Energy Conversion and Economics最新文献

With the ever-growing demand for electricity, fast development of intermittent renewable energy generation (IREG), and evolving electricity pricing mechanisms, different network tariff schemes are implemented in various countries to address emerging challenges in power system planning and operation as well as electricity market evolution. Given this background, a survey of current practices on dynamic network tariffs in some representative countries is first presented. Subsequently, key issues of dynamic network tariffs including prerequisite, implementation and effects are described. Finally, from the perspective of electricity consumers, distribution system operators (DSOs) and regulatory authorities, the challenges associated with the implementation of dynamic network tariffs are discussed.

Flexible combined cooling, heating, and power (CCHP) systems are effective in integrating wind sources. As an attractive, clean, and large-scale energy storage technique, the advanced adiabatic compressed air energy storage (AA-CAES) can store and generate both electricity and heating, and also provide cooling during expansion under certain conditions. Although AA-CAES has immense potential in multi-energy supply systems, CCHP dispatch with AA-CAES and wind power generation (WPG) is yet to be systematically studied. In this study, the economic dispatch of an AA-CAES system equipped with WPG is addressed. The AA-CAES system is comprehensively modelled by considering its thermal characteristics, air-temperature changes due to heating exchange, air storage constraint, and other factors, particularly the heat supply to the air for expansion, which is a key factor that influences the cooling supply. Subsequently, the cooling, heating, and power of the AA-CAES system are dispatched to minimise the operating cost under different supply modes. In conclusion, the proposed method is demonstrated using an integrated energy system in an industrial park, and the operation cost of the AA-CAES system is minimised. The numerical results demonstrate that the participation of AA-CAES in CCHP dispatch can curtail WPG and reduce operation costs. The economics of the different supply modes of AA-CAES are also discussed.

Restructuring the power system with higher penetration of distributed energy resources (DERs) and intelligent devices offers the potential for more efficient, reliable, and better resource utilisation of power systems through the transactive energy framework (TEF). This article provides a general overview of the mathematical models and formulations of the TEF reported in the literature. TEF concepts can be applied to various levels of the power system. Here, the TEF-related literature is divided into individual DER-, building-, microgrid-, and macrogrid-level TEF. The mathematical models of transactive agents corresponding to each level and power system network models are presented. Furthermore, TEF models for energy management and trading of integrated multi-energy systems are analysed. Finally, the potential challenges and future research directions for transactive energy are discussed.

The real-time carbon emission estimation of generators helps quantify the carbon emission costs and reduce power system emissions of power generation. Accurate estimation relies on the accuracy of the carbon emission factors (EFs) and power generation measurements. The dynamic carbon emission factor (DEF) of generators was proposed recently as a linear function of the output power. However, there is a significant deviation between the modelled DEFs and the actual measurement EFs, especially when the output power is low. This paper first presents the general definition of the DEF to characterize the emissions and focuses on the unit-level DEF (UDEF). The piecewise non-linear UDEF (P-UDEF) model is then proposed, which can better represent the unit emission characteristics. Then an accurate piecewise linear cost approximation method is proposed considering the segment points and extreme points of both P-UDEF and generation costs function. Last, the system carbon emissions and costs estimation are estimated by combined economic emission dispatch (CEED), and the reduction potential is evaluated. Case studies on an IEEE 30-bus system with piecewise linear cost functions show that the proposed P-UDEF can realize real-time emission and cost estimation as well as reduce the total system emissions by considering the incomplete combustion cost of generating units.

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.