Fuzzy Logic Power Management Strategy for a Residential DC-Microgrid

Abstract

Power management strategies (PMS) are applied to keep a balance, between different energy sources (i.e. solar, wind, geothermal, hydro), storage units (i.e. fuel cell, batteries, fly wheel) and loads. Up to date, there has been reported several advance techniques to solve this task, for instance: Fuzzy Logic, Deep Learning, Droop Control, Bayesian Networks, among others. Nevertheless, some of those PMS are over simplified and others are too complex to be programmed in devices with limited resources. To solve these issues, this paper proposes a PMS based on Fuzzy Logic, which keeps a balance between those two goals. Characteristics of the proposed PMS are a small number of rules; fulfillment of the demanded power at every time; reducing use of the storage unit; and keeping a balance between the different sources, storage unit and loads. The proposed PMS is numerically evaluated by using SIMULINK-MATLAB®, in a 10kW residential DC Microgrid (MG), and validated by using a Hardware in the Loop platform (NI myRio-1900 and Typhoon HIL402). A comparison with three popular advance techniques demonstrates the feasibility of the proposed PMS.

Application Conditions of Bounded Rationality and a Microgrid Energy Management Control Strategy Combining Real-Time Power Price and Demand-Side Response

“…If it is the minimum, keep the current value; (5). Input the data into the execution network, train the execution network, update its weights, and finally output the calculated RTPP and purchase electricity, which are the control strategies of the system; (6). Input the collected data and the control strategy obtained from the execution network to the critic network for training, update its weight, and then get the corresponding cost function; (7).…”

“…Since microgrid consists of various types of distributed power supplies and energy storage systems, generally, the typical discrete nonlinear optimization method is used to establish the energy management model [2]- [6]. As an optimization problem, the aim of energy managing for the microgrid is to realize local energy consumption, the balance between supply and demand, stable operation and optimal dispatch through establishing objective functions and constraints.…”

Differential Evolution-Based Overcurrent Protection for DC Microgrids

“…In addition, due to the low cable resistance, the fault current increases rapidly, making protection coordination more difficult. To address above challenges, one of the solutions is artificial intelligent based method, which has been drawn attention recently in DC microgrids [18][19][20][21][22]. Although these intelligent methods have been demonstrating promising results, there are still many technical barriers preventing them from being applied to industry such as limited/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time implementation.…”

Fuzzy logic power management for a PV/Wind microgrid with backup and storage systems

“…References from [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] discussed the energy management of MG based on RES. In [9], a supervisor energy management strategy based on FLC of hybrid WEG/PVEG/DG with BSS is implemented.…”

“…Results show that intermittency in RES can be controlled by FLCs to offer uninterrupted power. In [23] a balance in power flow from different energy sources is preserved by the aid of FLC. A small number of rules is used to keep continuous, decreasing the use of BSS; and observing a balance between power sources, storage, and load.…”