Iet Generation Transmission & Distribution最新文献

Electric grid faults are increasingly the source of ignition for major wildfires. To reduce the likelihood of such ignitions in high risk situations, utilities use preemptive de-energization of power lines, commonly referred to as Public Safety Power Shutoffs (PSPS). Besides raising challenging trade-offs between power outages and wildfire safety, PSPS removes redundancy from the network at a time when component faults are likely to happen. This may leave the network particularly vulnerable to unexpected line faults that may occur while the PSPS is in place. Previous works have not explicitly considered the impacts of these outages. To address this gap, the Security Constrained Optimal Power Shutoff problem is proposed which uses post-contingency security constraints to model the impact of unexpected line faults when planning a PSPS. This model enables, for the first time, the exploration of a wide range of trade-offs between both wildfire risk and pre- and post-contingency load shedding when designing PSPS plans, providing useful insights for utilities and policy makers considering different approaches to PSPS. The efficacy of the model is demonstrated using the EPRI 39-bus system as a case study. The results highlight the potential risks of not considering security constraints when planning PSPS and show that incorporating security constraints into the PSPS design process improves the resilience of current PSPS plans.

Thanks to reinforcement learning (RL), decision-making is more convenient and more economical in different situations with high uncertainty. In line with the same fact, it is proposed that prosumers can apply RL to earn more profit in the transactive energy market (TEM). In this article, an environment that represents a novel framework of TEM is designed, where all participants send their bids to this framework and receive their profit from it. Also, new state-action spaces are designed for sellers and buyers so that they can apply the Soft Actor-Critic (SAC) algorithm to converge to the best policy. A brief of this algorithm, which is for continuous state-action space, is described. First, this algorithm is implemented for a single agent (a seller and a buyer). Then we consider all players including sellers and buyers who can apply this algorithm as Multi-Agent. In this situation, there is a comprehensive game between participants that is investigated, and it is analyzed whether the players converge to the Nash equilibrium (NE) in this game. Finally, numerical results for the IEEE 33-bus distribution power system illustrate the effectiveness of the new framework for TEM, increasing sellers' and buyers' profits by applying SAC with the new state-action spaces. SAC is implemented as a Multi-Agent, demonstrating that players converge to a singular or one of the multiple NEs in this game. The results demonstrate that buyers converge to their optimal policies within 80 days, while sellers achieve optimality after 150 days in the games created between all participants.

Green hydrogen, the cleanest energy carrier, is receiving increased attention in recent years. Transporting hydrogen through a natural gas system (NGS) will significantly promote the use of hydrogen, moreover, hydrogen-enriched compressed natural gas (HCNG) has great potential for renewable energy accommodation. To solve the problem of altered gas flow caused by hydrogen injection into natural gas networks, an optimized operation model of integrated electricity-HCNG systems (IEHCNGS) with distributed hydrogen injecting is proposed in this paper. Firstly, a calculating model of hydrogen volume fraction based on minimum square summation and depth-first search is established to describe the gas flow distribution of NGS accurately. Secondly, a quantitative method of gas supply reliability based on maximum entropy is proposed to ensure the safe operation of the system. Finally, an optimization model of IEHCNGS is established considering the coupling constraints of the integrated system and the reliability of NGS. The case study shows that the hydrogen volume fraction calculation model can correct the heat value of gas in each pipeline in real-time, the maximum entropy model helps to improve the gas supply reliability of NGS, and the distributed hydrogen injecting mode is more capable of accommodating renewable energy.

Herein, the load power control of the stand-alone photovoltaic-battery hybrid power system (HPS) has been investigated. The underlying HPS consists of a boost DC-DC converter, a non-isolated bidirectional half-bridge converter, a photovoltaic (PV) panel, and a battery pack. On the PV side, a disturbance observer-based finite-time terminal sliding mode control (FTSMC) is used to regulate the DC bus to the desired voltage, in the presence of irradiation variation and load changes. On the battery side, the load power control system is constructed, based on a model predictive control (MPC) algorithm, with constraints on state-of-charge (SOC) and maximum current value of the battery to improve the battery life cycle and high reliability of the system. To highlight the benefits of the closed-loop system, the analytical proofs and numerical analysis are presented from a comparative viewpoint. The experimentally derived results, by implementation on TMS320F28335 digital signal processing (DSP), are also presented and discussed for practical justification.

When a severe fault occurs in the distribution network, all or parts of it may be disconnected from the upstream network. Partitioning of these islanded areas is a solution to supplying the affected loads. Due to the variable nature of loads and renewable distributed generation (DG), the static model of partitioning with a fixed nature during island operation cannot be suitable. Therefore, in this article, considering the variable nature of loads and renewable distributed generation, a dynamic model is presented for the island partitioning to restore more valuable loads, which is suitable for quick decision-making in emergencies. Also, a method for deciding on the mode of charging and discharging storage systems in emergencies is proposed. This model considers time limitation, uncontrollable DGs, controllable DGs and their control, controllability, and priority of loads, tie-switches, storage systems, simultaneous faults, different situations of unintentional islanding of the distribution network, position of switches, and variable nature of loads and distributed generations. So, it is more comprehensive than the previous methods. Applying the proposed model to the modified IEEE 69-bus system with controllable and uncontrollable generation and storage systems assuming different scenarios shows the effectiveness of the proposed scheme.

A new integration strategy of grid-forming-controlled wind farms connected to the bulk power systems through high-voltage direct current transmission based on the modular multi-level converter (MMC) is proposed to solve the problem of traditional uncontrollable DC overvoltage during the short circuit faults at the receiving end. However, a new DC overvoltage phenomenon appears after the fault is cleared, and the interaction between the wind farm and sending- and receiving-end MMCs makes the DC overvoltage mechanism more complex; further exploration shows that DC overvoltage during the sending-end fault recovery stage occurs under the new integration strategy. Therefore, the evolution process and mechanism of the new DC overvoltage are analysed. It is found that affected by the interaction between wind farms and MMCs, the alternate saturation of the integrators in the PI controller of MMCs is the main cause. Based on this understanding, additional controls are proposed to suppress this DC overvoltage during the fault recovery stage. Simulations are carried out on a test with MATLAB/Simulink, and the results verify the efficacy of the proposed methods in suppressing DC overvoltage.

Natural disasters would destroy power grids and lead to blackouts. To enhance resilience of distribution systems, the sequential load restoration strategy can be adopted to restore outage portions using a sequence of control actions, such as switch on/off, load pickup, distributed energy resource dispatch etc. However, the traditional strategy may be unable to restore the distribution system in extreme weather events due to random sequential contingencies during the restoration process. To address this issue, this paper proposes a distributionally robust sequential load restoration strategy to determine restoration actions. Firstly, a novel multi-time period and multi-zone contingency occurrence uncertainty set is constructed to model spatial and temporal nature of sequential line contingencies caused by natural disasters. Then, a distributionally robust load restoration model considering uncertain line contingency probability distribution is formulated to maximize the expected restored load amount with respect to the worst-case line contingency probability distribution. Case studies were carried out on the modified IEEE 123-node system. Simulation results show that the proposed distributionally robust sequential load restoration strategy can produce a more resilient load restoration strategy against random sequential contingencies. Moreover, as compared with the conventional robust restoration strategy, the proposed strategy yields a less conservative restoration solution.

With the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low-carbon operation of the future distribution system. To this end, this paper fully considers the multiple flexibility of DC and its impact on the active distribution network, and establishes a collaborative planning model of DC and active distribution network. Differing from most existing studies that apply robust optimization or stochastic optimization for uncertainty characterization, this study employs a novel interval optimization approach to capture the inherent uncertainties within the system (including the renewable energy source (RES) generation, electricity price, electrical loads, emissions factor and workloads). Subsequently, the planning model is reformulated as the interval multi-objective optimization problem (IMOP) to minimize economic cost and carbon emission. On this basis, instead of using a conventional deterministic-conversion approach, an interval multi-objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the proposed IMOP, which is able to fully preserve the uncertainty inherent in interval-typed information and allow to obtain an interval-formed Pareto front for risk-averse decision-making. Finally, an IEEE 33-node active distribution network is utilized for simulation and analysis to confirm the efficacy of the proposed approach.

Due to the limitations imposed by urban power grid outages for maintenance, on-line harmonic current detection technology for distribution network cables is expected to become an effective supplement to traditional offline diagnostic methods, enhancing the real-time diagnosis of distribution network cable insulation conditions. This study established a 10 kV distribution network cable test platform and prepared typical defective cables subjected to moisture and long-term thermal aging. Using COMSOL finite element electromagnetic simulation, the magnetic flux evolution laws of the cable insulation under typical defects were obtained. Experimental tests provided the harmonic current characteristics and statistical features of cables with typical defects. Based on these data, a method for analysing the degradation degree of distribution network cables was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis. Furthermore, a defect-type identification method based on cluster analysis was proposed. Results indicate that the odd harmonics and the 4th harmonic of the distribution network cable's harmonic current are closely related to the cable's degradation state. A model integrating principal component analysis (PCA) data dimensionality reduction and expectation-maximization clustering analysis achieved a recognition accuracy of up to 75.64% in distinguishing between moisture-affected and normal cable states. The proposed on-line detection and evaluation methods can effectively identify high-risk cables with latent defects.

Integrated demand response (IDR) is deemed as an effective tool to balance energy supply and demand. User’s uncertain information containing prior uncertain information and posterior uncertain information is a key factor affecting the implementation effectiveness of IDR, but existing studies fail to consider the two types of uncertain information, response risk caused by the uncertain information, and risk appetite comprehensively. Based on the principal-agent theory of optimal incentive contract under uncertain information and Markowitz's mean-variance portfolio theory, a new IDR model is established in this paper, and an IDR optimization strategy considering risk appetite under uncertain information is proposed. By proposing the user model considering multi-dimensional uncertain information and the risk appetite-based integrated energy service providers (IESP) model based on the principal-agent theory and Markowitz's mean-variance portfolio theory, we have achieved effective modelling of the user’s uncertain information and the risk borne by IESP. The arithmetic examples have verified advantages of the model in enhancing the accuracy of user’s actual response prediction and the superiority of incentive strategies, which is beneficial to reduce the cost of IESPs and enhance the benefit of users participating in IDR.