Day-ahead Unit Commitment Problem Research Articles

Distributionally Robust Unit Commitment Considering Unimodality-Skewness Information of Wind Power Uncertainty

The deepening penetration of renewable energy sources (RESs) into power grids imposes remarkable challenges that merit special attention. In this paper, we work out a distributionally robust chance-constrained (DRCC) day-ahead unit commitment problem while the stochasticity and variability arising from RESs are taken into consideration. The developed framework optimizes the expected objective function and stipulates that the DRCCs will hold within a moment-based ambiguity set, which additionally incorporates the unimodality information and mode skewness. The employed ambiguity set can not only account for the wind power uncertainty but also expressly alleviate the conservatism compared with the single moment metric. By deriving efficient mathematical reformulations to tackle the expected objective function and DRCCs, the proposed model is reduced to an exact mixed-integer second-order cone programming problem that can readily be implemented. Numerical experiments are carried out on two representative test systems to demonstrate the effectiveness and efficiency of the suggested methodology.

Elastic flexible unit commitment: A scenario-based power-electric fleet analysis

This paper presents an elastic flexible unit commitment (EFUC) in a coupled power and electric fleet (P-EF) network. In this framework, a master independent system operator (MISO) coordinates the day-ahead unit commitment problem (UCP) while various sources of both networks are integrated. On the power side, battery energy storage (BES) is allocated, and on the electric fleet side, private electric vehicles (PEVs), electric buses (EBs), and electric taxis (ETs) are integrated. The temporalities of PEVs and EBs are managed by PEV to grid (P2G) and bus to grid (B2G) technologies, respectively. Traveling patterns present the spatial impact of PEVs and ETs on day-ahead commitment. In this P-EF network, an incentive-based demand response (IBDR) program is implemented by MISO. A scenario-based technique is applied to generate operation scenarios for coupled uncertainties of load demand and the number of PEVs. The overall mixed integer linear programming (MILP) model is tested on IEEE 6-bus and 24-bus systems which are integrated with traveling patterns of PEVs and ETs. Results confirm that classifying the behavior of various electric vehicles leads to an explicit understanding of each source’s impact on UCP cost and required energy. The results indicate that MISO prioritizes the utilization of spatio-temporal sources (e.g., PEVs) over only stationary temporal ones (e.g., EBs or BES) since both time and location are under control. Also, MISO damps the spike prices by the IBDR program while offering incentives to responsive loads under uncertainty.

Proactive Resilient Day-Ahead Unit Commitment With Cloud Computing Data Centers

To enhance the resilience of power systems toward the temporal and spatial impacts caused by extreme weather events, e.g., hurricanes, the flexibility of cloud data centers (CDCs) is treated as a kind of efficient demand response. Since the workloads of CDCs have the shifting capacity between different locations and time slots, a day-ahead unit commitment problem including data centers is proposed to explore the integrated spatial and temporal flexibility of the workloads to its full extent through task migration. Considering the uncertainty of probability density functions, the line failure rates, workload arrival rates, power loads are integrated into an ambiguity set. The scheduling process of generators and CDCs is modeled as a two-stage distributionally robust optimization problem, which is reformulated as a large-scale deterministic mixed-integer linear programming problem and solved by the multicuts Benders decomposition method. The performance of the proposed scheduling strategy is tested in both the IEEE 24-bus RTS system and the three-area RTS-96 system. The results reveal that the method could mitigate the adverse impacts of hurricanes by enhancing the resilience of power systems and decreasing the dropping workloads of CDCs.

Addressing the Conditional and Correlated Wind Power Forecast Errors in Unit Commitment by Distributionally Robust Optimization

In this paper, a study of the day-ahead unit commitment problem with stochastic wind power generation is presented, which considers conditional, and correlated wind power forecast errors through a distributionally robust optimization approach. Firstly, to capture the characteristics of random wind power forecast errors, the least absolute shrinkage, and selection operator (Lasso) is utilized to develop a robust conditional error estimator, while an unbiased estimator is used to obtain the covariance matrix. The conditional error, and the covariance matrix are then used to construct an enhanced ambiguity set. Secondly, we develop an equivalent mixed integer semidefinite programming (MISDP) formulation of the two-stage distributionally robust unit commitment model with a polyhedral support of random variables. Further, to efficiently solve this problem, a novel cutting plane algorithm that makes use of the extremal distributions identified from the second-stage semidefinite programming (SDP) problems is introduced. Finally, numerical case studies show the advantage of the proposed model in capturing the spatiotemporal correlation in wind power generation, as well as the economic efficiency, and robustness of dispatch decisions.

Time-Adaptive Unit Commitment

The short-term operation of a power system is usually planned by solving a day-ahead unit commitment problem. Because of historical reasons, the commitment of power generating units is decided over a time horizon typically consisting of the 24 hourly periods of a day. In this paper, we show that, as a result of the increasing penetration of intermittent renewable generation, this somewhat arbitrary and artificial division of time may prove to be significantly suboptimal and counterproductive. Instead, we propose a time-adaptive day-ahead unit commitment formulation that better captures the net-demand variability throughout the day. The proposed formulation provides the commitment and dispatch of thermal generating units over a set of 24 time periods too, but with different duration. To do that, we use a clustering procedure to select the duration of those adaptive time periods taking into account the renewable generation and demand forecasts. Numerical results show that, without increasing the computational burden, the proposed time-adaptive unit commitment allows us for a more efficient use of the system flexibility, which translates into a lower operating cost and a higher penetration of renewable production than those achieved by a conventional hourly unit commitment problem.

Economic Valuation of Low-Load Operation with Auxiliary Firing of Coal-Fired Units

It is often claimed that coal-fired units are highly inflexible to accommodate variable renewable energy. However, a recently published report illustrates that making existing coal-fired units more flexible is both technically and economically feasible. Auxiliary firing is an effective and promising measure for coal-fired units to reduce their minimum loads and thus augment their flexibility. To implement the economic valuation of low-load operation with auxiliary firing (LLOAF) of coal-fired units, we improve the traditional fuel cost model to express the operating costs of LLOAF and present the economic criterion and economic index to assess the economics of LLOAF for a single coal-fired unit. Moreover, we investigate the economic value of LLOAF in the power system operation via day-ahead unit commitment problem and analyze the impacts on the scheduling results from unit commitment policies and from extra auxiliary fuel costs. Numerical simulations show that with the reduction of the extra auxiliary fuel costs LLOAF of coal-fired units can remarkably decrease the total operating costs of the power system. Some further conclusions are finally drawn.

System-Level Value of a Gas Engine Power Plant in Electricity and Reserve Production

Power systems require a certain amount of flexibility to meet varying demand and to be able to cope with unexpected events, and this requirement is expected to increase with the emergence of variable power generation. In this paper, we focus on gas engine power plant technology and the beneficial influence its flexible operation can have on a power system. The study introduces the concept of a combined-cycle gas engine power plant (CCGE), which comprises a combination of several gas-fired combustion engines and a steam turbine. The operation of CCGE is then comprehensively analyzed in electricity and reserve production in the South African power system and compared with combined-cycle gas turbine (CCGT) technology. Even though CCGE is a form of technology that has already been commercialized, it is rarely considered as a source of flexibility in the academic research. That is the notion providing the motivation for this study. Our core contribution is to show that the flexibility of CCGE can be valuable in power systems. The methodology is based on the unit-level model of the studied system and the solving of a day-ahead unit commitment problem for each day of the simulated 11-year period. The simulation studies reveal how a CCGE is able to offer system flexibility to follow hourly load variations and capacity to provide reserve power effectively.