Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific

**Distributed Stochastic Thermal Energy Management in Smart Thermal Grids.** / Rostampour, Vahab; Wayan Wicak Ananduta, Wayan Wicak; Keviczky, Tamas.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific

Rostampour, V, Wayan Wicak Ananduta, WW & Keviczky, T 2019, Distributed Stochastic Thermal Energy Management in Smart Thermal Grids. in P Palensky, M Cvetkovic & T Keviczky (eds), *Intelligent Integrated Energy Systems: The PowerWeb Program at TU Delft.* Springer, Cham, Switzerland, pp. 141-164. https://doi.org/10.1007/978-3-030-00057-8_7

Rostampour, V., Wayan Wicak Ananduta, W. W., & Keviczky, T. (2019). Distributed Stochastic Thermal Energy Management in Smart Thermal Grids. In P. Palensky, M. Cvetkovic, & T. Keviczky (Eds.), *Intelligent Integrated Energy Systems: The PowerWeb Program at TU Delft *(pp. 141-164). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-00057-8_7

Rostampour V, Wayan Wicak Ananduta WW, Keviczky T. Distributed Stochastic Thermal Energy Management in Smart Thermal Grids. In Palensky P, Cvetkovic M, Keviczky T, editors, Intelligent Integrated Energy Systems: The PowerWeb Program at TU Delft. Cham, Switzerland: Springer. 2019. p. 141-164 https://doi.org/10.1007/978-3-030-00057-8_7

@inbook{1e028aa47f384be1800932f3e1eab060,

title = "Distributed Stochastic Thermal Energy Management in Smart Thermal Grids",

abstract = "This work presents a distributed stochastic energy management framework for a thermal grid with uncertainties in the consumer demand profiles. Using the model predictive control (MPC) paradigm, we formulate a finite-horizon chance-constrained mixed-integer linear optimization problem at each sampling time, which is in general non-convex and hard to solve. We then provide a unified framework to deal with production planning problems for uncertain systems, while providing a-priori probabilistic certificates for the robustness properties of the resulting solutions. Our methodology is based on solving a random convex program to compute the uncertainty bounds using the so-called scenario approach and then, solving a robust mixed-integer optimization problem with the computed randomized uncertainty bounds at each sampling time. Using a tractable approximation of uncertainty bounds, the proposed formulation retains the complexity of the problem without chance constraints. We also present two distributed approaches that are based on the alternating direction method of multipliers (ADMM) to solve the robust mixed-integer problem. The performance of the proposed methodology is illustrated using Monte Carlo simulations and employing two different problem formulations: optimization over input sequences (open-loop MPC) and optimization over affine feedback policies (closed-loop MPC).",

author = "Vahab Rostampour and {Wayan Wicak Ananduta}, {Wayan Wicak} and Tamas Keviczky",

year = "2019",

doi = "10.1007/978-3-030-00057-8_7",

language = "English",

isbn = "987-3-030-00056-1",

pages = "141--164",

editor = "Peter Palensky and Milos Cvetkovic and Tamas Keviczky",

booktitle = "Intelligent Integrated Energy Systems",

publisher = "Springer",

}

TY - CHAP

T1 - Distributed Stochastic Thermal Energy Management in Smart Thermal Grids

AU - Rostampour, Vahab

AU - Wayan Wicak Ananduta, Wayan Wicak

AU - Keviczky, Tamas

PY - 2019

Y1 - 2019

N2 - This work presents a distributed stochastic energy management framework for a thermal grid with uncertainties in the consumer demand profiles. Using the model predictive control (MPC) paradigm, we formulate a finite-horizon chance-constrained mixed-integer linear optimization problem at each sampling time, which is in general non-convex and hard to solve. We then provide a unified framework to deal with production planning problems for uncertain systems, while providing a-priori probabilistic certificates for the robustness properties of the resulting solutions. Our methodology is based on solving a random convex program to compute the uncertainty bounds using the so-called scenario approach and then, solving a robust mixed-integer optimization problem with the computed randomized uncertainty bounds at each sampling time. Using a tractable approximation of uncertainty bounds, the proposed formulation retains the complexity of the problem without chance constraints. We also present two distributed approaches that are based on the alternating direction method of multipliers (ADMM) to solve the robust mixed-integer problem. The performance of the proposed methodology is illustrated using Monte Carlo simulations and employing two different problem formulations: optimization over input sequences (open-loop MPC) and optimization over affine feedback policies (closed-loop MPC).

AB - This work presents a distributed stochastic energy management framework for a thermal grid with uncertainties in the consumer demand profiles. Using the model predictive control (MPC) paradigm, we formulate a finite-horizon chance-constrained mixed-integer linear optimization problem at each sampling time, which is in general non-convex and hard to solve. We then provide a unified framework to deal with production planning problems for uncertain systems, while providing a-priori probabilistic certificates for the robustness properties of the resulting solutions. Our methodology is based on solving a random convex program to compute the uncertainty bounds using the so-called scenario approach and then, solving a robust mixed-integer optimization problem with the computed randomized uncertainty bounds at each sampling time. Using a tractable approximation of uncertainty bounds, the proposed formulation retains the complexity of the problem without chance constraints. We also present two distributed approaches that are based on the alternating direction method of multipliers (ADMM) to solve the robust mixed-integer problem. The performance of the proposed methodology is illustrated using Monte Carlo simulations and employing two different problem formulations: optimization over input sequences (open-loop MPC) and optimization over affine feedback policies (closed-loop MPC).

U2 - 10.1007/978-3-030-00057-8_7

DO - 10.1007/978-3-030-00057-8_7

M3 - Chapter

SN - 987-3-030-00056-1

SP - 141

EP - 164

BT - Intelligent Integrated Energy Systems

A2 - Palensky, Peter

A2 - Cvetkovic, Milos

A2 - Keviczky, Tamas

PB - Springer

CY - Cham, Switzerland

ER -

ID: 47928444