# Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid
## 1 Introduction
A common factor in this works is the management of constraints using the penalty technique, which implies adding more parameters to be defined by the end user. In Table 1, main proposals based on BFOA were grouped according to particular characteristics. Moreover, other proposals were added--each proposal derived in several contributions. In the first row, representing this work, BFOA is implemented in order to optimize a mathematical model minimizing an IMG, including renewable energy sources such as wind and solar, as well as a conventional generation unit based on diesel fuel. Two novel versions of the BFOA are implemented and tested: TS-MBFOA, and a new proposal called NTS-MBFOA. Results showed that TS-MBFOA obtained better numerical solutions compared to NTS-MBFOA and compared to LSHADE-CV, an EA found in the literature solving the same problem. However, the best solution found by NTS-MBFOA is better from a mechatronic point of view because it favors the lifetime of the IMG and therefore resulting in economic savings in the long term. In the second row of Table 1, [PERSON] and others [21] proposed the bacterial foraging tabu search (BFTS) technique, a hybridization of BFOA and tabu search (TS) using different operational time interval (OTI) to schedule appliances while balancing user comfort (UC). His goal was to reduce both the waiting time and electricity cost simultaneously. Real-time pricing (RTP) scheme was used to get the total cost of electricity consumed. For simulations, they studied an average size modern home with 11 appliances. The simulation results of BFTS-based scheduled clearly shows that the proposed technique is better as compared to BFOA, TS, and unscheduled electricity consumption. The electricity cost and waiting time were minimized thus increasing UC. In the third row of Table 1, [PERSON] and others [22] implemented two algorithms aimed at minimizing electricity cost and peak to average ratio (PAR) in a smartgrid by using BFOA and strawberry algorithm (SBA). Real-time pricing (RTP) pricing scheme was used to calculate the electricity cost. A single home with three types of appliances; fixed, shiftable and elastic appliances composed the simulated model. Authors found that these optimization schemes reduce the total electricity cost and peak to the average ratio by shifting the load from on-peak hours to off-peak hours. BFOA performed better than SBA regarding electricity cost minimization. However, the authors concluded that trade-off always exists between cost and user comfort. In the fourth row of Table 1, [PERSON] and others [23] gained electricity cost reduction up to 40% in a home energy management system (HEMS) with a single home using BFOA and pigeon inspired optimization (PIO). Cost, PAR and waiting time of the appliances were calculated on the bases of a 120 h time slot. Two types of appliances were used: interruptible and non-interruptible. Critical peak pricing (CPP) was used as a pricing signal to calculate the electricity bills. Simulation results showedthat PIO was identified as the best technique as it performs well in reducing cost. PIO gives 37% more waiting time than BFOA; it has 60% less cost by BFOA and PAR is 3% less by BFOA. In the fifth row of Table 1, [PERSON] and others [24] implemented a genetic algorithm to optimize a micro-grid operation considering distributed generation, environmental factors and demand response (DR). Experiments were conducted on a smart micro-grid from Tianjin, China. The building micro-grid system mainly includes distributed generation, energy storage device, electric vehicle, and various load resources. Two prices mechanisms were used, fixed price and DR prices. The main finding of this model is to optimize the cost in the context of considering demand response and system operation without reducing user comfort. Also, the authors found that the natural gas price dramatically influences both the operation cost of the micro-grid and demand response.