Energy Conversion and Economics最新文献

Long-term scenario generation of renewable energy is regarded as an important part of the optimal planning of renewable energy systems. This study proposes a scenario generation method for generating long-term correlated scenarios of wind and photovoltaic outputs from historical renewable energy data. The generation of scenarios was divided into two processes: long-term yearly sequence generation and intraday scenario generation of wind–solar energy. In the long-term yearly sequence generation process, the k-means clustering algorithm and Markov chain Monte Carlo simulation method were developed to capture the seasonal and long-term features of wind and photovoltaic energies. Furthermore, an attention-based conditional generative adversarial network (ACGAN) was proposed to capture short-term features. An attention structure and conditional classifiers were developed to capture features in the generated scenarios. To accelerate the convergence process and improve the quality of the generated scenarios, a gradient penalty was included in the ACGAN model. Numerical case studies were conducted to verify the validity of the proposed method using a real-world dataset.

Edge-side services provide new ideas for microgrid operational control, but as the microgrid control structure becomes increasingly large, the cost of configuring edge-side services also grows. In this context, it is necessary to find a modelling approach that can unify the mathematical models involved in microgrid control systems. First, a microgrid control structure with edge-computing services based on hybrid control theory is proposed, which can exploit the hybrid characteristics of the microgrid control and reduce the amounts of communication using event-triggered technology. Then, a hybrid control modelling method based on activity-on-edge networks is proposed, along with a standardised control strategy configuration method. The texts entered by the configurator can be parsed in an intuitive way. Complex control strategies can be configured with low-code input while improving the reliability of the strategies. Finally, a distributed control strategy for DC microgrids was studied and modelled using the hybrid control modelling approach based on activity-on-edge networks. The superiority of edge-computing services based on hybrid control theory and event-triggered technology in reducing communication and improving control in real time is demonstrated through the case study.

Developing renewable energy generation (REG)-rich power systems could contribute to achieving carbon neutrality. To ensure the secure and economic operation of power systems with high penetration of renewable energy, it is necessary to solve the problem of inefficient utilisation of demand-side resources by the current electricity market mechanism. The metaverse, an emerging technology attracting widespread attention, is expected to efficiently solve this problem. The metaverse can be regarded as a virtual-real interactive economic system built on advanced technologies such as blockchain, artificial intelligence, extended reality, avatars, and decentralised autonomous organisations (DAO). This paper first briefly introduces the concept, architecture, technologies, and features of the metaverse. Then, a metaverse-based DAO for energy systems is proposed and the corresponding business model is explored. The Energy DAO utilises algorithms and user consensus combined with smart contracts to solidify organisational operation rules. In this way, it organises users to directly participate in multiple types of electricity markets and carbon markets, as well as behavioural data production and transactions. Finally, an Energy DAO example for demand-side sources demonstrates how the Energy DAO could solve the problems of information asymmetry, information opacity, and incentive incompatibility in electricity market mechanisms.

A new concept of Battery Doctor is proposed for the next generation battery health assessment, first, the comprehensive assessment framework integrating the multiple health indices is formulated, where the bottom-up assessment hierarchy is used to provide the holistic health indicator from the battery cell to the large-format battery. Second, several options for defining a uniform indicator state of X is provided to effectively measure the battery health, which contributes to promoting the health assessment from state of charge and stage of health to state of X. Finally, the future challenges and opportunities of developing the battery doctor are disclosed from three different viewpoints, which is to incentivize the technology breakthrough for the next generation battery health assessment.

With the continuously increasing penetration of electric vehicles (EVs), the mutual match between the distribution of charging resources and the spatial–temporal distribution of EV charging demands is becoming increasingly important. To address this, this paper proposes a novel two-stage customized EV charging–navigation strategy. Building on previous research on the real-time information from dynamic traffic networks, a personalized dynamic road impedance (PDRI) model is built to transform three main criteria (distance, time, and finance) affecting charging–navigation into comprehensive road impedance. In the first navigation stage, fast-charging stations (FCSs) with the lowest overall objective are selected. In the second navigation stage, an improved Floyd–Warshall algorithm is utilized to identify the routes with the lowest personalized weight to the selected FCS in the PDRI model. Notably, the personalized preferences of EV drivers for the three primary criteria are considered in both stages of the navigation process. Finally, simulation results demonstrate a significant improvement in the degree of matching between charging navigation plans and drivers' personalized requirements, and a more balanced spatial–temporal distribution of EV charging demands among FCSs, which verifies the effectiveness of the proposed strategy.

This paper presents a simplified optimal power flow (OPF) framework to facilitate co-optimised active and reactive power scheduling for synchronous generators and price-sensitive demands. The proposed framework creates an opportunity for generators and loads to simultaneously participate in a combined market for active and reactive power. The co-optimisation of active and reactive power generation is constrained by the interdependence of the active and reactive power capacities, which is represented by the generator capability curve. Thus, a detailed mathematical derivation of the opportunity costs across various regions of the generator capability curve is presented. This study considers a detailed generator capability curve that considers the armature, field, under-excitation, and prime mover limits. The interdependence of active and reactive power consumption for demand is modelled using the concept of power-factor. The OPF problem for generator and load scheduling is formulated as a non-linear optimisation task, leveraging the inherent properties of the generator capability curve, that is, piecewise smoothness, continuity, and the monotonically increasing slope magnitudes. Furthermore, to simplify the OPF formulation, the non-linear capability curve is represented as a combination of the linear curves. To demonstrate the effectiveness of the proposed OPF methodologies, suitable case studies are conducted using different test systems.

In recent years, extreme weather events, such as typhoons, have led to large-scale power outages in distribution systems. As a result, developing strategies to bolster distribution system resilience has become imperative. This paper proposes a two-stage stochastic programming model aimed at enhancing this resilience. Prior to a typhoon, the first stage establishes a comprehensive wind field model based on extreme value distribution for accurate wind speed predictions. Simultaneously, a refined stress–strength interference model is used to determine the likelihood of distribution line failures. Taking into account the uncertainty of line damage, repair crews and mobile emergency generators are then strategically positioned at staging depots. Following the typhoon, the second stage coordinates network reconfiguration, dispatches repair crews, and mobilizes mobile emergency generators to minimize load shedding and expedite repairs. This model was validated on the IEEE 33-bus distribution system, coupled with a corresponding transportation network, utilizing data from the 2018 super typhoon Mangkhut'' in China. Simulations indicate that our approach can effectively reduce load shedding and power outage durations, thereby enhancing the resilience of distribution systems.

This study presents a comprehensive review of networked micro-grid (NMG) operations under the transactive energy paradigm. Specifically, we aimed to identify and analyze the key aspects of transactive NMG models, including operational scenarios, ownership models, transactive operation designs, prosumer behaviour, and business models. This is accompanied by a review of real-world applications and analysis of current research trends. With several research gaps identified, this study provides different views on the challenges in mathematical modelling and real-world deployment of NMG.

The characteristics of three-phase imbalances are present in medium- and low-voltage distribution networks. Integrating single-phase distributed generation (DG) exacerbates network imbalances, resulting in increased power losses and potential safety hazards. To address these issues, a dynamic reconfiguration strategy (DNR) for three-phase imbalanced distribution networks, considering soft open points (SOP), has been proposed. The objective is to alleviate the three-phase imbalance and minimize the operational costs of the distribution network. Within the reconfiguration strategy, the constraints of DG current imbalance in practical system operations are considered. A three-phase imbalanced DNR model that simultaneously considers the constraints of the SOP and DG current imbalances is introduced. This model aims to optimize the operation of all devices, including the SOP, to address the overall imbalance of the distribution network. This enables the authors to transform the non-linear model into a mixed-integer linear programming (MILP) model, significantly improving the solution efficiency. To validate the proposed strategy, simulations of a modified IEEE 34-node distribution system and an actual 78-node distribution system were conducted. The results demonstrate that this strategy offers significant economic benefits and ensures the security of the distribution network.

The integrated energy system leverages advanced information, communication, and control technology to integrate various energy subsystems, including electricity, heat, and gas, to achieve efficient and coordinated operation of the entire energy system. The integrated energy system further forms an integrated energy cyber physical system (IECPS) through resonant coupling between cyber and physical systems. However, integrating multiple energy subsystems and the deep coupling of cyber and physical procedures in the IECPS increases the risk of cyberattacks, necessitating enhanced cybersecurity measures. This paper provides a comprehensive overview of the cyber-physical coupling modelling, security performance evaluation, attack and defence methods, and operation and recovery strategies of IECPS in response to cybersecurity threats. The coupling modelling of cyber and physical systems is discussed, followed by an evaluation of security performance of IECPS. Next, a range of attack and defence methods and effective IECPS operation and recovery strategies are presented. At last, the future research direction of IECPS cybersecurity is pointed out.