Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration
The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead
... ochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-ahead scheduling results are tested against different real-time dispatch regimes, with none or limited look-ahead capability, or with the use of the variability reserve, utilizing a modified version of the Greek power system. The results demonstrate the enhanced reliability achieved by applying the multi-timing scheduling concept and explicitly considering the variability reserve, and certain features regarding the allocation and deployment of reserves are discussed. 2 of 25 power balance violations, price spikes and high volatility of electricity prices, frequency deviations and Area Control Error (ACE) augmentation, extensive use of regulation services to resolve the issue in real time or undesirable out-of-market corrections (e.g., committing/keeping additional units online), and often eventually relying on unavoidable curtailments of renewable generation. To face these reliability challenges in an economically efficient manner with existing power system infrastructure, advanced short-term scheduling strategies have evolved during the past years, enriching the Unit Commitment (UC) paradigm  . Co-optimization of energy and ancillary services in a deterministic modeling framework has been widely utilized by researchers and system operators, to attain improved efficiency and derive price incentives for the actual provision of these services [17, 18] . In deterministic UC (DUC), the net load is modeled as a single forecast and the associated uncertainty is handled using ad-hoc reserve requirements, which can be fixed during the course of a day  , or vary on a multi-hourly  or hourly [21, 22] basis. In    , the load, conventional and wind generation are considered as uncorrelated variables and the method of convolution is used to determine the reserve amount for different LOLP values; however, the load and wind power variability is not taken into account. Reference  presents a dynamic reserve quantification method for rolling UC models with variable time resolution, like the one proposed in  ; the approach takes into account contingency events, as well as both load and wind power uncertainty and variability using the standard deviation measure. In many cases, the literature underlines the need for defining different reserves associated with normal operation of the power system (i.e., operating reserves), like the regulating, load-following and ramping reserves. References [26,28-31] provide a comprehensive review of probabilistic methodologies to quantify requirements for such operating reserves under increased penetration of renewable generation. Focusing on real-time operations, some United States system operators have lately introduced in their deterministic real-time UC (RTUC) and dispatch (RTD) processes a specific ramping reserve product (CAISO , MISO ). This product, commonly called "flexiramp", is intended to reduce the frequency of ramp shortages caused by renewable variability and uncertainty in the real-time market, while producing sufficient price incentives for the eligible resources to actually provide their ramping capacity. The reduction of ramp shortages and relevant price spikes in RTD upon the introduction of the ramp product in CAISO is demonstrated in  . Navid et al.  analyze the trade-offs between the additional costs of procuring ramp capability, and the respective cost savings achieved in the real-time market. The operational implications of the ramp product on costs and system reliability are further examined by Krad et al. in ; simulations corroborate the reduction in scarcity pricing events caused by insufficient ramping capacity. In , Ela and O'Malley assessed the efficiency and the incentive structure (for providing ramp) of time-coupled (look-ahead) real-time market clearing models, as compared to incorporating ramp products and respective constraints in economic dispatch; look-ahead optimization horizon in real-time markets can prove more efficient in terms of reliability, however it may lack incentives as compared to the utilization of the ramp product. An efficient design of the requirements for the ramp product, which utilizes Monte Carlo simulation, is proposed by Wang et al. in . References [32-38] focus on the incorporation of the ramp product in the real-time markets. A dedicated analysis for a respective provision at the day-ahead stage is proposed in , considering an efficient allocation of the real-time (intra-hourly) system ramping requirements within the coarser (hourly) DUC optimization intervals utilized in day-ahead. Stochastic programming  has gained significant attention as an alternative scheduling strategy for handling the uncertainty associated with renewable generation. Instead of arranging a single net load forecast and allocating pre-determined amounts of reserves, stochastic UC (SUC) considers a set of possible net load realizations (scenarios) along with their respective probabilities of occurrence, and minimizes the operating cost over all scenarios considered. The driving factor for SUC is that-by actually accounting for different scenario realizations-the expected operating cost over all scenarios can be reduced  , as compared to DUC policies which do not consider the potential operating Energies 2017, 10, 140 3 of 25 conditions explicitly. This omission in the DUC policies can be further deteriorating, if the actual conditions deviate substantially from the assumptions made when determining the respective reserves. Various researchers have proposed two-stage SUC formulations, where wind uncertainty is arranged by either finding the optimal commitment of slow-start units in the first stage so that the system can cope with all possible realizations of wind output at the second (real-time) stage     , or explicitly determining the required level of load-following reserves    . Bouffard et al.  were among the first to present a two stage stochastic programming formulation that utilizes explicit decision variables for reserves while considering external reserve bids by the various resources. A similar two-stage formulation is utilized in  by Morales et al. for the quantification and economic valuation of load-following reserves. Reserve procurement at the day-ahead (first) stage is treated as a here-and-now decision, while reserve deployment is a wait-and-see decision determined at the real-time (second) stage, after the scenarios are revealed. Sahin et al.  presented a stochastic model for quantifying the appropriate spinning reserves, which can be provided by both generating units and demand response providers. In  , Wang et al. formulated a two-stage SUC model also incorporating demand response, where demand response resources can be scheduled both in the day-ahead and intra-day stage, depending on their relative responsive costs in each stage and their particular operating constraints. A respective security-constrained stochastic approach by Wu et al.  incorporates ramping costs and demonstrates the benefits of applying hourly demand response in managing the increasing renewable variability. In    , rolling stochastic unit-commitment models are presented and tested against their deterministic counterparts, either utilizing coarse (hourly or larger) time intervals  and making aggregations per generating unit technology , or using higher and variable time resolution to achieve more robust results  . Focusing on the utilization of the "flexiramp" product in RTUC  and RTD , Wang and Hobbs compared costs between the deterministic model with ramping reserve requirements as in [32, 34] and an ideal stochastic model, which endogenously determines the optimal amount of ramp capability to be acquired. Finally, Lee and Baldick  formulated a two-stage stochastic programming economic dispatch model for the determination of the optimal energy and reserve schedules, while adding simplified frequency constraints. In this paper, we examine both deterministic and stochastic day-ahead UC policies with focus on the determination, allocation and deployment of reserves. A fundamental distinction is proposed between (a) the uncertainty reserve, the procurement of which is intended to arrange the net load forecast errors, and (b) the variability reserve, the explicit procurement of which is intended to reduce the probability for ramp shortages and relevant price spikes in RTD. The mathematical problem formulations proposed for DUC and SUC policies minimize the costs of meeting the hourly net load forecasted in day-ahead, including the costs of uncertainty and variability reserve procurement, as well as the commitment costs. To enhance reliability, the concept of multi-timing scheduling is proposed and applied appropriately in all UC policies, which allows for the determination and optimal allocation of the uncertainty and variability reserves based on an intra-hourly ("real-time") resolution, when concurrently optimizing energy and the commitment status of the resources over longer scheduling intervals (hours). A modified version of the Greek power system is utilized in the case studies and detailed discussion is provided on the optimal allocation of the day-ahead uncertainty and variability reserves to the available resources, based on the resources' specific capacity and ramp rate capabilities and their respective economic offers. Finally, in order to better simulate the real operation of the power system and evaluate the attained margins of safety, the UC results (both DUC and SUC) are tested against different RTD regimes, with none or limited look-ahead capability, or with the use of the variability reserve determined by UC, to reveal several implications relative to the nature of RTD.