57,856 research outputs found
Mixed integer nonlinear programming for Joint Coordination of Plug-in Electrical Vehicles Charging and Smart Grid Operations
The problem of joint coordination of plug-in electric vehicles (PEVs)
charging and grid power control is to minimize both PEVs charging cost and
energy generation cost while meeting both residential and PEVs' power demands
and suppressing the potential impact of PEVs integration. A bang-bang PEV
charging strategy is adopted to exploit its simple online implementation, which
requires computation of a mixed integer nonlinear programming problem (MINP) in
binary variables of the PEV charging strategy and continuous variables of the
grid voltages. A new solver for this MINP is proposed. Its efficiency is shown
by numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:1802.0445
Minimum fuel coplanar aeroassisted orbital transfer using collocation and nonlinear programming
The fuel optimal control problem arising in coplanar orbital transfer employing aeroassisted technology is addressed. The mission involves the transfer from high energy orbit (HEO) to low energy orbit (LEO) without plane change. The basic approach here is to employ a combination of propulsive maneuvers in space and aerodynamic maneuvers in the atmosphere. The basic sequence of events for the coplanar aeroassisted HEO to LEO orbit transfer consists of three phases. In the first phase, the transfer begins with a deorbit impulse at HEO which injects the vehicle into a elliptic transfer orbit with perigee inside the atmosphere. In the second phase, the vehicle is optimally controlled by lift and drag modulation to satisfy heating constraints and to exit the atmosphere with the desired flight path angle and velocity so that the apogee of the exit orbit is the altitude of the desired LEO. Finally, the second impulse is required to circularize the orbit at LEO. The performance index is maximum final mass. Simulation results show that the coplanar aerocapture is quite different from the case where orbital plane changes are made inside the atmosphere. In the latter case, the vehicle has to penetrate deeper into the atmosphere to perform the desired orbital plane change. For the coplanar case, the vehicle needs only to penetrate the atmosphere deep enough to reduce the exit velocity so the vehicle can be captured at the desired LEO. The peak heating rates are lower and the entry corridor is wider. From the thermal protection point of view, the coplanar transfer may be desirable. Parametric studies also show the maximum peak heating rates and the entry corridor width are functions of maximum lift coefficient. The problem is solved using a direct optimization technique which uses piecewise polynomial representation for the states and controls and collocation to represent the differential equations. This converts the optimal control problem into a nonlinear programming problem which is solved numerically by using a modified version of NPSOL. Solutions were obtained for the described problem for cases with and without heating constraints. The method appears to be more robust than other optimization methods. In addition, the method can handle complex dynamical constraints
Urban Regeneration of Industrial Areas: Affordable Housing for Low Income Populations in Cities
The UK-China Sustainable Development Dialogue (SDD) is a partnership between the UK and Chinese Governments to promote collaboration and good practice on sustainable development. It is framed by a 2004 joint Prime Ministerial declaration and was formally established in 2005 by an agreement signed by UK Deputy Prime Minister John Prescott and State Councillor Tang. The SDD was founded on the recognition that, in an interdependent world, international co-operation is needed to ensure that learning is shared and efforts are made collectively towards achieving common goals. This report is the 13th and Final Paper in the SDD (Urban Strand. It draws together the core conclusions from the previous twelve papers and offers recommendations for taking the dialogue forward
Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations
Next-generation power grids will likely enable concurrent service for
residences and plug-in electric vehicles (PEVs). While the residence power
demand profile is known and thus can be considered inelastic, the PEVs' power
demand is only known after random PEVs' arrivals. PEV charging scheduling aims
at minimizing the potential impact of the massive integration of PEVs into
power grids to save service costs to customers while power control aims at
minimizing the cost of power generation subject to operating constraints and
meeting demand. The present paper develops a model predictive control (MPC)-
based approach to address the joint PEV charging scheduling and power control
to minimize both PEV charging cost and energy generation cost in meeting both
residence and PEV power demands. Unlike in related works, no assumptions are
made about the probability distribution of PEVs' arrivals, the known PEVs'
future demand, or the unlimited charging capacity of PEVs. The proposed
approach is shown to achieve a globally optimal solution. Numerical results for
IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this
approach
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