The proliferation of electric vehicles (EVs) and the increasing interdependence across power distribution networks (DNs) and transportation networks (TNs) have increased the complexity and vulnerability of the two systems in extreme circumstances. As the interdependence of two infrastructures tightens over time, it is viewed as a dire necessity to strengthen the resilience of the coordinated transportation-power distribution networks (TDNs) against natural disasters. This paper constructs a coordinated optimization method of TN traffic link reversing, DN line switching, and fast charging pile management, to improve the TDN performance in the emergency response stage after disasters. A dynamic TN model and a multi-period DN model are integrated in TDN modeling to capture flow propagations and state variations among time intervals. The coordinated optimization for TDN emergency response is designed as a mixed-integer nonlinear programming (MINLP) problem with high-order objective functions and nonlinear constraints to minimize TN travel costs and DN active and reactive power shortages. An accuracy-aware adaptive piecewise linearization approach combined with Gray code-based encoding is utilized to improve the computational efficiency for solving the TDN optimization problem. Numerical simulations show that the TDN performance is enhanced by coordinating various DN and TN resources, as compared with those of separate and conventional topology controls. The proposed TDN solution method has significantly reduced the computation time for managing extreme conditions while guaranteeing the accuracy of the results as compared with those of the nonlinear model and the linearized model by uniform piecewise linearization.

The uncertainties of single-phase integrated distributed energy resources (DERs) in the active distribution network (ADN) might lead to serious voltage unbalance (VU) problems. To cope with the corresponding VU problems, this paper proposes a two-stage uncertainty quantification and unbalance mitigation (UQUM) framework to quantify and mitigate the impacts of variable DERs on VU. In Stage one, the global sensitivity analysis based on the Rosenblatt transformation (RT-based GSA) method is proposed to quantify the impacts of correlated DERs with nonlinear correlation and tail dependence on VU. The RT-GSA method can identify the critical DERs which have significant impacts on VU. In Stage two, the joint allocation of fixed and mobile energy storage devices (ESDs) is proposed to compensate the uncertainties of critical DERs (identified in Stage one) for alleviating VU. The effectiveness of the proposed UQUM framework is verified in a 123-bus three-phase ADN system and the results are discussed.

The resilience of distribution networks (DNs) or transportation networks (TNs) has attracted a wide attention due to the frequent occurrence of extreme natural disasters. However, the existing works usually investigate the resilience of two systems independently, which neglects their coordinated operations. This paper models the interactions of DNs and TNs and coordinates multiple resilience enhancement strategies to make a more cost-effective resource allocation scheme for the operation of transportation-power distribution networks (TDNs) in hurricanes. Specifically, resource allocations and TDN operations are designed as a two-stage mixed-integer stochastic programming (TMISP) model, where the first stage is to make resource allocation decisions before the hurricane strikes, and the second stage is to perform multi-stage resilience enhancement strategies to minimize the expected TDN operational losses. The uncertain TDN outages with defensive resources in hurricanes are modeled as decision-dependent uncertainties, which are decoupled into decision-independent uncertainties for generating TDN outage scenarios. A comprehensive scenario reduction method is utilized to reduce the redundancies in simulated scenarios. The penalty-based Gauss-Seidel approach is combined with a scenario-skip technique to solve the TMISP problem with binary variables in both stages. Numerical simulations show that the proposed solution methods provide superior performances in computational efficiency and accuracy, over the traditional scenario reduction and scenario-wise decomposition algorithms in solving the TMISP problem. The investment efficiency is increased with the coordination of various DN and TN resources and multiple resilience enhancement strategies, compared with those of separate allocation schemes.

This paper proposes a lifted linear decision rule (LLDR) as a photovoltaic (PV) power control strategy for distribution system voltage regulation. First, the active-reactive power control is designed as a linear function in a lifted space of PV power, which is presented as a piecewise linear function without auxiliary integer variables for benefiting the parameter optimization of control functions. Then, a hierarchical central-local voltage regulation framework is designed. In the central control stage, the constant part of LLDR determines the reactive power of PV units, which is also coordinated with other voltage regulation devices. In the local control stage, the adjustable part of LLDR would control the PV power in real time for reducing voltage deviations. Finally, optimization problems and their solution methods are developed to obtain LLDR parameters. In particular, robust optimization (RO) and distributionally robust optimization (DRO) problems are designed to obtain the affine coefficients of LLDR considering multiple PV power uncertainty models. The tractable formula of RO is derived based on the convex hull of PV power uncertainty sets in the lifted space, and Benders decomposition is combined with a distribution separation method to solve the DRO problem. The proposed method is tested on distribution systems with actual PV power data, and the results are compared with those of other linear and piecewise-linear control methods to validate the effectiveness of the proposed method in diminishing voltage deviations caused by variable PV power.

Autonomous mobility on demand systems (AMoDS) will significantly affect the operation of coupled power distribution-urban transportation networks (PTNs) by the optimal dispatch of electric vehicles (EVs). This paper proposes an uncertainty method to analyze the operational states of PTNs with AMoDS. First, a PTN operation framework is designed considering the controllable EVs dispatched by AMoDS as well as the uncontrollable driving behaviors of other vehicle users. Then, a bi-level power-traffic flow (PTF) model is proposed to characterize the interaction of power distribution networks (PDNs) and urban transportation networks (UTNs). In the upper level, a social optimum model is established to minimize the operating cost of PDNs and UTNs embedded with controllable EVs. In the lower level, a stochastic user equilibrium (SUE) model is established to minimize the operating cost of uncontrollable EVs and gasoline vehicles (GVs) in UTNs. Finally, a probabilistic PTF analysis method is developed to evaluate PTN operations under environmental and human uncertainties. A regional sensitivity analysis method is proposed to identify the critical uncertainties and quantify the impacts of their distribution ranges on PTN operations. The effectiveness of the proposed method is verified by the PTN consisting of a 21-bus PDN and a 20-node UTN.

In modern cities, consumers with distributed energy resources (DERs) can trade energy by managing their consumption and supply. Blockchain is considered to provide technical support for establishing a distributed energy market, while the current mainstream blockchain technology cannot meet the requirement of efficiency and scalability under such a transaction scenario. In addition, the potential defaults of market players also hinder market implementation. Therefore, a reputation-based distributed energy trading mechanism and the corresponding blockchain consensus method considering reputation management are proposed in this paper. Firstly, a distributed reputation management mechanism is designed to systematically evaluate the user’s behavior in the market. Secondly, to address the limitations of traditional consensus methods in the distributed energy market distributed reputation control is introduced into the node model of blockchain, and a new energy blockchain consensus method named DPoR is proposed to solve the technical bottleneck under the distributed energy trading scenario. We also present a corresponding transaction mechanism to encourage market participants to maintain their reputations. Finally, the simulation results verify the effectiveness of the proposed transaction mechanism in this paper, and the presented blockchain consensus mechanism can meet the performance requirements under distributed energy trading scenarios in terms of high efficiency, low consumption, and openness.