Mingchen Ma

Power systems worldwide are undergoing a transition toward a low-carbon future. The structure of low-carbon power systems and the pathways to achieve them remain uncertain due to the rapid evolution of low-carbon technologies. Moreover, advanced planning algorithms should be developed to address the high complexity of planning problems. A standard test system is required to evaluate various prospects of power system transition and planning optimization algorithms. This paper introduces a novel test system, PROSPECT-43, dedicated to the power system's transition towards the carbon-mitigation target. The test system represents a load center region with an AC-DC hybrid transmission network. The test system incorporates multiple emerging technologies such as low-carbon generation technology, transmission technology and new types of loads (including data centers and power-to-hydrogen industry), enabling researchers to explore the impact of individual technologies and their synergies. The test system provides comprehensive data necessary for long-term power system planning, including candidate units, load growth, the carbon emission budget, etc. This paper provides a detailed description of the test system from the perspectives of the transmission network, generation, storage, and load. In addition, benchmark low-carbon transition pathways are proposed for the test system.

The increasing penetration of renewable energy leads to the frequent occurrences of zero-marginal-cost phenomenon in the spot market and causes missing money problems for renewable power producers (RPPs). Engagement in the forward market is a viable solution for RPPs to address this issue. This paper establishes a distributionally robust meanvariance model to optimize the RPPs' forward trading strategies. The model is further transformed into a tractable second-ordercone-programming one. Based on this model, RPPs are able to ensure their revenue sufficiency and mitigate revenue risks through the engagement in the forward market. We conduct case studies on RPPs' forward trading strategies under three typical market scenarios. In addition, we study the impacts of delivery mechanisms and the price of forward market on RPPs' trading strategies.

Carbon intensities are beginning to be used as incentives for consumer-driven carbon reduction. Guided by time-varying carbon intensities, consumers can schedule loads in priority order to use more electricity during low-carbon periods. However, carbon intensities cannot be perfectly forecasted in advance due to the fluctuation of loads and renewable energy. The probabilistic distribution of carbon intensities can be calculated by the grid operator and used as a reference for power consumers. This paper presents a probabilistic carbon emission flow model to calculate the distribution of carbon intensities for consumers. An adaptive regression-based calculation framework combined with a carbon pattern dictionary technique is proposed to handle the high calculation complexity caused by the nonlinearity of the carbon emission flow model. The simulation results from the case studies demonstrate the accuracy and efficiency of our proposed approach.