Power management for a DC MicroGrid integrating renewables and storages

Iovine, Alessio; Rigaut, Tristan; Damm, Gilney; Santis, Elena De; Benedetto, Maria Domenica Di

doi:10.1016/j.conengprac.2019.01.009

Cited by 61 publications

(38 citation statements)

References 30 publications

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“…The incorporation of models and forecasts allows for efficient machinery utilization as well as situation-dependent, predictive control decisions, and renders MPC well suited for systems governed by slow dynamics and/or large delays. Successful applications can be found for a variety of systems, such as heat pump systems (Fischer et al, 2017;Baeten et al, 2017), district heating systems (Dainese et al, 2019) and microgrids (Zachar and Daoutidis, 2019;Iovine et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

et al. 2020

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This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, free cooling facilities, and a large underground thermal storage. For solution of the arising Mixed-Integer Non-Linear Programs (MINLPs) we apply an existing general and optimalcontrol-suitable decomposition approach. To compensate deviation of forecast inputs from measured disturbances, we introduce a moving horizon estimation step within the MPC strategy. The MPC performance for this study, which consists of more than 50,000 real time suitable MINLP solutions, is compared to an elaborate conventional control strategy for the system. It is shown that MPC can significantly reduce the yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previously unknown, beneficial operation modes.

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Section: Introductionmentioning

confidence: 99%

A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

et al. 2020

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“…(Fig.11a, Fig.11b). The step, the reference trajectories generation obtained through differential flatness and B−spline parametrization are compared with MPC, as presented also in [34] and [15]. The simulation results are presented in Fig.12 and Fig.14 taking into account that N p is equal to 24 h with a sampling time T s equal to 1800 s. The obtained trajectories are similar.…”

Section: Simulation Resultsmentioning

confidence: 90%

Meshed DC microgrid hierarchical control: A differential flatness approach

Zafeiratou¹,

Prodan²,

Lefèvre³

et al. 2020

Electric Power Systems Research

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In this paper, a meshed DC microgrid control architecture whose goal is to manage load balancing and efficient power distribution is introduced. A novel combination of port-Hamiltonian (PH) modeling with differential flatness and B-splines parametrization is introduced and shown to improve the microgrid's performance. A three layer supervision structure is considered: i) B-spline parametrized flat output provide continuous profiles for load balancing and price reduction (high level); ii) the profiles are tracked through a MPC implementation with stability guarantees (medium level); iii) explicit switching laws applied to the DC/DC converters ensure appropriate power injection. Each level functions at a different timescale (from slow to fast), and the control laws are chosen appropriately. The effectiveness of the proposed approach is evaluated by simulations over a DC microgrid composed by a collection of solar panels (PV), an energy storage system (ES), a utility grid (UG) and a consumers' demand.

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“…Then, it is reused in the contrary case. In microgrids, DERs are connected to the load systems through converters to satisfy the energy demand [4,5].…”

Section: Introductionmentioning

confidence: 99%

Economic constrained optimization for power balancing in a DC microgrid: A multi-source elevator system application

Pham¹,

Prodan²,

Genon-Catalot³

et al. 2020

International Journal of Electrical Power & Energy Systems

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This paper considers a discrete-time scheduling method for the power balancing of a continuous-time DC microgrid system. A high-order dynamics and a resistor network are used for modelling the electrical storage unit and the DC bus of the centralized microgrid system, respectively. A PH (Port-Hamiltonian) formulation on graphs is employed to explicitly describe the microgrid topology. This modelling approach allows us to derive a discrete-time model which preserves the power and energy balance of the physical system. Next, a constrained economic MPC (Model Predictive Control) using the proposed control model is formulated for efficiently managing the microgrid operation. The systematic combination of the network modelling method and optimization-based control allows us to generate the appropriate power profiles. Finally, the benefits of the proposed approach are validated through simulation and comparison results over a particular DC microgrid elevator system under different scenarios and using real numerical data.

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Power management for a DC MicroGrid integrating renewables and storages

Cited by 61 publications

References 30 publications

A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

Meshed DC microgrid hierarchical control: A differential flatness approach

Economic constrained optimization for power balancing in a DC microgrid: A multi-source elevator system application

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