Energy Conversion and Economics最新文献

This paper presents a mathematical model to site and size the charging infrastructure for electric vehicles (EVs) in a distribution grid to minimize the required capital investments and maximize self-consumption of local PV generation jointly. The formulation accounts for the operational constraints of the distribution grid (nodal voltages, line currents, and transformers' ratings) and the recharging times of the EVs. It explicitly models the EV owners' flexibility in plugging and unplugging their vehicles to and from a charger to enable optimal utilization of the charging infrastructure and improve self-consumption (cooperative EV owners). The problem is formulated as a mixed-integer linear program (MILP), where nonlinear grid constraints are approximated with linearized grid models.

Most mobile battery energy storage systems (MBESSs) are designed to enhance power system resilience and provide ancillary service for the system operator using energy storage. As the penetration of renewable energy and fluctuation of the electricity price increase in the power system, the demand-side commercial entities can be more profitable utilizing the mobility and flexibility of MBESSs compared to the stational energy storage system. The profit is closely related to the spatiotemporal decision model and is influenced by environmental uncertainties, such as electricity price and traffic conditions. However, solving the real-time control problem considering long-term profit and uncertainties is time-consuming. To address this problem, this paper proposes a deep reinforcement learning framework for MBESSs to maximize profit through market arbitrage. A knowledge-assisted double deep Q network (KA-DDQN) algorithm is proposed based on such framework to learn the optimal policy and increase the learning efficiency. Moreover, two criteria action generation methods of knowledge-assisted learning are proposed for integer actions utilizing scheduling and short-term programming results. Simulation results show that the proposed framework and method can achieve the optimal result, and KA-DDQN can accelerate the learning process compared to the original method by approximately 30%.

With the rapid development of electric vehicles, they have become an important part of urban distribution and transportation networks. The power distribution network and transportation network are coupled by electric vehicle clusters and integrated through strong interactions, creating a coupled system. This paper presents the study on their collaborative responses is essential to reduce losses and improve urban resilience during unconventional events. First, the multidimensional and deep-level time-varying closed-loop coupling effects of the power distribution network and urban transportation network coupled by electric vehicle clusters are analysed under unconventional events. Second, based on the different scales of unconventional events, a summary of relevant studies is made on the collaborative response strategies of the coupled system to urban local power outages and large-scale blackouts following unconventional events. Finally, future research directions are discussed.

As a new energy-supply service solution to address massive, distributed energy access to the power system, a virtual power plant has higher transmission reliability and real-time communication requirements. To achieve collaborative optimisation, distributed load and energy resources must be aggregated using new information and communication technology. This study analyses underlying communication technologies for virtual power plant interaction from the perspective of standardisation, efficiency, reliability, and security, summarises the application of blockchain, cloud-edge collaboration, machine learning, and other new information and communication technologies in virtual power plant energy trading, interaction, and scheduling, and proposes ideas for addressing shortcomings in interaction. To improve virtual power plant interaction, performance parameter mapping between communication and business technology, and multilevel virtual power plant interaction technology are proposed.

The identification accuracy of low-voltage distribution consumer–transformer relationship and phase are crucial to three-phase unbalanced regulation and error correction in consumer–transformer relationships. However, owing to the rapid increase in the number of consumers and the upgrade of the feed lines for low-voltage distribution systems, the timely update of the consumer-transformer relationship and phase information of consumers is challenging. This influences the accuracy of the basic information of the power grid. Thus, this study proposes a low-voltage distribution network consumer–transformer relationship and phase identification method based on anomaly detection and the clustering algorithm. First, the improved fast dynamic time warping distance based on the filter search between voltage sequences is used to measure the similarity between voltage curves. Subsequently, an abnormal consumer detection method based on the local outlier factor is used to identify consumers with mismatched consumer-transformer relationships by determining the local outlier factor scores of voltage curves. Furthermore, the phase information of normal consumers is identified through clustering by fast search and find of density peaks. Finally, the proposed method is validated using case studies of practical low-voltage distribution systems in China. The proposed method can effectively improve phase identification accuracy and maintain high adaptability in various data environments.

With global concerns about carbon emissions, the proportion of renewable energy generation worldwide is increasing, and the demand for flexible resources in power systems is growing. In recent years, as a clean means of transportation, the number of electric vehicles has increased, and the optimal scheduling of electric vehicles has become a research hotspot. The rise of artificial intelligence, blockchain, and other innovative technologies has enriched research on optimal scheduling of electric vehicles. To reveal the latest developments in electric vehicle optimal scheduling studies, this paper summarises the application of state-of-the-art technologies, including deep learning, deep reinforcement learning, and blockchain technology in the optimal scheduling of electric vehicles. Moreover, the advantages and disadvantages of various technical applications are highlighted. Finally, considering the shortcomings and developmental status of applications of the above three technologies, some suggestions for future research directions are proposed.

Renewable energy is a key solution to address the challenges of energy shortages and climate change. Consequently, high-renewable-energy-penetrated power systems (HREPPS) have become a popular trend. This has led to the increasing use of voltage-source converters (VSCs) as renewable-energy interfaces. Meanwhile, other VSC-based applications such as flexible alternating current transmission systems (FACTS) are used for supporting the energy conversion of renewable energy. With the large integration of VSC-interfaced devices, there is a rising concern regarding their impact on the harmonic resonance of power systems. In this study, a new harmonically coupled impedance model is proposed to inspect the harmonic resonance caused by VSC-interfaced devices in HREPPS. The model is derived based on the penetration of a multifrequency harmonic, enabling it to fully reveal the frequency coupling effect in the system. Research has shown that frequency coupling plays an important role in harmonic resonance analysis. The correctness of the proposed harmonic impedance model and its effectiveness on harmonic resonance analysis were verified using time-domain simulations of a real-life photovoltaic integrated system.

The increasing popularity of electric vehicles (EVs) and the enhanced energy storage capability of batteries have made EVs adjustable resources in economic dispatching for power grids. The guidance and control of discharging EVs have become issues with ever-increasing concerns, and the EVs can be discharged to other entities in the grid, which is called vehicle to everything in the power grid (V2eG) technology. When V2eG technologies become widespread, EVs could be widely distributed, fully controllable, and marketable power sources. As ideal mobile power sources, EVs are geographically available all the time. In this paper, the application process of V2eG technology is first introduced, and the current research status of V2eG technology is discussed. The modelling technology involved in V2eG technology is next analysed and discussed in detail. Then, the applications of V2eG technology in economic dispatch are summarized. Furthermore, research on communication technology for V2eG technology is investigated. In addition, the policies and electricity market access criteria related to V2eG are discussed, and the corresponding trading mechanisms are analysed. Finally, the related V2eG technologies are summarized, and the future development direction of V2eG technology is prospected.

Participants in the supply and demand sides of electricity markets can potentially exercise their market power for their benefits. Hence, market power mitigation is an essential component of market management that physically and economically benefits operators by ensuring fairness in deregulated electricity markets. However, the mechanisms of mitigating market power in electricity markets pose several challenges in terms of market structure monitoring, market power evaluation, and market power mitigation. Therefore, this study provides a comprehensive analysis of market power mitigation mechanisms in electricity markets. First, a review of market structure monitoring and market power evaluation methods is provided to assess participants’ potential and degree of use/abuse of market power. Then, a detailed review of optimal bidding strategies in the supply and demand sides of electricity markets is provided. Accordingly, a systematic classification of market power mitigation is described. Finally, market power mitigation challenges and potential future research directions are discussed.

The transformation of the conventional power grid into a smart grid has revolutionised the power industry. However, this has also made the power grid vulnerable to various cyberattacks. This study evaluates the vulnerability of system states, which act as crucial inputs to other essential grid monitoring and control functions, against credible false data injection attacks on nonlinear AC state estimators. Accordingly, two approaches are proposed. The first maximises the yield of the attack, and the second reduces the number of physical meters to be manipulated to launch an attack. This study also develops an attack feasibility index, which can assist in quantifying the feasibility of an false data injection attack while considering various characteristics of a cyberattack. The attack feasibility index also aids in simultaneously comparing the effects of both approaches and assessing the vulnerability of the estimated states. The effectiveness of the proposed methods and the index are established by calculating the state vulnerability of the IEEE 39 bus and IEEE 118 bus test systems.