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In view of optimizing the configuration of each unit’s capacity for energy storage in the microgrid system, in order to ensure that the planned energy storage capacity can meet the reasonable operation of the microgrid’s control strategy, the power fluctuations during the grid-connected operation of the microgrid are considered in the planning and The economic benefit of hybrid energy storage is quantified. A multi-objective function aiming at minimizing the power fluctuation on the DC bus in the microgrid and optimizing the capacity ratio of each energy storage system in the hybrid energy storage system (HESS) is established. The improved particle swarm algorithm (PSO) is used to solve the objective function, and the solution is applied to the microgrid experimental platform. By comparing the power fluctuations of the battery and the supercapacitor in the HESS, the power distribution is directly reflected. Comparing with the traditional mixed energy storage control strategy, it shows that the optimized hybrid energy storage control strategy can save 4.3% of the cost compared with the traditional hybrid energy storage control strategy, and the performance of the power fluctuation of the renewable energy is also improved. It proves that the proposed capacity configuration of the HESS has certain theoretical significance and practical application value.

In many production environments, a fixed network of capacity is shared flexibly between multiple products with random demands. What is the best way to configure the capacity of the production network and to allocate the available capacity to meet predetermined fill rate requirements? We develop a new approach for network capacity configuration and allocation and characterize the relationship between the capacity of the network and the attainable fill rate levels for the products, taking into account the flexibility structure of the network. This builds on a new randomized allocation mechanism to deliver the desired services. We use this theory to investigate the connection between the flexibility structure and capacity configuration. We provide a new perspective to the well-known phenomenon that “long chain is almost as good as the fully flexible network”: for given target fill rates, the required capacity level in a long-chain network is close to that in a fully flexible network and is much lower than a dedicated system. We apply these insights and techniques on problems arising in the design of last-mile delivery operations and in semiconductor production planning, using real data from two companies. This paper was accepted by Terry Taylor, operations management.

In order to reasonably allocate the capacity of distributed generation and realize the goal of stable, economic and clean operation of the system, a multi-objective optimization model with investment cost, environmental protection and power supply quality as indicators has been established, and the multi-objective sparrow search algorithm is used to optimize the solution. Although the multi-objective search algorithm is more efficient than the traditional single objective algorithm, it is easy to fall into local optimum. To this end, the niche optimization technology is used to improve the optimization effect of multi-objective sparrow search algorithm, and the Levy flight strategy is introduced to enhance the ability of multi-objective sparrow search algorithm to jump out of local optimum. The calculation example uses the traditional multi-object search algorithm and the niche multi-objective sparrow search algorithm with levy disturbance to solve the proposed model. The simulation results verify the effectiveness of the multi-objective sparrow search algorithm improved by levy disturbance and niche optimization technology.

This study addresses the optimal capacity configuration of wind–photovoltaic–storage (WPS) systems under complex nonlinear constraints and economic requirements in grids with a high share of renewable energy. A multi-energy collaborative capacity planning model is developed, together with an energy management formulation that captures the coupling among wind, PV, and storage. To solve the resulting constrained optimization problem, an improved chaotic evolution optimization algorithm (ICEO) is proposed by embedding a self-learning perturbation strategy and an adaptive local search mechanism into the chaotic evolution framework. Specifically, Gaussian mutation and Lévy flight are combined to generate cooperative perturbations around high-quality solutions, while a stagnation-triggered local search refines solutions when the population evolution slows down. Simulation results on standard benchmark functions and a practical WPS case study demonstrate that ICEO achieves higher solution quality and robustness than several state-of-the-art meta-heuristics, thereby improving cost-effectiveness for WPS capacity planning.

The capacity of an energy storage device configuration not only affects the economic operation of a microgrid, but also affects the power supply’s reliability. An isolated microgrid is considered with typical loads, renewable energy resources, and a hybrid energy storage system (HESS) composed of batteries and ultracapacitors in this paper. A quantum-behaved particle swarm optimization (QPSO) algorithm that optimizes the HESS capacity is used. Based on the respective power compensation capabilities of ultracapacitors and batteries, a rational energy scheduling strategy is proposed using the principle of a low-pass filter and can help to avoid frequent batteries charging and discharging. Considering the rated power of each energy storage type, the respective compensation power is corrected. By determining whether the charging state reaches the limit, the value is corrected again. Additionally, a mathematical model that minimizes the daily cost of the HESS is derived. This paper takes an isolated micrgrid in north China as an example to verify the effectiveness of this method. The comparison between QPSO and a traditional particle swarm algorithm shows that QPSO can find the optimal solution faster and the HESS has lower daily cost. Simulation results for an isolated microgrid verified the effectiveness of the HESS optimal capacity configuration method.

To address a bi-objective optimization configuration problem of battery energy storage system (BESS) in distributed energy system (DES) considering energy loss and economy, a perturbation and observation approach (P&O) is proposed in this article. First, in a DES, the configuration model of BESS is established. Then, a novel way is designed that transforming a bi-objective optimization problem into a single objective optimization problem with variable conditions. And the P&O process of the proposed method is presented. Finally, in a simulation case, compared with a single optimization objective of energy loss or economy, the P&O method improves 5.71-fold or 2.94-fold in each direction, respectively and effectively balances the contradiction between them. In addition, with the efficiency and electricity cost of BESS increasing, the rated capacity of BESS changes by approximately 10%. And the location is a key factor affecting the configuration scheme of BESS in DES.

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

Substations’ auxiliary systems support the station’s operational loads and are crucial for grid security, often requiring backup power to ensure uninterrupted operation. A new alternative for this backup power supply is a microgrid composed of photovoltaic (PV) generation and storage. This paper proposes an electric–hydrogen microgrid as backup power supply for substation auxiliary systems. This microgrid ensures power supply during emergencies, provides clean and stable energy for daily operations, and enhances environmental friendliness and profitability. Firstly, using a 220 kV substation as an example, the construction principles of the proposed backup power microgrid are introduced. Secondly, operation strategies under different scenarios are proposed, considering time-sharing tariffs and different weather conditions. Following this, the capacity configuration optimization model of the electric–hydrogen microgrid is proposed, incorporating critical thresholds for energy reserves to ensure system robustness under fault conditions. Finally, the Particle Swarm Optimization (PSO) algorithm is used to solve the problem, and a sensitivity analysis is performed on hydrogen market pricing to evaluate its impact on the system’s economic feasibility. The results indicate that the proposed electric–hydrogen microgrid is more economical and provides better fault power supply time than battery-only power supply. With the development of hydrogen energy storage technology, the economy of the proposed microgrid is expected to improve further in the future.

Aiming to meet the low-carbon demands of power generation in the process of carbon peaking and carbon neutralization, this paper proposes an optimal PV-hydrogen zero carbon emission microgrid. The light–electricity–hydrogen coupling utilization mode is adopted. The hydrogen-based energy system replaces the carbon-based energy system to realize zero carbon emissions. Firstly, the mathematical models of photovoltaic, hydrogen and electric energy storage systems in a microgrid are built. Then, the optimal allocation model of the microgrid source storage capacity is established, and a scheduling strategy considering the minimum operational cost of energy storage equipment is proposed. The priority of equipment output is determined by comparing the operational costs of the hydrogen energy storage system and the electric energy storage system. Finally, the proposed scheme is compared with the scheduling scheme of the battery priority and the hydrogen energy system priority in an actual microgrid. It is verified that the scheme can ensure stable power-generating, zero carbon operation of a microgrid system while reducing the total annual power costs by 9.8% and 25.1%, respectively.

Currently, the integration of new energy sources into the power system poses a significant challenge to frequency stability. To address the issue of capacity sizing when utilizing storage battery systems to assist the power grid in frequency control, a capacity optimal allocation model is proposed for the primary frequency regulation of energy storage. Due to the requirement of a large number of actual parameters for the optimal allocation model, a simulation model of energy storage capacity is constructed based on the characteristics of primary frequency control to provide the necessary parameters. Subsequently, the primary frequency modulation output model of energy storage is established by considering the basic action output, the action in the frequency modulation dead zone, and a certain capacity margin. The antlion algorithm is employed to solve the capacity optimal allocation model. Finally, three groups of experiments are designed and compared to demonstrate the effectiveness of the proposed method in setting the capacity margin, which can increase profit to a certain extent.