Optimal fully electric vehicle load balancing with an ADMM algorithm in Smartgrids

Mercurio, Andrea; Giorgio, Alessandro Di; Purificato, Fabio

doi:10.1109/med.2013.6608708

Cited by 23 publications

(14 citation statements)

References 19 publications

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“…Because we deal with subgradients we use the inclusion sign instead of equality in (13). It simply means that zero must be included in the set.…”

Section: Operator Splittingmentioning

confidence: 99%

“…The resolvent of an operator F is the operator (I +tF ) −1 with scaling t > 0. In our case we require the resolvents of the functions ∂f and ∂g * from (13). The resolvent of ∂f is the proximal mapping…”

Section: Operator Splittingmentioning

confidence: 99%

“…A special case of Douglas-Rachford splitting is ADMM [12]. [13] use ADMM for Electric Vehicle charging coordination. [14] describe a general approach to Distributed MPC problems, while [15] provides convergence results for the splitting algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Model Predictive Control for Smart Energy Systems

Halvgaard

Vandenberghe

Poulsen

et al. 2016

IEEE Trans. Smart Grid

100

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Abstract-Integration of a large number of flexible consumers in a Smart Grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a Model Predictive Control (MPC) framework. To solve the large-scale control problem in real-time requires decomposition methods. We propose a decomposition method based on Douglas-Rachford splitting to solve this large-scale control problem. The method decomposes the problem into smaller subproblems that can be solved in parallel, e.g. locally by each unit connected to an aggregator. The total power consumption is controlled through a negotiation procedure between all cooperating units and an aggregator that coordinates the overall objective. For large-scale systems this method is faster than solving the original problem and can be distributed to include an arbitrary number of units. We show how different aggregator objectives are implemented and provide simulations of the controller including the computational performance.

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“…Because we deal with subgradients we use the inclusion sign instead of equality in (13). It simply means that zero must be included in the set.…”

Section: Operator Splittingmentioning

confidence: 99%

Section: Operator Splittingmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Model Predictive Control for Smart Energy Systems

Halvgaard

Vandenberghe

Poulsen

et al. 2016

IEEE Trans. Smart Grid

100

View full text Add to dashboard Cite

show abstract

“…Recent trends in the field of convex optimization, in particular the introduction of the alternating direction method of multipliers (ADMM), have enabled researchers to pursue distributed methods of load control [1][12] [13][14] [15]. Therefore, rather than using an aggregation model with centralized control, individual TCLs can coordinate amongst each other to drive the population towards a shared global objective.…”

Section: B Relevant Literaturementioning

confidence: 99%

Recursive parameter estimation of thermostatically controlled loads via unscented Kalman filter

Burger

Moura

2016

Sustainable Energy, Grids and Networks

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Abstract-For thermostatically controlled loads (TCLs) to perform demand response services in real-time markets, online methods for parameter estimation are needed. As the physical characteristics of a TCL change (e.g. the contents of a refrigerator or the occupancy of a conditioned room), it is necessary to update the parameters of the TCL model. Otherwise, the TCL will be incapable of accurately predicting its potential energy demand, thereby decreasing the reliability of a TCL aggregation to perform demand response. In this paper, we investigate the potential of various unscented Kalman filter (UKF) algorithm variations to recursively identify a TCL model that is nonlinear in the parameters. Experimental results demonstrate the parameter estimation of two residential refrigerators.

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“…These include adaptive strategies [6], Lagrange relaxation [7], stochastic pricing [8], [9] and the introduction of aggregators as mediating entities in the system [10]. Distributed optimization using the Alternating Direction Method of Multipliers (ADMM) has also received increasing attention [11], [12]. With this approach, both the devices and the system operator follow an iterative procedure that, under certain convexity assumptions, converges to a final result which is optimal for some combination of global and local objective functions.…”

Section: Introductionmentioning

confidence: 99%

Price-Based Schemes for Distributed Coordination of Flexible Demand in the Electricity Market

Paola

Angeli

Štrbac

2017

IEEE Trans. Smart Grid

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Abstract-This paper proposes novel distributed control schemes for large-scale deployment of flexible demand. The problem of efficiently coordinating price-responsive appliances operating in the electricity market is tackled within a gametheoretical framework. Adopting the concept of Nash equilibrium and Lyapunov-based techniques, a new iterative control algorithm is designed in order to always converge to a satisfactory solution for the individual customers, which aim at minimizing their energy costs. From the system perspective, it is shown that global quantities such as total generation costs are reduced at each algorithm iteration. These results are achieved for any penetration level of flexible demand and for all types of interruptible electrical appliances. The proposed control scheme can be applied in practice through a one-shot implementation that, at the price of a negligible degradation of the equilibrium performance, ensures faster convergence to a stable solution. Simulation results are also presented, testing the novel schemes in realistic future scenarios of the Great Britain power system with high penetration of flexible demand.

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Optimal fully electric vehicle load balancing with an ADMM algorithm in Smartgrids

Cited by 23 publications

References 19 publications

Distributed Model Predictive Control for Smart Energy Systems

Distributed Model Predictive Control for Smart Energy Systems

Recursive parameter estimation of thermostatically controlled loads via unscented Kalman filter

Price-Based Schemes for Distributed Coordination of Flexible Demand in the Electricity Market

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