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The electric power distribution system (PDS) and the water distribution system (WDS) are coupled with each other through electricity-driven water facilities (EdWFs), such as pumps, water desalination plants, and wastewater treatment facilities. However, they are generally owned and operated by different utilities, and there does not exist an operator that possesses full information of both systems. As a result, centralized methods are not applicable for coordinating the operation of the two systems. This paper proposes a decentralized framework where the PDS and WDS operators solve their own operation problems, respectively, by sharing only limited information. Nevertheless, the boundary variables (i.e., the variables shared between two systems) are discontinuous due to their dependence on the on/off nature of EdWFs. Unfortunately, mature decentralized/distributed optimization algorithms like the alternating direction method of multipliers (ADMM) cannot guarantee convergence and optimality for a case like this. Therefore, this paper develops a novel algorithm that can guarantee convergence and optimality for the decentralized optimization of PDS and WDS based on a recently developed algorithm called the SD-GS-AL method. The SD-GS-AL method is a combination of the simplicial decomposition (SD), gauss–seidel (GS), and augmented Lagrangian (AL) methods, which can guarantee convergence and optimality for mixed-integer programs (MIPs) with continuous boundary variables. Nonetheless, the original SD-GS-AL algorithm does not work for the PDS-WDS coordination problem where the boundary variables are discontinuous. This paper modifies and improves the original SD-GS-AL algorithm by introducing update rules to discontinuous boundary variables (called the Auxiliary Variables Update step). The proposed mixed-integer boundary compatible (MIBC) SD-GS-AL algorithm has the following benefits: (1) it is capable of handling cases whose boundary variables are discontinuous with convergence and optimality guaranteed for mild assumptions, and (2) it only requires limited information exchange between PDS and WDS operators, which will help preserve the privacy of the two utilities and reduce the investment in building additional communication channels. Simulations on two coupled PDS and WDS test cases (Case 1: IEEE-13 node PDS and 11-node WDS, and Case 2: IEEE-37 node PDS and 36-node WDS) show that the proposed MIBC algorithm converges to the optimal solutions while the original SD-GS-AL does not converge for both test cases. The ADMM does not converge for the first test case while it converges to a sub-optimal solution, 63 % more than the optimal solution for the second test case. • We propose a decentralized optimization of the water-energy nexus. • The proposed algorithm handles mixed-integer boundary variables. • The optimality and convergence of the proposed algorithm are guaranteed. • Outperforms mature distributed algorithms like ADMM. [ABSTRACT FROM AUTHOR]

In modern power system applications, security constrained optimal power flow (SCOPF), in which different contingency operating situations arise, is a complex, non-convex, and nonlinear optimization problem. With the inclusion of stochastic solar and wind energy sources in the power system, the energy efficiency is maximized. However, this increases the complexity of the SCOPF problem and makes its solution difficult. To tackle these challenges, it's essential to design an algorithm with a search behavior that aligns with the characteristics of the OPF/SCOPF problem. For this reason, in this paper, a novel dynamic fitness-distance balance based growth optimizer (dFDB-GO) algorithm was proposed to find the optimal solution of the OPF/SCOPF problem. By using the dFDB method, the guide selection in the learning stage of the GO algorithm was redesigned to improve the search capability and to provide a good balance between exploration and exploitation. To prove the performance of the dFDB-GO algorithm, the simulation study was performed on the SCOPF and benchmark problems. In the simulation study carried out on the optimization of benchmark problems using the six variations of GO and the base GO algorithms, all variations outperformed the base GO algorithm according to Friedman test results. In other simulation study, the performance of the proposed algorithm was tested on OPF/SCOPF problem including wind and solar energy sources. One of the important contribution of this study is to present a comprehensive simulation study to the literature, including twelve case studies using modified IEEE 30-bus and 57-bus systems and nine different objective functions. These case studies were solved by the dFDB-GO and eleven up-to-date MHS algorithms. The another important contribution was that a comprehensive analysis was carried out using the minimum, maximum, mean, and standard deviation values of the algorithms and the statistical analysis methods. Accordingly, the proposed dFDB-GO algorithm achieved better results than its rival in 11 of 12 case studies. Moreover, it ranked first with 1.3077 value among its competitors according to Friedman test. On the other hand, the stability of an algorithm on solving SCOPF problem was evaluated for the first time in this study. For this, while the dFDB-GO algorithm achieved 94.87% mean success rate for solving the SCOPF problem, the GO algorithm had the 12.09% mean success rate value. To sum up, all analysis results prove the superior performance of the proposed algorithm in solving the SCOPF problem against to its rivals. [ABSTRACT FROM AUTHOR]

The sustainable energies take increasing proportion in the power systems due to the "net-zero emission" goal, and the future trend is to make the new type power systems operate safely and stably while maintaining low carbon and economic efficiency. This paper proposes a novel smart generation system (SGS) architecture and a smart generation system consensus (SGSC) algorithm from the perspective of automatic generation control. The SGS is based on stratified sequencing method and multi-agent theory, which describes the dynamic and optimal generation dispatch of the units as a hierarchical decentralized multiple objectives programming problem. Further, the SGSC is proposed based on distributed Newton algorithm and leaderless consensus algorithm to solve the security performance degradation caused by decentralization. All the units are configured in parallel with the designed communication topology to achieve dispatch decentralization, thus ensuring low-carbon, economical and safe operation of the new type power system. The effectiveness of the proposed algorithm is verified on a multi-layer, multi-zone smart generation system model with a high proportion of sustainable energies from macro and micro aspects. Different control methods are also used for performance comparison on the two-area load frequency control model with lower regulation costs and lower carbon emissions achievement. • A decentralized control Architecture of autonomous SGS is developed. • A novel SGSC algorithm is proposed for dynamic generation command optimization of AGC. • The proposed algorithm not only minimizes regulation costs and carbon emissions but also ensures the stability of system operations, even during communication failures. [ABSTRACT FROM AUTHOR]

Traditional virtual power plants (VPPs) with fixed resource composition and coordination strategies struggle to cost-effectively exploit the flexibility of large-scale resources for adapting variable regulation requirements and resources characteristics. To this end, this paper proposes a dynamic aggregation mechanism to flexibly select and coordinate individual resources for forming aggregators according to grids regulation requirements and resource characteristics. The proposed mechanism is operated through a two-stage dynamic aggregation model comprising resource selection and coordination. Considering the two-stage dynamic aggregation model is a combinational optimization problem with high computational complexity, the submodular optimization method is utilized to swiftly address this problem. First, the complementarity and submodularity of the dynamic aggregation process are formulated to elaborate how the aggregation regulation characteristics (ARCs) evolve with flexible resource composition and coordination. Next, a submodularity-based algorithm is developed to promptly solve dynamic aggregation model under three scenarios, where aggregation operators focus on the resources quantity, quality, and cost-effectiveness, respectively. The polynomial computational complexity of the proposed algorithm has also been evaluated. Simulations using the IEEE 39-bus (New England) system consists of 10,000 flexible resources were executed to assess the submodularity approach. The proposed algorithm demonstrates superior computing speed and better performance guaranteed results (90%, 97%, 90% in three scenarios) compared to other methods—making it more suitable for implementation in practice. • The complementarity and submodularity of dynamic aggregation process are revealed and proved. • A two-stage dynamic aggregation model consists of resources selection and coordination is established. • A dynamic aggregation algorithm is proposed to deal with different aggregation scenarios. • The approximate guarantees and polynomial computational complexity of algorithm are proved. [ABSTRACT FROM AUTHOR]

Accurately capturing data on the external loads that large structural systems endure is crucial for improving the performance of energy equipment. This paper introduces a novel hybrid model-data-driven framework for the dynamic load identification of interval structures, which seamlessly combines finite-element modeling with machine learning techniques. To address potential ill-posed issues in model-driven methods and the interpretability limitations of data-driven methods, we propose a physics-informed neural network. This neural network effectively inverts uncertain modal responses with low data requirements and high predictive performance high by integrating the underlying modal transformation equation into the loss function of a fully connected neural network. To identify the modal loads using predicted modal displacement/acceleration responses, we introduce a pioneering dynamics inversion method. This method modifies the traditional Kalman filter with an assumption of unknown inputs to reduce the sensitivity of load identification process to different noises. In addition, our approach incorporates a subinterval Chebyshev expansion method to adaptively determine the interval boundaries of external loads. The efficiency of the proposed method is assessed through two numerical examples and validated through comparative research against baseline methods. Our findings suggest that this approach enhances precision, robustness, and generalization in dynamic load identification, even when facing challenges such as limited training data, significant noise interference, and non-zero initial conditions. • A novel hybrid model-data-driven dynamic load identification method for interval structures is proposed. • A physics-informed neural network is established to inverse the uncertain modal responses effectively. • Two improved Kalman filter algorithms are developed to identify the modal loads at specified parameters. [ABSTRACT FROM AUTHOR]

This work investigates the design optimization of aggregated energy systems (multi-energy systems, microgrids, energy districts, etc.) with (N-1) -reliability requirements. The problem is formulated as a two-stage stochastic Mixed Integer Linear Program which optimizes design (first stage variables) and operation variables (second stage variables) simultaneously considering a set of typical and extreme days. The analysis proposes and compares different approaches to include the (N-1) reliability requirement in the optimization problem. Moreover, the paper proposes two effective decomposition algorithms to solve the large-scale Mixed Integer Linear Program suitable for design problems with and without (N-1) reliability requirements. Depending on the instance, such decomposition algorithms allow reducing the computational time by one or more orders of magnitude (from days to a few hours, in the worst cases tested in this work). The proposed methodology is tested to design the aggregated energy system for a real case study considering both a grid-connected and off-grid installation. Results indicate that the actual reliability of the design solutions depends by the profiles of energy demand and renewable production considered in the failure scenarios included in the design problem. Including N-1 reliability requirements causes an increase in the total annual cost in the range 15–20%, due to the increase in capital costs. • N-1 reliability is included in MILP for the optimal design of Aggregated Energy Systems. • Different approaches to include reliability constraints are proposed and compared. • Two decomposition algorithms allow reducing the computational time considerably. • Including N-1 reliability results in a 15–20% Total Annual Cost increase. [ABSTRACT FROM AUTHOR]