The continuous growth of wind power requires greater power system operational flexibility owing to the variability and uncertainty of wind power generation. Retrofitting existing coal-fired units is a demonstrated cost-effective method for improving system flexibility. This study proposes a decision support tool that provides optimal investment decisions to enhance power system operational flexibility. A novel flexibility retrofit planning (FRP) model is proposed in which the embedded operational problem is a stochastic mixed-integer nonlinear programming (SMINLP) unit commitment problem. In the operational unit commitment model, novel mixed-integer nonlinear formulations are proposed to represent the coal-fired unit attribute (minimum power output, startup and shutdown times, and maximum ramp rate) modifications after retrofitting. A new solution method based on the Benders decomposition (BD) and stochastic dual dynamic integer programming (SDDIP) algorithms is proposed to solve the proposed FRP model. The modified IEEE 39-bus, 57-bus, and 118-bus test systems were used to verify the effectiveness of the proposed model and solution method. The results demonstrate that the proposed FRP model can balance the retrofitting and operational costs to minimize the total cost and accommodate more wind generation. The proposed solution method is computationally efficient for solving FRP models.

Demand response (DR) has high regulation potential, which can reduce the power supply–demand imbalance caused by extreme disasters. However, its actual effectiveness still needs to be improved because of low user willingness and incomplete compensation mechanisms. To address this issue, a symmetrical incentive mechanism for DR is proposed. Building upon this mechanism, a bi-level load restoration optimization model under extreme events is proposed. The upper-level model minimizes grid-side costs during load restoration, determining load restoration ratios and incentive coefficients transmitted to the lower level. The lower-level model maximizes user profits while considering comfort-level losses from DR participation, solving for actual response quantities that are fed back to the upper level. To efficiently solve the proposed load restoration model, an iterative mixed-integer load restoration solver is proposed. Case studies demonstrate that the proposed symmetrical mechanism achieved an 89.6% participation rate, showing a 2.46% improvement over fixed incentive schemes. Grid payment costs were reduced by CNY 365,400, achieving incentive compatibility that facilitates rapid load restoration post extreme disasters.

Ductless personalized ventilation (DPV) as a recently developed technique and having the potential to meet the requirement of local thermal comfort and increase a wider thermostat setpoint range. The majority of published studies focused on the performance of DPV combined with convective-dominant HVAC system, while few investigations employed DPV in the radiant heating, ventilation, and air conditioning (HVAC) system. This study conducted the numerical simulation to evaluate the local thermal comfort of a DPV combined with a radiant HVAC system. Flow rate of 2, 4 and 6 l/s were applied as personalized air which is drawn at the sucking height of 0.1 m, and ACH of 1.2 h⁻¹ (0.16 m/s) and 2.4 h⁻¹ (0.32 m/s) were considered for DV system. Different building enclosure temperature of 24, 26 and 28°C were assumed. Vertical air temperature difference, manikin-based equivalent temperature and draught rate expose to occupant face are mainly used as evaluation criteria. The results remarkably show that DPV can significantly decrease the equivalent temperature in head segment, and slightly increase the equivalent temperature in foot segment as well as calf and thigh segments. Larger draft discomfort with draught rate of about 16% occurred when DPV flow rate of 6 l/s was applied.