The problem of whether, where, when, and what types of transmission facilities to build in terms of minimizing costs and maximizing net economic benefits has been a challenge for the power industry from the beginning-ever since Thomas Edison debated whether to create longer dc distribution lines (with their high losses) or build new power stations in expanding his urban markets. Today?s planning decisions are far more complex, as grids cover the continent and new transmission, generation, and demand-side technologies emerge.

10aRM11-0021 aHobbs, Benjamin, F.1 aXu, Qingyu1 aHo, Jonathan1 aDonohoo, Pearl1 aKasina, Saamrat1 aOuyang, Jasmine1 aPark, Sang, Woo1 aEto, Joseph, H1 aSatyal, Vijay uhttps://certs.lbl.gov/publications/adaptive-transmission-planning01977nas a2200181 4500008003900000022001300039245010000052210006900152260001200221490000700233520138100240653001301621100002201634700002001656700002501676700002401701856007001725 2016 d a0195657400aThe Economic Effects of Interregional Trading of Renewable Energy Certificates in the U.S. WECC0 aEconomic Effects of Interregional Trading of Renewable Energy Ce c10/20160 v373 aIn 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-001340nas a2200193 4500008003900000245010000039210006900139260001200208520068900220653001300909100002100922700002400943700003100967700001500998700002001013700002001033700002001053856007301073 2016 d00aPlanning Transmission for Uncertainty: Applications and Lessons for the Western Interconnection0 aPlanning Transmission for Uncertainty Applications and Lessons f c01/20163 aThis 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.

10aRM11-0021 aHo, Jonathan, L.1 aHobbs, Benjamin, F.1 aDonohoo-Vallett, Pearl, E.1 aXu, Qingyu1 aKasina, Saamrat1 aPark, Sang, Woo1 aOuyang, Yueying uhttps://certs.lbl.gov/publications/planning-transmission-uncertainty01700nas a2200205 4500008003900000245009300039210006900132260003400201300001600235520100500251653001301256100002401269700002001293700001501313700002001328700002001348700002101368700003101389856007401420 2016 d00aWhat is the Benefit of Including Uncertainty in Transmission Planning? A WECC Case Study0 aWhat is the Benefit of Including Uncertainty in Transmission Pla aKoloa, HI, USAbIEEEc01/2016 a2364 - 23713 aThe 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.

10aRM11-0021 aHobbs, Benjamin, F.1 aKasina, Saamrat1 aXu, Qingyu1 aPark, Sang, Woo1 aOuyang, Jasmine1 aHo, Jonathan, L.1 aDonohoo-Vallett, Pearl, E. uhttps://certs.lbl.gov/publications/what-benefit-including-uncertainty02037nas a2200229 4500008003900000022001400039245008900053210006900142260001100211300001400222490000700236520132700243653001001570653002601580653002801606653001301634100002301647700002401670700001901694700001901713856007501732 2015 d a0885-895000aATC-Based System Reduction for Planning Power Systems With Correlated Wind and Loads0 aATCBased System Reduction for Planning Power Systems With Correl c1/2015 a429 - 4380 v303 aSimulations of production costs, flows, and prices are crucial inputs to generation and transmission planning studies. To calculate average system performance for many alternatives over long time periods, it is necessary to simulate large numbers of hourly combinations of renewable production and loads across large regions. As this is usually impractical for full network representations of such systems, aggregation of buses and lines is desirable. We propose an improved aggregation method for creating multi-area representations of power systems that yields more accurate estimates of the quantities required by planners. The method is based on partitioning the original large system into smaller areas and making a reduced equivalent for each area. The partitioning is based on available transfer capability (ATC) between each pair of network buses. Because ATC depends on net load conditions, separate partitions are defined for subsets of similar load and wind conditions, significantly enhancing the accuracy of optimal power flow solutions. We test the method on the IEEE 118-bus test system and the Polish 3120-bus system considering 150 load/wind scenarios, comparing the results to those of admittance-based partitioning methods. Accuracy is improved with only a negligible increase in simulation time.

10aCERTS10apower system planning10areliability and markets10aRM11-0021 aShayesteh, Ebrahim1 aHobbs, Benjamin, F.1 aSoder, Lennart1 aAmelin, Mikael uhttps://certs.lbl.gov/publications/atc-based-system-reduction-planning01638nas a2200205 4500008003900000022001400039245015000053210006900203260001200272520090900284653001301193100002101206700002101227700002401248700002001272700002401292700002701316700002201343856006701365 2015 d a1868-396700aCo-optimization of electricity transmission and generation resources for planning and policy analysis: review of concepts and modeling approaches0 aCooptimization of electricity transmission and generation resour c08/20153 aThe 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.10aRM11-0021 aKrishnan, Venkat1 aHo, Jonathan, L.1 aHobbs, Benjamin, F.1 aLiu, Andrew, L.1 aMcCalley, James, D.1 aShahidehpour, Mohammad1 aZheng, Qipeng, P. uhttps://certs.lbl.gov/publications/co-optimization-electricity02023nas 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-transmission01215nas a2200253 4500008003900000022001400039245009100053210006900144260001200213300001200225490000700237520045200244653001300696100001900709700001400728700001700742700002500759700002400784700001900808700002200827700002100849700001800870856007300888 2015 d a1540-797700aThe Evolution of the Market: Designing a Market for High Levels of Variable Generation0 aEvolution of the Market Designing a Market for High Levels of Va c11/2015 a60 - 660 v133 aRenewable energy was not the initial justification for electricity markets, but it is rapidly becoming a driver for new markets and market design changes. Starting in 1982 with market reforms in Chile, competition has been introduced into wholesale electricity markets around the world. This trend is likely to accelerate with countries such as China planning a major restructuring of power systems that could result in electricity markets.

10aRM11-0021 aAhlstrom, Mark1 aEla, Erik1 aRiesz, Jenny1 aO'Sullivan, Jonathan1 aHobbs, Benjamin, F.1 aO'Malley, Mark1 aMilligan, Michael1 aSotkiewicz, Paul1 aCaldwell, Jim uhttps://certs.lbl.gov/publications/evolution-market-designing-market01805nas 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.pdf01655nas a2200217 4500008003900000022001400039245007500053210006900128260001200197300001400209490000700223520099100230653001001221653002801231653002701259653001301286100002201299700002401321700002001345856007201365 2014 d a0885-895000aValue of Price Responsive Load for Wind Integration in Unit Commitment0 aValue of Price Responsive Load for Wind Integration in Unit Comm c03/2014 a675 - 6850 v293 aThe ability of load to respond to short-term variations in electricity prices plays an increasingly important role in balancing short-term supply and demand, especially during peak periods and in dealing with fluctuations in renewable energy supplies. However, price responsive load has not been included in standard models for defining the optimal scheduling of generation units in short-term. Here, elasticities are included to adjust the demand profile in response to price changes, including cross-price elasticities that account for load shifts among hours. The resulting peak reductions and valley fill alter the optimal unit commitment. Enhancing demand response also increases the amount of wind power that can be economically injected. Further, wind power uncertainty can be managed at a lower cost by adjusting electricity consumption in case of wind forecast errors, which is another way in which demand response facilitates the integration of intermittent renewables.

10aCERTS10areliability and markets10arenewables integration10aRM11-0021 aDe Jonghe, Cedric1 aHobbs, Benjamin, F.1 aBelmans, Ronnie uhttps://certs.lbl.gov/publications/value-price-responsive-load-wind00508nas a2200133 4500008003900000245009000039210006900129653001000198653002800208653001300236653002600249100002400275856007500299 2014 d00aWhat Investments Should be Made Now? Long Run Transmission Planning Under Uncertainty0 aWhat Investments Should be Made Now Long Run Transmission Planni10aCERTS10areliability and markets10aRM11-00210atransmission planning1 aHobbs, Benjamin, F. uhttps://certs.lbl.gov/publications/what-investments-should-be-made-now02473nas 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-transmission01949nas a2200253 4500008003900000022001400039245011400053210006900167260001200236300001600248490000700264520115900271653002401430653001801454653001201472653002801484653001801512653001301530653001501543100001501558700002401573700002301597856007501620 2012 d a0885-895000aDynamic Modeling of Thermal Generation Capacity Investment: Application to Markets With High Wind Penetration0 aDynamic Modeling of Thermal Generation Capacity Investment Appli c11/2012 a2127 - 21370 v273 aModeling the dynamics of merchant generation investment in market environments can inform the making of policies whose goals are to promote investment in renewable generation while maintaining security of supply. Such models need to calculate expected output, costs and revenue of thermal generation subject to varying load and random generator outages in a power system with high penetrations of wind. This paper presents a dynamic investment simulation model where the short-term energy market is simulated using probabilistic production costing using the Mix of Normals distribution (MOND) technique to represent residual load (load net of wind output). Price mark-ups due to market power are accounted for. An “energy-only” market setting is used to estimate the economic profitability of investments and forecast the evolution of security of supply. Simulated results for a Great Britain (GB) market case study show a pattern of increased relative security of supply risk during the 2020s. In addition, many new investments can recover their fixed costs only during years in which more frequent supply shortages push energy prices higher.

10aelectricity markets10aload modeling10apricing10areliability and markets10arisk analysis10aRM11-00210awind power1 aEager, Dan1 aHobbs, Benjamin, F.1 aBialek, Janusz, W. uhttps://certs.lbl.gov/publications/dynamic-modeling-thermal-generation01785nas 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-transmission02024nas a2200241 4500008003900000022001400039245008000053210006900133260001200202300001400214490000700228520127300235653002001508653002001528653002701548653002801575653002701603653001301630100002201643700002401665700002001689856007301709 2012 d a0885-895000aOptimal Generation Mix With Short-Term Demand Response and Wind Penetration0 aOptimal Generation Mix With ShortTerm Demand Response and Wind P c05/2012 a830 - 8390 v273 aDemand response, defined as the ability of load to respond to short-term variations in electricity prices, plays an increasingly important role in balancing short-term supply and demand, especially during peak periods and in dealing with fluctuations in renewable energy supplies. However, demand response has not been included in standard models for defining the optimal generation technology mix. Three different methodologies are proposed to integrate short-term responsiveness into a generation technology mix optimization model considering operational constraints. Elasticities are included to adjust the demand profile in response to price changes, including cross-price elasticities that account for load shifts among hours. As energy efficiency programs also influence the load profile, interactions of efficiency investments and demand response are also modeled. Comparison of model results for a single year optimization with and without demand response shows peak reduction and valley filling effects, impacting the optimal amounts and mix of generation capacity. Increasing demand elasticity also increases the installed amount of wind capacity, suggesting that demand response yields environmental benefits by facilitating integration of renewable energy.10ademand response10aload management10apower system economics10areliability and markets10arenewables integration10aRM11-0021 aDe Jonghe, Cedric1 aHobbs, Benjamin, F.1 aBelmans, Ronnie uhttps://certs.lbl.gov/publications/optimal-generation-mix-short-term01446nas a2200217 4500008003900000020002200039245006900061210006800130260003300198300001600231520074100247653001000988653002400998653002701022653002601049653001601075653002801091653001301119100002501132856007101157 2012 d a978-1-4577-1925-700aPlanning, Markets and Investment in the Electric Supply Industry0 aPlanning Markets and Investment in the Electric Supply Industry aMaui, HI, USAbIEEEc01/2012 a1923 - 19303 aEffective planning of the complex electricity supply network is essential because of the long lead times required for the development and placing in service of large new generators and transmission lines. Yet much can change while this physical process is being undertaken in terms of costs, prices, technological innovation and public policies, particularly about the environment and fuel diversity. The essential nature of well-constructed markets in yielding accurate, up-dated information about the likely effects of these many factors, as well as the use of advanced planning tools like "real-options" analysis are described, together with how these tools should be carefully staged and integrated with the planning process.

10aCERTS10aelectricity markets10apower system economics10apower system planning10areliability10areliability and markets10aRM11-0021 aSchuler, Richard, E. uhttps://certs.lbl.gov/publications/planning-markets-and-investment03083nas a2200229 4500008003900000022001400039245015100053210006900204260001100273300001200284490000700296520232000303653001002623653001802633653002002651653001902671653002802690653001302718653002802731100002502759856006902784 2012 d a0922-680X00aPricing the use of capital-intensive infrastructure over time and efficient capacity expansion: illustrations for electric transmission investment0 aPricing the use of capitalintensive infrastructure over time and c2/2012 a80 - 990 v413 aTraditional economic theory provides a conundrum for pricing large, lumpy infrastructure investments: very different short- and long-run pricing prescriptions. Unless the facility is congested, efficient short run prices should only cover operating costs (short-run marginal cost, SMC); any higher price designed to also recover capital costs would risk inefficient under-utilization. However, if the facility becomes crowded, capital costs should be included in the calculation of user-fees since that burgeoning demand is likely to cause the construction of more capacity, and users should be confronted with the cost-consequences of their decisions. Once additional capacity is completed, however, and if because of the large size of the addition the facility is no longer congested, then price should once again fall to SMC. The resulting jagged pattern of prices offers little assurance to investors of capital cost-recovery without a government guarantee, and it may lead to schizophrenic behavior by both customers and potential suppliers. Just because the physical investment is lumpy, should the price pattern also be dichotomous or can a smoother transition be employed? By integrating the use of congestion fees that are based upon the external costs imposed by one user on all others prior to the construction of added capacity, and then by using the same congestion charge to gauge the “willingness-to-pay” for new capacity and to set an “opportunity-cost”-based benchmark for capital cost recovery afterward, a smoother sequence of prices can evolve. The capital cost recovery portion of these prices, whose magnitude is based upon the congestion eliminated, is premised on a long-run, dynamic view of markets and the transitions they can facilitate, and these cost-recovery adders can be combined with “peak-load-pricing” and the “inverse-elasticity” rule, for example, to improve efficiency and fairness over both space and time. The resulting price patterns can provide compatible incentives for all parties, and they complement several existing electricity system planning processes in those regions where congestion rents are already assessed for the use of transmission. The net effect could be similar to a sequential “real-options” analysis of efficient capacity expansion.10aCERTS10acost recovery10adynamic pricing10ainfrastructure10areliability and markets10aRM11-00210atransmission investment1 aSchuler, Richard, E. uhttps://certs.lbl.gov/publications/pricing-use-capital-intensive00887nas a2200169 4500008003900000020002200039245007700061210006900138260003300207300001000240520030300250653002800553653001300581653002600594100002400620856007300644 2012 d a978-1-4673-2727-500aTransmission planning and pricing for renewables: Lessons from elsewhere0 aTransmission planning and pricing for renewables Lessons from el aSan Diego, CAbIEEEc07/2012 a1 - 53 aTransmission operating and planning procedures in Europe and elsewhere are changing in response to the new challenges posed by wind integration. Evolving procedures for managing transmission congestion and augmenting transmission capacity in Europe and Alberta are summarized and contrasted.

10areliability and markets10aRM11-00210atransmission planning1 aHobbs, Benjamin, F. uhttps://certs.lbl.gov/publications/transmission-planning-and-pricing01630nas a2200145 4500008003900000020002200039245013700061210006900198260002900267300001000296520107800306653001301384100001801397856006901415 2011 d a978-1-4244-9618-100aEfficient Pricing and Capital Recovery for Infrastructure over Time: Incentives and Applications for Electric Transmission Expansion0 aEfficient Pricing and Capital Recovery for Infrastructure over T aKauai, HIbIEEEc01/2011 a1 - 93 aClassic economic theory provides a conundrum: different short- and long-run pricing prescriptions for large capital-intensive infrastructure projects. Short run prices should cover only the operating costs of the facility; otherwise, the project may go under-utilized. Only as the facility becomes congested are additional fees warranted to allocate its use efficiently. But capital costs are included in user-fees only when additional demand would force the construction of more capacity. Once built, however, the price should fall, resulting in schizophrenic behavior by customers and investors. By integrating the assessment and assignment of congestion fees with other economic principles like "peak-load-pricing" and the "inverse-elasticity" rule for apportioning capital costs, a sequence of pricing rules is described that can lead to a smooth, efficient and fair evolution of prices over space and time. These pricing principles also result in compatible incentives for all parties, and they complement several existing electricity system planning processes.

10aRM11-0021 aSchuler, R, E uhttps://certs.lbl.gov/publications/efficient-pricing-and-capital01545nas a2200181 4500008003900000245015800039210006900197260001200266520087900278653002801157653001501185653001301200653002601213653001601239100002501255700002401280856005901304 2011 d00aPlanning electricity transmission to accommodate renewables: Using two-stage programming to evaluate flexibility and the cost of disregarding uncertainty0 aPlanning electricity transmission to accommodate renewables Usin c01/20113 aWe develop a stochastic two-stage optimisation model that captures the multistage nature of electricity transmission planning under uncertainty and apply it to a stylised representation of the Great Britain (GB) network. In our model, a proactive transmission planner makes investment decisions in two time periods, each time followed by a market response. This model allows us to identify robust first-stage investments and estimate the value of information in transmission planning, the costs of ignoring uncertainty, and the value of flexibility. Our results show that ignoring risk has quantifiable economic consequences, and that considering uncertainty explicitly can yield decisions that have lower expected costs than traditional deterministic planning methods. Furthermore, the best plan under a risk-neutral criterion can differ from the best under risk-aversion.10areliability and markets10arenewables10aRM11-00210atransmission planning10auncertainty1 avan der Weijde, A.H.1 aHobbs, Benjamin, F. uwww.econ.cam.ac.uk/research/repec/cam/pdf/cwpe1113.pdf