Collaborative dispatch of microgrid clusters under multi-strategy for electric vehicles based on improved BES algorithm

Port berth allocation and microgrid cluster joint optimization scheduling based on master-slave game

Electric vehicle charging scheduling considering urgent demand under different charging modes

This study investigates the influence of electric vehicle (EV) price, operational costs, and supporting infrastructure on the declining sales of conventional Toyota vehicles in Indonesia, with environmental awareness as a moderating factor. The research is grounded in the accelerating global shift from fossil- fuelled transportation towards sustainable mobility, a transition that is reshaping consumer preferences and competitive dynamics in the automotive industry. Using a quantitative design and the Structural Equation Modelling–Partial Least Squares (SEM-PLS) method, data were collected from 250 respondents comprising owners of both electric and conventional vehicles across various regions of Indonesia. Findings reveal that EV price, operational costs, and infrastructure exert a significant impact on conventional vehicle sales, with the effect being amplified when environmental awareness is high. These results highlight that adoption is not solely driven by economic considerations but also by environmental values aligned with Sustainable Development Goal 13: Climate Action. For PT Toyota Sales Operation, the implications are clear: accelerating EV adoption and expanding the Battery Electric Vehicle portfolio are critical to sustaining market competitiveness. This research contributes to the discourse on green supply chain strategy and environmentally responsible innovation in emerging automotive markets.

With the widespread integration of distributed generation into distribution network, the dispatching methods of distribution networks will undergo fundamental changes. Microgrids, as a way to absorb renewable sources, play an increasingly important role in new power systems. The coordination between distribution network and microgrid cluster helps promote the absorption of distributed resources, but traditional optimization methods struggle to quickly solve the complex problems of distribution network and microgrid cluster. Therefore, this paper proposes a multi-level collaborative dispatching method for distribution network and microgrid cluster based on safe deep reinforcement learning. First, considering the collaborative relationship between distribution network and microgrids, a multi-level dispatching model for distribution network and microgrid cluster is constructed. Then, the collaborative dispatching of distribution network and microgrid cluster is expressed as a Markov decision process. Addressing the constraint overshooting problem encountered when agent algorithms solve practical application problems, a multi-agent soft actor-critic with Lagrangian method algorithm is adopted. Finally, a case study is conducted based on the improved IEEE 33-node system to verify the effectiveness of the proposed model and algorithm.

The widespread adoption of Electric Vehicles (EVs) and their uncoordinated simultaneous charging puts additional stress on the grid due to an increase in peak load. The charging schedule of EVs can be coordinated by an EV Aggregator (EVA) through appropriate price signals. This paper presents an optimal scheduling framework for EV clusters using Time-of-use (TOU) price. Firstly, the EVA aggregates the total demand corresponding to the vehicle's arrival and departure times for optimal power scheduling using clustering algorithms. The accuracy of cluster-based aggregation plays a vital role, hence this study adopts the advanced clustering technique: spectral clustering algorithm, to accurately cluster the EVs. However, the optimal EV scheduling is motivated by dynamic prices like Real-Time Price (RTP) and Time of Use (TOU) but the acceptance rate of RTP is quite less due to its highly volatile nature. Hence, the proposed work focuses on the optimal scheduling of EV clusters based on TOU prices. For this, the historical data of RTPs are considered for TOU price design using hierarchical clustering. For the case study, 500 EVs are aggregated using spectral clustering and compared with the traditional k-means. The aggregation results are analyzed using Principal Component Analysis (PCA) decomposition; highlighting the increased accuracy of aggregation in terms of time. Further, the optimal scheduling of EV clusters is achieved based on the proposed pricing and aggregation strategies.

To integrate the combination of electric vehicles and wind power of the transmission system, a wind farm environment multi-objective transmission model considering plug-in electric vehicles was established. The fuzzy optimal scheduling model for the environment and economy target of wind farm power system considering grid interconnection of electric vehicles designed by the author has the following characteristics: A fuzzy multi-objective dynamic scheduling model for the design of wind farm power system considering grid connection of plug-in electric vehicle is established, and the original 10 systems are taken as an example for power system design. The results show that the better model shows that the electric vehicle grid connection can greatly reduce the disadvantage of wind power stochastic uncertainties of system scheduling. The model can be used under different conditions further analyzing the rationality of the model determined by the author. An improved particle swarm optimization algorithm is used to solve this problem. The model takes the environmental and economic benefits into account, which improves the efficiency of the model.

This study investigates the multi-objective scheduling optimisation of microgrid clusters to enhance power operation, energy management, and cost efficiency. An improved sparrow search algorithm is developed by integrating Bernoulli chaotic mapping, Cauchy mutation, dynamic adaptive weights, and reverse learning, enabling faster convergence and stronger global search capability. Simulation results show that for the F1 test function, the algorithm converges around 88 iterations with accuracy above 92.05%, and deviations remain within 2%. In a practical 24-hour grid-connected scheduling case, the original operating cost of the regional microgrid cluster was 31.82 × 104 yuan, while the optimised cost decreased to 25.36 × 104 yuan. Overall efficiency improved by approximately 25.46%. These findings demonstrate that the proposed method significantly enhances energy utilisation and provides a reliable theoretical basis for improving the economic performance and operational sustainability of microgrid clusters.

With the rapid growth of electric vehicle (EV) ownership, the problem of increased peak loads on distribution networks due to large-scale EV integration needs to be addressed. This study proposes an active distribution network multi-objective optimization scheduling method. It takes into account the charging demands of large-scale EVs and aims to minimize distribution network operating costs, reduce net load variance, and maximize the photovoltaic (PV) consumption rate. First, the Monte Carlo sampling method is used to analyze the charging load demands of a large number of EVs. Next, we construct a multi-objective optimization scheduling model for the active distribution network. This model integrates the charging demands of EVs with the operating constraints of the distribution network. To tackle the multi-objective optimization problem, we propose the NSGAII-NDAX algorithm. Additionally, we employ a fuzzy theory-based method to select the Pareto optimal solution set, addressing the challenge of decision-making complexity posed by the large size and information-rich content of the optimal solution set. Finally, the effectiveness of the proposed method in the comparative analysis of multiple scenarios is verified by an improved IEEE 33-node example. The experimental results show that the proposed model and method can effectively utilize EV charging load optimization to reduce the peak-to-valley difference in the system load while ensuring the system’s economic operation and the maximum PV consumption rate. Compared with the other algorithm, the NSGAII-NDAX algorithm has stronger optimization ability and can better handle multi-objective optimization problems.

With the rapid development of renewable energy, microgrid cluster systems are gradually being applied. To promote the development of microgrid cluster scheduling technology, maximize economic benefits while reducing the operating cost required for microgrid scheduling, an optimized scheduling scheme is proposed by constructing a function to minimize the operating cost of microgrids. Then, chaos mutation and Gaussian mutation are applied to improve the moth-flame algorithm that easily falling into local optima. A microgrid cluster optimization scheduling model on the basis of the improved moth-flame algorithm is constructed. The experimental results showed that the operating cost in islanding mode was 4286.21 yuan after 160 iterations. After optimizing the scheduling, the operating cost was 3912.3 yuan, with a decrease of 8.7%. The improved moth-flame algorithm had a stable average loss value of 20% and an operating efficiency of 97.19% after 10–50 iterations, which was significantly higher than other intelligent algorithms. This indicates that the improved moth-flame algorithm has high reliability and effectiveness in microgrid cluster optimization scheduling. Therefore, the proposed model effectively optimizes the scheduling scheme of microgrid cluster, providing new solutions for the efficient utilization of smart grids and renewable energy in the future.

This paper considers the economy and stability of the microgrid cluster system, and proposes a two-level energy optimization dispatching strategy for the microgrid cluster, which aims to coordinate the economic benefits and operational risks of the microgrid while reducing the interactive power and fluctuations between the microgrid cluster and the distribution network, and reduce line loss. The first level takes the microgrid as the research object, takes the highest economic benefit and the lowest operational risk as the optimization goals, uses the improved PSO algorithm to solve the problem, and builds the unit risk-economic benefit ratio screening solution set, and then formulates the internal dispatching candidate strategy of the microgrid. The second level is based on the premise that the interactive power between the microgrid cluster and the distribution network is minimized, and the optimal internal dispatching strategy of each microgrid is determined. Then the line loss of the microgrid cluster system is considered, and the microgrid cluster energy complementary scheme is formulated. Finally, a practical example verifies the feasibility and effectiveness of the two-level dispatching strategy and the improved algorithm.

Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040

Electric vehicle clusters scheduling strategy considering real-time electricity prices based on deep reinforcement learning

Now a days, Electric Vehicle (EV) integration to the distribution system is gradually increasing and is hitting with much power quality issues. This paper investigates the impacts of EVs integration into the distribution systems and highlights possible detrimental operational performance such as feeders and transformers overloading, lower voltage profiles, higher system losses and operational cost. EV integration is realized with two charging schemes; coordinated and uncoordinated for two EV penetration levels; 30% and 100%. A benchmark (RBTS) test system, and a real distribution network in Egypt (ShC-D8) are modeled. Each test system includes its own daily load and cost variations model. Simulations are performed to investigate the influence of EVs penetration and coordination on voltage profile, feeders and transformers loading, system losses, operational cost, voltage profile, and the daily load curves. Simulation results show that: the EVs penetration levels have a major effects on system performance, uncoordinated charging result in a negative impacts on system performance, and coordinate charging mitigate that negative impacts.

Although the rapid progress of the global economy and technology has advanced human civilization, it has also caused tremendous damage to the global ecological environment. Therefore, humans are thinking seriously about the environment and its sustainable development. One of the solutions to environmental problems is new energy vehicles. Since the promulgation of the “Energy Saving and New Energy Vehicle Industry Development Plan (2012–2020)” by the General Office of the State Council, the Chinese government has determined a strategy of pure electric driving technology. The electric vehicle market in China has expanded rapidly, making China the largest electric vehicle market in the world. Hence, research on the situation of electric vehicles in China is highly necessary and of reference value for other countries to develop electric vehicles. As a result, it is a critical issue to develop low-carbon, energy-saving, and intelligent electric vehicles to reduce the environmental impact. This paper establishes a theoretical framework based on the theory of planned behavior (TPB), technology acceptance model (TAM) and innovation diffusion theory (IDT), and explores the key factors influencing consumers’ purchase of electric vehicles. The results show that: The application of the key factor model constructed in this study to consumers’ behavioral intention regarding electric vehicle purchase is acceptable. According to the structural equation modeling (SEM) analysis results, (1) In terms of behavioral intention: Consumers’ control over the resources required to purchase electric vehicles has the highest influence on their behavioral intention, while consultation opinions from consumers’ surroundings also significantly affect their behavioral intention to purchase electric vehicles. In addition, consumers’ environmental awareness and acceptance of technology products will also influence their behavioral intention. (2) In terms of attitude toward behavior: When consumers believe that electric vehicles are more beneficial at the individual, environment or national level, or they believe that the usage of electric vehicles is simpler and more convenient, they will show a more positive attitude towards the purchase of electric vehicles. Consumers consider electric vehicles as forward-looking technology products with similar driving operation and usage cost compared to traditional vehicles. (3) In terms of regulations: The opinions of consumers’ family members, friends, colleagues or supervisors do not significantly affect the attitude or behavior of consumers regarding electric vehicle purchase. The key factors influencing consumers’ purchase of electric vehicles are not only applicable to the design and development of electric vehicles that better suit consumer demands, but also serve as a theoretical basis for the popularization of electric vehicles, and provide a reference for consumers’ choice and purchase. Therefore, the government and relevant manufacturers need to consider increasing the publicity of electric vehicles and launch more attractive battery and charging schemes to attract consumers and promote the sustainable development of the automobile industry.

As distributed energy storage, electric vehicles, adjustable loads, micro-grid clusters, 5G base stations, data centers and other flexibility resource-scale clusters are connected to the distribution network, is becoming more and more crucial in order to carry out their flexibility evaluation to guide distribution network planning. First, the connotation of distribution network flexibility and its influencing factors are analyzed. Second, based on the large-scale cluster access of flexibility resources, establish the distribution network flexibility evaluation index. Then, a method for calculating the distribution network flexibility assessment index while taking into account large-scale cluster access to flexibility resources is proposed. Finally, the method presented in this paper is validated using the IEEE 33-bus standard example system.