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• The grid frequency is reflected accurately in T&D co-simulation. • The impact of uncertainty in flexible load aggregation on SFR is analyzed. • The CUCB-Avg algorithm for user selection is integrated into co-simulation framework. • Time, power deviation, affected users, and regret of SFR are reduced. Aggregated flexible loads offer a promising solution for secondary frequency regulation (SFR) in power systems with increasing intermittent renewable energy sources. However, uncertainties in users' behaviors may create a mismatch between the aggregated power of flexible loads and the control target of SFR. Furthermore, as these loads are dispersed across distribution networks, distribution network's topology and its interplay with the transmission network may affect the performance of aggregated flexible loads in SFR. Therefore, this paper proposes an adaptive combinatorial multi-armed bandit (CMAB) flexible load aggregation strategy to enhance SFR performance in co-simulated transmission and distribution (T&D) networks. First, a dynamic T&D co-simulation framework is proposed based on the HELICS platform. Then, the combinatorial upper confidence bound-average (CUCB-Avg)-based CMAB algorithm is employed to manage users' uncertain responses. Case studies on the IEEE 14-bus system with five IEEE 8,500-node feeders demonstrate the effectiveness of the proposed framework and method. The SFR performance of the proposed strategy based on CUCB-Avg algorithm outperforms the average and CUCB strategies in terms of accuracy, rapidity, robustness, and the number of affected users. [ABSTRACT FROM AUTHOR]

The integration of inverter-based renewable energy sources into power grids presents a unique challenge: a reduction in grid inertia, leading to potential frequency stability issues. This paper introduces an innovative approach to address this challenge, focusing on the dynamic role of wind power plants equipped with dual-fed induction generators (DFIG). Unlike traditional methods that rely on fixed inertia control strategies and wind power droop coefficients, our method offers a dynamic solution tailored to the fluctuating nature of grid frequency dynamics. We propose a novel variable inertia support mechanism, coupled with an optimized power point tracking control and strategic reserve, to maximize the effective use of wind power reserves without waste. Additionally, we account for the inherent uncertainties in wind power generation, enhancing the frequency support capabilities of wind turbines through a fast frequency response strategy. A key feature of our approach is the application of an advanced piecewise linearization fitting method to establish a robust frequency nadir constraint. This is integrated into a two-stage robust frequency security-constrained unit commitment model, enabling optimal real-time decision making for frequency stability in wind power plants. The efficacy and practicality of our proposed method are verified through a detailed case study on a modified IEEE 39-bus system and a hardware-in-the-loop experiment. Compared with the traditional fixed parameter control method, the cost is reduced by approximately 0.42%, while the linearization error at frequency nadirs remains within the range of -4% to 1.5%. • The upper bound of the control parameter for variable virtual inertia is deduced. • A strategic reserve method is proposed considering wind uncertainties to improve the virtual inertia. • An Enhanced-PWL method is derived to eliminate redundant fitted samples for linearization accuracy improvement. • A two-stage robust FCUC model is proposed considering the variable fast-frequency support of DFIG-WTs. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy sources (RESs), adopting power‐to‐gas to improve the penetration of RESs is promising. This study designs a grid‐forming application for the fuel cell/electrolyser (FC/ELZ) system to supply the frequency support for the grid as well as the voltage source. The detailed control methods including the DC, AC voltage controls and the power synchronisation control are designed for the FC/ELZ system so that it can operate independently without any other energy sources. Several simulation results are provided to demonstrate the effectiveness of the designed grid‐forming application of the FC/ELZ system. [ABSTRACT FROM AUTHOR]

Developing integrated energy systems have been considered a feasible pathway to renewable‐powered energy systems. The power to hydrogen technology is recognised as a promising method to enhance the economics of integrated energy systems and help reduce renewable curtailments. However, oxygen‐enriched gas, which is the by‐product of power to hydrogen processes (electrolysation), has not been fully utilised yet. It can be purified to produce medical oxygen at a low cost and may further increase the economics of integrated energy systems. Particularly, at this very moment, the consideration of the combined production of hydrogen and medical oxygen also has the potential to relieve the shortage of medical oxygen due to the outbreak of the 2019 novel coronavirus (COVID‐19). This study proposes a model for the operation of integrated energy systems that consider the combined production of hydrogen and medical oxygen. This model is formulated as a convex mixed‐integer optimisation problem that balances the electricity, heat and hydrogen demands every hour in a 24‐hour period and balances oxygen demand on a daily basis. A test case of Taizhou City has been studied and results show that the combined production of hydrogen and medical oxygen improves the integrated energy system economics. [ABSTRACT FROM AUTHOR]

Owing to the limited operating regions of combined heat and power (CHP) units, the operation of integrated energy systems suffers from low flexibility, low‐cost efficiency, renewable curtailment etc. Meanwhile, as the capacity of renewable energies keeps growing and integrating into power systems, various methods, such as installing electric boilers to enable electricity‐heat conversion, have been developed to absorb excessive renewables and increase system operation flexibility. To further increase the system operation flexibility, this study explores the possibilities of utilising electrolysers and hydrogen storage tanks to enable electricity‐hydrogen‐heat conversion. To better visualise the enhanced flexibility, this study presents extra flexibilities from electric boilers, electrolysers and hydrogen tanks as the equivalent operating region expansion for CHP units. In this study, the system is modelled as a mixed‐integer optimisation problem which balances the electricity, heat and hydrogen demands in a 24‐hour period. A 6‐bus test system is used in the case studies to illustrate the effectiveness of implementing electrolysers and hydrogen storage tanks. The optimisation results show the application of hydrogen energy improves the system operation flexibility, reduces wind curtailment, thereby decreasing fuel consumption and carbon emission. [ABSTRACT FROM AUTHOR]

An enormous challenge for state estimation (SE) in power distribution networks with high penetration of photovoltaic generators lies in how to deal with measurement uncertainty. Driven by this motivation, an interval arithmetic (IA) based SE is proposed in this paper, which considers interval modeling for measurement uncertainty. The proposed SE is formulated as two interval optimization models based on the unknown-but-bounded theory, and solution bounds of state variables are obtained using a two-stage linear programming approach. In particular, the proposed SE is extended to realistic power distribution networks with mixed microphasor measurement units (μPMU), voltage magnitude, smart meters and feeder terminal units. Case studies and results are illustrated and discussed to demonstrate the performance of the proposed IA-based SE. It could provide the operators intuitive and effective bounds of system states for advanced power industrial applications under consideration of uncertainty. [ABSTRACT FROM AUTHOR]

The coordinated development of offshore wind and electricity–hydrogen systems (OW-EHS) has the potential to enhance the utilization of renewable energy sources and achieve carbon neutrality. This article presents a case study of nine coastal provinces in China and designs four scenarios to analyze the economic benefits and potential hydrogen production capacity of OW-EHS. A multi-stage stochastic dual dynamic programming based approach is proposed for integrating OW-EHS, exploring offshore wind energy as both a primary electricity source for coastal regions and a clean, sustainable energy source for electrolyzers. It accounts for variations in hydrogen demand, wind uncertainty, load profiles, and electricity price dynamics, offering a holistic perspective on the feasibility and benefits of collaborative offshore wind and electricity–hydrogen systems. A comparative analysis highlights the effectiveness of the proposed planning approach, demonstrating its capacity to maximize the overall system benefits, encompassing both the optimal levelized cost of energy and hydrogen production. The new discoveries elucidate the unique attributes and advantages of each coastal province in OW-EHS. This research provides a tailored blueprint for coastal provinces in China to harness their offshore wind potential and accelerate progress towards carbon neutrality. • Coordinated planning of OW-EHS tailored for coastal provinces of China. • Practical industrial electricity–hydrogen demands and offshore resources. • Multi-stage stochastic dynamic planning applied to accommodate the uncertainty. • Overall the provinces have appropriate H 2 production with LCOH below ¥18.8/kg. [ABSTRACT FROM AUTHOR]

In the context of power systems increasingly reliant on renewable energy sources, the consideration of uncertainty becomes paramount for year-round hourly operational simulations aimed at assessing the efficacy of power grid development strategies. While multi-stage stochastic programming has been effective in capturing multi-scale power fluctuations, its adoption faces challenges related to computational complexity and convergence performance. To address these issues, this paper presents a novel fast multi-stage stochastic unit commitment method tailored for year-round hourly operational simulation. This method strategically incorporates the expectations of a limited number of future stages to expedite the iteration process, thereby mitigating computational burdens. The annual time-series data is adaptively segmented based on the fluctuation characteristics of power and load, ensuring a balanced sub-problem scale aligned with the number of stages. Results from rigorous testing across multiple standard cases demonstrate that the proposed method consistently achieves optimal lower bounds within 6-8 iterations, resulting in significant computational time savings of up to 50%. Furthermore, the efficacy of the proposed method is showcased through its application in the annual operational simulation of a real-world provincial high-voltage power grid in China. • Multi-scale uncertainty introduced to year-round hourly operation simulation. • Integrity of time-series maintained and number of stages reduced by AGS method. • SDDIP algorithm reformulated to satisfy the extra unequal-length subproblems. • Fast finite SDDIP method proposed to accelerate the convergence performance. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy resources brings a number of uncertainties to modern power system operation. In particular, the frequent variation of wind power output causes a short-term mismatch between generation and demand, which causes system frequency fluctuation. The traditional approach to deal with this problem is to increase the amount of system spinning reserve. In recent years, researchers are actively exploring the utilization of residential and commercial loads in frequency regulation without affecting customers’ life quality. This paper first reviews the theoretical basis and application background of the dynamic demand control. Then, the paper summarizes the technical features and advantage/disadvantages of three types of dynamic demand control algorithms, namely centralized control, decentralized control and hybrid control. The technical and economic concerns of this research field are also discussed, which can be future research directions. [ABSTRACT FROM AUTHOR]

• A signal injection-based adaptive reclosing scheme reuses the original commutation branch of a mechanical DCCB with a simple control strategy, and thereby the DCCB can be turned into a signal injection source for fault nature identification after fault isolation. • The fault nature is identified by measuring the magnitudes of one know frequency of injected voltage signals. Based on the faulty line being isolating reliably, the fault nature is derived before reclosing to avoid a second strike on the network. Simultaneously, fault locations can be calculated under permanent faults with application of this proposed scheme. • The performance of the proposed reclosing scheme is compared with the currently used schemes to show its advantages. The scheme is simple in structure and affordable in cost, which makes it possible to popularize its application in MVDC distribution networks. The DC distribution network is an effective solution to distributed renewable energy applications. After a fault occurs in the network, it is necessary to quickly identify and isolate it to protect system components. Subsequent to fault isolation by DC circuit breakers (DCCBs), power transmission should be swiftly restored by reclosing to improve system reliability. However, conventional reclosing schemes risk a second strike on the grids and endanger the safety of the networks. Given those drawbacks, an adaptive reclosing scheme for medium-voltage DC (MVDC) networks is proposed in this paper. An additional signal injection branch with a simple control strategy is appended to the existing commutation branch of a mechanical DCCB for reclosing. The fault nature can be identified by analyzing the magnitudes of one know frequency of the voltage signals injected into the faulty lines while fault locations under permanent faults can also be calculated before reclosing by applying this scheme. Compared with currently widely used methods, this scheme is characterized by its simplicity in structure and affordability in costs. Simulation results by PSCAD/EMTDC verify the efficacy and robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

[Display omitted] • A full-life-cycle optimization model of future city-level energy systems is developed. • Feasibility of using power-to-hydrogen to achieve PV integration are highlighted. • City-level hydrogen demand potentials in industry and transport are evaluated. • Performances of two competing technologies of power-to-hydrogen (P2H) and electrochemical storage (ES) in carbon reductions are compared. Distributed renewable energy is penetrating the urban energy system with a continuously increasing proportion. However, due to its natural randomness, a large number of energy storage resources are necessary. Electrolytic hydrogen could quickly follow the fluctuations of renewable energy and transform excess renewable energy into hydrogen stored for future use, which not only promotes the accommodation of renewable energy, but also drives the low-carbon development of construction, transportation, electricity, and other industries, thereby helping construct future low-carbon energy systems (FCES). However, few existing studies have conducted a systematic investigation on the role of electrolytic hydrogen in smart city decarbonization from both the supply and demand sides. Here, we develop a full-life-cycle optimization model of FCES with photovoltaic (PV) and power-to-hydrogen (P2H) planning to achieve decarbonization goals in China. Our results show that the annual hydrogen potential of 10 selected cities with different energy structures and quantities varies from 0.236 to 9.795 megatons, with a gap of more than 40-fold. Considering PV and P2H integration in FCES, the CO 2 emissions of 10 cities in related fields decreased obviously under an 80 % photovoltaic penetration level (PPL). Compared with electrochemical storage, P2H shows better performance in PV integration of FCES from both economics and CO 2 emissions. However, this advantage needs to be based on diverse terminal uses of electrolytic hydrogen. [ABSTRACT FROM AUTHOR]

As a critical session of cyber-energy system operation, the state estimation (SE) is becoming more vulnerable to various malicious cyber-attacks like false data injection attacks (FDIAs), which can circumvent the detection mechanism and tamper with the data transmission to cause abnormal state deviation. Driven by this motivation, this paper tries to address the specific issue and propose a novel secure forecasting-aided SE (FASE) framework for the power distribution system with high penetration of renewable energy. The unbalanced distribution system FASE model is constructed first, and then the modeling process of FDIAs with incomplete system information considering limited attacking costs is illustrated. Afterward, a modified noise statistics estimator is combined with an unscented Kalman filter (UKF) to perform FASE and establish a historical state database beforehand. Moreover, a novel security enhancement strategy for FASE against FDIAs based on sliding time window (STW) theory and optimal state forecasting is innovatively proposed to correct the compromised measurements introduced by potential attacks. Comprehensive case studies are conducted on two modified distribution systems and the results demonstrate the effectiveness and credibility of the proposed secure FASE framework. [ABSTRACT FROM AUTHOR]