Ant Colony Optimization Based Energy Management Controller for Smart Grid

2016 ◽  
Author(s):  
Sahar Rahim ◽  
Zafar Iqbal ◽  
Nusrat Shaheen ◽  
Zahoor Ali Khan ◽  
Umar Qasim ◽  
...  
Keyword(s):  
Smart Grid ◽  
Ant Colony Optimization ◽  
Energy Management ◽  
Ant Colony

scholarly journals Energy Optimization in Smart Urban Buildings Using Bio-Inspired Ant Colony Optimization

2021 ◽  
Author(s):  
Fakhri Alam Khan ◽  
Kifayat Ullah ◽  
Atta ur Rahman ◽  
Sajid Anwar
Keyword(s):  
Ant Colony Optimization ◽  
Real Time ◽  
Energy Management ◽  
Management System ◽  
Ant Colony ◽  
Consumption Pattern ◽  
Energy Management System ◽  
Electricity Cost ◽  
Home Appliances ◽  
Home Energy Management

Abstract Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where Demand Response (DR) strategy is used to modify the nature of demand of consumer. In return, utility pay incentives to the consumer. The increasing load demand in residential area and irregular electricity load profile have encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. In order to meet the electricity demand of the consumers, the energy consumption pattern of a consumer is maintained through scheduling the appliances in day-ahead and real-time bases. In this paper we propose a hybrid algorithm Bacterial foraging Ant colony optimization is proposed (HB-ACO) which contain both BFA and ACO properties. Primary objectives of scheduling is to shift load from On-peak hour to Off-peak hours to reduce electricity cost and peak to average ratio. A comparison of these algorithms is also presented in terms of performance parameters electricity cost, reduction of PAR and user comfort in term of waiting time. The proposed techniques are evaluated using two pricing scheme time of use and critical peak pricing. The HB-ACO shows better performance as compared to ACO and BFA which is evident from the simulation results Moreover the concept of coordination among home appliances is presented for real time scheduling. We consider this is knapsack problem and solve it through Ant colony optimization algorithm.

scholarly journals Optimal Operation of Micro-Grids to Reduce Energy Production Costs and Environmental Pollution Using Ant Colony Optimization Algorithm (ACO)

2021 ◽  
Vol 54 (1) ◽  
pp. 9-19
Author(s):  
Giulio Lorenzini ◽  
Mehrdad Ahmadi Kamarposhti ◽  
Ahmed Amin Ahmed Solyman
Keyword(s):  
Environmental Pollution ◽  
Ant Colony Optimization ◽  
Energy Management ◽  
Optimization Algorithm ◽  
New Technologies ◽  
Optimal Operation ◽  
Ant Colony ◽  
Production Costs ◽  
Ant Colony Optimization Algorithm ◽  
Micro Grids

With increasing demand for electric energy and requesting higher quality by subscribers, the electric power industry has moved towards using new technologies. Many modern societies seek to use the systems of new energy management in order to reduce environmental pollutants and operational costs in electrical energy systems. Therefore, exploiting various resources of renewable energies, micro-grids can be considered as a significant tool to attain these objectives. According to the subject, Ant Colony Optimization algorithm (ACO) is used in this paper to optimize one micro-grid sample in order to reduce the cost of generating power and to reduce environmental pollution to increase reliability. The recommended algorithm has been done in two scenarios and each in two sections. In the first scenario, energy management will be conducted for all distributed generation resources and in the second scenario it is assumed that wind and solar products, produce their maximum power and energy management are conducted for the reminder elements and the results are compared with other optimization algorithms.

scholarly journals An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 810
Author(s):  
Yongbing Xiang ◽  
Xiaomin Yang
Keyword(s):  
Ant Colony Optimization ◽  
Fuel Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Layer Structure ◽  
Ant Colony ◽  
Mode Switching ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Target Value

In order to reduce fuel consumption and reduce the deviation between the final battery state-of-charge (SOC) value and the target value at the same time, a novel double-layer multi-objective optimization method is proposed, which adopts an improved ant colony optimization (ACO) algorithm and the equivalent fuel consumption minimization strategy (ECMS) considering mode switching. The proposed strategy adopts a two-layer structure. In the inner layer, the ECMS considering mode switching was adopted to optimize the working mode and working point, so as to achieve the goal of reducing fuel consumption. In the outer layer, aiming at the shortcomings of traditional ACO, the heuristic factor and adaptive volatilization factor were introduced. An improved ACO method was proposed to optimize the equivalent factor, so as to achieve the goal of reducing the deviation between the final value of SOC and the target value. In order to verify the effectiveness of the proposed algorithm, it is compared with the traditional ECMS strategy and the rule-based (RB) ECMS strategy. The simulation results show that the proposed energy management strategy combining an improved ACO algorithm with ECMS considering mode switching can reduce the energy consumption of the whole ship and control the battery power.

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