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Electric vehicles (EVs) are now attracting increasing interest from both industries and countries as an environmentally friendly and energy efficient mode of travel. Therefore, the EV charging and/or discharging issue has become an important challenge and research topic in power systems in recent years. An advanced and economic EV charging process, however, should employ smart scheduling, which depends on effective and robust algorithms. To that end, a comprehensive intelligent scatter search (ISS) algorithm within the frame of a basic scatter search has been designed with both unidirectional and bidirectional charging considered. The ISS structure also supports both a flexible and constant charging power rate by respectively employing filter-SQP (sequential quadratic programming) and mixed-integer SQP as local solvers with module control. The detailed design of ISS is presented and the objectives of smoothing the daily load profile and minimizing the charging cost have been tested. Compared with methods based on GS (global search), GA (genetic algorithm), and PSO (particle swarm optimization), the outcome-verified ISS can produce attractive results with a significantly short computational time. Moreover, to handle a large scale EV charging scenario, a hybrid method comprised of a GA and ISS approach has been further developed. Simulation results also verified its prominent performance, plus superbly low computational time.

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The electricity spot market is now being implemented in China. Demand response, as a kind of flexible resource, is also being studied and explored for the constructed power market. Among the many demand response applications, the virtual power plant (VPP) as an aggregator of distributed energy resources (DERs), receives ever-increasing attention. However, the participation manner and related impacts of the VPP to the electricity spot market are still unknown within the current power market rules. Under this background, obeying the present trading rules of China’s electricity spot market, a two-stage dispatching model with optimized bidding and operating strategy in the day-ahead (DA) and real-time (RT) market for the VPP is proposed. In the designed model, the conditional risk value (CVaR) is adopted to address the risk encountered by the uncertainty of the electricity spot market price. The impact of the user-side over-deviated revenue mechanism (UORM) of the China spot market on the income of the VPP in the DA and RT market is also analyzed. For a full evaluation, different coefficients for the influence of DA and RT risk, UORM, and energy storage system (ESS) are tested to investigate their respective impacts on the revenue of the VPP. The simulation cases prove that the proposed method is helpful for the VPP to optimize DERs’ output in the electricity spot market according to its own risk preference.

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Electric vehicles (EVs) are an attractive solution to make traditional transportation energy-efficient and environmentally friendly. EVs are also considered an important element to bring new opportunities for power grids. In addition to the role of a pure load, along with the development of discharging technology for batteries, EVs can deliver energy back to power grids or another power consumption entity or community, performing various discharging activities such as vehicle-to-home (V2H), vehicle-to-building (V2B), vehicle-to-vehicle (V2V), vehicle-to-grid (V2G), etc. These charging scenarios can be uniformly named the 'Vehicle-to-anything' (V2A) charging mode. The paradigm of this charging mode has already been given; however, the modeling and solving approaches are still insufficient. Therefore, the analysis and modeling of V2A are investigated herein. The aim is to propose a generic model suitable for different applications of V2A in different places. The characteristics of the V2A charging mode are given and analyzed first. Then a model that can adapt to the V2A scenario is illustrated. The optimization problem is transformed and solved as an INLP (integer nonlinear programming) problem. The effectiveness of the approach is verified with simulation results. The outcome also indicates that V2A applications have great potential for lowering power consumption costs; for instance, a reduction of 4.37% in energy expense can be achieved in a case study with EV discharging enabled for a household.