Microgrid energy management in grid-connected and islanding modes based on SVC

Abdelsalam, Abdelazeem A.

doi:10.1016/j.enconman.2014.06.070

Cited by 73 publications

(32 citation statements)

References 22 publications

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“…Installing sensors and telecommunication devices to the system makes these methods having high system and operation cost [19,20]. The power quality does not change, and the system is stable in remote methods.…”

Section: Remote Islanding Detection Methodsmentioning

confidence: 99%

A remote islanding detection and control strategy for photovoltaic-based distributed generation systems

Bayrak

2015

Energy Conversion and Management

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Section: Remote Islanding Detection Methodsmentioning

confidence: 99%

A remote islanding detection and control strategy for photovoltaic-based distributed generation systems

Bayrak

2015

Energy Conversion and Management

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“…In order to start the simulations, the nine fuzzy rule set must first be invoked from the command window in Matlab [10]. In Figure 3, the input data are measured based on the generalized theory of instantaneous reactive power values are calculated for active and reactive power to A-B-C phase.…”

Section: Implementation Of the Proposed Protection Schemementioning

confidence: 99%

“…After acquiring the required data, they are processed using the decision maker, ANFIS employs the theory of fuzzy sets and fuzzy if-then rules The Simulink model with the ANFIS for the proposed protection of the microgrid was developed in Matlab, as shown in Figure 2. In order to start the simulations, the nine fuzzy rule set must first be invoked from the command window in Matlab [10].…”

Section: Implementation Of the Proposed Protection Schemementioning

confidence: 99%

A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting

Abdulwahid

Wang

2016

Energies

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Nowadays, the use of distributed generation (DG) has increased because of benefits such as increased reliability, reduced losses, improvement in the line capacity, and less environmental pollution. The protection of microgrids, which consist of generation sources, is one of the most crucial concerns of basic distribution operators. One of the key issues in this field is the protection of microgrids against permanent and temporary failures by improving the safety and reliability of the network. The traditional method has a number of disadvantages. The reliability and stability of a power system in a microgrid depend to a great extent on the efficiency of the protection scheme. The application of Artificial Intelligence approaches was introduced recently in the protection of distribution networks. The fault detection method depends on differential relay based on Hilbert Space-Based Power (HSBP) theory to achieve fastest primary protection. It is backed up by a total harmonic distortion (THD) detection method that takes over in case of a failure in the primary method. The backup protection would be completely independent of the main protection. This is rarely attained in practice. This paper proposes a new algorithm to improve protection performance by adaptive network-based fuzzy inference system (ANFIS). The protection can be obtained in a novel way based on this theory. An advantage of this algorithm is that the protection system operates in fewer than two cycles after the occurrence of the fault. Another advantage is that the error detection is not dependent on the selection of threshold values, and all types of internal fault can identify and show that the algorithm operates correctly for all types of faults while preventing unwanted tripping, even if the data were distorted by current transformer (CT) saturation or by data mismatches. The simulation results show that the proposed circuit can identify the faulty phase in the microgrid quickly and correctly.Keywords: microgrid protection; adaptive network-based fuzzy inference system (ANFIS); Hilbert space-based power (HSBP); total harmonic distortion (THD)

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“…By virtue of flexible charge and discharge characteristics, BESS has prospects in smoothing and stabilizing the output of PV system, peak load shifting, operating reserve, improving power quality and scheduling flexibility of PV-MG. Therefore, optimal sizing of BESS with reasonable volume can significantly improve the efficiency of PV system and promote the local PV consumptive rate [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

et al. 2016

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Abstract:In order to solve the influence of uncertain photovoltaic power (PV) on the stable operation of microgrid (MG), demand response (DR) and battery energy storage system (BESS) need to be introduced simultaneously into the operation optimal scheduling of PV-based microgrid (PV-MG). Therefore, it is of great significance for commercial investment decisions of PV-MG to consider the influence of DR on BESS optimal sizing. Under the peak-valley time-of-use (TOU) price, this paper builds cross-time DR models based on price elasticity matrix. Furthermore, through the introduction of DR and BESS into PV-MG scheduling optimization, the MG investment and benefit model is proposed. Considering the constraint condition such as co-ordination of supply and demand, electricity price elasticity and energy loss of storage system, the improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimal allocation model of the BESS with the target of maximum PV consumptive rate and annual net profits. The optimization method was applied to a PV-MG in Guangdong. Through the regulation and control effect of demand response and BESS on load distribution, the uncertainties PV power can be suppressed so as to improve the PV system consumptive level, which is of great guiding significance for BESS optimal sizing under this situation.Keywords: time-of-use (TOU) price; multi-period demand response (DR); battery energy storage system (BESS); photovoltaic consumptive rate; investment and benefit model; multi-objective optimization; non-dominated sorting genetic algorithm II (NSGA-II) algorithm

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Microgrid energy management in grid-connected and islanding modes based on SVC

Cited by 73 publications

References 22 publications

A remote islanding detection and control strategy for photovoltaic-based distributed generation systems

A remote islanding detection and control strategy for photovoltaic-based distributed generation systems

A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting

Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

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