| 1. |
- Fachrizal, Reza, 1993-, et al.
(författare)
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Direct forecast of solar irradiance for EV smartcharging scheme to improve PV self-consumptionat home
- 2021
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Ingår i: 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665448758
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Konferensbidrag (refereegranskat)abstract
- The integration of electric vehicle (EV) chargingand Photovoltaic (PV) systems at residential buildings has increased in recent years and poses new challenges for the power system. Smart charging of EVs is believed to be one ofthe solutions to problems arising from PV and EV integration since it can improve the synergy between PV generation and EV charging. Accurate forecasts of PV generation plays an important role in smart charging schemes to optimally utilize the PV electricity for EV charging. This paper presents an assessment of a direct forecasting method applied to an EV smart charging scheme. Direct forecasting is a forecasting method which focus directly on the link between the forecast origin and the targeted horizon. The objective of the smart charging in this study is to minimize the net-load variability, which will also increase the self-consumption of PV electricity and reduce the peak loads. The PV self-consumption ratios in different forecast scenarios are compared. Results show that the smart charging with the direct forecast can achieve up to 89% of the PV self-consumption performance of the scheme with perfect forecast
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| 2. |
- Fachrizal, Reza, 1993-, et al.
(författare)
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Smart charging of electric vehicles considering photovoltaic power production and electricity consumption : a review
- 2020
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Ingår i: eTransporation. - : Elsevier. - 2590-1168. ; 4
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Forskningsöversikt (refereegranskat)abstract
- Photovoltaics (PV) and electric vehicles (EVs) are two emerging technologies often considered as cornerstones in the energy and transportation systems of future sustainable cities. They both have to be integrated into the power systems and be operated together with already existing loads and generators and, often, into buildings, where they potentially impact the overall energy performance of the buildings. Thus, a high penetration of both PV and EVs poses new challenges. Understanding of the synergies between PV, EVs and existing electricity consumption is therefore required. Recent research has shown that smart charging of EVs could improve the synergy between PV, EVs and electricity consumption, leading to both technical and economic advantages. Considering the growing interest in this field, this review paper summarizes state-of-the-art studies of smart charging considering PV power production and electricity consumption. The main aspects of smart charging reviewed are objectives, configurations, algorithms and mathematical models. Various charging objectives, such as increasing PV utilization and reducing peak loads and charging cost, are reviewed in this paper. The different charging control configurations, i.e., centralized and distributed, along with various spatial configurations, e.g., houses and workplaces, are also discussed. After that, the commonly employed optimization techniques and rule-based algorithms for smart charging are reviewed. Further research should focus on finding optimal trade-offs between simplicity and performance of smart charging schemes in terms of control configuration, charging algorithms, as well as the inclusion of PV power and load forecast in order to make the schemes suitable for practical implementations.
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| 3. |
- Frimane, Âzeddine, 1990-, et al.
(författare)
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Infinite hidden Markov model for short-term solar irradiance forecasting
- 2022
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Ingår i: Solar Energy. - : Elsevier. - 0038-092X .- 1471-1257. ; 244, s. 331-342
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Tidskriftsartikel (refereegranskat)abstract
- Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models – in terms of the number of states – is usually needed to be specified a priori. For solar irradiance data this assumption is very difficult to justify.In this paper, an infinite hidden Markov model (InfHMM) is introduced for short-term probabilistic forecasting of solar irradiance, where the assumption of fixed number of states a priori is relaxed and model complexity is determined during the model training. InfHMM is a non-parametric Bayesian model (NPB) indexed with an infinite dimensional parameter space which allows the automatic adaptation of the model to the “correct” complexity. This facilitates the automatic adaptation of the model to all weather conditions and locations. Posterior inference for InfHMM is performed using the Markov chain Monte Carlo algorithm, namely the beam sampler.Data from 13 different sources are used to validate the proposed model and subsequently it is compared to two well-established models in the literature: Markov-chain mixture distribution (MCM) and complete-history persistence ensemble (CH-PeEn) models. Important results are found, that cannot be derived from the existing finite models, such as the variation of the number of states within and across sites. The comparison of the models shows that the InfHMM is more consistent in term of the forecasting horizon.For reproducibility of the methodology presented in this paper, we have provided an R script for the InfHMM as supplementary material.
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| 4. |
- Lindberg, Oskar, et al.
(författare)
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Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden : Trading and forecast verification
- 2023
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Ingår i: Advances in Applied Energy. - : Elsevier. - 2666-7924. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates.
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| 5. |
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| 6. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
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Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model
- 2019
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Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 184, s. 688-695
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Tidskriftsartikel (refereegranskat)abstract
- This study presents a Markov-chain mixture (MCM) distribution model for forecasting the clear-sky index-normalized global horizontal irradiance. The model is presented in general, but applied to, and tested or minute resolution clear-sky index data for the two different climatic regions of Norrkoping, Sweden, and Hawaii USA. Model robustness is evaluated based on a cross-validation procedure and on that basis a reference con figuration of parameter settings for evaluating the model performance is obtained. Simulation results ar compared with persistence ensemble (PeEn) and quantile regression (QR) model simulations for both data set and for D = 1,...,5 steps ahead forecasting scenarios. The results are evaluated by a set of probabilistic fore casting metrics: reliability mean absolute error (reliability MAE), prediction interval normalized average widti (PINAW), continuous ranked probability score (CRPS) and continuous ranked probability skill score (skill). Botl in terms of reliability MAE and CRPS, the MCM model outperforms PeEn for all simulated scenarios. In terms c reliability MAE, the QR model outperforms the MCM model for most simulated scenarios. However, in terms c mean CRPS, the MCM model outperforms the QR model in most simulated scenarios. A point forecasting esti mate is also provided. The MCM model is concluded to be a computationally inexpensive, accurate and pars meter insensitive probabilistic model. Based on this, it is suggested as a candidate benchmark model in prop abilistic forecasting, in particular for solar irradiance forecasting. For applicability, a Python script of the MCA model is available as SheperoMah/MCM-distribution-forecasting at GitHub.
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| 7. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
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Probabilistic forecasting of the clear-sky index using Markov-chain mixture distribution and copula models
- 2019
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Ingår i: 2019 Ieee 46Th Photovoltaic Specialists Conference (PVSC). - New York : IEEE. - 9781728104942 ; , s. 2428-2433
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Konferensbidrag (refereegranskat)abstract
- Two probabilistic forecasting models for the clear-sky index, based on the Markov-chain mixture distribution (MCM) and copula clear-sky index generators, are presented and evaluated. In terms of performance, these models are compared with two benchmark models: a Quantile Regression (QR) model and the Persistence Ensemble (PeEn). The models are tested on minute resolution clear-sky index data, which was estimated from irradiance data for two different climatic regions: Hawaii, USA and Norrkoping, Sweden. Results show that the copula model generally outperforms the PeEn, while the MCM and QR models are superior in all tested aspects. Comparing MCM and QR reliability, the QR is superior, while the MCM is superior in mean CRPS and skill score. The MCM model is proposed as a potential benchmark for probabilistic solar forecasting. The MCM model is available in Python as SheperoMah/MCM-distribution-forecasting at GitHub.
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| 8. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
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Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model
- 2021
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 282
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Tidskriftsartikel (refereegranskat)abstract
- This study utilizes the Markov-chain mixture distribution model (MCM) for very short term load forecasting of residential electricity consumption. The model is used to forecast one step ahead half hour resolution residential electricity consumption data from Australia. The results are compared with Quantile Regression (QR) and Persistence Ensemble (PeEn) as advanced and simple benchmark models. The results were compared in terms of reliability, reliability mean absolute error (rMAE), prediction interval normalized average width (PINAW) and normalized continuous ranked probability score (nCRPS). For 10 steps conditioning for QR and PeEn, the MCM results were on par with QR, and superior to PeEn. As a sensitivity analysis, simulations were performed where the number of data points for conditioning QR and PeEn was varied and compared to the MCM output, which is based on only one data point for conditioning. It was shown that in terms of nCRPS and rMAE the QR results converged towards the MCM results for lower number of conditioning points included in QR. The nCRPS of PeEn never reached the superior MCM and QR results, but in rMAE, for number of conditioning points above 24, PeEn was the most reliable. Based on the sparse complexity design of MCM, high computational speed and competitive performance, it is suggested as a candidate for benchmark model in probabilistic forecasting of electricity consumption.
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| 9. |
- Shepero, Mahmoud, 1992-, et al.
(författare)
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Residential probabilistic load forecasting : A method using Gaussian process designed for electric load data
- 2018
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 218, s. 159-172
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Tidskriftsartikel (refereegranskat)abstract
- Probabilistic load forecasting (PLF) is of important value to grid operators, retail companies, demand response aggregators, customers, and electricity market bidders. Gaussian processes (GPs) appear to be one of the promising methods for providing probabilistic forecasts. In this paper, the log-normal process (LP) is newly introduced and compared to the conventional GP. The LP is especially designed for positive data like residential load forecasting—little regard was taken to address this issue previously. In this work, probabilisitic and deterministic error metrics were evaluated for the two methods. In addition, several kernels were compared. Each kernel encodes a different relationship between inputs. The results showed that the LP produced sharper forecasts compared with the conventional GP. Both methods produced comparable results to existing PLF methods in the literature. The LP could achieve as good mean absolute error (MAE), root mean square error (RMSE), prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP) as 2.4%, 4.5%, 13%, 82%, respectively evaluated on the normalized load data.
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| 10. |
- van der Meer, Dennis, et al.
(författare)
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An alternative optimal strategy for stochastic model predictive control of a residential battery energy management system with solar photovoltaic
- 2021
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 283
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Tidskriftsartikel (refereegranskat)abstract
- Scenario-based stochastic model predictive control traditionally considers the optimal strategy to be the expectation of the optimal strategies across all scenarios. However, while the stochastic problem involving uncertainties can be substantiated by a large number of scenarios, the expectation of the respective optimal control strategies derived from all scenarios as the optimal control strategy to the problem is challenging to justify. We therefore propose a different approach in which we artfully have the optimization program find the common optimal strategy across all scenarios for the first prediction step at each sample time, which, if it exists, yields the true optimal strategy with greater confidence. We demonstrate the efficacy of the proposed formulation through a case study of a research villa in Borås, Sweden, that is equipped with a battery and a photovoltaic system. We compute a covariance matrix that contains time-dependent information of the data and use it to sample autocorrelated scenarios from the probabilistic forecasts that serve as the uncertain input to the energy management system. We justify the credibility of the optimal solution derived from the proposed formulation with compelling reasoning and quantitative results such as improved self-consumption of photovoltaic power.
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