Coordinated planning is an effective method to balance investment costs and benefits in achieving high renewable target under the renewable-driven power system expansion wave. This paper proposes a coordinated planning model to support the efficient achievement of renewable target considering economy of the system by accounting for the interaction among source, grid, and energy storage system. An adaptive two-stage min-max-min robust optimization model is formulated to take into account renewable target as well as the uncertainty associated with renewable production and load demand. To reduce the conservatism of robust optimization, uncertain budget, multiple uncertain sets, and data-driven method are used to design uncertain sets. The resulting model is transformed into a tractable bi-level programming through strong duality theory and big-M method. A customized column-and-constraint generation algorithm is used to solve the bi-level programming. Simulation results presented for the modified IEEE 30-bus test system corroborates the effectiveness of the methodology, which finds siting and sizing of renewable energy sources and energy storage systems as well as transmission expansion schemes. It is capable to provide a flexible planning tool driven by renewable target under a reasonable computational burden.

This paper proposes a pricing and scheduling method for shared mobile energy storage systems (SMSs) in coupled power distribution and transportation networks. Different from existing shared energy storage studies, which mostly focus on stationary resources, the paper investigates the SMS operation considering the negotiation of rental prices as well as mobility and charging/discharging among SMS owners and different users. Specifically, the SMS pricing and scheduling with variable renewable energy are established as a bilevel mixed-integer chance-constrained distributionally robust optimization problem. In the upper-level problem, the SMS owner determines pricing and day-ahead mobility strategy to maximize its payoff. In the lower-level problem, the SMS users, i.e., distribution grid operators, determine the SMS charging/discharging power according to the SMS day-ahead pricing results and intra-day distribution grid operation strategies for accommodating variable renewable energy. The distributionally robust chance constraint is designed to cope with the intra-day operational risk caused by the variability of renewable power generation. To cope with the solution difficulty in the proposed bilevel optimization problem, the chance constraint is reformulated as second-order cone constraints, which are further transformed into a set of linear constraints, and then the reformulated bilevel mixed-integer linear programming problem is decomposed and iteratively solved to avoid enumerating lower-level integer variables. Simulation results show that the utilization rate of SMS batteries is increased and the excess renewable power is fully consumed when SMSs are shared among different distribution grids. The proposed distributionally robust optimization achieves higher revenue for the SMS owner and smaller operating costs of distribution grids than robust optimization under uncertain environments.

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.

In the high-renewable penetrated power grid, mobile energy-storage systems (MESSs) enhance power grids’ security and economic operation by using their flexible spatiotemporal energy scheduling ability. It is a crucial flexible scheduling resource for realizing large-scale renewable energy consumption in the power system. However, the spatiotemporal regulation of MESS is affected by the complex operating environments in the power and transportation networks. Numerous challenges exist in modeling and decision-making processes, such as incorporating uncertainty into the optimization model and handling a considerable quantity of integer decision variables. This paper provides a systematic review of MESS technology in the power grid. The basic modeling methods of MESS in the coupled transportation and power network are introduced. This study provides a detailed analysis of mobility modeling approaches, highlighting their impact on the accuracy and efficiency of MESS optimization scheduling. The applications of MESS in the power grid are presented, including the MESS planning, operation, and business model. The key challenges encountered by MESS in power grid operations across various scenarios are analyzed. The corresponding modeling methods, solution algorithms, and typical demonstration projects are summarized. At last, this study also proposes the MESS system research and application prospects based on the consideration of its promotion.

Disruptive technologies have been employed in various fields with a strategic priority in several countries as the core driving force of the fourth industrial revolution, significantly impacting the development of new power systems. It is a kernel to effectively identify the future potential of disruptive technologies. To overcome the subjectivity and limitations of existing disruptive technology identification methods, we propose a disruptive technology identification method based on patent evolution analysis. Firstly, the evolution matrix of the patent data is calculated. Afterward, we dig into the characteristics of disruptive technologies to build a more targeted identification index system. Finally, the fields of electric power communication and energy generation are selected as typical cases to build the patent data sets. The future development of the identified technologies, including the identified quantum technologies and controlled fusion, is analyzed. The results demonstrate that the proposed model can identify the key technologies of new power systems accurately and contribute to completing the industrial upgrading and transformation more rapidly.