The power-to-gas (P2G) storage, compressed air energy storage (CAES) unit, and power-to-heat (P2H) storage are considered as energy conversion/storage technologies in the form of a hybrid storage unit to participate in multiple energy markets. This strategy is proposed from the perspective of a storage system owner to maximize the profit of the hybrid storage unit. To this end, this chapter presents an optimal bidding/offering strategy for the economic participation of the hybrid energy storage unit in the multi-carrier energy markets. The preferable characteristic of energy storage systems raises the need to use a comprehensive energy management strategy to connect and manage different layers of energy networks in the scheduling process. Among the existing elements, energy storage systems and energy conversion facilities play a special role in the optimal operation of multi-carrier energy networks to supply different energy demands. Nowadays, multi-carrier energy networks are efficient solutions to boost energy efficiency, decrease energy supply cost, and increase the flexibility of the traditional systems. Simulation results suggest that the proposed scheduling methods can help quantify the daily operating cost, balance real-time power demands and PV output solar power, and achieve considerable operating cost savings by appropriately arranging and utilizing all the devices in the multi-energy system. Thus, the scheduling of MES operation can be conducted in much shorter time interval while considering more possible future scenarios. Besides, a new two-stage optimization formulation is proposed, which help greatly reduce computation time comparing with traditional integrated methods in the literature. Specifically, Random Forest forecasting model is applied and further improved with online adaptability feature to provide input for the subsequent optimization. In this thesis, optimal scheduling methods based on machine learning and optimization techniques of a real multi-energy system, Stone Edge Farm, CA, are proposed from an economic point of view. Through integrating as a multi-energy system, different energy carriers can be coupled and optimized as one unit to improve overall energy utilization efficiency, reduce system operating cost, and improving solar power integration. Traditionally, different energy infrastructures are scheduled and operated independently, which results in less efficient energy usage and resource wasting. ![]() In response to the challenge of improving energy production and consumption efficiencies due to environmental problems and energy crisis, multi-energy systems composed of electrical power, natural gas, heating power, cooling power networks and energy storage are attracting more attention and are being developed rapidly in recent years. The simulations show that the adaptive random forest model is better than the benchmark models in terms of prediction accuracy. ![]() Besides, an online self-adaptability feature is implemented with the model so it can adapt to the new forecasting environment when new observations are detected. This allows users to consider a variety of scenarios that may occur in the future for further system operation optimization and help users evaluate the reliability of the results. The adaptive random forest model can provide a probability distribution of the prediction results. This paper proposes a prediction model based on adaptive random forest for demands and solar power of a real MES, Stone Edge Farm, in California. One of the main stages in the optimal scheduling of a multi-energy system is the predictions of various demands and sustainable energy in the scheduling horizon. ![]() Through integration as a multi-energy system, different energy infrastructures can be scheduled and managed as one unit. In order to address the challenges of improving energy efficiency and integration of renewable energy, multi-energy systems, composed of electric, natural gas, heat and other energy networks, have received more and more attention in recent years and have been rapidly developed.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |