A hierarchical optimization approach to maximize hosting capacity for electric vehicles and renewable energy sources through demand response and transmission expansion planning.

Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs' hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4-23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Integrated transmission expansion planning incorporating fault current limiting devices and thyristor-controlled series compensation using meta-heuristic optimization techniques.

Transmission expansion planning (TEP) is a vital process of ensuring power systems' reliable and efficient operation. The optimization of TEP is a complex challenge, necessitating the application of mathematical programming techniques and meta-heuristics. However, selecting the right optimization algorithm is crucial, as each algorithm has its strengths and limitations. Therefore, testing new optimization algorithms is essential to enhance the toolbox of methods. This paper presents a comprehensive study on the application of ten recent meta-heuristic algorithms for solving the TEP problem across three distinct power networks varying in scale. The ten meta-heuristic algorithms considered in this study include Sinh Cosh Optimizer, Walrus Optimizer, Snow Geese Algorithm, Triangulation Topology Aggregation Optimizer, Electric Eel Foraging Optimization, Kepler Optimization Algorithm (KOA), Dung Beetle Optimizer, Sea-Horse Optimizer, Special Relativity Search, and White Shark Optimizer (WSO). Three TEP models incorporating fault current limiters and thyristor-controlled series compensation devices are utilized to evaluate the performance of the meta-heuristic algorithms, each representing a different scale and complexity level. Factors such as convergence speed, solution quality, and scalability are considered in evaluating the algorithms' performance. The results demonstrated that KOA achieved the best performance across all tested systems in terms of solution quality. KOA's average value was 6.8% lower than the second-best algorithm in some case studies. Additionally, the results indicated that WSO required approximately 2-3 times less time than the other algorithms. However, despite WSO's rapid convergence, its average solution value was comparatively higher than that of some other algorithms. In TEP, prioritizing solution quality is paramount over algorithm speed.

Enhancement of neural regeneration after spinal cord injury using muscle graft in experimental dogs.

AIMS: Spinal cord injuries (SCIs) can cause severe disability or death. The principal treatments for traumatic SCI include surgical stabilization and decompression. Using muscle as a scaffold is a new approach. The aim of this work is to evaluate the clinical efficacy of muscle graft as a scaffold for the growing axons organizing their growth, preventing gliosis in the damaged area and enhancing neural recovery in canine model of traumatic spinal cord injury. METHODS: 14 dogs were divided into group I (Control group) 4 control dogs subjected to Sham operation, group II (Trauma control group) 5 dogs subjected to dorsal laminectomy with excision of 1 cm segment of the spinal cord and group III (Muscle graft group) 5 dogs subjected to dorsal laminectomy then muscle graft was taken from the longissimus thoraces and inserted into the spinal cord gap. The animals of all groups were euthanatized after 8 weeks. Olby and modified Tarlov scores were used to clinically evaluate the therapeutic effects. Spinal cord specimens were subjected to histological, morphometric and statistical studies. RESULTS: Olby and modified Tarlov scores revealed significant clinical improvement in the muscle graft group. Histological sections showed overgrowth of axons on the muscle graft and the sections started to organize as central gray matter and peripheral white matter. CD44 & CD105 stains were positive for endogenous stem cells. CONCLUSIONS: This study proved the clinical efficacy of muscle grafting as a tool for induction of neuroregeneration after traumatic spinal cord injury.

Prolonged maturation culture favors a reduction in the tumorigenicity and the dopaminergic function of human ESC-derived neural cells in a primate model of Parkinson's disease.

For the safe clinical application of embryonic stem cells (ESCs) for neurological diseases, it is critical to evaluate the tumorigenicity and function of human ESC (hESC)-derived neural cells in primates. We have herein, for the first time, compared the growth and function of hESC-derived cells with different stages of neural differentiation implanted in the brains of primate models of Parkinson's disease. We herein show that residual undifferentiated cells expressing ESC markers present in the cell preparation can induce tumor formation in the monkey brain. In contrast, a cell preparation matured by 42-day culture with brain-derived neurotrophic factor/glial cell line-derived neurotrophic factor (BDNF/GDNF) treatment did not form tumors and survived as primarily dopaminergic (DA) neurons. In addition, the monkeys with such grafts showed behavioral improvement for at least 12 months. These results support the idea that hESCs, if appropriately matured, can serve as a source for DA neurons without forming any tumors in a primate brain.

Matrigel supports survival and neuronal differentiation of grafted embryonic stem cell-derived neural precursor cells.

Cell replacement therapy holds great promise as a means of treating neurological disorders, including Parkinson's disease. However, one of the major obstacles to the success of this treatment is the low survival rate of grafted cells, which probably results from mechanical damage, acute inflammation, and immunological rejection. To overcome this problem, we investigated the effect of different types of extracellular matrix (ECM) on the survival and differentiation of embryonic stem (ES) cell-derived neural precursor cells (NPCs). We tested materials from natural sources, including collagen, ornithine/laminin, and growth factor-reduced Matrigel (gfrMG), as well as the synthetic biomaterial PuraMatrix, which consists of self-assembling polypeptides. GfrMG efficiently supported cell survival, migration, and neurite outgrowth in vitro and promoted proliferation of grafted cells in vivo, resulting in larger graft volume and an increase in the number of TH-positive dopaminergic neurons in the graft. GfrMG did not induce dopaminergic differentiation directly; rather, it reduced the invasion of pan-leukocytic CD45-positive cells into the graft. Insofar as the inflammatory or immune response in the host brain inhibits neuronal differentiation of grafted NPCs, gfrMG may increase the number of TH-positive cells by suppressing this effect. Thus, gfrMG appears to provide a suitable scaffold that supports survival and differentiation of NPCs. However, because it is derived from mouse sarcomas, a human-derived matrix or synthetic biomaterial must be developed for clinical applications.