IET Energy Systems Integration最新文献

Coal mines consume electricity from grids while making use of various derived energy resources to generate heat and power. This allows coal mines to act as energy prosumers. This paper presents an integrated optimisation model of optimal operation and offers strategies for profit-seeking of a coal-mine energy prosumer in a liberalised market. The discussed problem is formulated as a tri-stage bi-level programming model. In the upper level, the coal mine-integrated energy system (CMIES) operator decides the retail contract management, equipment operation plan, and retail electricity price offered to end-users through three stages to maximise its expected profits at a predefined risk level. In the lower level, the end-users react to the price bids offered by the CMIES operator under study and other market retailers to minimise their costs for energy procurement. Unlike previous approaches, the endogenous uncertainties associated with market subjects' strategic behaviours are explicitly considered. To solve the proposed problem efficiently, the Karush–Kuhn–Tucker conditions and multicut benders decomposition method are employed. The simulation results show that the collaborative optimisation of the energy cycle and production scheduling can reduce daily operating costs by nearly $3576 and carbon emissions by 6.72 tons, which verifies the effectiveness of the proposed method.

The rapid evolution of renewable energy sources and the increasing demand for sustainable power systems have necessitated the development of efficient and reliable large-scale energy storage technologies. As the backbone of modern power grids, energy storage systems (ESS) play a pivotal role in managing intermittent energy supply, enhancing grid stability, and supporting the integration of renewable energy. This special issue is dedicated to the latest research and developments in the field of large-scale energy storage, focusing on innovative technologies, performance optimisation, safety enhancements, and predictive maintenance strategies that are crucial for the advancement of power systems.

This special issue encompasses a collection of eight scholarly articles that address various aspects of large-scale energy storage. The articles cover a range of topics from electrolyte modifications for low-temperature performance in zinc-ion batteries to fault diagnosis in lithium-ion battery energy storage stations (BESS). They also include predictive models for capacity decay in vanadium redox flow batteries, safety improvements through arc voltage and temperature analysis, and data-driven approaches for predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs). Additionally, the articles explore lithium inventory estimation, surface modification of electrodes in zinc-bromine flow batteries (ZBFBs), and the impact of water on battery performance and safety. These contributions provide a comprehensive view of the current state and future directions of energy storage technologies in the context of power systems.

Jin et al. review various anti-freezing electrolyte modification strategies for low-temperature aqueous zinc-ion batteries (AZIBs), which are promising for energy storage due to their safety and environmental benefits. They highlight the challenges posed by conventional aqueous electrolytes that freeze in sub-zero temperatures, leading to poor electrochemical performance. The authors emphasise the need for further research to optimise these electrolytes for better performance in extreme conditions, providing insights into future directions for developing effective low-temperature AZIBs.

Lin et al. investigate the impact of water on battery performance and safety. It is found that the reaction of water with LiPF₆ in battery electrolytes ultimately causes electrical contact loss and capacity decay. Excess water reduces electrolyte conductivity, increases internal resistance, and affects lithium-ion migration, altering the electrode structure and performance. The presence of water accelerates exothermic reactions, decreasing thermal stability and increasing heat release rates during thermal events. Experimental results also show that internal resistance and self-discharge rates increase with water content, indicating significant impacts on battery performance and safety.

Li et al. analyse the si

In recent years, bifacial solar panels are accelerating to replace single-side PV devices in traditional PV power generation system due to their high utilisation rate and price advantages. This makes the stability and control strategy of grid-connected bifacial PV systems (GCBPVS) to be different from the traditional method after it is connected to the power systems. This paper fully considers each detailed module in GCBPVS using virtual synchronous generator (VSG) technology and derives the small-signal model of the fully grid-connected (GC) system using the linearisation method of each sub-module. Then, it analyses the small disturbance stability and oscillation mode characteristics of GCBPVS by combining the effects of partial system parameters change on eigenvalues. Especially for the key parameters that affect the control stability of the system, this paper proposes a novel global optimisation design method of key control parameters to reform the distribution of system eigenvalues and improve the stability of GCBPVS. Finally, case simulation and result analysis show that the accuracy of the above small-signal model is very high and the related stabilisation control method is very effective. In addition, hardware-in-the-loop (HIL) experiments demonstrate that the proposed control method has strong engineering practicability and is better suitable for application.

The inverter side of hybrid cascaded HVDC adopts the structure of Modular Multilevel Converter (MMC) in series with Line Commutated Converter (LCC). The complete system amalgamates the advantages of LCC with MMC, but it also makes the interaction process of multi-controller more complicated during the failure of the system. Therefore, through the analysis of the controller interaction process during the system fault, this paper proposes a multi-controller coordinated control strategy based on the inverter side of the hybrid cascaded HVDC system, which can suppress the subsequent commutation failure of the system and take into account the recovery characteristics of the system during the fault, which has certain practical application value. Initially, the operational properties of current error control (CEC) and voltage-dependent current limit control (VDCOL) are examined, and a coordinated control technique for subsequent commutation failure suppression and rapid power recovery during fault is proposed. Secondly, aiming at the problem of power return between MMC after VDCOL regulation, the new VDCOL control curve is coordinated to improve the MMC control strategy to ensure stable recovery during system failure. Finally, the simulation model is built in PSCAD/EMTDC simulation environment. The simulation results indicate that the proposed control technique can successfully achieve the synchronisation of commutation failure suppression and rapid, stable power restoration, thereby enhancing the operational performance of hybrid cascaded HVDC.

The inherent randomness and intermittent nature of wind speed fluctuations pose significant challenges in accurately predicting future wind speeds. To address this complexity, a wind speed prediction model based on a multiscale temporal-preserving embedding broad learning system (MTPE-BLS) is proposed. MTPE-BLS used the localised behaviour of wind speed data, which is simpler to model and analyse than global patterns. Firstly, frequency clustering-based variational mode decomposition (FC-VMD) is proposed to deal with the non-stationary wind speed data into multiple intrinsic mode functions (IMFs). Then, temporal-preserving embedding (TPE) is proposed to extract the underlying temporal manifold structure from the decomposed IMFs. Finally, the extracted features are mapped into the broad learning system (BLS) to establish an accurate prediction model. Experimental results on two real-world wind speed datasets demonstrate the best performance of the proposed MTPE-BLS model compared to that of others. Compared to the original BLS, the MTPE-BLS achieves significant improvements, reducing the root mean square error (RMSE) and mean absolute error (MAE) by an average of 48.57% and 47.72%, respectively.

Enhancing the stability of the Virtual Synchronous Generator (VSG) under transient conditions has become a new challenge for VSG operation. This paper presents the design of a Virtual Power System Stabiliser (VPSS) with virtual inertia calculations for the stability enhancement of the VSG system under transient conditions. The virtual inertia is calculated by considering the transient conditions resulting from a three-phase ground fault and the allowable phase margin in the VSG. This aims to prevent the coupling effect, which can cause the active power loop control and reactive power loop control to operate non-independently. Subsequently, the VPSS is specifically designed based on the determined virtual inertia characteristics. The VPSS design is developed by taking into account the phase angle shift of the VSG. The proposed combination of virtual inertia and VPSS is capable of providing accurate compensation for phase angle changes under transient conditions. To evaluate the performance of the proposed virtual inertia and VPSS, a system-level VSG model is used to thoroughly analyse the system's performance. Based on the results and analysis, it is shown that the control strategy utilising the combination of virtual inertia and the proposed VPSS design can improve VSG stability under transient conditions.

Decarbonising the energy supply system is crucial to mitigate climate challenges. An emerging type of the multi-energy system, that is, the low-temperature electrified district heating system is gaining increasing popularity as a potential solution for future low-carbon heat supply. This paper investigated its operational optimisation with thermal energy storage (TES) installed at building sides. The optimisation model was to obtain the minimum operation costs of all heat pumps in this system. The TES was meant to achieve energy arbitrage through load shift, but it was observed from the optimised results that the TES did not play an active role in the optimisation. Five possible causes were identified and further investigated to reveal their impacts on the optimisation process. Results showed that the thermal capacitance, thermal resistance, and indoor temperature range of the building were major influencing factors, while the electricity price tariff and heat loss parameters of TES were minor ones. The results indicate that there is no need to equip the TES for operational optimisation purposes when the building thermal capacitance is larger than a threshold value, the thermal resistance is smaller than a threshold value, or the indoor temperature range is broader than a threshold value. These threshold values are case-specific and can be determined with the simulation model and method developed in this paper.

State estimation plays an important role in the monitoring and control of integrated electric–gas systems (IEGSs), but it faces limitations due to insufficient measurement configurations and low data redundancy in these systems; additional measurement configurations are needed to increase the overall system observability. Owing to the lack of suitable observability analysis methods, optimal measurement configurations for IEGSs remain underexplored. This paper presents an IEGS observability analysis method that incorporates gas flow dynamics via the Lie derivative. This method incorporates the complex topological structure of the gas network and the dynamic process of gas flow into the IEGS observability analysis. Furthermore, the measurement configuration problem for IEGSs considering gas flow dynamics is formulated as a rank-constrained optimization problem. To handle the rank constraints effectively, an iterative cutting method is developed with convergence guarantees. Finally, the efficacy and practicality of the proposed methods are validated through case studies of varying scales. The proposed optimal measurement configuration model reduces measurement configuration costs while maintaining system observability.

In this paper, the two level stochastic optimisation approach has been suggested. In the lower level, the probability distribution functions (pdfs) for bus voltages and branch currents have been determined using the Monte Carlo simulation (MCS) to be employed in chance-constrained probabilistic optimisation by taking into account solar radiation and power consumption uncertainties in the distribution networks (DNs). In the upper level, artificial hummingbird algorithm (AHA) handles the expected power loss minimisation subjected to chance constraints, which are related to bus voltages and branch currents, by optimising photovoltaic (PV) system capacities. This research examines the effect of uncertainties in PV system performing under diverse solar radiation and varying PV penetration level scenarios on expected power losses with stochastic DN limits. The stochastic optimisation approach has been compared with the deterministic method for observing the efficiency with optimal power usage. This research improves the knowledge base for optimal PV installation in DN by combining AHA with MCS and emphasising chance-constrained methods. To indicate the efficacy of proposed strategy, the optimisation outcomes are tested utilising MCS under various uncertainty circumstances and DN parameters are assessed in terms of probabilities of exceeding limitations. The results are compared with the application of firefly algorithm (FA) using stochastic assessment and simulations. The simulation results show that the AHA technique outperforms the FA method in terms of effectively minimising power losses with less simulation time.

This research paper introduces a comprehensive formulation for robust dynamic network reconfiguration (NR) of unbalanced active electric distribution networks (DNs). Network reconfiguration is a potent strategy to minimise active power loss in DN as it involves altering network topology through sectionalising (normally closed) and tie-line switches (normally open). However, NR is usually a mixed integer NP-hard non-linear optimisation problem due to the discrete nature of the switches. Hence, including variable injection uncertainties (from generation or load) for an unbalanced active DN with all its attributes further poses a significant challenge in solving NR. The proposed formulation addresses these challenges in a robust optimisation (RO) framework to get a robust topology and power and voltage set points for dispatchable Distributed Generators (DGs). Also, Chance-Constrained robust formulations are proposed to regulate the conservatism of RO. Numerical analyses demonstrate the impact of conservative robust NR on DG set points compared to the non-robust NR method. Tests on a modified unbalanced IEEE 34-bus system and comparison with previous formulations verify the efficacy of the proposed approach, showcasing its effectiveness.