Smart green charging scheme of centralized electric vehicle stations

This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process. The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively

Integrated energy system optimization and scheduling method considering the source and load coordinated scheduling of thermal-storage electric boilers and electric vehicles

The northern regions of China face the challenges of the mismatch of the power supply and demand, as well as serious wind curtailment issues, caused mainly by the limitation of the “with heat to determine electricity” mode for combined heat and power generation during the winter season. To further absorb the surplus wind power and alleviate restrictions, a comprehensive energy system optimization method for parks based on coordinated scheduling between sources and loads is proposed in this paper. First, the implementation of a heat-storage electric boiler on the source side further achieves the decoupling of heat and power. Second, an optimized scheduling method for electric vehicles combining incentive scheduling and orderly scheduling is proposed on the load side, which helps flatten the load curve. Finally, a tiered carbon trading mechanism is introduced and a community integrated energy system (CIES) optimization scheduling model is established with the aim of minimizing the total cost of the CIES, and the problem is solved using the CPLEX commercial solver. The simulation results indicate that the overall system efficiency is significantly improved through the coordinated scheduling of power sources and loads. Specifically, the integration rate of wind power increases by 3.91% when compared to the sole consideration of the integrated demand response. Furthermore, the peak shaving and off-peak filling effect is considerably enhanced compared to the utilization of only thermal-storage electric boilers. Additionally, the implementation of coordinated scheduling leads to a reduction in the total system cost by 2764.32 yuan and a decrease in total carbon emissions by 3515.4 kg. These findings provide compelling evidence that the coordinated scheduling of power sources and loads surpasses the limitations of thermal power units, strengthens the demand response capability of electric vehicles, and enhances the economic benefits of the CIES

The electric vehicle routing problem with energy consumption uncertainty

Compared with conventional freight vehicles, electric freight vehicles create less local pollution and are thus generally perceived as a more sustainable means of goods distribution. In urban areas, such vehicles must often perform the entirety of their delivery routes without recharging. However, their energy consumption is subject to a fair amount of uncertainty, which is due to exogenous factors such as the weather and road conditions, endogenous factors such as driver behaviour, and several energy consumption parameters that are difficult to measure precisely. Hence we propose a robust optimization framework to take into account these energy consumption uncertainties in the context of an electric vehicle routing problem. The objective is to determine minimum cost delivery routes capable of providing strong guarantees that a given vehicle will not run out of charge during its route. We formulate the problem as a robust mixed integer linear program and solve small instances to optimality using robust optimization techniques. Furthermore, we develop a two-phase heuristic method based on large neighbourhood search to solve larger instances of the problem, and we conduct several numerical tests to assess the quality of the methodology. The computational experiments illustrate the trade-off between cost and risk, and demonstrate the influence of several parameters on best found solutions. Furthermore, our heuristic identifies 42 new best solutions when tested on instances of the closely related robust capacitated vehicle routing problem

Optimisation-based Approaches for Evaluating the Aggregation of EVs and PVs in Unbalanced Low-Voltage Networks

214 p.In the near future, it is expected that the distribution system operators face different technical challenges derived from the massification of electric mobility and renewable energy sources in the low voltage networks. The purpose of this thesis is to define different smart coordination strategies among different agents involved in the low voltage networks such as the distribution system operator, the aggregators and the end-users when significant penetration levels of these resources are adopted. New models for representing the uncertainty of the photovoltaic output power and the connection of the electric vehicles are introduced. A new energy boundary model for representing the flexibility of electric vehicles is also proposed. In combination with the above models, four optimisation models were proposed as coordination strategies into three different approaches: individual, population, and hybrid. The first model was defined at the aggregator level, whereas the other models were proposed at the distribution system operator level. Complementary experimental cases about the proposed optimisation model in the individual-based approach and the quadratic formulation in the hybrid approach for the PV power curtailment were carried out to test its response in real-time. Simulations results demonstrated that the proposed coordination strategies could effectively manage critical insertion levels of electric vehicles and photovoltaic units in unbalanced low voltage networks

An Improved ABC Algorithm for Energy Management of Microgrid

Microgrids are an ideal way of electricity generation, distribution, and regulation for customers by means of distributed energy resources on the community level. However, due to the randomness of photovoltaic and wind power generation, it is a crucial and difficult problem to achieve optimal economic dispatch in microgrids. In this paper, we present an optimal economic dispatch solution for a microgrid by the improved artificial bee colony (ABC) optimization, with the aim of satisfying load and balance demand while minimizing the cost of power generation and gas emission. Firstly, we construct a mathematical model according to different characteristics of distributed generation units and loads, and improve the performance of global convergence for ABC in order to fit such model. Secondly, we explore how to solve the optimal economic dispatch problem by the improved ABC and give the essential steps. Thirdly, we carry out several simulations and the results illustrate the benefits and effectiveness of the proposed approach for optimal economic dispatch in microgrid

Deep reinforcement learning for the dynamic vehicle dispatching problem: An event-based approach

The dynamic vehicle dispatching problem corresponds to deciding which vehicles to assign to requests that arise stochastically over time and space. It emerges in diverse areas, such as in the assignment of trucks to loads to be transported; in emergency systems; and in ride-hailing services. In this paper, we model the problem as a semi-Markov decision process, which allows us to treat time as continuous. In this setting, decision epochs coincide with discrete events whose time intervals are random. We argue that an event-based approach substantially reduces the combinatorial complexity of the decision space and overcomes other limitations of discrete-time models often proposed in the literature. In order to test our approach, we develop a new discrete-event simulator and use double deep q-learning to train our decision agents. Numerical experiments are carried out in realistic scenarios using data from New York City. We compare the policies obtained through our approach with heuristic policies often used in practice. Results show that our policies exhibit better average waiting times, cancellation rates and total service times, with reduction in average waiting times of up to 50% relative to the other tested heuristic policies.Comment: 42 pages, 22 figure

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research