| 1. |
- Fachrizal, Reza, 1993-, et al.
(author)
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Smart charging of electric vehicles considering photovoltaic power production and electricity consumption : a review
- 2020
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In: eTransporation. - : Elsevier. - 2590-1168. ; 4
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Research review (peer-reviewed)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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| 2. |
- Munkhammar, Joakim, 1982-, et al.
(author)
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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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In: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 184, s. 688-695
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Journal article (peer-reviewed)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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| 3. |
- Munkhammar, Joakim, 1982-, et al.
(author)
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Probabilistic forecasting of the clear-sky index using Markov-chain mixture distribution and copula models
- 2019
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In: 2019 Ieee 46Th Photovoltaic Specialists Conference (PVSC). - New York : IEEE. - 9781728104942 ; , s. 2428-2433
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Conference paper (peer-reviewed)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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| 4. |
- Munkhammar, Joakim, 1982-, et al.
(author)
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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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In: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 282
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Journal article (peer-reviewed)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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| 5. |
- Shepero, Mahmoud, 1992-, et al.
(author)
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Residential probabilistic load forecasting : A method using Gaussian process designed for electric load data
- 2018
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In: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 218, s. 159-172
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Journal article (peer-reviewed)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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| 6. |
- van der Meer, Dennis, et al.
(author)
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Clear-sky index space-time trajectories from probabilistic solar forecasts : Comparing promising copulas
- 2020
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In: Journal of Renewable and Sustainable Energy. - : AMER INST PHYSICS. - 1941-7012. ; 12:2
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Journal article (peer-reviewed)abstract
- Short-term probabilistic solar forecasts are an important tool in decision-making processes in which uncertainty plays a non-negligible role. Purely statistical models that produce temporal or spatiotemporal probabilistic solar forecasts are generally trained individually, and the combined forecasts therefore lack the temporal or spatiotemporal correlation present in the data. To recover the spatiotemporal dependence structure, a copula can be employed, which constructs a multivariate distribution from which spatially and temporally correlated uniform random numbers can be sampled, which in turn can be used to generate the so-called space-time trajectories via the inverse probability integral transform. In this study, we employ the recently introduced ultra-fast preselection algorithm to leverage the spatiotemporal information present in a pyranometer network and compare its accuracy to that of quantile regression forecasts that only consider temporal information. We show that the preselection algorithm improves both the calibration and sharpness of the predictive distributions. Furthermore, we employ four copulas, i.e., (1) Gaussian, (2) Student-t, (3) Clayton, and (4) empirical, to generate space-time trajectories. The results highlight the necessity to rigorously assess the calibration of the space-time trajectories and the correct modeling of the spatiotemporal dependence structure, which we show through techniques introduced in atmospheric sciences. The code used to generate the results in this study can be found at https://github.com/DWvanderMeer/SpaceTimeTrajectories.
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| 7. |
- van der Meer, Dennis, et al.
(author)
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Data-Enabled Reactive Power Control of Distributed Energy Resources via a Copula Estimation of Distribution Algorithm
- 2022
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In: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665412117
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Conference paper (peer-reviewed)abstract
- The increase in the number of distributed energy resources (DERs) in the low-voltage (LV) grid causes reverse active power flow, which induces voltage regulation issues across the feeder. We employ the copula estimation of distribution algorithm (copula EDA) that optimally controls the reactive power of DERs to minimize voltage deviations. EDAs iteratively learn from data and sample an explicit probability distribution that models the dependencies between variables, allowing for a more effective exploration of the optimal solution space with fewer iterations. A copula offers additional flexibility, since the dependence structure between the decision variables and the marginal distributions can be modeled independently. The effectiveness of the proposed method is illustrated on a modified IEEE 123 node test feeder with 10 smart photovoltaic inverters. The results show that the proposed method achieves improved voltage profiles and offers many opportunities for further adaptability.
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| 8. |
- van der Meer, Dennis, et al.
(author)
-
Probabilistic clear-sky index forecasts using Gaussian process ensembles
- 2018
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In: 2018 IEEE 7th World Conference On Photovoltaic Energy Conversion (WCPEC). - : IEEE. - 9781538685297 ; , s. 2724-2729
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Conference paper (peer-reviewed)abstract
- In this paper, we investigate the performance of ensembles of Gaussian processes (GPs) to provide more accurate probabilistic forecasts of the clear-sky index (CSI), based on data from a network of pyranometers on Hawaii. This idea follows from the multiple-state model of the CSI in which its probability density can he represented as a combination of Gaussian densities, and the well-documented advantage of ensembles of prediction models. More specifically, we employ a Gaussian mixture model (GMM) and convolution to produce ensembles of GPs, and show that the GMM ensemble outperforms the individual and convoluted GP models, especially by improving the lower limit of the skill score.
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| 9. |
- van der Meer, Dennis, et al.
(author)
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Probabilistic forecasting of solar power, electricity consumption and net load : Investigating the effect of seasons, aggregation and penetration on prediction intervals
- 2018
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In: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 171, s. 397-413
-
Journal article (peer-reviewed)abstract
- This paper presents a study into the effect of aggregation of customers and an increasing share of photovoltaic (PV) power in the net load on prediction intervals (PIs) of probabilistic forecasting methods applied to dis- tribution grid customers during winter and spring. These seasons are shown to represent challenging cases due to the increased variability of electricity consumption during winter and the increased variability in PV power production during spring. We employ a dynamic Gaussian process (GP) and quantile regression (QR) to produce probabilistic forecasts on data from 300 de-identified customers in the metropolitan area of Sydney, Australia. In case of the dynamic GP, we also optimize the training window width and show that it produces sharp and reliable PIs with a training set of up to 3 weeks. In case of aggregation, the results indicate that the aggregation of a modest number of PV systems improves both the sharpness and the reliability of PIs due to the smoothing effect, and that this positive effect propagates into the net load forecasts, especially for low levels of aggregation. Finally, we show that increasing the share of PV power in the net load actually increases the sharpness and reliability of PIs for aggregations of 30 and 210 customers, most likely due to the added benefit of the smoothing effect.
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| 10. |
- van der Meer, Dennis, et al.
(author)
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Review on probabilistic forecasting of photovoltaic power production and electricity consumption
- 2018
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In: Renewable & sustainable energy reviews. - : Elsevier BV. - 1364-0321 .- 1879-0690. ; , s. 1484-1512
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Research review (peer-reviewed)abstract
- Accurate forecasting simultaneously becomes more important and more challenging due to the increasing penetration of photovoltaic (PV) systems in the built environment on the one hand, and the increasing stochastic nature of electricity consumption, e.g., through electric vehicles (EVs), on the other hand. Until recently, research has mainly focused on deterministic forecasting. However, such forecasts convey little information about the possible future state of a system and since a forecast is inherently erroneous, it is important to quantify this error. This paper therefore focuses on the recent advances in the area of probabilistic forecasting of solar power (PSPF) and load forecasting (PLF). The goal of a probabilistic forecast is to provide either a complete predictive density of the future state or to predict that the future state of a system will fall in an interval, defined by a confidence level. The aim of this paper is to analyze the state of the art and assess the different approaches in terms of their performance, but also to what extent these approaches can be generalized so that they not only perform best on the data set for which they were designed, but also on other data sets or different case studies. In addition, growing interest in net demand forecasting, i.e., demand less generation, is another important motivation to combine PSPF and PLF into one review paper and assess compatibility. One important finding is that there is no single preferred model that can be applied to any circumstance. In fact, a study has shown that the same model, with adapted parameters, applied to different case studies performed well but did not excel, when compared to models that were optimized for the specific task. Furthermore, there is need for standardization, in particular in terms of filtering night time data, normalizing results and performance metrics.
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