9 Electrical grid constraints

In this chapter, we investigate the influence of constraints applied to the transformer. Specifically, we consider two constraints: 2 and 3 times the maximum power of domestic electricity. For each constraint, we conduct two separate optimisations. The first optimisation is the baseline approach that does not consider the orientation of the PVs. The second optimisation employs a method that incorporates this orientation. The results presented are based on the second optimisation, as it yields superior TOTEX optimisation outcomes, resulting in lower overall costs. Moreover, this approach seems more realistic compared to the alternative assumption of all roofs being flat.

The initial result reveals that the trends are the same across the three districts regardless of whether the constraint is set at 2 or 3. However, it is important to note that each district do not react in the same way to the implementation of the constraint. Consequently, we will conduct separate analyses for each of them before providing a brief comparison.

Table 9.1: Value of the transformer constraints applied in REHO corresponding to 2 and 3 times the maximal domestic electricity power
District	2x the maximal power [kW]	3x the maximal power [kW]
Vessy	92.756	139.134
Jonction	1706.988	2560.482
Florissant	1945	2917.5

As Vessy is a residential area, the solar surface area of the roofs is nearly equal to the living area, resulting in a high GUd. This indicates that a significant amount of energy is being sold, as observed in Figures 9.1 and 9.2, where the OPEX and GWP from operations show negative values. However, the introduction of constraints leads to a substantial decrease in GUd. This reduction implies an increase in CAPEX, transitioning from negative to positive values. The implementation of constraints reduces the export of electricity, causing the negative values of OPEX and GWP to approach zero. When the constraints are taken into account, TOTEX becomes positive, indicating that the prosumer spends money as the income from selling electricity no longer offsets investments and consumption. Analysis of the KPIs reveals an increase in the PVC value, resulting in curtailment due to excessive production compared to consumption and exportable energy quantity.

Table 9.2: KPI for different transformer capacity of Vessy
Scenario	SC	SS	PVC	GUs	GUd	PVP	LCoE1
Without	0.135	0.580	0	2.03	15.4	4.281	-0.088
With 3x	0.404	0.574	0.079	2.19	3	1.422	-0.111
With 2x	0.492	0.559	0.100	2	2	1.128	-0.116

Figure 9.1: TOTEX comparison between the different transformer capacity of Vessy

Figure 9.2: GWP comparison between the different transformer capacity of Vessy

As Jonction and Florissant are districts with buildings, the solar surface area of the roofs divided by the ERA is much smaller than the residential area in Vessy. This explains the lower GUd observed in these districts. As a result, sales are lower in proportion to consumption. Figures 9.3 and 9.5 illustrate that the revenue from selling electricity does not offset the expenses associated with investments and electricity purchases for the Jonction and Florissant districts respectively. In other words, even without constraints, the costs outweigh the sales, resulting in positive TOTEX values. Similar to Vessy, the GUd decreases when constraints are considered. An special case arises in the district of Florissant when the constraint is set to 3. Indeed, the system does not recognize the constraints as the GUd and GUs values are already lower than 3 without the constraints.

Table 9.3: KPI for different transformer capacity of Jonction
Scenario	SC	SS	PVC	GUs	GUd	PVP	LCoE1
Without	0.385	0.546	0	2.06	4.82	1.417	-0.123
With 3x	0.448	0.547	0.020	2.06	3	1.219	-0.130
With 2x	0.534	0.561	0.088	2	2	1.051	-0.134

Figure 9.3: TOTEX comparison between the different transformer capacity of Jonction

Figure 9.4: GWP comparison between the different transformer capacity of Jonction

When comparing the GUd values of Jonction and Florissant, we observe that Jonction has a higher GUd than Florissant. This difference can be attributed to the variation in solar roof area, despite having a similar maximum domestic electricity output, as presented in Table 7.1. The discrepancy in solar roof area directly influences the GWP as shown on Figures 9.4 and 9.6. In the case of Florissant, the GWP remains consistently positive, while for Jonction, it only becomes positive when constraints are taken into account.

Table 9.4: KPI for different transformer capacity of Florissant
Scenario	SC	SS	PVC	GUs	GUd	PVP	LCoE1
Without	0.527	0.437	0	2.34	2.93	0.828	-0.141
With 3x	0.527	0.437	0	2.34	2.93	0.828	-0.141
With 2x	0.545	0.431	0.010	2	2	0.790	-0.143

Figure 9.5: TOTEX comparison between the different transformer capacity of Florissant

Figure 9.6: GWP comparison between the different transformer capacity of Florissant

In the following analyses, we focus on a specific scenario characterized by a constraint of 2 times the maximum power of domestic electricity, optimised by considering the orientation of the PVs. This particular value was selected as it corresponds to a more important constraint on the grid. Although the run time is longer, we have chosen to favour this optimisation as it aligns with the aim of minimizing TOTEX and maintaining realism in the analysis. In addition, some of the plots generated that are not included in this section of the results are included in the appendix.