Dataset Information

Kepler Algorithm for Large-Scale Systems of Economic Dispatch with Heat Optimization.

ABSTRACT: Combined Heat and Power Units Economic Dispatch (CHPUED) is a challenging non-convex optimization challenge in the power system that aims at decreasing the production cost by scheduling the heat and power generation outputs to dedicated units. In this article, a Kepler optimization algorithm (KOA) is designed and employed to handle the CHPUED issue under valve points impacts in large-scale systems. The proposed KOA is used to forecast the position and motion of planets at any given time based on Kepler's principles of planetary motion. The large 48-unit, 96-unit, and 192-unit systems are considered in this study to manifest the superiority of the developed KOA, which reduces the fuel costs to 116,650.0870 USD/h, 234,285.2584 USD/h, and 487,145.2000 USD/h, respectively. Moreover, the dwarf mongoose optimization algorithm (DMOA), the energy valley optimizer (EVO), gray wolf optimization (GWO), and particle swarm optimization (PSO) are studied in this article in a comparative manner with the KOA when considering the 192-unit test system. For this large-scale system, the presented KOA successfully achieves improvements of 19.43%, 17.49%, 39.19%, and 62.83% compared to the DMOA, the EVO, GWO, and PSO, respectively. Furthermore, a feasibility study is conducted for the 192-unit test system, which demonstrates the superiority and robustness of the proposed KOA in obtaining all operating points between the boundaries without any violations.

SUBMITTER: Hakmi SH

PROVIDER: S-EPMC10741829 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Kepler Algorithm for Large-Scale Systems of Economic Dispatch with Heat Optimization.

Hakmi Sultan Hassan SH Shaheen Abdullah M AM Alnami Hashim H Moustafa Ghareeb G Ginidi Ahmed A

Biomimetics (Basel, Switzerland) 20231214 8

Combined Heat and Power Units Economic Dispatch (CHPUED) is a challenging non-convex optimization challenge in the power system that aims at decreasing the production cost by scheduling the heat and power generation outputs to dedicated units. In this article, a Kepler optimization algorithm (KOA) is designed and employed to handle the CHPUED issue under valve points impacts in large-scale systems. The proposed KOA is used to forecast the position and motion of planets at any given time based on ...[more]

PMID: 38132548

Similar Datasets

Project description:The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxic air pollutant, has been causing harm to earth habitats. In this new situation, insufficiency of energy supplies, rising power generating costs, high capital cost of renewable energy equipment, environmental concerns of wind power turbines, and ever-increasing demand for electrical energy need efficient economic dispatch. The objective function in practical economic dispatch (ED) problem is nonlinear and non-convex, with restricted equality and inequality constraints, and traditional optimization methods are incapable of resolving such non-convex problems. Over the recent decade, meta-heuristic optimization approaches have acquired enormous reputation for obtaining a solution strategy for such types of ED issues. In this paper, a novel soft computing optimization technique is proposed for solving the dynamic economic dispatch problem (DEDP) of complex non-convex machines with several constraints. Our premeditated framework employs the genetic algorithm (GA) as an initial optimizer and sequential quadratic programming (SQP) for the fine tuning of the pre-optimized run of GA. The simulation analysis of GA-SQP performs well by acquiring less computational cost and finite time of execution, while providing optimal generation of powers according to the targeted power demand and load, whereas subject to valve point loading effect (VPLE) and multiple fueling option (MFO) constraints. The adequacy of the presented strategy concerning accuracy, convergence as well as reliability is verified by employing it on ten benchmark case studies, including non-convex IEEE bus system at the same time also considering VPLE of thermal power plants. The potency of designed optimization seems more robust with fast convergence rate while evaluating the hard bounded DEDP. Our suggested hybrid method GA-SQP converges to achieve the best optimal solution in a confined environment in a limited number of simulations. The simulation results demonstrate applicability and adequacy of the given hybrid schemes over conventional methods.

Project description:BackgroundSmartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers' availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched.ObjectiveThis study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert.MethodsWe developed and compared 4 analytical models to predict VFRs' response behaviors based on emergency event characteristics, volunteers' demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder's previous behavior.ResultsThe highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs' demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs' previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy.ConclusionsVFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.

Project description:BackgroundCombined Heat and Power (CHP) systems can provide a range of benefits to users with regards to efficiency, reliability, costs and environmental impact. Furthermore, increasing the amount of electricity generated by CHP systems in the United States has been identified as having significant potential for impressive economic and environmental outcomes on a national scale. Given the benefits from increasing the adoption of CHP technologies, there is value in improving our understanding of how desired increases in CHP adoption can be best achieved. These obstacles are currently understood to stem from regulatory as well as economic and technological barriers. In our research, we answer the following questions: Given the current policy and economic environment facing the CHP industry, what changes need to take place in this space in order for CHP systems to be competitive in the energy market?MethodsWe focus our analysis primarily on Combined Heat and Power Systems that use natural gas turbines. Our analysis takes a two-pronged approach. We first conduct a statistical analysis of the impact of state policies on increases in electricity generated from CHP system. Second, we conduct a Cost-Benefit analysis to determine in which circumstances funding incentives are necessary to make CHP technologies cost-competitive.ResultsOur policy analysis shows that regulatory improvements do not explain the growth in adoption of CHP technologies but hold the potential to encourage increases in electricity generated from CHP system in small-scale applications. Our Cost-Benefit analysis shows that CHP systems are only cost competitive in large-scale applications and that funding incentives would be necessary to make CHP technology cost-competitive in small-scale applications.ConclusionFrom the synthesis of these analyses we conclude that because large-scale applications of natural gas turbines are already cost-competitive, policy initiatives aimed at a CHP market dominated primarily by large-scale (and therefore already cost-competitive) systems have not been effectively directed. Our recommendation is that for CHP technologies using natural gas turbines, policy focuses should be on increasing CHP growth in small-scale systems. This result can be best achieved through redirection of state and federal incentives, research and development, adoption of smart grid technology, and outreach and education.

Dataset Information

Kepler Algorithm for Large-Scale Systems of Economic Dispatch with Heat Optimization.

Publications

Kepler Algorithm for Large-Scale Systems of Economic Dispatch with Heat Optimization.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets