Design of robust model predictive controllers for frequency and voltage loops of interconnected power systems including wind farm and energy storage system

Othman, Mohamed I. A.; El‐Fergany, Attia A.

doi:10.1049/iet-gtd.2018.5568

Cited by 35 publications

(30 citation statements)

References 44 publications

(61 reference statements)

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“…The controller1 is deployed for ALFC to coordinate BEGS output, and controller2 is solely deployed for AVR of the proposed hμG, as viewed in Figure B. Hence, the proposed system is studied for coordinated frequency and voltage regulations with linear and nonlinear loads by tuning these two controllers for different scenarios of load and source variations based on optimal utilisation of renewable powers in different climatic conditions. The key contributions of this work are Developing a linearised model of the proposed renewable‐bioenergy cogeneration‐based isolated hμG with a nominal system frequency of 50 Hz, including HEV charging station‐based DRC support for coordinated load frequency and voltage regulation. Modelling an IEEE type‐I excitation‐based AVR for voltage regulation with cross coupling coefficients for simultaneous frequency and voltage regulation. Designing the linearised model of an ORC‐based LFR type STP unit by deriving system parameters from practical data. Developing a novel QSHO algorithm by hybridising QOBL with SHO for coordinated regulation of frequency and voltage. Deriving a novel objective function called ISWAE, considering the minimisation of deviation in net apparent power of the proposed system. Estimating a combined performance index considering figures of demerits (FOD) of both frequency and voltage deviations of the system to compare the responses. Simulating four cases of extreme source variations due to seasonal/climatic changes throughout a year with linear loading and one case including nonlinear loadings, incorporating real‐time recorded monthly average solar/wind data to study the reliability of proposed system round the year. …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The ALFC regulates the active power demand of the grid only, whilst reactive power control is also crucial in real‐world paradigm which depends on voltage regulation of the grid with automatic voltage regulator (AVR) 21 . Some works are recently reported on combined voltage and frequency control of isolated system in Gupta et al 22 and interconnected multi‐area systems in Othman and El‐Fergany and Rajbongshi and Saikia 23‐26 for a nominal frequency ( f ) of 60 Hz. However, the coordinated frequency and voltage regulation of a 50‐Hz frequency‐based isolated hμG are unexplored so far.…”

Section: Introductionmentioning

confidence: 99%

“…The controller1 is deployed for ALFC 33 to coordinate BEGS output, and controller2 is solely deployed for AVR 34 of the proposed hμG, as viewed in Figure 1B. Hence, the proposed system is studied for coordinated frequency and voltage regulations 22‐26 with linear and nonlinear loads by tuning these two controllers for different scenarios of load and source variations based on optimal utilisation of renewable powers in different climatic conditions. The key contributions of this work are Developing a linearised model of the proposed renewable‐bioenergy cogeneration‐based isolated hμG with a nominal system frequency of 50 Hz, including HEV charging station‐based DRC support for coordinated load frequency and voltage regulation.…”

Section: Introductionmentioning

confidence: 99%

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Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

Barik

Das

2019

Int Trans Electr Energ Syst

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Summary This work is the earliest attempt towards coordinated regulation of both frequency and voltage of an isolated multisource‐based hybrid microgrid with demand response support using a modified selfish herd optimisation. It is a maiden effort to propose a 50‐Hz system frequency‐based hybrid microgrid model, utilising biorenewable sustainable energy sources like sunrays, wind, waste waters, and agricultural and domestic wastes, with available support from demand response contributors for a dual objective of waste and power management. The intermittency of these resources due to climatic variations creates a great challenge to meet the timely consumer demands of the microgrid with reliable quality power supply. To overcome these issues, the performances of the proposed system are studied considering different typical Indian‐climatic scenarios round the year with real‐time recorded data. Initially, the system performances are compared proposing a new objective function called integral square of weighted absolute error in order to minimise the net deviation in system apparent power, using six controllers tuned with four‐popular/recent optimisation algorithms and four‐objective functions in MATLAB. Then, the simulation results are analysed for five different extreme scenarios of both source and load variations in the proposed system using a novel quasi‐oppositional selfish herd optimisation and reported adaptive performances with demand response support. Finally, the sensitivity of the proposed system with variation in system inertia and load damping factor are analysed to study the robustness of the proposed system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

Barik

Das

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Furthermore, renewable energy is clean, and it has a low effect on the environment. So new researches in the power systems are focused on the utilizing of renewable energy in electricity generation . Among the RES, wind energy is a more effective renewable energy source, and it can produce high power during the conversion into electrical energy .…”

Section: Introductionmentioning

confidence: 99%

New design of robust PID controller based on meta‐heuristic algorithms for wind energy conversion system

Elsisi

2019

Wind Energy

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This paper proposes a new robust control method for a wind energy conversion system. The suggested method can damp the deviations in the generator speed because of the penetration of wind speed and load demand fluctuations in the electrical grid. Furthermore, it can overcome the uncertainties of the plant parameters because of load demand fluctuations and the errors of the implementation. The new method has been built based on new simple frequency-domain conditions and the whale optimization algorithm (WOA). This method is utilized to design a robust proportionalintegral-derivative (PID) controller based on the WOA in order to enhance the damping characteristics of the wind energy conversion system. Simulation results confirm the superiority and robustness of the proposed technique against the wind speed fluctuations and the plant parameters uncertainties compared with other meta-heuristic algorithms. KEYWORDS frequency-domain constraints, meta-heuristic algorithms, robust PID controller, wind energy conversion system | INTRODUCTIONRecently, electrical power generation from renewable energy sources (RES) is considered one of the most promising solutions to overcome the energy problems in the world. Furthermore, renewable energy is clean, and it has a low effect on the environment. So new researches in the power systems are focused on the utilizing of renewable energy in electricity generation. 1,2 Among the RES, wind energy is a more effective renewable energy source, and it can produce high power during the conversion into electrical energy. 3,4 However, wind energy produces fluctuated electrical power because of the fluctuations of the wind speed. In addition, it causes many problems in electrical system operation such as generator speed regulation. 5,6 So the wind turbine (WT) speed and its output power must be regulated. The regulation of the WT speed and the output power is developed by varying the pitch angle of its blades. The WT is connected with a blade pitch control (BPC) to enhance the operation of the wind energy conversion system (WECS), and it ensures the energy production limit against the fluctuations of the wind speed. Furthermore, the BPC preserves the quality of the generated power, and it protects the WT from any damaging because of the high wind speed.In recent years, several control methods are used for the BPC in WECS such as the proportional-integral-derivative (PID) controller, 7,8 statefeedback controller, 9 H 2 /H ∞ control, 10 sliding mode control, 11 fuzzy logic, 12 neural networks, 13 and model predictive control (MPC). 14 Among NOMENCLATURE: P, active power (p.u. MW); Q, reactive power (p.u. MVAR); vto, infinite bus voltage (p.u. /journal/we 391 400 ELSISI | CONCLUSIONThe tuning of PID controller gains by a novel AI technique named WOA is suggested in this paper. The proposed method is applied to the BPC of a WT in the WECS. In addition, simple frequency-domain conditions are developed based on Hermite-Biehler theorem to ensure the system stability over the plant parameters uncertai...

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Adaptive frequency control support of a DFIG based on second‐order derivative controller using data‐driven method

Kazemi

Sadati

Gholamian

2020

Int Trans Electr Energ Syst

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Design of robust model predictive controllers for frequency and voltage loops of interconnected power systems including wind farm and energy storage system

Cited by 35 publications

References 44 publications

Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

New design of robust PID controller based on meta‐heuristic algorithms for wind energy conversion system

Adaptive frequency control support of a DFIG based on second‐order derivative controller using data‐driven method

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