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First, we construct an MG energy management model with the objective of minimizing the operation cost. Second, the energy management model is formulated as
In this paper, an optimization solution is introduced to address an Energy Management problem for a Microgrid comprising Photovoltaic arrays, Wind Turbines, Combined Heat and Power units, and a Battery Energy Storage System. The goal is to establish an optimal Energy Management System that minimi
A large number of intelligent algorithms based on social intelligent behavior have been extensively researched in the past few decades, through the study of natural creatures, and applied to various optimization fields. The learning-based intelligent optimization algorithm (LIOA) refers to an intelligent optimization algorithm with a
Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently demanded. Herein, a complete workflow for designing a fuel blending scheme is presented, which is theoretically supported, efficient, and reliable. Based on the data distribution of the composition and properties of the
Optimization models have been proposed to solve this problem including a mixed-integer linear program for optimizing the dispatch based on optimal allocation of units in [35], while in [36] it was combined with the Genetic Algorithm to optimize the unit-commitment by considering optimum sizing of units in a microgrid consisting electric
With the continuous development of the power Internet of Things (PIoT), smart devices (SDs) have been widely used in electric power inspections. Due to the limited resources of intelligent inspection SDs and the distance of overhead transmission lines, many inspection tasks cannot be processed promptly. This paper proposes a multi-hop-based end-edge
The optimization methods of genetic algorithm, ant colony algorithm and particle swarm optimization algorithm in AGV path planning are emphatically summarized. It is found that genetic algorithm is suitable for complex and highly nonlinear path planning problems, ant colony algorithm is suitable for discrete path planning
The Tuna Swarm Optimization (TSO) algorithm is a metaheuristic algorithm based on swarm intelligence, inspired by the cooperative foraging behavior of tuna schools. Although tuna swim very fast, they are less agile than smaller fish regarding reaction speed, so they have developed various effective predation strategies.
Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general
Most other intelligent optimization algorithms are based on physically driven models, which are difficult to modify once the model and algorithm are established. Also, they often overlook the significance of historical data and decisions. Once a model and algorithm are set, their computational efficiency and solution accuracy do not change.
To solve the above problems, a multiobjective bilevel optimization algorithm based on preference selection is proposed to solve the energy hub system planning problem. Energy hub system planning is a multiobjective mixed integer planning problem, which also belongs to the Stackelberg game [ 19, 20 ].
In order to improve the efficiency of solving problems and solve large-scale problems, scholars use intelligent optimization algorithms to solve production scheduling problems. The fast non-dominated sorting genetic algorithm II (NSGA-II) was proposed by Srinivas and Deb in 2000 on the basis of NSGA [ 35 ].
With the highly increasing demand in engineering, traditional algorithms may fail to meet required performances. Recently, intelligent algorithms have been widely studied, gradually achieving successful applications in industry. Among them, swarm intelligent algorithms are a combination of intelligent algorithms and bionic swarm
Zhou et al. [17] proposed an energy dispatch flexibility strategy for IES by energy regulation of electric heat pump and energy storage system. Yan et al. [18] integrated electric storage, thermal storage, electric boiler and bypass compensation technology to enhance the operational flexibility of CHP plant.
A large number of intelligent algorithms based on social intelligent behavior have been extensively researched in the past few decades, through the study of natural creatures, and applied to various optimization fields. The learning-based intelligent optimization algorithm (LIOA) refers to an intelligent optimization algorithm with a
In this paper, the proposed improved second-order oscillating PSO algorithm is used to study the ship energy efficiency from the viewpoint of route optimization by considering the sea conditions and constraints. Firstly, a nonlinear optimization model for ship FOC (fuel oil consumption) considering the time-varying sea
Dual delay deterministic gradient algorithm is proposed for optimization of energy storage. • Uncertain factors are considered for optimization
9 · A robust optimization-based AC OPF framework was developed for systems with imprecise wind power generation [20], while novel strategies were devised for integrating wind, photovoltaic, and energy storage components [21]. Modified algorithms like JAYA were tailored for power systems with diverse renewable sources [22].
A novel LSA-based optimized algorithm for solving optimization problems to obtain minimum operating costs, achieve optimum usage of DER,
The sparrow search algorithm (SSA) is an efficient swarm-intelligence-based algorithm and has been widely studied in recent years. Nevertheless, as with other swarm intelligence optimization approaches, the SSA is prone to fall into local solutions, which weakens the exploration ability. In order to cope with this problem, in this paper, a
The development of solar PV energy throughout the world is presented in two levels, one is the expansion of solar PV projects and research and the other is the research and development (R&D) advancements (Gul et al., 2016).On the research side, the number of research papers concerning the deployment of optimization methods in the
To verify the effectiveness of the RTP-based DR project, two deterministic models with and without DR are used for simulation experiments. Because these two models are single-objective optimization problems, the efficient ASTA [39] algorithm was proposed to solve them and compare ASTA with five advanced optimization algorithms
A bi-level capacity optimization model of BESS is established to solve the problem that the economy and technology are difficult to balance. Based on the prediction errors
Combined with Figures 3A–C, it can be explained that the solution framework based on intelligent algorithm can determine an effective unit output scheduling scheme according to load changes, and EO algorithm can effectively solve this optimization problem and has better search ability than GWO.
By integrating phase change energy storage, specifically a box-type heat bank, the system effectively addresses load imbalance issues by aligning building
Because many discrete optimization models divide optimization problems into a sequence of continuous sub-problems, then continuous optimization models are used to solve each of these sub-problems. Therefore, one of the basic components of discrete optimization models is the method of solving continuous
As a new optimization technique, the POS algorithm can be used to solve the problem of cold chain logistics distribution path optimization of agricultural products. Acknowledgments This project is supported by Program for Research Development of BNUZ.
The swarm intelligent optimization algorithms were used to optimize the parameters. The path constraints were enforced by the penalty function method. Finally, extensive simulations were carried out in both nominal and dispersed cases, and the simulation results showed that the proposed guidance framework was effective, high
The battery energy storage system (BESS) is an effective means to compensate the photovoltaic (PV) power prediction errors, so as to improve the reliability of the PV power prediction results as the power grid dispatching reference. A bi-level capacity optimization model of BESS is established to solve the problem that the economy and technology are
Therefore, this paper proposes a novel scheduling strategy based on computational optimization starting point for energy storage, which can provide an appropriate
Ouyang et al. [105] proposed a multi-algorithm gene adaptive multi-objective method (AMALGAM), as a multi-objective optimization solver, by incorporating the uncertainty of surrogate modeling into the optimization model through chance-constrained
A distributed energy storage control strategy aiming at economy is proposed. This method optimizes the active power output between each energy storage unit by
1 · This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using
2 · Renewable energy can provide a clean and intelligent solution for the continually increasing demand for electricity. In order to rationally determine the locations and capacities of DG and ESS, this paper conducts site selection analysis and capacity planning based on different objective functions and optimization methods.
Aiming at the problems of high dimensionality, multi-constraint, multi-phase, non-linearity, and the fact that the established model is not easy to solve for the optimal scheduling of reservoir group flood control, this paper improved the dung beetle optimization algorithm by adopting a variety of strategies, proposes a new intelligent
After presenting the theoretical foundations of renewable energy, energy storage, and AI optimization algorithms, the paper focuses on how AI can be applied to improve the
The diagram of the SAC DRL algorithm for solving the IES dispatching problem is shown in Fig. 1. (kW·h); the initial charge state of the electric and thermal energy storage units is set to 0.5; Comparison of iteration curves of five intelligent optimization algorithms for solving the deterministic IES model with DR.
Reliable AI-based optimization algorithms and models from machine learning can accurately capture the environmental features and make the optimal decision of BESS
This paper makes a systematic review of multi-objective optimization methods. •. This paper introduces intelligent algorithms developments and (dis)advantages. •. This paper makes a summary of trade-off methods for compromising objectives. •. This paper illustrates methods applications in energy and environment issues.
A wind farm energy storage capacity optimization allocation scheme considering the battery operation state was proposed in which constructed a multi-objective optimization model for energy storage capacity allocation. However, these studies mainly focus on capacity allocation and cost optimization of energy storage systems in
The uncertainty of wind resources is one of the main reasons for wind abandonment. Considering the uncertainty of wind power prediction, a robust optimal dispatching model is proposed for the wind fire energy storage system with advanced adiabatic compressed air energy storage (AA-CAES) technology. Herein, the operation constraints of the power
Reasonable energy storage optimization allocation and operation can effectively mitigate these disadvantages. In this paper, the optimal location, capacity and charge/discharge strategy of the energy storage system were simultaneously performed based on two objective functions that include voltage deviations and active power loss.
Review of optimization techniques used in microgrid energy management systems. Mixed integer linear program is the most used optimization technique. Multi-agent systems are most ideal for solving unit commitment and demand management. State-of-the-art machine learning algorithms are used for forecasting applications.
In general, the weighted sum approach is used to solve the current bi-objective optimization problem (i.e., makespan and total energy consumption). This approach has high ability to determine a single unique solution for the tested problem.
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