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Hybrid storage system composed of battery energy storage systems (BESSs) and supercapacitors is a promising solution to mitigate the high frequency power fluctuations of pulsed power loads (PPLs) in medium voltage DC (MVDC) shipboard power systems. Due to the presence of multiple storage units, a power sharing algorithm needs to be considered within the energy management system (EMS) in the shipboard power system. In this paper, an optimal power flow problem is formulated for MVDC shipboard power systems with hybrid energy storage systems. Battery energy storage systems (BESSs) and conventional generation units are enhanced with virtual resistive droop controllers to share the steady-state power fluctuations. Supercapacitors are enhanced with virtual capacitive droop controllers to share the high frequency fluctuations in the load. The optimal flow accounts for minimizing the operational cost of the generation units in the shipboard power system considering the constraints of network, load balance, voltage profile, and power/energy limits. Second-order cone programming (SOCP) relaxation is used to approximate the nonconvexity of the optimal flow formulation and necessary conditions for global optimality of the solution are discussed. Results confirm the effectiveness of the proposed formulation in load support and managing the energy between storage units. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed control architecture for output power regulation of doubly fed induction generator (DFIG) based wind turbines (WTs) with on-site battery energy storage systems (BESSs). The proposed distributed control architecture receives information from adjacent WTs+BESSs to control the DFIG's grid side converter (GSC) and the storage unit for output active/reactive power regulation and energy management purposes. The proposed method guarantees the equal active power sharing and energy management of WTs+BESSs in various wind speed conditions. A distributed controller is also designed to respond to the reactive power demand and share the reactive load demand between the GSC and BESS controllers based on their capacities. Furthermore, a controller is designed to account for the communication delays caused by the communication systems based on the IEEE standards. The proposed method is tested on the modified IEEE 14-bus power system with 10 WTs+BESSs. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) and smart grids of urban systems are in a strong nexus. Energy consumption of WWTPs will directly impact the economy of the smart grids, while the energy forecast, marketing, and resource allocation of smart grids will affect the economy of WWTPs. As such, these two critical infrastructures need to be operated and managed cooperatively to maximize the mutual economic benefits. In this paper, a mixed integer nonlinear programming (MINLP) co-optimization model is developed to address the day-ahead economic dispatch problem of smart grids embedded with interdependent links among wastewater treatment plants and smart grids. The energy demand of WWTPs is optimized by minimizing the energy consumption of the aeration module, mixed liquor pumping, primary clarifier's influent pumping, secondary clarifier's sludge pumping, and mixing devices that are integrated in the dynamic economic dispatch problem of smart grid. Additionally, biochemical oxygen demand (BOD) and total Kjedhal nitrogen (TKN) concentrations of the treated wastewater is also incorporated in the economic dispatch problem to ensure of the high quality of the WWTP's effluent. Electricity consumption of the buildings is also added as a constraint to the economic dispatch, serving as a dynamic load for smart grid. Case studies are conducted to examine the impact of wastewater flow rate, influent BOD and TKN concentrations, battery efficiency, and end-of-day battery's state of the charge constraints on economic dispatch of the smart grid. The results show that total operational cost and energy consumption of the integrated WWTP-smart grid is increased only by 3.4% and 1.75%, respectively, as the daily influent flow rate and BOD & TKN concentrations are increased from their minimum to maximum thresholds. • A co-optimization model for combined wastewater plant-smart grid is proposed. • Wastewater plant includes aeration and pumping units. • Effluent's biochemical oxygen demand and total Kjedhal nitrogen are minimized. • Effect of battery's efficiency and end-of-day state of charge are studied. • Demand response and energy resource allocation of smart grid are considered. [ABSTRACT FROM AUTHOR]

A sustainable water infrastructure cannot be made possible without improving its energy efficiency in terms of the consumption value and the type of supplying resources. One solution to reduce the external energy requirement of the water systems is to decrease the energy consumption, while the internal energy recovery capacity is increased. Such is achieved by the present work through (1) optimizing the water demand and tanks' and pumps' scheduling and operations and (2) installing pumps-as-turbines (PATs) where there is excess pressure head in the water. In this paper, two optimization models are developed. The first optimization problem is designed to find the optimal number and location of the PATs among the eligible positions across a water system. The optimal solution will be incorporated in the second optimization problem, i.e., an economic dispatch problem, where the cost of energy generation of an integrated water-energy system is minimized while the water and electrical load demands, as well as the hydraulic integrity of the system are satisfied. The energy system consists of conventional power generation units aggregated of natural gas and diesel based generators, solar photovoltaic units, wind generators, and battery energy storage systems. The proposed models are verified with case studies, and the numerical results show more than 30% reduction in the daily electricity consumption of water systems and $79 in daily operating cost of energy generation in water-energy systems when 3 PATs were installed at optimal locations. • Two optimization models using mixed integer nonlinear programming are proposed. • First optimization model finds optimal place and number of pumps-as-turbines (PATs). • Next optimization model takes in optimal PATs to minimize cost of energy generation. • Effect of demand response on energy savings of water-energy system is studied. • Effect of optimal PATs on resource allocation of water-energy system is studied. [ABSTRACT FROM AUTHOR]

• Dynamic economic dispatch of a water-energy microgrid with intelligent thermal management. • Optimizing the energy consumption of pumps in water distribution network. • Considering occupants' temperature comfort range. • Adding thermal equilibrium of buildings to dynamic economic dispatch. • Use of renewable energy sources and energy storage in water-energy microgrid. In this paper, a mixed integer nonlinear programming formulation is proposed to intelligently dispatch the distributed generators, manage buildings' thermal energy, and support the water demand in an islanded water-energy microgrid. To optimize the demand of water distribution system, pump's nonlinear scheduling and hydraulic factors, and daily water usage of buildings are added to the formulation. In addition, thermal management of electric chillers considering customers' comfort level and outside temperature forecast was formulated. For a summer cooling schedule, it is shown that adjusting the indoor temperature settings at 20–25 °C for both occupied and unoccupied hours offers the lowest daily operation cost and peak demand charges. A fixed temperature setting of 25 °C during the occupied and unoccupied hours results in a lower daily cost and demand charges than a wide temperature setting of 15–30 °C. Lowest daily costs and electricity demand can be achieved within the proposed integrated water-energy microgrid with optimal water demand and building thermal energy management. By incorporating the water system constraints in the optimization model, hourly cost savings up to $150 can be achieved. In addition, incorporating building's thermal management in the proposed economic dispatch model can result in up to $20 hourly energy saving in the water-energy system. [ABSTRACT FROM AUTHOR]