Active Distribution System Planning for Low-carbon Objective using Cuckoo Search Algorithm

Abstract

-In this study, a method for the low-carbon active distribution system (ADS) planning is proposed. It takes into account the impacts of both network capacity and demand correlation to the renewable energy accommodation, and incorporates demand response (DR) as an available resource in the ADS planning. The problem is formulated as a mixed integer nonlinear programming model, whereby the optimal allocation of renewable energy sources and the design of DR contract (i.e. payment incentives and default penalties) are determined simultaneously, in order to achieve the minimization of total cost and CO 2 emissions subjected to the system constraints. The uncertainties that involved are also considered by using the scenario synthesis method with the improved Taguchi's orthogonal array testing for reducing information redundancy. A novel cuckoo search (CS) is applied for the planning optimization. The case study results confirm the effectiveness and superiority of the proposed method.

Improved Global Maximum Power Point Tracking for Photovoltaic System via Cuckoo Search under Partial Shaded Conditions

“…This algorithm is simple and easy to install; thus, it has gradually become popular among the intelligent algorithms [30]- [33].…”

A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches

“…Aiming to addressing these undesirable conditions, several AM key approaches are introduced and listed in Figure 1. All these AM approaches, such as active network reconfiguration [35,77,97,110], coordinated volt-var control [98,109], coordinated dispatch of DERs, including DGs [22,52,94], ESSs [46,71,88,111], demand response (DR) [53,65,89,112], and optimal charging strategy of electric vehicles (EVs) [55,99], offer many potential benefits to the planning of ADSs and affect quality of the planning solution. The coupling of operation and planning is able to achieve the simultaneous optimization of planning and operation, and to identify the benefits and the effects of optimal operation on the planning solution.…”

“…·Minimization of carbon emission [25], [34], [36], [48], [53], [66], [81], [86], [107]... ·Maximization of RDGs penetration [22], [51], [68], [88], ·Maximization of subsidy for RDGs [76] Technical objectives Enviromental objectives As shown in Figure 6, economic objectives are the most common planning objectives, and 95.40% of the surveyed papers involve economic objectives. On the contrary, only 22.98% of these planning models focus on the environmental objectives.…”

“…Although more than 53% of these single objective models also involve the technical and/or environmental factors, all of these factors are converted to economic ones by economic parameters, such as reliability costs [24,31,32,34,36,[41][42][43]56,60,63,64,66,71,75,76,87,91] and emission costs [25,[34][35][36]48,53,66,75,81,86,92,94,107]. However, these economic parameters are always experience dependent, and may affect the objectivity of planning solutions.…”

“…As the main sources of uncertainties, wind speed, solar irradiance, and load demand, are approximately assumed to follow Weibull distribution, Beta distribution, and normal distribution, respectively, as shown from Equations (2)-(4). Therefore, these PDFs are adopted to represent the high fluctuation and randomness features of wind speed, solar irradiance, and load demand in many articles [25,35,44,48,53,65,66,75,81,90].…”