2,419 research outputs found

    The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

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    Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation

    Learning-based short-time prediction of photovoltaic resources for pre-emptive excursion cancellation

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    There is a growing interest in using renewable energy resources (RES) such as wind, solar, geothermal and biomass in power systems. The main incentives for using renewable energy resources include the growing interest in sustainable and clean generation as well as reduced fuel cost. However, the challenge with using wind and solar resources is their indeterminacy which leads to voltage and frequency excursions. In this dissertation, first, the economic dispatch (ED) problem for a community microgrid is studied which explores a community energy market. As a result of this work, the importance of modeling and predicting renewable resources is understood. Hence, a new algorithm based on dictionary learning for prediction of solar production is introduced. In this method, a dictionary is trained to carry various behaviors of the system. Prediction is performed by reconstructing the tail of the upcoming signal using this dictionary. To improve the accuracy of prediction, a new approach based on a novel clustering-based Markov Switched Autoregressive Model is proposed that is capable of predicting short-term solar production. This method extracts autoregressive features of the training data and partitions them into multiple clusters. Later, it uses the representative feature of each cluster to predict the upcoming solar production level. Additionally, a Markov jump chain is added to improve the robustness of this scheme to noise. Lastly, a method to utilize these prediction mechanisms in a preemptive model predictive control is explored. By incorporating the expected production levels, a model predictive controller is designed to preemptively cancel the upcoming excursions --Abstract, page iv

    Microgrid Energy Management

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    In IEEE Standards, a Microgrid is defined as a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries, which acts as a single controllable entity with respect to the grid and can connect and disconnect from the grid to enable it to operate in both grid-connected or island modes. This Special Issue focuses on innovative strategies for the management of the Microgrids and, in response to the call for papers, six high-quality papers were accepted for publication. Consistent with the instructions in the call for papers and with the feedback received from the reviewers, four papers dealt with different types of supervisory energy management systems of Microgrids (i.e., adaptive neuro-fuzzy wavelet-based controls, cost-efficient power-sharing techniques, and two-level hierarchical energy management systems); the proposed energy management systems are of quite general purpose and aim to reduce energy usages and monetary costs. In the last two papers, the authors concentrate their research efforts on the management of specific cases, i.e., Microgrids with electric vehicle charging stations and for all-electric ships

    Efficient Decentralized Economic Dispatch for Microgrids with Wind Power Integration

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    Decentralized energy management is of paramount importance in smart microgrids with renewables for various reasons including environmental friendliness, reduced communication overhead, and resilience to failures. In this context, the present work deals with distributed economic dispatch and demand response initiatives for grid-connected microgrids with high-penetration of wind power. To cope with the challenge of the wind's intrinsically stochastic availability, a novel energy planning approach involving the actual wind energy as well as the energy traded with the main grid, is introduced. A stochastic optimization problem is formulated to minimize the microgrid net cost, which includes conventional generation cost as well as the expected transaction cost incurred by wind uncertainty. To bypass the prohibitively high-dimensional integration involved, an efficient sample average approximation method is utilized to obtain a solver with guaranteed convergence. Leveraging the special infrastructure of the microgrid, a decentralized algorithm is further developed via the alternating direction method of multipliers. Case studies are tested to corroborate the merits of the novel approaches.Comment: To appear in IEEE GreenTech 2014. Submitted Sept. 2013; accepted Dec. 201

    A systematic review of machine learning techniques related to local energy communities

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    In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.publishedVersio
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