The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP/congestion is obtained. The proposed method incorporates load/generation forecast, time varying operation constraints, and contingency models. By shifting the computation associated with multiparametric programs offline, the online computational cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.

10aRM13-0021 aJi, Yuting1 aThomas, Robert, J.1 aTong, Lang uhttps://certs.lbl.gov/publications/probabilistic-forecasting-real-time02742nas a2200157 4500008003900000245006500039210006200104260002900166520215900195653001002354653002802364653001302392100001802405700002302423856013802446 2015 d00aOn Bus Type Assignments in Random Topology Power Grid Models0 aBus Type Assignments in Random Topology Power Grid Models aKauai, HIbIEEEc01/20153 aIn order to demonstrate and test new concepts and methods for the future grids, power engineers and researchers need appropriate randomly generated grid network topologies for Monte Carlo experiments. If the random networks are truly representative and if the concepts or methods test well in this environment they would test well on any instance of such a network as the IEEE model systems or other existing grid models. Our previous work [1] proposed a random topology power grid model, called RT-nested-small world, based on the findings from a comprehensive study of the topology and electrical properties of a number of realistic grids. The proposed model can be utilized to generate a large number of power grid test cases with scalable network size featuring the same small-world topology and electrical characteristics found from realistic power grids. On the other hand, we know that dynamics of a grid not only depend on its electrical topology but also on the generation and load settings, and the latter closely relates with an accurate bus type assignment of the grid. Generally speaking, the buses in a power grid test case can be divided into three categories: the generation buses (G), the load buses (L), and the connection buses (C). In [1] our proposed model simply adopts random assignment of bus types in a resulting grid topology, according to the three bus types' ratios. In this paper we examined the correlation between the three bus types of G/L/C and some network topology metrics such as node degree distribution and clustering coefficient. We also investigated the impacts of different bus type assignments on the grid vulnerability to cascading failures using IEEE 300 bus system as an example. We found that (a) the node degree distribution and clustering characteristic are different for different type of buses (G/L/C) in a realistic grid, (b) the changes in bus type assignment in a grid may cause big differences in system dynamics, and (c) the random assignment - f bus types in a random topology power grid model should be improved by using a more accurate assignment which is consistent with that of realistic grids.10aCERTS10areliability and markets10aRM14-0031 aWang, Zhifang1 aThomas, Robert, J. uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070137&refinements%3D4255068112%26filter%3DAND%28p_IS_Number%3A7069647%2902450nas a2200301 4500008003900000245010300039210006900142260002900211520144400240653002801684653001301712653001301725100002401738700002001762700002301782700001201805700002501817700002501842700002501867700002301892700002501915700001201940700001201952700001501964700001401979700001701993856013802010 2015 d00aA Detailed Power System Planning Model: Estimating the Long-Run Impact of Carbon-Reducing Policies0 aDetailed Power System Planning Model Estimating the LongRun Impa aKauai, HIbIEEEc01/20153 aIn this paper, a much more detailed representation of the nation's electricity system than has been traditionally used in policy models is employed. This detailed representation greatly increases the computational difficulty of obtaining optimal solutions, but is necessary to accurately model the location of new investment in generation. Given the proposed regulation of CO2 emissions from US power plants, an examination of economically efficient policies for reducing these emissions is warranted. The model incorporates realistic physical constraints, investment and retirement of generation, and price-responsive load to simulate the effects of policies for limiting CO2 emissions over a twenty-year forecast horizon. Using network reductions for each of the three electric system regions in the U.S. And Canada, an optimal economic dispatch, that satisfies reliability criteria, is assigned for 12 typical hour-types in each year. Three scenarios are modeled that consider subsidies for renewables and either CO2 emissions regulation on new investment or cap-and-trade. High and low gas price trends are also simulated and have large effects on prices of electricity but small impacts on CO2 emissions. Low gas prices with cap-and-trade reduce CO2 emissions the most, large subsidies for renewables alone do not reduce carbon emissions much below existing levels. Extensive retirement of coal-fired power plants occurs in all cases.10areliability and markets10aRM12-00210aSuperOPF1 aShawhan, Daniel, L.1 aTaber, John, T.1 aZimmerman, Ray, D.1 aYan, J.1 aMarquet, Charles, M.1 aSchulze, William, D.1 aSchuler, Richard, E.1 aThomas, Robert, J.1 aTylavsky, Daniel, J.1 aShi, Di1 aLi, Nan1 aJewell, W.1 aHardy, T.1 aHu, Zhouxing uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070115&refinements%3D4254321466%26filter%3DAND%28p_IS_Number%3A7069647%2902664nas a2200157 4500008003900000245008200039210006900121260004200190300001000232520211000242653001302352100001802365700002502383700002302408856007502431 2015 d00aA novel measure to characterize bus type assignments of realistic power grids0 anovel measure to characterize bus type assignments of realistic aEindhoven, NetherlandsbIEEEc06/2015 a1 - 63 aElectric power engineers and researchers need appropriate randomly generated grid network topologies for Monte Carlo experiments to test and demonstrate new concepts and methods. Our previous work proposed a random topology power grid model, called RT-nested-small-world, based on a comprehensive study of the real-world grid topologies and electrical properties. The proposed model can be used to produce a sufficiently large number of power grid test cases with scalable network size featuring the same kind of small-world topology and electrical characteristics found in realistic grids. However, the proposed RT-power grid model has a shortcoming that is its random assignment of bus types. And our recent study has shown that the bus type assignment of a realistic power grid is not random but a correlated one. Generally speaking,the buses in a power grid can be grouped into three categories: generation buses (G), load buses (L), and connection buses (C). When studying the dynamics of a grid we need to take into account not only its "electrical" topology but also the generation and load settings including their locations, which are equivalent to the bus type assignments in our model. In this paper we define a novel measure to characterize typical bus type assignments of realistic power grids. The proposed measure will enable the recognition of the specific set of bus type assignments, consistent with that of a realistic grid, from those generated from random permutation. This will prove useful for designing an optimal algorithm to improve our random topology power grid modeling. The proposed measure, called the Bus Type Entropy, incorporates both bus type ratios and the link type ratios. Therefore it provides a quantitative means to identify the presence of correlation among the bus type assignments of a realistic grid. We then experiment with this entropy measure on a NYISO system and the IEEE 300-bus system. The numerical results from both test cases verify the effecti- eness of the proposed measure to characterize the bus type assignment of a real-world power grid.

10aRM14-0031 aWang, Zhifang1 aElyas, Seyyed, Hamid1 aThomas, Robert, J. uhttps://certs.lbl.gov/publications/novel-measure-characterize-bus-type01319nas a2200181 4500008003900000245007600039210006900115260002900184520065000213653001000863653003200873653002800905653001300933100001500946700002300961700001500984856013800999 2015 d00aProbabilistic Forecast of Real-Time LMP via Multiparametric Programming0 aProbabilistic Forecast of RealTime LMP via Multiparametric Progr aKauai, HIbIEEEc01/20153 aThe problem of short-term probabilistic forecast of real-time locational marginal price (LMP) is considered. A new forecast technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability mass function of the real-time LMP is estimated using Monte Carlo techniques. The proposed methodology incorporates uncertainty models such as load and stochastic generation forecasts and system contingency models. With the use of offline computation of multiparametric linear programming, online computation cost is significantly reduced.10aCERTS10alocational marginal pricing10areliability and markets10aRM13-0021 aJi, Yuting1 aThomas, Robert, J.1 aTong, Lang uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070121&refinements%3D4260156971%26filter%3DAND%28p_IS_Number%3A7069647%2901288nas a2200229 4500008003900000022001400039245006600053210006500119260001200184300001400196490000700210520060600217653002400823653003200847653002800879653001300907100001500920700001600935700002300951700001500974856006900989 2014 d a0885-895000aImpact of Data Quality on Real-Time Locational Marginal Price0 aImpact of Data Quality on RealTime Locational Marginal Price c03/2014 a627 - 6360 v293 aThe problem of characterizing impacts of data quality on real-time locational marginal price (LMP) is considered. Because the real-time LMP is computed from the estimated network topology and system state, bad data that cause errors in topology processing and state estimation affect real-time LMP. It is shown that the power system state space is partitioned into price regions of convex polytopes. Under different bad data models, the worst case impacts of bad data on real-time LMP are analyzed. Numerical simulations are used to illustrate worst case performance for IEEE-14 and IEEE-118 networks.10aelectricity markets10alocational marginal pricing10areliability and markets10aRM11-0031 aJia, Liyan1 aKim, Jinsub1 aThomas, Robert, J.1 aTong, Lang uhttps://certs.lbl.gov/publications/impact-data-quality-real-time01582nas a2200205 4500008003900000245007600039210006900115260004200184300001400226520091200240653001001152653003201162653002801194653001301222100001501235700001601250700002301266700001501289856007201304 2013 d00aForecasting real-time locational marginal price: A state space approach0 aForecasting realtime locational marginal price A state space app aPacific Grove, CA, USAbIEEEc11/2013 a379 - 3833 aThe problem of forecasting the real-time locational marginal price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future locational marginal prices with forecast horizons of 6-8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.10aCERTS10alocational marginal pricing10areliability and markets10aRM13-0021 aJi, Yuting1 aKim, Jinsub1 aThomas, Robert, J.1 aTong, Lang uhttps://certs.lbl.gov/publications/forecasting-real-time-locational02231nas a2200265 4500008003900000022001400039245010500053210006900158260001200227300001600239490000600255520139000261653002301651653001001674653002601684653002901710653002801739653001501767653001301782100003201795700002301827700002201850700002301872856007001895 2013 d a1949-305300aSecure Planning and Operations of Systems With Stochastic Sources, Energy Storage, and Active Demand0 aSecure Planning and Operations of Systems With Stochastic Source c12/2013 a2220 - 22290 v43 aThis work presents a stochastic optimization framework for operations and planning of an electricity network as managed by an Independent System Operator. The objective is to maximize the total expected net benefits over the planning horizon, incorporating the costs and benefits of electricity consumption, generation, ancillary services, load-shedding, storage and load-shifting. The overall framework could be characterized as a secure, stochastic, combined unit commitment and AC optimal power flow problem, solving for an optimal state-dependent schedule over a pre-specified time horizon. Uncertainty is modeled to expose the scenarios that are critical for maintaining system security, while properly representing the stochastic cost. The optimal amount of locational reserves needed to cover a credible set of contingencies in each time period is determined, as well as load-following reserves required for ramping between time periods. The models for centrally-dispatched storage and time-flexible demands allow for optimal tradeoffs between arbitraging across time, mitigating uncertainty and covering contingencies. This paper details the proposed problem formulation and outlines potential approaches to solving it. An implementation based on a DC power flow model solves systems of modest size and can be used to demonstrate the value of the proposed stochastic framework.10aancillary services10aCERTS10apower system planning10apower system reliability10areliability and markets10arenewables10aRM07-0021 aMurillo-Sanchez, Carlos, E.1 aZimmerman, Ray, D.1 aAnderson, Lindsay1 aThomas, Robert, J. uhttps://certs.lbl.gov/publications/secure-planning-and-operations02265nas a2200229 4500008003900000022001300039245013000052210006900182260001200251490000700263520150200270653001001772653002801782653002001810653001301830653001501843100003201858700002301890700002201913700002301935856007701958 2013 d a0167923600aA stochastic, contingency-based security-constrained optimal power flow for the procurement of energy and distributed reserve0 astochastic contingencybased securityconstrained optimal power fl c12/20130 v563 aIt is widely agreed that optimal procurement of reserves, with explicit consideration of system contingencies, can improve reliability and economic efficiency in power systems. With increasing penetration of uncertain generation resources, this optimal allocation is becoming even more crucial. Herein, a problem formulation is developed to solve the day-ahead energy and reserve market allocation and pricing problem that explicitly considers the redispatch set required by the occurrence of contingencies and the corresponding optimal power flow, static and dynamic security constraints. Costs and benefits, including those arising from eventual demand deviation and contingency-originated redispatch and shedding, are weighted by the contingency probabilities, resulting in a scheme that contracts the optimal amount of resources in a stochastic day-ahead procurement setting. Furthermore, the usual assumption that the day-ahead contracted quantities correspond to some base case dispatch is removed, resulting in an optimal procurement as opposed to an optimal dispatch. Inherent in the formulation are mechanisms for rescheduling and pricing dispatch deviations arising from realized demand fluctuations and contingencies. Because the formulation involves a single, one stage, comprehensive mathematical program, the Lagrange multipliers obtained at the solution are consistent with shadow prices and can be used to clear the day-ahead and spot markets of the different commodities involved.10aCERTS10areliability and markets10areserve markets10aRM07-00210asmart grid1 aMurillo-Sanchez, Carlos, E.1 aZimmerman, Ray, D.1 aAnderson, Lindsay1 aThomas, Robert, J. uhttps://certs.lbl.gov/publications/stochastic-contingency-based-security02181nas a2200253 4500008003900000022001400039245006700053210006600120260001200186300001600198490000700214520143600221653001001657653002701667653002401694653001801718653002001736653001301756653001501769100002301784700002001807700002301827856007701850 2012 d a0733-871600aFrom Packet to Power Switching: Digital Direct Load Scheduling0 aFrom Packet to Power Switching Digital Direct Load Scheduling c07/2012 a1027 - 10360 v303 aAt present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.10aCERTS10ademand-side management10aelectricity markets10aload modeling10aload scheduling10aRM11-00710asmart grid1 aAlizadeh, Mahnoosh1 aScaglione, Anna1 aThomas, Robert, J. uhttps://certs.lbl.gov/publications/packet-power-switching-digital-direct01140nas a2200217 4500008003900000020002200039245008200061210006900143260003300212300001600245520042500261653002400686653002200710653002800732653001300760653002400773100001500797700002300812700001500835856007200850 2012 d a978-1-4577-1925-700aImpacts of Malicious Data on Real-Time Price of Electricity Market Operations0 aImpacts of Malicious Data on RealTime Price of Electricity Marke aMaui, HI, USAbIEEEc01/2012 a1907 - 19143 aImpacts of malicious data data attack on the real-time electricity market are studied. It is assumed that an adversary has access to a limited number of meters and has the ability to construct data attack based on what it observes. Different observation models are considered. A geometric framework is introduced based on which upper and lower bounds on the optimal data attack are obtained and evaluated in simulations.10aelectricity markets10areal-time pricing10areliability and markets10aRM11-00310asmart grid security1 aJia, Liyan1 aThomas, Robert, J.1 aTong, Lang uhttps://certs.lbl.gov/publications/impacts-malicious-data-real-time01186nas a2200181 4500008003900000020002200039245007300061210006600134260003300200300001000233520059200243653002800835653001300863100001500876700002300891700001500914856007500929 2012 d a978-1-4673-2727-500aOn the nonlinearity effects on malicious data attack on power system0 anonlinearity effects on malicious data attack on power system aSan Diego, CAbIEEEc07/2012 a1 - 83 aThere has been a growing literature on the malicious data attack (or data injection attack) on power systems. Most existing work focuses on the DC (linear) model with linear state estimators. This paper examines the effects of nonlinearity in the power systems on the effectiveness of malicious data attack on state estimation and real-time market. It is demonstrated that attack algorithms designed for the DC model may not be effective when they are applied to nonlinear system with nonlinear state estimators. Discussion and experiments results about nonlinearity are provided.

10areliability and markets10aRM11-0031 aJia, Liyan1 aThomas, Robert, J.1 aTong, Lang uhttps://certs.lbl.gov/publications/nonlinearity-effects-malicious-data02308nas a2200253 4500008003900000020002200039245008800061210006900149260002900218300001000247520150600257653002401763653001201787653001601799653002801815653001301843653001501856100002301871700002501894700002301919700002301942700002301965856006601988 2011 d a978-1-4244-9618-100aIntegrating Wind Power: Can Controllable Load Substitute for Transmission Upgrades?0 aIntegrating Wind Power Can Controllable Load Substitute for Tran aKauai, HIbIEEEc01/2011 a1 - 93 aThe Cornell SuperOPF is used to illustrate how the system costs can be determined for a reliable network (the amount of conventional generating capacity needed to maintain System Adequacy is determined endogenously). Eight cases are studied to illustrate the effects of geographical distribution, ramping costs and load response to customers payment in the wholesale market, and the amount of potential wind generation that is dispatched. The proposed regulatory changes for electricity markets are 1) to establish a new market for ramping services, 2) to aggregate the loads of customers on a distribution network so that they can be represented as a single wholesale customer on the bulk-power transmission network and 3) to make use of controllable load to mitigate the variability of wind generation as an alternative to upgrading the capacity of the transmission network. The cost of ramping reduces the amount of potential wind generation that is dispatched because of the inherent variability of wind speeds. The analysis evaluates whether the ability to dispatch some load that is not time-sensitive, such as charging the batteries in electric vehicles over night above the minimum usage requirement, can be an effective way to use more of the potential wind generation without upgrading the transfer capacity of a transmission network. The expectation is that more wind generation can be dispatched at times when load is relatively low and congestion on the network is not a major limitation.10aelectricity markets10aramping10areliability10areliability and markets10aRM12-00410awind power1 aMount, Timothy, D.1 aLamadrid, Alberto, J1 aManeevitjit, Surin1 aZimmerman, Ray, D.1 aThomas, Robert, J. uhttps://certs.lbl.gov/publications/integrating-wind-power-can01709nas a2200241 4500008003900000022001400039245010900053210006900162260001200231300001200243490000700255520093400262653001001196653001401206653002901220653002701249653002801276653001301304100002301317700003201340700002301372856007201395 2011 d a0885-895000aMATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education0 aMATPOWER SteadyState Operations Planning and Analysis Tools for c02/2011 a12 - 190 v263 aMATPOWER is an open-source Matlab-based power system simulation package that provides a high-level set of power flow, optimal power flow (OPF), and other tools targeted toward researchers, educators, and students. The OPF architecture is designed to be extensible, making it easy to add user-defined variables, costs, and constraints to the standard OPF problem. This paper presents the details of the network modeling and problem formulations used by MATPOWER, including its extensible OPF architecture. This structure is used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits. Simulation results are presented for a number of test cases comparing the performance of several available OPF solvers and demonstrating MATPOWER's ability to solve large-scale AC and DC OPF problems.10aCERTS10aload flow10aoptimal power flow (OPF)10apower system economics10areliability and markets10aRM07-0021 aZimmerman, Ray, D.1 aMurillo-Sanchez, Carlos, E.1 aThomas, Robert, J. uhttps://certs.lbl.gov/publications/matpower-steady-state-operations01450nas a2200241 4500008003900000245007500039210006900114260001200183490000700195520068800202653001000890653002400900653001800924653002700942653002800969100001800997700002501015700002301040700002101063700002501084700002501109856007401134 2010 d00aEfficient Market Design and Public Goods, Part II: Theoretical Results0 aEfficient Market Design and Public Goods Part II Theoretical Res c01/20100 v113 aElectric power is traditionally comprised of valued services, including real and reactive power, voltage, frequency and reliability in its most general sense. In this second part of our two-part paper we show mathematically that of these, only real and reactive power are purely private goods, in that power consumed by one customer cannot be used by another and customers can be excluded from receiving any power. The other ancillary services, including voltage, frequency and reliability are shown to be public goods. The first order conditions presented clearly illustrate that the public goods occurring in electric power systems comprise a significant problem for market design.10aCERTS10aelectricity markets10amarket design10apower system economics10areliability and markets1 aToomey, David1 aSchulze, William, D.1 aThomas, Robert, J.1 aThorp, James, S.1 aTylavsky, Daniel, J.1 aSchuler, Richard, E. uhttps://certs.lbl.gov/publications/efficient-market-design-and-public03217nas a2200205 4500008003900000245008200039210006900121260001800190520255300208653002302761653001602784653002802800653001302828653001302841653001502854100002302869700002302892700002502915856007102940 2010 d00aGeographical averaging and ancillary services for stochastic power generation0 aGeographical averaging and ancillary services for stochastic pow bIEEEc09/20103 aThe distribution of stochastic generation from renewables across different geographical locations can, in certain cases, help to mitigate the inherent variability in output. This variability of generation from renewables may (1) increase the operating costs of the conventional generators used to follow the net load not supplied by stochastic capacity and (2) increase the amount of reserve conventional generating capacity needed to maintain Operating Reliability. In this scenario, customers have lower wholesale prices, due to reductions in the total annual generation from fossil fuels, while generators face higher operating costs for conventional generators caused by additional ramping that partly offset the customer benefits. However, the lower wholesale prices ($/MWh) imply lower annual earnings for conventional generators that lead to higher amounts of missing money ($/MW) needed to maintain the financial adequacy of installed generating units. The objective of this paper is to determine how variability from a stochastic generation resource affects the optimal hour-to-hour dispatch of generating units and the corresponding operating costs and wholesale prices. The results show that the inclusion of ramping costs for conventional generation affect the amount of energy dispatched from the stochastic generator, and the total costs composition observed in the system. The Cornell SuperOPF is used to illustrate how the operating costs and wholesale prices can be determined for a reliable network (the amount of conventional generating capacity needed to maintain Operating Reliability is determined endogenously). The results in this paper use a typical daily pattern of load and capture the cost of ramping by including additions to the operating costs of the generating units associated with the hour-to-hour changes in their optimal dispatch. The calculations for determining endogenous up and down reserves are included, and the wind generation cost is assumed to be zero. Ad- - ditionally, the maximum and minimum available capacities for all hours in the day are constrained to the optimal capacities for the hours with the highest and the lowest loads. Different scenarios are evaluated for a given hourly realization of wind speeds using specified amounts of installed wind capacity with and without ramping costs. The analysis also evaluates the effects of eliminating network constraints, as well as the elimination of wind variability by accounting for the effects of spatial aggregation of different wind locations.10aancillary services10areliability10areliability and markets10aRM12-00410aSuperOPF10awind power1 aMount, Timothy, D.1 aThomas, Robert, J.1 aLamadrid, Alberto, J uhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5649447&tag=103364nas a2200265 4500008003900000020002200039245008300061210006900144260004100213300001100254520249800265653002302763653002402786653002702810653002802837653001302865653001302878653001502891100002302906700002502929700002302954700002302977700002303000856007503023 2010 d a978-1-4244-5509-600aThe Hidden System Costs of Wind Generation in a Deregulated Electricity Market0 aHidden System Costs of Wind Generation in a Deregulated Electric aHonolulu, Hawaii, USAbIEEEc01/2010 a1 - 103 aEarlier research has shown that adding wind capacity to a network can lower the total annual operating cost of meeting a given pattern of loads by displacing conventional generation. At the same time, the variability of wind generation and the need for higher levels of reserve generating capacity to maintain reliability standards impose additional costs on the system that should not be ignored. The important implication for regulators is that the capacity cost of each MW of peak system load is now much higher. Hence, the economic benefits to a network of using storage and controllable load to reduce the peak system load will be higher with high penetrations of wind generation. These potential benefits will be illustrated in a case study using a test network and the SuperOPF. An important feature of the SuperOPF is that the amount of conventional generating capacity needed to maintain Operating Reliability is determined endogenously, and as a result, it is possible to determine the net social benefits of relying more on an intermittent source of generation, such as wind capacity, that lowers operating costs but increases the cost of maintaining System Adequacy. The capabilities of the SuperOPF provide a consistent economic framework for evaluating Operating Reliability in real-time markets and System Adequacy for planning purposes. Basically, a financially viable investment requires that the reductions in the total annual costs of the existing system should be larger than the annualized cost of financing the addition of, for example, wind generation to a network. The scenarios considered make it possible to determine 1) the amount of conventional generating capacity needed to meet the peak system load and maintain System Adequacy, 2) the amount of missing money paid to generators to maintain Financial Adequacy, 3) changes in the congestion rents for transmission that are collected by the system operator, and finally, 4) the total annual system costs paid by customers- directly in the Wholesale Market and, indirectly, as missing money. The results show that the benefits (i.e. the reduction in the total annual system costs) from making an investment in wind capacity and/of upgrading a tie line are very sensitive to 1) how much of the inherent variability of wind generation has to be accommodated on the network, and 2) how the missing money paid to conventional generators is determined (e.g. comparing a regulated market and a deregulated market).

10acontrollable loads10aelectricity markets10apower system economics10areliability and markets10aRM12-00410aSuperOPF10awind power1 aMount, Timothy, D.1 aLamadrid, Alberto, J1 aManeevitjit, Surin1 aThomas, Robert, J.1 aZimmerman, Ray, D. uhttps://certs.lbl.gov/publications/hidden-system-costs-wind-generation02442nas a2200217 4500008003900000020002200039245009600061210006900157260004100226300001100267520169800278653002801976653001302004653001302017100002302030700002502053700002302078700002302101700002302124856007702147 2009 d a978-0-7695-3450-300aEvaluating the Net Benefits of Investing in New Wind and Transmission Capacity on a Network0 aEvaluating the Net Benefits of Investing in New Wind and Transmi aWaikoloa, Hawaii, USAbIEEEc01/2009 a1 - 103 aThe SuperOPF provides a framework linking the short-run criterion of "operating reliability" and the long-run criterion of "system adequacy" on an AC network. This is accomplished by allowing for load shedding as an expensive option to meet contingencies. The high cost of "energy-not-served" implies that some equipment can be very valuable in contingencies if it reduces the amount of energy-not-served. Calculating the nodal prices correctly for different states of the system provides the basis for determining the economic value of improved reliability and investments in additional network capacity. The objective of this paper is to extend the cooptimization framework to determine the net economic benefit of adding an intermittent source of generation, such as wind capacity, and new transmission capacity to a network. Using the cooptimization framework, this situation can be represented by defining new contingencies that correspond to unpredicted changes in the level of generation from the wind capacity. Using the SuperOPF, the amount of additional reserve capacity that is required to maintain reliability with higher levels of wind penetration is determined endogenously. An empirical example illustrates how the SuperOPF can be used to determine the expected annual cost of meeting load after wind capacity is installed at a remote location. These annual costs are reduced when additional transmission capacity is installed to reduce congestion in transferring wind generation to a load center. Hence, the economic issue is to determine whether the savings in production costs are high enough to cover the annualized cost of investing in the new transmission capacity.

10areliability and markets10aRM05-00110aSuperOPF1 aMount, Timothy, D.1 aLamadrid, Alberto, J1 aManeevitjit, Surin1 aThomas, Robert, J.1 aZimmerman, Ray, D. uhttps://certs.lbl.gov/publications/evaluating-net-benefits-investing-new02463nas a2200265 4500008003900000245013200039210006900171260001200240300000700252520157200259653002201831653002801853653002701881653001301908100002501921700002301946700002301969700002501992700002302017700002502040700002402065700002202089700002002111856006602131 2009 d00aFacilitating Environmental Initiatives While Maintaining Efficient Markets and Electric System Reliability Final Project Report0 aFacilitating Environmental Initiatives While Maintaining Efficie c10/2009 a533 aWe use an alternating-current model of the power network in northeastern North America to predict the effects of several different incentive-based carbon dioxide regulations. We use all of the kinds of flow equations and constraints that govern the actual system. To our knowledge, this report is the first to analyze an environmental policy using an alternating-current model of a power network. This report makes three contributions to the environmental and energy economics literature. The first is to demonstrate and further develop the use of alternating current modeling. The second is to compare the predictions of an alternating-current model with those of a direct-current approximation of the same model and with an unlimited-transmission model of the same region. This comparison is a test of whether our more complex modeling is warranted. The third contribution is to predict the effects of different incentive-based carbon dioxide emission regulations on emissions and total variable cost. Among other scenarios, we simulate a U.S.-only regulation, a Canada-only regulation, the Regional Greenhouse Gas Initiative ("RGGI") in the presence of a drought, the effects of exempting smaller generators from a carbon dioxide regulation (as done in RGGI), and the interaction of incentive-based carbon dioxide and sulfur dioxide regulations. We have not previously seen any of these examined in the literature. In addition, we consider the impact of long-run demand response that can mitigate the impact of regulation by reducing demand for electricity.

10aMarket mechanisms10areliability and markets10areliability management10aRM12-0021 aSchulze, William, D.1 aThomas, Robert, J.1 aMount, Timothy, D.1 aSchuler, Richard, E.1 aZimmerman, Ray, D.1 aTylavsky, Daniel, J.1 aShawhan, Daniel, L.1 aMitarotonda, Doug1 aTaber, John, T. uhttps://certs.lbl.gov/publications/facilitating-environmental01290nas a2200205 4500008003900000020002200039245008400061210006900145260003900214300001000253520057600263653002400839653002900863653002800892653001300920100002300933700003200956700002300988856007301011 2008 d a978-1-4244-1905-000aAn advanced security constrained OPF that produces correct market-based pricing0 aadvanced security constrained OPF that produces correct marketba aPittsburgh, PA, USAbIEEEc07/2008 a1 - 63 aSecurity constrained optimal power flow programs are important tools for ensuring correct dispatch of supply while respecting the many constraints imposed by the delivery system. In addition to getting the dispatch right, locational prices must be calculated with equal precision in order to infuse market participants with the proper incentives for operation and investment. In this paper we discuss a co-optimization framework in which contingencies, ancillary services, and network constraints are correctly accounted for in determining both dispatch and price.

10aelectricity markets10aoptimal power flow (OPF)10areliability and markets10aRM07-0021 aThomas, Robert, J.1 aMurillo-Sanchez, Carlos, E.1 aZimmerman, Ray, D. uhttps://certs.lbl.gov/publications/advanced-security-constrained-opf02125nas a2200241 4500008003900000022001300039245009700052210006900149260001200218300000700230490000600237520139800243653002601641653002801667653001301695100001301708700001301721700001301734700002301747700002301770700001901793856007101812 2008 d a1751868700aNodal probabilistic production cost simulation considering transmission system unavailabilty0 aNodal probabilistic production cost simulation considering trans c01/2008 a320 v23 aA nodal probabilistic production cost simulation method is described for power system long-term expansion planning considering unavailability and delivery limitation constraints of the transmission system. This new nodal production cost simulation model includes capacity constraints and unavailabilities of generators as well as transmission lines. This simulation methodology comes from the nodal composite power system equivalent load duration curve (CMELDC), based on a new effective load model at load points developed by the authors. The nodal CMELDC can be obtained from convolution integral processing of the outage capacity probability distribution function of the fictitious generator and the original LDC. It is expected that the new simulation model based on the nodal CMELDC proposed here will provide solutions to many problems based on nodal and decentralised operation and control of electric power systems under a competition environment. The nodal CMELDC based on the new model at load points can extend application areas of nodal probabilistic production cost simulation, probabilistic congestion cost assessment, analytical outage cost assessment and nodal reliability evaluation and so on at load points. The characteristics and effectiveness of this new proposed methodology are illustrated by a small system case study using a network flow and enumeration method.

10apower system planning10areliability and markets10aRM05-0011 aChoi, J.1 aTran, T.1 aKwon, J.1 aThomas, Robert, J.1 aMount, Timothy, D.1 aBillinton, Roy uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=443610301382nas a2200241 4500008003900000245002700039210002300066260001200089300000700101520072200108653002200830653002800852653002700880653001300907653001300920100002500933700002300958700002300981700003201004700002301036700002301059856005801082 2008 d00aA "SuperOPF" Framework0 aSuperOPF Framework c12/2008 a593 aThe objective of the SuperOPF project is to develop a framework that will provide proper allocation and valuation of resources through true co-optimization across multiple scenarios. Instead of solving a sequence of simpler and approximate sub-problems, the SuperOPF approach combines as much as possible into a single mathematical programming framework, with a full AC network and simultaneous co-optimization across multiple scenarios with stochastic costs.

This effort involves development of the problem formulations, implementation of research grade software codes, and testing of the methods and algorithms on a range of case studies to demonstrate their added value over currently available tools.

10aMarket mechanisms10areliability and markets10areliability management10aRM05-00310aSuperOPF1 aLamadrid, Alberto, J1 aManeevitjit, Surin1 aMount, Timothy, D.1 aMurillo-Sanchez, Carlos, E.1 aThomas, Robert, J.1 aZimmerman, Ray, D. uhttps://certs.lbl.gov/publications/superopf-framework01912nas a2200241 4500008003900000245006200039210006100101260003200162300000700194520115200201653002201353653002801375653002701403653001301430100002301443700002301466700002501489700002501514700002301539700002401562700001801586856006601604 2006 d00aMarkets for Reactive Power and Reliability: A White Paper0 aMarkets for Reactive Power and Reliability A White Paper bCornell Universityc12/2006 a573 aThis analysis addresses that issue, but in the much broader context of determining an optimal bulk power supply system, both in terms of such a system's operation and of its investments for capacity in transmission lines, generation, and capacitors. This broad, integrated perspective is required because of the complex interactions between individual aspects of the electricity supply system. By using the economic objective of maximizing net benefits to society (gains from consumption of electricity that is reliably provided at stable voltages minus the cost of efficient provision), we not only determine optimal levels of energy consumption and installed capacity of facilities but also estimate the socially optimal level of reliability endogenously by weighing its benefits and costs. By adopting the perspective of a benevolent social planner, the analysis identifies the decisions that can be decentralized and determined efficiently through markets and the services that, because they have public-good-like attributes, require some intervention by a central authority to properly provide that aspect of the electric power supply.

10aMarket mechanisms10areliability and markets10areliability management10aRM06-0031 aThomas, Robert, J.1 aMount, Timothy, D.1 aSchuler, Richard, E.1 aSchulze, William, D.1 aZimmerman, Ray, D.1 aShawhan, Daniel, L.1 aToomey, David uhttps://certs.lbl.gov/publications/markets-reactive-power-and01930nas a2200217 4500008003900000245007800039210006900117260001200186300000700198520123400205653001001439653003201449653002201481653002801503653002701531100001401558700002101572700002301593700002301616856007301639 2003 d00aLocational Pricing and Scheduling for an Integrated Energy-Reserve Market0 aLocational Pricing and Scheduling for an Integrated EnergyReserv c01/2003 a103 aIt is well known that given a network that can become constrained on voltage or real power flows, reserves must also be spatially located in order to handle all credible contingencies. However, to date, there is no credible science-based method for assigning and pricing reserves in this way. Presented in this paper is a new scheduling algorithm incorporating constraints imposed by grid security considerations, which include one base case (intact system) and a list of possible contingencies (line-out, unit-lost, and load-growth) of the system. By following a cost-minimizing co-optimization procedure, both power and reserve are allocated spatially for the combined energy and reserve markets. With the Lagrange multipliers (dual variables) obtained, the scheduling algorithm also reveals the locational shadow prices for the reserve and energy requirements. Unlike other pricing and scheduling methods in use, which are usually ad-hoc and are based on engineering judgment and experience, this proposed formulation is likely to perform better in restructured markets when market power is a potential problem. An illustrative example of a modified IEEE 30-bus system is used to introduce concepts and present results.

10aCERTS10alocational marginal pricing10aMarket mechanisms10areliability and markets10areliability management1 aChen, Jie1 aThorp, James, S.1 aThomas, Robert, J.1 aMount, Timothy, D. uhttps://certs.lbl.gov/publications/locational-pricing-and-scheduling01823nas a2200193 4500008003900000245014700039210006900186260001200255300000700267520113500274653001001409653003201419653002801451653002001479100002101499700002301520700001401543856007201557 2002 d00aTime-space Methods for Determining Locational Reserves: A Framework for Location Based Pricing and Scheduling for Reserve Markets Final Report0 aTimespace Methods for Determining Locational Reserves A Framewor c12/2002 a673 aAn overriding factor in the power system operation is the maintenance of system security. Historically, the term security, when applied to the electric power system, refers to the ability of the bulk system to withstand sudden disturbances such as electric short circuits or unanticipated loss of system components. The static nature of the problem, that is, guaranteeing that in the post-contingency state all power system components are operating within established limits, is tractable once the set of credible contingencies is known. Generally, the most severe contingency is the sudden and unanticipated loss of a large generating unit although the loss of a critical line or a sudden and large increase in load at strategic locations could be just as catastrophic. The problem of whether or not the system can survive the transition, that is, the dynamic nature of system security, is still a hard and unresolved problem. Since in most systems load is not dispatchable, the security of the system depends on having the proper level, location and type of operating reserves available when needed to meet a contingency.

10aCERTS10alocational marginal pricing10areliability and markets10areserve markets1 aThorp, James, S.1 aThomas, Robert, J.1 aChen, Jie uhttps://certs.lbl.gov/publications/time-space-methods-determining-001327nas a2200169 4500008003900000245014800039210006900187260001200256300000700268520068400275653003200959653002000991100002101011700003201032700002301064856007001087 2001 d00aTime-space Methods for Determining Locational Reserves: A Framework for Location Based Pricing and Scheduling for Reserve Markets Annual Report0 aTimespace Methods for Determining Locational Reserves A Framewor c11/2001 a203 aThis project is exploring several options for the solution of the reserve scheduling problem that are also compatible with the idea of a deregulated reserve market structure. One of the major questions is whether it is possible to devise a cost-minimizing scheduling algorithm for the spatially distributed reserve problem that reveals the location-based shadow prices for the reserve requirements. The constraints imposed by grid security considerations should be taken into account in the procedure. If this major question is answered in the affirmative, a framework for a reserve power market based on the computed shadow prices should be derived automatically from it.

10alocational marginal pricing10areserve markets1 aThorp, James, S.1 aMurillo-Sanchez, Carlos, E.1 aThomas, Robert, J. uhttps://certs.lbl.gov/publications/time-space-methods-determining01399nas a2200205 4500008003900000245011600039210006900155260001200224300000700236520069800243653002300941653002200964653002800986653002701014100002301041700002301064700002301087700001501110856006801125 2000 d00aExperimental and Theoretical Evaluation of Current and Proposed Markets Including Effects of Ancillary Services0 aExperimental and Theoretical Evaluation of Current and Proposed c03/2000 a323 aThis report first discusses the unique experimental platform POWERWEB and the LEEDR lab, and their roles in this project. PJM and New England market data were analyzed to determine their efficiency as well to try and quantify the effects of similarities and differences between market rules. Finally, experiments were performed to try and determine if market characteristics were indeed captured. We are especially interested in ancillary service market design. An important result is that we are able to design autonomous agents to operate the markets, where these agents capture essential market characteristics. This means we can use these agents to examine the operation of systems.

10aancillary services10aMarket mechanisms10areliability and markets10areliability management1 aThomas, Robert, J.1 aMount, Timothy, D.1 aZimmerman, Ray, D.1 aEde, Simon uhttps://certs.lbl.gov/publications/experimental-and-theoretical