Multisource hybrid power generation systems are a type of representative application of the renewables' technology. In this investigation, wind turbine generators, photovoltaic panels, and storage ...batteries are used to build hybrid generation systems that are optimal in terms of multiple criteria including cost, reliability, and emissions. Multicriteria design facilitates the decision maker to make more rational evaluations. In this study, an improved particle swarm optimization algorithm is developed to derive these nondominated solutions. Hybrid generation systems under different design scenarios are designed based on the proposed approach. First, a grid-linked hybrid system is designed without incoroprating system uncertainties. Then, adequacy evaluation is conducted based on probabilistic methods by accounting for equipment failures, time-dependent sources of energy, and stochastic generation/load variations. In particular, due to the unpredictability of wind speed and solar insolation as well as the random load variation, time-series models are adopted to reflect their stochastic characteristics. An adequacy evaluation procedure including time-dependent sources, is adopted. Sensitivity studies are also carried out to examine the impacts of different system parameters on the overall design performance.
This paper proposes an integrated electric vehicle (EV) charging navigation framework, which takes into consideration the impacts from both the power system and transportation system. The proposed ...framework links the power system with transportation system through the charging navigation of massive EVs. It benefits the two systems by attracting EVs to charge at off-peak hours and saving the time of EV owners with real-time navigation. Based on the formulated framework, a hierarchical game approach is proposed in this paper to effectively navigate EVs to electric vehicle charging stations (EVCSs). At the upper level of the hierarchical game, a non-cooperative game is proposed to model the competition between EVCSs. Based on the pricing strategies obtained from the non-cooperative game, multiple evolutionary games are formulated at the lower level to evolve EVs' strategies in choosing EVCSs. The simulation results show that the proposed integrated charging navigation approach is effective in improving both the reliability of the power distribution grid and economic profits of the charging stations.
It is still a challenging task to segment real-world images, since they are often distorted by unknown noise and intensity inhomogeneity. To address these problems, we propose a novel segmentation ...algorithm via a local correntropy-based K-means (LCK) clustering. Due to the correntropy criterion, the clustering algorithm can decrease the weights of the samples that are away from their clusters. As a result, LCK based clustering algorithm can be robust to the outliers. The proposed LCK clustering algorithm is incorporated into the region-based level set segmentation framework. The iteratively re-weighted algorithm is used to solve the LCK based level set segmentation method. Extensive experiments on synthetic and real images are provided to evaluate our method, showing significant improvements on both noise sensitivity and segmentation accuracy, as compared with the state-of-the-art approaches.
•We propose a level set segmentation method based on the local correntropy-based K-means (LCK) clustering.•Due to LCK clustering, our segmentation algorithm is robust to complex noise.•Segmentation accuracy is improved as compared with the state-of-the-art approaches.
The concept of energy hub (EH) was proposed to facilitate the synergies among different forms of energy carriers. Under the new electricity market environment, it is of great significance to build a ...win-win situation for prosumers and the hub manager (HM) at the community level without bringing extra burden to the utility grid. This paper proposes a cooperative trading mode for a community-level energy system (CES), which consists of the energy hub and PV prosumers with the automatic demand response (DR) capability. In the cooperative trading framework, a real-time rolling horizon energy management model is proposed based on cooperative game theory considering the stochastic characteristics of PV prosumers and the conditional value at risk (CVaR). The validity of the proposed model is analyzed through optimality proof of the grand coalition. A contribution-based profit distribution scheme and its stability proof are also provided. Moreover, in order to solve the optimization model, it is further transformed into a more easily resolved mixed integer linear programming (MILP) model by adding auxiliary variables. Finally, via a practical example, the effectiveness of the model is verified in terms of promoting local consumption of PV energy, increasing HM's profits, and reducing prosumers' costs, etc.
The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a ...multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article.
► A multi-agent system is developed to manage the indoor environment, including temperature, illumination, and air quality. ► A personal agent is designed to enable the interactions between the ...occupants and the environment. ► The energy efficiency and users’ comfort are optimized through the proposed control system. ► The system features an open architecture so that it can be adapted to different building types.
Energy and comfort management is the major task for a building automation system. As a trend of next-generation's commercial buildings, intelligent buildings are capable of facilitating intelligent control of the building to fulfill occupants’ needs. Since occupants’ behaviors have a direct impact on the system performance, the building should be able to interact with occupants by responding to their requests and obtaining feedbacks based on their behaviors. In this paper, a multi-agent based intelligent control system is developed for achieving effective energy and comfort management in a building environment. The developed multi-agent system turns out to be capable of facilitating the building to interact with its occupants for realizing user-centered control of buildings.
Adequacy assessment of power-generating systems provides a mechanism to ensure proper system operations in the face of various uncertainties including equipment failures. The integration of ...time-dependent sources such as wind turbine generators (WTGs) makes the reliability evaluation process more challenging. Due to the large number of system states involved in system operations, it is normally not feasible to enumerate all possible failure states to calculate the reliability indices. Monte Carlo simulation can be used for this purpose through iterative selection and evaluation of system states. However, due to its dependence on proportionate sampling, its efficiency in locating failure states may be low. The simulation may thus be time-consuming and take a long time to converge in some evaluation scenarios. In this paper, as an alternative option, four representative population-based intelligent search (PIS) procedures including genetic algorithm (GA), particle swarm optimization (PSO), artificial immune system (AIS), and ant colony system (ACS) are adopted to search the meaningful system states through their inherent convergence mechanisms. These most probable failure states contribute most significantly to the adequacy indices including loss of load expectation (LOLE), loss of load frequency (LOLF), and expected energy not supplied (EENS). The proposed solution methodology is also compared with the Monte Carlo simulation through conceptual analyses and numerical simulations. In this way, some qualitative and quantitative comparisons are conducted. A modified IEEE Reliability Test System (IEEE-RTS) is used in this investigation.
This paper proposes a holistic framework for plug-in hybrid electric vehicles (PHEVs) to participate in frequency regulation in a competitive electricity market. It is challenging to use PHEVs as ...frequency regulation units since conflicts of interests exist among PHEVs, aggregators, and transmission system operator (TSO). PHEVs are also facing various uncertainties from power prices and the available regulation capacities. These challenges motivate us to model the system using a hierarchical game. At the upper level of the hierarchical game, the frequency regulation capacity bids of aggregators are formulated as a non-cooperative game. Based on the frequency regulation prices obtained from the non-cooperative game, we formulate a Markov game to coordinate the charging process of PHEVs at the lower level. The Markov game will optimize the regulation capacity of the aggregator and strengthen its ability in bidding a more favorable frequency regulation price in the upper level game. Thus, the benefits are well coordinated among PHEVs, aggregators, and TSO in the proposed game-theoretic framework. Furthermore, the uncertainties from power prices and available regulation capacities are elegantly handled by the proposed non-cooperative game and Markov game, respectively. Finally, various simulations are carried out to validate the effectiveness of the proposed hierarchical game approach.
Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. Integration of smart meters into ...electricity grid involves implementation of a variety of techniques and software, depending on the features that the situation demands. Design of a smart meter depends on the requirements of the utility company as well as the customer. This paper discusses various features and technologies that can be integrated with a smart meter. In fact, deployment of smart meters needs proper selection and implementation of a communication network satisfying the security standards of smart grid communication. This paper outlines various issues and challenges involved in design, deployment, utilization, and maintenance of the smart meter infrastructure. In addition, several applications and advantages of smart meter, in the view of future electricity market are discussed in detail. This paper explains the importance of introducing smart meters in developing countries. In addition, the status of smart metering in various countries is also illustrated.
The environmental issues that arise from the pollutant emissions produced by fossil-fueled electric power plants have become a matter of concern more recently. The conventional economic power ...dispatch cannot meet the environmental protection requirements, since it only considers minimizing the total fuel cost. The multi-objective generation dispatch in electric power systems treats economic and emission impact as competing objectives, which requires some reasonable tradeoff among objectives to reach an optimal solution. In this paper, a fuzzified multi-objective particle swarm optimization (FMOPSO) algorithm is proposed and implemented to dispatch the electric power considering both economic and environmental issues. The effectiveness of the proposed approach is demonstrated by comparing its performance with other approaches including weighted aggregation (WA) and evolutionary multi-objective optimization algorithms. All the simulations are conducted based on a typical test power system.
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