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Microgrids, or distributed systems of local energy generation, transmission, and demand, are now technologically and operationally capable of providing power to communities, especially in rural and peri-urban regions of developing nations. The reliability of the system, the cost of power generation, and the operating environmental impact are the major issues when designing and evaluating the performance of an off-grid hybrid renewable energy microgrid (HREM). This paper presents an integrated method for optimal sizing and operation of an HREM for rural agricultural communities in the Southern Philippines composed of run-of-the-river hydropower, photovoltaics (PV), diesel generator, and a battery energy storage system (BESS) using multi-objective particle swarm optimization (MOPSO) and a proposed multi-case power management strategy. The three conflicting objective functions that were simultaneously minimized were: loss of power supply probability (LPSP), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions, subject to several constraints. The optimization generated 200 non-dominated or Pareto optimal alternative solutions, 4 of which were selected as solutions of interest. Based on the results, the optimal sizes of the main components for the reliable operation of the system are 100 panels with a rating of 0.25 kW for PV, 100 kWh for BESS, and 13 kW for the diesel generator, with corresponding LCOE, LPSP, and GHG emission values of 0.1795 USD/kWh, 0.05%, and 7874 kg, respectively, for 1 year. The effectiveness of the proposed HREM design was also analyzed, and the study yielded plenty of useful findings that could aid the electrification of the area.

In this paper, we introduce CSSRank, an improved version of the charged system search (CSS) algorithm, designed to address complex optimization problems more efficiently. CSSRank integrates a rank-based reduction selection strategy to enhance exploitation by progressively reducing the number of charged particles used in electric force calculations. To further balance exploration and exploitation, a ranking-based mutation strategy is incorporated, promoting diversity in early iterations and precision in later stages. We evaluated CSSRank on a set of standard benchmark functions and compared its performance with the original CSS algorithm. In addition, CSSRank was tested on two major benchmark suites, CEC 2014 and CEC 2024, and compared against a wide range of state-of-the-art metaheuristic algorithms. The results show that CSSRank outperforms many existing methods on CEC 2014 and performs competitively and close to the best-performing algorithms on CEC 2024, demonstrating both robustness and scalability. For real-world applications, CSSRank was applied to six UCI clustering datasets, where it consistently achieved higher clustering accuracy and more reliable objective values than baseline methods. It was also tested on three complex reservoir operation optimization problems, yielding superior engineering solutions with high reliability, and contributing to improvements in operational cost and resource efficiency. These results confirm the effectiveness, versatility, and reliability of CSSRank across both theoretical and practical optimization tasks, positioning it as a strong candidate for solving complex problems in optimization and operations research.

In view of the negative impact on the stable operation of the system caused by the disorderly charging of large-scale electric vehicles connected to the microgrid, an optimization method for the operation of microgrid considering the impact of electric vehicles is proposed. Based on the traditional microgrid, a grid-connected microgrid system with electric vehicles is designed, and the system is studied. Based on Monte Carlo simulation method, the load model of disorderly charging and orderly charging and discharging of electric vehicles is constructed. According to the influence of disorderly charging of electric vehicles, an orderly charging and discharging strategy at time-of-use price is proposed. Taking the minimum total operating cost and the minimum peak-valley difference of the microgrid in one day as the optimization objective, and considering many constraints such as power balance constraints and output constraints of distributed generation units, the multi-objective optimization function is transformed into a single-objective optimization function by linear weighting method, and the model is solved by particle swarm optimization algorithm. Finally, taking the typical daily load data of a micro-grid in a certain area as an example, the comparative results of economic cost and load curve after three scenarios optimization, namely, no EV access, EV access disorderly charging and discharging, are obtained respectively. The calculation results show that the orderly charging and discharging of electric vehicles access to the grid can effectively improve the utilization rate of clean energy, reduce the operating cost and the peak-valley difference of load, and have good practical value.

The Progressive Optimality Algorithm (POA) is a powerful technique widely used for optimizing multi-reservoir operations; however, two crucial downsides cumber its application to complex large-scale multi-reservoir systems, which are insufficient search directions and the dimensionality problem—the former limits the POA’s precision, while the latter reduces its efficiency. Although several POA variants have been developed to overcome these downsides, a further balance between the precision and efficiency of the algorithm is required to boost the POA’s capability of optimizing the operation of complex large-scale multi-reservoir systems. In view of this, we made modifications to the original algorithm and developed a new POA variant, referred to as the Direct Search Algorithm Based on Disturbance-Response Strategy (DRDSA). On one hand, we changed the POA’s uniform optimization window for all reservoirs to discrepant optimization windows for varying reservoirs to enrich the search direction set of the algorithm. On the other hand, we introduced a disturbance-response strategy into the solution of sub-problems to handle the POA’s dimensionality problem. Two multi-reservoir operation optimization problems were employed to test the performance of the DRDSA, and seven advanced alternatives including two existing POA variants were used for comparison. The results showed the improved precision and efficiency of the DRDSA. Thus, a new technique is available for optimizing the operation of complex large-scale multi-reservoir systems.

The optimal algorithm to ensure computational efficiency and accuracy remains to be challenging for the development of robust operation model to solve the optimization problem of reservoir operation, particularly for applications involving flood control with complex flood hydrograph. The dynamic programming (DP) is one of the most popular methods to solve optimization problem, but it is limited the “curse of dimensionality” problem. The improved dynamic programming (IDP) method has been proposed to overcome this defeat of DP, remaining the convergence problem. The relaxation method based on approximate monotonic relationship shows potential to ensure convergence of IDP. In this study, the theoretical search range of the relaxation method are analyzed. A stable improved dynamic programming (SIDP) method is proposed based on relaxation method and a prediction method of schedulable storage states. The proposed SIDP overcomes the complex computational problem of DP and the convergence problem of IDP. The case study on an ideal reservoir and the Guanting reservoir, shows that SIDP can achieve an accuracy as high as DP, but with a much higher efficiency than DP. This method shows a strong solution to optimization problems of reservoir flood control operation.

Hydrogen energy is regarded as a key path to combat climate change and promote sustainable economic and social development. The fluctuation of renewable energy leads to frequent start/stop cycles in hydrogen electrolysis equipment. However, electrochemical energy storage, with its fast response characteristics, helps regulate the power of hydrogen electrolysis, enabling smooth operation. In this study, a multi-objective constrained operation optimization model for a wind/battery storage/alkaline electrolyzer system is constructed. Both profit maximization and power abandonment rate minimization are considered. In addition, some constraints, such as minimum start/stop times, upper and lower power limits, and input fluctuation limits, are also taken into account. Then, the non-dominated sorting genetic algorithm II (NSGA-II) algorithm and the entropy method are used to optimize the operation strategy of the hybrid energy system by considering dynamic hydrogen production efficiency, and through optimization to obtain the best hydrogen production power of the system under the two objectives. The change in dynamic hydrogen production efficiency is mainly related to the change in electrolyzer power, and the system can be better adjusted according to the actual supply of renewable energy to avoid the waste of renewable energy. Our results show that the distribution of Pareto solutions is uniform, which indicates the suitability of the NSGA-II algorithm. In addition, the optimal solution indicates that the battery storage and alkaline electrolyzer can complement each other in operation and achieve the absorption of wind power. The dynamic hydrogen production efficiency can make the electrolyzer operate more efficiently, which paves the way for system optimization. A sensitivity analysis reveals that the profit is sensitive to the price of hydrogen energy.

At present, the energy-saving application of power system has become an important means to ensure the sustainable development of social economy. However, there are still many deficiencies and problems in energy-saving dispatching and demand side management of power system, which urgently needs innovation and improvement. Based on this, this paper first analyzes the development status of energy-saving power operation mechanism, then studies the power operation optimization based on computer-based energy-saving dispatching, and finally gives the power operation optimization strategy based on power demand side management.

In many microgrids with a lot of uncontrollable DGs, a well-planned operation of the energy storage station is an important guarantee for the stability and economy of the microgrid. To solve the operation optimization problem, the Chaos mechanism, differential evolution, and random inertia weight were introduced to propose the traditional PSO algorithm. A mathematical model for minimizing the running cost of Microgrid is established, and through MATLAB simulation, both algorithms are used to solve the same problem to observe the improvement of the algorithm. The experimental results show that the IPSO algorithm achieves performance improvement.

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks.

An integrated energy system interconnects multiple energies and presents a potential for economics improvement and energy sustainability, which has attracted extensive attention. However, due to the obvious volatility of energy demands, most existing integrated energy systems cannot operate in a totally self-sufficient way but interact with the upper grid frequently. With the increasingly urgent demand for energy saving and emissions reduction, renewable resources have occupied a larger and larger proportion in energy system, and at last they may be dominant in the future. Unlike conventional fossil fuel generation, the renewable resources are less controllable and flexible. To ease the pressure and guarantee the upper grid security, a more independent integrated energy system is required. Driven by that, this paper firstly reviews the optimal strategies considering both independence and benefit from perspectives of individual efforts and union efforts. Firstly, the general optimization process is summarized in terms of energy flows modelling and optimization methods to coordinate supply–demand side and realize benefit maximization. Based on that, handling with uncertainty of high-ratio renewable energy is reviewed from uncertainty modeling methods and multi-stage operation strategy perspectives to make the strategy accurate and reduce the adverse effects on the upper grid. Then, the hybrid timescale characteristics of different energy flows are explored to enhance operation flexibility of integrated energy systems. At last, the coordination among different participants is reviewed to reduce the whole adverse effect as a union. Remarks are conducted in the end of each part and further concluded in the final part. Overall, this study summarizes the research directions in operation optimization of integrated energy systems to cater for a renewable energy dominated scene to inspire the latter research.