A multiple objective mixed integer linear programming model for power generation expansion planning

TLDR: In this paper, a multiple objective mixed integer linear programming model for power generation expansion planning is presented, which allows the consideration of modular expansion capacity values of supply-side options, and demand-side management is also considered an option in the planning process, assuming there is a sufficiently large portion of the market under franchise conditions.

About: This article is published in Energy.The article was published on 2004-03-01 and is currently open access. It has received 171 citations till now.

Figures

Table 3 Peak load duration curves (MW)

Fig. 3. Contribution of groups of type i in subperiod r to the corresponding LDC.

Table 1 Generating system existing at the beginning of the planning period

Table 4 Coefficients and upper bounds for pollutant emissions

Table 2 Generating units considered for additions

Table 10 Addition of generation capacity for the nondominated integer solutions (SC ¼ simple cycle; CC ¼ combined cycle; the index refers to subperiods)

Frequently asked questions

Q1. What are the contributions in "A multiple objective mixed integer linear programming model for power generation expansion planning" ?

This paper presents a multiple objective mixed integer linear programming model for power generation expansion planning that allows the consideration of modular expansion capacity values of supply-side options. As DSM full costs are accounted in the model, including lost revenues, it is possible to perform an evaluation of the rate impact in order to further inform the decision process. 

Q2. What have the authors stated for future works in "A multiple objective mixed integer linear programming model for power generation expansion planning" ?

The model takes explicitly into account multiple evaluation aspects, DSM issues, and the modularity of expansion possibilities. 

Q3. What is the role of the DM in the solution search process?

The DM intervenes in the solution search process by inputting information into the procedure, which in turn is used to guide the computation phase towards solutions which correspond more closely to his/her (evolutionary) preferences. 

Q4. What is the purpose of the MOLP problem?

The MOLP problem is a relaxation of the MOMILP problem by ignoring the integer constraints on decision variables in order to reduce the computational burden. 

Q5. What is the role of the objective model in a decision-making process?

Multiple objective models can provide decision support to decision makers (DMs) by rationalizing the comparison among different alternative solutions, thus enabling the DM to grasp the inherent conflicts and trade-offs among the distinct objectives for selecting a satisfactory compromise solution from the set of nondominated solutions. 

Q6. What is the purpose of the MOMILP model?

An MOMILP model has been presented to provide decision support in the evaluation of power generation capacity expansion policies. 

Q7. What is the reason why nondominated solutions are not optimal?

Nondominated solutions located in the interior of the convex hull (i.e. those which are not vertices) cannot be reached using the weighted sum programme because they are dominated by a convex combination of vertex solutions, and hence cannot be optimal solutions to the weighted-sum function (no set of weights exists which define a supporting hyperplane for them). 

Q8. Why is peak clipping selected to be included in the model?

peak clipping has been selected to be incorporated into the model,because it may be implemented by means of procedures which are actually used by utilities, such as direct load control (DLC) and time-of-use rates. 

Q9. What is the role of the MOMILP model in the energy market?

As far as the transformations currently underway in the energy market are concerned, the model can also be applied, besides the case where the utility is still vertically integrated, in a context in which private investment is invited to fulfil capacity quotas previously defined in a strategic development plan. 

Q10. How are nondominated solutions to the MOMILP model computed?

Nondominated solutions to the MOMILP model are computed by means of an interactive algorithm based on a reference direction approach, which is not too demanding regarding the computational burden and the information required from the decision maker. 

Q11. What type of parameters are used to characterize the supply-side generating units?

The same type of parameters used to characterize the supply-side generating units are used to model the DSM unit: implementation costs (installation and removal costs, initial contacts with the customers and incentives paid at the beginning of the DSM programme), operation cost (equipment operation and maintenance charges, incentives paid on an annual basis and lost revenues) and environmental impact (included in the model for the sake of generality). 

Q12. What is the purpose of the interactive approach?

The block diagram of the proposed interactive approach to provide decision support inMOMILP problems is depicted in Fig. 2.The model considers three objective functions which quantify: the total expansion cost, theenvironmental impact associated with the installed power capacity and the environmentalimpact associated with the energy output. 

Q13. What is the DM offered in this example?

Although in this example only information concerning the values of the objective functions and the generation additions has been presented, the DM is offered all the solution attributes (including pollutant emissions, etc.) which can be used to refine and guide the search process as well as to identify a solution as a satisfactory compromise plan. 

Q14. What are the environmental costs of the equivalent DSM generating unit?

The costs of the equivalent DSM generating unit involve investment costs ($/MW), related tothe needed hardware and its installation, and operating costs ($/MW h).