Neuro-Fuzzy-Based Model Predictive Energy Management for Grid Connected Microgrids

Abstract

With constant population growth and the rise in technology use, the demand for electrical energy has increased significantly. Increasing fossil-fuel-based electricity generation has serious impacts on environment. As a result, interest in renewable resources has risen, as they are environmentally friendly and may prove to be economical in the long run. However, the intermittent character of renewable energy sources is a major disadvantage. It is important to integrate them with the rest of the grid so that their benefits can be reaped while their negative impacts can be mitigated. In this article, an energy management algorithm is recommended for a grid-connected microgrid consisting of loads, a photovoltaic (PV) system and a battery for efficient use of energy. A model predictive control-inspired approach for energy management is developed using the PV power and consumption estimation obtained from daylight solar irradiation and temperature estimation of the same area. An energy management algorithm, which is based on a neuro-fuzzy inference system, is designed by determining the possible operating states of the system. The proposed system is compared with a rule-based control strategy. Results show that the developed control algorithm ensures that microgrid is supplied with reliable energy while the renewable energy use is maximized.

Issues in Energy Vulnerability Assessment: Looking for a Sustainable Choice of Natural Resource for Power Generation

“…A blowout, due to mechanical or operational failures that lead to the loss of control of a well, causes a large volume of crude oil to leak directly into the environment, impacting vulnerable factors [4,56] (The EV map for this project is found in [57]). The potential consequences analyzed in this project's EIA only take into account the worst-case blowout scenario.…”

“…Comparatively, fossil resources, such as oil and natural gas (NG), are non-renewable, as their formation is on a millennial scale. Unlike solar energy, which is considered "clean", hydrocarbons (HC) damage the planet from the moment of its invasive extraction (that has a risk of spillage) until its use (which releases greenhouse gases (GHG)) [3][4][5][6][7][8][9].…”

“…The blasting process involves the injection of drilling fluids that assist in the disintegration of rocks, which in turn are returned to the surface in the form of gravel, thus damaging all life that depends on these sites (for reasons ranging from suffocation and habitat loss to pollution/intoxication) [14]. There are also a large number of more serious potential impacts, for example in the event of a blowout (uncontrollable flow of gas, oil, or other reservoir fluids) [3,4]. Therefore, it is necessary to understand the effective (actual) and potential environmental impacts before executing a venture.…”

Voltage Regulation for Photovoltaics-Battery-Fuel Systems Using Adaptive Group Method of Data Handling Neural Networks (GMDH-NN)

“…In [15], similarly to [14], the FNNs are optimized to power and voltage regulation and the superiority of FNNs based control systems is shown. In [16], a predictive controller is developed using FNNs for power consumption management and the voltage regulation in daylight irradiation times is studied. In [17], deep discharging problem in PV/battery systems is studied using FNNs and it is shown that by the use of FNNs a better energy balance can be achieved.…”

Energy Management of a PV-SB HPGS Based on Model Predictive Control

“…Actual solar radiation and load profile were used in simulation results, which showed the reliability of the system with disturbances such as load variation and PV shading. A similar EMS was proposed based on a neuro-fuzzy inference system [10]. It was compared to a rule-based control strategy to test its reliability.…”