This report documents the results of a project whose goal is to carefully evaluate the practicality and potential usefulness of a method for planning transmission under uncertainty. This method, called stochastic programming, quantifies the economic value of simultaneously considering multiple scenarios (or “study cases”) of economic, policy, and technology changes over a multidecadal time horizon in a single model. By considering several possible futures in one model, analysts can identify near‐term transmission additions that enhance the adaptability and robustness of the transmission grid in the face of these uncertainties.

%8 01/2016
%0 Conference Paper
%B 2016 49th Hawaii International Conference on System Sciences (HICSS)
%D 2016
%T What is the Benefit of Including Uncertainty in Transmission Planning? A WECC Case Study
%A Benjamin F. Hobbs
%A Saamrat Kasina
%A Qingyu Xu
%A Sang Woo Park
%A Jasmine Ouyang
%A Jonathan L. Ho
%A Pearl E. Donohoo-Vallett
%K RM11-002
%X The electricity industry has undergone a series of radical economic, policy, and technology changes over the past several decades. More changes are to come, to be sure, but their nature and magnitude is highly uncertain. Such changes in market fundamentals profoundly impact the economic value of transmission. This paper quantifies the economic value of stochastic programming for transmission planning over a multidecadal time horizon, considering how generation investment reacts to network reinforcements and how the grid can be adapted later on as circumstances change. The economic value is the difference between the probability-weighted present worth of cost of (1) a stochastic model that chooses first-stage (through 2024) lines to minimize that cost and (2) a stochastic model whose 2024 lines are constrained to be those that were chosen by a suboptimal process, such as deterministic decision making. Even considering a small number of scenarios can drastically improve solutions.

%B 2016 49th Hawaii International Conference on System Sciences (HICSS) %I IEEE %C Koloa, HI, USA %P 2364 - 2371 %8 01/2016 %R 10.1109/HICSS.2016.295 %0 Journal Article %J Energy Systems %D 2015 %T Co-optimization of electricity transmission and generation resources for planning and policy analysis: review of concepts and modeling approaches %A Krishnan, Venkat %A Jonathan L. Ho %A Benjamin F. Hobbs %A Liu, Andrew L. %A James D. McCalley %A Shahidehpour, Mohammad %A Zheng, Qipeng P. %K RM11-002 %X The recognition of transmission’s interaction with other resources has motivated the development of co-optimization methods to optimize transmission investment while simultaneously considering tradeoffs with investments in electricity supply, demand, and storage resources. For a given set of constraints, co-optimized planning models provide solutions that have lower costs than solutions obtained from decoupled optimization (transmission-only, generation-only, or iterations between them). This paper describes co-optimization and provides an overview of approaches to co-optimizing transmission options, supply-side resources, demand-side resources, and natural gas pipelines. In particular, the paper provides an up-to-date assessment of the present and potential capabilities of existing co-optimization tools, and it discusses needs and challenges for developing advanced co-optimization models. %B Energy Systems %8 08/2015 %! Energy Syst %R 10.1007/s12667-015-0158-4 %0 Journal Article %J IEEE Transactions on Power Systems %D 2015 %T Economic Analysis of Transmission Expansion Planning With Price-Responsive Demand and Quadratic Losses by Successive LP %A Ozdemir, Ozge %A Francisco D. Munoz %A Jonathan L. Ho %A Benjamin F. Hobbs %K RM11-002 %X The growth of demand response programs and renewable generation is changing the economics of transmission. Planners and regulators require tools to address the implications of possible technology, policy, and economic developments for the optimal configuration of transmission grids. We propose a model for economic evaluation and optimization of inter-regional transmission expansion, as well as the optimal response of generators' investments to locational incentives, that accounts for Kirchhoff’s laws and three important nonlinearities. The first is consumer response to energy prices, modeled using elastic demand functions. The second is resistance losses. The third is the product of line susceptance and flows in the linearized DC load flow model. We develop a practical method combining Successive Linear Programming with Gauss-Seidel iteration to co-optimize AC and DC transmission and generation capacities in a linearized DC network while considering hundreds of hourly realizations of renewable supply and load. We test our approach for a European electricity market model including 33 countries. The examples indicate that demand response can be a valuable resource that can significantly affect the economics, location, and amounts of transmission and generation investments. Further, representing losses and Kirchhoff’s laws is also important in transmission policy analyses. %B IEEE Transactions on Power Systems %P 1 - 12 %8 05/2015 %! IEEE Trans. Power Syst. %R 10.1109/TPWRS.2015.2427799 %0 Journal Article %J IEEE Transactions on Power Systems %D 2014 %T An Engineering-Economic Approach to Transmission Planning Under Market and Regulatory Uncertainties: WECC Case Study %A Francisco D. Munoz %A Benjamin F. Hobbs %A Jonathan L. Ho %A Saamrat Kasina %K RM11-002 %K transmission planning %K WECC %X We propose a stochastic programming-based tool to support adaptive transmission planning under market and regulatory uncertainties. We model investments in two stages, differentiating between commitments that must be made now and corrective actions that can be undertaken as new information becomes available. The objective is to minimize expected transmission and generation costs over the time horizon. Nonlinear constraints resulting from Kirchhoff's voltage law are included. We apply the tool to a 240-bus representation of the Western Electricity Coordinating Council and model uncertainty using three scenarios with distinct renewable electricity mandates, emissions policies, and fossil fuel prices. We conclude that the cost of ignoring uncertainty (the cost of using naive deterministic planning methods relative to explicitly modeling uncertainty) is of the same order of magnitude as the cost of first-stage transmission investments. Furthermore, we conclude that heuristic rules for constructing transmission plans based on scenario planning can be as suboptimal as deterministic plans. %B IEEE Transactions on Power Systems %V 29 %P 307 - 317 %8 01/2014 %N 1 %! IEEE Trans. Power Syst. %R 10.1109/TPWRS.2013.2279654