A Model Predictive Control-Based Energy Management Scheme for Hybrid Storage System in Islanded Microgrids

Nair, Unnikrishnan Raveendran; Costa‐Castelló, Ramon

doi:10.1109/access.2020.2996434

Cited by 67 publications

(25 citation statements)

References 48 publications

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“…The input to the MPC is a sampled state of the ESS, PVs, DG, EV and load demand at time t. The forecast of PVs, EV and load demand for the prediction window will also be provided as input. The output of the MPC becomes the set points for the ESS converters, the DG and the EV charging curtailment value [23].…”

Section: Mpc Based Energy Management Strategymentioning

confidence: 99%

MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids

Ryu

Kim

et al. 2021

Energies

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This paper presents the improvements of the hosting capacity of photovoltaics (PVs) and electric vehicles (EVs) in a stand-alone microgrid (MG) with an energy storage system (ESS) by consider-ing a model predictive control (MPC) based energy management system. The system is configured as an MG, including PVs, an ESS, a diesel generator (DG), and several loads with EVs. The DG is controlled to operate at rated power and the MPC algorithm is used in a stand-alone MG, which supplies the energy demanded for several loads with EVs. The hosting capacity of the load in-cluding the EV and PVs can be expanded through the ESS to the terminal node of the microgrid. In this case, the PVs and the load can be connected in excess of the capacity of the diesel genera-tor, and each bus in the feeder complies with the voltage range required by the grid. The effec-tiveness of the proposed algorithm to resolve the hosting capacity is demonstrated by numerical simulations.

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Section: Mpc Based Energy Management Strategymentioning

confidence: 99%

MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids

Ryu

Kim

et al. 2021

Energies

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“…Model predictive control (MPC) [14] is multivariable control strategy of many industrial applications [15], [16]. The MPC paradigm offers a unique ability to handle input/state constraints and to deal efficiently with time delay, instability, nonminimum phase, model mismatch disturbances and measurement noise.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Based on Linear Parameter-Varying Models of Active Magnetic Bearing Systems

et al. 2021

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Active magnetic bearing (AMB) system has been recently employed widely as an ideal equipment for high-speed rotating machines. The inherent challenges to control the system include instability, nonlinearity and constricted range of operation. Therefore, advanced control technology is essential to optimize AMB system performance. This paper presents an application of model predictive control (MPC) based on linear parameter-varying (LPV) models to control an AMB system subject to input and state constraints. For this purpose, an LPV model representation is derived from the nonlinear dynamic model of the AMB system. In order to provide stability guarantees and since the obtained LPV model has a large number of scheduling parameters, the parameter set mapping (PSM) technique is used to reduce their number. Based on the reduced model, a terminal cost and an ellipsoidal terminal set are determined offline and included into the MPC optimization problem which are the essential ingredients for guaranteeing the closed-loop asymptotic stability. Moreover, for recursive feasibility of the MPC optimization problem, a slack variable is included into its cost function. The goal of the proposed feedback control system is twofold. First is to demonstrate high performance by achieving stable levitation of the rotor shaft as well as high capability of reference tracking without violating input and state constraints, which increases the overall safety of the system under disturbances effects. Second is to provide a computationally tractable LPVMPC algorithm, which is a substantial requirement in practice for operating the AMB system with high performance over its full range. Therefore, we propose an LPVMPC scheme with frozen scheduling parameter over the prediction horizon of the MPC. Furthermore, we demonstrate in simulation that such frozen LPVMPC can achieve comparable performance to a more sophisticated LPVMPC scheme developed recently and a standard NL MPC (NMPC) approach. Moreover, to verify the performance of the proposed frozen LPVMPC, a comparison with a classical controller, which is commonly applied to the system in practice, is provided.INDEX TERMS Model predictive control, linear parameter-varying models, magnetic bearing systems, parameter set mapping, asymptotic stability.

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“…Other MPC strategies applied to microgrids with hybrid configurations have been described in [17]- [20]. In [17], MPC was used to regulate the power flow through a DC/DC converter and a voltage-source inverter of a hybrid PV/battery-based islanded AC microgrid.…”

Section: Introductionmentioning

confidence: 99%

“…The input variables were the electricity price, weather forecast, and energy demand, and the simulation was conducted over a week. An EMS based on MPC was presented in [20] to improve the use of renewable generation, the operational efficiency of the microgrid, and reduce the rate of degradation of storage systems. The inputs of the MPC were the ESS state, PV generation, and load, and the outputs were the power commands to the ESS converters.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control-Based Optimized Operation of a Hybrid Charging Station for Electric Vehicles

et al. 2021

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This paper presents an energy management system (EMS) based on a novel approach using model predictive control (MPC) for the optimized operation of power sources in a hybrid charging station for electric vehicles (EVs). The hybrid charging station is composed of a photovoltaic (PV) system, a battery, a complete hydrogen system based on a fuel cell (FC), electrolyzer (EZ), and tank as an energy storage system (ESS), grid connection, and six fast charging units, all of which are connected to a common MVDC bus through Z-source converters (ZSC). The MPC-based EMS is designed to control the power flow among the energy sources of the hybrid charging station and reduce the utilization costs of the ESS and the dependency on the grid. The viability of the EMS was proved under a long-term simulation of 25 years in Simulink, using real data for the sun irradiance and a European load profile for EVs. Furthermore, this EMS is compared with a simpler alternative that is used as a benchmark, which pursues the same objectives, although using a states-based strategy. The results prove the suitability of the EMS, achieving a lower utilization cost (-25.3%), a notable reduction in grid use (-60% approximately) and an improvement in efficiency.

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A Model Predictive Control-Based Energy Management Scheme for Hybrid Storage System in Islanded Microgrids

Cited by 67 publications

References 48 publications

MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids

MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids

Model Predictive Control Based on Linear Parameter-Varying Models of Active Magnetic Bearing Systems

Model Predictive Control-Based Optimized Operation of a Hybrid Charging Station for Electric Vehicles

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