Day-Ahead Reserve Capacity Procurement based on Mixed-Integer Bilevel Linear Programming
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Paper: Day-Ahead Reserve Capacity Procurement based on Mixed-Integer Bilevel Linear Programming
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Introduction
A. Introduction to the electricaty market in Nordic countries
1. Nord Pool
This market is shared by the four Nordic countries
This market closed at 12:00 before the
day of operation
day of operation
2. the Regulation Market
This market is the reserve market for trading FRR-M
This market at 45min before operation
The providers of the procured reserve capacity have the mandatory obligation to
provide regulating power bids in the regulation market
provide regulating power bids in the regulation market
3. Availabiltity Market
This market closes at 09:30 before the day of operation
TSO is the only buyer of this market
Note: The total regulating power bids submitted from Denmark (Availability Market) to the common regulation market consist of 2 aspects
Mandatory regulating power bids
Voluntary regulating power bids
B. Problems need to be considered in the dispatch of power
Problem 1: Location of the reserve
In case that the capacoty of the interconnection is fully utilizered or unabailable
Problem 2: The day-ahead forecast error of the system wind power production
C. Contribution of this paper
Approach using the market model based on unit commitment (UC) to determine the volume of reserve capacity to be procured in the day-ahead availability market in Denmark
Model the sequential clearing of the reserve availability market and energy market to account for the forecast error of system wind power and demand
Day-ahead Reserve Capacity Procurement Using Mixed-integer Bilevel Linear Programming
A. Diagram of bilevel formulation
Upper level problem
TSO determines the amount of reserve capacity to procure and the corresponding EENS
Lower level problem
The clearing of energy market is simulated by a UC formulation
Why choose the UC formulation ?
1. UC can provide the information of spare capacity of generators
Note: This spare capacity can be treated as the voluntary regulating power bids
2. The voluntary bids may also include generators that are not selected in the energy market (not considered in this paper)
3. Bidding curves are not available
Possible solution to this problem: forecast the bidding curves
The soultion may introduce another unceratinty
B. Mathematical formulation
Upper level problem of BLPP
minimize the operating cost
Cost of pruchase reserve capacity
Cost of loss of load
The load loss due to generators outage (Not considered in this paper)
System wind forecast error
Demand forcaset error
Objective function of the upper level problem
The cost of reserve capacity for up and down regulation
The cost of EENS due to forecast errors
Lower level problem to model the energy market clearing process
Objective function of the lower level problem
Constraints
Power balance in spot market
Energy sale limits in spot market
Start up cost
Other standard constraints for generation units
C. Calcluation of the Energy not Served
Formulation of the energy not served (ENS) due to the forecasting error
of the net demand
of the net demand
Formulation of the available up-regulating reserve capacity in the
system
system
D. Solution algorithm
Case Study and Result
A. Case and data description
B. No reserve capacity procurement
This could be the case that if the reserve capacity price is much higher than the VOLL
In this case, only Unit 3 & Unit 4 are engaged, because they have the lowest marginal cost
With reserve capacity procurement
Case 1: with no procurement of reserve capacity
Case 2: 10 MW reserve capacity is procured from Unit 5
Case 2 instead increases the EENS than that of Case 1
Reason: When Unit 5 is brought online, Unit 4 with a higher capacity will be offline because of minimizing the generators operating cost. Consequently, the total available up-regulating reserve capacity is reduced
This implies that the total available reserve capacity does not necessarily increase when procuring more
reserve capacity.
reserve capacity.
Case 3 & Case 4: 30 MW of reserve capacity is procured, but from different
units
units
Case 4 has a lower EENS than Case 3, but has a higher
operating cost.
operating cost.
Reason: the three units in Case 4 have the highest capacity, but the ones in Case 3 have the lowest
marginal cost.
marginal cost.
子主题
Case 5: an additional 10 MW reserve capacity procured from Unit 6 compared with Case 4
The EENS remains the same and does not decrease compared with VCae 4
Case 6: the additional 10 MW reserve capacity is produced
from Unit 2 instead
from Unit 2 instead
The EENS become zero
Conclusions
Modelled the sequential clearing of the reserve availability market and energy market as a stochastic MILP-BLPP problem accounting for the forecast error of system wind power and demand.
Procuring more reserve capacity does not necessarily increase the total available reserve capacity or decrease the EENS if it reduces the total online generation capacity by pushing a unit with a large capacity offline.
In order to fine tune the final optimal solution of the proposed stochastic MILP-BLPP model, a tailored branch and bound algorithm needs to be further investigated.
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