Energy Management System for Small Scale Hybrid Wind Solar Battery Based Microgrid

Kumar, P. Satish; Chandrasena, Ruwan P.S.; Velishala, Ramu; Srinivas, G.; Babu, K. Victor Sam Moses

doi:10.1109/access.2020.2964052

Cited by 153 publications

(70 citation statements)

References 16 publications

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“…During MG's islanded operation mode, EMS algorithms are focused on the maximization of the grid service capacity. In this configuration, besides the consideration of traditional technical and operational restrictions expressed by (7) and (9), these systems must ensure that the MG load supplying is continuously performed by (12) and respect loads' priority ranks described by (13). In this sense, for a generic allocation of RMPS denoted by , the EMS problem for MG (7)-(9) can be represented as…”

Section: A Dynamic Programming Ems For Mgs Under Scheduled Islanded mentioning

confidence: 99%

“…In this sense, starting from the general optimal solution, one can work backward to the actual optimal solution that satisfies the sub-problem EMS constraints, i.e. (7) and (9), and (12)- (13). For this, the first candidate solutions 〈ℓ〉 are obtained by inducing backwards from the general optimal solution 〈ℓ〉 .…”

Section: A Dynamic Programming Ems For Mgs Under Scheduled Islanded mentioning

confidence: 99%

“…Communication non-idealities are considered in the EMS design problem depicted in [12], in which a consensus algorithm is used to solve energy management (EM) problem with data losses. Further, EMS focused on hybrid RES generation based systems, and transactive energy frameworks are tackled in [13]- [14]. An optimal scheduling mode based on chance-constrained programming is proposed to assist isolated MGs with battery storage [15].…”

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confidence: 99%

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Intelligent RMPS Allocation for Microgrids Support During Scheduled Islanded Operation

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New opportunities enabled by renewable mobile power stations (RMPSs), in association with microgrids (MGs) ability to operate islanded to the main grid, present a likely solution to ensure power system novel reliability requirements in face of the increasing number of severe disruptive events. In this perspective, this paper proposes a novel intelligent RMPS allocation method to support MGs during scheduled islanded operation. In this mode, the isolated operation of a MG is beforehand planned to mitigate the effects caused by possible predictable contingencies, e.g. weather-related events, as well as programmed maintenance. For this, first a novel energy management strategy (EMS) based on dynamic programming specifically designed for MG scheduled islanded operation is proposed. Next, a particle swarm optimization (PSO) method modified to ensure a continuous search space in the face of discrete load shedding policies is developed to determine the adequate allocation of RMPS. The proposed method is simulated considering different scenarios for a MG system considering local distributed generation capacity, electric vehicles penetration, load shedding based on priority groups, as well as technical and operational limits. The obtained results showcase the proposed approach ability to meaningfully improve MG service capacity during scheduled islanded operation.

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Section: A Dynamic Programming Ems For Mgs Under Scheduled Islanded mentioning

confidence: 99%

Section: A Dynamic Programming Ems For Mgs Under Scheduled Islanded mentioning

confidence: 99%

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confidence: 99%

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Intelligent RMPS Allocation for Microgrids Support During Scheduled Islanded Operation

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“…Similarly, in [9], a PV/battery hybrid distributed power generation system was proposed with a power management strategy (PMS) of only one integrated three-port power converter. In [10], an efficient PMS for a small-scale hybrid wind-solar-battery RER based MG was designed to maintain the MG power balance and accommodate the different variations in the RERs and the load demand. In [11], an improved PMS of a hybrid direct current DC MG system consisting of a PV cell, WT generator, battery energy storage, fuel cell and an electrolyzer was adopted.…”

Section: Introductionmentioning

confidence: 99%

Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for AC Microgrid

et al. 2020

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Microgrids (MGs) have been widely implemented as they increase the efficiency and resiliency of electrical networks. However, the uncertain nature of renewable energy resources (RERs) integrated into the MGs usually results in different technical problems. System stability, the most challenging problem, can be achieved via a robust power management strategy (PMS) of the MG. This paper introduces a PMS based on adaptive neuro fuzzy inference system (ANFIS) for AC MG consisting of a diesel generator (DG), a double fed induction generator (DFIG) driven by a wind turbine (WT) and a solar photovoltaic (PV) panel. The proposed strategy aims to achieve MG power balance, decrease DG fossil fuel to minimum consumption, keep the MG voltage stability and finally tracking the maximum power point (MPP) of each RER. Metaheuristic optimization techniques; including genetic algorithm (GA) and particle swarm optimization (PSO), are employed to train the ANFIS to accomplish the desired objectives and fulfill the generation/consumption balance. The proposed AC MG with the PMS is simulated by the MATLAB/Simulink software in order to analyze the system performance under different climatic conditions. The simulation results under symmetrical and asymmetrical electrical faults validated the effectiveness of the proposed strategy. INDEX TERMS Microgrid, renewable energy resources, power management strategy, voltage stability, adaptive neuro fuzzy inference system, double fed induction generator, genetic algorithm, particle swarm optimization.. NOMENCLATURE efficiency coefficient of the wind turbine G solar irradiation PV generator current DFIG rotor leakage inductance DFIG stator leakage inductance diesel generator output active power grid side converter active power diesel generator input mechanical power PV generator active power DFIG rotor active power DFIG stator active power extracted wind power DFIG stator reactive power rotor radius DFIG stator resistance DFIG rotor resistance slip t time interval PV generator voltage wind speed rotor angular frequency synchronously rotating reference frame angular velocity slip angular frequency turbine rotational speed blade pitch angle tip-speed ratio air density This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…This IUPQC is able to improve voltage and current profiles in multi-bus/multi-feeder systems simultaneously. The proposed IUPQC is implemented between grid and the HRES [17].…”

Section: Introductionmentioning

confidence: 99%

A Novel IUPQC for Multi-Feeder Systems Using Multilevel Converters With Grid Integration of Hybrid Renewable Energy System

Prakash¹,

Kumar

Chandrasena

2020

IEEE Access

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This paper presents a novel topology of interline unified power quality conditioner (IUPQC) for multi-bus/multi-feeder systems, capable of compensating both voltage and current imperfections simultaneously for power quality improvement. In this system, four voltage-source converters (VSC) with multilevel configuration are considered and space vector pulse width modulation is used in hexagonal coordinate system to reduce the complexity in generating switching pulses. In the proposed IUPQC, all converters share a common dc-link capacitor. Hence, power can be transferred from healthy feeder to adjacent feeders for compensation of sag/swell and current/voltage harmonics. This IUPQC is implemented between two feeders with hybrid renewable energy system as one of the feeders. The performance of the proposed system for various power quality problems is analyzed and presented using MATALB/SIMULINK. INDEX TERMS Grid integration, multi-feeder systems, multi-level converters, power quality improvement, renewable energy systems.

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Energy Management System for Small Scale Hybrid Wind Solar Battery Based Microgrid

Cited by 153 publications

References 16 publications

Intelligent RMPS Allocation for Microgrids Support During Scheduled Islanded Operation

Intelligent RMPS Allocation for Microgrids Support During Scheduled Islanded Operation

Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for AC Microgrid

A Novel IUPQC for Multi-Feeder Systems Using Multilevel Converters With Grid Integration of Hybrid Renewable Energy System

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