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Shepero, M., Munkhammar, J. & Widén, J. (2019). A generative hidden Markov model of the clear-sky index. Journal of Renewable and Sustainable Energy, 11, Article ID 043703.
Open this publication in new window or tab >>A generative hidden Markov model of the clear-sky index
2019 (English)In: Journal of Renewable and Sustainable Energy, ISSN 1941-7012, E-ISSN 1941-7012, Vol. 11, article id 043703Article in journal (Refereed) Published
Abstract [en]

Clear-sky index (CSI) generative models are of paramount importance in, e.g., studying the integration of solar power in the electricity grid. Several models have recently been proposed with methodologies that are related to hidden Markov models (HMMs). In this paper, we formally employ HMMs, with Gaussian distributions, to generate CSI time-series. The authors propose two different methodologies. The first is a completely data-driven approach, where an HMM with Gaussian observation distributions is proposed. In the second, the means of these Gaussian observation distributions were predefined based on the fraction of time of bright sunshine from the site. Finally, the authors also propose a novel method to improve the autocorrelation function (ACF) of HMMs in general. The two methods were tested on two datasets representing two different climate regions. The performance of the two methodologies varied between the two datasets and among the compared performance metrics. Moreover, both the proposed methods underperformed in reproducing the ACF as compared to state-of-the-art models. However, the method proposed to improve the ACF was able to reduce the mean absolute error (MAE) of the ACF by up to 19%. In summary, the proposed models were able to achieve a Kolmogorov-Smirnov test score as low as 0.042 and MAE of the ACF as low as 0.012. These results are comparable with the state-of-the-art models. Moreover, the proposed models were fast to train. HMMs are shown to be viable CSI generative models. The code for the model and the simulations performed in this paper can be found in the GitHub repository:HMM-CSI-generativeModel.

Keywords
Markov processes, Photovoltaics, Machine learning, Solar energy, Statistical models, Solar irradiance
National Category
Energy Systems Other Environmental Engineering Environmental Sciences
Identifiers
urn:nbn:se:uu:diva-389945 (URN)10.1063/1.5110785 (DOI)
Funder
StandUpSwedish Energy Agency
Available from: 2019-08-01 Created: 2019-08-01 Last updated: 2019-08-14Bibliographically approved
Munkhammar, J. & Widén, J. (2019). A spatiotemporal Markov-chain mixture distribution model of the clear-sky index. Solar Energy, 179, 398-409
Open this publication in new window or tab >>A spatiotemporal Markov-chain mixture distribution model of the clear-sky index
2019 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 179, p. 398-409Article in journal (Refereed) Published
Abstract [en]

This study presents a spatiotemporal Markov-chain mixture distribution model of the clear-sky index for an arbitrary number of locations, and is particularly suited for simulations of small-scale spatial networks with a span of 10 km or less. The model is statistical, but in practice data-driven and based on clear-sky index input from an arbitrary number of locations to generate synthetic time-series for the same locations. When trained on clear-sky index data based on the NREL Hawaii network radiometer solar irradiance data, dispersed within 1 km x 1.2 km, the model is shown to have high goodness-of-fit compared with test data from the network in terms of probability distributions, autocorrelations, location pair-correlations and k-lag correlations between locations. It is also shown to perform comparably to state of the art temporal, spatial and spatiotemporal clear-sky index generators. All measures of model goodness-of-fit are shown to improve with increased number of bins, up to a certain limit of N > 4, where the performance improvements reaches a plateau. The results are also shown to be insensitive with respect to choice of training and test data sets as well as number of output time-steps.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2019
Keywords
Clear-sky index, spatiotemporal variability, Markov-chain modeling, Mixture distribution modeling
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-378739 (URN)10.1016/j.solener.2018.12.064 (DOI)000458942300039 ()
Funder
Swedish Energy Agency
Available from: 2019-03-11 Created: 2019-03-11 Last updated: 2019-03-11Bibliographically approved
Åberg, M., Lingfors, D., Olauson, J. & Widén, J. (2019). Can electricity market prices control power-to-heat production for peak shaving of renewable power generation?: The case of Sweden. Energy, 176, 1-14
Open this publication in new window or tab >>Can electricity market prices control power-to-heat production for peak shaving of renewable power generation?: The case of Sweden
2019 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 176, p. 1-14Article in journal (Refereed) Published
Abstract [en]

100% renewable energy systems require high penetration of variable renewable electricity (VRE) generation. This causes the net load in the system to be more variable and could cause operational problems in local power grids. Demand side management (DSM), such as fuel or energy carrier switching in response to a price signal, can provide flexibility to meet the increased variability. This study investigates the impact of VRE production on electricity prices and their potential to act as an incentive to control district heating power-to-heat (P2H) production in order to shave VRE production peaks. Also, the potential to increase P2H production flexibility with additional heat storages is studied. Electricity prices are simulated by modification of historical electricity market supply curves. A heat storage component is implemented in an existing model for district heat production. The results show that P2H production is significantly increased (up to 98%) when electricity prices are influenced by VRE production. Thermal storages further increase the P2H production by up to 46%. The increased P2H production, however, does not necessarily coincide with the peaks of VRE. Thus, in conclusion, the pricing mechanism on the Nord pool electricity market is insufficient to control P2H production for shaving VRE production peaks. (C) 2019 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Power-to-heat, District heating, Electricity market prices, Renewable electricity generation, Heat storage, Demand response
National Category
Energy Systems
Identifiers
urn:nbn:se:uu:diva-389819 (URN)10.1016/j.energy.2019.03.156 (DOI)000470939500001 ()
Funder
Swedish Energy Agency, P42904-1
Available from: 2019-07-30 Created: 2019-07-30 Last updated: 2019-07-30Bibliographically approved
Luthander, R., Shepero, M., Munkhammar, J. & Widén, J. (2019). Photovoltaics and opportunistic electric vehicle charging in the power system: a case study on a Swedish distribution grid. IET Renewable Power Generation, 13(5), 710-716
Open this publication in new window or tab >>Photovoltaics and opportunistic electric vehicle charging in the power system: a case study on a Swedish distribution grid
2019 (English)In: IET Renewable Power Generation, ISSN 1752-1416, E-ISSN 1752-1424, Vol. 13, no 5, p. 710-716Article in journal (Refereed) Published
Abstract [en]

Renewable distributed generation and electric vehicles (EVs) are two important components in the transition to a more sustainable society. However, both pose new challenges to the power system due to the intermittent generation and EV charging load. In this case study, a power system consisting of a low- and medium-voltage rural and urban distribution grid with 5174 customers, high penetration of photovoltaic (PV) electricity and a fully electrified car fleet were assumed, and their impact on the grid was assessed. The two extreme cases of two summer weeks and two winter weeks with and without EV charging and a PV penetration varying between 0 and 100% of the annual electricity consumption were examined. Active power curtailment of the PV systems was used to avoid overvoltage. The results show an increased electricity consumption of 9.3% in the winter weeks and 17.1% in the summer weeks, a lowering of the minimum voltage by 1% at the most, and a marginal contribution by the EV charging to lower the need of PV power curtailment. This shows the minor impact of EV charging on the distribution grid, both in terms of allowing more PV power generation and in terms of lower voltage levels.

Keywords
battery powered vehicles, power grids, power consumption, photovoltaic power systems, power distribution economics, distributed power generation, power generation economics, demand side management, opportunistic electric vehicle, power system, Swedish distribution grid, renewable distributed generation, electric vehicles, intermittent generation, EV charging load, photovoltaic electricity, fully electrified car fleet, summer weeks, winter weeks, PV penetration, annual electricity consumption, active power curtailment, PV systems, PV power curtailment, PV power generation, EV, medium-voltage rural distribution grid, medium-voltage urban distribution grid, low-voltage rural distribution grid, low-voltage urban distribution grid
National Category
Infrastructure Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-359430 (URN)10.1049/iet-rpg.2018.5082 (DOI)000462942900008 ()
Funder
Swedish Energy Agency, P41015-1
Available from: 2018-09-02 Created: 2018-09-02 Last updated: 2019-04-25Bibliographically approved
Munkhammar, J., van der Meer, D. & Widén, J. (2019). Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model. Solar Energy, 184, 688-695
Open this publication in new window or tab >>Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model
2019 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 184, p. 688-695Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
Clear-sky index, Probabilistic forecasting, Markov-chain mixture distribution forecasting, Quantile regression
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-387280 (URN)10.1016/j.solener.2019.04.014 (DOI)000466823400061 ()
Funder
Swedish Energy AgencySwedish Energy Agency
Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-06-24Bibliographically approved
Widén, J. & Munkhammar, J. (2019). Solar Radiation Theory (1ed.). Uppsala: Uppsala University
Open this publication in new window or tab >>Solar Radiation Theory
2019 (English)Book (Other academic)
Abstract [en]

One of the challenges in solar engineering is that the availability of the solar resource varies with time and location. An important engineering task is to design solar energy systems that are able to collect as much solar radiation as possible under these constraints. This book introduces the basic properties of solar radiation that are required to understand how the solar resource can be converted into useful heat and electricity, and what the limitations are. It also shows how solar radiation on planar surfaces can be modeled mathematically. This is useful when optimizing the orientation of collecting surfaces and predicting the performance of different system designs. The book builds upon lecture notes from solar engineering courses at Uppsala University, carefully edited to suit a wider scientific and engineering audience. The two authors have, together, more than two decades' experience of teaching, research and development in the field of solar irradiance modeling.

Place, publisher, year, edition, pages
Uppsala: Uppsala University, 2019. p. 50 Edition: 1
National Category
Civil Engineering
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-381852 (URN)10.33063/diva-381852 (DOI)978-91-506-2760-2 (ISBN)
Note

https://doi.org/10.33063/diva-381852

Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-05-13Bibliographically approved
Psimopoulos, E., Bee, E., Widén, J. & Bales, C. (2019). Techno-economic analysis of control algorithms for an exhaust air heat pump system for detached houses coupled to a photovoltaic system. Applied Energy, 249, 355-367
Open this publication in new window or tab >>Techno-economic analysis of control algorithms for an exhaust air heat pump system for detached houses coupled to a photovoltaic system
2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 249, p. 355-367Article in journal (Refereed) Published
Abstract [en]

Operational control strategies for the heating system and "smart" utilization of energy storage were developed and analyzed in a simulation based case study of a single-family house with exhaust air heat pump and photovoltaic system. Rule based control algorithms that can easily be implemented into modern heat pump controllers were developed with the aim to minimize final energy and maximize self-consumption by the use of the thermal storage of the building, the hot water tank and electrical storage. Short-term weather and electricity price forecasts are used in some of the algorithms. Heat supply from an exhaust air heat pump is limited by the ventilation flow rate fixed by building codes, and compact systems employ an electric heater as backup for both space heating and hot water. This heater plays an important role in the energy balance of the system. A typical system designed for new detached houses in Sweden was chosen for the study. This system, together with an independent photovoltaic system, was used as a base case and all results are compared to those for this base case system. TRNSYS 17 was used to model the building and system as well as the control algorithms, and special care was taken to model the use of the backup electric heater as this impacts significantly on final energy use. Results show that the developed algorithms can reduce final energy by 5-31% and the annual net cost for the end user by 3-26%, with the larger values being for systems with a battery storage. Moreover, the annual use of the backup electric heater can be decreased by 13-30% using the carefully designed algorithms.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2019
Keywords
Photovoltaics, Heat pump, Forecast services, Thermal storage, Electrical storage, Control algorithms
National Category
Energy Engineering
Identifiers
urn:nbn:se:uu:diva-390382 (URN)10.1016/j.apenergy.2019.04.080 (DOI)000472692200029 ()
Funder
Knowledge Foundation, 20160171
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-12Bibliographically approved
Munkhammar, J. & Widén, J. (2018). A Markov-chain probability distribution mixture approach to the clear-sky index. Solar Energy, 170, 174-183
Open this publication in new window or tab >>A Markov-chain probability distribution mixture approach to the clear-sky index
2018 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 170, p. 174-183Article in journal (Refereed) Published
Abstract [en]

This paper presents a Markov-chain probability distribution mixture approach to the clear-sky index (CSI). The main assumption is that the temporal variability of the state of clear and the state of cloudy can be described by a two-state Markov-chain, and the variability within each state can be approximated by a probability distribution, unique for each state. Measurables such as the mean clear-sky index, fraction of bright sunshine, expected duration of clearness and expected duration of cloudiness events are shown to be related to the parameters of the method. Additionally, the Ångström equation, which relates mean normalized solar irradiance to the fraction of bright sunshine, is shown to arise as the expectation of the method. In order to numerically verify the method, a simulation model is constructed based on data sets for two different climatic regions: Norrköping, Sweden and Oahu, Hawaii, USA. Results from the simulation model based on training data shows good agreement with testing data, and when comparing the results to existing models in the literature it is comparable to the state of the art. It is shown that the simulation model generates a non-trivial, generally non-zero, autocorrelation function. Finally, challenges with the method and open problems are discussed.

National Category
Other Environmental Engineering
Identifiers
urn:nbn:se:uu:diva-363524 (URN)10.1016/j.solener.2018.05.055 (DOI)000442713900018 ()
Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2019-03-19Bibliographically approved
Zhang, X., Lovati, M., Vigna, I., Widén, J., Han, M., Gal, C. & Feng, T. (2018). A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions. Applied Energy, 230, 1034-1056
Open this publication in new window or tab >>A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions
Show others...
2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 230, p. 1034-1056Article, review/survey (Refereed) Published
Abstract [en]

The emergence of renewable-energy-source (RES) envelope solutions, building retrofit requirements and advanced energy technologies brought about challenges to the existing paradigm of urban energy systems. It is envisioned that the building cluster approach-that can maximize the synergies of RES harvesting, building performance, and distributed energy management-will deliver the breakthrough to these challenges. Thus, this paper aims to critically review urban energy systems at the cluster level that incorporate building integrated RES solutions. We begin with defining cluster approach and the associated boundaries. Several factors influencing energy planning at cluster scale are identified, while the most important ones are discussed in detail. The closely reviewed factors include RES envelope solutions, solar energy potential, density of buildings, energy demand, integrated cluster-scale energy systems and energy hub. The examined categories of RES envelope solutions are (i) the solar power, (ii) the solar thermal and (iii) the energy-efficient ones, out of which solar energy is the most prevalent RES. As a result, methods assessing the solar energy potentials of building envelopes are reviewed in detail. Building density and the associated energy use are also identified as key factors since they affect the type and the energy harvesting potentials of RES envelopes. Modelling techniques for building energy demand at cluster level and their coupling with complex integrated energy systems or an energy hub are reviewed in a comprehensive way. In addition, the paper discusses control and operational methods as well as related optimization algorithms for the energy hub concept. Based on the findings of the review, we put forward a matrix of recommendations for cluster-level energy system simulations aiming to maximize the direct and indirect benefits of RES envelope solutions. By reviewing key factors and modelling approaches for characterizing RES-envelope-solutions-based urban energy systems at cluster level, this paper hopes to foster the transition towards more sustainable urban energy systems.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
RES, Building cluster, Energy system, Energy hub, Modelling, Optimization
National Category
Energy Engineering Energy Systems
Identifiers
urn:nbn:se:uu:diva-369938 (URN)10.1016/j.apenergy.2018.09.041 (DOI)000448226600077 ()
Funder
EU, Horizon 2020, 768766
Available from: 2018-12-18 Created: 2018-12-18 Last updated: 2018-12-18Bibliographically approved
Munkhammar, J. & Widén, J. (2018). An N-state Markov-chain mixture distribution model of the clear-sky index. Solar Energy, 173, 487-495
Open this publication in new window or tab >>An N-state Markov-chain mixture distribution model of the clear-sky index
2018 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 173, p. 487-495Article in journal (Refereed) Published
Abstract [en]

This paper presents an N-state Markov-chain mixture distribution approach to model the clear-sky index. The model is based on dividing the clear-sky index data into bins of magnitude and determining probabilities for transitions between bins. These transition probabilities are then used to define a Markov-chain, which in turn is connected to a mixture distribution of uniform distributions. When trained on measured data, this model is used to generate synthetic data as output. The model is an N-state generalization of a previously published two-state Markov-chain mixture distribution model applied to the clear-sky index. The model is tested on clear-sky index data sets for two different climatic regions: Norrköping, Sweden, and Oahu, Hawaii, USA. The model is also compared with the two-state model and a copula model for generating synthetic clear-sky index time-series as well as other existing clear-sky index generators in the literature. Results show that the N-state model is generally on par with, or superior to, state-of-the-art synthetic clear-sky index generators in terms of Kolmogorov–Smirnov test statistic, autocorrelation and computational speed.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-363525 (URN)10.1016/j.solener.2018.07.056 (DOI)000452940800047 ()
Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2019-01-16Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4887-9547

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