Decomposed Phase Analysis using Convex Inner Approximations: a Methodology for DER Hosting Capacity in Distribution Systems

This paper uses convex inner approximations (CIA) of the AC power flow to tackle the optimization problem of quantifying a three-phase distribution feeder's capacity to host distributed energy resources (DERs). This is often connoted hosting capacity (HC), but herein we consider separative bounds for each node on positive and negative DER injections, which ensures that injections within these nodal limits satisfy feeder voltage and current limits and across nodes sum up to the feeder HC. The methodology decomposes a three-phase feeder into separate phases and applies CIA-based techniques to each phase. An analysis is developed to determine the technical condition under which this per-phase approach can still guarantee three-phase constraints. New approaches are then presented that modify the per-phase optimization problems to overcome conservativeness inherent to CIA methods and increase HC, including selectively modifying the per-phase impedances and iteratively relaxing per-phase voltage bounds. Discussion is included on trade-offs and feasibility. To validate the methodology simulation-based analysis is conducted with the IEEE 37-node test feeder and a real 534-node unbalanced radial distribution feeder.Comment: 9 pages, submitted to PSCC 2024 conferenc

Enhancing grid reliability with coordination and control of distributed energy resources

The growing utilization of renewable energy resources (RES) within power systems has brought about new challenges due to the inherent uncertainty associated with RES, which makes it challenging to accurately forecast available generation. Further- more, the replacement of synchronous machines with inverter-based RES results in a reduction of power system inertia, complicating the task of maintaining a balance between generation and consumption. In this dissertation, coordinating Distributed Energy Resources (DER) is presented as a viable solution to these challenges.DERs have the potential to offer different ancillary services such as fast frequency response (FFR) when efficiently coordinated. However, the practical implementation of such services demands both effective local sensing and control at the device level and the ability to precisely estimate and predict the availability of synthetic damping from a fleet in real time. Additionally, the inherent trade-off between a fleet being available for fast frequency response while providing other ancillary services needs to be characterized. This dissertation introduces a fully decentralized, packet-based controller for a diverse range of flexible loads. This controller dynamically prioritizes and interrupts DERs to generate synthetic damping suitable for primary frequency control. Moreover, the packet-based control methodology is demonstrated to accu- rately assess the real-time availability of synthetic damping. Furthermore, spectral analysis of historical frequency regulation data is employed to establish a probabilis- tic bound on the expected synthetic damping available for primary frequency control from a fleet and the trade-off of concurrently offering secondary frequency control. It is noteworthy that coordinating a large number of DERs can potentially result in grid constraint violations. To tackle this challenge, this dissertation employs con- vex inner approximations (CIA) of the AC power flow to address the optimization problem of quantifying the capacity of a three-phase distribution feeder to accommo- date DERs. This capacity is often referred to as hosting capacity (HC). However, in this work, we consider separate limits for positive and negative DER injections at each node, ensuring that injections within these nodal limits adhere to feeder voltage and current constraints. The methodology dissects a three-phase feeder into individual phases and applies CIA-based techniques to each phase. Additionally, new approaches are introduced to modify the per-phase optimization problems to mitigate the inherent conservativeness associated with CIA methods and enhance HC. This includes selectively adjusting the per-phase impedances and proposing an iterative relaxation method for per-phase voltage bounds

A reliability based model for generation and transmission expansion planning

This paper presents a mixed integer linear multi-objective model based on information gap decision theory (IGDT), which is used to solve coordinated multiyear generation and transmission expansion planning (G&TEP) problems. The model maximizes the robustness of each uncertain parameter while a maximum allowable budget range is set. Fuel transportation price is considered. The results provide a numerical tool for system planner to help him adjust the appropriate level of robustness for each uncertain parameter of the problem. Extra limits on security, gaseous emission, and fuel availability are considered. A multi-objective method called the ε-constraint method is used here to maximize the robust region of load and investment costs simultaneously. The model is implemented on a six-bus Garver test system and 24 bus IEEE test system. The numerical results show the good performance of the model

Numerical investigation of the overall stiffness of carbon nanotube-based composite materials

In this study, a finite element model of a representative volume element that contains a hollow and filled single-walled Carbon nanotube (SWCNT) in two case studies was generated. It was assumed that the nanocomposites have geometric periodicity with respect to local length scale and the elastic properties can be represented by those of the representative volume element (RVE). Elastic properties in agreement with existing literature values for the Carbon nanotube and the matrix were assigned. Then for the two case studies, the tensile test was simulated to find the effect of the geometry, i.e. the volume fraction of matrix and SWCNT's properties variation, on the effective Young's modulus of the structure. In another approach, by applying perpendicular loading to the tube direction, the effect of matrix volume fraction on the transverse Young's modulus was studied. The investigations showed that for both RVEs with filled SWCNT and hollow SWCNT, the effective Young's modulus of the structure decreases approximately linear as the matrix volume fraction increases. The value of Young's modulus of the RVE with a filled Carbon nanotube was obtained to be higher than the RVE with the hollow Carbon nanotube. In addition, by increasing the tube diameter, the effective Young's modulus of the structure increases and the transverse Young's modulus decreases approximately linear for filled tubes but this parameter remains rather constant in the case of the hollow tube by increasing the matrix volume fraction

Multiobjective robust power system expansion planning considering generation units retirement

This paper presents a mixed-integer linear robust multiobjective model for the expansion planning of an electric power system. An information-gap decision theory-based framework is proposed to take into account the uncertainties in electrical demand and new power system elements prices. The model is intended to increase the power system resistance against the uncertainties caused by forecast errors. The normal boundary intersection method is used to obtain the Pareto front of the multiobjective problem. Since the planning problem is a large-scale problem, the model is kept linear using the Big M linearization technique that is able to significantly decrease the computational burden. The fuel transportation and availability constraints are taken into account. The model also enables the system planner to build new fuel transportation routes whenever it is necessary. The generating units' retirement is also incorporated into the model, and the simulation results are showed to the advantages of incorporating units' retirement in the power system expansion planning model instead of considering it separately. The proposed multiobjective method is applied to the Garver 6-bus, IEEE 24-bus, and IEEE 118-bus test systems, and the results are compared with the well-known epsilon-constraint method

Clinical manifestations and laboratory findings and mortality rate of kidney transplant recipients infected with COVID-19

Introduction: The emergence of a novel coronavirus (COVID-19) in late December 2019 and its rapid global spread has led World Health Organization (WHO) to introduce it as a very dangerous pandemic. People with underlying disease and a history of organ transplantation are at higher risk for COVID-19 disease compared with healthy people. Objectives: In the present study, clinical and laboratory manifestations in the patients with COVID-19 with a history of kidney transplantation has been investigated. Patients and Methods: This study conducted on 103 COVID-19-positive kidney transplant patients as a descriptive epidemiological study. Clinical and laboratory symptoms of hospitalized renal transplanted patients have been assessed. Statistical analysis of the collected data conducted using SPSS (Statistical Package for Social Sciences, version 22). Results: This study consisted of 103 COVID-19 patients with a history of kidney transplant, of which 64 males (62.1%) and 39 females (37.9%) with an average age of 48.5 ± 13.1 years. The most common clinical manifestations were headache (67%) and shortness of breath (66%). Elevated lactate dehydrogenase (LDH) levels, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) has been observed in 100%, 98.1% and 93.2% of patients, respectively. In 12.6% and 41.7% of patients, the degree of lung involvement was above 75% and 50%-75%, respectively. Moreover, 79.6% of patients has been discharged after improved, while 20.4% of patients died. Conclusion: We found, kidney transplantation may increase COVID-19-related mortality when compared to COVID-19-related mortality in the general population

Robust Switch Selection in Radial Distribution Systems Using Combinatorial Optimization

Selecting the best type of equipment among available switches with different prices and reliability levels is a significant challenge in distribution system planning. In this paper, the optimal type of switches in a radial distribution system is selected by considering the total cost and reliability criterion and using the weighted augmented epsilon constraint method and combinatorial optimization. A new index is calculated to assess the robustness of each Pareto solution. Moreover, for each failure, repair time is considered based on historical data. Monte Carlo simulations are used to consider the switch failure uncertainty and fault repair time uncertainty in the model. The proposed framework is applied to an RTBS Bus-2 test system. Furthermore, the model is also applied to an industrial system to verify the proposed method's excellent performance in larger practical engineering problems