There is increasing requests for noncontrollable distribution generation (DG) interconnections in the medium and low voltage networks. Many studies have suggested that with proper system planning, DG could provide benefits such as reliability enhancement, investment deferment, and reduced losses. However, without network reinforcements, the allowable interconnection capacity in a network is often restricted due to fault current level, voltage variation, and power flow constraints. This paper aims to address the issue of optimizing network operation and use for accommodating DG integrations. A new DG interconnection planning study framework that includes a coordinated feeder reconfiguration and voltage control to calculate the maximum allowable DG capacity at a given node in the distribution network is presented. A binary particle swarm optimization (BPSO) technique is employed to solve the discrete nonlinear optimization problem and possible uncertainties associated with volatile renewable DG resource and loads are incorporated through a stochastic simulation approach. Comprehensive case studies are conducted to illustrate the applicability of the proposed method. Numerical examples suggest that the method and procedure used in the current DG interconnection impact study should be modified in order to optimize the existing grid operation and usage to facilitate customer participation in system operation and planning.

This study presents a multi‐objective framework to evaluate the integration of distant wind farms with associated transmission network upgrades on optimal power system planning. The presented approach also extends the technique to include the consideration of energy limitations associated with the installed hydro generation facilities. This study attempts to emphasise on the reliability implications rather than the production cost evaluation aspects. The decision making is based on hierarchal level II (HL‐II) Expected Energy Not Served as an entire power system reliability assurance, and capital cost plus annual operational cost as an economical index. Non‐dominated Sorting Genetic Algorithm is adopted to achieve the Pareto front of the aforementioned multi‐objective problem. A fuzzy satisfying method, designated as the distance metric, is used to represents a trade‐off between different objectives. To numerically evaluate the efficiency of the proposed method, simulation results on three case studies are provided. In spite of huge computation burden at HL‐II reliability assessment, the results indicate high efficiency of the proposed method.

This paper presents a new formulation for the preventive security-constrained unit commitment problem modeling N - k line outages and transmission losses. The pre- and post-contingency transmission constraints, representing N - k line outages, are explicitly included by using generalized generation distribution factors. To account for security, a contingency selection procedure based on line outage distribution factors finds a list of worst-case contingencies. Transmission losses are incorporated using piecewise linear expressions. The proposed model is formulated as an instance of mixed-integer linear programming. The effectiveness of the proposed approach is illustrated with the IEEE 57-bus system and the 1,354-bus portion of the European transmission system. As empirically evidenced, the explicit consideration of N-k line outages and transmission losses leads to different decisions in the generation scheduling and dispatch, ensuring secure power system operation.