Published in IET Generation, Transmission & Distribution Received on 29th September 2009 Revised on 18th January 2010 doi: 10.1049/iet-gtd.2009.0548 ISSN 1751-8687 Evaluating maximum wind energy exploitation in active distribution networks P. Siano 1 P. Chen 2 Z. Chen 2 A. Piccolo 1 1 Department of Information and Electrical Engineering, University of Salerno, Fisciano, Italy 2 Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark E-mail: psiano@unisa.it Abstract: The increased spreading of distributed and renewable generation requires moving towards active management of distribution networks. In order to evaluate maximum wind energy exploitation in active distribution networks, a method based on a multi-period optimal power flow analysis is proposed. Active network management schemes such as coordinated voltage control, energy curtailment and power factor control are integrated in the method in order to investigate their impacts on the maximisation of wind energy exploitation. Some case studies, using real data from a Danish distribution system, confirmed the effectiveness of the proposed method in evaluating the optimal applications of active management schemes to increase wind energy harvesting without costly network reinforcement for the connection of wind generation. 1 Introduction The international concern over climate change is driving European countries to reduce carbon-dioxide emissions by means of political and regulatory pressure and to increase the total electrical supply energy from renewable sources. Electricity market liberalisation and the priority given to renewable sources under EU directive 03/54/EC along with the worldwide promotion of renewable encourage the development of the distributed generation (DG) and renewable sources. The connection of large amounts of DG to distribution systems presents a number of technical challenges to distribution network operators (DNOs) [1–6]. These challenges are partly caused by the mismatch between the location of energy resources and the capability of local networks to accommodate new generation. Particularly, the location of wind turbines (WTs) is determined by the local wind resources and geographical conditions. However, the current capacity of the network to which the WTs will be connected may not be sufficient to deliver the generated wind power. As a result, network reinforcement needs to be planned by the DNOs. Since such network reinforcement usually calls for high capital investment, DNOs would like to explore less costly means that can improve the capability of the network to accommodate new generation. One way is to make the best use of the existing network by encouraging development at the most suitable locations [3–6]. In order to do this, DNOs require a reliable and repeatable method of quantifying the capacity of new DG that may be connected to distribution networks without the need for reinforcement. The challenge of identifying the best network location and capacity for DG has attracted significant research effort, albeit referred to by several terms: optimal ‘capacity evaluation’ [3–7], ‘DG placement’ [8] or ‘capacity allocation’ [9–11]. These optimisation problems apply different numerical algorithms with various objectives and constraints. For example, genetic algorithms are used to find the optimal location of DG [12–14]. Several other algorithms are adopted to handle optimisation problems with discrete variables [9, 15]. Other approaches require network locations of interest to be pre-specified with algorithms guiding capacity growth within network constraints [7–9, 16]. Nevertheless, as values associated with WTs are time- and location-dependent, methods that simply consider one specific power value at a specific moment are not able to account for time dependence. Therefore, WTs optimal 598 IET Gener. Transm. Distrib., 2010, Vol. 4, Iss. 5, pp. 598–608 & The Institution of Engineering and Technology 2010 doi: 10.1049/iet-gtd.2009.0548 www.ietdl.org Authorized licensed use limited to: Universita degli Studi di Salerno. Downloaded on May 18,2010 at 07:59:21 UTC from IEEE Xplore. Restrictions apply.