Search

Time-of-use (TOU) pricing strategy is an important component of demand-side management (DSM), but the cost of supplying power during critical peak periods remains high under TOU prices. This affects power system reliability. In addition, TOU prices are usually applicable to medium- and long-term load control but cannot effectively regulate short-term loads. Therefore, this paper proposes an optimization method for TOU pricing and changes the electricity consumption patterns during the critical peak periods through a critical peak rebate (CPR). This reduces generation costs and improves power system reliability. An optimization model for peak-flat-valley (PFV) period partition is established based on fuzzy clustering and an enumeration iterative technique. A TOU pricing optimization model including grid-side and customer-side benefits is then proposed, and a simulated annealing particle swarm optimization (SAPSO) algorithm is used to solve the problem. Finally, a CPR decision model is developed to further reduce critical peak loads. The effectiveness of the proposed model and algorithm is illustrated through different case studies of the Roy Billinton Test System (RBTS). [ABSTRACT FROM AUTHOR]

Abstract The DBF algorithm of sporadic task systems on multiprocessors uses the approximation of the exact demand bound function on uniprocessor as a criterion. The systems which are feasible under the partitioned paradigm are flagged as 'infeasible' sometimes. In this paper, we present a novel efficient DBF( eDBF) partitioned scheduling algorithm. A criterion which tracks the demand bound function exactly as needed is used to avoid the incorrect judgment in determining whether a processor can accommodate an additional task in the new algorithm. We give the pseudo code of the new algorithm on least-number processors and fixed-number processors respectively. Then, we prove the correctness of, and evaluated the effectiveness of this new algorithm. The experimental results demonstrate that eDBF has better performance than DBF and Density algorithms. [ABSTRACT FROM AUTHOR]