Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review

Ssekulima, Edward Baleke; Anwar, Muhammad Bashar; Al-Hinai, Amer; Moursi, Mohamed Shawky El

doi:10.1049/iet-rpg.2015.0477

Cited by 167 publications

(94 citation statements)

References 98 publications

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“…Ssekulima et al. () present a review of both solar and wind forecasting in the context of integration of renewable energy to the grid and its challenges. More recently, Ahmed and Khalid () review several renewable energy forecast techniques in the literature with an application‐oriented approach.…”

Section: Related Workmentioning

confidence: 99%

Smart energy pricing for demand‐side management in renewable energy smart grids

Borges

Schouery

Miyazawa

et al. 2019

Int Trans Operational Res

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Smart grids are expected to provide various benefits to society by integrating advances in power engineering with recent developments in the field of information and communications technology. One of the advantages is the support to efficient demand‐side management (DSM), for example, changes in consumer demands for energy based on using incentives. Indeed, DSM is expected to help grid operators balance time‐varying generation by wind and solar units, and the optimization of their usage. This paper focuses on DSM considering renewable energy generation and proposes an auction, in which consumers submit bids to renewable energy usage plans. An additional model is introduced to allow consumers to compute their bid for a given usage plan. Both models have been extended to include energy storage devices. The proposed model is compared to a system with time‐varying pricing for energy, where it is shown to allow consumers to use more appliances, to lead to a larger profit, and to reduce the peak‐to‐average ratio of energy consumption. Finally, the impact of the use of energy storage in households and in the energy provider is also considered.

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Section: Related Workmentioning

confidence: 99%

Smart energy pricing for demand‐side management in renewable energy smart grids

Borges

Schouery

Miyazawa

et al. 2019

Int Trans Operational Res

View full text Add to dashboard Cite

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“…These synthetic prediction series can be obtained with any approach reported in the literature (see for instance the approaches reported in [23,24]), being the auto-regressive (ARMA) models the most widely used. In this paper, we propose an alternative model based on both spectral decomposition and ARMA models to generate the prediction scenarios.…”

Section: Generation Of Scenariosmentioning

confidence: 99%

A Novel Stochastic-Programming-Based Energy Management System to Promote Self-Consumption in Industrial Processes

2018

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Abstract:The introduction of non-conventional energy sources (NCES) to industrial processes is a viable alternative to reducing the energy consumed from the grid. However, a robust coordination of the local energy resources with the power imported from the distribution grid is still an open issue, especially in countries that do not allow selling energy surpluses to the main grid. In this paper, we propose a stochastic-programming-based energy management system (EMS) focused on self-consumption that provides robustness to both sudden NCES or load variations, while preventing power injection to the main grid. The approach is based on a finite number of scenarios that combines a deterministic structure based on spectral analysis and a stochastic model that represents variability. The parameters to generate these scenarios are updated when new information arrives. We tested the proposed approach with data from a copper extraction mining process. It was compared to a traditional EMS with perfect prediction, i.e., a best case scenario. Test results show that the proposed EMS is comparable to the EMS with perfect prediction in terms of energy imported from the grid (slightly higher), but with less power changes in the distribution side and enhanced dynamic response to transients of wind power and load. This improvement is achieved with a non-significant computational time overload.

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“…Nevertheless, RESs are extremely volatile due to their dependency on weather conditions [3]. The power produced by renewables is highly variable and challenging to predict [4].…”

Section: Introductionmentioning

confidence: 99%

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

et al. 2019

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The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.

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Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review

Cited by 167 publications

References 98 publications

Smart energy pricing for demand‐side management in renewable energy smart grids

Smart energy pricing for demand‐side management in renewable energy smart grids

A Novel Stochastic-Programming-Based Energy Management System to Promote Self-Consumption in Industrial Processes

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

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