Combined probabilistic and set-based uncertainties for a stochastic model predictive control of island energy systems

Sachs, Julia; Gienger, Andreas; Sawodny, Oliver

doi:10.1109/acc.2016.7526737

Cited by 4 publications

(7 citation statements)

References 19 publications

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“…, and the SOC is determined analytically as presented in [12]. A linear differential equation is used to described the battery dynamics given by ¶ ¶…”

Section: Analytical Reformulation Of Chance Constraints For the Formmentioning

confidence: 99%

“…These limits are given by physical restrictions and the dimensioning of the PV panels. For the consideration of the limits, the formulation is extended by a set based approach as described in [12]. Therefore, the limits in the load demand and PV power are transferred to the net power, which is assumed to be in the set…”

Section: +1mentioning

confidence: 99%

“…The SMPC approaches mainly use two-stage stochastic programming [8][9][10], scenario based solution methods [11], dynamic programming with empirical mean [6] or the evaluation of the chance constraints with the cumulative distribution function [7]. This paper discusses an effective SMPC approach presented in a previous work [12], which is based on an analytical formulation of the chance constraints. This formulation is used for the evaluation of the cumulative distribution function of normal distributed photovoltaic power and load demand, in which the physical limits are considered by a set-based approach.…”

mentioning

confidence: 99%

“…For this reason, the forecasted variables of load demand and PV power are summarized to the net power, which represents a disturbance on the system and is defined as w P . The variance at time step k is calculated by an exponentially weighted sum of the squared forecast error of past days with time horizon N. As presented in [12], the stochastic optimization problem with the expected operational cost E w is given by…”

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confidence: 99%

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Structural Transformations of Systems with Distributed Parameters.

Klevtsov¹

2016

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Microgrids are a promising approach for the integration of renewable energy sources in existing networks and the energy supply of rural areas. A cost effective option for a microgrid is given by a hybrid energy system, which combines e.g. diesel generators, photovoltaic panels and batteries as considered in this paper. However, the interaction of the components and uncertainties in the load demand and photovoltaic power make the controller design challenging. This paper discusses a Stochastic Model Predictive Control approach which yields promising results regarding effectiveness and reliability as shown in a simulation study. Èñïîëüçîâàíèå ýëåêòðîýíåðãåòè÷åñêèõ ìèêðîãðèä-ñèñòåì ÿâëÿåòñÿ ïåðñïåêòèâíûì ïîäõîäîì ê èíòåãðàöèè âîçîáíîâëÿåìûõ èñòî÷íèêîâ â ñóùåñòâóþùèå ñåòè è ýíåðãîîáåñïå-÷åíèå ñåëüñêîé ìåñòíîñòè. Ýêîíîìè÷åñêàÿ ýôôåêòèâíîñòü ýëåêòðîýíåðãåòè÷åñêèõ ìèêðîãðèä-ñèñòåì çàâèñèò îò ãèáðèäíîé ýíåðãîñèñòåìû, îáúåäèíÿþùåé äèçåëüíûå ãåíåðàòîðû, ôîòîýëåêòðè÷åñêèå ïàíåëè è áàòàðåè. Îäíàêî âçàèìîäåéñòâèå ñîñòàâëÿþùèõ è íåîïðåäåëåííîñòü ãðàôèêà íàãðóçêè è ôîòîýëåêòðè÷åñêîé ýíåðãèè îáóñëîâëèâàþò íåîáõîäèìîñòü ñîçäàíèÿ áëîêà óïðàâëåíèÿ. Ðàññìîòðåíî ïðèìåíåíèå ñòîõàñòè÷åñêîé ìîäåëè äëÿ èíòåëëåêòóàëüíîãî óïðàâëåíèÿ, ÷òî ïîçâîëèò îáåñïå÷èòü ýôôåêòèâíîñòü è íàäåaeíîñòü ýíåðãîñèñòåìû.

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“…, and the SOC is determined analytically as presented in [12]. A linear differential equation is used to described the battery dynamics given by ¶ ¶…”

Section: Analytical Reformulation Of Chance Constraints For the Formmentioning

confidence: 99%

Section: +1mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Structural Transformations of Systems with Distributed Parameters.

Klevtsov¹

2016

Èlektron. model.

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“…This paper discusses an effective SMPC approach presented in a previous work [12], which is based on an analytical formulation of the chance constraints. This formulation is used for the evaluation of the cumulative distribution function of normal distributed photovoltaic power and load demand, in which the physical limits are considered by a set-based approach.…”

mentioning

confidence: 99%

Stochastic Model Predictive Control for Hybrid Energy Systems.

Gienger¹,

Sachs²,

Sawodny³

2017

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Microgrids are a promising approach for the integration of renewable energy sources in existing networks and the energy supply of rural areas. A cost effective option for a microgrid is given by a hybrid energy system, which combines e.g. diesel generators, photovoltaic panels and batteries as considered in this paper. However, the interaction of the components and uncertainties in the load demand and photovoltaic power make the controller design challenging. This paper discusses a Stochastic Model Predictive Control approach which yields promising results regarding effectiveness and reliability as shown in a simulation study.Èñïîëüçîâàíèå ýëåêòðîýíåðãåòè÷åñêèõ ìèêðîãðèä-ñèñòåì ÿâëÿåòñÿ ïåðñïåêòèâíûì ïîäõîäîì ê èíòåãðàöèè âîçîáíîâëÿåìûõ èñòî÷íèêîâ â ñóùåñòâóþùèå ñåòè è ýíåðãîîáåñïå-÷åíèå ñåëüñêîé ìåñòíîñòè. Ýêîíîìè÷åñêàÿ ýôôåêòèâíîñòü ýëåêòðîýíåðãåòè÷åñêèõ ìèêðîãðèä-ñèñòåì çàâèñèò îò ãèáðèäíîé ýíåðãîñèñòåìû, îáúåäèíÿþùåé äèçåëüíûå ãåíåðàòîðû, ôîòîýëåêòðè÷åñêèå ïàíåëè è áàòàðåè. Îäíàêî âçàèìîäåéñòâèå ñîñòàâëÿþùèõ è íåîïðåäåëåííîñòü ãðàôèêà íàãðóçêè è ôîòîýëåêòðè÷åñêîé ýíåðãèè îáóñëîâëèâàþò íåîáõîäèìîñòü ñîçäàíèÿ áëîêà óïðàâëåíèÿ. Ðàññìîòðåíî ïðèìåíåíèå ñòîõàñòè÷åñêîé ìîäåëè äëÿ èíòåëëåêòóàëüíîãî óïðàâëåíèÿ, ÷òî ïîçâîëèò îáåñïå÷èòü ýôôåêòèâíîñòü è íàäåaeíîñòü ýíåðãîñèñòåìû.

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