Control of Parallel ULTC Transformers in Active Distribution Systems

Pouladi, Alireza; Zadeh, Alimorad Khajeh; Nouri, Alireza

doi:10.1109/jsyst.2019.2897771

Cited by 9 publications

(9 citation statements)

References 24 publications

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“…This paper tackles the short term DSP and therefore, for each hour a separate problem is solved. The hourly load data and energy prices can be found in [15] and ρ R = 0.1ρ A . The load coefficients are provided in [16] for residential loads.…”

Section: B Scheduling Resultsmentioning

confidence: 99%

“…2 (b) shows the equivalent per unit pi circuit. It is assumed that for a well manufactured transformer Z n p,t = Z n s,t = Z n t /2 [15]. For N t parallel transformers indexed by t, each impedance in the resultant equivalent pi model is found by aggregating the respective impedances in the pi model of all transformers, i.e, 1/Z sr = Nt t=1 1/Z srt , 1/Z pr,p = Nt t=1 1/Z pr,s t and 1/Z pr,s = Nt t=1 1/Z pr,p t (see Fig.…”

Section: A Transformers and Upstream Network Modelsmentioning

confidence: 99%

“…The cost curve is concave up and therefore, the linearized cost is always lower than the accurate cost. This can be generalized based on the results reported in [15]. For the sake of brevity, the transformer core loss is selected here as one of the main components of the system loss which varies widely as the tap position is changed.…”

Section: A Illustrative Examplementioning

confidence: 99%

“…V. CASE STUDIES IEEE 33-bus distribution test system [15] is used to analyze the effectiveness of the proposed framework. Two parallel transformers under OLTC control (Table I) connect this system to the upstream system.…”

Section: A Illustrative Examplementioning

confidence: 99%

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Fast Resource Scheduling for Distribution Systems Enabled With Discrete Control Devices

Nouri

Soroudi

Keane

2020

IEEE Systems Journal

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This paper proposes a framework for fast shortterm scheduling and steady state voltage control in distribution systems enabled with both continuous control devices, e.g., inverter interfaced DGs and discrete control devices (DCDs), e.g., on-load tap changers (OLTCs). The voltage-dependent nature of loads is taken into account to further reduce the operating cost by managing the voltage levels. Branch and cut method is applied to handle the integrality constraints associated with the operation of DCDs. A globally convergent trust region algorithm (TRA) is applied to solve the integer relaxed problems at each node during the branching process. The TRA sub-problems are solved using interior point method. To reduce the branching burden of branch and cut algorithm, before applying TRA at each node, a simplified optimization problem is first solved. Using the convergence status and value of objective function of this problem, a faster decision is made on stopping the regarding branch. Solving the simplified problem obviates the application of TRA at most nodes. It is shown that the method converges to the optimal solution with a considerable saving in computation time according to the numerical studies. Index Terms-Mixed integer programming, resource scheduling, smart grids, voltage control LIST OF ABBREVIATIONS BC Branch and cut CB Capacitor bank DCD Discrete controllable device DER Distributed energy resource DSP Distribution scheduling problem FR Feasible region MINLP Mixed integer nonlinear programming NLP Nonlinear programming OLTC On load tap changer OPF Optimal power flow RR Renewable resource SDP Semidefinite programming SOCP Second-order cone programming SVR Static voltage regulator TRA Trust region algorithm I. INTRODUCTION T HE RESOURCE scheduling in current distribution systems is a critical task due to the high penetration of distributed energy resources (DERs), fast load variations [1], A. Nouri (alireza.nouri@ucd.ie), A. Soroudi (alireza.soroudi@ucd.ie) and A. Keane (andrew.keane@ucd.ie) are with the

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Section: B Scheduling Resultsmentioning

confidence: 99%

Section: A Transformers and Upstream Network Modelsmentioning

confidence: 99%

Section: A Illustrative Examplementioning

confidence: 99%

Section: A Illustrative Examplementioning

confidence: 99%

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Fast Resource Scheduling for Distribution Systems Enabled With Discrete Control Devices

Nouri

Soroudi

Keane

2020

IEEE Systems Journal

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“…The per unit value of the secondary series impedance (Z sr,s ), X M and R c viewed from the secondary terminal do not change. It is assumed that for a well manufactured transformer Z n sr,p = Z n sr,s = Z n sr /2 [22]. For OLTC transformers, the independent optimization variable is tap which is an integer variable and tap = (r−1)/∆U .…”

Section: A Fast Control Devicesmentioning

confidence: 99%

Strategic Scheduling of Discrete Control Devices in Active Distribution Systems

Nouri

Soroudi

Keane

2020

IEEE Trans. Power Delivery

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The frequent actuation of discrete control devices (DCDs), e.g., on-load tap changers, drastically reduces their lifetime. This, in turn, imposes a huge replacement cost. Simultaneous scheduling of these DCDs and continuous control devices, e.g., distributed energy resources, is imperative for reducing the operating cost. This also increases the lifetime of DCDs and helps to avoid the sub-optimal/infeasible solutions. Considering the high cost of discrete control actions (DCAs), they may never be justified against the other options in a short scheduling horizon (SH). With a longer SH, their benefits over a long period justify DCAs. However, a shorter SH helps to hedge against the risk impelled by uncertainties. Here, the system future is modeled as a set of multi-period scenarios. The operator exploits a long SH, but solely applies the decisions made for the first period and waits for updated data to make the next decisions. This enables cost reduction by strategically applying DCAs prior to the time that they are inevitable, while avoiding them when unneeded. The proposed branch-and-cut-based solution methodology accurately deals with DCAs while applying some expediting heuristics. During the branching process, a globally convergent trust region algorithm solves the integer relaxed problems. BC Branch and cut CB Capacitor bank DCA Discrete controllable action DCC Discrete control cost DCD Discrete controllable device DER Distributed energy resource DSP Distribution scheduling problem LP Linear programming MILP Mixed integer linear programming MINLP Mixed integer nonlinear programming NLP Nonlinear programming OC Operation cost OLTC On load tap changer PDF Probability density function RES Renewable energy sources RHC Receding horizon control SH Scheduling horizon SOCP Second-order cone programming SVR Static voltage regulator SA Solution approaches A. Nouri (alireza.nouri@ucd.ie), A. Soroudi (alireza.soroudi@ucd.ie) and A. Keane (andrew.keane@ucd.ie) are with the

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