2.7 Storage Incentivisation
2.7.1 Proposed Method for Storage Incentivisation
As presented in Section2.5.5, storage has not proven to be attractive for private investors. As pointed out in Section 2.1, due to the high capital cost, relatively low round-trip effi- ciency, and smaller electricity price arbitrage, large-scale storage may not be economical in current electricity markets; however, storage deployment will be becoming more eco- nomical in the near future due to the growing storage technologies and higher arbitrage benefits in future electricity markets.
Large-scale storage diffusion for energy shifting can also result in peak shaving. In this way, peak-shaving generators, which usually cause air pollution, can be shut/turned down, thereby generating less CO2emission. Moreover, large-scale energy-shifting storage
can allow a higher penetration of wind and solar energy into electric grids since sporadic availability of renewable sources can be addressed by introducing storage to (partially) decouple energy generation from demand, thereby increasing system security [61]. Due to their considerable environmental and technical benefits, privately owned storage could be financially supported by utility regulators [60].
One approach to encourage potential investors to invest in storage is that utility regulators incentivize storage owners in contract setting for storage capital cost. This could be realized through constant monthly/annual payments to storage owners. In this approach, however, storage owners are not directly encouraged to operate effectively in the market to obtain their incentives; therefore, this approach is not appropriate in competitive electricity markets.
In this chapter of the thesis, price modulation is proposed as part of the RTOS algorithm to virtually increase energy price arbitrage to competitively offer incentive to storage owners to fill the gap between current and a stable expected ROR. The use of modulation factor also demonstrates how much the energy price arbitrage shall increase until the storage plant becomes economical. By implementing the proposed approach, the more the storage is operated to support the grid by energy shifting/peak shaving, the more incentives it can receive from the utility regulator since the amount of incentive is dependent on charging in off-peak periods and discharging in peak periods which are appropriate for both the utility regulator/system operator and storage investor.
One of the advantages of this method is that the level of the price modulation can be adjusted by utility regulators to incentivize all eligible market players, including stor- age, according to their technical and environmental benefits. By including the proposed approach to incentivize the storage as part of the optimization problem, the objective function would be expressed as follows:
Maximize PtS,Chg, PtS,Dhg X t∈Ni (PtS,Dhg−PtS,Chg).(I)EtF M rk−CS,DhgO. PtS,Dhg−CS,ChgO. PtS,Chg ! .∆T.(2.9)
As expressed in (2.9), the electricity price (i.e., EF M rk
t ) is multiplied by a constant
“I”, called modulation factor where I >1. Since I >1, the price arbitrage, the difference
between high and low levels of the price, increases. This causes to increase revenue for storage owners by purchasing and selling electricity. The extra profit is provided for storage owners indirectly by the utility regulator. The value of modulation factor “I”
included in (2.9) should be so that the total revenue at least covers the expected revenue due to investment. In such a case, the extra revenue at least reaches to zero; the zero extra revenue is the border between economic and uneconomic operations of the storage. The storage extra revenue is defined in (2.10), as follows:
Extra Revenue: X t∈Ni (PtS,Dhg −PtS,Chg).(I)EtF M rk−CS,DhgO. PtS,Dhg −CS,ChgO. PtS,Chg ! .∆T −N ×(CS,EInc+CS,Cap), (2.10)
where the total revenue and expected revenue over the optimization horizon are expressed by (2.11) and (2.12), respectively, as follows:
Total Revenue: X t∈Ni (PtS,Dhg −PtS,Chg).(I)EtF M rk −CS,DhgO. PtS,Dhg −CS,ChgO. PtS,Chg ! .∆T (2.11)
Expected Revenue: N ×(CS,EInc+CS,Cap), (2.12)
whereCS,Capis the hourly capital cost, defined by wasting the capital cost over the life of
the plant, and CS,EInc is the hourly expected income due to investment As expressed in
(2.10), the summation of these constant parameters has been subtracted from the total revenue to express the storage revenue excluding the capital cost and expected income over the optimization horizon, named extra revenue in this study.
2.7. Storage Incentivisation 35 Time (Hour) P ri c e ( $ /M W h ) A B C 5 h 8 h 3 h 8 h
Figure 2.6: Generic electricity price profile.
Table 2.13: Different Levels of the Generic Price Profile Shown in Fig. 2.6
Price Levels ($/MWh) Price Profiles For Weekdays For Weekends
A B C A B C
Profile 1 60 90 120
Profile 2 60 120 180 50 60 70
Profile 3 60 150 240
Theoretically,Imincan be calculated as stated in the following where the extra revenue
(see (2.10)) equals zero:
Imin = P t∈Ni(C S,DhgO. PS,Dhg t +CS,ChgO. P S,Chg t ) P t∈Ni(P S,Dhg t −P S,Chg t ). EtF M rk +P N ×(CS,EInc+CS,Cap) t∈Ni(P S,Dhg t −P S,Chg t ). EtF M rk . (2.13) Imin in (2.13) is the minimum required modulation factor to meet the expected revenue
in each optimization horizon. However, in practice, Imin cannot be calculated simply by
using (2.13) since electricity prices do not follow a constant pattern in each optimization horizon. In this case, the objective is not to make the storage work economically in every single optimization horizon; instead, it is expected that the monthly or annual extra revenue of storage at least reaches to zero. This is investigated in Section 2.7.2.1.