This paper is concerned with the application of a new method to the problem of transmission planning expressed as a large finite Markovian sequential process over time. The method recognizes that in solving large sequential problems the complete enumeration of strategies is seldom feasible. The basic idea of Discrete Dynamic Optimizing (DDO) is to combine the deterministic search procedure of dynamic programming with a probabilistic search and a heuristic stopping criterion. The method is designed to take advantage of whatever information is known about the problem. Test results obtained with an experimental program are also given.

Transmission Planning Using Discrete Dynamic Optimizing

A new method is proposed in this paper to solve a single-stage expansion problem for a transmission network, given future generation and load patterns, and alternative types of lines available, subject to overload, reliability and right-of-way constraints. The problem is formulated as a series of zero-one integer programs which are solved by an efficient branch-and-bound algorithm. Complexity is reduced by the concepts of optimal cost-capacity curves and screening algorithms. A sample study is shown and the method is implemented in a computer program.

Transmission Expansion by Branch-and-Bound Integer Programming with Optimal Cost - Capacity Curves

This paper discusses the need for the integration of storage systems on transmission networks having renewable sources, and presents a tool for energy storage planning. The tool employs robust optimization to minimize the investment in storage units that guarantee a feasible system operation, without load or renewable power curtailment, for all scenarios in the convex hull of a discrete uncertainty set; it is termed ROSION—Robust Optimization of Storage Investment On Networks. The computa- tional engine in ROSION is a specific tailored implementation of a column-and-constraint generation algorithm for two-stage robust optimization problems, where a lower and an upper bound on the optimal objective function value are successively calculated until convergence. The lower bound is computed using mixed-integer linear programming and the upper bound via linear programming applied to a sequence of similar problems. ROSION is demon- strated for storage planning on the IEEE 14-bus and 118-bus networks, and the robustness of the designs is validated via Monte Carlo simulation.

https://spiral.imperial.ac.uk/bitstream/10044/1/19452/5/ROSION_R2.pdf

Robust Optimization of Storage Investment on Transmission Networks

A model for optimal long-range transmission planning is proposed. The model is capable of dealing with both static and dynamic modes of transmission planning. Given the existing network and anticipating a requirement for future facilities and loads, an accurate nonlinear cost function for the system is formulated. It includes both the fixed and the variable costs for all planned lines, in addition to the cost of the power losses. The cost function is minimized subject to demand satisfaction and overloading constraints. The model includes security constraints on bus voltage magnitudes and swing angles. The AC load flow solution is included in the optimization model. The results of applying the model to a previously reported power system from the literature are discussed. >

New transmission planning model

Many of the most commonly used generation planning models have been formulated in a way that neglects the chronological sequence of demand and the mixed-integer nature of generating units. The generator schedules assumed by these models are inaccurate and become increasingly divorced from real schedules with increasing variability. This paper seeks to characterize and quantify the limitations of these models over a broad set of input parameters. For an illustrative set of test systems, wind capacities and generator types, annual system costs are determined for all combinations of generating units using a unit-commitment model, which captures the chronological behavior of units and a dispatch model which does not. It is seen that the relative performance of the dispatch model is highly system specific but generally degrades with increasing variability. The difference in cost estimates between the models is decomposed into start costs, starts avoidance and average cost estimation error. The impact on least-cost portfolios is shown and finally sensitivities are performed with the addition of hydro and nuclear power to assess their impact.

/pdf/accommodating-variability-in-generation-planning-tsk9hlp77w.pdf

Accommodating Variability in Generation Planning

The determination of the least costly long-term expansion of a power system in the presence of uncertainty about future loads is formulated as a mathematical optimization problem. It is recognized that more information about these future loads will be acquired with time. The objective is to find an expansion policy such that the investment decision is based on an up-to-date estimate of the system requirements.

Power System Long-Term Planning in the Presence of Uncertainty

This paper extends previously published results on the application of a dynamic programming optimization approach to the planning of systems, with special emphasis on the long-term expansion of power systems. Future uncertainties about loads, equipment costs, etc. are considered explicitly in the method described. A practical computational solution to the resulting stochastic optimization is obtained by means of the recently developed open-loop feedback approximation. Further, it is shown how various known approaches of production cost simulation can be used in conjunction with the dynamic programming optimization to accommodate large systems that may be represented with great technical detail.

Patrice H. Henault

Papers

Power System Long-Term Planning in the Presence of Uncertainty

Long-term power system expansion planning by dynamic programming and production cost simulation