Solar Irradiance Forecasting in Remote Microgrids Using Markov Switching Model

Shakya, Ayush; Michael, Semhar; Saunders, Christopher T.; Armstrong, Douglas; Pandey, Prakash; Chalise, Santosh; Tonkoski, Reinaldo

doi:10.1109/tste.2016.2629974

Cited by 69 publications

(18 citation statements)

References 32 publications

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“…, T ], produced by those households m ∈ M owning RESs is considered predicted with sufficient accuracy over the whole period T , hence it is considered known as well. Methods for prediction of residential electricity demand and of renewable energy generation are proposed, for example, in [16], [17] and [18], [19], respectively. Note that for those households from the set M that do not own RESs, but only ESSs, the renewable energy vector is zero, i.e.…”

Section: B the Energy Consumption Modelmentioning

confidence: 99%

“…The sum above is computed over all possible subsets G (even of single players) of M not containing player m. G ∪ {m}) and υ(G) are computed using the formulation in (19), in case of (P II ), and the formulation in (20), in case of (P III ), respectively. The payoffs determined by Shapley value for each individual household are discounted from the household's individual cost c indiv m .…”

Section: The Coalitional Game Model For Householdsmentioning

confidence: 99%

“…The monetary gains of the RESs and ESSs owners are actually decreasing while the capacity increases. This is due of the fact that the result of (P I ) is used as a comparison benchmark in (19) and (20) for the calculation of the revenues of the coalitional optimizations. The increase of the ESSs capacities allows for better performance of the (P I ) problem.…”

Section: Simulation Examplesmentioning

confidence: 99%

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Coalitional Game-Based Cost Optimization of Energy Portfolio in Smart Grid Communities

Chis

Koivunen

2019

IEEE Trans. Smart Grid

119

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Abstract-In this paper we propose two novel coalitional game theory based optimization methods for minimizing the cost of electricity consumed by households from a smart community. Some households in the community may own renewable energy sources (RESs) conjoined with energy storage systems (ESSs). Some other residences own ESSs only, while the remaining households are simple energy consumers. We first propose a coalitional cost optimization method in which RESs and ESSs owners exchange energy and share their renewable energy and storage spaces. We show that by participating in the proposed game these households may considerably reduce their costs in comparison to performing individual cost optimization. We further propose another coalitional optimization model in which RESs and ESSs owning households not only share their resources, but also sell energy to simple energy consuming households. We show that through this energy trade the RESs and ESSs owners can further reduce their costs, while the simple energy consumers also gain cost savings. The cost savings obtained by the coalition are distributed among its members according to the Shapley value. Simulation examples show that the proposed coalitional optimization methods may reduce the electricity costs for the RESs and ESSs owning households by 18%, while the sole energy consumers may reduce their costs by 3%.Index Terms-Coalitional game, demand side management, smart households, renewable energy, energy storage, cost reduction. NOMENCLATURE Abbreviations DSM Demand side management ESS Energy storage system RES Renewable energy sourceSets, cardinalities and indicesOptimization period, number of time-slots, and their index N , N, n Households in the community, their number, and index M, M, m Households owning RESs and/or ESSs (grand coalition), their number, and index P, P, p Sole energy consuming households, their number, and index G, G, gHouseholds forming a coalition, their number, and indexWe acknowledge the financial support received from KAUTE Foundation for the research reported in this article.The Cost incurred by household m, g in periodPayoff assigned to a player m by Shapley value This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TSG.2017.2784902, IEEE Transactions on Smart Grid 2 a n,m,p (t) Amount of energy exchanged by household n, m, p with other households in time-slot t b n,m,p (t) Amount of energy bought by household n, m, p from utility company in time-slot t

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Section: B the Energy Consumption Modelmentioning

confidence: 99%

Section: The Coalitional Game Model For Householdsmentioning

confidence: 99%

Section: Simulation Examplesmentioning

confidence: 99%

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Coalitional Game-Based Cost Optimization of Energy Portfolio in Smart Grid Communities

Chis

Koivunen

2019

IEEE Trans. Smart Grid

119

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“…In the same class of statistical methods there exist other works that capture variability in solar PV power [16]- [18] and black box models like using artificial neural networks (ANN) [19]- [21] and support vector machines (SVM) [22] based pattern matching techniques to predict solar power when class labels are known. Additionally, there are also methods based on the Markovianity assumption of solar power such as [23]- [25] in order to forecast solar PV power.…”

Section: Introductionmentioning

confidence: 99%

A Model for Joint Probabilistic Forecast of Solar Photovoltaic Power and Outdoor Temperature

Ramakrishna

Scaglione

Vittal

et al. 2019

IEEE Trans. Signal Process.

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In this paper, a stochastic model with regime switching is developed for solar photo-voltaic (PV) power in order to provide short-term probabilistic forecasts. The proposed model for solar PV power is physics inspired and explicitly incorporates the stochasticity due to clouds using different parameters addressing the attenuation in power. Based on the statistical behavior of parameters, a simple regime-switching process between the three classes of sunny, overcast and partly cloudy is proposed. Then, probabilistic forecasts of solar PV power are obtained by identifying the present regime using PV power measurements and assuming persistence in this regime. To illustrate the technique developed, a set of solar PV power data from a single rooftop installation in California is analyzed and the effectiveness of the model in fitting the data and in providing short-term point and probabilistic forecasts is verified. The proposed forecast method outperforms a variety of reference models that produce point and probabilistic forecasts and therefore portrays the merits of employing the proposed approach.

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“…These images are subsequently used for automatic cloud coverage computation, cloud tracking, and cloud base height estimation. In our research group, we use these imagers to study the effects of clouds on satellite communication links (Dev et al, 2018b;Yuan et al, 2016;Dev et al, 2017e).Localized and short-term forecasting of cloud movements is an on-going research topic (Shakya et al, 2017;Jiang et al, 2017;Feng et al, 2018). Optical flow techniques can be used to forecast images using anterior frames (Dev et al, 2016d).Similarly, cloud motion vectors are exploited for solar power prediction from satellite images (Jang et al, 2016).…”

mentioning

confidence: 99%

Estimating solar irradiance using sky imagers

2019

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Ground-based whole sky cameras are extensively used for localized monitoring of clouds nowadays. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers co-located with our whole sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations as compared to other related methods using ground-based observations. IntroductionClouds have a significant impact on solar energy generation. They intermittently block the sun and significantly reduce the solar irradiance reaching solar panels. A short-term forecast of solar irradiance is needed for grid operators to mitigate the effects of a drop in power generation. With rapid developments in photogrammetric techniques, ground-based sky cameras are now widely used (Dev et al., 2016e). These cameras, known as Whole Sky Imagers (WSIs) are upward looking devices that capture images of the sky at regular intervals of time. These images are subsequently used for automatic cloud coverage computation, cloud tracking, and cloud base height estimation. In our research group, we use these imagers to study the effects of clouds on satellite communication links (Dev et al., 2018b;Yuan et al., 2016;Dev et al., 2017e).Localized and short-term forecasting of cloud movements is an on-going research topic (Shakya et al., 2017;Jiang et al., 2017;Feng et al., 2018). Optical flow techniques can be used to forecast images using anterior frames (Dev et al., 2016d).Similarly, cloud motion vectors are exploited for solar power prediction from satellite images (Jang et al., 2016). Our proposed method for estimating solar irradiance is thus a first step towards solar irradiance forecasting, as the input data used to estimate the irradiance is the same as the one used to forecast the sky condition.The accurate estimation and prediction of solar energy generation is a challenging task, as clouds greatly impact the total irradiance received on the earth's surface. In the event of clouds covering the sun for a short time, there is a sharp decline of the 1 arXiv:1910.04981v1 [eess.IV]

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Solar Irradiance Forecasting in Remote Microgrids Using Markov Switching Model

Cited by 69 publications

References 32 publications

Coalitional Game-Based Cost Optimization of Energy Portfolio in Smart Grid Communities

Coalitional Game-Based Cost Optimization of Energy Portfolio in Smart Grid Communities

A Model for Joint Probabilistic Forecast of Solar Photovoltaic Power and Outdoor Temperature

Estimating solar irradiance using sky imagers

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