Increasingly the prevalence of electric vehicles (EVs) calls for an effective planning of charging stations by coupling transportation network (TN) with power distribution network (PDN). This paper conducts an interdisciplinary study on the coordinated planning strategy of EV charging stations and coupled traffic-electric network. A comprehensive model is proposed, which determines the optimal expansion strategies for both TN and PDN, including sites and sizes of new charging stations, charging spots, TN lanes and PDN lines. In TN, we use an unconstrained traffic assignment model (UTAM) to explicitly capture the steady-state distribution of traffic flows. In PDN, operating conditions are described by a linearized DistFlow. Moreover, EV charging demands and interdependency of TN and PDN are characterized by EV charging station location model (ECSLM). A mixed-integer linear programming (MILP) with UTAM constraints is formulated for the coordinated planning model, where a global optimal solution is obtained through a deterministic branch-and-bound method. Numerical experiments on a coupled traffic-electric network are conducted to validate the effectiveness of the proposed model and the solution method.

Line hardening refers to strengthening certain distribution lines that will not be subject to outages during extreme conditions. Line hardening provides a resilient solution in the case of major faults in a radial or meshed distribution system. We propose a robust optimal line hardening method coupled with multiple provisional microgrids to improve the distribution system resilience against worst N-k contingencies. A tri-level optimal model is considered with the objectives of minimizing the costs of line hardening and the operation of multiple islanded provisional microgrids, which could include the cost of load shedding in each provisional microgrid considering the worst N-k contingencies. Specifically, a two-stage robust optimization model is formulated and Benders decomposition (BD) algorithm coupled with iterative relaxation procedure (IRP) is developed to enable the tractable computation. In our work, the master problem determines line hardening strategies and the subproblem discovers the impact of the worst N-k contingencies corresponding to the optimal operation of reformed provisional microgrids. The second-stage max-min subproblem is solved efficiently using linearization techniques, duality theory and IRP. Our computational results for the IEEE distribution test systems validate the effectiveness of the proposed model and reveal that distributed generation is critical in increasing the resilience of distribution systems corresponding to the worst N-k contingencies in provisional microgrids.

With rapid smart grid technology development, the customer can actively participate in demand-side management (DSM) with the mutual information communication between the distributor operation company and the smart devices in real-time. Controllable load management not only has the advantage of peak shaving, load balance, frequency regulation, and voltage stability, but is also effective at providing fast balancing services to the renewable energy grid in the distributed power system. The load management faces an enormous challenge as the customer has a large number of both small residential loads and dispersed renewable sources. In this paper, various controllable load management approaches are discussed. The traditional controllable load approaches such as the end users’ controllable appliances, storage battery, Vehicle-to-Grid (V2G), and heat storage are reviewed. The “broad controllable loads” management, such as the microgrid, Virtual Power Plant (VPP), and the load aggregator are also presented. Furthermore, the load characteristics, control strategies, and control effectiveness are analyzed.

A microgrid that consists of distributed energy sources, such as micro turbines, wind turbines, fuel cells, and photovoltaic panels, storage devices and a group of radial load feeders are quickly rising in popularity in recent years. Because there are various controllable units in a microgrid, it has the potential to provide ancillary services (A/Ss) for the grid in the grid-connected mode. This article proposes a novel microgrid energy management strategy with demand response to provide peak shaving for the grid by using the peak-time rebate scheme. Under this scheme, various controllable units, such as controllable loads, battery storage, and diesel generators, participate voluntarily in the demand-response program to achieve maximum profit for the microgrid, so the microgrid can respond actively to the grid. The optimal energy management for the microgrid is achieved under this scheme. In addition, the main grid can benefit from the scheme with a lower cost compared with other traditional peak-shaving programs. Various incentive rewards of the peak-time rebate and demand-response levels of the controllable loads are discussed in the peak-time rebate scheme. The results demonstrate that the peak power of the grid tie-line is shaved markedly while achieving optimal energy management.

Due to the gradual exhaustion of petroleum‐based energy resources and severe concern for environmental protection, renewable energy (RE) resources and demand response (DR) techniques have been wide deployed in power network. However, the insufficient management as well as technology bottleneck becomes the major obstacle in their further development. Based on the uniform clearing of electricity market, a centralised dispatch model of virtual power plant (VPP) is introduced to improve the competitiveness of distributed energy resources in electricity market. To neutralise the side effect of RE penetration, a bidding strategy optimisation model considering DR and the uncertainty of RE for VPP is proposed and numerical analysis is conducted to prove its applicability. In addition, scenario analysis method is applied to deal with the influence of elastic demand and potential risk, which are associated with utility users’ consumption patterns and VPP's bidding preference, respectively. The application of distributed algorithm into multi‐players’ strategy optimisation problem accelerated the convergence of bidding procedure, which verifies the applicability and effectiveness of the proposed models. Furthermore, numerical case studies demonstrate the distinctive superiority of VPP in the integration and management of RE and DR resources, which in turn contribute to its advantage position in electricity market.

The integration of natural gas in electricity network requires a more reliable operating plan for increasing uncertainties in the whole system. In this paper, a three-stage robust optimization model is proposed for resilient operation of energy system which integrates electricity and natural gas transmission networks with the objective of minimizing load curtailments caused by attacks. Non-convex constrains are linearized in order to formulate the dual problem of optimal energy flow. Then, the proposed three-stage problem can be reformulated into a two-stage mixed integer linear program (MILP) and solved by Benders decomposition algorithm. Numerical studies on IEEE 30-bus power system with 7-node natural gas network and IEEE 118-bus power system with 14-node natural gas network validate the feasibility of the proposed model for improving resilience of integrated energy system. Energy storage facilities are also considered for the resiliency analysis.