This paper investigates the issues of topology design and capacity configuration in multi-microgrid (MMG) systems. Firstly, we analyze the limitations of current researches about MMG planning, which mainly focus on either topology design or capacity configuration separately, and propose the idea of joint planning simultaneously considering both aspects. Secondly, we present a two-stage stochastic optimization model aimed at minimizing the planning and operational costs to derive the solutions for topology design and capacity configuration. Moreover, various constraints are taken into account, including maximum equipment capacity, load shedding penalty cost, wind and solar power curtailment costs, and energy coordination among microgrids. Finally, simulation experiments are conducted using the islanded multi-microgrid (IMMG) as the research object, and additional comparative experiments are included to demonstrate the effectiveness and feasibility of the proposed model. Experimental results demonstrate that our method can significantly improve the economic benefits of MMG systems and enhance their penetration of renewable energy, which helps promote the economic development and stable operation of MMG systems.

In this paper, we present a novel distributed sequential allocation mechanism for optimizing investments in unmanned systems, framed as a multi-agent dynamic planning problem. The core contribution lies in an advanced algorithm that integrates multi-agent parallel computing for global optimization with single-agent sequential allocation for local refinement. This hybrid approach ensures both optimality and polynomial-time complexity, effectively addressing the challenges of multi-field investment with uncertain costs and rewards. By employing sophisticated optimization techniques, our algorithm dynamically adjusts investment strategies based on the real-time data. Simulation results in typical scenarios demonstrate the algorithm’s superiority over benchmark methods, offering significantly enhanced investment solutions tailored to the unique requirements of unmanned systems. Our method not only improves investment efficiency and effectiveness, but also provides a robust and adaptable solution for the dynamic and uncertain nature of unmanned systems investment portfolios, thereby ensuring sustained performance and strategic advantage.

The significant wave height prediction holds critical value for marine energy development, coastal infrastructure planning, and ensuring safety in maritime operations. The precision of such predictions carries substantial theoretical and practical weight. This survey delivers an exhaustive evaluation and integration of the latest studies and advances in the domain of significant wave height prediction, serving as a methodical guidepost for academicians. The study introduces an all-encompassing predictive framework for significant wave height, which not only integrates diverse established forecasting techniques but also paves the way for novel research trajectories and creative breakthroughs. The framework is structured into four principal layers, i.e., feature selection, basic prediction, data decomposition, and parameter optimization. The ensuing sections meticulously dissect the methodologies within these strata, elucidating their core concepts, distinctive features, merits, and constraints, and their applicability to significant wave height prediction. To wrap up, the study delves into fresh research inquiries and avenues pertinent to the discipline, thereby broadening the comprehension of significant wave height prediction. In essence, this scholarly article imparts critical knowledge beneficial to the realm of marine technology.

The modern power system has evolved into a cyber-physical system with deep coupling of physical and information domains, which brings new security risks. Aiming at the problem that the “information-physical” cross-domain attacks with key nodes as springboards seriously threaten the safe and stable operation of power grids, a risk propagation model considering key nodes of power communication coupling networks is proposed to study the risk propagation characteristics of malicious attacks on key nodes and the impact on the system. First, combined with the complex network theory, a topological model of the power communication coupling network is established, and the key nodes of the coupling network are screened out by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method under the comprehensive evaluation index based on topological characteristics and physical characteristics. Second, a risk propagation model is established for malicious attacks on key nodes to study its propagation characteristics and analyze the state changes of each node in the coupled network. Then, two loss-causing factors: the minimum load loss ratio and transmission delay factor are constructed to quantify the impact of risk propagation on the coupled network. Finally, simulation analysis based on the IEEE 39-node system shows that the probability of node being breached (α) and the security tolerance of the system (β) are the key factors affecting the risk propagation characteristics of the coupled network, as well as the criticality of the node is positively correlated with the damage-causing factor. The proposed methodological model can provide an effective exploration of the diffusion of security risks in control systems on a macro level.

Time series clustering is a challenging problem due to the large-volume, high-dimensional, and warping characteristics of time series data. Traditional clustering methods often use a single criterion or distance measure, which may not capture all the features of the data. This paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization, termed i-MFEA, which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously, each with a different validity index or distance measure. Therefore, i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of decision-makers. Experiments on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering quality. The paper also discusses how i-MFEA can address two long-standing issues in time series clustering: the choice of appropriate similarity measure and the number of clusters.