In recent years, the energy consumption structure has been accelerating towards clean and low-carbon globally, and China has also set positive goals for new energy development, vigorously promoting the development and...
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In recent years, the energy consumption structure has been accelerating towards clean and low-carbon globally, and China has also set positive goals for new energy development, vigorously promoting the development and utilization of renewable energy, accelerating the implementation of renewable energy substitution actions, and focusing on improving the consumption capacity of new energy. However, due to the intermittent and unstable characteristics of renewable energy, it is difficult to meet the demands of the power load side in practical applications. Energy storage is an important link for the grid to efficiently accept new energy, which can significantly improve the consumption of new energy electricity such as wind and photovoltaics by the power grid, ensuring the safe and reliable operation of the grid system, but energy storage is a high-cost resource. Therefore, this paper focuses on the energy storage scenarios for a big data industrial park and studies the energy storage capacity allocation plan and business model of big data industrial park. Firstly, based on the characteristics of the big data industrial park, three energy storage application scenarios were designed, which are grid center, user center, and market center. On this basis, an optimal energy storage configuration model that maximizes total profits was established, and financial evaluation methods were used to analyze the corresponding business models. Finally, taking an actual big data industrial park as an example, the economic viability of energy storage configuration schemes under two scenarios was discussed, and an energy storage system construction plan was proposed to promote the zero-carbon target of the big data industrial park. (C) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
The integration of renewable energy sources into power systems has introduced complexities that necessitate advanced optimization strategies for efficient energy management. This paper presents a novel deep learning-b...
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Extreme natural disasters represent particularly significant challenges to the safe and economical operation of electric distribution networks (DNs) utilizing a high proportion of intermittent renewable energy. Howeve...
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Extreme natural disasters represent particularly significant challenges to the safe and economical operation of electric distribution networks (DNs) utilizing a high proportion of intermittent renewable energy. However, although topological reconstruction is regarded as an ideal approach to maximize the capability of DNs in rapidly restoring normal load supply in the face of emergencies, the randomness of fault occurrence and the uncertainty of renewable energy output have been relatively overlooked in previous research. The present work addresses this issue by proposing a pre-disaster distributionally robust scheduling model for active DNs considering topology reconfiguration with a high proportion of photovoltaic (PV) and energy storage system (ESS) resources while seeking to optimize the energy sources, electric power grids, electric loads, and ESSs (i.e., source-grid-load-storage coordination) collaboratively under PV output uncertainty. The model is a two-layer model. First, the upper-layer model aims to obtain the optimal DN topology that minimizes the total dispatch cost in the pre-disaster prevention stage under multiple fault scenarios using stochastic programming. Second, the lower-layer model applies a distributionally robust optimization (DRO) approach to optimize the source-load-storage scheduling strategy based on the DN topology given by the upper layer model, combined with the PV output scenarios. The DRO problem is solved using the column and constraint generation algorithm (C&CG). Finally, the effectiveness of the proposed two-layer scheduling scheme for improving the load survivability of a DN under extreme weather conditions and PV output uncertainties is verified based on the computational results obtained for an IEEE 33-bus system as an example.
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