A novel long-term power forecasting based smart grid hybrid energy storage system optimal sizing method considering uncertainties

Luo, Zhao; Zhang, Tingze; Peng, Xiuyan; Zhang, Xinan

doi:10.1016/j.ins.2022.07.161

Cited by 19 publications

(3 citation statements)

References 32 publications

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“…From the perspective of configuration methods, at present, most studies on energy storage configuration for new energy (especially solar PV) focus on the optimization of multi-objective configuration, such as light rejection rate and operation economy, or the optimization of mixed energy storage with different attributes, but few studies on the basic response capability configuration of energy storage for uncertainty calming demand. Table 9 shows the comparison results of the energy storage configuration method with the method proposed in this paper to compensate the prediction error [ 43 ].…”

Section: Case Studymentioning

confidence: 99%

The short-term intermittency evaluation of distributed photovoltaic power

Ma,

Huang,

Yuan

2024

Heliyon

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Section: Case Studymentioning

confidence: 99%

The short-term intermittency evaluation of distributed photovoltaic power

Ma,

Huang,

Yuan

2024

Heliyon

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“…To control and mitigate demand-side requirements, it is critical to initially understand the features of demand-side loads. The classification of an energy system's relationship with a smart measurement, as either BTM or front of meter (FOM), is pivotal [1,2]. BTM systems supply electricity directly to on-site locations, bypassing traditional metering, whereas FOM systems deliver energy to external locations, necessitating metering before reaching consumers.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

Zaboli,

Kasimalla,

Park

et al. 2024

Energies

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Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model’s precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model’s predictive capabilities. Comparing the model’s predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach.

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“…In [26], a storage system sizing technique is proposed, taking into account the possible errors made in the temporal prediction. The impact of hybrid power plants, with photovoltaic energy and solar thermal energy, from a technical point of view, is analyzed in [27].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Operation of Hybrid Renewable Power Plants Participating in Coupled Power Markets with Different Execution Times

et al. 2023

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The increasing limitations in the use of fossil fuels due to their limited availability and pollution have increased the use of renewable energies and storage systems for electricity generation. To achieve the goals of the integration of renewable energy, sizing and management methods for hybrid plants are needed to make investments profitable and attractive in these resources. This work presents an optimization method for the sizing and operation of hybrid plants with storage, choosing the best combination of technologies based on resource availability, installation costs and market prices, maximizing an economic index such as the net present value. One of the main contributions of this work is to reduce the oversizing that occurs in traditional methods through a penalty term for lost energy, encouraging investment in batteries to store excess energy above the point of interconnection (POI). In addition, it is intended to cover gaps such as the operation in coupled markets with different execution periods to maximize the benefits of the investment made and to contemplate different generation alternatives together with storage. The presented method is tested through sizing and operation simulations to demonstrate its potential. The presented method is tested through sizing and operation simulations to demonstrate its potential. In scenario A, the best combination of solar energy, photovoltaic energy and storage, is chosen. In scenario B, it is shown how the curtailment of the oversizing is reduced in some months by more than 5%. In scenario C, for daily operation in coupled markets, it is possible to improve the benefits from 0.7% to 37.04% in the days of the year.

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A novel long-term power forecasting based smart grid hybrid energy storage system optimal sizing method considering uncertainties

Cited by 19 publications

References 32 publications

The short-term intermittency evaluation of distributed photovoltaic power

The short-term intermittency evaluation of distributed photovoltaic power

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

Optimal Sizing and Operation of Hybrid Renewable Power Plants Participating in Coupled Power Markets with Different Execution Times

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