Wind turbine participation in micro‐grid frequency control through self‐tuning, adaptive fuzzy droop in de‐loaded area

Abazari, Ahmadreza; Dozein, Mehdi Ghazavi; Monsef, Hassan; Wu, Bin

doi:10.1049/iet-stg.2018.0095

Cited by 25 publications

(16 citation statements)

References 42 publications

(53 reference statements)

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“…In addition, the distribution system operator (DSO) encourages prosumers to achieve benefits and reduction in maintenance and expansion cost of equipment. In the U.S. and some European countries, the development of this kind of market has led to achieving a great balance between supplies and demand as well [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

et al. 2020

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In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647.

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Section: Introductionmentioning

confidence: 99%

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

et al. 2020

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“…For example: IF ∆ f is MN and ∆ f /∆t is SN, THEN output will be MP. It is important to say that membership function is called LN (Large negative), MN (medium negative), SN (small negative), ZO (Zero), SP (small positive), MP (medium positive), and LP (large positive), which have been shown in Figure 3 [28].…”

Section: Fuzzy Logic-based Controllermentioning

confidence: 99%

Coordination between Demand Response Programming and Learning-Based FOPID Controller for Alleviation of Frequency Excursion of Hybrid Microgrid

2020

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In recent years, residential rate consumptions have increased due to modern appliances which require a high level of electricity demands. Although mentioned appliances can improve the quality of consumers’ lives to a certain extent, they suffer from various shortcomings including raising the electricity bill as well as serious technical issues such as lack of balance between electricity generation and load disturbances. This imbalance can generally lead to the frequency excursion which is a significant concern, especially for low-inertia microgrids with unpredictable parameters. This research proposes an intelligent combination of two approaches in order to alleviate challenges related to the frequency control mechanism. Firstly, a learning-based fractional-order proportional-integral-derivative (FOPID) controller is trained by recurrent adaptive neuro-fuzzy inference (RANFIS) in the generation side during various operational conditions and climatic changes. In the following, a decentralized demand response (DR) programming in the load side is introduced to minimize consumption rate through controllable appliances and energy storage systems (ESSs). Furthermore, parameters uncertainties and time delay, which are generally known as two main concerns of isolated microgrids, are regarded in the frequency plan of a low-inertia microgrid including renewable energy sources (RESs), and energy storage systems (ESSs). Simulation results are illustrated in three different case studies in order to compare the performance of the proposed two methods during various operational conditions. It is obvious that the frequency deviation of microgrid can be improved by taking advantage of intelligent combination of both DR program and modern control mechanism.

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“…Both frequency and voltage oscillations can be alleviated by traditional generation units in grid-connected mode [10,11]; however, the aforementioned terms should be mitigated in islanded mode, based on intelligent control frameworks. An unusual frequency nadir, excessive rate of change of frequency (RoCoF), and catastrophic instability are examples of recent challenges in islanded MGs that stemmed from load/generation imbalance [12][13][14][15]. As the penetration of RESs into the modern power system is steadily increasing, the creation of a coordination between RESs appears to be a vital issue.…”

Section: Introductionmentioning

confidence: 99%

Optimal Distribution Coefficients of Energy Resources in Frequency Stability of Hybrid Microgrids Connected to the Power System

et al. 2021

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The continuous stability of hybrid microgrids (MGs) has been recently proposed as a critical topic, due to the ever-increasing growth of renewable energy sources (RESs) in low-inertia power systems. However, the stochastic and intermittent nature of RESs poses serious challenges for the stability and frequency regulation of MGs. In this regard, frequency control ancillary services (FCAS) can be introduced to alleviate the transient effects during substantial variations in the operating point and the separation from main power grids. In this paper, an efficient scheme is introduced to create a coordination among distributed energy resources (DERs), including combined heat and power, diesel engine generator, wind turbine generators, and photovoltaic panels. In this scheme, the frequency regulation signal is assigned to DERs based on several distribution coefficients, which are calculated through conducting a multi-objective optimization problem in the MATLAB environment. A meta-heuristic approach, known as the artificial bee colony algorithm, is deployed to determine optimal solutions. To prove the efficiency of the proposed scheme, the design is implemented on a hybrid MG. Various operational conditions which render the system prone to experience frequency fluctuation, including switching operation, load disturbance, and reduction in the total inertia of hybrid microgrids, are studied in PSCAD software. Simulation results demonstrate that this optimal control scheme can yield a more satisfactory performance in the presence of grid-following and grid-forming resources during different operational conditions.

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Wind turbine participation in micro‐grid frequency control through self‐tuning, adaptive fuzzy droop in de‐loaded area

Cited by 25 publications

References 42 publications

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

Coordination between Demand Response Programming and Learning-Based FOPID Controller for Alleviation of Frequency Excursion of Hybrid Microgrid

Optimal Distribution Coefficients of Energy Resources in Frequency Stability of Hybrid Microgrids Connected to the Power System

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