Energy Conversion and Economics最新文献

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.

Prolonged exposure of power transmission lines to extreme ice disasters in the atmosphere disrupts transmission. First, this study establishes a comprehensive failure probability model for the impact of extreme ice disasters on the transmission lines to better understand and improve the transmission lines' ability to withstand such disasters. It predicts the line failure probability based on the initial design data of the lines. Second, the system's weak points are identified, and the fault scenario set is established using the Monte Carlo state sampling method. Next, the system's state is calculated using the resilience assessment index and the DC optimal load reduction model. Finally, an optimal decision-making method for the transmission line maintenance sequence is proposed from the post-disaster maintenance scheduling perspective. Considering the travel time and maintenance effect, this method can effectively restore the system state in response to extreme ice disasters. The IEEE 30-bus power system is taken as an example of simulation verification. The results show that this method can effectively complete the system load state restoration and improve the transmission system's resilience. It also has certain practicality and good practical application values.

Smart grids are typically modelled as cyber–physical power systems, with limited consideration given to the social aspects. Specifically, traditional power system studies tend to overlook the behaviour of stakeholders, such as end-users. However, the impact of end-users and their behaviour on power system operation and response to disturbances is significant, particularly with respect to demand response and distributed energy resources. Therefore, it is essential to plan and operate smart grids by taking into account both the technical and social aspects, given the crucial role of active and passive end-users, as well as the intermittency of renewable energy sources. In order to optimize system efficiency, reliability, and resilience, it is important to consider the level of cooperation, flexibility, and other social features of various stakeholders, including consumers, prosumers, and microgrids. This article aims to address the gaps and challenges associated with modelling social behaviour in power systems, as well as the human-centred approach for future development and validation of socio-technical power system models. As the cyber–physical–social system of energy emerges as an important topic, it is imperative to adopt a human-centred approach in this domain. Considering the significance of computational social science for power system applications, this article proposes a list of research topics that must be addressed to improve the reliability and resilience of power systems in terms of both operation and planning. Solving these problems could have far-reaching implications for power systems, energy markets, community usage, and energy strategies.

Distribution system planning is a multifaceted topic involving financial, regulatory, and system level analysis. The wide nature of the topic warrants a holistic study considering all aspects of analysis. The distribution utility is a natural monopoly that is subjected to utility regulation. The regulator can impact customer experience by strategically influencing the planning decisions of the utility. Hence, this paper reviews the existing utility regulation methods in the context of the distribution system and their efficacy in improving certain reliability and efficiency objectives. A two-bus system is used to demonstrate the impact of classical models in alleviating reliability and efficiency issues through demand response. Further, a review is conducted on distribution system planning models without a regulatory regime, and suitable models for holistic analysis are identified. A two-person complete information regulator and utility game with a comprehensive distribution system model at the lower level is proposed. A framework based on the Mixed Integer Bilevel Linear Program (MIBLP) is discussed to find the equilibrium point of the proposed game.

Nowadays, transactive energy markets (TEMs) are emerging as interesting frameworks in deregulated power markets to control the balance of supply and demand in the entire electrical network. Due to wide deployment of renewable energy resources, grid connected micro-grids, and open access transmission and distribution networks, the planning and operation of TEMs become complex. So, an efficient optimal dispatch model for TEMs should be developed to achieve the objectives of TEMs, such as feasible sizes of transactions and optimal dispatch of these transactions with minimal operating costs. The transactive dispatch problem is similar to the resource allocation/matching problem. Recently, optimal transport (OT) has received significant attention in various fields including optimization theory and resource matching problems due to its potency and relevance in modeling and optimization. An OT-based approach is proposed here for optimal dispatch of transactions in energy markets while minimizing the cost of transactions considering the operating constraints of the system. The proposed approach can efficiently determine the feasible sizes of transactions without any security issues. The optimal solutions of the OT-based approach are obtained using a Sinkhorn iterative technique. Also, the load uncertainties are considered in this work to analyse the impacts of load uncertainties on the optimal dispatch of transactions. The numerical simulation results on the modified IEEE 9-bus system, modified IEEE 57-bus system, modified IEEE 118-bus system, and Indian Northern Regional Power Grid (NRPG) system illustrate the efficacy of the proposed OT-based framework.