Day-Ahead Scheduling and Real-Time Economic MPC of CHP Unit in Microgrid With Smart Buildings

Vasilj, Josip; Gros, Sébastien; Jakus, Damir; Zanon, Mario

doi:10.1109/tsg.2017.2785500

Cited by 63 publications

(27 citation statements)

References 21 publications

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“…The discretization of the state variable yk of TCL k can be described by (26). The real-time control problem at time step t can be optimized by minimizing the cost function expressed in (27). The external energy consumption of step t, denoted as t q , can be calculated with (28), utilizing (14), (15), and (26).…”

Section: B) Lower Level: Continuous Mpc Loadsmentioning

confidence: 99%

“…The calculation of 1 S Eq   in (30) for the current slot h is obtained from the real-time control model, whereas those for the other slots are estimated by the predicted on/off states based on the outdoor temperature and previous working states, in units of the length of each time slot. The optimization model of the lower level can be given as minimize (27) . .…”

Section: B) Lower Level: Continuous Mpc Loadsmentioning

confidence: 99%

“…Economic MPC, as its name suggests, usually optimizes the operating cost of the system under control, and it is used extensively in power industries [26], [27]. Implementations of economic MPC in controller design for dual-model powersplit electric vehicles (EVs) [28], voltage regulation in distribution networks [29], and the operation and control of modern thermal power plants [30] have been investigated, among other applications.…”

Section: Introductionmentioning

confidence: 99%

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Economic MPC-Based Smart Home Scheduling With Comprehensive Load Types, Real-Time Tariffs, and Intermittent DERs

et al. 2020

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Smart home scheduling, facilitated by advanced metering, monitoring, and manipulation technology, plays an important role in the energy transition in terms of accommodating intermittent renewable energy and improving energy consumption efficiency. The key functionalities of home energy scheduling are usually implemented by leveraging the flexibility of household appliances, such as thermostatically controlled loads (TCLs) and energy storage units, to improve the peak-to-average ratio for utilities and reduce energy bills for customers. However, the consumption patterns of appliances are greatly influenced by a variety of factors, including real-time tariffs, ambient temperature profiles, indoor activities, and solar irradiance. Hence, smart home energy scheduling is a challenging task because most of these impacting factors are stochastic and difficult to predict. To properly model and manage the uncertainty factors associated with smart home appliance scheduling, an economic model predictive control (MPC)-based bilevel smart scheduling scheme is proposed in this paper. The comprehensive modeling of distributed generation and household appliances is performed at the single-household level. The home energy scheduling problem is formulated on two levels, with the upper level emphasizing the economic impact and the lower level focusing on capturing TCL responses. The correlations among different TCLs and their performance under the influence of various uncertainty factors, such as environmental impacts and user behaviors, are considered. The efficiency of the proposed MPC-based bilevel optimization model and the effectiveness of the home energy scheduling strategy in managing uncertainties are validated and illustrated in numerical studies.

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Section: B) Lower Level: Continuous Mpc Loadsmentioning

confidence: 99%

Section: B) Lower Level: Continuous Mpc Loadsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Economic MPC-Based Smart Home Scheduling With Comprehensive Load Types, Real-Time Tariffs, and Intermittent DERs

et al. 2020

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“…The MPC uses the system model to predict the future evolution of the plant to generate the control action on receding control strategy [17], [22]. The goal of this controller is to switch the HP on/off in order to shift HP power consumption based on DRP while producing sufficient chilled/hot water.…”

Section: B Mpcmentioning

confidence: 99%

Demonstration of Peak-Load Shifting with Optimal Residential Thermal Energy Management

Baniasadi¹,

Habibi²,

Al-Saedi³

et al. 2019

EasyChair Preprints

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The high penetration of renewable energy resources (RES), in particular the rooftop photovoltaic (PV) systems in power systems, causes rapid ramps in power generation to supply load during peak-load periods. In smart residential buildings, variations in rooftop PV power causes a mismatch between generation and load demand. This paper deals with shifting heat pumps loads to either the lower electricity price period or whenever PV generation is available. A strategy is proposed for managing heat pump operation based on real-time pricing tariff to minimize the operation cost of a smart building by controlling the room temperature. Simulation results demonstrate the cost benefits and effectiveness of the proposed thermal energy management strategy.

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“…30 While Appino et al 31 discuss the effect of uncertain forecasts on storage scheduling, a distributed approach to scheduling is suggested in the work of Braun et al 32 The recent paper of Meyer-Hübner et al 33 considers mpc for storage scheduling in high-voltage grids. Stochastic economic nmpc of microgrids is proposed in the work of Vasilj et al 34 An approach to combined frequency regulation and economic dispatch via economic mpc, including stability analysis, with dc grid models is presented in the work of Köhler et al 35 With respect to the second branch, several works [36][37][38][39] consider voltage or frequency stabilization; frequency control and automatic generation control 40 as well as the use of quadratic stability constraints ensuring stability (which lead to state path constraints instead of terminal constraints) 41 have been investigated. Approaches to consider voltage control via minimizing the absolute value of total reactive power injection are presented in the work of Zima and Andersson.…”

Section: Introductionmentioning

confidence: 99%

Toward economic NMPC for multistage AC optimal power flow

Faulwasser

Engelmann

2019

Optim Control Appl Methods

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Summary Recently, there has been a considerable progress on the analysis of stability and performance properties of so‐called economic nonlinear model predictive control (nmpc) schemes, ie, schemes employing stage costs that are not directly related to distance measures of precomputed setpoints. At the same time, with respect to the energy transition, the use of nmpc schemes is proposed and investigated in a plethora of papers in different contexts. For example, receding‐horizon approaches to generator dispatch problems, which is also known as multistage optimal power flow (opf) in power systems, naturally lead to economic nmpc schemes based on nonconvex discrete‐time optimal control problems (ocp). This paper investigates the transfer of analysis results available for general economic nmpc schemes to receding‐horizon multistage opf. We propose a blueprint formulation of multistage opf including ac power flow equations. Based on this formulation, we present results on the dissipativity and recursive feasibility properties of the underlying ocp. Finally, we draw upon simulations using a 5‐bus system and a 118‐bus system to illustrate our findings.

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Day-Ahead Scheduling and Real-Time Economic MPC of CHP Unit in Microgrid With Smart Buildings

Cited by 63 publications

References 21 publications

Economic MPC-Based Smart Home Scheduling With Comprehensive Load Types, Real-Time Tariffs, and Intermittent DERs

Economic MPC-Based Smart Home Scheduling With Comprehensive Load Types, Real-Time Tariffs, and Intermittent DERs

Demonstration of Peak-Load Shifting with Optimal Residential Thermal Energy Management

Toward economic NMPC for multistage AC optimal power flow

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