Energy Conversion and Economics最新文献

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.

This paper focuses on the thermal analysis of the synchronous reluctance generator with a rating of 2.1 kW. It mainly uses explicit, and implicit finite difference methods for thermal analysis to reduce the complexity of thermal calculation for the machine's components. It compares the results with the results obtained using a finite element analysis (FEA) and includes the experimental verification of the obtained results. The explicit, and implicit finite difference thermal analysis is relatively simple and computationally fast. Once the design parameters are known, the electric losses and iron losses of the synchronous reluctance generator are evaluated. These machine parameters are utilized in developing the explicit finite difference (EFD), an implicit finite difference (IFD), and a 3D FEA model for thermal analysis. It is observed that the obtained results from the EFD, IFD, FEA, and experiments are very close to each other, and the temperature rise for the designed machine is within the desired and acceptable range.

This paper presents a deep reinforcement learning based data-driven solution to the microgrid bidding in the electricity market considering offers for the reserve market. The framework, based on the Markov decision process, models the microgrid's participation in the electricity market at different stages, including bidding, market-clearing, and reserve activation. The problem is split into two stages: day-ahead submission and real-time market period, and the proposed method mainly focus on the first stage. The state information from state-space models of distributed energy resources serves as input for the policy network. A deep deterministic policy gradient is employed to train the network and produce a deterministic bidding strategy. The second stage can then adjust this strategy based on the results from the first stage. The method is validated with real-world microgrid systems and data from the Singapore spot market.

This paper proposes a disturbance-observer-based control (DOBC) scheme for frequency and voltage regulation in modern power systems with high renewable energy sources (RES) penetration. The proposed approach acts as a feed-forward control that improves the dynamic performance of the conventional proportional-integral-derivative (PID) controller. The proposed voltage and frequency control has been validated through hardware-in-loop (HIL) implementation on OPAL-RT, and testing on laboratory-scale experimental test setup. The robustness of the proposed control scheme has been validated through simulations under worst-case and stochastic uncertainties to mitigate real-time variability in RES output and load. Real-time simulation results depict superior performance of the proposed control strategy in comparison to several well-established techniques under practical operating conditions, in the presence of communication delay and white noise. To validate the proposed control on laboratory-scale experimental setup, the digital twin of the physical plant transfer function has been designed. Results reveal that the proposed DOBC control scheme drastically improves the system performance without rendering much computational burden under practical operation scenarios.

The interest conflict among entities in the integrated energy system (IES) has a great challenge to operation decisions of IES. With regards to this, an optimal dispatching model of electric-heat-hydrogen IES based on Stackelberg game is proposed. Firstly, an energy producer (EP) model is formulated which considered the full utilization of hydrogen energy and involved the conversion of hydrogen energy to electricity and heat energy. Meanwhile, the demand response amount is integrated into the objective function of load aggregator (LA) in order to encourage consumers to adjust their consumption behaviour. Secondly, by analyzing the characteristics of price information interaction among EP, energy system operator (ESO), and LA, the payoffs of each entity in IES are reformulated. Finally, a Stackelberg game model is established with ESO as the dominator guiding price information, EP and LA as the followers whose private information is confidential. Genetic algorithm and quadratic programming algorithm (GA-QP) are employed to solve the developed model. Numerical experiments are carried out on an actual park-level IES in northern China to demonstrate the effectiveness of the proposed model in promoting the benefit equilibrium among various entities.

The energy risk associated with distributed energy resources (DERs) is inevitable in Peer-to-Peer (P2P) transactive energy markets owing to mismatches between energy commitments and metered measurements. However, adjusting these possible mismatches by progressive revision of the energy commitments in the rolling time horizon mitigates the energy risk, and thereby mitigates the financial risk for prosumers. In this study, the conditional value at risk (CVaR) is used to estimate the risk value for each prosumer. The energy offers that are riskier than CVaR-based threshold values are reduced in an “adjustment bid”. A new pricing mechanism for these adjustment bids is introduced, which varies with historical deviations of a prosumer from energy commitments. This market framework and pricing mechanism are simulated through a blockchain network hosted on a Python Django server using the practical Byzantine fault tolerance consensus algorithm to guarantee network immutability and data privacy. Efforts to mitigate such mismatches between ex-ante and ex-post energy values incentivise risk-aware participation in P2P markets. In addition, the welfare of both prosumers and consumers improves with their participation in the proposed market framework. Furthermore, implementing a network using blockchain technology guarantees the privacy of bidding data and provides a secure transaction platform.

Protecting cybersecurity is a non-negotiable task for smart grids (SG) and has garnered significant attention in recent years. The application of artificial intelligence (AI), particularly deep learning (DL), holds great promise for enhancing the cybersecurity of SG. Nevertheless, previous surveys and review articles have failed to comprehensively investigate the intersection between DL and SG cybersecurity. To address this gap, this study presents a survey of the latest advancements in DL technology and their relevance to SG cybersecurity. First, the functional mechanisms and scope of application of common DL techniques are explored. Subsequently, SG cyberthreats are categorised into distinct types of cyberattacks that have not been systematically examined in previous surveys. Based on this, a thorough review of the application of DL techniques in addressing each cyberthreat along with recommendations and a generalised framework for enhancing cyberattack detection using DL is offered. Finally, insights are provided into the emerging challenges presented by DL applications in SG cybersecurity that are yet to be widely acknowledged, and potential research avenues are proposed to address or alleviate these challenges.