Model Predictive Control for Cooperative Energy Management at City-District Level

Stoyanova, Ivelina; Jendoubi, Imen; Monti, Antonello

doi:10.23919/pscc.2018.8442619

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…This work completes the two‐phase EM for ETD and thermal storage at city district level introduced in previous works . In the following, we provide a summary of the concept as an overview of the background and the relation between the phases and to illustrate the transition from an aggregator‐led to a distributed approach.…”

Section: Introductionmentioning

confidence: 76%

“…Therefore, only deviations of 5 minutes and more are considered in the calculation. According to the work of Stoyanova et al, the time step is set to 15 minutes for the distributed and decomposition strategies and 1 hour for the rescheduling.…”

Section: Test Scenariomentioning

confidence: 99%

“…The definition of the performance indicators residual deviation and number of switching events is introduced in accordance with the work of Stoyanova et al Additionally, the computation time is included as a factor for the computational performance, and the scalability potential is evaluated for each method.…”

Section: Test Scenariomentioning

confidence: 99%

“…Within the planning phase, BEMSs solve local optimization problems based on weather and thermal demand forecasts and user behavior to generate a set of optimal or near‐optimal binary schedules for the heating systems for the next day in resolution of one hour . Based on the sets of schedules, an optimal combination of schedules is calculated, which enables a maximal integration of local and overregional renewable energy (RE) sources …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed model predictive control strategies for coordination of electro‐thermal devices in a cooperative energy management concept

Stoyanova

Gümrükcü

Aragón

et al. 2019

Optim Control Appl Methods

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Summary This work discusses three promising strategies for the compensation of deviations within the online phase of a cooperative energy management concept. Whereas in the planning phase, hourly schedules for the active electro‐thermal devices are negotiated on a daily basis, the second phase tracks the schedule, including forecast updates, and compensates the deviations. The latter online compensation is realized with a distributed and hierarchical‐distributed model predictive control strategies, which are compared with continuous rescheduling with receding horizon and evaluated in terms of computational and optimization performance and scalability. The optimization is implemented and deployed using an open‐source optimization framework, which automatically manages optimization control, integration of renewable generation and load forecasts, multi‐instantiation and communication over an internet of things communication protocol. We address the case of significant deviations from the day‐ahead forecast and the tracking of an obsolete schedule and suggest a combined method, which combines the advantages of rescheduling and a distributed MPC strategy. The method applies a dynamic threshold to evaluate the deviation trend over time and to decide if compensation is still feasible or if a rescheduling should be triggered. The methods are evaluated based on several performance indicators such as residual deviation, number of switching events and computation and communication requirements and scalability, and offer a methodology for the control design of systems of different dimensions.

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Section: Introductionmentioning

confidence: 76%

Section: Test Scenariomentioning

confidence: 99%

Section: Test Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed model predictive control strategies for coordination of electro‐thermal devices in a cooperative energy management concept

Stoyanova

Gümrükcü

Aragón

et al. 2019

Optim Control Appl Methods

Self Cite

View full text Add to dashboard Cite

show abstract

“…A recent review on dispatch via multistage opf can be found in the work of Xia and Elaiw. 27 Economic mpc for multi-energy systems including storage can be found in the works of Arnold and Andersson 28 and Stoyanova et al 29 Furthermore, multistage opf can be used for cost-optimal energy management of microgrids based on price signals using a linearized dc model. 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.…”

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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A critical review of control schemes for demand-side energy management of building clusters

Kaspar

Ouf

Eicker

2022

Energy and Buildings

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Model Predictive Control for Cooperative Energy Management at City-District Level

Cited by 8 publications

References 14 publications

Distributed model predictive control strategies for coordination of electro‐thermal devices in a cooperative energy management concept

Distributed model predictive control strategies for coordination of electro‐thermal devices in a cooperative energy management concept

Toward economic NMPC for multistage AC optimal power flow

A critical review of control schemes for demand-side energy management of building clusters

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