1,206 research outputs found
Utilizing Converter-Interfaced Sources for Frequency Control with Guaranteed Performance in Power Systems
To integrate renewable energy, converter-interfaced sources (CISs) keep penetrating into power systems and degrade the grid frequency response. Control synthesis towards guaranteed performance is a challenging task. Meanwhile, the potentials of highly controllable converters are far from fully developed. With properly designed controllers the CISs can not only eliminate the negative impacts on the grid, but also provide performance guarantees.First, the wind turbine generator (WTG) is chosen to represent the CISs. An augmented system frequency response (ASFR) model is derived, including the system frequency response model and a reduced-order model of the WTG representing the supportive active power due to the supplementary inputs.Second, the framework for safety verification is introduced. A new concept, region of safety (ROS), is proposed, and the safe switching principle is provided. Two different approaches are proposed to estimate the largest ROS, which can be solved using the sum of squares programming.Third, the critical switching instants for adequate frequency response are obtained through the study of the ASFR model. A safe switching window is discovered, and a safe speed recovery strategy is proposed to ensure the safety of the second frequency dip due to the WTG speed recovery.Fourth, an adaptive safety supervisory control (SSC) is proposed with a two-loop configuration, where the supervisor is scheduled with respect to the varying renewable penetration level. For small-scale system, a decentralized fashion of the SSC is proposed under rational approximations and verified on the IEEE 39-bus system.Fifth, a two-level control diagram is proposed so that the frequency of a microgrid satisfies the temporal logic specifications (TLSs). The controller is configured into a scheduling level and a triggering level. The satisfaction of TLSs will be guaranteed by the scheduling level, and triggering level will determine the activation instant.Finally, a novel model reference control based synthetic inertia emulation strategy is proposed. This novel control strategy ensures precise emulated inertia by the WTGs as opposed to the trial and error procedure of conventional methods. Safety bounds can be easily derived based on the reference model under the worst-case scenario
Supervisory model predictive control of building integrated renewable and low carbon energy systems
To reduce fossil fuel consumption and carbon emission in the building sector,
renewable and low carbon energy technologies are integrated in building energy
systems to supply all or part of the building energy demand. In this research, an
optimal supervisory controller is designed to optimize the operational cost and the
CO2 emission of the integrated energy systems. For this purpose, the building
energy system is defined and its boundary, components (subsystems), inputs and
outputs are identified. Then a mathematical model of the components is obtained.
For mathematical modelling of the energy system, a unified modelling method is
used. With this method, many different building energy systems can be modelled
uniformly. Two approaches are used; multi-period optimization and hybrid model
predictive control. In both approaches the optimization problem is deterministic, so
that at each time step the energy consumption of the building, and the available
renewable energy are perfectly predicted for the prediction horizon. The controller
is simulated in three different applications. In the first application the controller is
used for a system consisting of a micro-combined heat and power system with an
auxiliary boiler and a hot water storage tank. In this application the controller
reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent
respectively, with respect to the heat led operation. In the second application the
controller is used to control a farm electrification system consisting of PV panels, a
diesel generator and a battery bank. In this application the operational cost with
respect to the common load following strategy is reduced by 3.8 percent. In the
third application the controller is used to control a hybrid off-grid power system
consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank
and a fuel cell. In this application the controller maximizes the total stored energies
in the battery bank and the hydrogen storage tank
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Performance and economic analysis of hybrid microhydro systems
Microhydro (MHP) systems usually employ unregulated turbines and an electronic load controller, a demand-side control device. Existing analytical models for such systems are lacking details, especially supply-side flow control, for performance simulation at hourly or sub-hourly scales. This work developed stochastic models for downscaling of streamflow and an empirical model of MHP systems. We integrated these models within the framework of Hybrid2 tool to simulate the long-term performance of a tri-hybrid system consisting of hydropower, solar PV and wind turbine.
Based on an additive model of time series decomposition, we develop a Multiple Input Single Output (MISO) model in order to synthesize an hourly time series of streamflow. The MISO model takes into account daily precipitation dataset as well as regional hydrological characteristics. The model employs a constrained Monte-Carlo Markov Chain (MCMC) algorithm which is validated against an hourly time series of flow data at Blue River at Blue, Oklahoma. A non-dimensional performance model of MHP systems is developed based on empirical data from Nepal.
Three design configurations are presented for a case study. The results show that, along with a small pond that can store water for an hour at the rated capacity of MHP system, a hybrid system with half the size of the battery bank can supply the load year around at Thingan Project in Nepal. This system meets the availability requirements of the Multi-Tier Framework for measuring energy access for household supply. The new proposed system is marginal in the economic sense as well. This project can never recover the initial capital cost at a current rate of the tariff which is about 7 cents/kWh. Other O&M risks aside, the sensitivity analysis suggests that the system may barely recover the initial capital cost, excluding the subsidy, at twice the existing rate of tariff and half the interest rate.
This study aspires to come up with better techniques to simulate hybrid microhydro systems and enhance their design and operation through more effective utilization of resources
Wind Power
This book is the result of inspirations and contributions from many researchers of different fields. A wide verity of research results are merged together to make this book useful for students and researchers who will take contribution for further development of the existing technology. I hope you will enjoy the book, so that my effort to bringing it together for you will be successful. In my capacity, as the Editor of this book, I would like to thanks and appreciate the chapter authors, who ensured the quality of the material as well as submitting their best works. Most of the results presented in to the book have already been published on international journals and appreciated in many international conferences
Long-term research challenges in wind energy – a research agenda by the European Academy of Wind Energy
The European Academy of Wind Energy (eawe), representing universities and institutes with a significant wind energy programme in 14 countries, has discussed the long-term research challenges in wind energy. In contrast to research agendas addressing short- to medium-term research activities, this eawe document takes a longer-term perspective, addressing the scientific knowledge base that is required to develop wind energy beyond the applications of today and tomorrow. In other words, this long-term research agenda is driven by problems and curiosity, addressing basic research and fundamental knowledge in 11 research areas, ranging from physics and design to environmental and societal aspects. Because of the very nature of this initiative, this document does not intend to be permanent or complete. It shows the vision of the experts of the eawe, but other views may be possible. We sincerely hope that it will spur an even more intensive discussion worldwide within the wind energy community
Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration
This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid.
The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%.
Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization
algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was
utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the
conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response
of 1 ms in reaching the predetermined charging current stages.
Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by
28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)
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