Distribution system energy management through capacity constrained optimization

Abstract

Due to the widespread installation of Distributed Energy Resources (DER) and Demand SideManagement resources (DSM) into Distribution Systems (DS), the usage pattern of the DS has changed remarkably in recent times. As a result, the DS has the potential to lead its network operational parameters, such as bus voltage profiles and line flows, outside of their allowable limits. Therefore, they increase the variability and range of variation of loads in the network. However, the DS generally are not designed for such conditions. Thus, it becomes necessary to reassess and redesign the network capacity. A standard strategy would be to increase the capacity of the network, however, this is costly. A cost-effective strategy could be to improve the management of such a system by improving the determination of its actual capacity. Recent advances in ICT equipment make it appear possible to manage the critical loading conditions instead of increasing capacity of the network. Thus, an energy management system is required to perform that function during critical loading. In this thesis, a framework for controlling a network-oriented autonomous energy management system on the distribution network using network capacity constrained is proposed.The framework is based on a State Estimation (SE) technique, which is able to determine the most probable system state of a network. Practically, the system's operational parameters may be unobservable due to measurement deficiency, loss of communication etc. In such a situation, the observability analysis needs to be performed on the available measurement data to investigate whether the operational parameters are observable or unobservable. Such an analysis is taken into account in this thesis.The proposed framework is akin to an Optimal Power Flow (OPF) problem, where SE gives a starting point to the simulation layer. The simulation layer supports the optimization under the network constraints. In this framework, the challenging task is to formulate the network constraints.To this end, a linearization based map is generated from the functional relationships between an arbitrary set of control variables and an extensive variety of constrainable operational parameters.This map permits the formulation of linear inequality constraints to build a flexible system to manage the network within existing capacity. The feasibility of the proposed constrained generation is demonstrated using an extensive variety of cost functions from a simple Linear Programming (LP) to non-linear functions like absolute value. The significant feature of this approach is that it is flexible both in terms of constrained parameters and control variables.Along with observability, controllability is another crucial factor regarding capacity constrained state optimization. The controllability analysis would allow the decision to be made of whether or not available control variables allow changing the value of operational parameters to specific values without other influences. With this in mind, a technique to a...

Scenario-Based Design for Multiple Microgrids with High DG Penetration Considering Uncertainty on Demand and Generation Side

“…Currently, the DS is being impacted by an increasing installation of different types of distributed energy resources (DER), such as photovoltaic and wind turbines.as well as a wider range of emerging demandside management (DSM) resources, e.g. battery storage and electric vehicles (EVs) [1].…”

“…The main purpose of this paper is to cluster distribution networks into a set of microgrids with consideration of network reliability [9]. In [1], linearization system can be implemented mainly using three techniques, such as sampled sensitivity, Zbus matrix and analytically. The sampled based technique is easier to implement but processing time is slow, while the Zbus matrix method is the more precise linear model, but it is unable to capture non-linear behavior.…”