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• GBD, OCD and APP are adopted to solve the optimization model. • The characteristics of GBD, OCD and APP are compared and analyzed specifically. • Interconnected topology and bus topology are first made a comparison in terms of different aspects. • A comparison among independent operation, adding selfish constraints and fair allocation are given. Interconnected microgrids can enable mutual power support among microgrids (MGs) and improves the utilization of renewable energy sources, especially for CCHP-based (Combined Cooling, Heating, and Power) Microgrid Cluster (MGC). To preserve information privacy and achieve scheduling independence of microgrids, the problem of multi-area economic and environmental dispatch in CCHP plus MGC can be computed by distributed algorithm framework, i.e. , generalized benders decomposition (GBD), optimal condition decomposition (OCD) and auxiliary problem principle (APP), respectively for interconnected topology and bus topology. Moreover, chance constrained programming (CCP) is added to address the uncertainty factors of renewable energy, cooling, heating, and electrical loads. A consensus-based distributed fair cost allocation algorithm is proposed to make a comparison with the condition of adding selfish constraints and independent operation, so that guaranteeing the stability of economic coalition of MGC. A case study with four networked CCHP microgrids in two kinds of topology is tested to demonstrate the effectiveness of the proposed approach in summer scenario. In conclusion, distributed algorithms will have a prospective application on MGC as the result of the necessity from different entities in the future. [ABSTRACT FROM AUTHOR]

Highlights • Basic principles of graph theory yield an optimal communication network topology. • A combination of "centralized plus distributed" strategies is adopted for coordinated control. • The second-level power optimization controller determines the voltage and frequency reference values of distributed micro generators. Abstract Due to the increasing coupling degree of the power network, natural gas network, and thermal network, this paper discusses a flexible DC power distribution network based on the consistency algorithm theory regarding the advantages and disadvantages of centralized control and distributed control. A layered network control architecture is presented. Considering the multi-time-scale control characteristics of the system, the control architecture is divided into three layers: energy management layer, bus control layer, and converter control layer. In the energy management layer, the dispatch optimization center optimizes the system operating cost through the multi-objective energy optimization management of the integrated energy system consisting of the DC distribution network, natural gas network, and heating network. In the bus control layer, the DC bus voltage is taken into account. The control characteristics are divided into three categories corresponding to three working modes according to the range of the DC bus voltage, which is used to realize the conversion mode control of every distributed device in the distribution network. In the converter control layer, based on the original droop control, the voltage and frequency deviations of every distributed device are compensated twice, and every distributed device controller communicates only with the adjacent controller to achieve the voltage and frequency consistency control with the "virtual leader" node. Finally, using the typical eight-terminal VSC flexible DC distribution network control architecture, the proposed control strategy is verified by the PSCAD/EMTDC simulation software. [ABSTRACT FROM AUTHOR]

Carbon capture system (CCS) and power to gas (P2G), as key low-carbon technologies, have obtained the widespread attention. Thus, it is necessary to explore the operation pattern and benefits of CCS and P2G. This paper investigates their roles in the electric-thermal-carbon-natural gas integrated energy system (IES) and focuses on the multi-market participation and multi-energy management. IES participates in the electricity, carbon trading, and natural gas markets. In the electricity market, IES can provide two types of ancillary services: deep peak regulation and frequency regulation services. Then, owing to the small scale of the ancillary service market, the strategic behaviors of IES are discussed. A bi-level model is established to deal with the complex couplings in the peak regulation market. IES's strategic behavior in the frequency regulation market is depicted by the price quota curve. Next, a solution method based on the particle swarm optimization algorithm is proposed to deal with the nonconvexity of the lower-level model. Finally, case studies show that the strategic behavior modeling in this paper can increase the profits of the deep peak regulation and the frequency regulation services. When IES is equipped with CCS and P2G, the operation cost decreases by 16.6%. • The roles of CCS and P2G are investigated in the IES. • IES's ability to provide the ancillary services is considered in the operation model. • The strategic behaviors in the ancillary service markets are modeled. • A solution method based on PSO is designed for the proposed bi-level model. [ABSTRACT FROM AUTHOR]

With the deepening of the coupling between electric, gas, and thermal energy systems, traditional relatively independent energy systems are evolving towards an integrated energy system with multi-energy complementarity and multiple interactions. In order to improve the comprehensive utilization of distributed renewable energy, many multi-energy conversion devices have been introduced into the system. However, this increases the possibility of disorderly and recurrent conversion of renewable energy, resulting in a large amount of energy loss. In addition, the coupling relationship between different participants is complex, and they have different interests and goals. To solve these above issues, the system is firstly divided into the lower multi-energy microgrid users and the upper integrated energy service provider, and the causes for the recurrent energy conversion paths of renewable energy are discussed in detail. Secondly, the tie-line power between the upper and lower participants is regarded as the coupling variable of their corresponding objective functions, which is equivalent to the value of virtual generator and the virtual load respectively, and the Lagrangian penalty function is used to deal with the deviation between them, using the target cascade method to solve the upper and lower optimization objective functions respectively. Finally, a typical regional integrated energy system was selected for discussion and analysis, including an IEEE33-node electric-thermal coupling network and a 7-node natural gas network, which verifies the rationality and effectiveness of the proposed method. • High proportion of conversion devices may lead to recurrent energy conversion paths. • The target cascade method can solve the two-level model in parallel. • Coordination mode with two levels can run more efficiently. [ABSTRACT FROM AUTHOR]

This paper studies the energy trading among flexible demand response aggregators (DRAs) and a distribution company (Disco) with self-owned generators. Instead of the conventional non-cooperative game based approach, the trading problem is formulated as a bargaining based cooperative model, where Disco and DRAs collaboratively decide the amounts of energy trade and the associated payments. This cooperative interaction can be beneficial to both Disco and DRAs, by reducing the aggregated peak demand and increasing the potential cost savings. The increased benefits from cooperation are fairly allocated among these participants, based on the Nash bargaining theory. Compared with the non-cooperative game based approach, the proposed bargaining cooperative model can further improve the benefits of Disco and DRAs. Moreover, the bargaining outcome can maximize the social welfare of the system. Considering the privacy and autonomy issues of participants, we utilize a decentralized solution to solve the bargaining problem, with minimum information exchange. Numerical studies demonstrate the effectiveness of the bargaining -based cooperative framework, and also show the improvement of benefits of the system. [ABSTRACT FROM AUTHOR]

To enhance the delay-dependent stability of interconnected microgrids, a novel adaptive control strategy is designed in this paper. Firstly, an adaptive robust control model is established to coordinate multi-MGs energy management. By regulating control parameters, Lyapunov stability without delay can be guaranteed. Secondly, stability evaluation methods considering uncertainty, such as mode perturbation and communication failure, are proposed to analyze delay-independent system. On this basis, to eliminate the adverse effects of delay caused by wide-area measurement, an active disturbance rejection stabilizer is designed here, which can be self-adjust adaptively via delayed time, and doesn’t require multiple iterations in the parameters regulation. Through Schur complement theorem, the analytic expression of feedback gains is obtained from linear matrix inequalities. Finally, with an IEEE 118-node test feeder as an example, the numerical simulation verifies the feasibility of the proposed stabilizer under the cases of long delays. [ABSTRACT FROM AUTHOR]

AC microgrid is a promising approach to integrate various distributed generators and energy storage into the power system, and provide renewable and reliable energy to the customers. However, highly intermittent distributed energy sources and varying load demands pose increasing challenges for optimal energy management in an AC microgrid. In this paper, a distributed cooperative optimal control method based on the discrete consistency algorithm is proposed to realize the large-scale penetration of renewable energy in an AC microgrid. The proposed approach is implemented through a multi-agent distributed hierarchical control architecture, which only requires a local communication network to exchange the information. The optimal control is achieved through a three-level control architecture. The third control level aims at multi-objective optimal scheduling, considering the factors such as fuel consumption, pollution in the form of emissions and operational maintenance. The second control level is based on the discrete consistency algorithm that optimizes the frequency and voltage references of droop control and also shares the active and reactive power accurately according to the particular demand. The first control level is responsible for reference tracking of the associated components. Compared with the conventional centralized and decentralized controls, the proposed control method offers increased robustness and flexibility in the AC microgrid control, and only requires limited communication between neighboring agents to realize a global optimization. Finally, the effectiveness of the proposed strategy is verified in a typical AC microgrid model by using the PSCAD/EMTDC software. [ABSTRACT FROM AUTHOR]

To accommodate the large scale of renewable energy resources now widely integrated into power systems, an interactive two-level pricing mechanism for coupled microgrids (MG) in a smart energy system is proposed that considers operational quality and renewable generation uncertainty. In the upper level of the pricing mechanism, the distribution energy market operator (DEMO) guarantees operational quality by trading energy with coupled microgrids, while the actual transactions between networked microgrids is performed at the lower level. Stochastic programming is applied to handle the uncertainty caused by large-scale renewable integration. An innovative time-varying game vector and energy transaction strategy deal with the spatio-temporal market interaction of the networked microgrids, where each microgrid is able to directly trade all types of energy with any other microgrid at any time. The proposed model is solved using a customized hierarchical genetic algorithm. Case studies on an IEEE bus test feeder and an existing energy system in China demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Renewable generation is a key component in our generation mix to meet increasing electricity demand and decarburization targets. However, due to the constrained network capacity, a large volume of renewable generation is curtailed. There are typically three approaches to deal with excessive renewable: directly curtailed, reinforcing networks to expand transfer capacity, or converted into hydrogen to be transported. However, the costs and benefits of three approaches could vary significantly. This paper uses a 16 bus-bar UK network to analyze the performance of the three approaches. UK 2020 generation mix is used to quantify the saved energy from renewable and the triggered costs by using the approaches. The payback time and net present value (NPV) of the two investment techniques are also compared. From demonstration, it is reasonable to conclude that blending hydrogen from renewable is an environmentally friendly and cost-effective way to address wind curtailment. [ABSTRACT FROM AUTHOR]

• A VPP transaction structure integrating main-side chains and multi-agent technology is established with strong scalability; • A reputation evaluation and feedback mechanism matching the main chain of VPP is designed to maintain a good VPP operation environment; • The specific trading process of VPP based on the main-side chains and the key smart contract functions are clarified; • The VPP internal market rules are conducive to economic and efficient operation. To stimulate the market vitality of distributed resources, the virtual power plant (VPP) has gradually transformed from a third-party profit organization to a service-oriented trading platform, and the transaction has shown a trend of decentralization. In this case, a new VPP trading mode is designed to solve the difficulties in multi-layer coordination and the lack of trust between market entities from the same layer. First, the consortium chain is combined with the multi-agent system to build a VPP internal transaction framework. In order to match the characteristic of the VPP with multi-layer market entities, the main-side chain governance structure is adopted to form data barriers and ensure the scalability of the transaction framework. Second, the existing consensus protocol is improved in accordance with the feature of VPP main chain, and the consensus reputation is introduced to maintain the operation efficiency of the blockchain. In order to improve the execution quality of distributed transactions and the VPP operation stability, the trading reputation closely related to the transaction result is introduced to constrain the trading behavior of aggregators, and a feasible bidding strategy is provided. Finally, the specific trading process of VPP based on the main-side chains is analyzed, and the main functions of smart contracts are designed in combination with internal market rules. The simulation results prove the feasibility and effectiveness of the proposed VPP trading mode. [ABSTRACT FROM AUTHOR]

Recently, large-scale renewable energy resources have been widely integrated into power systems. To optimize large-scale integration of these resources and improve the operation performance of the distribution system, this paper proposes a novel dynamic energy management strategy with the cooperative interaction of an energy system: a multi-grid connected microgrids (MGs)-based active distribution system (ADS). A bi-level multi-objective optimization problem of the strategy is formulated with the active distribution network (ADN) in the upper level and MGs in the lower level. The interaction can be classified into two categories: the one between MGs and the ADN and the other one among MGs. The former is described by bi-level programming; the latter is innovatively explained by an interactive energy game matrix (IEGM) defined in this paper. The concept of the expanded energy storage system is defined and applied to the IEGM for the optimal operation of ADSs. The optimal operation includes improved technical performances in terms of power quality, energy utilization, adaptability and autonomy. The optimization problem is solved with a hybrid algorithm of Rough Set Theory–Hierarchical Genetic Algorithm–NSGA-II. Case studies of an ADS with different MGs and a real system would validate the efficiency of the proposed methodology. Results show that the proposed EM strategy can accurately quantify and guide the energy interaction among MGs and that between MGs and the ADN. Moreover, those technical performances of the ADS are improved. [ABSTRACT FROM AUTHOR]

• A tri-layer ancillary service market mechanism is presented for demand flexibility. • A two-loop Stackelberg game depicts the interactions between the market subjects. • Multi-type ancillary services are considered in the game model. • The two-loop Stackelberg game equilibrium is proved. • A distributed iterative interaction method is presented to clear the multi-type ancillary service market. In order to utilize demand-side flexibility and meet system requirements, the aggregator (AGG) has become a vital role in the ancillary service market. To reflect the willingness of each market subject, a tri-layer ancillary service market framework for ancillary service market operator (ASMO), AGG, and users is proposed in this paper, which contains the interactions between different layers including ASMO and AGG, AGG and users. We develop a detailed market timeline that takes multi-type ancillary services into account. Moreover, due to the hierarchical market structure, we conduct a two-loop Stackelberg game to depict the effect of each market subject's strategic behavior. Based on the game models of all the market subjects, the existence and uniqueness of the game equilibrium point are proved. In addition, considering the correlation between multi-type ancillary services, the clearing process is decomposed into two sub-problems and a distributed joint market clearing based on the particle swarm optimization algorithm is presented. Case study shows that the proposed method is profitable for all the market subjects, the market timeline is reasonable, and the joint clearing method is effective. [ABSTRACT FROM AUTHOR]

Abstract: With the widespread application of distributed generation (DG), their utilization rate is increasingly higher and higher in the power system. This paper analyzes the static and transient impact of large-scale DGs integrated with the distribution network load models on the power grid. Studies of static voltage stability based on continuous power flow method have shown that a reasonable choice of DG's power grid position will help to improve the stability of the system. The transient simulation results show that these induction motors in the distribution network would make effect on the start-up and fault conditions, which may cause the instability of DGs and grid. The simulation results show that modeling of distributed generations and loads can help in-depth study of the microgrid stability and protection design. [Copyright &y& Elsevier]

Abstract: Wind farms interconnected to power system bring new challenges to power system economic operation. It is imperative to study how to solve optimal power flow (OPF) formulation with wind farms. In this paper, a multi-period optimal power flow (DOPF) is studied based on traditional OPF algorithm. According to the characteristic equations of asynchronous generator, the paper deduces a new algorithm that fits the DOPF formulation with wind farms. Based on the primal–dual interior point algorithm (PDIPM), a new modified algorithm is proposed. Besides, the voltage stability indicator, L-indicator, is also introduced into DOPF to demonstrate voltage stability of power system after wind farms incorporation. By taking SVC reactive power compensation into account, the paper analyzes its influences on system voltage stability. In addition, four different static load models are researched to reveal their effects on system-operating characteristics. An example shows the algorithm''s effectiveness and computation performance of the proposed method and several conclusions are obtained as well. [Copyright &y& Elsevier]

Abstract: For power quality detection, several techniques have been proposed in the literature. In the paper, some shortcomings of the conventional methods that are usually used to analyze the power quality issues are firstly pointed out. A kind of artificial neural network, adaline, and its new algorithm for analysis of power quality are presented. The new algorithm has the advantages of being simply calculated and easily implemented through hardware. The simulating results of voltage quality disturbances detection and especially frequency tracking demonstrate that the new adaline and its algorithm can be applied to the precise analysis for power quality. [Copyright &y& Elsevier]

• Framework includes VAE-GAN training, and threshold-based detector formulation. • The VAE-GAN has good convergence performance and grasps the statistical properties. • The proposed feature extraction can learn data representation well. • The proposed detector can achieve higher accuracy with lower computational burden. • The proposed energy theft detector is robust against attack type changes. With the fast development of industrial Internet of Things (IoT) for smart energy, data processing and storing are closer to the end used side. Edge data center, an intermediate platform between end data source and centralized data center, can reduce the data transmission pressure and processing time. To provide dependable data source for decision making and to reduce property loss, energy theft detection is important to an edge data center. In this work, we propose a threshold-based abnormality detector for energy theft detection in an edge data center. The framework includes training feature extractor based on VAE-GAN, implementing k-means clustering to determine the representative features of normal load profiles, and finally formulating a threshold-based abnormality detector based on defined abnormality degree. We demonstrate that when VAE-GAN converges, it can grasp the temporal relationship and statistical distribution of real data. The encoder of VAE-GAN has good feature extraction performance and the distribution of normal and abnormal data can be easily separated. Also, we prove that the proposed feature representation is better than the feature extracted by other advanced feature extractors. By comparison with state-of-the-art detection models, the proposed detector is more computationally efficient and robust against the attack type changes. [ABSTRACT FROM AUTHOR]

• Giving a new aggregation mode of prosumers with a superior prosumer and some inferior prosumers operating in a VPP. • Proposing a two-stage robust Stackelberg game considering the uncertainties of renewable energy output and market prices. • Ensuring the realization of real-time optimal scheduling of aggregate prosumers by AGC units. • Linearizing and solving the two-stage robust Stackelberg game model with CC&G algorithm. In this paper, a novel two-stage robust Stackelberg game is proposed to solve the problem of day-ahead energy management for aggregate prosumers considering the uncertainty of intermittent renewable energy output and market price. The aggregate prosumers operate in the form of virtual power plant (VPP) and participate in day-ahead (DA) and real-time (RT) market transactions. As the initiator and leader of the VPP, the superior prosumer with thermal units and interruptible loads is responsible for formulating the internal price mechanism and energy management strategy of the aggregate prosumers. Inferior prosumers, including renewable energy and shiftable loads, are responsible for providing renewable energy output information and responding to the price signals from the superior prosumer. The two-stage robust Stackelberg game model is linearized and solved by column-and-constraint generation (CC&G) algorithm. In addition, the thermal unit operating in the automatic generation control (AGC) mode ensures the realization of real-time optimal scheduling of aggregate prosumers for the entire dispatching cycle. Simulation results prove the rationality and validity of the proposed model and method. [ABSTRACT FROM AUTHOR]

• Labeling model, conditional scenario generation and reduction are proposed. • The conditional WGAN-GP is trained to generate scenarios for multi-wind farms. • The generated scenarios have mode diversity and follow the statistical resemblance. • The proposed method is better than the WGAN using weight clipping. Because of environmental benefits, wind power is taking an increasing role meeting electricity demand. However, wind power tends to exhibit large uncertainty and is largely influenced by meteorological conditions. Apart from the variability, when multiple wind farms have geographical adjacency, their power generation also displays strong correlation. Thus, scenario generation considering the spatiotemporal relationships is a useful tool to model the stochastic process. In this work, we propose a wind power scenario generation framework based on the conditional improved Wasserstein generative adversarial network (WGAN). The framework includes a cluster analysis to establish the classification rule, a support vector classifier (SVC) to predict labels, a conditional scenario generation process based on improved WGAN, and finally a scenario reduction procedure. We demonstrate that the clustering analysis and SVC based labeling model can provide accurate classification results and the scenarios conditioned on input labels can not only follow the marginal distribution of each category but also capture the spatiotemporal relationships. We also illustrate that by adding a gradient penalty term to the discriminator's loss function to enforce the Lipschitz constraint, the quality of scenarios is better than that of the existing method. [ABSTRACT FROM AUTHOR]

• Analytical target cascading and column-and-constraint generation are integrated. • Column-and-constraint generation is realized in a decentralized manner. • Comparison and analysis are given in terms of different aspects. • Cost comparison between cooperative case and uncooperative cases are made. • Sensitivity analysis is discussed aiming at different robust adjustment parameters. This paper describes two kinds of decentralized economic dispatch framework for the coordinated operation of multi-microgrids in a distribution network. Considering the high uncertainties of renewable energy sources and load demands, robust optimization-based distributed framework exhibits a prospective application on networked multi-microgrids in the future. By integrating analytical target cascading and robust model which can be solved using a column-and-constraint generation method, the robust economic dispatch problem can be applied in different entities, respectively, distribution network operator and microgrid operator in a decentralized way. Second way, column-and-constraint generation algorithm can be naturally realized in a decentralized and coordinated manner, specifically, the sub-problems and master-problem can be separately executed in microgrid operators and distribution network operator. Decentralized framework can respect the independence and privacy of different operators, and be beneficial to solve technical and economic challenges brought by centralized optimization. Numerical results of a modified IEEE 33-bus test distribution system with three microgrids validate the effectiveness of the proposed methods, and compare the characteristics of two kinds of decentralized implementations, and it is found that the first methodology shows better information privacy and lower communication burden, while the second way can obtain quicker convergence speed. In addition, cost comparison between cooperative case and three kinds of uncooperative cases is analyzed to explain economic benefits of cooperative operation. Furthermore, sensitivity analysis of lateral uncertainty and longitudinal uncertainty are given to show that robustness is enhanced at the expense of adding costs. [ABSTRACT FROM AUTHOR]

Abstract The solute-membrane interactions play an important role in adsorption and consequently rejection of trace organic compounds (TrOCs) by nanofiltration (NF) membranes, while the various specific interactions are yet to be identified and quantified. In this study, molecular docking was for the first time explored as a simulation and computation approach to elucidating the solute-membrane interactions. The binding modes between several pharmaceuticals (PhACs) and the polyamide (PA) material in different protonation/deprotonation states were simulated, and the specific interactions including hydrogen bonding, π-π stacking, π-cation interaction and ionic bridge binding were identified. Binding energies consisting of van der Waals and electrostatic components were calculated by the Grid scoring of docking, which quantitatively confirmed the contributions of various interactions to the adsorption of PhACs onto the membrane. Regression analysis showed that the adsorbed amounts could be well described jointly by the binding energies and two molecular descriptors of PhACs (i.e., solubility (logS) and polarity (MR)), which depict the effects of solute-membrane and solute-water interactions, respectively. This study provided valuable information to better understanding the adsorption mechanisms which greatly affect the rejection of TrOCs by NF membranes. Graphical abstract fx1 Highlights • Molecular docking was for the first time explored to elucidate solute-membrane interactions; • Specific interactions between PhACs and PA layer were identified from the binding modes; • Binding energies were calculated to confirm effects of various interactions on adsorption; • Adsorbed amounts were well correlated with E binding and logS and MR values of PhACs. [ABSTRACT FROM AUTHOR]

A new optimization algorithm for optimal PMU configuration based on combination of graph theory and genetic algorithm is proposed. According to four topology reconstruction rules and three PMU configuration rules based on the graphic relationships between PMUs, constraints of PMU placement are put forward through topology constraint analysis, which dramatically limits the feasible solution space, thereby enhancing the algorithm speed. Meanwhile, an improved genetic algorithm based on serial number coding is presented to avoid infeasible solutions and improve the overall optimization performance. New corresponding crossover and mutation operator is also created. Examples show that the proposed algorithm performs very well and is highly valuable to large-scale networks. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) emerges as a promising integration and aggregation technology that facilitates the utilization of massive flexible demand-side resources (DSRs). However, non-negligible modeling errors and high-dimensional uncertainties involved in DSR aggregation threaten the delivery reliability and cost-effectiveness of VPP operation. To address this problem, this study proposes an integrated three-stage scheduling framework for VPPs and develops a model-assisted multi-agent reinforcement learning (MARL) approach. In the proposed framework, the VPP scheduling problem is formulated as a decentralized partially observable Markov Decision Process (Dec-POMDP), which depicts the complex interaction process among the three stages (bidding, re-dispatching and disaggregation). The interactions are evaluated by a comprehensive reward function, incorporating the trading and operation costs, as well as imbalance penalties. To enable decentralized decision-making, a model-assisted multi-agent proximal policy optimization (MA2PPO) algorithm is proposed, which trains a separate actor network for each aggregator. Additionally, the MA2PPO is augmented with a model-assisted safety decision-making method to accelerate the training process. Numerical simulation results verify that the proposed method enhances the delivery reliability and cost-effectiveness of the VPP, while achieving faster convergence time compared with purely model-free MARL methods. • Integrated three-stage VPP scheduling, incorporating bidding, re-dispatching and disaggregation. • Data-driven decentralized decision-making without accurate modeling and uncertainty representation. • Model-assisted safety decision-making for accelerating the algorithm learning speed. • Superior performance compared to model-based and model-free optimization methods. [ABSTRACT FROM AUTHOR]

Introducing operational flexibility in power system planning has motivated the development of planning models to be built upon and validated by unit commitment. While handling uncertainty, these models form a holistic robust optimization that makes three groups of decisions in a single model, namely investment, commitment, and dispatch. A dilemma arises when aligning commitment with other decisions, either on the same layer with investment or with dispatch—the former makes the model easy to solve but violates the decision-making process in reality, while the latter creates a triple-layer optimization with a mixed-integer inner layer that is harder to solve. A tractable routine is proposed for the latter with a nested decomposition algorithm to contribute a technique that splits the investment and commitment decisions for power system planning under uncertainty, which is well suited to the real-world infrastructure investment decision process. • Incorporating unit commitment and handling uncertainty simultaneously into planning. • Splitting commitment from investment into different decision-making stages. • Applying a decomposition to multi-layer optimization with a mixed-integer inner layer. [ABSTRACT FROM AUTHOR]

With the increasing proportion of variable renewable energy characterized by fluctuation and the promotion of the "Clean Heating" policy, the problem of seasonal energy imbalance of the system has become increasingly challenging. There is a lack of effective means to mitigate this challenge under the background of gradual compression of the construction space for traditional thermal units. Concentrated solar power (CSP) is a promising technology to replace thermal units by integrating emergency boilers to cope with extreme weather, and can meet long-time energy balance as a seasonal peak regulation source. In this paper, we propose a long-term high-resolution expansion planning model of energy systems integrating CSP to address seasonal energy imbalances. The model takes into account both investment and operation considerations in full hourly resolution for the whole year based on a fast cluster optimization method. With the projection to 2050, we take the energy system in Xinjiang province which is a typical area of the "Clean Heating" project with rich irradiance as a case study. It shows that the optimal deployment of CSP and electric boiler (EB) can result in a 8.73 % reduction in costs, a 19.72 % decrease in the peak-valley difference of net load, and a substantial 58.24 % reduction in renewable curtailment at 65 % renewable penetration compared to the base scenario. [ABSTRACT FROM AUTHOR]

Abstract: The binding of reactive polycyclic aromatic hydrocarbon (PAH) metabolites, formed enzymatically, to DNA is a crucial step in PAH carcinogenesis in vivo. We investigated the noncovalent binding interactions between 11 PAH metabolites and human p53 complementary DNA (p53 cDNA) using the fluorescence displacement method and molecular docking analysis. All of the examined metabolites predominantly interacted with p53 cDNA by intercalation instead of groove binding. The dissociation constants ranged from 0.02 to 12.34μM. Of the metabolites tested, 1-hydroxypyrene and 3-hydroxybenzo[a]pyrene showed the strongest binding affinities to DNA, while 2-naphthol was the weakest DNA intercalator. The intercalation of the metabolites was stabilized by stacking the PAH phenyl rings with the DNA base pairs and the formation of hydrogen bonds between the oxide or hydroxyl groups on the metabolites, and DNA bases or backbones. The binding of the metabolites to DNA showed some sequence selectivity. The binding affinities and hydrogen bonds for 3-hydroxybenzo[a]pyrene, benzo[a]pyrene-4,5-dihydroepoxide (BPE) and benzo[a]pyrene-r-7,t-8-dihydrodiol-t-9,10-epoxide (BPDE) differed. It seems that the functional groups on the periphery of the PAH aromatic ring play crucial roles in regulating its binding affinity with DNA. Although it was difficult to determine the correlation between DNA noncovalent binding affinity and carcinogenicity for some of the PAH metabolites, the present study improved our understanding of the formation of PAH metabolite-DNA adducts. [Copyright &y& Elsevier]

• The data-driven inverse optimization corrects the DR execution online. • The proposed strategy is realized in a distributed manner and free of initialization. • This paper provides rigorous stability proof for the proposed optimization. • D-PPDS algorithm solves the DR optimization with global inequality constrain. This paper provides an insight into the demand response (DR) optimization in distribution markets consisting of a retailer and multiple demand response aggregators (DRA), where a retailer determines DR incentives based on power consumption profile. Conventional DR optimization with global states and constraints is intractable to be implemented in a distributed framework, which restricts the application feasibility and the potential profit of DR. To handle these limitations, we design a multi-agent architecture for distributed demand response (DDR). An online data-mining method is developed to identify the characteristics of DR. A leader–follower structure decomposes the original problem into a leader problem with global variables and aggregators of sub-problems, where discrete singular consensus is designed to broadcast the leader's strategy to followers in real-time. The distributed perturbation primal–dual sub-gradient (D-PPDS) algorithm is proposed to solve the DDR problem with global inequality constraints in a completely distributed fashion. The proposed DDR strategy is tested by an actual case. The simulation results demonstrate that the asynchronous D-PPDS algorithm can obtain the near-optimal solution of the problem with global inequality constraints, and is robust against delay or plug-and-play. [ABSTRACT FROM AUTHOR]

Accurate demand response (DR) potential forecasting is the basis for load aggregators (LA) to make optimal bidding strategies in DR market trading. LAs usually face practical challenges when they perform forecasts for those new customers who have no historical response data. Transfer learning provides a promising solution to this problem by leveraging knowledge acquired from other existing contracted customers. However, traditional transfer learning methods are trained offline and cannot make use of the latest response information of these new customers, which may result in large forecasting errors since the response behavior of new customers usually dynamically changes. To address the above issues, this paper proposes an online transfer learning-based DR potential forecasting framework, in which two forecasting models are established. The first one is built using the historical data of existing customers and this model is then transferred to the target domain (i.e., new customers) by parameter sharing and fine-tuning. The second model is built using the local response data of new customers, which gradually accumulates with the increasing participation of DR events. These two models are combined by an adaptive ensemble framework based on their online performances, thus enabling it to dynamically track the changes in new customers' response behavior. Case studies on a real-world dataset validate the effectiveness of the proposed framework. • The challenge of forecasting new customers' DR potential is discussed. • An online transfer learning-based DR potential forecasting framework is proposed. • The proposed framework can combine advantages of transfer learning and online learning. • The proposed method outperforms other off-line benchmark methods. [ABSTRACT FROM AUTHOR]

Considering the flexible capacity and privacy needs of numerous flexible energy users in the context of demand response (DR), this study establishes an influence model (IM) to describe the DR participation capabilities of users considering privacy budget. Using the designed Stackelberg game mechanism that can achieve optimal selection for DR responders from users with different characteristics described by IM, a federated learning (FL)-based optimization method that uses differential privacy (DP) as the data transmission mechanism for DR economic optimal dispatch is proposed. Ideas of controlling the number of participating users and financially compensating for the privacy leakage risk by the FL-based optimization method are the guarantees for users with private data to participate in DR. The performance of the proposed optimization method is also compared with that of the Moth-flame optimization algorithm in a case study, and the guiding value of the former in selecting among user groups with different characteristics is then discussed. Results show that the proposed method exhibits good economic benefits and universal applicability. [Display omitted] • Users' influence models can accurately reflect historical power consumption characteristics. • Privacy and response incentive mechanisms collectively guide the optimal scheduling of demand response. • The federated learning-based optimization method achieves economic Nash equilibrium while upholding users' privacy. • Optimal strategies for the distribution network manager vary based on different privacy requirements. [ABSTRACT FROM AUTHOR]

• The proposed dynamic online robust framework can serve as a standby dispatch during emergency. • The proposed method guarantees queue stability and supports long-term operation. • Future costs are estimated and optimized online via deep deterministic policy gradient, and thus are reduced. Appropriate distributed dynamic standby dispatch schemes are of great importance to manage distributed energy systems and integrate large-scale renewables without the guidance of central intelligence or day-ahead forecasting. This study focuses on systems based on networked microgrids (MGs), which include self-contained medium-voltage MGs—consisting of renewable or dispatchable generators, energy storage systems (ESS), and flexible loads. An online dynamic dispatch scheme is proposed to support autonomous operation or to serve as a standby dispatch scheme in an emergency when a dispatch center is unavailable or day-ahead planning is infeasible. First, the multi-MG energy management problem is modeled into several distributed coupled subproblems. Afterwards, taking Lyapunov drift and online learning of future costs into consideration, the distributed problem is transformed into a robust long-term optimization. Moreover, a Lyapunov-based dynamic algorithm is employed to solve the problem in a fully distributed fashion. Finally, taking an actual system as an example, the superiority and feasibility of the proposed strategy are verified by simulation. The proposed distributed online standby dispatch has good performance in the long-term economy optimization and queue stability without any day-ahead forecasting and central decision-making compared with existing frameworks. [ABSTRACT FROM AUTHOR]

Energy efficiency and carbon mitigation are important issues in modern energy systems research. Considering the couplings between energy and carbon, this paper investigates a carbon-embedded energy coordination problem in a park-level integrated energy system (PIES). Firstly, based on energy hub, a multilateral interactive transaction framework is introduced, consisting of energy hub operator (EHO), building users with photovoltaics, and an electric vehicle (EV) charging agent. Secondly, a time-varying carbon emission measurement model for the outsourced electricity is designed, taking into account the dynamic composition of the supply-side generators. Based on the time-varying carbon factor and virtual carbon emission flow, an energy-carbon pricing strategy is proposed to balance the carbon responsibility among different participants. Then, a multi-agent interactive trading model in the PIES is constructed, where the energy consumption plans of building users and EVs are guided by the dynamic energy-carbon integrated prices. Through the iterative interaction among different agents, the optimal trading results are finally obtained. Simulation results illustrate the effectiveness of the proposed energy-carbon integrated pricing method in reducing carbon emissions and promoting energy sharing. • A carbon-embedded multilateral energy interaction mechanism in a PIES is discussed. • A time-varying carbon emission measurement model is established. • An energy-carbon integrated pricing strategy based on the dynamic carbon factor and virtual carbon emission flow is designed. • The impacts of energy-carbon integrated pricing on the scheduling of the PIES and carbon emission are investigated. [ABSTRACT FROM AUTHOR]

[Display omitted] • A colorimetric fluorescent probe was designed and synthesized for the detection of bisulfite with high sensitivity and selectivity in 100% aqueous solution. • The probe has been successfully used for the detection of SO 2 in food and animal serum samples with good recovery. • The probe was used as a solid sensor to visually detect the liquid and gas sulfur dioxide on the test strips. • The probe could detect both bisulfite in food and gaseous SO 2 in the environment. Bisulfite (HSO 3 −) has the functions of bleaching, antiseptic, antioxidant, inhibiting bacterial growth, and controlling enzymatic reactions in food. However, long-term consumption of foods containing excessive amounts of bisulfite can be harmful to health. In addition, large doses of sulfur dioxide (SO 2) can cause diarrhea, hypotension, allergic and asthmatic reactions in susceptible individuals. Therefore, it is urgent and essential to explore some rapid, reliable, and convenient tools to detect HSO 3 − in food and SO 2 gas. Herein, we exploited a fluorescent probe, NPO , to detect HSO 3 − in 100 % aqueous solution. The probe has the advantages of easy synthesis, excellent water solubility, significant colorimetric change, good selectivity, high sensitivity, and fast response (within 1 min). Probe NPO was successfully applied for testing strips to visualize the behavior of HSO 3 − and SO 2 gas. Moreover, the probe has been used to monitor the behavior of HSO 3 − in real food samples and animal serum samples. [ABSTRACT FROM AUTHOR]

Abstract: To investigate bioaccumulation, maternal transfer and elimination of polybrominated diphenyl ethers (PBDEs) in amphibians, we collected adult frogs (Rana limnocharis) from a rice field in an e-waste recycling site in China. We found that ∑PBDEs in the whole frogs and various tissues (brain, liver, testis and egg) ranged from 17.10 to 141.11ngg−1 wet weight. Various tissues exhibited a similar PBDE congener profile, which was characterized by intermediate brominated congeners (BDE-99 and BDE-153) as the largest contributors, with less lower brominated congeners (BDE-28 and BDE-47) and higher brominated congeners (BDE-209). The maternal transfer capacity of PBDEs declined with the increase in bromine numbers of PBDE congeners. We suggest that the bromine atom number (the molecular size, to some degree) might be a determining factor for the maternal transport of a PBDE congener rather than K ow (Octanol–Water partition coefficient), which expresses a compound’s lipophilicity. ∑PBDEs concentrations in frogs decreased over time during a depuration period of 54days when these wild frogs were brought to the lab from the e-waste recycling site. The half-life of ∑PBDEs was 35days, with about 14days for BDE-47, and 36 and 81days for BDE-99 and BDE-153, respectively. The data shows that the elimination of PBDEs has no essential difference from aquatic and terrestrial species. [Copyright &y& Elsevier]

Abstract: Polybrominated diphenyl ethers (PBDEs) have been shown to alter thyroid hormone level in experimental animals. One of the possible mechanisms for hormone disruption is the competitive binding of hydroxylated PBDEs (OH-PBDEs) with hormone transport proteins. In this study, binding interaction of 14 diversely structured OH-PBDEs with two thyroxine transport proteins was investigated by fluorescence displacement assay, circular dichroism, and molecular docking. Binding affinity of the 14 OH-PBDEs with transthyretin (TTR) and thyroxine-binding globulin (TBG) was measured by competitive fluorescence displacement assay. The binding constant was found to fall in the range of 1.4×107 M−1 and 6.9×108 M−1 for TTR, and between 6.5×106 M−1 and 2.2×108 M−1 for TBG. Binding affinity increased significantly with bromination number from 1 to 4, whereas 5- and 6-brominated diphenyl ethers did not show any further increase. Protein secondary structural change of TTR and TBG upon binding with 5-OH-BDE-047 was investigated by circular dichroism. The spectral change displayed a pattern similar to the one with thyroxine, suggesting that the environmental chemical binds to the two proteins at the same sites as the hormone. In molecular docking analysis, a ligand-binding channel in TTR was revealed for OH-PBDEs binding, which appeared to be mostly hydrophobic inside but guarded by positively charged residue Lys15 at the entrance. Binding affinity of the 14 OH-PBDEs with TTR could be rationalized reasonably well by their pocket binding mode and hydrophobic characteristics. Based on the binding constant obtained in this work, possibility of in vitro competitive displacement of thyroid hormones from the transport proteins by OH-PBDEs was evaluated. [ABSTRACT FROM AUTHOR]

Differentiation of resilience from reliability has been a heated topic ever since the emergence of the former upon the limitation, and as a complement, of the latter, while finding the common ground for both has been scarce. This study first picks out the steady-state performance of both to be the common ground, while differentiating their typical causes, namely, the extreme weather for the resilience threats and the component ageing for the reliability challenges. An original evaluation framework developed earlier for the sole reliability is then extended here towards this common ground to accommodate resilience, becoming the joint reliability and resilience evaluation framework. The joint evaluation framework is built upon the Monte Carlo simulation, which embeds the rolling unit commitment as the system operation module to balance the optimality of operation strategy and the punctuality of condition update, and the forecast module with the machine learning technique for generate varied operation conditions. The proposed evaluation framework that joins resilience with reliability not only has its efficacy validated on the 39-bus system, but also deepens the evaluation by analyzing scenarios created with variating the values of key factors that impact resilience, including the typhoon speed, load factor, restoration time, and typhoon direction. Such a joint reliability and resilience evaluation pioneers as an integrated approach to conduct both evaluations in three aspects, namely, differentiating the causes of reliability and resilience harms and paralleling the challenges of both on system performance, upgrading the existing evaluation methods by applying the rolling mechanism to the operation strategy, and offering an open framework to embed complex functions, such as forecast tools enabled by machine learning and proactive responses for resilience enhancement. • Research gap of common ground of resilience and reliability spotted from literature. • A joint resilience and reliability evaluation framework with the Monte Carlo simulation. • Rolling unit commitment as the embedded operation module of the evaluation. • An open framework extensible for more complex strategies of resilience enhancement. • Proven convenience of the framework for application to real systems. [ABSTRACT FROM AUTHOR]

Abstract: Bisphenol A (BPA) is the monomer component of polycarbonate plastics and classified as an endocrine disrupting chemical (EDC). The reproductive toxicity of BPA has been extensively studied in mammals; however, relatively little information is available on the immunotoxic responses of fish to BPA. In this study, we investigated the effects of BPA on the immune functions of lymphocytes and macrophages in Carassius auratus. The effects of BPA were compared with those of two natural steroid hormones, estradiol and hydrocortisone. Proliferation of the two types of cells in response to PHA was measured using colorimetric MTT assay. Macrophage respiratory burst stimulated by Con A was measured using chemiluminescence assay. Results showed that BPA (0.054–5.4 mg/L), estradiol (0.0002–2.0 mg/L) and hydrocortisone (5–50 mg/L) significantly induced Carassius auratus lymphocyte proliferation while higher doses of hydrocortisone (500–5000 mg/L) appeared to be inhibitory. BPA (0.005–50 mg/L), estradiol (0.005–800 mg/L) and hydrocortisone (0.005–500 mg/L) markedly enhanced macrophage proliferation, whereas higher doses of BPA (500–1000 mg/L) appeared to inhibit cell proliferation. Furthermore, higher dosage of BPA (50 mg/L) and hydrocortisone (50 and 500 mg/L) suppressed the macrophages respiratory burst while estradiol is stimulative all the doses tested (0.05–500 mg/L). In conclusion, BPA could have immunotoxicity to Carassius auratus and functional changes of lymphocyte and macrophage in Carassius auratus may be different between low and high dosages. [Copyright &y& Elsevier]