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Optimal power flow (OPF) represents one of the most important issues in the electrical power system for energy management, planning, and operation via finding optimal control variables with satisfying the equality and inequality constraints. Several optimization methods have been proposed to solve OPF problems, but there is still a need to achieve optimum performance. A Slime Mould Algorithm (SMA) is one of the new stochastic optimization methods inspired by the behaviour of the oscillation mode of slime mould in nature. The proposed algorithm is characterized as easy, simple, efficient, avoiding stagnation in the local optima and moving toward the optimal solution. Different frameworks have been applied to achieve single and conflicting multi-objective functions simultaneously (Bi, Tri, Quad, and Quinta objective functions) for solving OPF problems. These objective functions are total fuel cost of generation units, real power loss on transmission lines, total emission issued by fossil-fuelled thermal units, voltage deviation at load bus, and voltage stability index of the whole system. The proposed algorithm SMA has been developed by incorporating it with Pareto concept optimization to generate a new approach, named the Multi-Objective Slime Mould Algorithm (MOSMS), to solve multi-objective optimal power flow (MOOPF) problems. Fuzzy set theory and crowding distance are the proposed strategies to obtain the best compromise solution and rank and reduce a set of non-dominated solutions, respectively. To investigate the performance of the proposed algorithm, two standard IEEE test systems (IEEE 30 bus IEEE 57 bus systems) and a practical system (Iraqi Super Grid High Voltage 400 kV) were tested with 29 case studies based on MATLAB software. The optimal results obtained by the proposed approach (SMA) were compared with other algorithms mentioned in the literature. These results confirm the ability of SMA to provide better solutions to achieve the optimal control variables.

The Sine–Cosine algorithm (SCA) is efficient but faces challenges in exploitative abilities, slow convergence, and exploration–exploitation balance. This study proposes a novel optimization method, the learning-based sine–cosine algorithm (L-SCA), to solve the optimal power flow (OPF) problem. The basic SCA has been modified with a learning phase operator inspired by TLBO. The SCA handles global exploration, while the learner phase of teaching–learning based optimization (TLBO) offers strong local search capabilities, which can be utilized to enhance the solution neighborhood space provided by the SCA technique. The L-SCA and original SCA algorithms address OPF in IEEE 57-bus, Algerian 59-bus, and IEEE 118-bus power systems, considering twelve cases with a focus on cost savings, voltage stability, voltage profile, emissions, and power losses. The comparative study shows that the proposed L-SCA consistently outperforms standard SCA and other reported methods in all cases for varied-scale standard test systems as well as for a practical power system, within reasonable execution times. For instance, L-SCA in the Algerian 59-bus system cut fuel costs by around 13.13% compared to initial case, equating to annual savings of $2.2 million, while in the IEEE-118 bus system, power loss is significantly reduced to 17.881 MW, marking an 86.5% reduction compared to the base case.

Developing a low-carbon power system is critical and fundamental to cope with the challenges of global warming, in which the carbon capture and storage (CCS) technology will play a key role. In this study, the characteristics of energy flow and operation of carbon capture plants (CCPs) are clarified, while the mutual constraint between total generation output of CCPs and operation power consumption of carbon capture system is analysed. Then a generation output model and the optimal dispatch principle of CCPs is established, which can identify how the amount of carbon captured can represent a premium payment that can offset the increase in costs caused by the reduction on power output due to the CCS. On this basis, what with the low-carbon economy factors, a economic power dispatch model under low-carbon economy with CCPs considered is proposed. With the generation fuel cost and carbon emission cost incorporated in the objective function, the model proposed can effectively evaluate the power dispatch problem under low-carbon economy. Studies of the economic power dispatch of the 3-unit, 26-unit and 54-unit test systems show that the model proposed is effective and practical.

Economic dispatch (ED) is an optimisation tool that is used to allocate active load demands to the generating units through optimising the fuel generation cost function subject to the different operational constraints. The high non-linearity of the power system imposes mathematical challenges in formulating the generation cost function models, which makes the ED problem hard to solve. This study introduces two ideas to solve issues related to the ED problem. First, a dynamic formulation technique is developed to optimally allocate the change in the total active load demand to the generating units. This technique is shown to be insensitive to the optimality of the initial active load distribution unlike the base point and participation factor method. Moreover, it guarantees an optimal distribution among the generating units due the change in the active load demand. Second, a novel approximation of the non-convex generation cost function is developed to solve non-convex ED problem with the transmission losses. This approximation enables the use of gradient and Newton techniques to solve the non-convex ED problem with valve point loading effect and transmission losses in an analytic approach. This approximation is compared with some heuristic optimisation techniques.

Wind driven optimization (WDO) algorithm is a best optimization method based on atmospherically motion, global optimization nature inspired method. The method is based on population iterative analytical global optimization for multifaceted and multi prototype in the search domain for constraints to implement. In this paper, WDO algorithm is accustomed to find optimal power flow solution. To find the efficacy of the technique, it is applied to IEEE 30 bus systems to find fuel cost for generation of power as a main objective. Obtained results were compared with other techniques shows the better solution for optimal power flow problem.

Net anthropogenic emissions of carbon dioxide (CO 2 ) must approach zero by mid-century (2050) in order to stabilize the global mean temperature at the level targeted by international efforts 1 – 5 . Yet continued expansion of fossil-fuel-burning energy infrastructure implies already ‘committed’ future CO 2 emissions 6 – 13 . Here we use detailed datasets of existing fossil-fuel energy infrastructure in 2018 to estimate regional and sectoral patterns of committed CO 2 emissions, the sensitivity of such emissions to assumed operating lifetimes and schedules, and the economic value of the associated infrastructure. We estimate that, if operated as historically, existing infrastructure will cumulatively emit about 658 gigatonnes of CO 2 (with a range of 226 to 1,479 gigatonnes CO 2 , depending on the lifetimes and utilization rates assumed). More than half of these emissions are predicted to come from the electricity sector; infrastructure in China, the USA and the 28 member states of the European Union represents approximately 41 per cent, 9 per cent and 7 per cent of the total, respectively. If built, proposed power plants (planned, permitted or under construction) would emit roughly an extra 188 (range 37–427) gigatonnes CO 2 . Committed emissions from existing and proposed energy infrastructure (about 846 gigatonnes CO 2 ) thus represent more than the entire carbon budget that remains if mean warming is to be limited to 1.5 degrees Celsius (°C) with a probability of 66 to 50 per cent (420–580 gigatonnes CO 2 ) 5 , and perhaps two-thirds of the remaining carbon budget if mean warming is to be limited to less than 2 °C (1,170–1,500 gigatonnes CO 2 ) 5 . The remaining carbon budget estimates are varied and nuanced 14 , 15 , and depend on the climate target and the availability of large-scale negative emissions 16 . Nevertheless, our estimates suggest that little or no new CO 2 -emitting infrastructure can be commissioned, and that existing infrastructure may need to be retired early (or be retrofitted with carbon capture and storage technology) in order to meet the Paris Agreement climate goals 17 . Given the asset value per tonne of committed emissions, we suggest that the most cost-effective premature infrastructure retirements will be in the electricity and industry sectors, if non-emitting alternatives are available and affordable 4 , 18 . A comprehensive assessment of ‘committed’ carbon dioxide emissions—from existing and proposed fossil-fuel-based infrastructure—finds that these emissions may exceed the level required to keep global warming within 1.5 degrees Celsius.

Most countries, including the United States, have an array of greenhouse gas mitigation policies, which provide subsidies or restrictions typically aimed at specific technologies or sectors. Such climate policies range from automobile fuel economy standards, to gasoline taxes, to mandating that a certain amount of electricity in a state comes from renewables, to subsidizing solar and wind electrical generation, to mandates requiring the blending of biofuels into the surface transportation fuel supply, to supply-side restrictions on fossil fuel extraction. This paper reviews the costs of various technologies and actions aimed at reducing greenhouse gas emissions. Our aim is twofold. First, we seek to provide an up-to-date summary of costs of actions that can be taken now using currently available technology. These costs focus on expenditures and emissions reductions over the life of a project compared to some business-as-usual benchmark—for example, replacing coal-fired electricity generation with wind, or weatherizing a home. We refer to these costs as static because they are costs over the life of a specific project undertaken now, and they ignore spillovers. Our second aim is to distinguish between dynamic and static costs and to argue that some actions taken today with seemingly high static costs can have low dynamic costs, and vice versa. We make this argument at a general level and through two case studies, of solar panels and of electric vehicles, technologies whose costs have fallen sharply. Under the right circumstances, dynamic effects will offer a justification for policies that have high costs according to a myopic calculation.

Converting all U.S. onroad vehicles to hydrogen fuel-cell vehicles (HFCVs) may improve air quality, health, and climate significantly, whether the hydrogen is produced by steam reforming of natural gas, wind electrolysis, or coal gasification. Most benefits would result from eliminating current vehicle exhaust. Wind and natural gas HFCVs offer the greatest potential health benefits and could save 3700 to 6400 U.S. lives annually. Wind HFCVs should benefit climate most. An all-HFCV fleet would hardly affect tropospheric water vapor concentrations. Conversion to coal HFCVs may improve health but would damage climate more than fossil/electric hybrids. The real cost of hydrogen from wind electrolysis may be below that of U.S. gasoline.

The emission of CO 2 from industrial (24%) and different anthropogenic activities, like transportation (27%), electricity production (25%), and agriculture (11%), can lead to global warming, which in the long term can trigger substantial climate changes. In this regard, CO 2 sequestration and wastewater treatment in tandem with bioenergy production through photosynthetic microbial fuel cell (PMFC) is an economical and sustainable intervention to address the problem of global warming and elevating energy demands. Therefore, this review focuses on the application of different PMFC as a bio-refinery approach to produce biofuels and power generation accompanied with the holistic treatment of wastewater. Moreover, CO 2 bio-fixation and electron transfer mechanism of different photosynthetic microbiota, and factors affecting the performance of PMFC with technical feasibility and drawbacks are also elucidated in this review. Also, low-cost approaches such as utilization of bio-membrane like coconut shell, microbial growth enhancement by extracellular cell signalling mechanisms, and exploitation of genetically engineered strain towards the commercialization of PMFC are highlighted. Thus, the present review intends to guide the budding researchers in developing more cost-effective and sustainable PMFCs, which could lead towards the commercialization of this inventive technology.

The treatment of wastewater is an expensive and energy-extensive practice that not only ensures the power generation requirements to sustain the current energy demands of an increasing human population but also aids in the subsequent removal of enormous quantities of wastewater that need to be treated within the environment. Thus, renewable energy source-based wastewater treatment is one of the recently developing techniques to overcome power generation and environmental contamination issues. In wastewater treatment, microbial fuel cell (MFC) technology has demonstrated a promising potential to evolve as a sustainable approach, with the simultaneous recovery of energy and nutrients to produce bioelectricity that harnesses the ability of electrogenic microbes to oxidize organic contaminants present in wastewater. Since traditional wastewater treatment has various limitations, sustainable implementations of MFCs might be a feasible option in wastewater treatment, green electricity production, biohydrogen synthesis, carbon sequestration, and environmentally sustainable sewage treatment. In MFCs, the electrochemical treatment mechanism is based on anodic oxidation and cathodic reduction reactions, which have been considerably improved by the last few decades of study. However, electricity production by MFCs remains a substantial problem for practical implementations owing to the difficulty in balancing yield with overall system upscaling. This review discusses the developments in MFC technologies, including improvements to their structural architecture, integration with different novel biocatalysts and biocathode, anode, and cathode materials, various microbial community interactions and substrates to be used, and the removal of contaminants. Furthermore, it focuses on providing critical insights and analyzing various types, processes, applications, challenges, and futuristic aspects of wastewater treatment-related MFCs and thus sustainable resource recovery. With appropriate planning and further studies, we look forward to the industrialization of MFCs in the near future, with the idea that this will lead to greener fuels and a cleaner environment for all of mankind.