This study proposes a novel scheme for dynamic distribution expansion planning (DEP) in the presence of plug-in electric vehicles (PEVs). The model considers investment, production and maintenance cost and identifies ...
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This study proposes a novel scheme for dynamic distribution expansion planning (DEP) in the presence of plug-in electric vehicles (PEVs). The model considers investment, production and maintenance cost and identifies the substations and feeders to be built, reinforced or replaced. Owing to the increasing penetration of PEVs into the distribution network, traditional strategies to expand the network have to be updated to cope with the new uncertainties incurred by the PEVs integration. In this regard, a two-stage scenario-based strategy is presented, in which the uncertainties related to the PEVs are modelled via stochastic optimisation using Monte Carlo simulation. In the first stage, the binary decision variables (here and now decisionvariables) are determined, whereas in the second stage, the optimal production of substations and optimal charging of PEVs (wait and see decisionvariables) are identified in a day-ahead electricity market. Moreover, the daily electricity price and load volatilities have also been taken into account. The DEP problem is formulated as a mixed-integer linear programming problem and is solved using the efficient Benders' decomposition algorithm. The results of the case study based on a 24-node distribution system show the feasibility, tractability and effectiveness of the proposed model.
Intentional islanding operation (IIO) is a feasible solution to improve the reliability of active distribution network (ADN) by supplying critical loads through the local DG when a fault occurs. Aiming at this goal, a...
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Intentional islanding operation (IIO) is a feasible solution to improve the reliability of active distribution network (ADN) by supplying critical loads through the local DG when a fault occurs. Aiming at this goal, a new two-stage methodology is proposed to supply critical loads based on cost-effective improvement. In the first stage, the interruption cost is proposed as the load priority and the ON/OFF status of switches are considered as the binary decision variables. Therefore, IIO is considered as a mixed integer linear programming (MILP) problem to minimise the interruption cost. At the second stage, the power flow calculation is performed on the initial islands for the real-time operation. The proposed method can be utilised for both long- and short-term studies. In the long-term study, the inherent uncertainty of ADN is considered in MILP by using a Monte-Carlo simulation. This concept is used for clustering ADN into self-sufficient microgrids. Moreover, by taking a snapshot of the ADN status and performing operational feasibility, the proposed method can be considered as a real-time power regulation method. The proposed methodology is implemented on the IEEE 33-bus distribution network, and the results are discussed in detail.
This paper proposes a novel modeling scheme for model predictive control of a class of hybrid (continuous/discrete) flowshops, where permutation scheduling of jobs inserted into machines and control of continuous dyna...
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ISBN:
(纸本)9781424453634
This paper proposes a novel modeling scheme for model predictive control of a class of hybrid (continuous/discrete) flowshops, where permutation scheduling of jobs inserted into machines and control of continuous dynamics on jobs inside machines are simultaneously optimized in the conveyor-type flowshops. This modeling allows us to reduce the number of the binary decision variables in the resulting mixed integer programming (MIP) problem, thereby achieving a remarkable decrease in the computational burden of solving it. The proposed method is applied to slab reheating furnace control for a hot strip mill.
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