Generation self-scheduling and coal supply in coal-fired power plants are closely related but typically optimized separately. To enhance the optimal operation of power plants, we propose a coordinated optimal operation strategy of generation and coal management in this paper. Uncertainties in electricity prices and demands, coal prices, and coal inventory holding costs are captured and modeled by discrete scenarios. Emission constraints are introduced to control generation emissions. The heat loss caused by the weathering of coal during the storage is taken into account, which distinguishes the considered co-optimization problem from the previous ones. The proposed strategy is built on a mixed-integer linear programming-typed two-stage stochastic programming model, in which whether to purchase coal is determined in the first stage and the quantity of coal purchase, the coal inventory, and the economic generation dispatch are determined in the second stage. The objective is to maximize the expected profits. An improved Benders decomposition algorithm is developed to solve the problem where multiple Benders cuts are added in each iteration and valid inequalities are introduced to speed up the convergence of the algorithm. Numerical experiments demonstrate the effectiveness of the proposed strategy and algorithm.

We propose an emission-intensity-based carbon-tax policy for the electric-power industry and investigate the impact of the policy on thermal generation self-scheduling in a deregulated electricity market. The carbon-tax policy is designed to take a variable tax rate that increases stepwise with the increase of generation emission intensity. By introducing a step function to express the variable tax rate, we formulate the generation self-scheduling problem under the proposed carbon-tax policy as a mixed integer nonlinear programming model. The objective function is to maximize total generation profits, which are determined by generation revenue and the levied carbon tax over the scheduling horizon. To solve the problem, a decomposition algorithm is developed where the variable tax rate is transformed into a pure integer linear formulation and the resulting problem is decomposed into multiple generation self-scheduling problems with a constant tax rate and emission-intensity constraints. Numerical results demonstrate that the proposed decomposition algorithm can solve the considered problem in a reasonable time and indicate that the proposed carbon-tax policy can enhance the incentive for generation companies to invest in low-carbon generation capacity.

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