A Multi-Period Optimal Reactive Power Dispatch Approach Considering Multiple Operative Goals

Morán-Burgos, Jairo A.; Sierra-Aguilar, Juan Esteban; Villa-Acevedo, Walter M.; López-Lezama, Jesús M.

doi:10.3390/app11188535

Cited by 8 publications

(13 citation statements)

References 46 publications

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“…active and reactive power markets). Many studies have not considered opportunity costs when scheduling active and reactive powers [19][20][21][22][23][24][25][26][27][28][29][30][31]. In certain studies the opportunity cost is defined in different ways [18,[33][34][35].…”

Section: Status Of the Literaturementioning

confidence: 99%

“…In reference [25], the effect of capability curve limits on microgrid reconfiguration was analysed. In reference [26], a multi‐period optimal reactive power dispatch approach was proposed; however, the active and reactive power generations were modelled independently. In reference [27], a neural network‐based approach was proposed to virtually visualise the generator capability curve.…”

Section: Introductionmentioning

confidence: 99%

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An AC optimal power flow framework for active–reactive power scheduling considering generator capability curve

Vaishya

2023

Energy Conversion and Econom

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This paper presents a simplified optimal power flow (OPF) framework to facilitate co‐optimised active and reactive power scheduling for synchronous generators and price‐sensitive demands. The proposed framework creates an opportunity for generators and loads to simultaneously participate in a combined market for active and reactive power. The co‐optimisation of active and reactive power generation is constrained by the interdependence of the active and reactive power capacities, which is represented by the generator capability curve. Thus, a detailed mathematical derivation of the opportunity costs across various regions of the generator capability curve is presented. This study considers a detailed generator capability curve that considers the armature, field, under‐excitation, and prime mover limits. The interdependence of active and reactive power consumption for demand is modelled using the concept of power‐factor. The OPF problem for generator and load scheduling is formulated as a non‐linear optimisation task, leveraging the inherent properties of the generator capability curve, that is, piecewise smoothness, continuity, and the monotonically increasing slope magnitudes. Furthermore, to simplify the OPF formulation, the non‐linear capability curve is represented as a combination of the linear curves. To demonstrate the effectiveness of the proposed OPF methodologies, suitable case studies are conducted using different test systems.

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Section: Status Of the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An AC optimal power flow framework for active–reactive power scheduling considering generator capability curve

Vaishya

2023

Energy Conversion and Econom

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“…The optimization problem indicated by Equations ( 1)-( 9) is nonlinear and nonconvex. This is evident in Equations ( 2) and (3) which involve trigonometric functions as well as a nonlinear relation of the admittance matrix with the transformer taps [21,43]. Moreover, the presence of integer variables makes the problem even more complex.…”

Section: Inequality Constraintsmentioning

confidence: 99%

Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface

et al. 2022

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The Optimal Reactive Power Dispatch (ORPD) problem consists of finding the optimal settings of reactive power resources within a network, usually with the aim of minimizing active power losses. The ORPD is a nonlinear and nonconvex optimization problem that involves both discrete and continuous variables; the former include transformer tap positions and settings of reactor banks, while the latter include voltage magnitude settings in generation buses. In this paper, the ORPD problem is modeled as a mixed integer nonlinear programming problem and solved through two different metaheuristic techniques, namely the Mean Variance Mapping Optimization and the genetic algorithm. As a novelty, the solution of the ORPD problem is implemented through a Python-DIgSILENT interface that combines the strengths of both software. Several tests were performed on the IEEE 6-, 14-, and 39-bus test systems evidencing the applicability of the proposed approach. The results were contrasted with those previously reported in the specialized literature, matching, and in some cases improving, the reported solutions with lower computational times.

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“…The main complication of the optimization problem (1)-( 9) is associated with the non-convexity of the solution space mainly caused by the active and reactive power balance constraints and the nonlinear dependence of the admittance matrix with the transformer taps [9,36]. Additional to these complications, the presence of the integer variables that generate disconnections in the solution space, make necessary to propose efficient optimization techniques that ensure adequate solution with low computational requirements, even when these are sub-optimal solutions.…”

Section: Interpretation Of the Mathematical Modelmentioning

confidence: 99%

Solution of the Optimal Reactive Power Flow Problem Using a Discrete-Continuous CBGA Implemented in the DigSILENT Programming Language

2021

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The problem of the optimal reactive power flow in transmission systems is addressed in this research from the point of view of combinatorial optimization. A discrete-continuous version of the Chu & Beasley genetic algorithm (CBGA) is proposed to model continuous variables such as voltage outputs in generators and reactive power injection in capacitor banks, as well as binary variables such as tap positions in transformers. The minimization of the total power losses is considered as the objective performance indicator. The main contribution in this research corresponds to the implementation of the CBGA in the DigSILENT Programming Language (DPL), which exploits the advantages of the power flow tool at a low computational effort. The solution of the optimal reactive power flow problem in power systems is a key task since the efficiency and secure operation of the whole electrical system depend on the adequate distribution of the reactive power in generators, transformers, shunt compensators, and transmission lines. To provide an efficient optimization tool for academics and power system operators, this paper selects the DigSILENT software, since this is widely used for power systems for industries and researchers. Numerical results in three IEEE test feeders composed of 6, 14, and 39 buses demonstrate the efficiency of the proposed CBGA in the DPL environment from DigSILENT to reduce the total grid power losses (between 21.17% to 37.62% of the benchmark case) considering four simulation scenarios regarding voltage regulation bounds and slack voltage outputs. In addition, the total processing times for the IEEE 6-, 14-, and 39-bus systems were 32.33 s, 49.45 s, and 138.88 s, which confirms the low computational effort of the optimization methods directly implemented in the DPL environment.

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A Multi-Period Optimal Reactive Power Dispatch Approach Considering Multiple Operative Goals

Cited by 8 publications

References 46 publications

An AC optimal power flow framework for active–reactive power scheduling considering generator capability curve

An AC optimal power flow framework for active–reactive power scheduling considering generator capability curve

Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface

Solution of the Optimal Reactive Power Flow Problem Using a Discrete-Continuous CBGA Implemented in the DigSILENT Programming Language

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