Multi-converter-based architectures are common in hybrid energy storage systems used in electric vehicles, increase a high cost, high power losses, and high weight. The current work presents a conceptual break to such traditional designs with a direct-coupled HESS that incorporates a proton exchange membrane fuel cell, a lithium-ion battery, and a supercapacitor into one DC bus so that the interconnection power converters are not required. This suggested configuration is based on the new demand-driven model, where the distribution of power is determined depending on the actual load requirements and without referring to predictive algorithms. The proposed system was modeled to evaluate the feasibility of this approach and was heavily simulated in an environment based on MATLAB/Simulink, with three standardized drive cycles FTP75, EPA Highway and WLTC Class. The performance is shown with EMS distribution efficiency of 94.8% and response time of 3.2 milliseconds. It also shows a more stress-free architecture of components, 55.7% decrease in peak current of the battery and better capacity retention. Through successful decoupling of power distribution and predictive models, the results demonstrate that a simplified, converter-free topology is a high-performance alternative providing a possible future, more solid, efficient, and commercially viable route to more robust and efficient powertrains in electric vehicles.
To address the problems of insufficient consideration of source-load temporal characteristics, weak coordination between site and equipment configuration, and low efficiency of multi-objective optimization in the planning of urban smart local energy systems, this paper constructs a system planning method that integrates temporal characteristic analysis and two-layer multi-objective optimization. By constructing a source-load temporal coupling model, the dynamic characteristics of combined heat and power loads and renewable energy outputs such as wind, solar, hydrogen, and storage are accurately characterized. The temporal model provides dynamic data input for the lower-layer equipment configuration optimization. A two-layer optimization framework based on Voronoi diagrams and genetic algorithms is designed. The upper layer uses Voronoi diagrams to partition the space and select station sites. The lower layer employs an improved NSGA-II algorithm to optimize equipment selection and capacity configuration. The two layers interact iteratively: the upper layer provides zoning and load information to the lower layer, and the lower layer feeds back configuration costs to update the upper-layer planning. The framework achieves collaborative site selection and zoned energy supply for multiple energy stations. A fuzzy membership decision mechanism is introduced to enhance the engineering applicability of the Pareto solution set. Experimental results show that, for economics, the total annual cost of the system is reduced by approximately 12% compared to the traditional step-by-step optimization method. In terms of environmental protection, carbon emissions are reduced by approximately 15%. In terms of robustness, even considering demand-side response and load fluctuations of up to 20%, the system can still maintain an energy supply reliability of over 88%. The core innovation lies in the deep integration of high-resolution temporal dynamics with spatial-device collaborative optimization through a closed-loop iterative mechanism, which significantly improves planning accuracy, efficiency, and system adaptability. This method provides a theoretical and technical support for the low-carbon and efficient layout of urban smart local energy systems.
Off-grid power generation has become increasingly efficient for remote and developing regions with the advancement of renewable energy technologies. Areas with limited access to the conventional grid can now depend on self-sustaining hybrid systems that offer higher reliability and lower carbon emissions compared to traditional power sources. The main objective of this study is to propose an economically viable and optimally designed system model for electric vehicle (EV) charging infrastructure. The system aims to provide a practical and sustainable solution for EV charging, considering electric vehicles as a primary mode of transportation. The motivation behind this work is to promote the adoption of renewable energy in the transportation sector, thereby contributing to a cleaner and pollution-free environment. The selected location is the capital city of the central region of Oman. The electric load of a community was synthesized for 25 households, with 60% of them owning an electric vehicle. Comprehensive system modelling, optimization, and performance evaluation were carried out under varying operating conditions. The total Net Present Cost (NPC) and Cost of Electricity (COE) of the proposed system were estimated to be $7,11,333 and $28,394 respectively. In addition, a sensitivity analysis was conducted to study the impact of variations in average solar irradiance and diesel fuel prices in Oman, as these parameters significantly influence the overall production cost of the system.
To address the challenges of absorption difficulty and insufficient economy caused by the grid connection of high-proportion renewable energy, this paper constructs a non-cooperative asymmetric game model between a wind-PV-thermal integrated energy system and external independent power generation units participating in the spot and frequency regulation coordinated market under the framework of Liaoning Province’s frequency regulation market rules. Taking the profit maximization of each participant as the core objective, a joint clearing model that incorporates the costs, compensations, and constraints of both the spot and regulation service markets are established, and the Nash equilibrium is solved through the application of the strategy iteration algorithm. Case studies verify that through internal resource coordination, the integrated energy collaborative system significantly reduces marginal costs and frequency regulation quotes, effectively improves its own market revenue while promoting renewable energy absorption, providing an economical and reliable operation support for the new power system.
The long-term stable adequacy of power generation capacity and flexible adjustable resources is one of the keys to the stable operation of the power market. The uncertainty of new energy and the periodicity of hydropower output pose greater challenges to the adequacy of power market capacity and adjustable resources. To address this challenge, probabilistic modeling of error probabilities in different periods is conducted through the kernel density estimation method with separate parameters. Dynamic reserve capacity demand is calculated under confidence probability, and capacity adequacy performance payment is introduced. Finally, the incentive effects of the new method are compared with those of the traditional capacity payment system and market auction in the market, leading to the conclusion that the new method helps flexible adjustable units better recover costs.
