122,147 research outputs found
Perancangan Sistem Informasi Persediaan Barang Menggunakan Visual Basic 2010 (Vb.Net) Pada PT. Solusi Rekatama Makmur
The warehousing management system is a key in the supply chain, where the main objective is to control all the processes that take place such as shipping and receiving. Currently the update of inventory data in PT. The Rekatama Makmur solution is periodically not updated every time a transaction occurs and is performed by a single computer performed by a warehouse officer, so that the other part of the employee is constrained in getting the latest data needed to make a business decision. Problems in this research how to make inventory information system for PT. Rekatama Makmur solution that produces updated inventory information every transaction and can generate information for warehouse officer, purchasing, and sales department. This research uses design and development which is done using VB.Net and MySql programming language as database management system
Robust Optimal Power Flow with Wind Integration Using Conditional Value-at-Risk
Integrating renewable energy into the power grid requires intelligent
risk-aware dispatch accounting for the stochastic availability of renewables.
Toward achieving this goal, a robust DC optimal flow problem is developed in
the present paper for power systems with a high penetration of wind energy. The
optimal dispatch is obtained as the solution to a convex program with a
suitable regularizer, which is able to mitigate the potentially high risk of
inadequate wind power. The regularizer is constructed based on the energy
transaction cost using conditional value-at-risk (CVaR). Bypassing the
prohibitive high-dimensional integral, the distribution-free sample average
approximation method is efficiently utilized for solving the resulting
optimization problem. Case studies are reported to corroborate the efficacy of
the novel model and approach tested on the IEEE 30-bus benchmark system with
real operation data from seven wind farms.Comment: To Appear in Proc. of the 4th Intl. Conf. on Smart Grid
Communication
Distributed Stochastic Market Clearing with High-Penetration Wind Power
Integrating renewable energy into the modern power grid requires
risk-cognizant dispatch of resources to account for the stochastic availability
of renewables. Toward this goal, day-ahead stochastic market clearing with
high-penetration wind energy is pursued in this paper based on the DC optimal
power flow (OPF). The objective is to minimize the social cost which consists
of conventional generation costs, end-user disutility, as well as a risk
measure of the system re-dispatching cost. Capitalizing on the conditional
value-at-risk (CVaR), the novel model is able to mitigate the potentially high
risk of the recourse actions to compensate wind forecast errors. The resulting
convex optimization task is tackled via a distribution-free sample average
based approximation to bypass the prohibitively complex high-dimensional
integration. Furthermore, to cope with possibly large-scale dispatchable loads,
a fast distributed solver is developed with guaranteed convergence using the
alternating direction method of multipliers (ADMM). Numerical results tested on
a modified benchmark system are reported to corroborate the merits of the novel
framework and proposed approaches.Comment: To appear in IEEE Transactions on Power Systems; 12 pages and 9
figure
Efficient Decentralized Economic Dispatch for Microgrids with Wind Power Integration
Decentralized energy management is of paramount importance in smart
microgrids with renewables for various reasons including environmental
friendliness, reduced communication overhead, and resilience to failures. In
this context, the present work deals with distributed economic dispatch and
demand response initiatives for grid-connected microgrids with high-penetration
of wind power. To cope with the challenge of the wind's intrinsically
stochastic availability, a novel energy planning approach involving the actual
wind energy as well as the energy traded with the main grid, is introduced. A
stochastic optimization problem is formulated to minimize the microgrid net
cost, which includes conventional generation cost as well as the expected
transaction cost incurred by wind uncertainty. To bypass the prohibitively
high-dimensional integration involved, an efficient sample average
approximation method is utilized to obtain a solver with guaranteed
convergence. Leveraging the special infrastructure of the microgrid, a
decentralized algorithm is further developed via the alternating direction
method of multipliers. Case studies are tested to corroborate the merits of the
novel approaches.Comment: To appear in IEEE GreenTech 2014. Submitted Sept. 2013; accepted Dec.
201
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