ﻻ يوجد ملخص باللغة العربية
The seasonal and variable electricity production of renewable sources, such as wind and solar power, needs to be compensated by resources that can guarantee a reliable supply of power at all times. As the penetration of variable renewable energy increases globally for economic reasons, so do the requirements for additional sources of flexible operation. The permanent balance between demand and supply of electricity is one of the reasons of the increased interest on energy storage systems in recent years. By far, the largest technology used globally to this end is Pump Hydro Storage (PHS) because of the fast response of power, large storage capacity and competitiveness. PHS project are highly site specific, and the selection and design of these projects is critical. In this article, an integer programming problem is formulated for their siting and sizing. The approach is to select grid cells from a Digital Elevation Model (DEM) that will conform reservoirs of PHS to meet minimum storage requirements. The objective function includes the costs of embankments, water conveyance systems, and electromechanical equipment. The model can be executed for different instances of DEM, and the best local solutions can be aggregated to provide regional or national requirements of power systems.
In this paper, we consider the problem of joint antenna selection and analog beamformer design in downlink single-group multicast networks. Our objective is to reduce the hardware costs by minimizing the number of required phase shifters at the trans
We propose a trust-region method that solves a sequence of linear integer programs to tackle integer optimal control problems regularized with a total variation penalty. The total variation penalty allows us to prove the existence of minimizers of
This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form. This method is particularly effective for weakly constrained SDPs. The key idea is to formulate an approximate complementa
Cutting plane methods play a significant role in modern solvers for tackling mixed-integer programming (MIP) problems. Proper selection of cuts would remove infeasible solutions in the early stage, thus largely reducing the computational burden witho
We propose a dual dynamic integer programming (DDIP) framework for solving multi-scale mixed-integer model predictive control (MPC) problems. Such problems arise in applications that involve long horizons and/or fine temporal discretizations as well