Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm

Mohagheghi, Erfan; Gabash, Aouss; Alramlawi, Mansour; Li, Pu

doi:10.1016/j.renene.2018.03.072

Cited by 57 publications

(35 citation statements)

References 53 publications

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“…An OPF problem was formulated [25], employing active-reactive power capabilities of BSSs [117,118] to increase the total yield of a distribution network. It is worth mentioning that utilizing the reactive power capability of distributed energy resources leads to significant economic [119] and technical benefits [120,121]. In addition, the BSSs described in reference [25] operate on the basis of a fixed length of charge and discharge periods, which was later extended [26] for flexible charge and discharge periods, though still with a fixed length of charge-discharge cycles.…”

Section: X(t) = G(x(t) U(t) L(t) Y(t)) X(tmentioning

confidence: 99%

“…It means, with a certain degree of conservativeness, that the RT-OPF framework can offer a feasible solution for 'any' actual values of wind power. The RT-OPF framework in reference [46] was further extended [119] to incorporate reactive power dispatch of wind farms. In addition, the study in reference [119] proposed a reconciliation algorithm to overcome convergence issues [206] in RT-OPF computation.…”

Section: Opf-based Rt-emssmentioning

confidence: 99%

“…The RT-OPF framework in reference [46] was further extended [119] to incorporate reactive power dispatch of wind farms. In addition, the study in reference [119] proposed a reconciliation algorithm to overcome convergence issues [206] in RT-OPF computation. Furthermore, using graphical processing units and parallel computing, an approach to accelerate the OPF computations was proposed [129].…”

Section: Opf-based Rt-emssmentioning

confidence: 99%

“…The real-time active-reactive OPF framework in reference [119] was further extended [218] to optimally deal with spontaneous changes of wind power in the distribution networks with BSSs. The multi-phase multi-time-scale dynamic active-reactive OPF framework in reference [218] provides the possibility of simultaneous optimization of a huge number of mixed-integer decision variables (e.g., decision variables of wind farms, BSSs, slack bus voltage, active-reactive import/export powers, etc.)…”

Section: Opf-based Rt-emssmentioning

confidence: 99%

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A Survey of Real-Time Optimal Power Flow

et al. 2018

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There has been a strong increase of penetration of renewable energies into power systems. However, the renewables pose new challenges for the operation of the networks. Particularly, wind power is intermittently fluctuating, and, therefore, the network operator has to fast update the operations correspondingly. This task should be performed by an online optimization. Therefore, real-time optimal power flow (RT-OPF) has become an attractive topic in recent years. This paper presents an overview of recent studies on RT-OPF under wind energy penetration, offering a critical review of the major advancements in RT-OPF. It describes the challenges in the realization of the RT-OPF and presents available approaches to address these challenges. The paper focuses on a number of topics which are reviewed in chronological order of appearance: offline energy management systems (EMSs) (deterministic and stochastic approaches) and real-time EMSs (constraint satisfaction-based and OPF-based methods). The particular challenges associated with the incorporation of battery storage systems in the networks are explored, and it is concluded that the current research on RT-OPF is not sufficient, and new solution approaches are needed.Energies 2018, 11, 3142 2 of 20 time. However, the incorporation of BSSs into the network leads to dynamic state operations and thus introduces dynamic model equations to the OPF, which makes the problem more complex to solve. This complexity is due to the fact that the dynamic optimization is described by differential/difference equations, whereas static optimization requires only algebraic equations [30].Another issue is that the REG rate is uncertain. It means their output cannot be forecasted accurately, and there may be discrepancies between the forecasted and actually realized values. The uncertainties can lead to constraint violations and thus safety problems if they are not handled properly. Therefore, deterministic OPF methods are not suitable for the real operation of the networks, or their solutions need further modifications before realization. Besides, owing to fast fluctuating REG, in particular wind power, the network operators need to fast update-i.e., in real-time-the operation strategies correspondingly, in order for the network to operate economically and in its feasible region. Therefore, the real-time energy management systems (RT-EMSs) have attracted increasing interest from many researchers. In this paper, we classify the RT-EMSs into two main categories: (1) constraint satisfaction-based RT-EMSs, and (2) OPF-based RT-EMSs. The former can satisfy the technical constraints of the network in real time, but the solutions may not be optimal. The latter provides optimal solutions in real time while satisfying the constraints. It is noted that the technical constraints could be different for these two categories, depending on the components of the network (details can be seen in Section 2).The remainder of the paper is organized as follows: Section 2 provides the general formulation of OPF...

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Section: X(t) = G(x(t) U(t) L(t) Y(t)) X(tmentioning

confidence: 99%

Section: Opf-based Rt-emssmentioning

confidence: 99%

Section: Opf-based Rt-emssmentioning

confidence: 99%

Section: Opf-based Rt-emssmentioning

confidence: 99%

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A Survey of Real-Time Optimal Power Flow

et al. 2018

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“…Although the inner-outer approximation is a general approach of solving chance constrained optimization problems for linear and nonlinear models, here we solve a DC (linear) OPF under uncertain penetration of wind power. The wind power is described by the Beta PDF [18,19]. The input data for the optimization is given in Table I and Fig.…”

Section: Chance Constrained Opfmentioning

confidence: 99%

Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach

Mohagheghi

Geletu

Bremser

et al. 2018

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Syst

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In recent years, there has been a huge trend to penetrate renewable energy sources into energy networks. However, these sources introduce uncertain power generation depending on environmental conditions. Therefore, finding 'optimal' and 'feasible' operation strategies is still a big challenge for network operators and thus, an appropriate optimization approach is of utmost importance. In this paper, we formulate the optimal power flow (OPF) with uncertainties as a chance constrained optimization problem. Since uncertainties in the network are usually 'non-Gaussian' distributed random variables, the chance constraints cannot be directly converted to deterministic constraints. Therefore, in this paper we use the recently-developed approach of inner-outer approximation to approximately solve the chance constrained OPF. The effectiveness of the approach is shown using DC OPF incorporating uncertain non-Gaussian distributed wind power. Keywords-Optimal power flow (OPF); chance constrained programming; inner-outer approximation approach; uncertain wind power generation; non-Gaussian distribution. NOMENCLATURE , ijIndices for buses. BSusceptance. () IsPiecewise continuous function.f Objective function.

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Real Power loss reduction by hybrid pan troglodytes optimization: extreme learning machine based augmented sine: cosine algorithms

Lenin

2021

Int J Syst Assur Eng Manag

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Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm

Cited by 57 publications

References 53 publications

A Survey of Real-Time Optimal Power Flow

A Survey of Real-Time Optimal Power Flow

Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach

Real Power loss reduction by hybrid pan troglodytes optimization: extreme learning machine based augmented sine: cosine algorithms

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