This paper proposes a new methodology to include financial risk management in the framework of two-stage stochastic programming for energy planning under uncertainties in demands and fuel price. A deterministic mixed integer linear programming formulation is extended to a two-stage stochastic programming model in order to take into account random parameters that have discrete and finite probabilistic distributions. Thus the expected value of the total cost of power generation is minimized, while the carbon emission reduction constraint is satisfied. Furthermore, the so-called “conditional value at risk”, a risk measure in financial risk management, is incorporated within the framework of two-stage mixed integer programming.

The proposed new methodology is implemented in an existing Ontario Power Generation (OPG) fleet with existing technologies and new technologies individually, while reducing carbon emissions under uncertain factors. The new methodology is analyzed under the integrated mode, which minimizes the cost of power generation through a power grid consisting of power generating plants. Taking into account environmental and economic issues, new technologies are integrated into the existing technologies such that much stricter carbon reduction requirements can be achieved and cost of electricity decreased dramatically.

Existing technologies are fuel switching and fuel balancing. Fuel balancing is used to decrease carbon emissions by adjusting the operation of the fleet of existing electricity-generating stations. Fuel switching involves switching from carbon-intensive fuels to less carbon-intensive fuels, such as switching from coal to natural gas. New technologies are carbon capture and sequestration. Power plants with existing technologies consist of fossil fuel stations, nuclear stations, hydroelectric stations, a wind station, pulverized coal stations, and natural gas combined cycle (NGCC) stations. Hypothesized power plants with new technologies include solar, wind , pulverized coal, NGCC and integrated gasification combined cycle (IGCC) stations, with and without capture and sequestration.

The optimization model is implemented in GAMS (General Algebraic Modeling System) and solved using CPLEX, a commercial solver available in GAMS. The computational results demonstrate the effectiveness of the proposed new methodology.