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• Planning for multi-energy system integrating active distribution network is modeled. • A probabilistic scenario generation method is proposed considering the robustness. • A modified piecewise linearization method is developed. • Mixed integer second order cone programming is employed to solve the model. As an effective pattern to promote efficient use of energy, multi-energy system has become the main focus of development. On the other hand, active distribution network is now also the promoting concept for developing power distribution system, since the active network management technologies can make the network more flexible and controllable. To facilitate and utilize the advantages of both systems, this paper proposes an expansion planning model for multi-energy system integrating active distribution network, natural gas network and energy hub; and the positive impact of active network managements on expanding multi-energy system is originally investigated. With the aim of minimizing the total cost over the planning horizon, this model centers on the optimal determination of the type, location and size of all the infrastructures, where the active network managements are modeled and incorporated. In addition, in order to improve the robustness of the planning results, a probabilistic scenario generation approach is proposed to test the model. In order to solve the proposed mixed-integer nonlinear model, the second order cone programming as well as a modified piecewise linearization approach is applied to convert the original model to a mixed integer second order cone programming model. A multi-energy system (including a modified IEEE 33-node distribution system, a new 23-node gas system and 8 energy hubs) are employed to verify the effectiveness of the prosed model and methods. The simulation results exhibit the superiority of the joint expansion planning model for multi-energy system and the beneficial impact of considering active network management. [ABSTRACT FROM AUTHOR]

Previous research has found total mercury (THg) and methylmercury (MeHg) levels increase with litterfall decay, thus suggesting litterfall decomposition plays an essential role in the biogeochemical transformation of mercury (Hg). However, it remains unclear how Hg accumulates in the decaying litter, how bacterial taxa networks vary and what roles various microorganisms play during litterfall decomposition, especially nitrogen (N)-fixing, MeHg-degrading and Hg-methylating microbes. Here, we demonstrated as degradation proceeded, a gradually-complex network evolved for litterfall bacteria for the subtropical mixed broadleaf-conifer (MBC) forest, whereas a relatively static network existed for the evergreen broadleaf (EB) forest. N-fixing and MeHg-degrading bacteria dominated throughout litterfall decomposition process, with relative abundances of N-fixing genera and nifH copies maximum and relative abundances of MeHg-degrading bacteria and merAB copies minimum in summer. Hence, N-fixing bacteria likely mediate THg increase in the decomposing litterfall, while MeHg enhancement may be regulated by aerobic MeHg-degrading microbes which can transform MeHg to inorganic divalent Hg (Hg2+) or further to elemental Hg (Hg0). Together, this work elucidates variations of N-fixing and MeHg-degrading microbes in decaying litterfall and their relationships with Hg accumulation, providing novel insights into understanding the biogeochemical cycle of Hg in the forest ecosystem. [Display omitted] • A gradually-complex network evolved for the decaying litterfall of MBC forest. • A relatively static network existed for the decomposing EB forest litterfall. • N-fixing bacteria may mediate the increase of THg levels in the decomposing litterfall. • MeHg enhancement is likely regulated by aerobic MeHg-degrading microbes. • Underlying soil is the source of litterfall THg, while MeHg is transported from litterfall to soil. [ABSTRACT FROM AUTHOR]

• A novel structure of the smart multi-energy system is proposed. • Transportation, natural gas, and active distribution networks are jointly considered. • Integrated energy stations are served to achieve the synergies of various energies. • A mixed user equilibrium is developed to obtain both routing and charging guidance. As the energy demand for multiple purposes increases, smart multi-energy systems have become the trend in the development of cities. In such an intelligent environment, it is possible and imperative to consider the traffic flow guidance for electric and non-electric vehicles in the urban transportation network. However, this has not yet been considered in the challenges of planning multi-energy systems. Based on these considerations, a novel optimal planning framework is proposed in the present study, that takes into account the coupling of transportation, natural gas and active distribution networks. Firstly, a novel structure of integrated energy stations is proposed, which serves as coupling components where various sources of energy can be converted, distributed, and stored. The model optimizes the investment and operational strategies of renewable energy generation, energy converters, storage devices and charging facilities in a coordinated way. To capture the impact of traffic flow, a mixed user equilibrium state was established. Thus, the route selections for non-electric vehicles and the navigations of electric vehicles to charging facilities could both be incorporated into the model. Since the proposed planning model is a mixed-integer nonlinear program problem, convex relaxation was conducted to transform the model formulations into a mixed-integer second order cone program version. Finally, four cases were designed and tested in a practical urban area. The quantitative comparison revealed that unguided traffic flow will increase the traffic costs by more than 23%. Moreover, when both charging and routing guidance are considered the energy losses will decrease by at least 5.6%. [ABSTRACT FROM AUTHOR]

• A two-stage robust planning model for the coupled system is proposed. • The uncertainty of renewable energy, load and traffic demand are jointly considered. • The interactions between transportation and active distribution system are explored. • A two-level method is used to solve the proposed robust optimization model. Widespread utilization of electric vehicles has incurred more uncertainties and led to profound interdependencies across the power grid and transportation network. This creates an urgent demand for uncertainty modelling tools. Under this context, this paper proposes a two-stage robust optimization model for expansion planning of coupled active distribution and transportation. This model identifies the best type, location and capacity for roads, distribution lines, distributed generations, static VAR compensators, on-load tap changers, energy storage systems and charging facilities. From a system-level perspective, the steady-state pattern of the traffic flow in a transportation network is characterized by the Wardrop user equilibrium. In the active distribution system, the conic relaxation-based branch flow equations are employed, and the operating status of related devices are formulated by the linearized convex constraints. Considering the interdependency between two coupled systems, a deterministic expansion planning model is first established. Furthermore, with multiple uncertainties considered, a two-stage robust optimization model is proposed to optimize the investment and operation strategies coordinately, and thus a robust optimal solution is achieved. A two-level algorithm based on a combination of the column-and-constraint generation and outer approximation technique is developed for solving the two-stage model. The results demonstrate that the robust approach can effectively reduce the operation cost of coupled systems under the worst-case scenarios. Moreover, a comparative analysis indicates that operation costs in both systems increase linearly as the uncertainty level of traffic demand increases, while increasing that of the power load does not show an obvious change in the traffic operation cost. [ABSTRACT FROM AUTHOR]

• A planning model of coupled transportation and active distribution system is proposed. • Scenario generation technique is extended to incorporate traffic demand. • Scenario-based user equilibrium constraint is derived and embedded into the model. • A three dimensional piecewise method is designed to relax the bivariate expressions. • The importance of considering interdependencies of the coupled system is verified. The widespread utilization of electric vehicles has inspired the emerging trend of coupled transportation and distribution system, which entails the systematic methodologies to model the new planning problems. This paper proposes a scenario-based comprehensive expansion planning model for a coupled transportation and active distribution system. With the aim of minimizing the investment and operation costs, this model determines the best alternatives, locations and sizes for candidate assets, including traffic roads, distribution lines, distribution generators, capacitor banks, static var compensators, voltage regulators, energy storage systems and charging facilities, as well as their operation strategies. First, a generated scenario method is extended to incorporate the uncertainty of the traffic flow demand. Based on multiple scenarios, the steady-state distribution of traffic flow is characterized by the Wardrop user equilibrium principle, and the corresponding equivalent constraints are derived and incorporated into the model. For active distribution system, we formulate the operation constraints for related infrastructures. Considering the interdependency between the two systems, an expansion planning model is proposed, which simultaneously optimizes the investment and operation strategies. Due to the nonlinear nature of the model, we have developed a three-dimensional piecewise linear approximation and applied second order cone relaxation to reformulate the model as a mixed-integer second order conic program; thus, the global optimal solution can be found in a reasonable time frame. Results on a test system reveal that the variations of both systems have some influences on each other, indicating the significance of considering the interdependencies between transportation and active distribution system. [ABSTRACT FROM AUTHOR]

• A novel expansion planning model for urban integrated energy system is proposed. • The transportation network is incorporated into the model, in which fast and slow charging modes are considered. • A layered-based framework for the optimization model of the urban integrated energy system is proposed. • A two-layer optimization algorithm is designed to solve the proposed model. • Simulation results based on a real case in China indicate the superiority of the proposed approach. In the modern metropolitan regions, the coupling among distribution networks (DNs), gas networks (GNs), and transportation networks (TNs) is increasingly intensified, which gives rise to the urgent demand for efficient modeling and solving methods. In this context, a mathematical model for the configuration planning of integrated energy systems is proposed in this paper. With the aim of minimizing total planning costs, the model jointly considers the investment and operation strategies for distribution lines, distributed wind power generators (DWGs), combined heat and power units (CHPs), power to gas units (P2Gs), gas furnaces (GFs), fast charging facilities (FCFs) and slow charging facilities (SCFs). Since the proposed model comes down to a non-convex and nonlinear optimization problem, a two-layer algorithm is designed to solve it. In the outer layer, a genetic algorithm (GA) is performed to obtain a set of individuals representing the investment decisions on distribution lines, DWGs, and FCFs, which will be delivered to the inner layer model. With fixed decision variables, the inner model optimizes the operating costs, charging profit coefficients, and strategies for DWGs and energy converters in a coordinated manner. A real urban area in China is employed in the numerical experiment, which corroborates the effectiveness of the proposed model and solution algorithm. [ABSTRACT FROM AUTHOR]

• The structure of urban multi-energy network is provided. • Three energy flows are generally defined and their convex relaxations are performed. • A mixed user equilibrium is applied to describe rational route and charging choices. • A two-stage bound-tightening algorithm is proposed to strengthen convex relaxations. The ever-growing interdependence among power distribution, natural gas networks and transportation infrastructure entails efficient and coordinated optimization techniques. This paper proposes an operational optimization model of urban multi-energy networks that encompasses the synergies among the above components, energy converters and electric vehicles. The proposed model defines and encapsulates three energy flows in a convex optimization problem. In the distribution network, the conic relaxation-based branch flow equation is employed to characterize the alternating current power flow. The static characteristics of gas flow are described via the Weymouth equation with convex envelop relaxations. In the transportation network, the traffic flow patterns associated with the interplay among routing and charging behaviors are studied. Since these behaviors are amenable to the Wardrop principle such that the travel cost cannot be reduced by unilaterally altering travel choice, a mixed user equilibrium model is established to describe the related traffic flows. Although the relaxation quality has been improved by deriving the tighter bounds of the variables, to date, bound-tightening approaches have not yet been effectively applied to multi-energy network problems. To strengthen convex relaxations, we enhance the proposed model with a two-stage approach. The first stage improves the variable bounds through sequential optimality-based bound contraction. The second stage iteratively and successively solves the model with a dynamic bound-tightening algorithm. Based on case studies, the interdependence among energy networks is discussed. In addition, the numerical experiments corroborate the solution quality and computational efficiency benefits of the proposed approach. [ABSTRACT FROM AUTHOR]

• A novel expansion planning model for active distribution system with multiple active network managements is proposed. • Optimal load shedding is originally proposed and shows an obvious better stability performance in case study. • A SOCP approach and some linearization means are applied to solve the MINLP model. • A two-stage optimization algorithm is developed and proved to have great ability to solve this problem. • Simulation results based on a modified 37-bus system indicate the technical and economic superiority of the proposed model. In this paper, a new expansion planning model for an active distribution system considering multiple active network management schemes is proposed, where demand management considering the optimal load-shedding direction is innovatively introduced into the planning model. This model optimizes the investment decisions of all the infrastructures as well as the corresponding operational strategies in a coordinated manner. Therefore, the final solution is identified by the optimal type, location and size of the feeders, distributed generators, capacitor banks, static var compensators, voltage regulators, energy storage systems, and transformer substations. To incorporate the optimal load reduction into active network management, the optimal load-shedding direction is first introduced, and an iterative method is developed to compute it. On this basis, the coordination planning model of an active distribution system is established, and the operational strategy of all the active management devices is integrated into the expansion planning problem of an active distribution system. By applying the second-order cone relaxation method, the original mixed-integer nonlinear model is converted into a mixed-integer second-order cone programming model that be efficiently solved. Finally, a two-stage optimization algorithm is proposed to obtain the final solutions. Simulation results based on a 37-bus system and a 122-bus system corroborate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]