This paper presents a holistic modeling framework for the interdependent transportation network and power distribution network. From a system-level perspective, on-road fast charging stations would simultaneously impact vehicle routing in the transportation system and load flows in the distribution system, therefore tightly couple the two systems. In this paper, a dedicated traffic user equilibrium model is proposed to describe the steady-state distribution of traffic flows comprised of gasoline vehicles and electric vehicles. It encapsulates route selections, charging opportunities, electricity prices, and individual rationalities of minimum travel expense in a convex traffic assignment problem over an extended transportation network. An adaptive path generation oracle is suggested to solve the problem in a tractable manner. Economic operation of the power distribution system is formulated as an alternating current optimal power flow problem. Convex relaxation is performed. The optimal generation dispatch and nodal electricity prices can be computed from a second-order cone program. It is revealed that an equilibrium state will emerge due to the rational behaviors in the coupled systems, which is characterized via a fixed-point problem. A best-response decomposition algorithm is suggested to identify the network equilibrium through iteratively solving the traffic assignment problem and the optimal power flow problem, both of which entail convex optimization. Illustrative examples are presented to validate related concepts and methods.

This paper conducts an interdisciplinary study on the coordinated operation of both transportation system and power system. We consider an electrified transportation network enabled by wireless power transfer technology and coupled with a power distribution network (PDN) in the future city. The independent system operator, which is a public entity, is eligible to manage generation assets and charge congestion tolls (CTs) on electrified roads with the purpose of minimizing social cost. The route choices of electric vehicles are amenable to the Wardrop user equilibrium (UE) principle, such that no one can reduce his travel cost by changing route unilaterally. The traffic UE pattern further influences the spatial distribution of the electrical loads of the PDN. The power flow of the PDN is modeled through Dist-Flow equations. To find out the best generation schedule and CTs, we propose an optimal traffic-power flow model, which is a mixed integer nonlinear program with traffic UE constraints and further reformulated as a mixed integer second-order cone program, whose global optimal solution is accessible with reasonable computation effort. Case studies corroborate the benefits from the joint operation of the coupled networks, and demonstrate that ignoring the interdependency between the two critical infrastructures may lead to an insecure operation.

A large-scale integrated energy system can represent several subsystems representing areas that are tied by electricity and natural gas networks. Accordingly, we propose a decentralized optimal energy flow (DOEF) calculation as compared with a centralized solution method. The proposed approach demonstrates the merits of the decentralized operation and control of a multi-area integrated electricity-natural gas system (IEGS), in terms of large-scale modeling requirements, faster computations, and data management for local sensitivity analyses. Using the proposed decentralized structure, the communication burden is relatively light as individual area operators in a multi-area IEGS will make optimal dispatch decisions independently and the corresponding information is shared with adjacent subsystems. The reformulation of the second-order cone (SOC) is proposed using advanced sequential cone programming (SCP) to handle the nonlinear steady-state natural gas flow, which provides a feasible solution with a high degree of computational efficiency. Furthermore, an iterative alternating direction method of multipliers (I-ADMM) is adopted to manage the nonconvexity of integer variables, which guarantees a satisfactory convergence performance. Case studies on three multi-area IEGS validate the effectiveness of the proposed model in a multi-area IEGS.

Recent revolution of the electricity distribution sector, especially a deeper penetration of gas-fired distributed generations (DGs), intensifies the interdependence of electricity on natural gas distribution systems. Such integrated electricity and natural gas distribution systems (IENDS) are facing with significant threats from frequent natural disasters that cause enormous economic losses. Network hardening is regarded as an effective technique for enhancing resilience of IENDSs against natural disasters. This paper presents a trilevel robust optimization-based network hardening model for minimizing worst-case total weighted electricity and gas load shedding of IENDSs with respect to hardening budget limits and random damages caused by disasters of different severity levels. Specifically, distinct failure probabilities of overhead power lines and underground gas pipelines are considered, while DGs and gas storages are modeled as effective emergency response resources for supplying high-priority electricity/gas loads during disasters. The proposed model is solved by a column-and-constraint generation approach, in which nonlinear gas network constraints are linearized via Taylor series expansion. Numerical case studies evaluate the proposed robust hardening strategy against natural disasters.

As the sharp growth of gas-fired power plants and the new emergence of power-to-gas (PtG) technology intensify the interdependence between electricity and natural gas systems, it is imperative to co-optimize the two systems for improving the overall efficiency. This paper presents a long-term robust co-optimization planning model for interdependent systems, for minimizing total investment and operation costs. Beside generators, transmission lines, gas suppliers, and pipelines, PtGs and gas compressor stations are also considered as investment candidates to effectively handle wind power uncertainties in the power system and compensate pressure losses in the gas network. Furthermore, the proposed model includes a joint N-1 and probabilistic reliability criterion to promote economical and reliable planning solutions. The proposed model is solved via a decomposition approach, by iteratively solving a base-case master problem and two operation subproblems to check N-1 and probabilistic reliability criteria. Numerical case studies illustrate the effectiveness of the proposed robust co-optimization planning approach.

This paper studies the multi-stage real-time stochastic operation of grid-tied multi-energy microgrids (MEMGs) via the hybrid model predictive control (MPC) and approximate dynamic programming (ADP) approach. In the MEMG, practical power and thermal network constraints, heterogeneous energy storage devices, and distributed generations are involved. Given the relatively large thermal inertia and slow thermal energy fluctuation, only uncertainties of renewable energy sources and active/reactive power loads are considered. Then, historical data are adopted as training scenarios for the MPC-ADP method to acquire empirical knowledge for dealing with all the diverse uncertainties. Further, piecewise linear functions are used to approximate value functions with respect to the operation status of energy storage assets, which enables sequentially solving the Bellman’s equation forward through time to minimize MEMG operation cost. Finally, numerical case studies are conducted to illustrate the effectiveness and superiority of the proposed MPC-ADP approach. Simulation results indicate that with sufficient information embedded, the MPC-ADP approach could obtain good-enough real-time operation solutions with the successively updated forecast. Further, it outperforms alternative real-time operation benchmarks in terms of optimality and convergence for various application scenarios.