Weather forecasts for microgrid energy management: Review, discussion and recommendations

Agüera-Pérez, Agustín; Palomares-Salas, José Carlos; Rosa, Juan José González de la; Florencias-Oliveros, Olivia

doi:10.1016/j.apenergy.2018.06.087

Cited by 136 publications

(83 citation statements)

References 97 publications

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“…In contrast, customer day-ahead demand, is generally harder to predict accurately, because it depends on more uncertain factors when compared to PV output prediction [42,43]. One reasonable way to forecast a day's demand is to use load profile for the previous week, as consumers usually have a typical weekly consumption pattern [11].…”

Section: Load and Pv Generation Forecastingmentioning

confidence: 99%

Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation

Azuatalam

Paridari

et al. 2019

Renewable and Sustainable Energy Reviews

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The home energy management problem has many different facets, including economic viability, data uncertainty and quality of strategy employed. The existing literature in this area focuses on individual aspects of this problem without a detailed, holistic analysis of the results with regards to practicality in implementation. In this paper, we fill this gap by performing a comprehensive comparison of seven different energy management strategies, each with different levels of practicality, sophistication and computational requirements. We analyse the results in the context of these three characteristics, and also critique the modelling assumptions made by each strategy. Our analysis finds that using a more sophisticated energy management strategy may not necessarily improve the performance and economic viability of the PV-battery system due to the effects of modeling assumptions, such as the treatment of uncertainties in the input data and battery degradation effects.

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Section: Load and Pv Generation Forecastingmentioning

confidence: 99%

Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation

Azuatalam

Paridari

et al. 2019

Renewable and Sustainable Energy Reviews

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“…For the purchase pricing scheme (c p (k), k ∈ {0, · · · , N }), we use the P G&E schedule E6 [27] for the residential building and the P G&E schedule A10 [28] for the commercial building. For the selling pricing scheme (c s (k), k ∈ {0, · · · , N }), we use 7 We note that ξp < 1.0 during the charge phase and ξp > 1.0 during the discharge phase. In other words, more power is discharged from the battery than what is output and less power is used to charge the battery than what is input.…”

Section: Grid Transactionsmentioning

confidence: 99%

Designing near-optimal policies for energy management in a stochastic environment

Poolla

Ishihara

Milito³

2019

Applied Energy

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With the rapid growth in renewable energy and battery storage technologies, there exists significant opportunity to improve energy efficiency and reduce costs through optimization. However, optimization algorithms must take into account the underlying dynamics and uncertainties of the various interconnected subsystems in order to fully realize this potential. To this end, we formulate and solve an energy management optimization problem as a Markov Decision Process (MDP) consisting of battery storage dynamics, a stochastic demand model, a stochastic solar generation model, and an electricity pricing scheme. The stochastic model for predicting solar generation is constructed based on weather forecast data from the National Oceanic and Atmospheric Administration. A near-optimal policy design is proposed via stochastic dynamic programming.Simulation results are presented in the context of storage and solar-integrated residential and commercial building environments. Results indicate that the near-optimal policy significantly reduces the operating costs compared to several heuristic alternatives. The proposed framework facilitates the design and evaluation of energy management policies with configurable demand-supply-storage parameters in the presence of weather-induced uncertainties.

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“…Weather predictions are generated using Numerical Weather Prediction (NWP) models associated with the atmospheric processes which are solved by using the physical equations which can predict meteorological parameters for several days. However, some complex problems such as calculations, a lack of local considerations and computational cost may affect the results [1]. Varying the parameterizations, boundary and initial conditions of the NWP models can calculate the different possible evolutions of the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of solar radiation forecasting between WRF and LSTM in Gifu, Japan

Araujo

2020

Environ. Res. Commun.

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Three months comparison of hourly solar radiation forecasting from 1st January to 31st March 2017 between Weather Research and Forecasting (WRF) mesoscale model and Long short-term memory (LSTM) algorithm is presented in this study. One-way grid nesting technique of the WRF model is applied for the simulation with a six-hourly input dataset downloaded from the National Oceanic and Atmospheric Administration -National Operational Model Archive and Distribution System (NOMADS) website. Three years'data of solar radiation from 1st January 2014 to 31st December 2016 are used as input data for Long Short Term Memory (LSTM) algorithm to simulate solar radiation. The results show the root mean square error of the LSTM algorithm is 310 W m −2 higher compared to 210 W m −2 from the WRF model. The MBE and the nMBE of the WRF model are obtained positive value 96 W m −2 and 9% compared to −101 W m −2 and −9% of LSTM for 2160 h prediction. Meanwhile, the performance error percentage of WRF is 19% lower compared to 28% of LSTM for the nRMSE error metric. Although this study found that the WRF model performed better and lower error compared to the LSTM algorithm, however, it also recommends the LSTM algorithm configuration can be used for long-term prediction.

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Weather forecasts for microgrid energy management: Review, discussion and recommendations

Cited by 136 publications

References 97 publications

Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation

Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation

Designing near-optimal policies for energy management in a stochastic environment

Performance comparison of solar radiation forecasting between WRF and LSTM in Gifu, Japan

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