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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 the increasing market penetration of electric vehicles (EVs), the charging behavior and driving characteristics of EVs have an increasing impact on the operation of power grids and traffic networks. Existing research on EV routing planning and charging navigation strategies mainly focuses on vehicle-road-network interactions, but the vehicle-to-vehicle interaction has rarely been considered, particularly in studying simultaneous charging requests. To investigate the interaction of multiple vehicles in routing planning and charging, a routing optimization of EVs for charging with an event-driven pricing strategy is proposed. The urban area of a city is taken as a case for numerical simulation, which demonstrates that the proposed strategy can not only alleviate the long-time queuing for EV fast charging but also improve the utilization rate of charging infrastructures. Note to Practitioners—This article was inspired by the concerns of difficulties for electric vehicle (EV)’s fast charging and the imbalance of the utilization rate of charging facilities. Existing route optimization and charging navigation research are mainly applicable to static traffic networks, which cannot dynamically adjust driving routes and charging strategies with real-time traffic information. Besides, the mutual impact between vehicles is rarely considered in these works in routing planning. To resolve the shortcomings of existing models, a receding-horizon-based strategy that can be applied to dynamic traffic networks is proposed. In this article, various factors that the user is concerned about within the course of driving are converted into driving costs, through which each road section of traffic networks is assigned the corresponding values. Combined with the graph theory analysis method, the mathematical form of the dynamic traffic network is presented. Then, the article carefully plans and adjusts EV driving routes and charging strategies. Numerical results demonstrate that the proposed method can significantly increase the adoption of EV fast charging while alleviating unreasonable distributions of regional charging demand. [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]

An improved explicit estimation algorithm is proposed for reliability estimation of the distribution network. Firstly, hierarchical clustering is used to identify and cluster typical feeders based on the topology structure. Secondly, the explicit formula of reliability index under each typical feeder topology is derived by regression analysis, to establish the model for network reliability estimation. Numerical simulations show the suitability of the proposed method in obtaining accurate reliability index for diversified network topology. [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]

Scale evolution pattern recognition of plug-in electric vehicles (PEVs) and charging demand modeling are essential for various involved sectors to promote PEV proliferation and integration into power systems. Considering that the market penetration development of PEVs will drive the evolution of charging demand, an integrated dynamic method based on an agent-based modeling technology is proposed in this paper by combining scale evolution model with charging demand model to jointly detect the possible PEVs evolution patterns and long-term charging demand profiles. Heterogeneous consumers presenting different preferences in making vehicle purchase decisions and the interactions with other consumers via social dynamics are taken into consideration in the scale evolution model. After obtain the scale of PEVs by aggregating individual consumer agents purchase behavior, the driving patterns, charging behavior habits, and charging strategies are systematically incorporated into the charging demand model. Case studies demonstrate the feasibility and effectiveness of the proposed methodology by taking an urban area as an example. Furthermore, the factors that affect the market evolution of PEVs and the charging demand are also simulated and analyzed. [ABSTRACT FROM AUTHOR]

A novel method for optimal sizing of energy storage system (ESS) in active distribution networks using state of energy (SOE) function is proposed in this letter. The SOE of ESS in an operational cycle is characterized by an approximately continuous function based on Fourier–Legendre series expansion. Then, the ESS capacity can be determined by the optimized SOE function extreme. The effectiveness and advantages of the proposed method are verified by test cases. [ABSTRACT FROM PUBLISHER]

The water system management problem has been widely investigated. However, the interdependencies between water and energy systems are significant and the effective co-optimization is required considering strong interconnections. This paper proposes a two-stage distributionally robust operation model for integrated water-energy nexus systems including power, gas and water systems networked with energy hub systems at a distribution level considering wind uncertainty. The presence of wind power uncertainty inevitably leads to risks in the optimization model. Accordingly, a coherent risk measure, i.e., conditional value-at-risk, is combined with the optimization objective to determine risk-averse operation schemes. This two-stage mean-risk distributionally robust optimization is solved by Bender's decomposition method. Both the day-ahead and real-time operation cost are minimized with an optimal set of scheduling the multi-energy infrastructures. Case studies focus on investigating the strong interdependencies among the four interconnected energy systems. Numerical results validate the economic effectiveness of IES through optimally coordinating the multi-energy infrastructures. The proposed model can provide system operators a powerful two-stage operation scheme to minimise operation cost under water-energy nexus considering risk caused by renewable uncertainties, thus benefiting customers with lower utility bills. [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]

The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish–Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city. [ABSTRACT FROM AUTHOR]