Italy is one such country. Data on day-ahead market prices show a decrease in the last few years which has been principally driven by technological variables. Just think about the fact that Italy covers 38.6% of its power production with renewable energy sources (wind and solar). In Figure 1 the unique price is compared between 2013 and 2014 and a significant decrease is evident. Otherwise, in this article we will focus also on zonal price interaction.
Figure 1: Annual average Unique Price on Italian Power EXchange (IPEX)
So firstly, what is the mechanism to determine electricity prices on a power exchange? And further, what is the weight of congestion on the network? Italy adopted a Zonal pricing model, which differs from US power exchanges – PJM as example – which instead adopted Nodal pricing. The network is divided in market zones and electricity flows between them. In power transmission the rule of “communicating vessels” is literally applied. The capacity of connection between two zones is fundamental information to the day-ahead market.
If the transit limits are observed then the market is unique and prices between two zones perfectly match. Where the flows overcharge the connections, the market is then split in two different zonal markets and zonal prices diverge. In particular, the importing zone will get a higher price than the exporting zone. This problem is exacerbated when the grid is poorly cross-linked. An example of this network setting is represented by the following Figure 2 which consists of a “tree connection” where electricity must necessarily flow through zone 2 to get from zone 1 to zone 3. Figure 2 is aimed only to represent a simulation of the tree-like connection between three zones, since Italy has at least six geographical zones and several virtual trading zones.
This example illustrates the importance and usefulness of a mashed grid when current flows could congest the connection between zones 1 and 2.
Further, finding the combination of zonal prices and quantities, means resolving an optimization problem, where the surplus of consumers and producers (buyers and sellers) is maximized under several constraints:
Lagrangian can be used freely but, you cannot escape the lack of significant storage and poor connection between zones. One could think that only in the ancillary services market such problems could intervene, and instead these technical constraints are strong enough to manifest themselves in the resolution of the price in a typically economical market, such as the day-ahead. Since it is supposed that shadow prices do their work, in the case of strong use of the network (as to cause congestions), the divergence between prices causes in its tu a separation of market areas as presented in Figure 3.
Figure 3: Market Splitting
The two economic assessments are quite different and the equilibrium prices are found to be different as well. Since the zones are, of course, in the same country, then the mechanism of the unique price is applied. As a result of this exercise, the different prices are represented in a graph (Figure 4) which shows these variations.
Figure 4: Zonal Prices
Is this able to match Italy’s great need to easily dispatch the energy produced from renewable sources, such as wind in the south or solar in the central region? And further, is it in line with the necessity of an electrical system dedicated to the transmission of electricity, not only at the national level but also European-wide? This analysis highlights the difficulty in integrating renewables into such a system, as long as paradoxically, the case of an only renewable production at zero marginal price becomes a problem at the level of network management.
For more on this issue, see: “The More Renewables You Have The More Transmission You’ll Need”.
Alberto D’Antoni holds a M.S. in Economics and a Postgraduated Degree in Energy Management. He is a member Italian Association of Energy Economists the local affiliate of IAEE.