In low-carbon transition, carbon emission will be an additional concern in power system dispatch under uncertainty. In this paper, we propose a distributionally robust chance-constrained (DRCC) model for the low-carbon economic dispatch (LCED) with wind power uncertainty to achieve a low-carbon and economical operations. The DRCC-LCED model characterizes the wind power uncertainty via the variable moment-based ambiguity set, which avoids the assumptions on both uncertainty distribution and exact moment information. We incorporate the choice of wind power curtailment as an optimization variable into the proposed DRCC-LCED model and then reformulate the model as a tractable second-order cone programming. Case studies demonstrate the effectiveness of the proposed DRCC-LCED approach and show the value of incorporating the wind power curtailment in terms of feasibility enhancement of DRCC modeling and reduction in system cost and carbon emission.

Rapid development of power-to-gas technology provides a potential solution for virtual power plants (VPP) to achieve near-zero carbon emissions. In this paper, a bi-level hybrid stochastic/robust optimization model is proposed for low-carbon VPP day-ahead dispatch considering uncertainties from renewable generation and market prices. First, Karush-Kuhn-Tucker optimality conditions are employed to convert the bi-level model to a single level one. Next, the single level problem is decomposed into a master problem in the base case and several subproblems in extreme cases, which can then be solved by using the column-and-constraint generation algorithm iteratively. Numerical results indicate the proposed approach can effectively satisfy system operation constraints including the carbon emission limit, enhance computational efficiency and algorithm robustness compared with the stochastic method, and improve VPP revenue compared with the robust method.

A promising way to boost popularity of electric vehicles (EVs) is to properly layout fast charging stations (FCSs) by jointly considering interactions among EV drivers, power systems and traffic network constraints. This paper proposes a novel sensitivity analysis-based FCS planning approach, which considers the voltage sensitivity of each sub-network in the distribution network and charging service availability for EV drivers in the transportation network. In addition, energy storage systems are optimally installed to provide voltage regulation service and enhance charging capacity. Simulation tests conducted on two distribution network and transportation network coupled systems validate the efficacy of the proposed approach. Moreover, comparison studies demonstrate the proposed approach outperforms a Voronoi graph and particle swarm optimization combined planning approach in terms of much higher computation efficiency.

While price schedules can help improve the economic efficiency of renewable energy-powered microgrids, time-of-use (TOU) pricing has been identified as an effective way for microgrid development, which is presently limited by its high costs. In this study, we propose an evolutionary game theoretic model to explore optimal TOU pricing for development of renewable energy-powered microgrids by applying a multi-agent system, that comprises a government agent, local utility company agent, and different types of consumer agents. In the proposed model, we design objective functions for the company and the consumers and obtain a Nash equilibrium using backward induction. Two pricing strategies, namely, the TOU seasonal pricing and TOU monthly pricing, are evaluated and compared with traditional fixed pricing. The numerical results demonstrate that TOU schedules have significant potential for development of renewable energy-powered microgrids and are recommended for an electric company to replace traditional fixed pricing. Additionally, TOU monthly pricing is more suitable than TOU seasonal pricing for microgrid development.

Microgrids (MGs) with high penetration of distributed generators may cause congestion in the distribution network during operation. To address this issue, this paper proposes a two-time-scale congestion management scheme for multiple MGs integrated distribution networks. Day-ahead hourly-scale dynamic congestion management (DCM) is formulated as a constrained optimization problem, which can be solved by utilizing the proposed alternating iterative method, with the privacy of both the distribution network and MGs being preserved. The sub-hourly-scale contract energy tracking aims at fully utilizing the controllable resources of the MGs to minimize the difference of the contract and actual exchanged energy between the MG and distribution network. Through coordination of the proposed two timescales of management schemes, the MGs integrated distribution networks can operate economically while avoiding the probable congestion predicament with high penetration of renewable energy. Simulation studies with a 13-bus system MGs integrated distribution network demonstrated this proposed approach is effective to manage the congestion problem in the distribution network, while the energy tracking approach can improve the welfare of the MGs engaged in energy contracts execution.

In addition to renewable energy sources and market prices uncertainties, the regulation commands issued by the superior market operator are unpredictable factors within the virtual power plant (VPP) bidding range. To avoid branch power flow outage and voltage violation under uncertain regulation commands, a tri-level robust optimization-based day-ahead energy and regulation service bidding strategy for a generalized VPP, considering various distributed energy resources is proposed, in which the VPP bidding strategy, worst scenario estimation approach and regulation service scheduling method are formulated at three optimization layers, respectively. Then, the proposed tri-level model is transformed into an equivalent, single-level, mixed integer, second-order cone programming problem with rigid proof. Numerical simulations illustrate the effectiveness and superiority of the proposed approach by comparison with other prevailing methods in recent literature.