The increasing penetration of renewable generation presents challenges for system frequency regulation due to short-term power fluctuations and system inertia reductions. This paper presents a coordinated control strategy for frequency regulation in which inverter air-conditioning (IAC) units are used to perform primary frequency regulation (PFR) and fixed frequency air-conditioning (FFAC) units are used to perform secondary frequency regulation (SFR). In PFR, the regulation power is provided by adjusting the setpoints of the IAC units. A random number generation method is proposed to stochastically trigger IAC units based on the frequency deviation in real time. Furthermore, a recovery method is presented to stably restore the IAC units to their initial operating states after regulation. In SFR, a constant equivalent duty ratio method is raised to keep the regulation power stable over a long instruction interval. Based on this, the transforming time interval method is presented to determine the ON/OFF status of FFAC units to provide the required regulation power. Additionally, a recovery method for FFAC units is proposed to mitigate the power rebound after regulation. The proposed control strategy achieves an improved frequency regulation effect with fewer communication demands. Dynamic simulations in a six-machine two-area system and an isolated microgrid with wind power verify the effectiveness of the proposed control strategy.

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.

Increased wind power penetration and load demands lead to an increase in the peak-valley difference, which challenges system stability. This paper presents a hierarchical control strategy to coordinate multiple units of aggregated air-conditioning (AC) loads for power regulation to help reduce the peak-valley difference. First, the independent system operator (ISO) determines the purchased reserve capacity according to the roughly expected regulation effect, and the range of adjustable instructions is derived based on the purchased reserve capacity to help reduce the computation burden. Second, the economy optimizations between the ISO and load aggregators (LAs) are carried out to determine the distribution of purchased control units for AC. Third, after implementing unified planning by the ISO, the LAs deliver the scheduling instructions, including the triggering times and unit numbers, to the AC units to achieve short-term power regulation. Additionally, the scheduling accuracy can be set by changing the scale of the AC control units according to the requirements of the instructions. The simulation results show the effectiveness of the proposed control framework in Lianyungang, with 1000 control units participating. The method proposed in this paper could relieve the burden of the peak-valley difference effectively with less economic expenditure.

The emergence of load aggregators (LAs) has significantly elevated the trading position of residential users in demand response (DR). Effective incentive designs can greatly enhance the responsiveness of residential users. However, a key but often overlooked issue in practical implementation is strengthening the connection between pricing and actual responses to ensure the executability of DR scheduling results. To address this issue, this paper presents a competitive incentive mechanism for multi-agents in DR through a hierarchical game that considers joint uncertainties, and examines the rationality and interactive behaviors of different agents to ensure the efficient implementation of scheduling. The agents are the independent system operator (ISO), LAs, and residential users. First, the rationality levels of the various agents are analyzed, and corresponding utility functions are developed to target each agent specifically. Then, the interactive relationships among the various agents are examined, and a bi-level game model that incorporates a “pricing-response” approach is developed to enhance the feasibility of scheduling results, utilizing both Stackelberg and evolutionary game theories. Finally, uncertain factors are introduced, and the correlations between them are analyzed using copula theory, which results in model refinements that improve accuracy and pertinence. Simulations verify the effectiveness and economic efficiency of the proposed competitive incentive mechanism in terms of achieving a win-win situation for all agents.

High penetration of renewable energy resources poses technical and economic challenges to system operation. Utilizing demand response to provide ancillary services is beneficial to the security of the grid, but for this, an adequate level of reserves to support possible ancillary services is required. Reasonable allocation of the demand response reserves (DRREs) deployed by different ancillary services can guarantee performance and participant profits during ancillary service provision. An allocation mechanism that periodically allocates DRREs for various ancillary services considering uncertainty and demand response resource (DRR) sufficiency is proposed to address this problem. The participants include purchasers and providers of DRRs. First, K-means clustering is used to obtain the occurrence characteristics of different ancillary service events. Second, the utility functions of the participants in the allocation process are modeled while fully considering the uncertainty in periodic allocation. Then, different types of Stackelberg games are formulated to determine the DRREs deployed by different ancillary services under different DRR sufficiency situations. Finally, an intraday scheduling method is presented to optimize the use of the allocated DRREs and achieve the expected regulation performance. Simulations verify the effectiveness of the proposed allocation mechanism in terms of achieving a win-win situation for all participants.