Optimization methods for electric utility resource planning

We give the reader a tour of good energy optimization models that explicitly deal with uncertainty. The uncertainty usually stems from unpredictability of demand and/or prices of energy, or from resource availability and prices. Since most energy investments or operations involve irreversible decisions, a stochastic programming approach is meaningful. Many of the models deal with electricity investments and operations, but some oil and gas applications are also presented. We consider both traditional cost minimization models and newer models that reflect industry deregulation processes. The oldest research precedes the development of linear programming, and most models within the market paradigm have not yet found their final form.

https://www.pogc.ir/Portals/0/maghalat/890812.13.pdf

Stochastic Programming Models in Energy

In this paper, the authors describe the basic technical concepts involved in developing cost based transmission prices. They introduce the concepts of transmission pricing paradigms and methodologies to better illustrate how transmission costs are transformed into transmission prices. They also briefly discuss the role of these paradigms and methodologies in promoting "economic efficiency" which is narrowly defined in this paper. They conclude the paper with an example of the application of some of these paradigms and methodologies for pricing transmission services in Brazil.

Some fundamental, technical concepts about cost based transmission pricing

In addition to long-existing load uncertainty on power systems, continuously increasing renewable energy injections (such as wind and solar) have further made the power grid more volatile and uncertain. Stochastic and recently introduced robust optimization approaches have been studied to provide the day-ahead unit commitment decision with the consideration of real-time load and supply uncertainties. In this paper, we introduce an innovative minimax regret unit commitment model aiming to minimize the maximum regret of the day-ahead decision from the actual realization of the uncertain real-time wind power generation. Our approach will ensure the robustness of the unit commitment decision considering the inherent uncertainty in wind generation. Meanwhile, our approach will provide a system operator a clear picture in terms of the maximum regret value among all possible scenarios. A Benders' decomposition algorithm is developed to solve the problem. Finally, our extensive case studies compare the performances of three different approaches (robust optimization, minimax regret, and stochastic optimization) and verify the effectiveness of our proposed algorithm.

Two-Stage Minimax Regret Robust Unit Commitment

This paper provides an overview of the key contributions within the planning and scheduling communities with specific emphasis on uncertainty analysis. As opposed to focusing in one particular industry, several independent sectors have been reviewed in order to find commonalities and potential avenues for future interdisciplinary collaborations. The objectives and physical constraints present within the planning and scheduling problems may vary greatly from one sector to another; however, all problems share the common attribute of needing to model parameter uncertainty in an explicit manner. It will be demonstrated through the literature review that two-stage stochastic programming, parametric programming, fuzzy programming, chance constraint programming, robust optimization techniques, conditional value-at-risk, and other risk mitigation procedures have found widespread application within all of the analyzed sectors. This review is the first work which attempts to provide a comprehensive description of t...

Planning and Scheduling under Uncertainty: A Review Across Multiple Sectors

A methodology is described for expansion planning of power systems under uncertainty in factors such as demand growth, fuel cost, delay in project completion, and financial constraints The approach draws upon three classes of techniques: decomposition and stochastic optimization provide the basic framework, and allow an implicit representation of alternative investment strategies; decision analysis is used to represent the dynamic aspects of decision-making as uncertainties are resolved over time; and hedging objectives from tradeoff analysis help select flexible and resilient expansion strategies. Case studies within the Brazilian system are presented and discussed. >

Power system expansion planning under uncertainty

A methodology for the optimal scheduling of hydrothermal power systems is described taking into account multiple hydro reservoir characteristics, inflow stochasticity, and transmission network represented by a linearized power flow model. The solution algorithm is based on stochastic dual dynamic programming (SDDP), which decomposes the multistage stochastic problem into several one-stage subproblems. Each subproblem corresponds to a linearized optimal power flow with additional constraints representing the hydro reservoir equations and a piecewise linear approximation of the expected future cost function. Each subproblem is solved by a customized network flow/dual simplex algorithm which takes advantage of the network characteristics of the hydro reservoirs and of the transmission system. The application of the methodology is illustrated in a case study with a Brazilian system comprising 44 hydroplants, 11 thermal plants, 463 buses and 834 circuits. >

Stochastic optimization of a hydro-thermal system including network constraints

This paper describes a methodology for expansion planning of interconnected power systems under several uncertainty factors such as demand growth, fuel cost, delay in project completion, financial constraints etc. that includes a detailed representation of inflow stochasticity. The solution approach is based on stochastic optimization techniques, decision analysis, and multi-objective tradeoff analysis. A case study with the system of Costa Rica is presented and discussed. 22 refs, 8 figs, 2 tabs

N.M. Campodonico

Papers

Power system expansion planning under uncertainty

Stochastic optimization of a hydro-thermal system including network constraints

Application of stochastic optimization to power system planning and operation