Intelligent energy management control for independent microgrid

Abstract

This work presents a new adaptive scheme for energy management in an independent microgrid. The proposed energy management system has been developed to manage the utilization of power among the hybrid resources and energy storage system in order to supply the load requirement based on multi-agent system (MAS) concept and predicted renewable powers and load powers. Auto regressive moving average models have been developed for predicting the wind speed, atmospheric temperature, irradiation, and connected loads. The structure proposed in this paper includes renewable sources as primary source and storage system as secondary source. A wind generator and solar PV array system together acts as primary source, which supplies power to the local load most of the time in this energy management strategy. When they fail to meet the load demand, the secondary source present in the system will assist the primary source and help to attain the goal of satisfying load demand without interruption. If the primary source and secondary source together are not able to meet the load demand then load shedding will be executed according to the priority set. Thus the developed MAS algorithm co-ordinates the hybrid system components and achieves energy management among renewable energy sources, storage units, and load under varying environmental conditions and varying loads. STATCOM based compensation has been implemented to balance the reactive power demand and to mitigate the voltage fluctuations and harmonics on the AC bus. The proposed microgrid has been simulated with MAS concept in Matlab/Simulink environment. The results presented in this paper show cases the effectiveness of the proposed energy management controller.

Future research tendencies for solar energy management using a bibliometric analysis, 2000–2019

“…To evaluate future research tendencies, the articles which were published between 2010 and 2019 were analyzed respecting the scientific literature gaps which they propose. Of the 3260 articles on solar PV energy management that were published between 2010 and 2019, 235 were not accessible, 955 did not apply to research and 1917 did not include proposals for further research ( Figure 3 ) [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 …”

Review and clustering of optimal energy management problem studies for industrial microgrids

“…The objective was to analyze and compare centralized and decentralized deployment of energy sources, regarding bus voltage, power balance and cost criteria. Bogaraj and Kanakaraj 27 put forward an energy usage optimization and forecasting study by using simulation and ARMA software. In another multi‐objective study, Borhanazad et al, 28 optimization of both cost of electricity and the probability of losses in the power supply were handled.…”

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

“…Bogaraj and Kanakaraj [69] presented an energy management proposal based on intelligent multi-agents for a stand-alone MG, which maintains the energetic balance between the loads, distributed generators, and batteries. The agents consist of photovoltaic systems, wind turbines, fuel cells, and battery banks.…”