Energy Conversion and Economics最新文献

Power system dynamics are generally modeled by high dimensional nonlinear differential-algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system nonlinearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.

The expansion of the DC fast-charging (DCFC) network is expected to accelerate the transition to sustainable transportation by offering drivers additional charging options for longer journeys. However, DCFC places significant stress on the grid, leading to costly system upgrades and high monthly operational expenses. Incorporating energy storage into DCFC stations can mitigate these challenges. This article conducts a comprehensive review of DCFC station design, optimal sizing, location optimization based on charging/driver behaviour, electric vehicle charging time, cost of charging, and the impact of DC power on fast-charging stations. The review is closely aligned with current state-of-the-art technologies and encompasses academic research contributions. A critical assessment of 146 research articles published from 2000 to 2023 identifies research gaps and explores avenues for future study based on the literature review.

In recent years, the frequent occurrence of natural disasters has seriously threatened the security and stability of distribution networks. Amidst these natural disasters, researchers have increasingly focused on ensuring fault recovery in distribution networks. Soft open points (SOPs) are new types of power electronic devices that can effectively recover faults in distribution networks. When a fault occurs, SOPs can quickly block and isolate fault short-circuit current, provide power access to non-fault areas, and optimise the power flow of distribution networks. Based on the critical role of distributed generation (DG) power supply in fault recovery, this study proposes a method that leverages the synergistic capabilities of SOPs and DG to recover faults in distribution networks. By employing a binary particle swarm optimisation algorithm, this method effectively improves load recovery, reduces the number of switching operations, and minimises network loss. The proposed method was simulated and verified using the IEEE 33-node and 99-node systems.

The carbon market plays a critical role in promoting the transition toward renewable energy sources and reducing greenhouse gas emissions in the electricity generation and transmission. Extant research has overlooked the dynamic bilateral causality that exists between electricity and carbon markets. Moreover, these studies have frequently treated the macroeconomic effect as exogenous. To bridge this research gap, this paper presents a holistic modeling framework that comprehensively captures the intertwined nature of electricity and carbon markets and their concomitant interactions with the overarching economy. The suggested modeling framework is an integration of three principal modules, namely, a carbon market, an electricity market, and economic system. This synergistic blend provides an exhaustive understanding of the entire market operation cycle. It offers detailed clearance rules, and most importantly, it adopts a macroeconomic systematic modeling approach for evaluating the impact emanating from the interconnected electricity and carbon markets. To illustrate the practicality and effectiveness of the proposed approach, a case study anchored on empirical data sourced from the electricity and carbon markets in China is conducted. The empirical findings underscore the fact that incorporating a green certificate market into the modeling framework can precipitate a reduction in greenhouse gas emissions. Additionally, the results indicate that expanding the scale of the green certificate market from 1.9% in 2021 to 33% by 2023 will increase the generation of green electricity by 10%.

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.