This paper discusses a bilayer coordinated operation scheme for the multi-energy building microgrid (MEBM) with comprehensive uncertainty sources. First, a building model considering the battery degradation, practical/detailed thermal loads, and various operating tasks of residential appliances is presented. Second, to alleviate the adverse effects from diverse uncertainties of electricity demands, cooling water temperature, outdoor temperature, occupants metabolism as well as solar irradiance, a bilayer model is applied which makes full utilization of historical data and manages the solution conservativeness. The first layer is the stochastic-weighted robust optimization-based day-ahead operation. It determines the dispatch of heterogeneous energy storage assets, multi-energy demand response, and on-off status of combined cooling, heat and power (CCHP) plants on an hourly basis for the entire day. The second layer sequentially finalizes the operation of the power-to-thermal conversion unit, CCHP plant, and electricity transactions between the MEBM and utility grid hourly with uncertainty realizations. Numerical case studies demonstrate the effectiveness of the proposed approach in obtaining the economic MEBM operation with the high computational performance and immunizing against uncertainties.

The increasing prevalence of electric vehicles (EVs) has intensified the coupling between power distribution networks (PDNs) and transportation networks (TNs) in both temporal and spatial dimensions. In order to accurately model the coupled network, this paper studies the dynamic network equilibrium to capture the temporally-dynamic interactions between PDNs and TNs. This equilibrium encapsulates the driver’s choices of route, departure time, and charging location, and the electricity price. In the TN, a dynamic traffic model with point queues is proposed to describe the spatial and temporal evolution of traffic flows that is congruent with established user equilibrium choices. In particular, the queues formed at charging stations are, for the first time, modeled by a point queue. In the PDN, the electricity prices are accurately determined from a second-order conic program with a guarantee of strong duality. After theoretically proving the existence of an equilibrium solution, an improved fixed-point algorithm based on extrapolation is also proposed to solve the equilibrium problem efficiently. Numerical results show that the dynamic network equilibrium can be efficiently solved to capture the temporally variant nature of traffic flows, queues, and electricity prices.

Given the rapid development of the ship industry and increasing ship operating cost and gas emissions, this article studies the optimal operation of a multienergy ship (MES) microgrid to simultaneously reduce the ship operating cost and gas emissions. Apart from the conventional diesel generators, electric energy storage, and photovoltaic cells, the combined cooling heat and power (CCHP) unit is introduced for higher energy utilization efficiency. Besides, to flexiblize the CCHP operation and enhance the multienergy interaction, the thermal storage and power-to-thermal conversion unit are also included in the MES microgrid. Then, a multiobjective coordinated energy dispatch and voyage scheduling method are presented. After the constraints linearization, the coordination method is formulated as a mixed-integer linear programming problem, which is solved by the augmented ϵ-constraint method through Cplex solver. Finally, the proposed method is verified on a testing MES microgrid with necessary sensitivity analysis, and by comparing it with traditional benchmarks, its advantages are demonstrated. The simulation results indicate that the proposed method is effective for reducing the operating cost and emission, and it could provide references for the optimal operation of the emerging multienergy cruising ships.

In this letter, we propose a tractable formulation and an efficient solution method for the Wasserstein-metric-based distributionally robust unit commitment (DRUC-dW) problem. First, a distance-based data aggregation method is introduced to hedge against the dimensionality issue arising from a huge volume of data. Then, we propose a novel cutting plane algorithm to solve the DRUC-dW problem much more efficiently than state-of-the-art. The novel solution method is termed extremal distribution generation, which is an extension of the column-and-constraint generation method to the distributionally robust cases. The feasibility and cost efficiency of the model, and the efficiency of the solution method are numerically validated.

In this paper, an optimal coordination method for energy dis-patch and voyage scheduling is proposed for a renewable-energy-integrated hybrid AC/DC multi-energy ship (MES) mi-crogrid under the continuous ship swinging. In the MES mi-crogrid, different units are coordinated to provide multiple en-ergies with enhanced flexibility. To guarantee reliable ship oper-ation, diverse uncertainties from solar irradiation, ship swinging angle, and onboard multi-energy demands are managed by an adaptive risk-averse stochastic optimization approach to mini-mize the voyage cost and conditional value-at-risk. In addition, chance constraints are introduced to leverage the quality of thermal service given thermal inertia. To speed up the solution process, original nonlinear/nonconvex operation constraints are reformulated to the mixed-integer quadratically constrained programming (MIQCP) form by linearization/convexification and scenario generation/reduction methods, which can be effi-ciently solved by commercial solvers. Finally, case studies are conducted on a test MES microgrid. The simulation results veri-fy that the proposed method is effective in coordinating multi-energy dispatch and voyage scheduling, minimizing operating cost/risk, and immunizing against diverse uncertainties.