Due to environmental and fuel cost concerns more and more wind- and solar-based generation units are embedded in distribution networks (DNs). However, a part of such an embedded generation would be curtailed due to system constraints and variations of the energy penetration. This part of energy can be recovered by introducing energy storage systems (ESSs) and an optimal dispatch of both active and reactive powers. Therefore, we propose a combined problem formulation for active-reactive optimal power flow (A-R-OPF) in DNs with embedded wind generation and battery storage. The solution provides an optimal operation strategy which ensures the feasibility and enhances the profit significantly. Results of a 41-bus distribution network are presented. It can be demonstrated that more than 12% of energy losses and a large amount of reactive energy to be imported from the transmission network (TN) can be reduced using the proposed approach in comparison to the operation strategy where only active OPF is considered.

Active-reactive optimal power flow (A-R-OPF) in distribution networks (DNs) with embedded wind generation and battery storage systems (BSSs) was proposed recently. The solution was based on a fixed length in the charge and discharge cycle for daily operations of BSSs. This can lead to a low profit when the profiles of renewable generators, demand and prices vary from day to day. In this paper, we extend the A-R-OPF method by developing a flexible battery management system (FBMS). This is accomplished by optimizing the lengths (hours) of charge and discharge periods of BSSs for each day, leading to a complex mixed-integer nonlinear program (MINLP). An iterative two-stage framework is proposed to address this problem. In the upper stage, the integer variables (i.e., hours of charge and discharge periods) are optimized and delivered to the lower stage. In the lower stage the A-R-OPF problem is solved by a NLP solver and the resulting objective function value is brought to the upper stage for the next iteration. It can be shown through a case study that a flexible operation strategy will achieve a considerably higher profit than by a fixed operation strategy of BSSs.

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.

Developing a suitable framework for real-time optimal power flow (RT-OPF) is of utmost importance for ensuring both optimality and feasibility in the operation of energy distribution networks (DNs) under intermittent wind energy penetration. The most challenging issue thereby is that a large-scale complex optimization problem has to be solved in real-time. Online simultaneous optimization of the wind power curtailments of wind stations and the discrete reference values of the slack bus voltage which leads to a mixed-integer nonlinear programming (MINLP) problem, in addition to considering variable reverse power flow, make the optimization problem even much more complicated. To address these difficulties, a two-phase solution approach to RT-OPF is proposed in this paper. In the prediction phase, a number of MINLP OPF problems corresponding to the most probable scenarios of the wind energy penetration in the prediction horizon, by taking its forecasted value and stochastic distribution into account, are solved in parallel. The solution provides a lookup table for optional control strategies for the current prediction horizon which is further divided into a certain number of short time intervals. In the realization phase, one of the control strategies is selected from the lookup table based on the actual wind power and realized to the grid in the current time interval, which will proceed from one interval to the next, till the end of the current prediction horizon. Then, the prediction phase for the next prediction horizon will be activated. A 41-bus medium-voltage DN is taken as a case study to demonstrate the proposed RT-OPF approach.

It has recently been shown that using battery storage systems (BSSs) to provide reactive power provision in a medium-voltage (MV) active distribution network (ADN) with embedded wind stations (WSs) can lead to a huge amount of reverse power to an upstream transmission network (TN). However, unity power factors (PFs) of WSs were assumed in those studies to analyze the potential of BSSs. Therefore, in this paper (Part-I), we aim to further explore the pure reactive power potential of WSs (i.e., without BSSs) by investigating the issue of variable reverse power flow under different limits on PFs in an electricity market model. The main contributions of this work are summarized as follows: (1) Introducing the reactive power capability of WSs in the optimization model of the active-reactive optimal power flow (A-R-OPF) and highlighting the benefits/impacts under different limits on PFs. (2) Investigating the impacts of different agreements for variable reverse power flow on the operation of an ADN under different demand scenarios. (3) Derivation of the function of reactive energy losses in the grid with an equivalent-π circuit and comparing its value with active energy losses. (4) Balancing the energy curtailment of wind generation, active-reactive energy losses in the grid and active-reactive energy import-export by a meter-based method. In Part-II, the potential of the developed model is studied through analyzing an electricity market model and a 41-bus network with different locations of WSs.