Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 4, Issue 4, Pages 2081-2088Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2013.2261565
Keywords
Decentralized control; distributed algorithms; energy management; load management; optimal scheduling; power system economics; power systems
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Funding
- Semiconductor Research Corporation (SRC) Energy Research Initiative (ERI) for Smart Grid Research Center (SGRC) Task 2111.001 Adaptive Load Management
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This paper concerns mathematical conditions under which a system-level optimization of supply and demand scheduling can be implemented as a distributed optimization in which users and suppliers, as well as the load serving entities, are decision makers with well-defined sub-objectives. We start by defining the optimization problem of the system that includes the sub-objectives of many different players, both supply and demand entities in the system, and decompose the problem into each player's optimization problem, using Lagrange dual decomposition. A demand entity or a load serving entity's problem is further decomposed into problems of the many different end-users that the load serving entity serves. By examining the relationships between the global objectives and the local/individual objectives in these multiple layers and the optimality conditions of these decomposable problems, we define the requirements of these different objectives to converge. We propose a novel set of methods for coordinating supply and demand over different time horizons, namely day-ahead scheduling and real-time adjustment. We illustrate the ideas by simulating simple examples with different conditions and objectives of each entity in the system.
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