A Distributed Optimization Algorithm for the Predictive Control of Smart Grids

Braun, Philipp; Grüne, Lars; Kellett, Christopher M.; Weller, Steven R.; Worthmann, Karl

doi:10.1109/tac.2016.2525808

Cited by 72 publications

(49 citation statements)

References 27 publications

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“…In [2], a predictive control algorithm is developed for a single distribution optimization problem. Here, a comparison between centrally-controlled systems and distributed control systems is done when applying a predictive control algorithm.…”

Section: Related Workmentioning

confidence: 99%

Performance Assessment of Black Box Capacity Forecasting for Multi-Market Trade Application

2017

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Abstract:With the growth of renewable generated electricity in the energy mix, large energy storage and flexible demand, particularly aggregated demand response is becoming a front runner as a new participant in the wholesale energy markets. One of the biggest barriers for the integration of aggregator services into market participation is knowledge of the current and future flexible capacity. To calculate the available flexibility, the current aggregator pilot and simulation implementations use lower level measurements and device specifications. This type of implementation is not scalable due to computational constraints, as well as it could conflict with end user privacy rights. Black box machine learning approaches have been proven to accurately estimate the available capacity of a cluster of heating devices using only aggregated data. This study will investigate the accuracy of this approach when applied to a heterogeneous virtual power plant (VPP). Firstly, a sensitivity analysis of the machine learning model is performed when varying the underlying device makeup of the VPP. Further, the forecasted flexible capacity of a heterogeneous residential VPP was applied to a trade strategy, which maintains a day ahead schedule, as well as offers flexibility to the imbalance market. This performance is then compared when using the same strategy with no capacity forecasting, as well as perfect knowledge. It was shown that at most, the highest average error, regardless of the VPP makeup, was still less than 9%. Further, when applying the forecasted capacity to a trading strategy, 89% of the optimal performance can be met. This resulted in a reduction of monthly costs by approximately 20%.

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Section: Related Workmentioning

confidence: 99%

Performance Assessment of Black Box Capacity Forecasting for Multi-Market Trade Application

2017

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“…With the interest in large-scale multi-agent systems, many distributed optimization algorithms have been recently proposed; e.g., [3], [4], [5] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

“…However, as proposed in [5], the introduction of a hierarchical structure with a trusted central entity that can aggregate information and then broadcast only the aggregated information alleviates the need for subsystems to share detailed information with their neighbors.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present an alternative hierarchical distributed optimization algorithm to that proposed in [5]. Here, we propose a variant of the well-known alternating direction method of multipliers (ADMM) algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

“…Similar to the work in [7], [5], we consider the problem of smoothing the energy demand from a group of residences equipped with energy storage. However, the proposed ADMM variant is somewhat more flexible than the approach considered in [7], [5] and we also address two additional problems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hierarchical distributed ADMM for predictive control with applications in power networks

Braun

Faulwasser

Grüne

et al. 2018

IFAC Journal of Systems and Control

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Abstract-We present a variant of the alternating direction method of multipliers (ADMM) that can be implemented in a hierarchical distributed fashion for large-scale systems where the coupling between subsystems occurs in a structured way in the cost function. We show that this ADMM algorithm can be embedded in a model predictive control (MPC) implementation and subsequently apply this to three battery scheduling problems in the context of residential microgrid electricity networks.

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Towards multiobjective optimization and control of smart grids

Sauerteig

Worthmann

2019

Optim Control Appl Methods

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Summary The rapid uptake of renewable energy sources in the electricity grid leads to a demand in load shaping and flexibility. Energy storage devices such as batteries are a key element to provide solutions to these tasks. However, typically, a trade‐off between the performance‐related goal of load shaping and the objective of having flexibility in store for auxiliary services, which is, for example, linked to robustness and resilience of the grid, can be observed. We propose to make use of the concept of Pareto optimality in order to resolve this issue in a multiobjective framework. In particular, we analyze the Pareto frontier and quantify the trade‐off between the nonaligned objectives to properly balance them.

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A Distributed Optimization Algorithm for the Predictive Control of Smart Grids

Cited by 72 publications

References 27 publications

Performance Assessment of Black Box Capacity Forecasting for Multi-Market Trade Application

Performance Assessment of Black Box Capacity Forecasting for Multi-Market Trade Application

Hierarchical distributed ADMM for predictive control with applications in power networks

Towards multiobjective optimization and control of smart grids

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