On efficient computation of time constrained optimal power flow in rectangular form

Meyer-Huebner, Nico; Suriyah, Michael

doi:10.1109/ptc.2015.7232378

Cited by 12 publications

(13 citation statements)

References 7 publications

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“…It is also worth to be noted that Proposition 1 does not require any conditions on the input penalization, ie, no restriction on R and r in (14). The main reason for this is the special system structure with A = I n x .…”

Section: Constant Disturbancesmentioning

confidence: 99%

“…For both systems, we investigate two different load scenarios: (i) we consider a piecewise-constant load to study the behavior of the system at steady state and (ii) we consider a random demand sequence to investigate the system behavior in the time-varying case. For the stage cost , we consider the standard economic cost for active power generation (14); in both cases, we add a small quadratic cost for the state of charge for all storages to ensure positive definiteness of Q. The sampling period is set to Δ = t b = 1h in all scenarios, which also serves as the base time for the per-unit (p.u.)…”

Section: Simulation Examplesmentioning

confidence: 99%

“…The parameters of the stage cost from (14) are as follows: Q = diag(diag (10,11,12,13), 10 −3 · I 9×9 ) $ h(p.u.) 2 , R = 0 18 × 18 , q = 10 2 · (15, 30, 40, 10, 0 1×9 ) ⊤ $ h(p.u.)…”

Section: -Bus Systemmentioning

confidence: 99%

“…Furthermore, we consider occasionally varying demands at bus 59 and 116 with results shown in Figure 5 and consider randomly generated demands as above in Figure 6. The parameters for the stage cost (14) are obtained from the matpower database with additional small quadratic coefficients of 10 −3 $ h (p.u.) 2 for q g and e in Q to ensure Q ≻ 0.…”

Section: -Bus Systemmentioning

confidence: 99%

“…This paper aims at partially closing this gap, ie, we try to transfer and verify the dissipativity notions established in *Note that there is no unified notion for these problems. In the literature, these problems are also referred to as dynamic optimal power flow, 13 time-constrained optimal power flow, 14 or dynamic economic dispatch, 15 see other works [16][17][18] for tutorial introductions on opf problems. economic nmpc to multistage opf problems while explicitly considering the nonlinear ac power flow equations as equality constraints.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Constant Disturbancesmentioning

confidence: 99%

Section: Simulation Examplesmentioning

confidence: 99%