Cost allocation of spinning reserve based on risk contribution

Abstract

Current cost allocation methods require generating companies (GENCOs) to afford spinning reserve (SR) costs according to their energy production rather than the impact on grid stabilization. The differences in generator reliability and forecast accuracy of renewables cause difficulty in quantifying the contribution of individual factors on the SR requirements (SRRs). First, this paper employs a reliability-constrained unit commitment (RCUC) model to determine the SRR and SR costs according to the grid reliability. Then, a cost allocation method is proposed to allocate these SR costs based on risk contribution theories. The risk contribution theories, marginal contribution, and stand-alone contribution are employed to measure the effect of individual risk factors on grid safety. The cost allocation method is demonstrated and discussed in the IEEE-RTS. The proposed risk contribution method can quantify the impacts of risk factors on grid safety and allocate SR costs into them according to their contributions. Additionally, this risk-based cost allocation method can encourage GENCOs to enhance the reliability level of generators and continuously improve the forecast accuracy of renewables to lower SR costs.

An optimisation model to determine the capacity of a distributed energy resource to contract with a balancing services aggregator

“…Several mathematical models exist to determine the optimal operational schedule of a generation unit: Chang, Tsai, Lai & Chung [52] formulated a mixed-integer linear programming (MILP) unit commitment (UC) model to obtain power schedule and marginal price information associated with system constraints such as load demand requirements, spinning reserve (SR) [53], generation and reserve capacity to assist strategic bidding in a flexibility market. The demand data was represented using 24 time periods from a single data, and results could improve by selecting a whole year of plant generation and dual price data.…”

Spinning reserve quantification considering confidence levels of forecast in systems with high wind and solar power penetration

“…Compared to the contingency concern, the inclusion of a forecast error in the spinning reserve for normal operation is more frequently mentioned [10][11][12][13][14][15][16][17], but the approach in the literature was simple. For instance, the intrahour variation forecast error was simplified in [11,12] as a fixed percentage of the net load for solving the problem of unit commitment.…”

“…The number can be a formula of 2% of demand, 15% of wind power (WP), and 15% of solar power (PV) or, as given in [13], with a square root of a sum of squares of 3% of demand, ±95 percentile of the actual wind fluctuation, and ± 95 percentile of the actual PV fluctuation. Similarly, the unit commitment problem in [14] fixed the standard deviations of forecast errors of loads, wind power, and solar power; then, the standard deviation of the net load was estimated by assessing the root mean square of the sum of squares of these three standard deviations. A more complicated approach can be found in [15], where authors tried to predict the forecast error using artificial neural networks to calculate the required reserve for each hour in an urban microgrid.…”

“…commonly considers other basic constraints such as loadgeneration balance (9), generation limits (10a), (10b)- (11), emergency spinning reserve (12), generator states (13), ramp rate (14), one operating mode (generating/pumping/startingup/shutting-down) for a hydro pump at a time (15a)-(15b):…”

Fuzzy random cost–profit tradeoff location model for a vehicle inspection station with regional constraints

“…The risk loss of inspection customers is defined as a probability that is greater than or equal to a specified level . The specified level is the given/expected transportation cost in this work.…”