The continuous growth of renewable generation in power systems brings serious challenges to electricity markets due to their characteristics different from conventional generation technologies. These challenges come from two dimensions, including short‐term (energy and ancillary service markets) and long‐term (long‐term bilateral and capacity markets) aspects. Under this background, the design of energy and ancillary service markets is studied for power systems with a high penetration level of variable renewable generation. In the proposed spot market mechanism, energy and frequency regulation service (FRS) bids are jointly cleared, where renewable generators are motivated to proactively manage the intermittency and uncertainty of their power outputs. The proposed market mechanism can also ensure the adequacy of FRS capacity for compensating variability of renewables. Besides, in order to ensure the execution of spot market clearing outcomes, this paper established a penalty scheme for mitigating the real‐time fluctuations of renewable generation outputs in the spot market. Differences between real‐time generation outputs and market clearing outcomes are managed within a certain limit by imposing the designed penalty prices on deviations. Finally, the feasibility and efficiency of the developed market mechanism and algorithms are manifested in the case studies.

With the significant decrease in natural gas prices in many parts of the world, the employment of gas turbine (GT) units has increased steadily in recent years. The ever-increasing deployment of GT units is strengthening the interconnections between electric power and natural gas systems, which could provide a higher level of operational flexibility and reliability. As a result, the planning and operation issues in the interconnected electric power and natural gas systems have aroused concern. In these circumstances, the impacts of increasing deployment of GT units in power system operation are studied and evaluated through well-being analysis (WBA). The fast responsive characteristics of GT units are analyzed first, and the definition and adaption of WBA in a power system with increasing deployment of GT units are addressed. Then the equivalent reserve capacity of GT units is estimated, taking demand fluctuations, commitment plans, and operational risks of GT units into account. The WBA of a power system with increasing deployment of GT units is conducted considering the uncertainties of system operation states and renewable energy sources. Finally, the proposed methods are validated through an integrated version of the IEEE 118-bus power system and a 10-bus natural gas system, and the impacts of GT units on power system security under various penetration levels are examined. Simulation results demonstrate that the role of a GT unit as a low-cost electricity producer may conflict with its role as a reserve provider, but through maintaining a proper proportion of idle GT capacities for reserve, the well-being performance of the power system concerned can be significantly improved.

Increasing deployment of distributed energy resources (DERs) is re-sculpturing the modern power systems in recent years. Future smart power distribution systems should be competent at accommodating extensive integration of DERs and managing the associated uncertainties at the distribution level. The electricity market has been proved to be an efficient way to employ market signals to direct behaviors of users and DERs with large capacity and homogeneous pattern. However, existing market frameworks cannot effectively handle a large number of small-scale DERs due to their diverse characteristics and arbitrary behavior patterns. In this context, an aggregated model which can represent and manage a diverse collection of DER, load, and storage is proposed. An additional trading platform, namely the energy sharing market, is established to reinforce the coordination and collaboration among various aggregators as well as operators. Energy sharing scheme is applied and a corresponding dynamic dispatch platform is designed to solve the crowdsource problem. The efficiency of the proposed model is validated by the numerical studies, and the market performance and impacts of energy sharing on the power systems are illustrated.INDEX TERMS Distributed power generation, electricity supply industry deregulation, energy management, energy sharing, crowdsourcing behavior.

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Smart home scheduling, facilitated by advanced metering, monitoring, and manipulation technology, plays an important role in the energy transition in terms of accommodating intermittent renewable energy and improving energy consumption efficiency. The key functionalities of home energy scheduling are usually implemented by leveraging the flexibility of household appliances, such as thermostatically controlled loads (TCLs) and energy storage units, to improve the peak-to-average ratio for utilities and reduce energy bills for customers. However, the consumption patterns of appliances are greatly influenced by a variety of factors, including real-time tariffs, ambient temperature profiles, indoor activities, and solar irradiance. Hence, smart home energy scheduling is a challenging task because most of these impacting factors are stochastic and difficult to predict. To properly model and manage the uncertainty factors associated with smart home appliance scheduling, an economic model predictive control (MPC)-based bilevel smart scheduling scheme is proposed in this paper. The comprehensive modeling of distributed generation and household appliances is performed at the single-household level. The home energy scheduling problem is formulated on two levels, with the upper level emphasizing the economic impact and the lower level focusing on capturing TCL responses. The correlations among different TCLs and their performance under the influence of various uncertainty factors, such as environmental impacts and user behaviors, are considered. The efficiency of the proposed MPC-based bilevel optimization model and the effectiveness of the home energy scheduling strategy in managing uncertainties are validated and illustrated in numerical studies.