Abstract:In this paper, we propose a dynamic economic dispatch (DED) model with sharing of responsibility for supply-demand balance under uncertain demands in a microgrid (MG). For developing the proposed model, an energy band operation scheme, including a tie-line flow (TLF) contraction between the main grid and the microgrid (MG), is constructed for preventing considerable changes in the TLFs caused by DED optimization. The proposed scheme generalizes the relationship between TLF contractions and MG operational costs. Moreover, a chance-constrained approach is applied to prevent short-and over-supply risks caused by unpredictable demands in the MG. Based on this approach, it is possible to determine the reasonable ramping capability versus operational cost under uncertain power demands in the MG.
This paper presents the important features of structure-dependent model predictive control (MPC)-based approaches for automatic generation control (AGC) considering network topology. Since power systems have various generators under different topologies, it is necessary to reflect the characteristics of generators in power networks and the control system structures in order to improve the dynamic performance of AGC. Specifically, considering control system structures is very important because not only can the topological problems be reduced, but also a computing system for AGC in a bulk-power system can be realized. Based on these considerations, we propose new schemes in the proposed controller for minimizing inadvertent line flows and computational burden, which strengthen the advantages of MPC-based approach for AGC. Analysis and simulation results in the IEEE 39-bus model system show different dynamic behaviors among structure-dependent control schemes and possible improvements in computational burden via the proposed control scheme while system operators in each balancing area consider physical load reference ramp constraints among generators.
The current trend of CAD system is to make the system intelligent. CAD systems need to be intelligent in the sense that they must be able to use knowledge to achieve the designer’s goal. In the early stages of ship design, more experienced and higher level knowledge is required rather than that of detail design. The existing CAD systems have several limitations in terms of satisfying the requirements of real design. Accordingly, a more powerful and capable CAD system is required to support the activities in the early stage of design. Recently the application of expert systems has been considered as a tool for extending the capability of existing CAD systems. In this paper, we present an approach to implement a practical knowledge-based system for the machinery layout design of a ship engine room. The knowledge-base is implemented and verified in the actual CAD environment of a ship engine room, named MADES, which we develop in this study. The approaches presented in this paper provide a practical example of a knowledge-based system for complex design problems, and can also provide guidance on implementing an integrated design expert system that extends the capability of existing CAD systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.