The frequency stability of microgrids is of paramount importance due to their low inertia and high share of renewable energy sources. In this paper, a frequency stability-constrained microgrid scheduling (FSC-MS) model is developed based on a time-dependent discretized frequency response model considering seamless unintentional islanding events. The optimal dispatch of controllable units, as well as optimal upward and downward primary reserves, are determined with a minimum operating cost while ensuring frequency stability criteria are maintained in their safe ranges following an unintentional islanding event, such as rate of change of frequency (RoCoF) and frequency nadir and overshoot. The limitation of generators with respect to primary frequency response is formulated in the form of primary reserve capacity and primary ramp rate limits. A cost-based model is presented to describe the available primary reserve of synchronous generators. The proposed FSC-MS model is formulated as a mixed-integer linear programming (MILP) problem using Benders decomposition. Simulation results on a medium voltage microgrid test system demonstrate the effectiveness of the proposed model to meet the frequency stability constraints while minimizing the total operating cost.

The grid integration of renewable energy sources necessitates using energy storage systems (ESSs) to provide more flexibility and controllability. This article proposes a frequency stability-constrained battery energy storage system (BESS) sizing model for microgrids formulated as a mixed-integer linear programming (MILP) problem and decomposed using Benders decomposition. The optimal size of BESS is determined as a trade-off between minimizing the operating costs or maximizing the benefits and the high investment costs of BESS. Both the grid-connected and stand-alone operating modes are modeled for the microgrid along with the corresponding generation contingencies. The microgrid scheduling optimization model is built for cost-benefit analysis considering unit commitment and frequency stability (FS) constraints. The transient frequency dynamics are considered using an accurate time-domain model based on the discretized swing equation. The proposed method ensures frequency stability criteria, such as frequency nadir/overshoot, rate of change of frequency (RoCoF), and steady-state frequency, are within their allowable ranges following generation contingencies. Simulation results based on a medium-voltage microgrid test case with historical data of load, PV generation, and market price verify the effectiveness of the proposed method.

The proliferation of renewable generation brings challenges to the power supply-demand balance. To relieve the power fluctuations caused by photovoltaic (PV) generation variability, a multi-timescale allocation algorithm that considers the allocation of available energy and power is proposed. Contracted demand response energy (CDRE) refers to the regulation energy, which is specified in contracts in advance, that can be used to relieve power fluctuations. First, the capacity of CDRE and available regulation power (RP) provided by load aggregators (LAs) that aggregate different demand response resources (DRRs) are estimated. In hour-timescale, CDRE is allocated to maximize the control economy of all participants, where a sample average approximation-based Stackelberg game is proposed to optimize the behavior of each participant based on considering PV generation uncertainty. In minute-timescale, RP is allocated to smooth the power fluctuations and minimize the power deviations based on the allocated CDRE results. Simulation on a modified IEEE-24 bus system verifies the effectiveness of the proposed algorithm in terms of reducing the power supply-demand imbalance with maximum revenue.

This paper proposes a price-maker bidding and offering model for networked microgrids (NMG) in a pool-based day-ahead electricity market. The objective of this model is to maximize the net revenue of NMG by coordinating the joined individual microgrids to submit aggregated offers/bids to the market operator. A hybrid stochastic-robust optimization framework is developed to offset multiple associated uncertainties. The bidding and offering model is first formulated as a hard-to-solve mixed-integer nonlinear programming (MINLP) problem, which is later converted to its easy-to-solve mixed-integer linear programming (MILP) counterpart. To resolve privacy concerns of each microgrid and improve the scalability of the proposed bidding and offering model, a coordinated scheduling framework for NMG based on the Dantzig-Wolfe decomposition (DWD) method is proposed to obtain the global optimum. Numerical simulations with real-world measured data validate the effectiveness of the proposed price-maker bidding model, which is shown to outperform existing price-taker models.

Uncertainties associated with wind power integration challenge the operational adequacy of conventional power systems. A set of wind power ramp limits (WPRLs) is proposed in this paper to evaluate the operational adequacy of power systems and to provide operating references to wind farms in the form of a ramp power limit (RPL) and ramp rate limit (RRL). The RPL is used to evaluate the minimum and maximum allowable generation of a wind farm by considering the power reserve capacities of generators and power flow constraints. A robust second-order cone programing (SOCP) RPLs formulation with AC power flow constraints and a column-and-constraint generation (C&CG) based solution method are proposed to maximize the total operating range of all wind farms. Meanwhile, a Pareto optimality-based RPLs evaluation approach is proposed to handle the coupled relationship among the operating ranges of the wind farms to achieve a balanced RPLs solution for each wind farm. The RRL is used to evaluate the most rapid wind power ramp behavior that can be handled by system frequency regulation without exceeding the designated frequency range. A comprehensive criterion is proposed to evaluate the RRLs by considering primary and secondary frequency regulation. Finally, the effectiveness of the proposed evaluation approach is verified through case studies

With the increasing integration of renewable energy sources (RESs) and the implementation of dynamic line rating (DLR), the accompanying uncertainties in power systems require intensive management to ensure reliable and secure operational planning. However, while numerous approaches and methods in the literature deal with uncertainties, they have not been analyzed axiomatically. This paper presents an analysis of risk in power system operation using coherent risk measures, elaborating on the origin of risk and the mechanisms of its management in the presence of various sources of uncertainty. To illustrate the practicality and benefits of coherent risk measures, a risk-averse asymmetry robust unit commitment (UC) model is established. It is based on coherent reformulations of the uncertain reserve and line flow constraints and is formulated in the form of a compact computationally efficient mixed-integer second-order conic program (SOCP). The overall performance of the proposed framework is verified using the updated 2019 IEEE Reliability Test System and the ACTIVSg2000 test system over a year-long period.