The day-ahead spot market and the markets for control reserve power are among the most important markets for short-term trading of power plant capacities in Germany. Especially the prices for control reserve power hav...
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(纸本)9781509012985
The day-ahead spot market and the markets for control reserve power are among the most important markets for short-term trading of power plant capacities in Germany. Especially the prices for control reserve power have been varying severely in the last years. Considering the applied pay-as-bid auction design and market power of certain market participants, strategic bidding behavior must be assumed. Such strategic behavior cannot be modelled by fundamental simulation models alone. Thus, it is necessary to simulate individual bidding curves and trading decisions of market participants in order to simulate realistic market results and prices. Therefore, the aim of this paper is to model the bidding considerations on the day-ahead and control reserve power markets, to implement them in an agent based simulation model using a reinforcement learning algorithm and to investigate the resulting market prices.
The purpose of this work was to get acquainted with wireless communication technologies and the information security challenges they create from the viewpoint of 5G and its preceding technologies. 5th Generation (5G) ...
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The large-scale application of renewable energy can promote the global goal of carbon neutrality. However, the stochastic nature of wind and solar energy aggravates the active power imbalance and increases the frequen...
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The large-scale application of renewable energy can promote the global goal of carbon neutrality. However, the stochastic nature of wind and solar energy aggravates the active power imbalance and increases the frequency deviation. These obstacles hinder the load frequency control with the traditional proportional-integral-derivative as the primary approach for automatic generation control. Inspired by the "Divide and Conquer" strategy, a mode-decomposition memory reinforcement network strategy is proposed to reduce the impact of random fluctuations and uncertainties on power systems. The proposed strategy combines the traditional methods and intelligent algorithms for smart generation control. The proposed strategy includes empirical mode decomposition, proportional-integral-derivative, long short-term memory networks, and reinforcement learning algorithms. Firstly, the historical data that has been decomposed by the empirical mode decomposition is utilized to train long short-term memory networks. Then, the trained long short-term memory networks decompose and reorganize the frequency deviation into the high-frequency and low-frequency signals in real-time. Finally, reinforcementlearning and proportional-integral-derivative respectively optimize the generation commands by the high-frequency and low-frequency signals to mitigate frequency deviation. Two cases results prove that the mode-decomposition memory reinforcement network has a higher control effect and lower generation cost than the other four strategies. Significantly, the frequency deviation and generation cost are respectively reduced by at least 9.77% and 4.39% in the four-area power system.
The Third Generation Partnership Project launched the Narrowband Internet of Things (NB-IoT) as part of 5G technology under Release-13. These networks are authorized Low Power Wide Area Networks. It uses less power an...
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The Third Generation Partnership Project launched the Narrowband Internet of Things (NB-IoT) as part of 5G technology under Release-13. These networks are authorized Low Power Wide Area Networks. It uses less power and sends message over long distances. The NB-IoT is can be used widely in industries, environment, and home for automation. While comparing NB-IoT to regular Long Term Evaluation Machine Type Communication, the repetition in uplink is 128. The extreme coverage by 164 dB in terms of Maximum Coupling Loss. The NB-IoT is employed in deep coverage for users. If we use many repetitions in NB-IoT technology, the results are shorter battery life and higher maintenance cost. Hence, we suggest abandoning the random spectrum access strategy in favor of a novel technique termed Dynamic Spectrum Access, which uses reinforcement learning algorithm to eliminate the shortcomings. This method is used to decrease the number of re-transmissions so that NB-IoT system power goes to less and augments coverage. The Third Generation Partnership Project launched the Narrowband Internet of Things (NB-IoT) as part of 5G technology under Release-13. These networks are authorized Low Power Wide Area Networks. It uses less power and sends message over long distances. The NB-IoT can be used widely in industries, environment, and home for ***
The Cognitive Internet of Things (CIoT) is a rapidly evolving field that combines artificial intelligence (AI) with the Internet of Things (IoT) ecosystem. By augmenting IoT with AI, objects can sense, perceive, think...
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The Cognitive Internet of Things (CIoT) is a rapidly evolving field that combines artificial intelligence (AI) with the Internet of Things (IoT) ecosystem. By augmenting IoT with AI, objects can sense, perceive, think, and make decisions independently with minimal initial knowledge. These cognitive objects form a society and work toward achieving their goals through cooperation. However, trust is a significant security challenge in such a society. To address this challenge, this paper proposes a soft security approach to model trust in CIoT using Collaborative Multi-Agent Systems (CMAS). Our model introduces an interactive, autonomous, and self-taught agent that can move toward a secure situation without needing a supervisor. We use a state machine to model an insecure ecosystem where agents can behave honestly, adversarially, neutrally, or hypocritically. We define inner trust as a combination of direct and indirect experiences and observations and global trust as the weighted average of inner trust and recommendations. Additionally, we employ the reinforcement learning algorithm to train agents. To evaluate our model, we developed a proprietary tool called ASSOCIATE and assessed our model using data from the Santander Smart City dataset and simulator-generated data. Our evaluations encompass three aspects: the success rate in achieving the goal, the recognition of agents' behavior, and the quality of the diagnosis.
Losses in the electrical power transmission and distribution systems are considered two of the most critical challenges in power grids. Reducing the related losses plays a significant role in increasing system efficie...
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Losses in the electrical power transmission and distribution systems are considered two of the most critical challenges in power grids. Reducing the related losses plays a significant role in increasing system efficiency in addition to diminishing costs. Therefore, optimum power transfer as well as finding a convenient route, are essential factors in electrical grids. This paper intends to substantially reduce the transmission/distribution-related losses by finding the shortest and most optimal path between the renewable energy power plant (producer) and the substations/consumers. A genetic algorithm (GA) is proposed for optimal routing to increase the system's reliability and minimize the losses of the entire network. In this work, by presenting a coding with chromosomes of variable length and considering the construction costs and the power transmission line/path as the fitness function, the appropriate route is obtained. The efficiency of the proposed method is compared with Dijkstra's algorithm, one of the conventional graph search approaches. The ant colony optimization (ACO) algorithm and a reinforcement learning algorithm, namely the Q-learning model, are employed to further explore the optimization efficiency of the proposed renewable energy-based transmission system. The simulation results demonstrate that the proposed models accurately determine the optimal pathway within an excellent time.
Increasing display process complexity, Automated Guided Vehicle becomes more important and the delivery time constraint is getting shorter. The complicated process causes traffic congestion and deadlocks between AGVs....
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Increasing display process complexity, Automated Guided Vehicle becomes more important and the delivery time constraint is getting shorter. The complicated process causes traffic congestion and deadlocks between AGVs. Preventing these issues, this paper suggests digital twin technology that allows AGV to choose the best route among various paths. This research validates the AGV simulation for the display plant. The result shows AGV average delivery time was shortened by 14.1% compared to the traditional algorithm. In addition, the AGV capacity rate can be reduced by 21.5% and the number of AGVs can be reduced by 10.1%.
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