The instability induced by high voltage direct current (HVdc) contingencies is challenging operation of ac/dc synchronous system. The asynchronization of ac/dc synchronous system can be a promising strategy for this concern which, however, is lack of practical applications. This paper analyzed the necessity of replacing the existing parallel ac/dc interconnection by pure dc interconnection in China Southern Power Grid (CSG), Guangzhou, China. A comprehensive solution is proposed to mitigate the risk of frequency instability in a small power grid with large amount of power sending out by HVdcs. Meanwhile, the strategy that combines the use of line-commuted converter-HVdc, voltage source converter-HVdc, and flexible ac transmission system is proposed to ensure the secure operation of HVdc feeding in weak ac system. The principle and application of asynchronous operation of CSG provide a reference for other grid development.

The widespread penetration of electric vehicles (EVs) will cause an increase in energy demand. To achieve an effective demand response approach, the participation of EV users is a crucial factor. Nevertheless, the discharging price found in the literature is generally set in accordance with the spot electricity price. A fair pricing strategy is thus desirable to motivate EV users to regulate their charging behavior for less bill payment, and at the same time to achieve load regulation as well as benefit for power companies. The charging and discharging prices should be cohesively equilibrated to achieve the task. This paper presents a new V2G (Vehicle-to-Grid) pricing policy by incorporating the system load condition, maximum power limit, and price rate for user load in a fair manner. The price setting follows a hierarchical optimization procedure between the operator and end users with the aim to maximize operator's profit and balance end users' bill and comfort level. The optimization for the operator is achieved via genetic algorithm whereas the energy management of each user is decomposed as a single power optimization problem. Simulation results have confirmed the benefit of liberating the discharging price from the user load tariff and have verified the effectiveness of the proposed method.

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.

The use of distributed computation and control is pervasive for a wide range of smart grid research topics. However, recent developments of smart grid co-simulation platforms have not been able to provide effective support for the modeling and simulation of distributed software systems. In particular, cosimulation literatures only focused on the integration of electrical and communication network simulators, and the responsibility for modeling distributed software is often delegated to one of these two simulators. Since these domain specific simulators are not designed for this purpose, such delegation incurs many limitations that prevent convenient, effective, and accurate modeling of software behaviors. To mitigate the problem, this paper presents, to our knowledge, the first co-simulation integration of direct-execution simulators to provide dedicated support for distributed smart grid software. We first present the development of the novel DecompositionJ framework (DEterministic, COncurrent Multi-PrOcessing SImulaTION for Java programs), which is a compiler-based code analyzer and transformer to automatically convert multi-thread Java programs into direct-execution simulators, eliminating the need for manual code or model development. Next, we apply DecompositionJ framework to generate simulators for a popular multi-agent platform JADE. The JADE simulators are then integrated with electromagnetic transient simulator (PSCAD) and packet-level network simulator (OPNET) using standardized co-simulation runtime infrastructure. At last, we conduct a case study on agent-based smart grid restoration using this novel co-simulation platform. Through the analysis of simulation results, it is shown that the proposed direct-execution simulation framework is able to facilitate the understanding, evaluation, and debugging of distributed smart grid software.

Coordinated charging activities of electric vehicles (EVs) in charging stations with distributed photovoltaic (PV) resources play an essential role in promoting PV consumption, saving electricity costs, and improving profits for charging stations. However, the uncertainties caused by PV power output and EV behaviors impose grand challenges to achieving the desired optimal control performance. This paper presents a novel real-time EV-PV coordination strategy using deep reinforcement learning (RL) algorithm for providing real-time controls based on short-term prediction of PV outputs and EV arrivals, which can effectively avoid large deviations between actual and optimal conditions. The EV-PV coordination problem is first described by EV station model and a Markov Decision Process (MDP). A charging control strategy inside the station is also proposed. Then, based on the soft actor-critic (SAC) algorithm, an RL training process for EV-PV coordination strategy is proposed. Though only short-term prediction is used in real-time control, the proposed strategy can balance short-term and long-term profit and achieve optimal charging control for EV stations. Comprehensive case studies with realistic EV charging station data verify the feasibility and effectiveness of the proposed method.