Ayman Bahgat Abdelazim Ibrahim Eltantawy
Online Assessment of Distributed Generation Connection for Smart Grid
Increasing renewable energy generation is among the most important objectives of smart grid, especially due to the increased environmental concerns, energy demand, and depletion of fossil energy resources. Introducing incentive feed-in tariff (FIT) programs to promote renewable distributed generation (DG) in distribution systems is an essential step towards smart grid implementation. However, current regulations of FIT programs for small-scale DG sources strictly limit the aggregated installed DG capacity to a small fraction of the system peak load. Limiting the DG capacity avoids the need for detailed connection impact assessment studies for the DG connection. Conducting detailed CIA studies for each small-scale DG project application is impractical due to the large number of applications, which can lead to delaying the DG connection process. However, avoiding assessment studies and imposing such strict limits result in rejecting numerous applications for renewable DG projects, and therefore losing a significant amount of renewable DG capacity.
Such situations underscore the need for research that suggests new directions for increasing small-scale renewable DG projects under FIT programs. In order to accomplish this target, this thesis presents a planning model and a management scheme for DG connection online assessment in smart grids. The planning model achieves two objectives: insuring an adequate profit for DG owners and maximizing the number of installed DG sources in the systems. The management scheme controls the curtailment of the connected DG units to satisfy the system operational constrains. Implementing the proposed work evades the need for detailed connection impact assessment studies prior to installing small-scale DG units since the assessment is performed on an online basis. This feature can therefore reduce the number of rejected applications for renewable DG projects under FIT programs while accelerating the DG connection process.
The proposed planning model and management scheme for DG connection online assessment are based on dividing the capacity of each DG unit into two components: unconditional and conditional. Unconditional DG capacity refers to the minimum DG capacity that guarantees adequate profit for the DG investors. A DG unit with a capacity less than or equal to the unconditional DG capacity is granted permission to inject power into the system without curtailment for all online conditions of the system. Conditional DG capacity denotes the DG capacity that exceeds the value of the unconditional DG capacity. Conditional DG capacity is subject to curtailment based on the online condition of the system. The curtailment of the conditional DG capacities is controlled using the proposed management scheme for DG connection online assessment.
The first phase of this work introduces an economic model for calculating the unconditional DG capacity. This model ensures that the unconditional DG capacity, which is not susceptible to curtailment, yields adequate profit for DG investors. The first part also presents a techno-economic planning model that maximizes the number of DG units installed based on the technical and economic constraints.
The second phase of this work presents a novel algorithm for DLF analysis that can interact with the continual changes of load and network topology in smart grids. This algorithm can solve the DLF problem in a specific area of interest in a distribution system without necessitating the inclusion of all of the system buses. This "zooming" feature leads to a significant reduction in the required DLF solution time, especially for large distribution systems. This DLF algorithm is utilized in obtaining load ?ow results in the proposed management scheme for DG connection online assessment, presented in the third phase of this work.
The third phase of this work introduces a management scheme for DG connection online assessment in smart grids. The assessment is performed using a novel scalable optimization model that utilizes the "zooming" feature of the proposed DLF algorithm, presented in the second phase of this work. The scalable optimization model can therefore minimize the curtailment of the conditional DG capacities in a specific area of interest in the system without including all the system buses in the optimization problem. This feature ensures fast calculation of the minimum DG power to be curtailed based on the online condition of the system.
The simulation results include a comparison between two maximum renewable DG capacities -that which can be installed according to the current FIT rules in Ontario and that which can be installed by implementing the proposed planning model with the management scheme for DG connection online assessment. The comparison indicates that implementing the proposed work would significantly increase the number of small-scale renewable DG projects that can be installed.