Aouss Gabash

In response to the pressing global challenges around climate change and the imperative of transitioning the energy market towards sustainability, this paper presents a comprehensive review starting from the late 18th century. The study examines the pivotal role of Transmission System Operators (TSOs) and Distribution System Operators (DSOs) in shaping the evolving energy landscape, with a specific emphasis on the C+++ Framework. This framework emphasizes coordination, cooperation, and collaboration between TSOs and DSOs to achieve sustainable energy systems through the integration of renewable energy technologies, storage systems, and efficient energy demand management. In addition, the review provides a historical overview of global warming from 1800 to the present, highlighting key events and developments related to greenhouse gas emissions. Furthermore, the paper delves into the significance of international agreements such as the Paris Agreement and the importance of reducing greenhouse gas emissions for a sustainable future. Recognizing the vital role of the C+++ Framework, the paper concludes with a discussion of future hybrid sustainable technologies incorporating various storage and efficient lighting technologies that can optimize energy management and reduce carbon emissions. This research aims to contribute valuable insights to inform energy policy and decision-making processes for a reliable, efficient, and sustainable energy delivery system.

This paper provides an overview of power electronics and its applications in various fields, emphasizing power conditioning and minimizing losses for high energy efficiency. It discusses the distinction between unidirectional and bidirectional converters and their applications in power systems. The significance of unidirectional and bidirectional power flow in different scenarios is explored. The importance of battery storage systems (BSSs) for grid stabilization, frequency regulation, and renewable energy integration is highlighted. The paper focuses on flexible active-reactive optimal power flow (A-R-OPF) frameworks in battery storage and power electronic systems, reviewing existing research, identifying gaps, and offering new perspectives. It addresses the challenges and potential of grid-scale energy storage for reliable and cost-effective power systems with high renewable energy penetration. The need for energy curtailment, demand response, and smart grid implementation is discussed. The paper emphasizes comprehensive coordination, new power lines, European collaboration, and smart grid implementation to meet the dynamic needs of Europe’s power grids.

Passive loads in power systems are well-known, but sustainable loads (SL's), due to distributed renewable energy sources and possible reverse active power flow (RAPF) from a low voltage level through a medium-voltage (MV) cable towards a high voltage level, introduce new challenges. In this paper, a simple model for the shortest MV cable required to supply SL's is introduced. The concept of unconstrained and constrained domains to design the shortest cable, at which the limiting constraint for the thermal capacity and the limiting constraint for the voltage drop are equal, is proposed. In this case, the cable can be fully used, that is, using the whole feasible region at the shortest length. A sample 2-node/1-cable network shows that the power-voltage relationship can theoretically be nonlinear within the unconstrained domains, but it can be well linearized within the proposed constrained domains. Another 3-node/2-cable network is developed to show the effects when using different cables types (copper and aluminium) and power factors. It is shown that the developed model can simply be used for estimating the active power domains of new SL's while considering the shortest length of MV cables.

In this paper, a multi-phase multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework is developed to optimally deal with spontaneous changes in wind power in distribution networks (DNs) with battery storage systems (BSSs). The most challenging issue hereby is that a large-scale ‘dynamic’ (i.e., with differential/difference equations rather than only algebraic equations) mixed-integer nonlinear programming (MINLP) problem has to be solved in real time. Moreover, considering the active-reactive power capabilities of BSSs with flexible operation strategies, as well as minimizing the expended life costs of BSSs further increases the complexity of the problem. To solve this problem, in the first phase, we implement simultaneous optimization of a huge number of mixed-integer decision variables to compute optimal operations of BSSs on a day-to-day basis. In the second phase, based on the forecasted wind power values for short prediction horizons, wind power scenarios are generated to describe uncertain wind power with non-Gaussian distribution. Then, MINLP AR-OPF problems corresponding to the scenarios are solved and reconciled in advance of each prediction horizon. In the third phase, based on the measured actual values of wind power, one of the solutions is selected, modified, and realized to the network for very short intervals. The applicability of the proposed RT-DAR-OPF is demonstrated using a medium-voltage DN.

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.

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.

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.

This is the second part of a companion paper on variable reverse power flow (VRPF) in active distribution networks (ADNs) with wind stations (WSs). Here, we propose an electricity market model considering agreements between the operator of a medium-voltage active distribution network (MV-ADN) and the operator of a high-voltage transmission network (HV-TN) under different scenarios. The proposed model takes, simultaneously, active and reactive energy prices into consideration. The results from applying this model on a real MV-ADN reveal many interesting facts. For instance, we demonstrate that the reactive power capability of WSs will be never utilized during days with zero wind power and varying limits on power factors (PFs). In contrast, more than 10% of the costs of active energy losses, 15% of the costs of reactive energy losses, and 100% of the costs of reactive energy imported from the HV-TN, respectively, can be reduced if WSs are operated as capacitor banks with no limits on PFs. It is also found that allocating WSs near possible exporting points at the HV-TN can significantly reduce wind power curtailments if the operator of the HV-TN accepts unlimited amount of reverse energy from the MV-ADN. Furthermore, the relationships between the size of WSs, VRPF and demand level are also uncovered based on active-reactive optimal power flow (A-R-OPF).

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.