Plug-and-Play Model Predictive Control for Load Shaping and Voltage Control in Smart Grids

Floch, Caroline Le; Bansal, Somil; Tomlin, Claire J.; Moura, Scott J.; Zeilinger, Melanie N.

doi:10.1109/tsg.2017.2655461

Cited by 23 publications

(10 citation statements)

References 37 publications

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“…The individual subsystems are connected via a common point of coupling, the grid operator (simply termed Central Entity [CE]), see Figure (left). So far, we essentially follow the modeling approach presented in the works of Ratnam et al and Worthmann et al and further developed in the work of Braun et al An alternative would be to also model the underlying network structure as graph and then to incorporate the physical connections within the microgrid, see, eg, the work of Le Floch et al…”

Section: Problem Formulationmentioning

confidence: 99%

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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Section: Problem Formulationmentioning

confidence: 99%

Towards multiobjective optimization and control of smart grids

Sauerteig

Worthmann

2019

Optim Control Appl Methods

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“…Last but not the least, on one hand, users behaviour in a LVDN is dynamic, which is hard to predict accurately, meanwhile, collecting the data of user behaviour risks infringing users' privacy, which is one of the main concerns about the implementation of smart grid 8 . On the other hand, managing a large group of users with ensuring system constraints and user satisfaction is the key objective of active control in LVDN 9 , which demands access to users behaviour data to some extent.…”

Section: Introductionmentioning

confidence: 99%

Stochastic distributed optimization of shapeable energy resources in low voltage distribution networks under limited communications

2020

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Due to the rising renewable penetration rate, modern low voltage distribution network (LVDN) calls for active control with tractable computation and limited communication. To tackle this, the paper proposes a novel stochastic distributed optimization approach. The computation of optimum is completely decentralized, with a global broadcast signal is used as public reference in order to guarantee the consistency among individual shapeable energy resources. The proposed approach employs Bernoulli trials to imitate the searching process in classical gradient descent approach, and player compatible relationship is employed to play the role of gradients to indicate the direction of the search. Working in a model-free manner without relying on iterations, the proposed approach offers an approximate optimization to minimize the accumulated compensation of reshaping/deferring the shapeable energy resources in a given LVDN while respecting the system constraints. A 103 nodes test network based on a realistic Belgian semi-urban distribution network is used for validation. With two different profiles and a special case of communication failure, the proposed approach is validated and benchmarked with a classical AC optimal power flow algorithm. The results prove that the proposed approach is able to deliver a good approximation to the theoretical optimum with reasonable gap.

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“…e traditional multi-time scale optimal methods have slow response speed which may lead to a large forecast error. In [18], MPC was used for handling plug-and-play charging requests of flexible loads in a distribution system. In [19], MPC was applied to control voltage in the ADN according to roll optimization of unknown variables.…”

Section: Introductionmentioning

confidence: 99%

Multi-Time Scale Optimal Dispatch for AC/DC Distribution Networks Based on a Markov Chain Dynamic Scenario Method and MPC

Liu

et al. 2020

Journal of Electrical and Computer Engineering

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A multi-time scale optimal dispatch model based on the scenario method and model predictive control (MPC) in the AC/DC distribution network is established due to the uncertainty of wind and load. A Markov chain dynamic scenario method is proposed, which generates scenarios by characterizing the forecast error via empirical distribution. Considering the time correlation of the forecast error, Markov chain is adopted in the Markov chain dynamic method to simulate the uncertainty and variability in wind and load with time. A multi-time scale optimal dispatch strategy based on MPC is proposed. The operation scheduling of operation units is solved in day-ahead and intraday optimal dispatch by minimizing the expected value of total cost in each scenario. In the real-time optimal dispatch, the stability and robustness of system operation are considered. MPC is adopted in the real-time optimal dispatch, taking the intraday scheduling as reference and using the roll optimization method to compute real-time optimal dispatch scheduling to smooth the output power. The simulation results in a 50-node system with uncontrollable distributed energy demonstrate that the proposed model and strategy can effectively eliminate fluctuations in wind and load in AC/DC distribution networks.

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Plug-and-Play Model Predictive Control for Load Shaping and Voltage Control in Smart Grids

Cited by 23 publications

References 37 publications

Towards multiobjective optimization and control of smart grids

Towards multiobjective optimization and control of smart grids

Stochastic distributed optimization of shapeable energy resources in low voltage distribution networks under limited communications

Multi-Time Scale Optimal Dispatch for AC/DC Distribution Networks Based on a Markov Chain Dynamic Scenario Method and MPC

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