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Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability. [ABSTRACT FROM AUTHOR]

The effective pricing of electric vehicle (EV) charging by aggregators constitutes a key problem toward the realization of the significant EV flexibility potential in deregulated electricity systems and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging/discharging levels. Although reinforcement learning (RL) can tackle this challenge, state-of-the-art RL methods require discretization of state and/or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This article proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners. [ABSTRACT FROM AUTHOR]

Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics. [ABSTRACT FROM AUTHOR]

The envisaged decarbonisation of electricity systems has attracted significant interest around the role and value of energy storage systems (ESSs). In the deregulated electricity market, there is a need to investigate the complex impacts of ESSs, considering the potential exercise of market power by strategic players. This study aims at comprehensively analysing the impacts of both price‐taking and price‐making storage behaviours on energy market efficiency, corresponding to potential settings with small and large storage players, respectively. In order to achieve this and in contrast to previous papers, this work develops a multi‐period equilibrium programming market model to determine market equilibrium stemming from the interactions of independent strategic producers and ESSs, while capturing the time‐coupling operational constraints of ESSs as well as network constraints. The results of case studies on a test market capturing the general conditions of the GB electricity system demonstrate that the introduction of ESSs mitigates market power exercise and improves market efficiency, with this beneficial impact being higher when ESSs act as price takers. When the electricity network is congested, the location of ESSs also affects the market outcome, with their beneficial impact on market efficiency being higher when they are located in higher‐priced areas. [ABSTRACT FROM AUTHOR]

Previous work on the role of the demand side in imperfect electricity markets has demonstrated that its self-price elasticity reduces electricity producers’ ability to exercise market power. However, the concept of self-price elasticity cannot accurately capture consumers’ flexibility, as the latter mainly involves shifting of loads’ operation in time. This paper provides for the first time theoretical and quantitative analysis of the beneficial impact of demand shifting (DS) in mitigating market power by the generation side. Quantitative analysis is supported by a multiperiod equilibrium programming model of the imperfect electricity market, accounting for the time-coupling operational constraints of DS as well as network constraints. The decision making process of each strategic producer is modeled through a bi-level optimization problem, which is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. The oligopolistic market equilibria resulting from the interaction of multiple independent producers are determined by employing an iterative diagonalization method. Case studies on a test market reflecting the general generation and demand characteristics of the GB system quantitatively demonstrate the benefits of DS in mitigating producers’ market power, by employing relevant indexes from the literature. [ABSTRACT FROM AUTHOR]

In this paper, Painlevé integrability of a generalized Hirota-Satsuma (gHS) equation is confirmed by using the Weiss-Tabor-Carnevale (WTC) test. Then, a new exact solution with two arbitrary functions is constructed. Some new soliton structures are illustrated analytically by selecting appropriate functions. [ABSTRACT FROM AUTHOR]

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]

Based on a matrix Lie algebra consisting of block matrices, new tri-integrable coupling of the Kaup-Newell soliton hierarchy is constructed. Then, the bi-Hamiltonian structure which leads to Liouville integrability of this coupling is furnished by the variational identity. [ABSTRACT FROM AUTHOR]

In this article, Painlevé integrability of the multi-component Sasa-Satsuma equation is confirmed by using the standard WTC approach and the Kruskal simplification. Then, by means of the multi-linear variable separation approach, a new exact solution with lower-dimensional arbitrary functions is constructed. For the physical quantity new coherent structure which possesses peakons at x-axis and compactons at y-axis is illustrated both analytically and graphically. [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]

Uniform marginal pricing cannot generally support competitive equilibrium solutions in markets with non-convexities, yielding surplus sub-optimality effects. Previous work has identified non-convexities associated with the generation side of electricity markets and proposed different approaches to address surplus sub-optimality. This paper extends this concept to incorporate the demand side. Non-convexities of flexible demand (FD) are identified, including options to forgo demand activities as well as discrete and minimum power levels, and resulting surplus sub-optimality effects are demonstrated through simple examples and a larger case study. Generalized uplift and convex hull pricing approaches addressing these effects are extended to account for FD non-convexities. Concerning the former, generalized uplift functions for FD participants are proposed, and a new rule is introduced for equitable distribution of the total surplus loss compensation among market participants. Regarding the latter, it is demonstrated that convex hull prices are flattened at periods when FD is scheduled to eliminate surplus sub-optimality associated with the FD ability to redistribute energy requirements across time. [ABSTRACT FROM PUBLISHER]

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]

Psoriasis is a chronic inflammatory skin disease whose etiology has not yet been determined. MicroRNAs (miRs) regulate the early stages of psoriasis and are targets for therapeutic intervention. The present study aimed to investigate the functional role of miR-489-3p in psoriasis. The present study first assessed the expression levels of miR-489-3p and Toll-like receptor (TLR)4 mRNA using reverse transcription-quantitative PCR, and also detected the protein expression levels of TLR4 and NF-κB via western blot analysis. TargetScan and miRDB target gene prediction tools were used to confirm the regulation of Toll-like receptor (TLR)4 by miR-489-3p. Moreover, a Cell Counting Kit (CCK)-8 assay was conducted to evaluate cell viability, while cell cycle and colony formation assays were performed to evaluate cell proliferation. Human keratinocytes (HaCaT) were co-transfected with TLR4-small interfering RNA and miR-489-3p-inhibitor plasmids, and analysis of cell proliferation and inflammatory cytokine secretion was performed using CCK-8 assay and ELISA. It was found that miR-489-3p expression was downregulated in patients with psoriasis. Bioinformatics analysis identified that TLR4 was a direct target of miR-489-3p. This was confirmed via luciferase reporter assays in HaCaT cells. The overexpression of miR-489-3p inhibited the TLR4/NF-κB signaling pathway and reduced cell proliferation. TLR4 silencing alleviated the effects of miR-489-3p, and enhanced cell proliferation and inflammatory cytokine secretion. Taken together, these data suggested that miR-489-3p may be a key effector of psoriasis, which promotes inflammatory responses by direct targeting of TLR4. miR-489-3p therefore represents a promising prognostic biomarker and therapeutic target for psoriasis treatment. [ABSTRACT FROM AUTHOR]

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]

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user's energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances' operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household's energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods. [ABSTRACT FROM AUTHOR]

• P2P energy trading is modeled as a prosumer coalitional game • A computationally efficient pricing mechanism is proposed to construct a stable grand coalition of prosumers • The effectiveness of the proposed mechanism is validated against state-of-the-art mechanisms using real-world system data Load balancing issues in distribution networks have emerged alongside the large-scale deployment of distributed renewable generation sources. In light of this challenge, peer-to-peer (P2P) energy trading constitutes a promising approach for delivering secure and economic supply-demand balance when faced with variable load and intermittent renewable generation through matching energy demand and supply locally. However, state-of-the-art mechanisms for governing P2P energy trading either fail to suitably incentivize prosumers to participate in P2P trading or suffer severely from the curse of dimensionality with their computational complexity increase exponentially with the number of prosumers. In this paper, a P2P energy trading mechanism based on cooperative game theory is proposed to establish a grand energy coalition of prosumers and a computationally efficient pricing algorithm is developed to suitably incentivize prosumers for their sustainable participation in the grand coalition. The performance of the proposed algorithm is demonstrated by comparing it to state-of-the-art mechanisms through numerous case studies in a real-world scenario. The superior computational performance of the proposed algorithm is also validated. [ABSTRACT FROM AUTHOR]

• Local energy markets enable direct energy trading among participating customers. • The interactions among electricity retailers and local energy markets are modelled. • A novel approach is employed for solving the developed bi-level optimization model. • Local energy markets are shown to enhance the competitiveness of retailers. • Local energy markets are shown to increase the economic surplus of the customers. Local energy markets (LEM) have recently attracted great interest as they enable effective coordination of small-scale distributed energy resources (DER) at the customer side, and avoidance of distribution network reinforcements. However, the introduction of LEM has also significant implications on the strategic interactions between the customers and incumbent electricity retailers. This paper explores for the first time these interactions by proposing a novel multi-period bi-level optimization model, which captures the pricing decisions of a strategic retailer in the upper level (UL) and the response of both independent customers and the LEM (both including flexible consumers, micro-generators and energy storages) in the lower level (LL). Since the LL problem representing the LEM is non-convex, a new analytical approach is employed for solving the developed bi-level problem. The examined case studies demonstrate that the introduction of an LEM reduces the customers' energy dependency on the retailer and limits the retailer's strategic potential of exploiting the customers through large differentials between buy and sell prices. As a result, the profit of the retailer is significantly reduced while the customers, primarily the LEM participants and to a lower extent non-participating customers, achieve significant economic benefits. [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]

The use of hydropower has expanded in recent years in southwest China, playing a key role in the success of China's West-to-East energy strategy. Although installed hydropower capacity has reached a record high, the problem of abandoned water has intensified and has been gradually highlighted by researchers. Abandoned water has resulted in a significant waste of carbon-free and inexpensive renewable energy, which is not conducive to China's goals to reduce emissions to save energy. This waste has seriously affected the development of China's hydropower industry. Therefore, now is the time to analyze the main reasons for the abandoned water problem and to identify the crux of the problem. In this paper, we collected the actual operation data of all the power stations in the case area and established the mathematical model according to the quantity of abandoned water as well as the actual and the theoretical output curve of the power station to quantify the abandoned water into energy loss. We then classified the abandoned water according to three groups of causes, namely, generation-side, line-side, and market-side causes. In 2016, the total energy loss of abandoned water of Sichuan Province was 28.73 × 109 kW h, of which the generation-, line-, and market-side causes account for 7.76%, 11.68%, and 80.56% of the energy loss, respectively. Therefore, the market-side causes are the main cause for the abandoned water problem. Such market-side causes primarily include the imbalance between electricity supply and demand, the provincial barriers of the local government system and its mechanisms, and the hysteresis congestion of the transmission lines. Seizing the favorable opportunity presented by new power system reforms in China, this study proposes several market-oriented measures to improve hydropower consumption capacity. [ABSTRACT FROM AUTHOR]

The investigation of the effects of demand flexibility on the pricing strategies and the profits of electricity retailers has recently emerged as a highly interesting research area. However, the state‐of‐the‐art, bi‐level optimisation modelling approach makes the unrealistic assumption that retailers treat wholesale market prices as exogenous, fixed parameters. This study proposes a tri‐level optimisation model, which drops this assumption and represents the wholesale market clearing process endogenously, thus capturing the realistic implications of a retailer's pricing strategies and the resulting demand response on the wholesale market prices. The scope of the examined case studies is three‐fold. First of all, they demonstrate the interactions between the retailer, the flexible consumers and the wholesale market and analyse the fundamental effects of the consumers' time‐shifting flexibility on the retailer's revenue from the consumers, its cost in the wholesale market and its overall profit. Furthermore, they analyse how these effects of demand flexibility depend on the retailer's relative size in the market and the strictness of the regulatory framework. Finally, they highlight the added value of the proposed tri‐level model by comparing its outcomes against the state‐of‐the‐art bi‐level modelling approach. [ABSTRACT FROM AUTHOR]

The development of renewable‐dominated power systems (RDPS) in the context of pursuing deep decarbonisation targets necessitates suitable exploitation of the peak shaving capacity (PSC) of central air conditioning loads (CACL) in multiple time scales. Based on the evaluation framework of CACL participating in the RDPS PSC and the CAC apparent temperature model and its operation mode, the CACL PSC evaluation model related to the resource allocation in multiple time scales is proposed. For the time scale of online dispatching, the capacity evaluation model for rapid CACL response to power system emergency peak shaving command and its continuous response potential is constructed. The method of optimising CACL PSC based on the load aggregator model is developed. For the intraday pre‐scheduling time scale, the evaluation model of CACL's remaining capacity after undertaking the peak shaving task and the ability to respond continuously to the peak shaving command is constructed. For the day‐ahead dispatching time scale, the PSC of CACL under different weather conditions, temperatures, holidays, and other typical scenarios is evaluated. Finally, case studies carried out on 14 central air‐conditioned buildings in Shanghai city comprehensively validate the effectiveness of the proposed CACL PSC evaluation model. [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]

• 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]

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]

• Patients with SUD have decrease glutamate levels in DLPFC. • Higher glutamate levels in MPFC and basal ganglia were showed in SUD patients. • Remission period and cumulative years of drug use had no effect on the glutamate levels. Animal studies have reported the brain glutamatergic dysfunction in substance dependence. However, proton magnetic resonance spectroscopy (1H-MRS) studies of glutamate in substance-dependent patients published contradicting results. In order to investigate the characteristics of brain glutamatergic alterations in substance-dependent patients, we conducted systematic reviews and meta-analyses of 1H-MRS studies that have investigated the glutamate, glutamine, and Glx (glutamate + glutamine) concentration in substance-dependent patients. Multiple databases were searched until Sep 10, 2020. Twenty-nine studies comprising 982 patients and 787 controls were included. There was significantly decreased glutamate level in dorsolateral prefrontal cortex in patients compared with controls. Higher glutamate levels in medial prefrontal cortex and basal ganglia region were also demonstrated in patients compared with controls. Subgroup analyses based on the substance type and abstinence period (short vs medium-term abstinence period) were performed. The results revealed Glx and glutamate concentrations in all investigated brain regions were not different in patients with any types of substance dependence compared with controls. The abstinence period had no effect on the glutamate levels. In summary, substance dependence is associated with glutamatergic dysfunction of prefrontal cortex and basal ganglia. Present findings partially support the hypothesis that addiction is associated with abnormal brain glutamatergic neurotransmission. [ABSTRACT FROM AUTHOR]