Energy management with application to HVAC systems based on bacterial colony chemotaxis algorithm

This work addresses an energy management problem in smart grid with the relationship between the operating states and the energy consumption of the loads taken into consideration. The energy management problem is modeled as a nonlinear optimization problem. In the constraints, the operation-to-energy functions and the probability of exceeding the energy supply under forecast errors are included. Furthermore, we devote to achieve the energy management of distributed heating, ventilation, and air conditioning (HVAC) systems and establish the consumers' cost functions based on the predicted mean vote (PMV) thermal comfort model. The bacterial colony chemotaxis (BCC) algorithm is adopted to seek the optimal temperature settings and the optimal energy supply for the consumers and the energy provider, respectively. Finally, the simulation results demonstrate that the BCC algorithm shows good convergence performance to seek the optimal solution and the demand is approximately equal to the energy supply with margin for reliability.