Demand estimation in a water distribution network provides crucial data for monitoring and controlling systems. Because of budgetary and physical constraints, there is a need to estimate water demand from a limited number of sensor measurements. The demand estimation problem is underdetermined because of the limited sensor data and the implicit relationships between nodal demands and pressure heads. A simulation optimization technique using the water distribution network hydraulic model and an evolutionary algorithm is a potential solution to the demand estimation problem. This paper presents a detailed process simulation model for water demand estimation using the particle swarm optimization (PSO) algorithm. Nodal water demands and pipe flows are estimated when the number of estimated parameters is more than the number of measured values. The water demand at each node is determined by using the PSO algorithm to identify a corresponding demand multiplier. The demand multipliers are encoded with varying step sizes and the optimization algorithm particles are also discretized in order to improve the computation time. The sensitivity of the estimated water demand to uncertainty in demand multiplier discrete values and uncertainty in measured parameters is investigated. The sensor placement locations are selected using an analysis of the sensitivity of measured nodal heads and pipe flows to the change in the water demand. The results show that nodal demands and pipe flows can be accurately determined from a limited number of sensors.
Due to increase in integration of renewable energy into the grid and power quality issues arising from it, there is need for analysis and power improvement of such networks. This paper presents voltage profile, Q-V sensitivity analysis and Q-V curves analysis for a grid that is highly penetrated by renewable energy sources; solar PV, wind power and small hydro systems. Analysis is done on IEEE 39 bus test system with Wind power injection alone, PV power injection alone, with PV and wind power injection and with PV, wind and micro hydro power injection to the grid. The analysis is used to determine the buses where voltage stability improvement is needed. From the results, it was concluded that injection of the modeled wind power alone helped in stabilizing the voltage levels as determined from voltage profiles and reactive power margins. Replacing some of the conventional sources with PV power led to reduction of voltages for weak buses below the required standards. Injection of power from more than one renewable energy source helped in slightly improving the voltage levels. Distribution Static compensators (D-STATCOMs) were used to improve the voltage levels of the buses that were below the required standards.
This paper presents a power and energy management strategy for a wind-photovoltaic (PV) microgrid with an energy storage system (ESS) and PV array current injection on the DC-link. The ESS consists of a battery and a supercapacitor interfaced to the DC-link through bi-directional DC-DC converters. Cascade PI control is used to regulate the current supplied by the ESS to the DC-link. A fuzzy logic controller is proposed for efficient power sharing between the battery and the supercapacitor. A discrete-time simulation model is used to study the microgrid operation. The PV array model is implemented as an s-function. Using the proposed system, the power supplied to the grid is maintained at the desired reference value during changes in wind speed, solar radiation, and grid power demand. Results also show that the ESS improves the DC-link voltage stability during three
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