Flexible ramping products (FRPs) have been implemented by several independent system operators (ISOs) to procure adequate flexible resources. Currently, system operators estimate the system level ramping requirements ignoring the spatiotemporal correlations among various uncertainty sources. This leads to overestimates/underestimates of ramping requirements. In addition, the explicit FRPs model considers only the generation ramping limitation. Other security constraints, such as the transmission limits, are not considered, which leads to deliverability issues with FRPs. To deal with these shortcomings of current FRPs models, this paper proposes a deliverable FRPs based on a distributionally-robust chance constrained multi-interval optimal power flow considering the spatiotemporal correlation of wind power and demand uncertainties endogenously. Furthermore, an asymmetrical affine policy (AAP) is proposed to leverage generation flexibility and mitigate the uncertainty in different directions. The pricing mechanism of the FRPs is proposed using a novel uncertainty-contained locational marginal price (U_LMP) which is derived from the proposed AAP-DRCC-MIOPF model. New components representing the price of FRPs are added into the traditional LMP formulation. Finally, the PJM 5-bus, IEEE 39-bus and IEEE 118-bus systems case studies validate the proposed FRPS approach. The payment of uncertain demand and wind power on FRPs are analyzed.

The continuous growth of variable renewable integration has greatly increased uncertainty and variability in the power system, resulting in unprecedentedly high demand for flexibility. Recently, there has been much attention in the power industry to manage the flexibility and uncertainty via market mechanisms. This paper presents a novel approach to jointly clearing energy, reserve, and uncertainty with moment information based on distributionally robust chance constraints (DR-CC). Locational marginal prices (LMP) are derived for both energy and reserve. Uncertainty is modeled with statistical moments of historical forecast errors, and uncertainty marginal prices (UMP) are defined to reflect the marginal cost of uncertainty following the cost-causation principle. The congestion cost of reserve trading is explicitly modelled, and is shown to fully cover financial transmission right (FTR) underfunding. We show that the proposed mechanism guarantees revenue adequacy and provides appropriate incentives for efficient operation and investment. Case studies are carried out to illustrate the impact of the proposed market scheme, and its feasibility is demonstrated on a testbed system based on a real-world 1934-bus grid.

The increasing penetration of renewable energy sources (RES) requires more flexibility resources (FR), such as thermal units and storages. FR kept in the system can help accommodate the uncertainties from RES. The challenge is how the system can survive when the RES level is very high. In this paper, RESs are considered as full-role market participants. They can bid in the day-ahead market, and the powers they deliver to the market are controllable up to their maximum available powers. Therefore, RESs are effectively dispatchable and can function as FR. To integrate dispatchable renewables, a two-stage robust unit commitment (UC) and dispatch model is established. In the first stage, a base UC and dispatch is determined. In the second stage, all FRs including RESs are used to accommodate the uncertainties, which is a mixed-integer programming (MIP) problem. It is proved that the solution to the max-min problem can be identified directly whether the strong duality holds or not for the inner minimization problem. The solution robustness can be guaranteed by considering only one extra scenario. Numerical results show the effectiveness of the proposed model and its advantages over the traditional robust UC model with high-level RES penetration.

Security constraint is a key component in unit commitment to guarantee reliable generator commitment. Large set of security constraints are notorious for making the problem difficult to solve. Constraint screening, i.e., filtering out non-dominating constraints, is regarded as a powerful tool to address this challenge. This work presents a security-constraint screening that can effectively integrate virtual transaction and capture changes online in real-time or look-ahead markets. The proposed approach takes advantage of both deterministic and statistical methods, which leverages mathematical modeling and historical data. Effectiveness are verified using Midcontinent Independent System Operator (MISO) data.

The power and transportation systems are urban interdependent critical infrastructures (CIs). During the post-disaster restoration process, transportation mobility and power restoration process are interdependent, and their functionalities significantly affect other well-beings of other urban CIs. Therefore, to enhance the resilience of urban CIs, successful recovery strategies should promote CI function cooperatively and synergistically to distribute goods and services efficiently. This paper develops an integrative framework that addresses the challenges of enhancing the recovery efficiency of urban power and transportation systems in short-term recovery period. Specifically, the post-storm recovery process is considered as a scheduling problem under the constraints representing crew dispatch, equipment and fuel limit. We propose a new framework for co-optimizing the recovery scheduling of power and transportation systems, respecting precedency requirement and network constraints. The advantages and benefits of co-optimized recovery scheduling are validated in a testing system.

Many communities, such as college campuses, purchase electricity at a rate that includes a peak demand charge (averaged over 15-minute intervals). Energy cost reduction and demand response require actively managing the peak demand. In this paper, we develop transactive control mechanisms that enable buildings in an active distribution system or microgrid to cooperatively manage peak demand. A decentralized transactive control (DTC) approach is designed based on a novel decomposition algorithm that preserves the detailed load profile and information. By directly solving a mixed integer linear program, the scalable algorithm determines the optimal or near-optimal operating point for the system. Selected buildings on a real-world campus are used to evaluate the proposed platform. The results show the peak demand can be dramatically reduced while maintaining the largest possible building energy revenue.