A load frequency control for microgrid including stochastic elements based on Hebb Learning

Khalghani, Mohammad Reza; Khushalani-Solanki, Sarika; Solanki, Jignesh

doi:10.1109/naps.2017.8107340

Cited by 10 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where For generators and energy storage batteries, the available power mainly depends on their rated capacity, so the constraints of diesel engines and energy storage batteries during frequency regulation can be expressed as P DU,min − P DU ≤ ∆U DU (k) ≤ P DU, max − P DU P BESS,min − P BESS ≤ ∆U BESS (k) ≤ P BESS,max − P BESS (17) where P DU,min , P DU, max , P BESS,min , and P BESS,max are the upper and lower power limits of the diesel engine and energy storage unit, respectively. P DU , P BESS is the upper dispatch power value at the current moment.…”

Section: Prediction Modelmentioning

confidence: 99%

“…In [14,15], the black hole optimization search algorithm and particle swarm optimization algorithm were used for secondary frequency control. The authors in [16,17] used a fuzzy adaptive controller and self-tuning techniques based on AI for secondary frequency control. However, these AI-based methods do not consider the control quantity in the objective function, which may cause power overlimit and waste renewable energy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model Predictive Secondary Frequency Control for Islanded Microgrid under Wind and Solar Stochastics

Zhao,

Zhang,

Zhong

2023

Electronics

View full text Add to dashboard Cite

As microgrids are the main carriers of renewable energy sources (RESs), research on them has been receiving more attention. When considering the increase in the penetration of renewable energy sources/distributed generators (DGs) in microgrids, their low inertia and high stochastic power disturbance pose more challenges for frequency control. To address these challenges, this paper proposes a model predictive control (MPC) secondary control that incorporates an unknown input observer and where RESs/DGs use a deloading virtual synchronous generator (VSG) control to improve the system’s inertia. An unknown input observer is employed to estimate the system states and random power disturbance from the RESs/DGs and load to improve the effect of the predictive control. The distributed restorative power of each DG is obtained by solving the quadratic programming (QP) optimal problem with variable constraints. The RESs/DGs are given priority to participate in secondary frequency control due to the proper weighting factors being set. An islanded microgrid model consisting of multiple photovoltaic and wind power sources was built. The simulation results demonstrate that the proposed method improves the system frequency, restoration speed, and reduces frequency deviations compared with the traditional secondary control method.

show abstract

Section: Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Predictive Secondary Frequency Control for Islanded Microgrid under Wind and Solar Stochastics

Zhao,

Zhang,

Zhong

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…A load frequency control method (3,4) can be applied to support the operation of a microgrid. However, more and more battery storage systems (BSSs) are currently working on the power grid.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Island Microgrid Operation by Frequency-sensor-based Controller of Battery Storage System

Hsu¹,

Cheng²,

Huang³

2020

Sensors and Materials

View full text Add to dashboard Cite

In this paper, we proposed a frequency-sensor-based controller (FSBC) of the battery storage system (BSS) to regulate the frequency variation of an island microgrid with a very high penetration level of renewable energy sources. We selected the Kinmen Island microgrid for our study. Grid data including the network topology, system load profile, photovoltaic power generation (PVPG), wind power generation (WPG), and load shedding strategy are collected and analyzed. Then, the mathematical models of the governor and exciter of diesel generators are confirmed and derived. The BSS with an FSBC is designed and applied to enhance the operation of the microgrid. After that, the transient stability analysis is carried out by considering various operation scenarios and contingency events, such as the tripping of diesel and wind generators or the ramping change in PVPG power output. Moreover, the FSBC of the BSS with different rating capacities is included in the simulation process to improve the frequency variation of the microgrid by regulating its active power output rapidly. It is found that the proposed FSBC of the BSS is very effective in enhancing the operation of the grid and preventing load shedding.

show abstract

“…In this manner, a selftuning PID controller is adopted for LFC of this microgrid. These four learning rules are mentioned in (3.8-3.11) [97]: Non-supervised Hebb learning rule: The controller output and the corresponding PID signals error are multiplied in this learning rule:…”

Section: Learning Methods For Weightsmentioning

confidence: 99%

“…In order to present a quantitative comparison among controllers in different cases, the Integral of Time multiplied by the Absolute value of Error IT AE is applied. This criterion can consider overshoot, steady-state error, and settling time [97].…”

Section: Scenariomentioning

confidence: 99%

Resilient Stochastic Control Strategies in Cyber-Physical Microgrids

Khalghani¹

Self Cite

View full text Add to dashboard Cite

Microgrids can locally fulfill the demand and operate isolated from the main grid to sustain critical services even in case of large-scale outages and cascading power failures. Microgrids are the integration of a large number of distributed energy resources in a decentralized way such that the energy supply reliability and resiliency are enhanced against natural disasters, physical and cyber disruptions. This dissertation focuses on three main challenges regarding technologies emerging in microgrids. 1) According to the fast growing number of Electric Vehicles (EV) deployment, their impacts on microgrids resulted from their uncertain behaviors are new concerns for the microgrid operators. These concerns can be EV charging and discharging schedule and locations of EV parking-lots in the system which are considered and solved using stochastic modeling and optimization algorithms based on Artificial Intelligence (AI) techniques. First, charging and discharging scheduling of EVs is minimized using Particle Swarm Optimization (PSO) Algorithm such that the daily load profile is flattened (peak and off-peak shaving) with considering constraints of State-Of-Charging (SoC). Due to the uncertain nature of daily travelling EVs between residential and administrative areas, chronological stochastic modeling is suggested. Furthermore, a multi-objective optimization based on PSO and fuzzification theory is proposed to find the best location of parking lots for these EV aggregators. Two indices, voltage unbalance and power loss, for locating the EV aggregators are considered. During peak hour, these indices can be more critical for a three-phase distribution system. 2) As the penetration of renewable energies, generally, uncertainties, increase in microgrids, a more dynamic and complex system is emerging that makes frequency control of the islanded microgrid more challenging due to stochastic dynamic encumbrances. These stochastic encumbrances can create oscillatory frequency response, which eventually leads to astray controls and instability even under primary and traditional secondary controllers. This dissertation develops AI-based and analytical based secondary control for the islanded microgrids to compensate for the frequency deviation in the presence of intermittent energy resources and uncertain load changes. Two types of artificial I wish to express my deep and sincere gratitude to my supervisor Prof. Jignesh Solanki for his guidance, support and encouragement in entire my Ph.D. program. Prof. Jignesh Solanki was always there to patiently listen and to give valuable advice. I will not forget his great feedback for enhancing my research presentation skills. Besides my supervisor, I cannot begin to express my thanks to Prof. Sarika Khushalani Solanki for participating in lengthy discussions to perfectly align my research and present my accomplishments. She has been very supportive and sympathetic in both my professional and daily lives. Working under these two great professors was a golden opportunity for me to leverage a...

show abstract

A load frequency control for microgrid including stochastic elements based on Hebb Learning

Cited by 10 publications

References 23 publications

Model Predictive Secondary Frequency Control for Islanded Microgrid under Wind and Solar Stochastics

Model Predictive Secondary Frequency Control for Islanded Microgrid under Wind and Solar Stochastics

Enhancement of Island Microgrid Operation by Frequency-sensor-based Controller of Battery Storage System

Resilient Stochastic Control Strategies in Cyber-Physical Microgrids

Contact Info

Product

Resources

About