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By using symbolic computation software(Maple), a generalized Dirac soliton hierarchy is derived from a new matrix spectral problem associated with the Lie algebra sl ( 2 , R ) . A bi-Hamiltonian structure yielding Liouville integrability is furnished by the trace identity. [ABSTRACT FROM AUTHOR]

The increasing penetration of electric vehicles (EV) and fast charging stations (FCS) is tightly coupling the operation of power and transportation systems. In this context, the characterization of the EV flows and charging demand in response to varying traffic conditions and coordinated optimization strategies play a vital role. Previous work on the computation of stochastic traffic user equilibrium (TUE) involve non-linearities in the traffic link and FCS congestion representations and are generally inefficient in dealing with the multi-source uncertainties associated with the operating conditions of the traffic network (TN) and power distribution network (PDN). To address this, this paper proposed a novel deep learning (DL) based surrogate modeling method, leveraging the strength of edge-conditioned convolutional network (ECCN) and deep belief network (DBN). ECCN enables automatic extraction of spatial dependencies, taking into account both node and edge features characterizing the operation of TN. DBN leverages the value of the extracted features and achieves an accurate mapping between the latter to the EV charging demand and EV flows in the TUE, while adaptively generalizing to the multi-dimensional uncertainties. Case studies on three test systems of different scales (including a real-world case involving the matched TN and PDN of Nanjing city) demonstrate that the proposed surrogate model achieves a higher solution accuracy with respect to the state-of-the-art DL-based methods, and exhibits favorable computational performance. Quantitative results also corroborate the benefits brought by the proposed coordinated spatial optimization of EV flows and charging demand on the operation of both TN and PDN. • Coordinated optimization of EV flows and charging demand is investigated. • A novel deep learning based surrogate modeling framework is proposed. • Edge-conditioned convolutional network is employed to extract spatial dependencies. • Deep belief network forecasts the EV charging demand with generalization capability. • Significant techno-economic benefits for the operation of network nexus. [ABSTRACT FROM AUTHOR]

Abstract: The Lie symmetry analysis method is extended to deal with the time fractional KdV-type equation. It is shown that this equation can be reduced to an equation with the Erdélyi–Kober fractional derivative. This method can also be applied to symmetry classification of fractional equations with some arbitrary functions. [Copyright &y& Elsevier]

The increasing penetration of small-scale distributed energy resources (DER) has the potential to support cost-efficient energy balancing in emerging electricity systems, but is also fundamentally affecting the conventional operation paradigm of the latter. In this context, innovative market mechanisms need to be devised to better coordinate and provide incentives for DER to utilize their flexibility. Peer-to-Peer (P2P) energy trading has emerged as an alternative approach to facilitate direct trading between consumers and prosumers interacting in an energy collective and fosters more efficient local demand–supply balancing. While previous research has primarily focused on the technical and economic benefits of P2P trading, little effort has been made towards the incorporation of prosumers' heterogeneous characteristics in the P2P trading problem. Here, we address this research gap by classifying the participating prosumers into multiple clusters with regard to their portfolio of DER, and analyzing their trading decisions in a simulated P2P trading platform. The latter employs the mid-market rate (MMR) local pricing mechanism to enable energy trading among prosumers and penalizes the contribution to the system demand peak of each prosumer. We formulate the P2P trading problem as a multi-agent coordination problem and propose a novel multi-agent deep reinforcement learning (MADRL) method to address it. The proposed method is founded on the combination of the multi-agent deep deterministic policy gradient (MADDPG) algorithm and the technique of parameter sharing (PS), which not only enables accelerating the training speed by sharing experiences and learned policies between all agents in each cluster, but also sustains the policies' diversity between multiple clusters. To address the non-stationarity and computational complexity of MADRL as well as persevering the privacy of prosumers, the P2P trading platform acts as a trusted third party which augments the market collective trading information to help training of prosumer agents. Experiments with a large-scale real-world data-set involving 300 residential households demonstrate that the proposed MADRL method exhibits a strong generalization capability in the test data-set and outperforms the state-of-the-art MADRL methods with regard to the system operation cost, demand peak as well as computational time. • The coordinated management of large-scale P2P energy trading is investigated. • The heterogeneity of prosumers with diverse resources' portfolios is accounted. • A novel multi-agent deep reinforcement learning approach is proposed. • The proposed approach achieves significant operating cost and peak demand benefits. • The proposed approach deals effectively with uncertain parameters of the problem. [ABSTRACT FROM AUTHOR]

Under the background of green certificate trading (GCT)‑carbon emission trading (CET) market, the application of P2P trading in regional integrated energy system (RIES) has not been fully explored, and it is urgent to design a reasonable prosumer P2P trading mechanism to adapt to the development of diversified electricity market. Therefore, this paper proposes a prosumer P2P cooperative trading model based on generalized Nash bargaining. Specifically, based on the differential modeling of various types of prosumers, this paper constructs a prosumer P2P cooperative trading model based on generalized Nash bargaining theory, and quantifies the bargaining power of prosumers through the contribution value of interactive operation. The GCT-CET collaborative mechanism is introduced to promote the consumption of distributed energy by transforming carbon quotas through green certificates. In addition, this paper designs a warm start and adaptive step alternating direction multiplier method decentralized algorithm (WAS-ADMM) to improve the solution efficiency and protect the trading privacy of prosumers. Through a case study, it analyzes the impact of prosumers' P2P cooperative trading on the consumption of distributed energy, prosumers' GCT-CET costs and comprehensive costs in the context of GCT and CET market. The simulation results show that the proposed trading model reduces the GCT-CET costs of the prosumers' alliance by about 24.98%. • A P2P cooperative trading model of integrated energy is constructed based on GNB. • A GCT-CET collaborative mechanism is proposed to promote the consumption of renewable energy. • An improved decentralized algorithm based on WAS-ADMM is designed to solve the P2P trading model. [ABSTRACT FROM AUTHOR]

The increasing effects of global warming and energy depletion have raised concerns about the pollution caused by traditional oil and fossil energy usage. Distributed energy resources (DERs) have emerged as promising techniques to address these issues. However, the growing proportion of power injection from DERs presents technical challenges such as power quality decline and power flow reversal, which may destabilize the grid. Non-intrusive load monitoring (NILM) is a promising and cost-effective approach that can provide residential power information to improve grid scheduling, dispatching, and optimize residential power consumption behavior. This paper proposes a novel NILM method that can decompose the power of both household appliances and DERs integrated with residential houses. The proposed approach employs a data segment method that utilizes adaptive window lengths to identify the behavior of DERs based on the operating periods and characteristics of different equipment. A non-intrusive load monitoring method is proposed based on the ResNet-seq2seq framework to address the problem of model degradation and gradient vanishing. Post-processing techniques are applied to the output results using a feature list of the target equipment. Experiments are conducted on a dataset organized based on the REDD and Pecan Street datasets to demonstrate the accuracy and effectiveness of the proposed method. The results show that compared to the start-of-the-art CNN-seq2seq model, the proposed method achieved 54.95%, 69.18%, 56.95%, and 84.98% reduction in MAE for PV, EV, refrigerators, and microwaves, respectively. • A novel ResNet-seq2seq-based NILM approach is proposed for load disaggregation of residential houses integrating DERs. • Wavelet denoising and adaptive window length data segments are used for data pre-processing and accurate disaggregation. • Residual connections are employed to solve model degradation and vanishing gradient issues as network depth increases. • A post-processing technique is applied to modify the output of the network and improve disaggregation accuracy. [ABSTRACT FROM AUTHOR]

• A two-stage localisation framework for forced oscillations is proposed. • Graphical representation is generated to characterise forced oscillations. • Knowledge of image recognition is transferred to the localisation problem. • It has high accuracy, good anti-noise performance and robustness. Accurately locating forced oscillations (FOs) disturbance source in a large-scale power system is a challenging task. In this paper, a localisation method featuring system-level and area-level localisation is proposed. Compared with traditional localisation methods which process the FOs signals only in control centre phasor data concentrator (PDC), the proposed method can not only relieve the data communication pressure, but also meets the data privacy and confidentiality requirements of utility companies. Besides, with the two-stage deep transfer learning, high accuracy of localisation can be realised with far less training. Firstly, by adopting principle component analysis (PCA) to extract the most representative information at system-level and smooth pseudo Wigner-Ville distribution (SPWVD) to characterize FOs signals, graphical representations of FOs at system-level and area-level are obtained respectively, transforming the localisation problem to image recognition problem. Subsequently, a two-stage deep transfer learning (DTL) algorithm is developed to locate FOs disturbance source. The first stage involves repurposing the learnt features from a pre-trained deep convolutional neural network (previously trained for universal image recognition) to improve the learning of system-level localisation. In a similar logic, the second stage entails transferring the knowledge acquired from the first stage is exploited to aid area-level localisation learning. Case studies carried out on the WECC 179-bus system demonstrate that the proposed method achieves a significantly higher accuracy in the presence of measurement noise, topology variation and load disturbances of the system with respect to the traditional machine learning method. The proposed method also exhibits a more favorable computational performance and learning efficiency due to the employment of the tactfully designed two-stage DTL approach. [ABSTRACT FROM AUTHOR]

• Essential similarities between gas linepack and thermal inertia are found. • Unified definition and modelling of gas and thermal inertia is formulated. • The abundant flexibility from slow dynamics of gas and thermal systems is exploited. • A multitype reserve procurement scheme considering inertia reserves is proposed. • Flexible features of inertia reserves are compared with conventional reserves. The multi-energy coupling characteristic of integrated energy system helps to promote energy efficiency improvement and facilitate renewable energy integration. However, the growing complexities from multi-energy coupling and the increasing uncertainties of renewable generations are both threatening the synergistic operation. It is imperative to fully exploit the flexibility of integrated energy system for operating reliability and economy. In view of the essential similarities from the slow dynamics of gas and thermal systems, a unified gas and thermal inertia model for integrated energy system is proposed in this paper for flexibility excavation and optimal allocation. First, gas and thermal inertia is uniformly defined considering inertial characteristics. Furthermore, a method for unified modelling of gas and thermal inertia is accurately illustrated based on the dynamic model of gas and thermal system. To prove the validity of the unified model, a multitype reserve procurement scheme considering gas and thermal inertia is proposed. A typical community-scale integrated energy system is employed as a test system. The simulation results demonstrate the potential ability of gas and thermal inertia to improve the operational flexibility, reliability and economy of integrated energy system. [ABSTRACT FROM AUTHOR]

The increasing frequency of the occurrence of high impact low probability (HILP) disruptive events has posed huge threats to the power system. Therefore, power system resilience improvement has drawn world-wide attention. Electric buses (EB) are equipped with a large capacity of batteries which could provide sufficient energy and capacity values to run islanded micro-grids (MG) for hours, with a particular focus on V2G, which will deliver system resilience during extreme operational conditions. In this paper, an innovative reciprocal transport-power system coordination scheme is proposed to simultaneously mitigate the adverse impacts on both systems. By using this economic incentive-free approach, EBs are endogenously motivated to provide resilience-oriented V2G service to the power system, while increasing the transport service survivability during HILP disruptive events. Additionally, a novel EB dispatch rescheduling method considering the trade-off of both the requirement of energy supply security and transport service fulfilment is proposed. By using this method, the essential load curtailment of local MGs can be further alleviated, while the impacts of reduced charging service on transport service can also be further mitigated. Through a series of case studies, we illustrate that EBs have significant potential to provide resilience enhancement to the urban energy system based on appropriate dispatch strategies. Meanwhile, the interests of the power system and the transport system can be well reconciled by using the proposed approach during HILP disruptive events. • Present an algorithm for resilient operation of power system and transport system. • Propose a reciprocal coordination scheme reconciling the interests of both systems. • Investigate the values of strategic EB dispatch rescheduling in improving resilience. • Quantify energy values and capacity values of EBs in enhancing system resilience. [ABSTRACT FROM AUTHOR]

Craving and cognitive deficits are potential treatment targets for methamphetamine use disorder (MUD). Previous studies implied that transcranial direct current stimulation (tDCS) and cognitive training respectively improve these symptoms, but the combined effect is unknown. In this study, we investigated the combined effects of tDCS over dorsolateral prefrontal cortex (DLPFC) and computerized cognitive addiction therapy (CCAT) on cue-induced craving and cognitive functions among female individuals with MUD. Seventy-five patients with MUD were randomly assigned to three groups: CCAT + tDCS group, CCAT + sham tDCS group and the control group. The former two groups received 20 sessions of cognitive training combined 1.5 mA active/sham tDCS over DLPFC (20min/session, 5times/week), while the control group received usual care which includes routine medical care, health education, physical exercises and psychological support related to relapse prevention. The cue-induced craving and cognitive functions were tested at the baseline, the end of 2nd week and 4th week. The CCAT + tDCS group showed a significant reduction in cue-induced craving after 4-week intervention. Moreover, the craving score of the real CCAT + tDCS group was significantly lower than that of the CCAT + sham tDCS group and that of the control group at the end of 4th week. A significant improvement in accuracy of TWOB task was only observed in the CCAT + tDCS group at the end of 4th week when compared to baseline. Unexpectedly, participants who received CCAT plus active or sham tDCS did not change their discounting, whereas those in the control group performed more impulsively over time. The study found that the intervention of tDCS over DLPFC combined with CCAT may have potential benefit in improving treatment outcome in patients with MUD. More research is needed to explore the underlying mechanism. [ABSTRACT FROM AUTHOR]

• The long-term impacts of demand flexibility on deregulated systems are investigated. • Bi-level optimization model of a generation company's investment problem is proposed. • The proposed model captures energy shifting and reserve provision of flexible demand. • Different market design options around the allocation of reserve payments are tested. Demand flexibility has attracted significant interest given its potential to address techno-economic challenges associated with the decarbonisation of electricity systems. However, previous work has investigated its long-term impacts through centralized generation planning models which do not reflect the current deregulated environment. At the same time, existing market-based generation planning models are inherently unable to capture the demand flexibility potential since they neglect time-coupling effects and system reserve requirements in their representation of the electricity market. This paper investigates the long-term impacts of demand flexibility in the deregulated environment, by proposing a time-coupling, bi-level optimization model of a self-interested generation company's investment planning problem, which captures for the first time the energy shifting flexibility of the demand side and the operation of reserve markets with demand side participation. Case studies investigate different cases regarding the flexibility of the demand side and different market design options regarding the allocation of reserve payments. The obtained results demonstrate that, in contrast with previous centralised planning models, the proposed model can capture the dependency of generation investment decisions and the related impacts of demand flexibility on the electricity market design and the subsequent strategic response of the self-interested generation company. [ABSTRACT FROM AUTHOR]

Integrable hierarchies provide many important physical models. Firstly, with the help of symbolic computation software Maple, two generalized integrable hierarchies of Ablowitz–Kaup–Newell–Segur (AKNS) type are constructed from the matrix spectral problem associated with the Lie algebra s l (2). Then, two generalized integrable hierarchies of super Dirac type and super nonlinear Schrödinger-modified Korteweg–de Vries (NLS–mKdV) type are obtained to illustrate the use of the super Lie algebra B (0 , 1). [ABSTRACT FROM AUTHOR]

• Proposed an original concept called spatial load migration (SLM), which is supposed to fully utilize the space connection between power loads, and provide a new load dispatch approach to address power transmission limitation. • Analyzed the feasibility and constraints of the typical power loads applied on SLM base on a holistic literature review and a utilized assessment architecture, including data center, base station, electric vehicle, mobile storage, and multi-energy equipment. • Provided a brief case study describing the potential of SLM on effective power distribution. • Concluded the power level and accessible appliance of each load type, and discussed the future research direction of SLM. As the transmission capabilities of power systems face challenges from renewable generation and massive electricity usage, power load resources have an increasingly important role in power dispatch. Traditional load dispatch methods are mainly based on the dispatch of load behavior in the time dimension, disregarding the potential of heterogeneous space connections existing in loads, such as communication connections and transportation connections. Although some studies, such as energy hubs or data networks, have utilized some part of space connections, no unified framework has been proposed to distinguish the problems caused by applying space connections or a horizontal review of spatial resources. In this paper, a framework named spatial load migration (SLM), which provides solutions to solving spatial network constraint problems with the enhancement of space connections, is proposed. With respect to SLM, this paper analyzes the mechanisms of typical power load types participating in SLM and qualitative appliance potential. A multi-system-coupled case study is designed with the occasion of power line disconnection. The results show that applying space connections to load dispatch can both expand the controllable objects and increase system performance. [ABSTRACT FROM AUTHOR]