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Massive residential power consumption information provides data support for the mining and analysis of load patterns. This article proposes a complete framework for load pattern identification, which mainly includes the clustering module and the classification module. Considering that the high-dimensional load profiling dataset will bring a heavy computational burden, multiple dimensional scaling is introduced in the process of data preprocessing. Then, an innovative mixture model based on regular vine copula mixture model (RVMM) is adopted for clustering typical load patterns. Finally, a random forest (RF) classifier constructed with certain load characteristic indexes and RVMM clustering results is employed as a supervised classification model to predict the categories of subsequent new customers, and the accuracy is calculated by the 10-fold cross-validation. It is demonstrated in the case study that the proposed RVMM algorithm exhibits better performance in the clustering validity evaluation. Besides, higher accuracy is achieved by the RF classifier. [ABSTRACT FROM AUTHOR]

Economic scheduling is an important solution to improve the operational efficiency of integrated energy system (IES). Active power is the majority being considered in economic scheduling, ignoring the influence of var-voltage effect on scheduling. In this article, an agent-based var-voltage control (VVC) capability is developed, and integrated in the economic scheduling modeling of IES. First, we build the VVC agent model based on the Markov decision process, and the economic scheduling model of IES is built with the decision function of the VVC agent as a constraint. Then, because the decision function is highly nonconvex and nonlinear, a deep learning enabled surrogate model is proposed to solve the scheduling problem. In the surrogate model, we train a pooling multilayer perception (P-MLP) to fit the mapping relationship between scheduling and maximum voltage deviation under VVC. And the trained P-MLP is used to surrogate the decision function constraint. After surrogating, the scheduling problem is transformed into a mixed integer programming problem, which can be solved directly by calling the solver. Finally, the effectiveness and accuracy of the proposed method are tested by cases. The results show that the scheduling integrated with the VVC capability model can strictly meet safety constraints and reduce the operation cost of IES as well. [ABSTRACT FROM AUTHOR]

The emergence of prosumers in the distribution networks provides the proactive local management of distributed energy resources (DERs) and distributed energy trading. A bilevel framework of prosumers is proposed, enabling the joint optimization of the internal scheduling and external peer-to-peer (P2P) trading. Interactive benefit is defined as the marginal response cost. On the lower prosumer scheduling level, instead of concurrent scheduling process considering merely internal DERs’ response, interactive benefits of the DERs and the P2P trading are both taken into consideration while solving prosumers’ scheduling problem. Subsequently, a cooperative interaction method using the interactive benefit prioritization principle is proposed for the joint optimization problem in prosumer, considering both internal and external strategies. On the upper P2P trading level, an iterative bilateral negotiation algorithm is proposed, integrated with network charges. The proposed algorithm enables trading peers to reach an agreement, without the intervention of DSO. As a means to attribute the costs incurred by P2P trades to prosumers, the network charges include the congestion and loss costs, and the maintenance and modernization costs. Finally, the proposed method is implemented and benchmarked on a test case using the IEEE 33 bus system. The effectiveness of the method is verified in terms of promoting the consumption of DERs and prosumers’ benefits. Also, it is verified that the appropriate network charges may facilitate both the P2P trades and the secure operation. [ABSTRACT FROM AUTHOR]

In recent years, the idea of green aviation and environmental protection has received increasing attention from the aviation industry. Hydrogen energy has an important role in the transition to low-carbon energy systems. To address that, this article conducts the technoeconomic analysis for the hydrogen energy system, photovoltaic energy, battery storage system, electric auxiliary power unit (APU) of aircraft, and electric vehicles (EVs) into the electrified airport energy system. Specifically, the model quantifies aircraft electrical load based on passenger’ travel behavior, establishes a corresponding APU load characteristic model, and establishes an EV charging load profile based on the flight schedule and sequencing algorithm. A mixed-integer linear programming optimization method based on life cycle theory was proposed to minimize the total costs of hydrogen-integrated energy systems for airports (HIES). However, the resilience advantages of hydrogen energy concerning power failure are little explored in existing academic research. Thus, a resilience assessment method and improvement measure were proposed for HIES. Case studies have been conducted under different optimal hydrogen energy integration configurations and disaster times with resilience assessment by considering periods when the power supply capacity of the grid is insufficient. The results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With rapid development of the electric vehicle (EV) industry, charging infrastructures are built fast. However, the unreasonable deployments with increasing EVs contribute to a long queuing time for charging demand of EVs, especially in the peak hours. How to navigate a specific EV to economically satisfy its charging demand, while relieve the traffic burden, is an urgent problem. To address that, a price incentive-based charging navigation strategy for EVs is proposed. Unlike previous charging navigation studies that mainly focus on the EVs-transportation-power systems modeling, it considers the spatial-temporal influence of EVs’ charging decision, especially the simultaneous charging requests. Specifically, the charging navigation framework with the collaborative working mode of EV-charging station-information exchange center-intelligent transportation system is established first. Following this, spatiotemporal distribution of the charging demand is obtained through the origin–destination analysis. After this, an event-driven dynamic queue model is constructed. It contributes to the modeling of the charging strategy, together with the proposed reservation opportunity cost mechanism. Finally, the simulation results indicate that the presented charging navigation strategy can not only reduce the EV's charging cost but also improve the utilization rate of charging facilities, which verify its effectiveness. [ABSTRACT FROM AUTHOR]

With the development of distributed energy resources (DERs) and the increasing flexibility in active distribution networks (ADNs), the economic operation of ADN coordinating both generation and demand sides is required to be studied. In this paper, considering the electricity price response of DER, a coordinated operation strategy for ADN is presented based on a bi-level agent framework. The DER agent makes their own response based on the technical operability and economic consideration, while the ADN agent will finally coordinate each participant by using the interactive benefit prioritization (IBP) principle. The simulation results indicate that the proposed strategy cannot only reduce the power imbalance but also improve the economic operation of ADN. Moreover, consumption of renewable energy is ameliorated as well. [ABSTRACT FROM AUTHOR]

Proper selection of electric vehicle (EV) path will be helpful to improving the travel efficiency of EV users and alleviate their difficulties in charging during peak hours. In this context, a personalized fast-charging navigation strategy based on mutual effect of dynamic queuing is proposed. First, an effective charging navigation system is presented to enable the integration of EV information terminals, fast-charging stations (FCSs), intelligent transportation system, and information processing center (IPC). Next, by incorporating the real-time mutual effect of dynamic queuing as well as considering the priority reservation cost of the EV drivers who reserve charging priority before others, a dynamic reservation-waiting queue model is established. After that, on the basis of the above model, the coupling relations of the criteria that may affect the charging experience are investigated for the first time and the charging navigation plans in IPC are optimized for diverse EVs. Finally, the validity of the method is verified by conducting the simulation under the participation of 2250 EVs in the urban areas of a city. Results show that the proposed charging strategy will increase charging satisfaction on average by more than 10.9% in all analyzed cases. Additionally, the navigation can save the charging cost of EV owners while also improve the operation efficiency of FCSs. [ABSTRACT FROM AUTHOR]