Abstract
It has recently been shown that using battery storage systems (BSSs) to provide reactive power provision in a medium-voltage (MV) active distribution network (ADN) with embedded wind stations (WSs) can lead to a huge amount of reverse power to an upstream transmission network (TN). However, unity power factors (PFs) of WSs were assumed in those studies to analyze the potential of BSSs. Therefore, in this paper (Part-I), we aim to further explore the pure reactive power potential of WSs (i.e., without BSSs) by investigating the issue of variable reverse power flow under different limits on PFs in an electricity market model. The main contributions of this work are summarized as follows: (1) Introducing the reactive power capability of WSs in the optimization model of the active-reactive optimal power flow (A-R-OPF) and highlighting the benefits/impacts under different limits on PFs. (2) Investigating the impacts of different agreements for variable reverse power flow on the operation of an ADN under different demand scenarios. (3) Derivation of the function of reactive energy losses in the grid with an equivalent-π circuit and comparing its value with active energy losses. (4) Balancing the energy curtailment of wind generation, active-reactive energy losses in the grid and active-reactive energy import-export by a meter-based method. In Part-II, the potential of the developed model is studied through analyzing an electricity market model and a 41-bus network with different locations of WSs.
Highlights
Buy-back is well-known in electricity markets where utilities or customers are buying or selling electric energy in a designed energy marketplace [1]
Before formulating the active-reactive optimal power flow (A-R-OPF) problem in a MV network, we would like to note that there are some major differences between optimal power flow problems in transmission and distribution systems
That stability constraints are not included in the above model and the formulated A-R-OPF problem is solved by the same general algebraic modeling system (GAMS) using the nonlinear programming (NLP) solver CONOPT3 as in [15,16]
Summary
Buy-back is well-known in electricity markets where utilities or customers are buying or selling electric energy in a designed energy marketplace [1] This issue becomes more complex if renewable energies and/or storage systems are considered in connected power systems with bidirectional active and reactive power flows as seen in Figure 1 [2]. The works in [4,5] used a model for reactive power production from wind turbines based network. We note that reactive power contribution can be modeled differently form on an electrical model of a wind turbine with a full scale converter. We that reactive power form contribution can[8] beof modeled differently form photovoltaic [8]note or energy storage systems [2].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
