Multi-objective model predictive control for microgrids

Schmitt, Thomas; Rodemann, Tobias; Adamy, Jürgen

doi:10.1515/auto-2020-0031

Cited by 16 publications

(14 citation statements)

References 38 publications

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“…Electrical powers are denoted by P and thermal powers by Q. For details of the modeling, the reader is referred to [26]. Note that the battery charging power P charge is no input on its own, but is implicitly given by P charge (k) = E(k + 1) − E(k), see (7).…”

Section: Methodology and Simulation Setup 21 Microgrid Modelingmentioning

confidence: 99%

“…where c chp , c gas , c grid,buy , c grid,sell and c grid,peak are constants [26]. P grid,peak (k) refers to the maximum peak in P grid , which has occurred until time step k. In Germany, the peak costs c grid,peak • P grid,peak are evaluated annually.…”

Section: Economic Mpc Approachmentioning

confidence: 99%

“…Furthermore, they tend to increase, while c grid,peak = 100.01 e kW for 2020 has already been comparatively high. Note that the max-terms in ( 5) and ( 6) can be translated into a linear expression by an epigraph reformulation (see [26] for details).…”

Section: Economic Mpc Approachmentioning

confidence: 99%

“…The weights w comf and w mon have been derived from 2D Pareto optimization over a longer time periods [26,27]. w bat is then chosen to be large enough to prevent the arbitrary charging and discharging of the battery, but small enough to keep the rewarding incentives of battery use described above.…”

Section: Economic Mpc Approachmentioning

confidence: 99%

See 3 more Smart Citations

The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing

2021

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Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability to respect the forecasts of loads and generation of renewable energies. However, while there are lots of approaches to accounting for uncertainties in these forecasts, their impact is rarely analyzed systematically. Here, we use a simplified linear state space model of a commercial building including a photovoltaic (PV) plant and real-world data from a 30 day period in 2020. PV predictions are derived from weather forecasts and industry peak pricing is assumed. The effect of prediction accuracy on the resulting cost is evaluated by multiple simulations with different prediction errors and initial conditions. Analysis shows a mainly linear correlation, while the exact shape depends on the treatment of predictions at the current time step. Furthermore, despite a time horizon of 24h, only the prediction accuracy of the first 75min was relevant for the presented setting.

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Section: Methodology and Simulation Setup 21 Microgrid Modelingmentioning

confidence: 99%

Section: Economic Mpc Approachmentioning

confidence: 99%

Section: Economic Mpc Approachmentioning

confidence: 99%

Section: Economic Mpc Approachmentioning

confidence: 99%

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The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing

2021

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“…For the higher level, Pareto optimization is applied with the monetary costs and the quadratic temperature deviation from 21 °C as two objectives. For a detailed insight into both modeling and the Pareto approach, the reader is referred to [14], [15].…”

Section: Hairmentioning

confidence: 99%

PARODIS: One MPC framework to control them all - Almost

Schmitt

Engel

Hoffmann

et al. 2021

2021 IEEE Conference on Control Technology and Applications (CCTA)

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We introduce the MATLAB framework PARODIS, the Pareto optimal Model Predictive Control framework for distributed Systems. It is a general-purpose, flexible and easy-touse framework for discrete state space models. Special features are the support of distributed (hierarchical) systems, scenariobased optimization and built-in methods for determination of the Pareto front and selection of a solution. It uses the popular MATLAB framework YALMIP for the symbolic formulation of optimization problems and models.

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Relaxed dissipativity assumptions and a simplified algorithm for multiobjective MPC

et al. 2022

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We consider nonlinear model predictive control (MPC) with multiple competing cost functions. In each step of the scheme, a multiobjective optimal control problem with a nonlinear system and terminal conditions is solved. We propose an algorithm and give performance guarantees for the resulting MPC closed loop system. Thereby, we significantly simplify the assumptions made in the literature so far by assuming strict dissipativity and the existence of a compatible terminal cost for one of the competing objective functions only. We give conditions which ensure asymptotic stability of the closed loop and, what is more, obtain performance estimates for all cost criteria. Numerical simulations on various instances illustrate our findings. The proposed algorithm requires the selection of an efficient solution in each iteration, thus we examine several selection rules and their impact on the results. and we also examine numerically how different selection rules impact the results

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Multi-objective model predictive control for microgrids

Cited by 16 publications

References 38 publications

The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing

The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing

PARODIS: One MPC framework to control them all - Almost

Relaxed dissipativity assumptions and a simplified algorithm for multiobjective MPC

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