Generation Planning Problem Research Articles

A scalable solution framework for stochastic transmission and generation planning problems

Current commercial software tools for transmission and generation investment planning have limited stochastic modeling capabilities. Because of this limitation, electric power utilities generally rely on scenario planning heuristics to identify potentially robust and cost effective investment plans for a broad range of system, economic, and policy conditions. Several research studies have shown that stochastic models perform significantly better than deterministic or heuristic approaches, in terms of overall costs. However, there is a lack of practical solution techniques to solve such models. In this paper we propose a scalable decomposition algorithm to solve stochastic transmission and generation planning problems, respectively considering discrete and continuous decision variables for transmission and generation investments. Given stochasticity restricted to loads and wind, solar, and hydro power output, we develop a simple scenario reduction framework based on a clustering algorithm, to yield a more tractable model. The resulting stochastic optimization model is decomposed on a scenario basis and solved using a variant of the Progressive Hedging (PH) algorithm. We perform numerical experiments using a 240-bus network representation of the Western Electricity Coordinating Council in the US. Although convergence of PH to an optimal solution is not guaranteed for mixed-integer linear optimization models, we find that it is possible to obtain solutions with acceptable optimality gaps for practical applications. Our numerical simulations are performed both on a commodity workstation and on a high-performance cluster. The results indicate that large-scale problems can be solved to a high degree of accuracy in at most 2 h of wall clock time.

Hybrid Dynamic Programming/Branch-and-Bound Strategies for Electric Power Generation Planning

Abstract This paper espouses the application of hybrid dynamic programming/branch-and-bound theory to the generation planning problem (GPP) of electric utility companies. A hybrid approach retains the advantages of previous dynamic programming formulations of the GPP; these advantages include the relative ease with which the more sophisticated and realistic measures of the system's reliability and variable operating cost can be represented within the model framework. Meanwhile, the disadvantages of pure dynamic programming, including the relatively large amounts of CPU time and high speed storage space required in solving GPP's, can be ameliorated by a hybrid approach. This reduction in CPU time and high speed storage space requirements could allow electric utilities to solve larger GPP's, thereby permitting planners to study problems with longer planning horizons and a greater number of feasible solutions.

Decomposition techniques in power system planning: the Benders partitioning method

The relevance of decomposition techniques for power system planning is reviewed in this paper. A general framework for the formulation of the generation planning problem is presented which serves to illustrate the underlying structure of the problem. This structure rests on the occurrence of two different types of decisions in generation planning problems in general: decisions relating to investment in additional capacity and decisions relating to the operation of the equipment available to meet the load. It is shown how one particular decomposition technique, the Benders partitioning method, is particularly well suited to take advantage of this structure. The flexibility of the method is illustrated with an example on a thermal system. Results confirm the advantages to be derived from the method, both in terms of memory requirements and solution times.

Optimum Fuel Loading and Operation Planning for Light Water Reactor Power Stations. Part I: Pressurized Water Reactor Case Study

An extended application of the FAPMAN planning model has been made to typical boiling water reactor (BWR) fuel loading and generation planning problems. The additional constraint on reactive fuel loading pertinent to BWRs was implemented, together with some improvements on the back-end fuel cycle economic model and the gradient projection method of searching for better generation plans. The flexibility of the FAPMAN planning tool has been demonstrated in numerical studies made in several degrees of optimization, for disposal or reprocessing options in the back end of the fuel cycle and for the bounded or free refueling cycle length.