| 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. |
- 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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| 3. |
- van der Meer, Dennis, et al.
(författare)
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A comparison of strategies for net demand forecasting in case of PV power production and electricity consumption
- 2017
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Konferensbidrag (refereegranskat)abstract
- This paper aims to investigate the relative difference in accuracy between forecasting net demand, i.e., electricity con- sumption less the photovoltaic (PV) power production, directly and indirectly, where the latter implies forecasting consumption and production separately before subtraction. Depending on the variability and penetration of PV power production, variability of the net demand time series is likely to increase as well, which may influence accuracy of the forecast. The well-known AutoRegressive Integrated Moving Average (ARIMA) model is employed to forecast the univariate time series. We show that the direct strategy leads to a forecast with higher accuracy. Moreover, the difference in accuracy between the strategies appears to increase with lead time.
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| 4. |
- van der Meer, Dennis, et al.
(författare)
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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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Ingår i: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665412117
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Konferensbidrag (refereegranskat)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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| 5. |
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| 6. |
- van der Meer, Dennis, et al.
(författare)
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Predicting hosting capacity of photovoltaic power production in low-voltage grids using regressive techniques
- 2017
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Konferensbidrag (refereegranskat)abstract
- In this study we predict the hosting capacity (HC) of photovoltaic (PV) power of low-voltage (LV) grids utilizing explanatory variables that are straightforward for stakeholders to determine. The motivation of this study is to avoid the necessity of simulating electricity grids using power flow analysis, which are generally time consuming in terms of both coding and solving. In order to achieve this, we utilize extensive power flow simulations performed on two medium-voltage (MV) grids in Herrljunga, Sweden, and extract explanatory variables that show high correlation with HC. Furthermore, we employ multiple linear regression (MLR), gradient boosting (GB) and Gaussian process (GP) to predict HC. The results reveal that HC can be predicted with reasonable accuracy, achieving MAE between 12.2 kW and 14.0 kW, and RMSE between 15.7 kW and 17.4 kW, and can therefore guide stakeholders by providing an accurate first estimate.
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| 7. |
- van der Meer, Dennis, et al.
(författare)
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Probabilistic clear-sky index forecasts using Gaussian process ensembles
- 2018
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Ingår i: 2018 IEEE 7th World Conference On Photovoltaic Energy Conversion (WCPEC). - : IEEE. - 9781538685297 ; , s. 2724-2729
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Konferensbidrag (refereegranskat)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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