Another plausible issue that can occur in the distribution grid is potential congestion within the grid. When such an unexpected event occurs, the use of flexibility to alleviate this issue can be highly beneficial. For this sub-chapter, a hypothetical lost grid capacity occurs due to the appearance of an unforeseen event after the day ahead production has been scheduled. The reduced capacity results in the line between nodes 26 and 27 having a new transfer limit of 0,0173 pu. Implementing this congestion has caused some challenges due to the introduction of square root for calculation of the apparent power, as shown in equation(32).
Sij =q
Pij2 +Q2ij (32)
The problem presented by this equation has been avoided by not including the square root but rather calcu-lating the squared apparent power flow instead. The resulting inequality constraint is shown with equation (33). This equation takes into account the power flowing from node 26 to 25 shown with variable (PF L) and (QF L), load (PL,AC) and (QL,AC) and power provided from flexible load (PL,F lex) and generation (PG,F lex) within node 26.
(−P26−25,t,uF L −P26,t,uL,AC−P26,t,uL,F lex+P26,t,uG,F lex)2+ (−QF L26−25,t,u−QL,ACm,t,u)2≤(P26−27,t,uF L,max )2 (33) Three cases have been simulated in this chapter to answer how the model would respond to such congestion.
The first case is modeled without any flexibility present in the system, manifesting what actions are necessary to acquire feasibility. In the second case, flexibility is included in the grid to alter the power flows and satisfy the new line constraint. For the third case, in addition to the flexibility, voltage regulation is also introduced.
All data used for these simulations are given in Appendix C and F. The case starts by analyzing the voltage to determine its magnitudes and the effect flexibility has on it, as shown by figure 29.
Figure 29: Voltage magnitudes for all nodes for all three cases during 09:00.
With no flexibility or voltage regulation presented in the base case in figure 29, low voltage magnitudes can be seen close to the lower bounds for nodes 31, 32, and 33. The second case improves these results with the inclusion of flexibility provided by DFRs. These improved voltage results are due to the lower voltage drops caused by a more localized production from DFRs. A similar result could be seen in 5.2, where the DFRs have been used to optimize the operation of the grid. Although the new power flow constraint has been satisfied, flexibility has introduced a new issue. During 09:00, nodes 13 to 18 experience overvoltages due to the high production from DFRs, leading to voltage violation. In order to counter this issue, case 3 presents a solution where flexibility is also supplemented by voltage regulation. The resulting voltage has a similar profile across the grid compared to case 2, with voltage magnitude on nodes 13 to 18 now below the upper voltage bound. The previous issue with low voltages for nodes 31, 32, and 33 is also alleviated, resulting in a far better voltage profile. Plots showcasing the combined used volume of each particular flexible asset are shown in figures 30 to 33. The plots also present the total load profile in the distribution grid, which is used to reference the acquired results.
Figure 30: Load shedding used to handle the congestion for case 1.
For the first case, where no flexibility is present, the only available option to acquire feasibility in this grid situation is load shedding. Due to its high costs, this is a highly inefficient solution and DSO’s last resort to handle such an issue. Flexibility is a much more attractive and cost-efficient solution, which is showcased in figure 31.
Figure 31: Flexibility and load shedding used to handle the congestion for case 2.
Figure 31 presents the required flexibility dispatch to handle the occurring congestion in the grid. The flexible load has been increased at the simulation start and decreased accordingly during peak load hours when the congestion occurs. The battery has a similar response, where it is being charged at the beginning of the simulation and discharged during peak load hours. A short charging period also takes place during the last hour of the simulation. This interval results from the battery’s need to end the simulation with the same charge as the initial one. From the model, it is also apparent that no load shedding is required to acquire feasibility, which is a highly desirable outcome.
Figure 32: Flexibility and load shedding used to handle the congestion for case 3.
For case 3, voltage regulation has been applied to constraint the voltage magnitude during peak load hours, resulting in a flexibility dispatch as shown in figure 32. When comparing the flexibility dispatch between cases 2 and 3, one can see a similar response. A noticeable difference is the use of load shedding. Due to the voltage issues in case 2, re-dispatching flexibility alone was insufficient to acquire a feasible solution for case 3. Therefore, load shedding had to be used to satisfy the voltage requirements. Lastly, figure 33 present flexibility generation. Due to the higher production capacity than the other DFRs, the flexible generation is shown separately from the other results to ensure data visibility.
Figure 33: Flexible generation used to handle the congestion for case 2 and 3.
From the figure 33, one can see a constant provision of power from the flexible generation, with a sudden increase between 08:00 and 09:00. This increase is due to the occurring line congestion between nodes 26 and 27. This sudden increase is lower for case 3 due to the implementation of a voltage constraint.