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Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The proposed MIQCP-based EMS optimizes the power flows between the grid, EVs, and PV system by scheduling both active and reactive power dispatches of EVs in real-time. • The presented algorithm benefits all three entities, including the distribution system operator (DSO), charging station owner (CSO), and EV prosumer, respectively. • The proposed EMS incorporates linearized transformer's aging constraints into optimization problem formulation to avoid premature aging of the transformer caused by additional EV charging demand. • The proposed EMS algorithm is capable of handling both daily uncertainties and seasonal variations of PV generation and non-EV load by utilizing the stochastic optimization framework. • The implementation of the proposed EMS algorithm under receding horizon approach allows to update the EV parameters in real-time, resulting in reliable EV scheduling. This study presents a new energy management system (EMS) for a grid-tied photovoltaic (PV) – electric vehicle (EV) integrated workplace charging station. The proposed EMS is developed as a convex stochastic mixed-integer quadratically constrained problem (MIQCP) to minimize the expected apparent power demand while limiting the distribution transformer's accelerated aging and satisfying the EV driving needs. This is accomplished by scheduling real and reactive powers of EVs in real-time through Vehicle-to-grid (V2G) mode. The inherent diurnal uncertainties and seasonal variations associated with the workplace non-EV load, PV generation are incorporated using probabilistic and hierarchical clustering techniques, respectively. The implementation of the developed EMS under receding horizon model enables real-time operation by adapting to dynamic arrivals of EVs. The effectiveness of the proposed EMS is validated through numerous simulations from the view point of the distribution system operator (DSO), charging station owner (CSO), and EV prosumer. The results indicate a substantial reduction in the peak demand, minimized transformer's loss-of-life (LoL), and operating cost saving while satisfying the EV driving needs in comparison to uncoordinated charging method. [ABSTRACT FROM AUTHOR]

To enhance the utilization efficiency of wind and solar renewable energy in industrial parks, reduce operational costs, and optimize the charging experience for electric vehicle (EV) users, this paper proposes a real-time scheduling strategy based on the "Dual Electricity Price Reservation—Surplus Refund Without Additional Charges Mechanism" (DPRSRWAC). The strategy employs a Gaussian Mixture Model (GMM) to analyze EV users' charging and discharging behaviors within the park, constructing a behavior prediction model. It introduces reservation, penalty, and ticket-grabbing mechanisms, combined with the Interval Optimization Method (IOM) and Particle Swarm Optimization (PSO), to dynamically solve the optimal reservation electricity price at each time step, thereby guiding user behavior effectively. Furthermore, linear programming (LP) is used to optimize the real-time charging and discharging schedules of EVs, incorporating reservation data into the generation-side model. The generation-side optimal charging and discharging behavior, along with real-time electricity prices, is determined using Dynamic Programming (DP). In addition, this study explicitly considers the battery aging cost associated with V2G operations and proposes a benefit model for EV owners in V2G mode, thereby incentivizing user participation and enhancing acceptance. A simulation analysis demonstrates that the proposed strategy effectively reduces park operation costs and user charging costs by 8.0% and 33.1%, respectively, while increasing the utilization efficiency of wind and solar energy by 19.3%. Key performance indicators are significantly improved, indicating the strategy's economic viability and feasibility. This work provides an effective solution for energy management in smart industrial parks. [ABSTRACT FROM AUTHOR]

The expansion of electric vehicles (EVs) challenges electricity grids by increasing charging demand, thereby making Demand-Side Management (DSM) strategies essential to maintaining balance between supply and demand. Among these strategies, the Valley-Filling approach has emerged as a promising method to optimize renewable energy utilization and alleviate grid stress. This study introduces a novel heuristic, Load Conservation Valley-Filling (LCVF), which builds on the Classical and Optimistic Valley-Filling approaches by incorporating dynamic load conservation principles, enabling better alignment of EV charging with grid capacity. We conducted a comprehensive analysis of the heuristic across five EV charging scenarios. In both the Original and Flexible scenarios, LCVF reduced energy demand by up to 10.65%, demonstrating its adaptability and effectiveness. Notably, in the 24-hour Availability scenario, LCVF achieved a reduction of over 20% in energy demand compared to CVF. These findings indicate that LCVF could play a crucial role in enhancing real-world EV charging infrastructure, boosting energy efficiency and grid stability. By integrating DSM strategies like LCVF, energy grids can better accommodate renewable energy sources, promoting more sustainable operations. [ABSTRACT FROM AUTHOR]

In a sustainable energy system, managing the charging demand of electric vehicles (EVs) becomes increasingly critical. Uncontrolled charging behaviors of large-scale EV fleets will exacerbate loads imbalanced in a multi-microgrid (MMG). At the same time, the time cost of users will increase significantly. To improve users' charging experience and ensure stable operation of the MMG, we propose a new joint scheduling strategy that considers both time cost of users and spatial load balancing among MMGs. The time cost encompasses many factors, such as traveling time, queue waiting time, and charging time. Meanwhile, spatial load balancing seeks to mitigate the impact of large-scale EV charging on MMG loads, promoting a more equitable distribution of power resources across the MMG system. Compared to the Shortest Distance Matching Strategy (SDMS) and the Time Minimum Matching Strategy (TMMS) methods, our approach improves the average peak-to-valley ratio by 9.5% and 10.2%, respectively. Similarly, compared to the Load Balancing Matching Strategy (LBMS) and the Improved Load Balancing Matching Strategy (ILBMS) methods, our approach reduces the average time cost by 31.8% and 25% while maintaining satisfactory spatial load balancing. These results demonstrate that the proposed method achieves good results in handling electric vehicle scheduling problems. [ABSTRACT FROM AUTHOR]

Due to numerous distributed power sources connecting to the grid, which results in strong grid volatility and diminished power quality, the traditional energy storage configuration is limited in terms of flexibility and economy. Based on this, integrating electric vehicles (EVs) into the distribution network as energy storage devices has emerged as a promising development direction. This paper proposes a frequency-response optimization study considering the strong uncertainty model of EVs. First, from the perspective of temporal-spatial characteristics, energy storage resources, and users' willingness to respond, the strong uncertainty model of EVs is constructed by fitting the trip chain and the access probability of their participation in energy storage. Second, the frequency optimization model is integrated and constructed according to the response capability of a single EV. Finally, examples and scenarios are analyzed to verify that the maximum and minimum frequency offsets are reduced by 69.41% and 66.69%, respectively, which significantly reduces frequency fluctuations and stabilizes the output of EV clusters. [ABSTRACT FROM AUTHOR]

Smart cities embody a transformative approach to modernizing urban infrastructure and harness the power of deep learning (DL) and Vehicle-to-Home (V2H) technology to redefine home energy management. Neural network-based Q-learning algorithms optimize the scheduling of household appliances and the management of energy storage systems, including batteries, to maximize energy efficiency. Data preprocessing techniques, such as normalization, standardization, and missing value imputation, are applied to ensure that the data used for decision making are accurate and reliable. V2H technology allows for efficient energy exchange between electric vehicles (EVs) and homes, enabling EVs to act as both energy storage and supply sources, thus improving overall energy consumption and reducing reliance on the grid. Real-time data from photovoltaic (PV) systems are integrated, providing valuable inputs that further refine energy management decisions and align them with current solar energy availability. The system also incorporates battery storage (BS), which is critical in optimizing energy usage during peak demand periods and providing backup power during grid outages, enhancing energy reliability and sustainability. By utilizing data from a Tunisian weather database, smart cities significantly reduce electricity costs compared to traditional energy management methods, such as Dynamic Programming (DP), Rule-Based Systems, and Genetic Algorithms. The system's performance is validated through robust AI models, performance metrics, and simulation scenarios, which test the system's effectiveness under various energy demand patterns and changing weather conditions. These simulations demonstrate the system's ability to adapt to different operational environments. [ABSTRACT FROM AUTHOR]

Rapid advances in renewable energy technologies offer significant opportunities for the global energy transition and environmental protection. However, due to the fluctuating and intermittent nature of their power generation, which leads to the phenomenon of power abandonment, it has become a key challenge to efficiently consume renewable energy sources and guarantee the reliable operation of the power system. In order to address the above problems, this paper proposes an electric vehicle aggregator (EVA) scheduling strategy based on a two-layer game by constructing a two-layer game model between renewable energy generators (REG) and EVA, where the REG formulates time-sharing tariff strategies in the upper layer to guide the charging and discharging behaviors of electric vehicles, and the EVA respond to the price signals in the lower layer to optimize the large-scale electric vehicle scheduling. For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. Based on the case study of wind power, photovoltaic, and wind–solar complementary data in Jilin Province, the results show that the strategy significantly improves the rate of renewable energy consumption (up to 97.88%) and reduces the cost of power purchase by EVA (an average saving of RMB 0.04/kWh), realizing a win–win situation for all parties. The study provides theoretical support for the synergistic optimization of the power system and renewable energy and is of great practical significance for the large-scale application of electric vehicles and new energy consumption. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of grid stability posed by the integration of renewable energy sources, focusing on regions, like Jeju Island, where overgeneration complicates energy management. We introduce a novel sensitivity-based control algorithm for Virtual Power Plants (VPPs) that optimizes the deployment of distributed energy resources (DERs). Unlike conventional curtailment methods, such as Last In, First Out (LIFO) and Pro-rata, which often overlook the nuanced impacts of individual DERs on grid stability, our approach leverages electrical sensitivity analysis. This analysis quantifies the effect of each DER on voltage stability, allowing resources to be grouped and managed based on their specific sensitivity. By deriving the sensitivity matrix from the Jacobian matrix of power flow equations, the algorithm identifies critical DERs that significantly influence grid conditions, enabling more strategic curtailment decisions. The proposed method was validated using real-world data from Jeju Island, demonstrating improved efficiency and reduced curtailment compared to existing techniques. These findings underscore the potential of sensitivity-based control to enhance the efficiency of renewable energy systems, supporting sustainable energy transitions. [ABSTRACT FROM AUTHOR]