The increasingly complex residential microgrids (r-microgrid) consisting of renewable generation, energy storage systems, and residential buildings require a more intelligent scheduling method. Firstly, aiming at the radiant floor heating/cooling system widely utilized in residential buildings, the mathematical relationship between the operative temperature and heating/cooling demand is established based on the equivalent thermodynamic parameters (ETP) model, by which the thermal storage capacity is analyzed. Secondly, the radiant floor heating/cooling system is treated as virtual energy storage system (VESS), and an optimization model based on mixed-integer nonlinear programming (MINLP) for r-microgrid scheduling is established which takes thermal comfort level and economy as the optimization objectives. Finally, the optimal scheduling results of two typical r-microgrids are analyzed. Case studies demonstrate that the proposed scheduling method can effectively employ the thermal storage capacity of radiant floor heating/cooling system, thus lowering the operating cost of the r-microgrid effectively while ensuring the thermal comfort level of users.
Optimal scheduling of building microgrid could yield economical savings and reduce the pollutants emission, while ensuring the comfort level of users. A multi-objective optimal scheduling problem is studied for one kind of building microgrid-redundant residential microgrid (RR-microgrid). The radiant floor heating/cooling system widely utilized in residential buildings is treated as the virtual energy storage system (VESS) to dig up the considerable thermal storage capacity. Interval number is employed to describe the random fluctuation of uncertainty factors, such as renewable energy power, electric load, and weather conditions in the process of microgrid operation, while the exchange power with external grid and the output power of micro-gas turbines are, respectively, taken as the mean to smooth the electric power fluctuation, and two different optimization scheduling models which take the operating cost (OC), thermal comfort level (TCL), and pollutant emission (PE) as the optimization objectives are developed, and an improved non-dominate sorting genetic algorithm II (NSGA-II) is proposed to search the Pareto front of the scheduling models. The case study for heating in winter is performed and the results demonstrate the effectiveness of the proposed optimal scheduling method. INDEX TERMSMicrogrid, optimal scheduling, virtual energy storage (VESS), interval. NOMENCLATURE University (XJTU), Xi'an, China, in 2008. Since 2008, she has been a Lecturer with the Department of Automation, NCEPU. Her research interest includes signal analysis and processing.
This paper investigates robust output voltage control of battery energy storage systems (BESS) inverter in stand-alone micro-grid. The transfer function model between the output voltage and duty cycle of the BESS inverter is established, base on which the main factors affecting the output voltage are analyzed theoretically. Then, the expanded inverse model of the BESS inverter is developed based on BP neural network, and the gravity search algorithm (GSA) is employed to search the initial values of the network's parameters in the training process. Further, a control method for the output voltage of BESS inverter is proposed by putting the expanded inverse model in series with the original system as well as performing the single-loop control with PI controller. Both simulation experiment results and prototype experiment results show that the proposed expanded inverse model control method has a strong capability of disturbance rejection and could ensure the high power quality for the output voltage of the BESS inverter. INDEX TERMS BESS inverter, inverse model, BP neural network, gravity search algorithm, load disturbance.
Optimal scheduling of a redundant residential microgrid (RR-microgrid) could yield economical savings and reduce the emission of pollutants while ensuring the comfort level of users. This paper proposes a novel multi-objective optimal scheduling method for a grid-connected RR-microgrid in which the heating/cooling system of the RR-microgrid is treated as a virtual energy storage system (VESS). An optimization model for grid-connected RR-microgrid scheduling is established based on mixed-integer nonlinear programming (MINLP), which takes the operating cost (OC), thermal comfort level (TCL), and pollution emission (PE) as the optimization objectives. The non-dominate sorting genetic algorithm II (NSGA-II) is employed to search the Pareto front and the best scheduling scheme is determined by the analytic hierarchy process (AHP) method. In a case study, two kinds of heating/cooling systems, the radiant floor heating/cooling system (RFHCS) and the convection heating/cooling system (CHCS) are investigated for the RR-microgrid. respectively, and the feasibility and validity of the scheduling method are ascertained.
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