Yilin Wen

Distributed energy resources (DERs) in distribution systems offer substantial flexibility potential for power system operations, necessitating effective quantification and optimization methods for their utilization. This paper presents a unified time-coupled flexibility cost quantification approach that aligns with time-coupled flexibility models of various types of DER, whether individual or aggregated. The superior capability in accurately capturing the user inconvenience costs compared to traditional power-range-based, time-decoupled methods is demonstrated, thereby enhancing user willingness to provide flexibility to support the power system operation. Based on the quantification method, a DSO optimization model that activates DER flexibility for energy arbitrage and ancillary services provision in the transmission system while maintaining the distribution system's security and economic efficiency, i.e., TSO-DSO coordination, is developed. The optimization model introduces the concept of marginal flexibility prices (MFPs), which quantify the marginal contribution of flexibility in TSO-DSO coordination. Using MFPs for revenue allocation, incentive compatibility, revenue adequacy, and the non-profit status of DSO are guaranteed, enabling the proposed flexibility provision framework to operate sustainably. Numerical tests on the IEEE test system verify the proposed methods.

Uncertainty modeling has become increasingly important in power system decision-making. The widely-used tractable uncertainty modeling method—chance constraints with Conditional Value at Risk (CVaR) approximation, can be over-conservative and even turn an originally feasible problem into an infeasible one. This paper proposes a new approximation method for multiple joint chance constraints (JCCs) to model the uncertainty in dispatch problems, which solves the conservativeness and potential infeasibility concerns of CVaR. The proposed method is also convenient for controlling the risk levels of different JCCs, which is necessary for power system applications since different resources may be affected by varying degrees of uncertainty or have different importance to the system. We then formulate a data-driven distributionally robust chance-constrained programming model for the power system multiperiod dispatch problem and leverage the proposed approximation method to solve it. In the numerical simulations, two illustrative examples clearly demonstrate the superiority of the proposed method, and the results of the multiperiod dispatch on IEEE test cases verify its practicality.

Concise and reliable modeling for the aggregated power flexibility of distributed energy resources (DERs) in active distribution networks (ADNs) is essential for coordinating transmission and distribution networks. Inner approximated aggregation models are desirable for power system operators because they ensure a safe dispatchable range for the distribution network. However, previous inner approximation methods often rely on experience and intuition due to the absence of an explicit expression of the exact aggregation model (EAM), leading to accuracy and computational efficiency challenges. This paper proposes a novel inner approximation method to overcome these challenges, leveraging the properties of our recently derived closed-form EAM. Specifically, we divide the process of inner approximation methods into two steps: defining a geometric prototype and calculating its parameters. The first step is accomplished by selecting a subset of all the coefficient vectors forming the EAM so as to improve accuracy. When calculating parameters, on the other hand, we exploit the regularity of these coefficient vectors to improve the computational efficiency. The inner approximated flexibility model of ADNs is further incorporated into the security-constrained unit commitment problem as an application, which helps improve the system’s economic benefits. Numerical simulations verify the effectiveness of the proposed method.

Coordinated dispatch of integrated transmission-distribution systems (ITDs) continues to be a hotspot due to the growing demand-side flexibility. Decentralized optimization, which has been widely studied, does not conform to the current hierarchical framework of power system operation. This paper proposes a centralized distributionally robust chance-constrained dispatch model for ITDs. The transmission system operator considers the flexible ranges of large-scale distributed energy resources (DERs) in distribution systems via flexibility aggregation. A price-guided flexibility model is proposed to describe the temporally coupled aggregate flexibility of distribution systems. It is constructed by filtering those constraints with a high probability of activeness in the ITD dispatch problem, reducing the number of constraints from quadratic to linear with the number of divided time slots while maintaining high accuracy. Furthermore, we use the data-driven distributionally robust chance constraint to deal with the uncertainty from both renewable generations and the flexibility of DERs. Numerical simulations on IEEE test systems verify the effectiveness of the proposed methods.

The high penetration of converter-based renewable energy sources has brought challenges to the power system frequency control. It is essential to consider the frequency security constraints and frequency control reserve requirements in unit commitment (UC). First, a novel extreme learning machine-based method is proposed to approximate the highly nonlinear frequency nadir constraint (FNC) by a set of linear constraints. Second, considering the variation of the frequency insecurity risk under the changing operational condition, we propose to optimize the primary frequency control (PFC) droop gains and reserve capacities in the UC model to provide diverse control efforts in different risk levels adaptively. Third, a secondary frequency control (SFC) reserve capacity quantification method is proposed by combining the Copula theory and distributionally robust optimization technique. The UC simulation is conducted on the IEEE 118-bus system to test the proposed optimal PFC droop gain strategy and SFC reserve capacity quantification method. Simulation results show that the proposed optimal PFC droop gain strategy is better than the traditional fixed PFC droop gain setting on economic efficiency and operational flexibility. Besides, the SFC reserve capacity calculated by the proposed method is more appropriate than the actual SFC reserve capacity in the historical operation.

With the development of electric buses (EBs) in urban areas, how to effectively utilize their charging demand flexibility and the considerable energy stored in the on-board batteries becomes an urgent issue for the electric bus operator (EBO) to consider. This paper focuses on the bidding decision in energy and reserve markets for the urban EBO owning multiple electric bus stations with energy storage systems (ESSs), and the charging scheduling of all EBs. A trip-chain based electric vehicle boundary model is proposed firstly to concisely describe the flexibility region for an EB with multiple sequential trips, which can be further aggregated via a state-based approach to adapt to large-scale problems. The uncertain boundaries are also derived taking the uncertainty of trip energy consumption into account. In order to deal with the issue that the boundary aggregation will possibly influence the feasibility of decomposing the total power to each individual EB, the stochastic bidding and EB scheduling optimization problem in a hierarchical optimization framework is proposed. Accelerated Benders decomposition is utilized to efficiently solve the problem. Case studies verify that the proposed model can dramatically reduce the computational burden, effectively consider the uncertainty and take advantage of the flexibility of both EBs and ESSs to achieve better economic performance.