The prediction of electric city bus energy demand is crucial in order to estimate operating costs and to size components such as the battery and charging systems. Unfortunately, there are unpredictable dynamic factors that can cause variation in the energy demand, particularly concerning driver choices and traffic levels. The impact of these factors on energy demand has been difficult to study since fast computing sufficiently accurate dynamic simulation models have been missing, properly quantified in terms of relevant inputs which contribute to energy demand. The objective is to develop and validate a novel electric city bus model for computing the energy demand, to study the nature and impact of various input factors. The developed equation-based model predicted real-world electric city bus energy consumption within 0.1% error. The most crucial unmeasurable input factors were the driven bus route, the number of stops, the elevation profile, the traffic level and the driving style. This understanding can be used to specify routes and stops for a given electric bus battery capacity. Worst-case scenarios are also necessary for electric bus sizing analysis. The best- and worst-case levels of the crucial factors were identified and with them synthetic best- and worst-case speed profiles were generated to demonstrate their effect to the energy demand. While the measured nominal consumption was 0.70 kWh/km, the computed range of variation was between 0.19 kWh/km and 1.34 kWh/km. For design sizing purposes, an electric city bus can have a broad range of possible energy consumption rates due to mission condition variations.

Development and validation of energy demand uncertainty model for electric city buses

Developing local driving cycle for accurate vehicular CO2 monitoring: A case study of Tehran

The electric engine cooling system with the active grille shutter requires intelligent and predictive control to reach its full benefits on fuel economy and thermal management. Conventional control methods regulate the coolant temperature to a fixed value but do not directly minimize the vehicle's fuel/energy consumption. By contrast, we design a fuel minimization controller through solving constraint nonlinear optimization problems, whose cost function is the total fuel consumption and constraints are the vehicle's physical limits. To achieve high computational efficiency and sufficient accuracy, the optimization problem is solved by iterative convex quadratic programming and quasilinearization. The advantages of the proposed control method on both fuel economy and engine thermal management are demonstrated by simulations.

/pdf/fuel-minimization-of-the-electric-engine-cooling-system-with-1gbrjj5t0r.pdf

Fuel Minimization of the Electric Engine Cooling System With Active Grille Shutter by Iterative Quadratic Programming

Optimal capacity configuration and dynamic pricing strategy of a shared hybrid hydrogen energy storage system for integrated energy system alliance: A bi-level programming approach

This paper presents the development of a global and integrated sizing approach under different performance indexes applied to fuel cell/battery hybrid power systems. The strong coupling between the hardware sizing process and the system supervision (energy management strategy EMS) makes it hard for the design to consider all the possibilities, and today’s methodologies are mostly experience-based approaches that are impervious to technological disruption. With a smart design approach, new technologies are easier to consider, and this approach facilitates the use of new technologies for transport applications with a decision help tool. An automotive application with a hybrid fuel cell (PEMFC)/battery (Li-Ion) is considered to develop this approach. The proposed approach is based on imbricated optimization loops and considers multiple criteria such as the fuel consumption, reliability, and volume of the architecture, in keeping with industry expectations to allow a good trade-off between different performance indexes and explore their design options. This constitutes a low computational time and a very effective support tool that allows limited overconsumption and lifetime reduction for designed architecture in extreme and non-optimal use. We obtain, thanks to this work, a pre-design tool that helps to realize the first conception choice.

/pdf/multi-criteria-optimal-design-for-fuel-cell-hybrid-power-30977655.pdf

Multi-Criteria Optimal Design for FUEL Cell Hybrid Power Sources

Reducing energy consumption of ground vehicles is a paramount pursuit in academia and industry. Even though the road infrastructural has a significant influence on vehicular fuel consumption, the majority of the R&D efforts are dedicated to improving vehicles. Little investigation has been made in the optimal design of the road infrastructure to minimize the total fuel consumption of all vehicles running on it. This paper focuses on this overlooked design problem and the design parameters of the optimal road infrastructure is the profile of road grade angle between two fixed points. We assume that all vehicles on the road follow a given acceleration profile between the two given points. The mean value of the energy consumptions of all vehicles running on the road is defined as the objective function. The optimization problem is solved both analytically by Pontryagin’s minimum principle and numerically by dynamic programming. The two solutions agree well. A large number of Monte Carlo simulations show that the vehicles driving on the road with the optimal road grade consume up to 31.7% less energy than on a flat road. Finally, a rough cost analysis justifies the economic advantage of building the optimal road profile.

https://www.mdpi.com/1996-1073/10/5/700/pdf

The Optimal Road Grade Design for Minimizing Ground Vehicle Energy Consumption

Shared energy storage is an energy storage business application model that integrates traditional energy storage technology with the sharing economy model. Under the moderate scale of investment in energy storage, every effort should be made to maximize the benefits of each main body. In this regard, this paper proposes a distributed shared energy storage double-layer optimal allocation method oriented to source-grid cooperative optimization. First, considering the regulation needs of the power side and the grid side, a distributed shared energy storage operation model is proposed. Second, a distributed shared energy storage double-layer planning model is constructed, with the lowest cost of the distributed shared energy storage system as the upper-layer objective, and the lowest daily integrated operation cost of the distribution grid-distributed new energy stations as the lower-layer objective. Third, a double-layer iterative particle swarm algorithm combined with tide calculation is used to solve the distributed shared energy storage configuration and distribution grid-distributed new energy stations’ economic operation problem. Finally, a comparative analysis of four scenarios verifies that configuring distributed shared energy storage can increase the new energy consumption rate to 100% and reduce the net load peak-valley difference by 61%. Meanwhile, distributed shared energy storage operators have realized positive returns. 

Distributed Shared Energy Storage Double-Layer Optimal Configuration for Source-Grid Co-Optimization

The current energy-only market can only guarantee the recovery of marginal operating costs of flexible resources, especially for thermal power units, and the external costs generated by flexible resources to guarantee the consumption of variable renewable energy in the form of capacity have not been repaid. As a result of this, this paper focuses on the research of capacity compensation mechanism design to ensure flexible resource adequacy. First, we analyze the necessity of capacity compensation mechanism. When determining the capacity compensation quota, it should consider the impact of the decision-making behavior of thermal power enterprises on the capacity adequacy and flexibility of system operation. Therefore, this paper establishes a two-level decision-making model of thermal power enterprises considering the capacity compensation mechanism. The upper level determines whether the thermal power enterprises will exit the market with the goal of maximizing the expected income of thermal power enterprises, and the lower level is the market clearing model based on the decision-making behavior of each thermal power enterprise. Finally, based on IEEE 30 bus system, the validity of the model is verified, the impact of different capacity compensation prices on the exit decision of thermal power enterprises and the overall operation of the market is analyzed.

Design of Capacity Compensation Mechanism to Ensure Flexibility and Resource Adequacy

In view of the current situation of the global energy crisis and environmental pollution, the energy industry transition and environmental governance are urgently needed. To deal with the problem above, the construction of a power system dominated by renewable energy (PSDRE) with wind turbine (WT), photovoltaic (PV), biomass power (BP), and other clean, low-carbon, renewable energy sources as the principal part has become a consensus all over the world. However, the random and uncertain power output of renewable energy will not only put pressure on the power system but also lead to the unreasonable and insufficient usage of renewable energy. In this context, the energy storage (ES) effects of flexible resources, such as physical energy storage of batteries and demand response (DR), are analyzed first. Next, a modeling method for the operational characteristics of physical and virtual shared energy storage (PVSES) in regional PSDRE (RPSDRE) is proposed. Finally, an optimal scheduling strategy for RPSDRE that considers PVSES is proposed to achieve coordination of WT, PV, PVSES, and other flexible resources. The case study on RPSDRE in Lankao county, Kaifeng city, Henan province of China verifies the effectiveness and practicability of the proposed strategy.

/pdf/optimal-scheduling-strategy-of-regional-power-system-mzj7bdxg.pdf

Optimal Scheduling Strategy of Regional Power System Dominated by Renewable Energy Considering Physical and Virtual Shared Energy Storage

Optimizing the transmission curve of DC power transmitted to Henan Province is of great significance for serving the new energy consumption of Henan Province and improving the overall operational efficiency of the power system. Based on the analysis of the net load characteristics of the power system in Henan Province, a two-step optimization method for the transmission curve of DC power transmitted to Henan Province is proposed. On the premise of meeting the full amount of transmission energy in the power transmission agreement, reasonably determine the transmission power, duration, and period distribution in the peak and valley sections of the power transmission curve. Through the simulation of production, the effect of optimizing the power transmission curve in matching the power demand of the provincial grid, reducing the abandonment of new energy, reducing the cost of ancillary services, and electricity purchases are evaluated quantitatively. This method has reference significance for provincial power companies to organize curve transactions and coordinate system peak shaving.

Optimization of UHVDC power transmission curve for auxiliary peak shaving

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