Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling

Ιωάννου, Αναστασία; Fuzuli, Gulistiani; Brennan, Feargal; Yudha, Satya Widya; Angus, Andrew

doi:10.1016/j.eneco.2019.02.013

Cited by 82 publications

(24 citation statements)

References 40 publications

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“…New energy resources under uncertainties are modeled as interval-stochastic variables. The uncertainties, such as load growth and fuel price, were taken into account [22], however, to restrict the focus of this study to the effect of smart grid resources, only the effects of smart grid resources are considered. A Benders' decomposition technique is used to determine both deterministic and stochastic variables simultaneously [23].…”

Section: Formulation Of Integrated Generation and Transmission Expansmentioning

confidence: 99%

Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model

Moon

Joo

2020

Energies

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In the power industry, the deployment of smart grid resources in power systems has become an issue of major interest. The deployment of smart grid resources represents an additional uncertainty in the integrated generation and transmission planning that raises uncertainties in investment-related decision making. This paper presents a new power system planning method for the integration of electric vehicles (EVs) and wind power generators into power systems. An interval-stochastic programming method is used to account for the heterogeneous uncertainties attributable to natural variability and lack of knowledge. The numerical results compare the multiple integration scenarios and verifies the effectiveness of the proposed method in terms of cost distribution and regret cost.

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Section: Formulation Of Integrated Generation and Transmission Expansmentioning

confidence: 99%

Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model

Moon

Joo

2020

Energies

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“…By increasing the number of variables and constraints, the planning problem requires a lot of computational effort. Some useful examples of multi-stage models have been addressed in [31][32][33][34]. In this work, an AC model for multi-stage TEP is used to assess the loss of real and reactive power accurately.…”

Section: Multi-stage Tepandrpp Modelmentioning

confidence: 99%

An application of energy efficiency programs on multi-stage transmission network expansion and reactive power planning

2020

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Energy efficiency programs as an energy source with multiple advantages (low cost, more reliable and better clean) are considered as one of the most effective solutions to meet future energy needs. In this paper, energy efficiency programs (EEPs) are modelled as virtual sources of power generation to investigate their impacts on expanding transmission network and reactive power planning (RPP). A multi-stage model based on AC power flow has been used to indicate the possibility of postponing the investment actions as the investment deferral in transmission expansion planning (TEP). Two groups of regulatory support schemes are considered as investor income to promote participation in EEPs, namely purchasing certified emission reductions (CERs) and shared saving model. In addition, two standard case studies (Garver & 46-bus Brazilian) are evaluated to demonstrate the promising potential of the proposed model in handling the planning problems of practical power systems. The simulation results show the effectiveness of the proposed model to solve such planning problems in the presence of EEPs.

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“…Otherwise, it is generally intractable to optimize a stochastic problem by incorporating uncertain parameters as continuous random variables. Thus, these parameters can be represented as a multi-period scenario tree, which grows with scenario tree nodes based on approximating continuous distributions into discrete distributions or Monte Carlo simulation random generated nodes (a random generation of information) from the common continuous distributions [41]. A scenario tree is represented by a set of nodes, k K, and branches.…”

Section: Generating Scenario Tree For Uncertain Parametersmentioning

confidence: 99%

“…To illustrate, if two uncertain parameters described by three nodes (high, medium, and low) and four multiple periods represented by four seasons (e.g., spring, summer, fall, and winter) were considered, Hence, at the end of the fourth period, 3 8 scenarios were generated. Similar approaches have been adopted in the literature of other applications [29,41,49]. The next step was to keep fewer scenarios possible to ensure that the problem of stochastic optimization could be solved with a reasonable computational effort.…”

Section: Generating Scenario Tree For Uncertain Parametersmentioning

confidence: 99%

A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty

Al-Aboosi

El‐Halwagi

2019

Sustainability

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The production of shale gas and oil is associated with the generation of substantial amounts of wastewater. With the growing emphasis on sustainable development, the energy sector has been intensifying efforts to manage water resources while diversifying the energy portfolio used in treating wastewater to include fossil and renewable energy. The nexus of water and energy introduces complexity in the optimization of the water management systems. Furthermore, the uncertainty in the data for energy (e.g., solar intensity) and cost (e.g., price fluctuation) introduce additional complexities. The objective of this work is to develop a novel framework for the optimizing wastewater treatment and water-management systems in shale gas production while incorporating fossil and solar energy and accounting for uncertainties. Solar energy is utilized via collection, recovery, storage, and dispatch of heat. Heat integration with an adjacent industrial facility is considered. Additionally, electric power production is intended to supply a reverse osmosis (RO) plant and the local electric grid. The optimization problem is formulated as a multi-scenario mixed integer non-linear programming (MINLP) problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational conditions through a finite set of scenarios. The results show the capability of the system to address water-energy nexus problems in shale gas production based on the system’s economic and environmental merits. A case study for Eagle Ford Basin in Texas is solved by enabling effective water treatment and energy management strategies to attain the maximum annual profit of the entire system while achieving minimum environmental impact.

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Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling

Cited by 82 publications

References 40 publications

Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model

Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model

An application of energy efficiency programs on multi-stage transmission network expansion and reactive power planning

A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty

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