A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

Silvente, Javier; Kopanos, Georgios M.; Dua, Vivek; Papageorgiou, Lazaros G.

doi:10.1016/j.cherd.2017.09.013

Cited by 45 publications

(29 citation statements)

References 62 publications

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“…Since Equation 7yields the possibility for no power on the storage but power on the grid side, which in return is beneficial for the objective function (14), the following two equations are necessary and state that the efficiency of each operating mode ought to be always below one:…”

Section: Constraints To Model Energy Storage Devicesmentioning

confidence: 99%

“…As a model of the electricity market is not yet integrated in the optimisation problem, the objective function (14) is set to minimise the emissions throughout the regarding time frame:…”

Section: Objective Functionmentioning

confidence: 99%

“…It would allow for modification of the MILP results in order to adjust for uncertainties or new information, but was not used in [10]. The framework was updated in [14] to model start-up times and tested for different rolling horizon interval lengths between two and 24 h. [15] presented a different framework, where the rolling horizon was implemented for a similar reason, but added an optional economic dispatch algorithm to adjust the suggested UC. This is to model market behaviour more properly.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Different Interval Lengths in a Rolling Horizon MILP Unit Commitment with Non-Linear Control Model for a Small Energy System

2019

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In this paper, a fixed electricity producer park of both a short- and long-term renewable energy storage (e.g., battery, power to gas to power) and a conventional power plant is combined with an increasing amount of installed volatile renewable power. For the sake of simplicity, the grid is designed as a single copper plate with island restrictions and constant demand of 1000 MW; the volatile input is deducted from scaled 15-min input data of German grid operators. A mixed integer linear programming model is implemented to generate an optimised unit commitment (UCO) for various scenarios and configurations using CPLEX® as the problem solver. The resulting unit commitment is input into a non-linear control model (NLC), which tries to match the plan of the UCO as closely as possible. Using the approach of a rolling horizon the result of the NLC is fed back to the interval of the next optimisation run. The problem’s objective is set to minimise CO2 emissions of the whole electricity producer park. Different interval lengths are tested with perfect foresight. The results gained with different interval lengths are compared to each other and to a simple heuristic approach. As non-linear control model a characteristic line model is used. The results show that the influence of the interval length is rather small, which leads to the conclusion that realistic forecast lengths of two days can be used to achieve not only a sufficient quality of solutions, but shorter computational times as well.

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Section: Constraints To Model Energy Storage Devicesmentioning

confidence: 99%

“…As a model of the electricity market is not yet integrated in the optimisation problem, the objective function (14) is set to minimise the emissions throughout the regarding time frame:…”

Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Different Interval Lengths in a Rolling Horizon MILP Unit Commitment with Non-Linear Control Model for a Small Energy System

2019

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“…On the other hand, the obtained solutions may result in being too conservative, because the model must consider all the possibilities. This technique has been used to consider different sources of uncertainty, such as uncertainty in demand, supply of raw materials and processing times (Shapiro et al, 2013), and applied to supply chain management (Schildbach and Morari, 2016) and energy systems (Silvente et al, 2018), among other examples.…”

Section: Introductionmentioning

confidence: 99%

Scenario tree reduction for optimisation under uncertainty using sensitivity analysis

Silvente

Papageorgiou

Dua

2019

Computers & Chemical Engineering

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This work addresses the optimal management of a system through a two-stage stochastic Non-Linear Programming (NLP) formulation. This approach uses a scenario-based mathematical formulation to tackle uncertain information. Accurate representation of uncertainty usually involves increased number of scenarios, which may result in large-scale optimisation models. Thus, the proposed formulation aims to reduce the number of scenarios through a sensitivity analysis approach. The proposed model investigates the use of scenario reduction techniques to reduce computational requirements while maintaining good quality of the final optimal solution.

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“…Among a limited number of works that combine reactive and proactive approaches, Silvente et al (2015) developed a rolling horizon stochastic programming approach for the energy supply and demand management of microgrids. The authors further developed their model to consider a rolling horizon approach for optimal management of microgrid under stochastic uncertainty (Silvente et al, 2017). In addition, Gupta and Maranas (2000) studied a two-stage stochastic programming model to solve supply-chain planning problem under demand uncertainty through a rolling horizon framework.…”

Section: Introductionmentioning

confidence: 99%

A rolling horizon stochastic programming approach for the integrated planning of production and utility systems

Zulkafli

Kopanos

2018

Chemical Engineering Research and Design

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This study focuses on the operational and resource-constrained condition-based cleaning planning problem of integrated production and utility systems under uncertainty. For the problem under consideration, a two-stage scenario-based stochastic programming model that follows a rolling horizon modelling representation is introduced; resulting in a hybrid reactiveproactive planning approach. In the stochastic programming model, all the binary variables related to the operational status (i.e., startup, operating, shutdown, under online or offline cleaning) of the production and utility units are considered as first-stage variables (i.e., scenario independent), and most of the remaining continuous variables are second-stage variables (i.e., scenario dependent). In addition, enhanced unit performance degradation and recovery models due to the cumulative operating level deviation and cumulative operating times are presented. Terminal constraints for minimum inventory levels for utilities and products as well as maximum unit performance degradation levels are also introduced. Two case studies are presented to highlight the applicability and the particular features of the proposed approach as an effective means of dealing with the sophisticated integrated planning problem considered in highly dynamic environments.

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A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

Cited by 45 publications

References 62 publications

Effect of Different Interval Lengths in a Rolling Horizon MILP Unit Commitment with Non-Linear Control Model for a Small Energy System

Effect of Different Interval Lengths in a Rolling Horizon MILP Unit Commitment with Non-Linear Control Model for a Small Energy System

Scenario tree reduction for optimisation under uncertainty using sensitivity analysis

A rolling horizon stochastic programming approach for the integrated planning of production and utility systems

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