Multi-objective economic emission dispatch based on an extended crisscross search optimization algorithm

Tang, Xiongmin; Li, Zhengshuo; Xu, Xuancong; Zeng, Zhijun; Jiang, Tianhong; Fang, Wenrui; Meng, Anbo

doi:10.1016/j.energy.2021.122715

Cited by 22 publications

(5 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another factor that plays a crucial role in the convergence speed is the random direction vector PHI. Due to the random process being included in the definition of the random direction vector, the convergence speed of the algorithm is limited [ 34 ]. For this reason, this paper proposes a particle swarm algorithm speed formula using dynamic inertia weight factors to replace the traditional random direction vector.…”

Section: An Improved Bacterial Foraging Optimization and Its Applicationmentioning

confidence: 99%

Research on Economic Optimal Dispatching of Microgrid Based on an Improved Bacteria Foraging Optimization

Zhou

2023

Biomimetics

View full text Add to dashboard Cite

This paper proposes an improved Bacterial Foraging Optimization for economically optimal dispatching of the microgrid. Three optimized steps are presented to solve the slow convergence, poor precision, and low efficiency of traditional Bacterial Foraging Optimization. First, the self-adaptive step size equation in the chemotaxis process is present, and the particle swarm velocity equation is used to improve the convergence speed and precision of the algorithm. Second, the crisscross algorithm is used to enrich the replication population and improve the global search performance of the algorithm in the replication process. Finally, the dynamic probability and sine-cosine algorithm are used to solve the problem of easy loss of high-quality individuals in dispersal. Quantitative analysis and experiments demonstrated the superiority of the algorithm in the benchmark function. In addition, this study built a multi-objective microgrid dynamic economic dispatch model and dealt with the uncertainty of wind and solar using the Monte Carlo method in the model. Experiments show that this model can effectively reduce the operating cost of the microgrid, improve economic benefits, and reduce environmental pollution. The economic cost is reduced by 3.79% compared to the widely used PSO, and the economic cost is reduced by 5.23% compared to the traditional BFO.

show abstract

Section: An Improved Bacterial Foraging Optimization and Its Applicationmentioning

confidence: 99%

Research on Economic Optimal Dispatching of Microgrid Based on an Improved Bacteria Foraging Optimization

Zhou

2023

Biomimetics

View full text Add to dashboard Cite

show abstract

“…For stability of the boiler operation and thermal considerations, the real power generation of the i-th TGU must be ranged from minimum (P min i ) and maximum (P max i ) production limits in MW. Therefore, the power generation capacity of each generator can be described by the following inequality constraint [2].…”

Section: Power Generation Capacitymentioning

confidence: 99%

“…Therefore, there have been widespread calls for electricity companies to manage and reduce quantities of these pernicious gases coming from classical TGUs. To fulfill those requirements, power system operators have combined the total emission generated by TGUs and total production Energies 2023, 16, 4554 2 of 34 cost in one problem called the economic emission dispatch problem (EEDP) [2,3]. The EEDP, which is a multiobjective optimization problem (MOP), has been solved subject to various operating constraints, such as energy balance constraints and generation limits of TGUs.…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

2023

View full text Add to dashboard Cite

The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD).

show abstract

“…where SOC min is the lower limit of battery capacity, SOC max is the upper limit of battery capacity, and SOC t is the capacity of the battery at time t. The remaining battery power constraint [21,22] is given by:…”

Section: Electric Vehicle Constraintmentioning

confidence: 99%

“…The mainstream of current research is dynamic economic dispatch. The integration of plug-in electric vehicles (PEVs) into DED problems has gained attention with the rise of new energy vehicles [21][22][23][24][25][26]. Connecting PEVs to the grid (V2G) can help mitigate the fluctuations of the grid and achieve peak clipping and valley filling.…”

Section: Introductionmentioning

confidence: 99%

Chaos Moth Flame Algorithm for Multi-Objective Dynamic Economic Dispatch Integrating with Plug-In Electric Vehicles

et al. 2023

View full text Add to dashboard Cite

Dynamic economic dispatch (DED) plays an important role in the operation and control of power systems. The integration of DED with space and time makes it a complex and challenging problem in optimal decision making. By connecting plug-in electric vehicles (PEVs) to the grid (V2G), the fluctuations in the grid can be mitigated, and the benefits of balancing peaks and filling valleys can be realized. However, the complexity of DED has increased with the emergence of the penetration of plug-in electric vehicles. This paper proposes a model that takes into account the day-ahead, hourly-based scheduling of power systems and the impact of PEVs. To solve the model, an improved chaos moth flame optimization algorithm (CMFO) is introduced. This algorithm has a faster convergence rate and better global optimization capabilities due to the incorporation of chaotic mapping. The feasibility of the proposed CMFO is validated through numerical experiments on benchmark functions and various generation units of different sizes. The results demonstrate the superiority of CMFO compared with other commonly used swarm intelligence algorithms.

show abstract

Multi-objective economic emission dispatch based on an extended crisscross search optimization algorithm

Cited by 22 publications

References 68 publications

Research on Economic Optimal Dispatching of Microgrid Based on an Improved Bacteria Foraging Optimization

Research on Economic Optimal Dispatching of Microgrid Based on an Improved Bacteria Foraging Optimization

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

Chaos Moth Flame Algorithm for Multi-Objective Dynamic Economic Dispatch Integrating with Plug-In Electric Vehicles

Contact Info

Product

Resources

About