The AGC participation factors’ optimization, coupled with the renewable energy uncertainties, changes the distribution of status variables. Consequently, it poses both modeling and computational challenges for existing chance-constrained alternating current optimal power flow (CC-ACOPF) methods. This paper proposes a data-driven method to reformulate and solve the CC-ACOPF, optimizing participation factors for automatic generation control (AGC) without prior assumptions about uncertainties. Based on historical data, a surrogate model is constructed to describe the relationship between uncertainties and chance constraints. A linearized power flow model, including reactive power and voltage magnitude, is introduced to reduce the dimensionality of the surrogate model. By embedding the surrogate model into the chance-constrained optimization, a tractable data-driven CC-ACOPF model is developed. A two-stage, data-driven Monte Carlo-based method is established to correct errors in power flow linearization and surrogate learning. Numerical results are presented for the PJM 5-bus and IEEE 118-bus systems to validate the effectiveness and robustness of the proposed method.

With the further advancement of electricity market reform, the introduction of time-of-use price and other policies has provided a favorable market environment for the application and development of demand response technology. The cooling electric load demand response technology represented by the cold storage air conditioning system can effectively alleviate the problem of power supply and demand balance. The demand response capability and potential benefits of cooling electric load are closely related to the investment planning and operation methods of demand response equipment. The-existing-cooling-technology-based demand response equipment investment planning methods mostly consider a single equipment type, and based on fixed or simplified system operation strategy for operation simulation, it is difficult to achieve the optimal matching of different types of equipment and user cold demand characteristics, resulting in the limited optimization space of the system demand response capacity and economic value. Therefore, this paper investigates the unified investment planning modeling method for cooling electric load demand response equipment considering the optimization of operation strategy. Firstly, this paper proposes a demand response model considering multiple types of chillers and operation strategy optimization in the electricity market. It realizes flexible response of cooling electric load to price signals and fully saves electricity cost. Then, this paper constructs a demand response equipment investment planning model with embedded unified operation strategy optimization to minimize the total cost of investment and operation. This model considers the price policy, cold demand characteristics, investment cost and operating characteristics of related equipment, and adopts linearization techniques to build a mixed-integer linear programming model. It can be efficiently solved by a commercial solver. The analysis shows that the proposed method can fully adapt to the current electricity market environment, effectively improve the investment efficiency of customer, optimize the electricity load curve, and leverage the value of demand response technology.

This paper introduces a new type of cutting plane for the unit commitment (UC) problem, namely “infeasibility cutting plane”. The infeasibility cutting plane refers to a type of logic constraint that eliminates the combination of integer variables causing infeasibility of the UC problem while not affecting any feasible integer solutions. This paper demonstrates that under certain conditions, such a cutting plane is effective for tightening the linear programming (LP) relaxation of UC, thus achieving a valid acceleration of UC without the loss of accuracy. A theoretical and easy-to-implement criterion is provided to identify valid infeasibility cutting planes. Then, an efficient framework for constructing the infeasibility cutting planes is presented to quickly obtain multiple combinations of integer variables causing infeasibility of UC through solving a batch of relaxed LP problems. The condition that the constructed infeasibility cutting plane does not provide overlapped information is provided. Based on the test on 30 public and utility cases, the proposed cutting plane method achieves an acceleration of 1.14 to 2.41 times with full optimality guarantee. Also, results show that the proposed cutting planes also work with existing cutting plane methodologies embedded in modern solvers.

Stochastic electricity markets have drawn attention due to fast increase of renewable penetrations. This results in two issues: one is to reduce uplift payments arising from non-convexity under renewable uncertainties, and the other one is to allocate reserve costs based on renewable uncertainties. To resolve the first issue, a convex hull pricing method for stochastic electricity markets is proposed. The dual variables of system-wide constraints in a chance-constrained unit commitment model are shown to reduce expected uplift payments, together with developing a linear program to efficiently calculate such prices. To resolve the second issue, an allocation method is proposed to allocate reserve costs to each renewable power plant by explicitly investigating how renewable uncertainties of each renewable power plant affect reserve costs. The proposed methods are validated in a 24-period 3-unit test example and a 24-period 48-unit utility example.