Practical Power Management of PV/ESS Integrated System

Jeon, Seoung Uk; Noh, Jai Sung; Kang, Seok‐Ju; Park, Jung–Wook

doi:10.1109/access.2020.3031591

Cited by 17 publications

(5 citation statements)

References 15 publications

(23 reference statements)

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“…Nevertheless, it is noted that grid extensions might remain necessary in the long term. Further, the works of [25][26][27] delved into the investigation of battery storage systems in power grids for providing decongestion services, highlighting that storage is an appealing option in alleviating congestion problems and enhancing grid hosting capacity.…”

Section: Introductionmentioning

confidence: 99%

Export Constraints Applicable to Renewable Generation to Enhance Grid Hosting Capacity

Chatzistylianos,

Psarros,

Papathanassiou

2024

Energies

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This paper investigates grid export constraints applicable to photovoltaic (PV) and wind farm (WF) installations, both with and without behind-the-meter storage, aimed at enhancing grid hosting capacity. The study focuses on static output power limitations, i.e., simple export constraints that are preventively imposed on renewable energy source (RES) plants regardless of the prevailing network congestion conditions. These constraints are easy to apply, implemented ex ante and out of market, and do not require additional investments in energy storage or advanced devices. They also yield a measurable increase in hosting capacity and grid utilization, with their impact on RES plant operation and return of investment straightforwardly calculable. Analysis defines the level and shape of these constraints assuming an indicative acceptable curtailment level of 5% for each RES technology attributed to the preventing action of the imposed limitations, while the respective implications for RES energy yield and investment viability are explored. The findings indicate that an export power limitation of ca. 68% is effective for stand-alone PVs, while a stepwise static limitation is necessary for stand-alone WFs and WFs with integrated storage to manage midday solar generation peaks. PV plants tightly coupled with storage facilities can handle static limitations as low as 35% with minimal impact on the economic feasibility of the investments.

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Section: Introductionmentioning

confidence: 99%

Export Constraints Applicable to Renewable Generation to Enhance Grid Hosting Capacity

Chatzistylianos,

Psarros,

Papathanassiou

2024

Energies

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“…For example, when using a PV-linked energy storage system (ESS), ultra-short term PV forecasting is employed to control the ESS charging in response to PV volatility [12]. Furthermore, for a distributed conservation voltage reduction (CVR) [13] with continuous voltage regulation [14], [15], power generation data predicted in short time units can be used as key information for energy optimization.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short Term Photovoltaic Generation Forecasting Based on Data Decomposition and Customized Hybrid Model Architecture

Lee,

Kang,

Lee

et al. 2024

IEEE Access

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As photovoltaic (PV) systems have been successfully adopted worldwide, accurate power generation forecasting becomes increasingly essential to stable power grid operation and smart grid applications to cope with the variability of PV systems. Several data-driven models have recently been proposed for the more accurate prediction of PV power generation and have shown good performance. In particular, hybrid models that combine the characteristics of single-structure deep learning-based models have achieved better accuracies. To this end, a novel ultra-short term PV power generation forecasting model with a hybrid structure is proposed for instantaneous response to PV fluctuations. For higher forecasting accuracy, the proposed model decomposes the input feature data into trend and residual components and employs customized sub-models such as the linear, Transformer, and long short-term memory (LSTM). Furthermore, the proposed model is trained with data from the self-built PV site to implement a model suitable to real-world applications. Finally, the experimental results demonstrate that the proposed model has the best forecasting performance compared to conventional and state-of-the-art deep learning-based forecasting models with reasonable computational complexity.

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“…Common examples are: crow search algorithm auto-drive particle swarm optimization solver [27], tabu search optimization solver [28], genetic-based optimization solver [29], artificial ecosystem-based optimization [30], simulated annealing optimization solver [31], and ant colony optimization solver [32]. Regarding energy storage systems and PEVs, their vital contribution to growing the RES hosting capacity has been explored in [33]. The authors of [34] have developed control schemes of PEV batteries to raise the RES capacity in distribution systems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids With PEV-Parking Lots

Ali

Mahmoud

Lehtonen

2022

IEEE Systems Journal

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Lately, the integration of renewable energy sources (e.g. photovoltaic and wind generation systems) has been raised into microgrids interconnected with plug-in electric vehicles (PEV). Such intermittent generation and charging/discharging PEV profiles are challenging to ensure the secure and optimal operation of microgrids. In this paper, an optimization approach is proposed to determine the optimal locations and sizes of photovoltaic and wind generation systems in microgrids with PEV-parking lots. The developed approach addresses 1) the uncertainty of generation profiles of photovoltaic and wind generation systems and loads, 2) the DSTATCOM feature of photovoltaic and wind generation systems, and 3) microgrid constraints. The feasible PEV conditions are also considered, i.e. initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. To solve the planning model, we have developed a bi-level metaheuristic-based approach. The upper-level and lower-level optimization tasks are to optimize the decision variables of renewable energy sources and PEV, respectively. Both energy losses through the lines and energy from the main grid are considered as sub-objectives to be minimized. Various simulations and study cases are performed to assess the effectiveness of the proposed approach. The outcomes show the efficacy of the proposed approach to simultaneously plan renewable energy sources and manage PEV to form an autonomous microgrid.

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Practical Power Management of PV/ESS Integrated System

Cited by 17 publications

References 15 publications

Export Constraints Applicable to Renewable Generation to Enhance Grid Hosting Capacity

Export Constraints Applicable to Renewable Generation to Enhance Grid Hosting Capacity

Ultra-Short Term Photovoltaic Generation Forecasting Based on Data Decomposition and Customized Hybrid Model Architecture

Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids With PEV-Parking Lots

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