Distributed generation allocation considering uncertainties

Abstract

Summary This paper presents a method for distributed generation (DG) allocation planning and investigates the extent to which system load and generation output uncertainties influence the final optimisation results. The problem is presented as a multiobjective constrained optimization problem in which the objective functions are power loss reduction and voltage profile improvements, while the constraints are the voltage, current, and short‐circuit power limits. The optimization is performed by using a multiobjective genetic algorithm. The best trade‐off among candidate solutions from Pareto front is achieved by using Bellman‐Zadeh method. The system load and DG power output uncertainties are addressed by using the possibilistic, α‐cut method. The proposed method is applied to a real 35 kV distribution system. It was demonstrated that appropriately planned DG allocation has potential to positively influence network losses and voltage profile. Further, it was determined that load and generation uncertainty, inherently present in the DG allocation problem, have significant impact on optimization results. This paper makes a contribution to the existing knowledge by applying, to a realistic test power system, a DG allocation method and by determining the extent to which load and DG output uncertainties influence the final optimization results.

Hybrid wind‐solar grid‐connected system planning using scenario aggregation method

“…Bansal et al 5 proposed biogeography-based optimization to find optimal configuration of hybrid energy system, which reduces overall system cost. Saric et al 6 investigated the effects of renewable energy and load uncertainties on the optimization results of the hybrid energy system. The problem was formulated as a multi-objective optimization problem and NSGA-II was used to find the Pareto front.…”

Review of VSG Control-Enabled Universal Compatibility Architecture for Future Power Systems with High-Penetration Renewable Generation

“…When VSC-HVDC stations are controlled by the VSG strategy, they provide virtual inertia and regulate the frequency, which enhances the system's controllability. Moreover, when feeding a weak AC system (in which the short circuit ratio (SCR) is below three) [69], the PLL in the conventional VSC-HVDC control block degrades the system's stability and restricts its transmission ability. On the contrary, a VSG-controlled VSC-HVDC can self-synchronize and feed an isolated system [70,71].…”

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

“… Step 1 : Variable setting with respect to desired applications: that is, types of supports to be provided by DGs, including (a) placement only, for example, find the best location in a radial feeder 36,37 ; (b) sizing and placement, for example, add power to city grid 38 ; (c) sizing, placement and numbers of DG; (d) sizing, placement and types of DG; and (e) sizing, placement, numbers of DG and types. Step 2 : Problem formulation: Objective function can be presented in either single‐objective or multi‐objective manner, including economic or technic requirements. Besides, the ever‐increasing uncertainties in renewable energy, market participants' behaviours, load patterns make stochastic programming or robust programming more and more popular in DG planning 39‐42 Step 3 : Select the most suitable optimization algorithm based on mathematical properties of the formulated problem: In most cases, DG planning is to be formulated as a high‐dimension and non‐convex problem.…”

“…[33][34][35][36] However, they heavily rely on an accurate model developed on a set of assumptions or simplifications, making the results not suitable in practice. By contrast, numerical algorithms such as linear programming (LP), 37 mixed non-linear programming (MINLP), [38][39][40][41][42] dynamic programming (DP) 43 reduce the reliance on accurate modelling which are easy to be implemented. However, this method may obtain inaccurate solution in complex systems.…”