In the U.S., individual states enact Renewable Portfolio Standards (RPSs) for renewable electricity production with little coordination. Each state imposes restrictions on the amounts and locations of qualifying renewable generation. Using a co-optimization (transmission and generation) planning model, we quantify the long run economic benefits of allowing flexibility in the trading of Renewable Energy Credits (RECs) among the U.S. states belonging to the Western Electricity Coordinating Council (WECC). We characterize flexibility in terms of the amount and geographic eligibility of out-of-state RECs that can be used to meet a state’s RPS goal. Although more trade would be expected to have economic benefits, neither the size of these benefits nor the effects of such trading on infrastructure investments, CO2 emissions and energy prices have been previously quantified. We find that up to 90% of the economic benefits are captured if approximately 25% of unbundled RECs are allowed to be acquired from out of state. Furthermore, increasing REC trading flexibility does not necessarily result in either higher transmission investment costs or a substantial impact on CO2 emissions. Finally, increasing REC trading flexibility decreases energy prices in some states and increases them elsewhere, while the WECC-wide average energy price decreases.

10aRM11-0021 aPerez, Andres, P.1 aSauma, Enzo, E.1 aMunoz, Francisco, D.1 aHobbs, Benjamin, F. uhttps://certs.lbl.gov/publications/economic-effects-interregional02088nas a2200181 4500008003900000022001300039245014900052210006900201260001200270300001400282490000800296520145000304653001301754100002501767700002401792700001801816856007201834 2016 d a0377221700aNew bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints0 aNew bounding and decomposition approaches for MILP investment pr c02/2016 a888 - 8980 v2483 aWe propose a novel two-phase bounding and decomposition approach to compute optimal and near-optimal solutions to large-scale mixed-integer investment planning problems that have to consider a large number of operating subproblems, each of which is a convex optimization. Our motivating application is the planning of power transmission and generation in which policy constraints are designed to incentivize high amounts of intermittent generation in electric power systems. The bounding phase exploits Jensen’s inequality to define a lower bound, which we extend to stochastic programs that use expected-value constraints to enforce policy objectives. The decomposition phase, in which the bounds are tightened, improves upon the standard Benders’ algorithm by accelerating the convergence of the bounds. The lower bound is tightened by using a Jensen’s inequality-based approach to introduce an auxiliary lower bound into the Benders master problem. Upper bounds for both phases are computed using a sub-sampling approach executed on a parallel computer system. Numerical results show that only the bounding phase is necessary if loose optimality gaps are acceptable. However, the decomposition phase is required to attain optimality gaps. Use of both phases performs better, in terms of convergence speed, than attempting to solve the problem using just the bounding phase or regular Benders decomposition separately.

10aRM11-0021 aMunoz, Francisco, D.1 aHobbs, Benjamin, F.1 aWatson, J.-P. uhttps://certs.lbl.gov/publications/new-bounding-and-decomposition-002023nas a2200181 4500008003900000022001400039245012400053210006900177260001200246300001100258520140100269653001301670100001801683700002501701700002101726700002401747856007001771 2015 d a0885-895000aEconomic Analysis of Transmission Expansion Planning With Price-Responsive Demand and Quadratic Losses by Successive LP0 aEconomic Analysis of Transmission Expansion Planning With PriceR c05/2015 a1 - 123 aThe 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.10aRM11-0021 aOzdemir, Ozge1 aMunoz, Francisco, D.1 aHo, Jonathan, L.1 aHobbs, Benjamin, F. uhttps://certs.lbl.gov/publications/economic-analysis-transmission01805nas a2200217 4500008003900000022001400039245012100053210006900174260001200243300001400255490000700269520110400276653001301380653002601393653000901419100002501428700002401453700002101477700002001498856006901518 2014 d a0885-895000aAn Engineering-Economic Approach to Transmission Planning Under Market and Regulatory Uncertainties: WECC Case Study0 aEngineeringEconomic Approach to Transmission Planning Under Mark c01/2014 a307 - 3170 v293 aWe 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.10aRM11-00210atransmission planning10aWECC1 aMunoz, Francisco, D.1 aHobbs, Benjamin, F.1 aHo, Jonathan, L.1 aKasina, Saamrat uhttps://certs.lbl.gov/publications/engineering-economic-approach02042nas a2200157 4500008003900000245012300039210006900162260001200231520143200243653001301675653002601688100002501714700002401739700001701763856010401780 2014 d00aNew Bounding and Decomposition Approaches for Multi-Area Transmission and Generation Planning Under Policy Constraints0 aNew Bounding and Decomposition Approaches for MultiArea Transmis c05/20143 aWe propose a novel two-phase bounding and decomposition approach to compute optimal and near-optimal solutions to large-scale mixed-integer investment planning problems that have to consider a large number of operating subproblems, each of which is a convex optimization. Our motivating application is the planning of transmission and generation in which policy constraints are designed to incentivize high amounts of intermittent generation in electric power systems. The bounding phase exploits Jensen's inequality to define a new lower bound, which we also extend to stochastic programs that use expected-value constraints to enforce policy objectives. The decomposition phase, in which the bounds are tightened, improves upon the standard Benders algorithm by accelerating the convergence of the bounds. The lower bound is tightened by using a Jensen's inequality-based approach to introduce an auxiliary lower bound into the Benders master problem. Upper bounds for both phases are computed using a sub-sampling approach executed on a parallel computer system. Numerical results show that only the bounding phase is necessary if loose optimality gaps are acceptable. Attaining tight optimality gaps, however, requires the decomposition phase. Use of both phases performs better, in terms of convergence speed, than attempting to solve the problem using just the bounding phase or regular Benders decomposition separately.10aRM11-00210atransmission planning1 aMunoz, Francisco, D.1 aHobbs, Benjamin, F.1 aWatson, J.P. uhttps://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/New-Bounding-and-Decomposition-Approaches.pdf02473nas a2200229 4500008003900000022001400039245012600053210006900179260001100248300001400259490000700273520170400280653001001984653002801994653004002022653001302062653002602075100002502101700002002126700002402146856007302170 2013 d a0922-680X00aApproximations in power transmission planning: implications for the cost and performance of renewable portfolio standards0 aApproximations in power transmission planning implications for t c6/2013 a305 - 3380 v433 aRenewable portfolio standards (RPSs) are popular market-based mechanisms for promoting development of renewable power generation. However, they are usually implemented without considering the capabilities and cost of transmission infrastructure. We use single- and multi-stage planning approaches to find cost-effective transmission and generation investments to meet single and multi-year RPS goals, respectively. Using a six-node network and assuming a linearized DC power flow, we examine how the lumpy nature of network reinforcements and Kirchhoff’s Voltage Law can affect the performance of RPSs. First, we show how simplified planning approaches that ignore transmission constraints, transmission lumpiness, or Kirchhoff’s voltage law yield distorted estimates of the type and location of infrastructure, as well as inaccurate compliance costs to meet the renewable goals. Second, we illustrate how lumpy transmission investments and Kirchhoff’s voltage law result in compliance costs that are nonconvex with respect to the RPS targets, in the sense that the marginal costs of meeting the RPS may decrease rather than increase as the target is raised. Thus, the value of renewable energy certificates (RECs) also depends on the network topology, as does the amount of noncompliance with the RPS, if noncompliance is penalized but not prohibited. Finally, we use a multi-stage planning model to determine the optimal generation and transmission infrastructure for RPS designs that set multiyear goals. We find that the optimal infrastructure to meet RPS policies that are enforced year-by-year differ from the optimal infrastructure if banking and borrowing is allowed in the REC market.10aCERTS10areliability and markets10arenewable portfolio standards (RPS)10aRM11-00210atransmission planning1 aMunoz, Francisco, D.1 aSauma, Enzo, E.1 aHobbs, Benjamin, F. uhttps://certs.lbl.gov/publications/approximations-power-transmission01785nas a2200205 4500008003900000020002200039245007200061210006900133260003300202300001000235520111600245653001001361653002801371653001301399653002601412100002501438700002401463700002001487856007201507 2012 d a978-1-4673-2727-500aEfficient proactive transmission planning to accommodate renewables0 aEfficient proactive transmission planning to accommodate renewab aSan Diego, CAbIEEEc07/2012 a1 - 73 aThere is a growing need for tools to help decision makers to proactively plan for transmission infrastructure to accommodate renewables under gross market and regulatory uncertainties. In this paper, we make three contributions. First, we discuss how the current approaches aiming to proactively plan for transmission to accommodate renewables in the US are mathematically inaccurate, particularly with regards to their treatment of uncertainty. Second, improving existing models, we develop a two-stage stochastic network-planning model that takes into account Kirchhoff's laws, uncertainties, generators' response, and recourse investment decisions. Third, for large-scale networks, we demonstrate the use of Benders decomposition, taking advantage of the block-structure of the constraints. Testing our model on a simplified representation of California, we show that there are costs of ignoring uncertainty and that trying to identify robust solutions from a series of deterministic solutions is not necessarily effective, and indeed could result in higher costs than ignoring uncertainty altogether.

10aCERTS10areliability and markets10aRM11-00210atransmission planning1 aMunoz, Francisco, D.1 aHobbs, Benjamin, F.1 aKasina, Saamrat uhttps://certs.lbl.gov/publications/efficient-proactive-transmission