A Two-Stage Economic Optimization and Predictive Control for EV Microgrid

The aggregation of electric vehicles (EVs) has been advocated as an effective means to manage and control large-scale plug-in EVs. However, the efficient aggregation is challenging due to the heterogeneity and uncertainty of individual EVs. To cope with this challenge, this paper puts forth a fast polytope-based aggregation approach, which could deal with the time-coupled, uncertain, and heterogeneous individual EV models. First, the feasible region of each EV's power profiles is expressed as a polytope under H-representation considering various operational constraints. To improve the aggregation efficiency, H-representation reconstruction and polytopic projection processes are designed to address the heterogeneity of EVs. The uncertainties of EVs are modeled by the distributionally robust joint chance-constrained programming. The original individual and aggregate feasible region of EVs are subsequently inner approximated by the homothets of a basic prototype. Last, a day-ahead optimal bidding model for charging service provider in a joint market, including energy, peak-shaving and reserve markets, is developed. Numerical simulation results based on real-world dataset demonstrate that the proposed aggregation method can improve both the computation efficiency and accuracy of approximate feasible region.

Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices

At present, our power supply structure is still dominated by thermal power, and the power system has limited ability to adjust flexibly. In the past, the power supply side dispatching method of the power system is gradually approaching the limit operation, and the uncertainty of electric vehicles caused frequent start and stop of units, large scale climbs, etc. For these problems, we need to dig flexible interactive resources from both the source and the load to improve the flexible adjustment capability of the system. As an important part of the smart grid architecture, we use big data demand response to guide users to adjust electricity consumption behavior through price signals or incentive signals to respond to the increase in grid uncertainty caused by the integration of electric vehicles. We use big data technology to optimize the electric vehicle charging grid-connected automation system to improve the flexible adjustment capability of the power system itself.

With the development of renewable energy power systems, electric vehicles, as an important carrier of green transportation, are gradually having an impact on the power grid load curve due to their charging behavior. However, the significant influx of electric vehicles (EVs) and distributed power sources has led to multiple uncertainties, increasing the difficulty in making grid scheduling decisions. Traditional robust scheduling strategies tend to be overly conservative, resulting in poor economic performance. Therefore, this paper proposes a robust and economic dispatch strategy for park power grids based on the information gap decision theory (IGDT). Firstly, based on the probabilistic characteristics of the spatial and temporal distribution of EVs charging, the Monte Carlo method is used to generate typical electricity usage scenarios for EVs. Simultaneously, an economic dispatch model for the park power grid is established with the objective of minimizing operating costs. Taking into account the uncertainty of renewable energy output, simulation analysis is conducted through the IGDT model. Finally, through the verification of the improved IEEE-33 node test system and comparison with other methods, the proposed approach in this paper can reduce decision conservatism and effectively reconcile the contradiction. Through analysis, the proposed method in this paper can reduce the total operational cost of the system by up to 3.2%, with a computational efficiency of only 8.9% of the traditional stochastic optimization time.

The increasing number of electric vehicles (EVs) necessitates the installation of more charging stations. The challenge of managing these grid-connected charging stations leads to a multi-objective optimal control problem where station profitability, user preferences, grid requirements and stability should be optimized. However, it is challenging to determine the optimal charging/discharging EV schedule, since the controller should exploit fluctuations in the electricity prices, available renewable resources and available stored energy of other vehicles and cope with the uncertainty of EV arrival/departure scheduling. In addition, the growing number of connected vehicles results in a complex state and action vectors, making it difficult for centralized and single-agent controllers to handle the problem. In this paper, we propose a novel Multi-Agent and distributed Reinforcement Learning (MARL) framework that tackles the challenges mentioned above, producing controllers that achieve high performance levels under diverse conditions. In the proposed distributed framework, each charging spot makes its own charging/discharging decisions toward a cumulative cost reduction without sharing any type of private information, such as the arrival/departure time of a vehicle and its state of charge, addressing the problem of cost minimization and user satisfaction. The framework significantly improves the scalability and sample efficiency of the underlying Deep Deterministic Policy Gradient (DDPG) algorithm. Extensive numerical studies and simulations demonstrate the efficacy of the proposed approach compared with Rule-Based Controllers (RBCs) and well-established, state-of-the-art centralized RL (Reinforcement Learning) algorithms, offering performance improvements of up to 25% and 20% in reducing the energy cost and increasing user satisfaction, respectively.

This work studies the power quality impacts of electric transportation charging including electric buses (EBs) and electric vehicles (EVs) on distribution systems. The stochastic harmonic models for EBs and EVs and the stochastic models for their usage scenarios are built utilising the measured charging data. Based on these models, a power quality analysis method considering the charging uncertainties of EBs and EVs is proposed in this study and then their impacts on distribution systems are investigated. The Monte Carlo simulation is integrated into the proposed method to study the under voltage, overcurrent, voltage unbalance, transformer overload, harmonic current, voltage etc. caused by the stochastic charging of EBs and EVs. An actual distribution system acquired from Taiwan Power Company is taken as an example to validate the practicability of the proposed method. The test results of the proposed stochastic model are compared with those of the conservative model.

Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks

The transportation sector is under increasing pressure to reduce greenhouse gas emissions by decarbonizing its operations. One prominent solution that has emerged is the adoption of electric vehicles (EVs). As the electric vehicles market experiences rapid growth, the utilization of lithium-ion batteries (LiB) has become the predominant choice for energy storage. However, it is important to note that lithium-ion battery technology is sensitive to factors, like excessive voltage and temperature. Therefore, the development of an accurate battery model and a reliable state of charge (SOC) estimator is crucial to safeguard against the overcharging and over-discharging of the battery. Numerous studies have been conducted to address lithium-ion battery cell modeling and SOC estimations. These studies have explored variations in the number of RC networks within the model and different estimation methods. However, it is worth mentioning that the capacity of a single lithium-ion battery cell is relatively low and cannot be directly employed in electric vehicles. To meet the total capacity and voltage requirements for electric vehicles, multiple cells are typically connected in series or parallel configurations to form a battery pack. Surprisingly, this aspect has often been overlooked in previous research. To tackle this overlooked challenge, our study introduces a comprehensive battery pack model and an advanced Battery Management System (BMS). We then integrate these components into an electric vehicle model. Subsequently, we simulate the integrated EV-BMS model under the conditions of four different urban driving scenarios to replicate real-world driving conditions. The BMS that we have developed includes an Extended Kalman Filter (EKF)-based SOC estimation system, a mechanism for controlling coolant flow, and a passive cell-balancing algorithm. These components work together to ensure the safe and efficient operation of the battery pack within the electric vehicles.

Demand response programs (DRPs) have paved a meaningful role in the power supply–demand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT‐based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively.

As the number of electric vehicles (EVs) continues to grow and the demand for charging infrastructure is also increasing, how to improve the charging infrastructure has become a bottleneck restricting the development of EVs. In other words, reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system. Considering the construction and maintenance of the charging station, the distribution network loss of the charging station, and the economic loss on the user side of the EV, this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function, and thus proposes a location and capacity planning model for the EV charging station. Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm, the simulated annealing immune particle swarm optimization algorithm (SA-IPSO) is adopted in this paper. The simulated annealing algorithm is used in the global update of the particle swarm optimization (PSO), and the immune mechanism is introduced to participate in the iterative update of the particles, so as to improve the speed and efficiency of PSO. Voronoi diagram is used to divide service area of the charging station, and a joint solution process of Voronoi diagram and SA-IPSO is proposed. By example analysis, the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost, while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%. The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1% and basically does not fall into local convergence.

Plug-in electric vehicles (PEVs) are becoming increasingly popular. In the next several years, all major vehicle manufacturers will introduce PEVs, either as battery electric vehicles (BEVs), such as the Nissan Leaf, or plug-in hybrid electric vehicles (PHEVs) with a dual powertrain, such as the General Motors Volt and the Toyota Prius. This new generation of vehicles relies on electricity as a power source and a battery as an energy storage medium. Naturally, for PEVs, refueling occurs when the battery connects to an electrical outlet linked to a distribution network. The primary objective of this paper is to study optimal locations for building facilities (i.e., smart garages) that will maximize integrated benefits from the transportation system (i.e., parking fee) and the electric power system [i.e., revenue from vehicle-to-building (V2B) services]. A deterministic smart garage development problem (SGDP) is formulated and modeled as a bilevel program and solved by using a genetic algorithm. The results of a simple numerical example show the sensitivity of the model results with respect to parameters, including PEV penetration rate and battery capacity.

Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles

Electric vehicles use lots of ECU controllers in the CAN network, the quality of ECU parts used in vehicles is closely related to safe driving. This paper develops a kind of fault diagnostic equipment for electric vehicles' ECU controllers. This device has designed digital input signal acquisition module, the output switch signal module, power supply module and fault simulation module based on CAN bus communication principle. Then we use LabVIEW developing computer program in order to monitor and control the whole system. Keywords-Electric Vehicle; ECU; Fault diagnosis LabVIEW;CAN Bus I. INTRODUCTION The rapid development of Chinese economy has made car ownership in China surpassed the global average level and grow rapidly. But China is lack of oil resources, problems of over-consumption of oil resources caused by fuel vehicles, and air pollution caused by fuel vehicle emissions should not be ignored any more. In the face of enormous pressure on energy and environmental protection, clean energy vehicles, especially pure electric vehicles have become the mainstream in today's world. The electric car uses controller area (CAN bus) structure, information can be shared, wires harnessed on the car is significantly reduced, the reliability of data has been improved. CAN network of electric vehicles use a variety of electronic control units (ECU), whether ECUs could operate reliably is closely related with the safe and reliable operation of the electric vehicles. As a new energy vehicle, the detection equipment of electric vehicles remains to be the developed, and detection methods are lack of a certain standard. Not only the detection of vehicle products is not strong, but the detection of key components such as power battery has lagged behind more which is extremely detrimental to the independent development of the industry in the future. Therefore, there is a great significance in developing a kind of diagnostic equipment for Electric Vehicle ECU test.

When a large-scale random charging load is connected to the regional power grid, it can negatively affect the safe and stable operation of the power grid. Therefore, we need to study its charging load and response potential in advance so that electric vehicles can interact well with the grid after being connected to the regional grid. Firstly, after analyzing the influencing factors of regional electric vehicle ownership, an electric vehicle ownership prediction model based on the sparrow search algorithm-improved BP neural network (SSA-BPNN) is established. On this basis, an electric vehicle charging load prediction model is established based on the sparrow search algorithm-improved BP neural network and Monte Carlo algorithm (SSA-BPNN-MC). Secondly, the charging behavior of different types of electric vehicles is analyzed and modeled, and the data from a certain area are taken as an example for the prediction. Then, according to the load forecasting results, the potential of electric vehicles participating in demand response in the region in the future is deeply analyzed using the scenario analysis method. Finally, with the aim of resolving the problems of massive multi-source heterogeneous data processing and the management of electric vehicles participating in the regional power grid demand response, a basic framework of electric vehicles participating in the regional power grid demand response is developed, which provides effective support for promoting electric vehicles to participate in regional grid demand response.

Electric vehicles (EVs) are flexible demand-side response resources in a power distribution system. Reasonable and orderly control of charging/discharging processes of aggregated EVs can improve their coordination and interaction with the distribution system and ensure its efficient and stable operation. Aiming at the problem that the fluctuation of wind power output may affect the stable operation of distribution system, a real-time control strategy for aggregated EVs to smooth the fluctuation of wind power is proposed. Firstly, considering the dispatchability of EVs, the charging/discharging energy boundary model is established to determine the charging/discharging margin of an EV at each moment. Then, first-order low-pass filtering is used to determine the total dispatching power of aggregated EVs. Finally, the total charging power of aggregated EVs is determined and power allocation is carried out. Simulation results show that the proposed strategy can achieve real-time smoothing for the fluctuation of wind power output while meeting the charging requirements of EVs, and the proposed strategy can not only reduce the fluctuation rate of total load, but also realize peak shaving and valley filling for the distribution system.