Analysis of optimal energy management in smart homes using MPC

Sundström, Christofer; Jung, Daniel; Blom, A.F.

doi:10.1109/ecc.2016.7810596

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…( 1) ( ) ( ) ( ) Therefore, the capacitor voltage at (k+1)th instant can be predicted according to (19). To control the capacitor voltage tightly, the cost function is formulated to…”

Section: Mpvp Of Ac/dc Interlinking Convertermentioning

confidence: 99%

“…In the last few years, the model predictive control (MPC) scheme, in which the optimal switching state of the power converter is determined according to a specified cost function, has been adopted to obtain better performance [13]- [16]. Still, MPC is seldom reported in the coordinated control of multiple converters in microgrids, although some system-level algorithms have been proposed to achieve a variety of goals such as minimizing system operating costs [17], optimized power flow management [18] and economic load dispatch [19]. These algorithms are designed and implemented at the system level.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model Predictive Control of Bidirectional DC–DC Converters and AC/DC Interlinking Converters—A New Control Method for PV-Wind-Battery Microgrids

Shan

Chan

et al. 2019

IEEE Trans. Sustain. Energy

214

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In renewable energy systems, fluctuating outputs from energy sources and variable power demand may deteriorate the voltage quality. In this paper, a model predictive control strategy without using any proportional-integral-derivative (PID) regulators is proposed. The proposed strategy consists of a model predictive current and power (MPCP) control scheme and a model predictive voltage and power (MPVP) control method. By controlling the bidirectional dc-dc converter of the battery energy storage system based on the MPCP algorithm, the fluctuating output from the renewable energy sources can be smoothed, while stable dc-bus voltage can be maintained. Meanwhile, the ac/dc interlinking converter is controlled by using the MPVP scheme to ensure stable ac voltage supply and proper power flow between the microgrid and the utility grid. Then, a system-level energy management scheme (EMS) is developed to ensure stable operation under different operation modes by considering fluctuating power generation, variable power demand, battery state of charge (SOC), and electricity price. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation based on a 3.5 MW pv-wind-battery system with realworld solar and wind profiles.

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“…( 1) ( ) ( ) ( ) Therefore, the capacitor voltage at (k+1)th instant can be predicted according to (19). To control the capacitor voltage tightly, the cost function is formulated to…”

Section: Mpvp Of Ac/dc Interlinking Convertermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Bidirectional DC–DC Converters and AC/DC Interlinking Converters—A New Control Method for PV-Wind-Battery Microgrids

Shan

Chan

et al. 2019

IEEE Trans. Sustain. Energy

214

View full text Add to dashboard Cite

show abstract

“…An MPC scheme with a sample time of one hour is presented in [35]. It includes hot water usage, electric vehicle, domestic heating and with an actuator with water tank to use it as heat storage.…”

Section: Optimal Energy Usagementioning

confidence: 99%

Phase Balancing Home Energy Management System Using Model Predictive Control

2018

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Most typical distribution networks are unbalanced due to unequal loading on each of the three phases and untransposed lines. In this paper, models and methods which can handle three-phase unbalanced scenarios are developed. The authors present a novel three-phase home energy management system to control both active and reactive power to provide per-phase optimization. Simplified single-phase algorithms are not sufficient to capture all the complexities a three-phase unbalance system poses. Distributed generators such as photo-voltaic systems, wind generators, and loads such as household electric and thermal demand connected to these networks directly depend on external factors such as weather, ambient temperature, and irradiation. They are also time dependent, containing daily, weekly, and seasonal cycles. Economic and phase-balanced operation of such generators and loads is very important to improve energy efficiency and maximize benefit while respecting consumer needs. Since homes and buildings are expected to consume a large share of electrical energy of a country, they are the ideal candidate to help solve these issues. The method developed will include typical distributed generation, loads, and various smart home models which were constructed using realistic models representing typical homes in Austria. A control scheme is provided which uses model predictive control with multi-objective mixed-integer quadratic programming to maximize self-consumption, user comfort and grid support.

show abstract

“…In the past few years, the model predictive control (MPC) scheme, in which the optimal switching state of power converter is determined according to a specified cost function, has been adopted to obtain better converter performance than PID control [14]- [17]. Although some system level algorithms have been proposed to achieve a variety of goals such as minimal system operating costs [18], optimal power flow management [19], economic load dispatch [20], realizing voltage coordination [21], and stabilizing terminal voltages [22], the MPC is seldom reported in the coordinated control of multiple converters in microgrids, and flexible power regulation for voltage support has not been studied. At the device level, a review of various MPC-controlled power converters is presented in [23,24].…”

Section: Introductionmentioning

confidence: 99%