systems are used. The results show that the proposed method is robust and improves the accuracy of DSSE compared with the traditional DSSE approach.

Distribution System State Estimation Based on Nonsynchronized Smart Meters

The power management strategy (PMS) plays an important role in the optimum design and efficient utilization of hybrid energy systems. The power available from hybrid systems and the overall lifetime of system components are highly affected by PMS. This paper presents a novel method for the determination of the optimum PMS of hybrid energy systems including various generators and storage units. The PMS optimization is integrated with the sizing procedure of the hybrid system. The method is tested on a system with several widely used generators in off-grid systems, including wind turbines, PV panels, fuel cells, electrolyzers, hydrogen tanks, batteries, and diesel generators. The aim of the optimization problem is to simultaneously minimize the overall cost of the system, unmet load, and fuel emission considering the uncertainties associated with renewable energy sources (RES). These uncertainties are modeled by using various possible scenarios for wind speed and solar irradiation based on Weibull and Beta probability distribution functions (PDF), respectively. The differential evolution algorithm (DEA) accompanied with fuzzy technique is used to handle the mixed-integer nonlinear multi-objective optimization problem. The optimum solution, including design parameters of system components and the monthly PMS parameters adapting climatic changes during a year, are obtained. Considering operating limitations of system devices, the parameters characterize the priority and share of each storage component for serving the deficit energy or storing surplus energy both resulted from the mismatch of power between load and generation. In order to have efficient power exploitation from RES, the optimum monthly tilt angles of PV panels and the optimum tower height for wind turbines are calculated. Numerical results are compared with the results of optimal sizing assuming pre-defined PMS without using the proposed power management optimization method. The comparative results present the efficacy and capability of the proposed method for hybrid energy systems.

A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems

In designing procedure of a power sharing controller for a voltage source converter (VSC)-based microgrid with no communication link, three issues should be considered. Firstly, in VSC-based microgrids, which use droop controller method, the desired frequency of VSCs is altering regarding the output active power. Consequently, the conventional load frequency control techniques are inappropriate since their operation is based on a fixed pre-specified desired frequency. Secondly, to prevent circulating current and thermally overstressing, all DGs should participate in active power supply. In addition, since there is no communication link, the steady state value of each micro-source active power is unknown. Therefore, the conventional fixed active power control method for DGs is not appropriate. Thirdly, when the microgrid loads are increased, the output power of VSCs is increased rapidly; however, the output power of each VSC's primary source could not change in the same rate to respond. It causes the DC voltage of VSCs to decrease, which could affect the appropriate performance of VSCs. In this paper, a novel control strategy for VSCs and an energy storage system in a VSC-based microgrid without communication link accompanied with a novel hybrid model of VSC-based DGs, which considers primary source effect, is proposed.

Decentralized Cooperative Control Strategy of Microsources for Stabilizing Autonomous VSC-Based Microgrids

The optimal reactive power dispatch (ORPD) problem is a non-linear mixed-variable optimisation problem. This study employs a new evolutionary algorithm that expands the original shuffled frog leaping algorithm (SFLA) to solve this problem. In order to fully exploit the promising solution region, a local search algorithm known as Nelder-Mead (NM) algorithm is integrated with SFLA. The resultant NM-SFLA is very efficient in solving ORPD problem. The most important benefit of the proposed method is higher speed of convergence to a better solution. The proposed method is applied to ORPD problem on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus power systems and compared with four versions of particle swarm optimisation algorithm, two versions of differential evolutionary algorithm and SFLA. The optimal setting of control variables including generator voltages, transformer taps and shunt VAR compensation devices for active power loss minimisation in a transmission system is determined while all the constraints are satisfied. The simulation results show the efficiency of the proposed method.

Hybrid shuffled frog leaping algorithm and Nelder-Mead simplex search for optimal reactive power dispatch

Wind and solar (photovoltaic) power generations have rapidly evolved over the recent decades. Efficient and reliable planning of power system with significant penetration of these resources brings challenges due to their fluctuating and uncertain characteristics. In this paper, incorporation of both PV and wind units in the unit commitment of power system is investigated and a risk-constrained solution to this problem is presented. Considering the contribution of PV and wind units, the aim is to determine the start-up/shut-down status as well as the amount of generating power for all thermal units at minimum operating cost during the scheduling horizon, subject to the system and unit operational constraints. Using the probabilistic method of confidence interval, the uncertainties associated with wind and PV generation are modeled by analyzing the error in the forecasted wind speed and solar irradiation data. Differential evolution algorithm is proposed to solve the two-stage mixed-integer nonlinear optimization problem. Numerical results indicate that with indeterminate information about the wind and PV generation, a reliable day-ahead scheduling of other units is achieved by considering the estimated dependable generation of PV and wind units.

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A. Alimardani

Papers

Distribution System State Estimation Based on Nonsynchronized Smart Meters

A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems

Decentralized Cooperative Control Strategy of Microsources for Stabilizing Autonomous VSC-Based Microgrids

Hybrid shuffled frog leaping algorithm and Nelder-Mead simplex search for optimal reactive power dispatch

Risk-Constrained Unit Commitment of Power System Incorporating PV and Wind Farms