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The COVID-19 has slowed down global economic growth. Meanwhile, it also significantly cuts the global carbon emission, which provides a golden opportunity for the whole world to combat the climate change together. While the former policies (e.g., the CAFE standards, renewable portfolio standards, etc.) have reduced certain level of fossil fuel consumption, the most effective measures (such as carbon tax, cap-and-trade programs) are still far from ready for global implementation. This paper investigates an alternative way to achieve a more carbon efficient power grid using the uplift payment scheme. Specifically, we propose an effective algorithm to guarantee carbon efficiency with minimal uplift payments. We also submit that this scheme provides more flexibility to realize carbon reduction than carbon tax, which is exemplified by thorough numerical studies. Furthermore, we show that the stability of the power grid can be ensured under our uplift payment scheme, both from theoretical analysis and numerical studies. The results strengthen our belief that our uplift payment scheme is practicable for carbon reduction in the electricity market. [ABSTRACT FROM AUTHOR]

In the electricity sector the carbon tax is a common environmental policy aiming to reduce CO2 emissions, but is often regarded as economically unfriendly, especially for areas relying on coal-fire and other carbon-intensive generators. A power grid utilizing an energy storage system can be a promising solution to alleviate the regional economy pressure in a grid where the carbon tax is enforced. With the increasing exploitation of clean energy, e.g., solar and wind power, in this work, we characterize the stochastic emission-aware economic dispatch with a storage system utilizing two frameworks, namely a chance-constrained framework and a robust optimization framework. We highlight their differences and connections by studying the trade-offs between robustness and overall cost. Specifically, we bridge the two frameworks with a novel distributed robust optimization framework that considers practical bounds to estimate the optimal system performance under the reliability requirement. Numerical studies on the six-bus model and the IEEE-118 bus model further justify our findings. [ABSTRACT FROM AUTHOR]

The expected increase in the penetration of renewables in the approaching decade urges the electricity market to introduce new products—in particular, flexible ramping products—to accommodate the renewables' variability and intermittency. A risk-limiting economic dispatch scheme provides the means to optimize the dispatch and provision of these products. In this paper, we adopt the extended loss-of-load probability as the definition of risk. We first assess how the new products distort the optimal economic dispatch by comparing to the case without such products. Specifically, using parametric analysis, we establish the relationship between the minimal generation cost and the two key parameters of the new products: the up- and down-flexible ramping requirements. Such relationship yields a novel routine to efficiently solve the non-convex risk-limiting economic dispatch problem. Both theoretical analysis and simulation results suggest that our approach may substantially reduce the cost for incorporating the new products. We believe our approach can assist the ISOs with utilizing the ramping capacities in the system at the minimal cost. [ABSTRACT FROM AUTHOR]

Market power assessment is a prime concern when designing a deregulated electricity market. In this paper, we propose a new functional market power measure, termed transmission constrained network flow (TCNF), that unifies three large classes of transmission constrained structural market power indices in the literature: residual supply based, network flow based, and minimal generation based. Furthermore, it is suitable for demand-response and renewable integration and hence more amenable to identifying market power in the future smart grid. The measure is defined abstractly, and allows incorporation of power flow equations in multiple ways; we investigate the current market operations using a DC approximation and further explore the possibility of including detailed AC power flow models through semidefinite relaxation, and interior-point algorithms from Matpower. Finally, we provide extensive simulations on IEEE benchmark systems and highlight the complex interaction of engineering constraints with market power assessment. [ABSTRACT FROM AUTHOR]

Distributed energy resources and demand-side management are expected to become more prevalent in the future electric power system. Coordinating the increased number of grid participants in an efficient and reliable way is going to be a major challenge. A potential solution is the employment of a distributed energy management approach, which uses intelligence distributed over the grid to balance supply and demand. In this paper, we specifically consider the situation in which distributed resources and loads form microgrids within the bulk power system in which the load is supplied by local generation. A distributed energy management approach based on the consensus + innovations method is presented and used to coordinate local generation, flexible load, and storage devices within the microgrid. The approach takes advantage of the fact that, at the optimal allocation settings, the marginal costs given as a function of the power output/consumption need to be equal for all nonbinding network resources. Solutions for single time step, as well as multitime step optimization including intertemporal constraints, are presented. [ABSTRACT FROM PUBLISHER]