Markets, Policy, and Computation Minitrack

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Public concerns about the adverse environmental and health effects of using fossil fuels and nuclear power to generate electricity have led to a greater reliance on renewable sources of generation that are inherently variable and uncertain. This trend is accompanied by increased proliferation of distributed resources, storage and smart grid technologies that facilitate demand response and greater observability of the grid. As a result the electric power industry faces new challenges in planning and operation of the power system that require new market mechanisms and computational optimization tools to achieve productive and allocative efficiencies. A key concept in addressing the new challenges associated with renewables integration and in mobilizing a diverse resource portfolio to mitigate the uncertainty and variability of these intermittent resources is flexibility. Hence the central theme of this mini track revolves around identifying metrics and the needs for flexibility based on available data, characterizing market products and public policies that incentivizes flexibility and optimizing resource use to meet flexibility needs so as to assure system reliability in face of uncertainty at least cost. The first session focuses on identifying the needs for flexibility and market constructs that will facilitate the mobilization procurement and deployment of resources that provide flexibility (including generation, storage, transmission and demand). The second session focuses on computational aspects of optimizing the planning, operation and procurement of a diverse resource portfolio, including power flow and unit commitment optimization under uncertainty.

Minitrack Chair:

Shmuel S. Oren
University of California at Berkeley

Session 1: Market Design and Analysis
Session Organizer and Session Chair: Richard D. Tabors,

High penetration levels of variable resources such as wind and solar power introduce significant uncertainty and variability into the power system and electricity markets creating high and unpredictable ramps in net load. Such ramps present a great challenge for system operators who must maintain reliable operation and efficient markets with simultaneous maximum utilization of renewable energy. Furthermore, procuring and maintaining sufficient reserves on hand in anticipation of such ramps often requires out of merit dispatch and high levels of costly reserves. Incentivizing investment and market participation by diverse resources on the supply and demand side who can provide flexibility economically requires new market mechanisms and operating procedures.

This session aims to bring together leading researchers for comprehensive discussion of appropriate metrics and market constructs for flexibility in electric energy systems. The flexibility will be inherently multi-time-scale, and can be extracted from supply/demand side, energy storage, as well as the delivery infrastructure of the electric energy system (e.g. topology control and FACTS). Papers that discuss innovative assessment and provision of flexibility from both theoretical and empirical perspectives are encouraged.

Session 2: Frontiers in Power Systems Optimization
Session Organizer and Session Chair: Bill Rosehart,

The evolution of the Electric Power system with increased data and information being available for planning, operations and control is presenting new challenges in optimization and computation. Furthermore, the proliferation of intermittent and variable resources, distributed generation, storage and demand response requires new analytic tools capable of handling far greater levels of both temporal and spatial data and dealing with uncertainty and variability on the supply and demand side. Issues of meeting ramping requirements given greater penetration of renewable resources; of increased technology options that can provide flexibility in real time operations; and new sources of distributed generation and storage require advances in both the development and application of optimization tools. Recent progress in optimization theory and software development complimented by dramatic increases in the capability of low cost computer hardware is opening the door for a new generation of software for Electric Power Systems to address the above challenges through new problem formulations and algorithm design.

The objective of this session is to bring together papers focused on new problem formulations, algorithmic developments and computational advances focusing on improvements of solution quality and computational efficiency in optimization of planning, operation and market clearing for the electric power industry. Papers focusing on new approaches to power flow optimization, stochastic and robust unit commitment, topology control, chance constrained optimization and multi-objective programming are particularly encouraged.


Recent Submissions

Now showing 1 - 10 of 10
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    Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning
    ( 2017-01-04) Deng, Weisi ; Ji, Yuting ; Tong, Lang
    The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators.
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    The Case for a Simple Two-Sided Electricity Market
    ( 2017-01-04) Lamadrid, Alberto ; Jeon, Wooyoung ; Lu, Hao ; Mount, Tim
    This paper builds on the results from our earlier research on the design of electricity markets that have to accommodate the uncertainty associated with high penetrations of renewable sources of energy. The key results show that 1) distributed storage (deferrable demand) is an effective way to reduce total system costs, 2) a simple market structure for energy allows aggregators to meet their customers' energy needs and provide ramping services to the system operator, and 3) using a receding-horizon optimization to dispatch units for the next market time-step benefits from the availability of more accurate forecasts of renewable generation and allows market participants to adjust their bids and offers in response to this new information. In our two-sided market, distributed storage in the form of deferrable demand is controlled locally by independent aggregators to minimize their expected payments for energy in the wholesale market, subject to meeting the energy needs of their customers. In addition, these aggregators are responsible for maintaining a stable power factor by installing local capabilities that automatically deal with local power imbalances. Failure to do this triggers penalties paid to the system operator. \ \ Our earlier results have shown that it is optimal for an aggregator to submit demand bids into a day-ahead market that include threshold prices for charging and discharging storage and also ensure that the expected amount of stored energy is consistent with the capacity limits of their storage. Because departures from the expected daily pattern of renewable generation are generally persistent (highly positive serial correlated), it is likely that the system operator determines an optimum pattern of demand for the aggregator that violates the capacity limits of storage by the end of the 24-hour period. If the market uses a receding horizon, the results in this paper show that aggregators can modify their bids to ensure that the capacity limits of storage are never violated in the next market time-step. \ \ In an empirical application, a stochastic form of multi-period security constrained unit commitment with optimal power flow (the MATPOWER Optimal Scheduling Tool, MOST) using a receding-horizon optimization determines the optimum dispatch and reserves for the next hour and forecasts of the nodal prices for the next 24 hours. The results show that locally controlled deferrable demand is almost as effective as centrally controlled deferrable demand as a way to reduce system costs and mitigate the variability of renewable generation. The additional advantage from using a receding horizon is that the system operator always charges/discharges the storage managed locally by aggregators within the capacity constraints of the storage.
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    Power System State Estimation and Bad Data Detection by Means of Conic Relaxation
    ( 2017-01-04) Madani, Ramtin ; Lavaei, Javad ; Baldick, Ross ; Atamtürk, Alper
    This paper is concerned with the power system state estimation problem, which aims to find the unknown operating point of a power network based on a set of available measurements. We design a penalized semidefinite programming (SDP) relaxation whose objective function consists of a surrogate for rank and an l1-norm penalty accounting for noise. Although the proposed method does not rely on initialization, its performance can be improved in presence of an initial guess for the solution. First, a sufficient condition is derived with respect to the closeness of the initial guess to the true solution to guarantee the success of the penalized SDP relaxation in the noiseless case. Second, we show that a limited number of incorrect measurements with arbitrary values have no effect on the recovery of the true solution. Furthermore, we develop a bound for the accuracy of the estimation in the case where a limited number of measurements are corrupted with arbitrarily large values and the remaining measurements are perturbed with modest noise values. The proposed technique is demonstrated on a large-scale 1354-bus European system.
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    Locational Marginal Pricing of Natural Gas subject to Engineering Constraints
    ( 2017-01-04) Rudkevich, Alex ; Zlotnik, Anatoly
    We derive a price formation mechanism to maximize social welfare for a pipeline network that delivers natural gas from suppliers to consumers. The system is modeled as a metric graph subject to physical balance laws for steady-state hydraulic flow on edges and mass balance at nodes. The pricing mechanism incorporates engineering constraints on local pressures and energy applied by gas compressors. Optimality conditions yield expressions for locational marginal prices for gas (gLMPs) and a decomposition of gLMPs into components corresponding to energy, compression, and two types of congestion. We demonstrate that price and pressure differentials between nodes have the opposite sign, so that price cannot decline in the direction of flow, and prove that the pricing mechanism is revenue adequate. We also present computational examples of congestion pricing for a small test network and a large-scale case study.
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    Electric Vehicle Storage Management in Operating Reserve Auctions
    ( 2017-01-04) Kahlen, Micha ; Ketter, Wolfgang ; Gupta, Alok
    Carsharing operators, which rent out electric vehicles for minutes or hours, lose money on idle vehicles. We develop a model that allows carsharing operators to offer the storage of these vehicles on operating reserve markets (market for quickly rampable back-up power sources that replace for instance failing power plants). We consider it a dispatch and pricing problem with the tradeoff between the payoffs of offering vehicles for rental and selling their storage. This is a problem of stochastic nature taking into account that people can rent electric vehicles at any time. To evaluate our model we tracked the location and status of 350 electric vehicles from the carsharing company Car2Go and simulated the dispatch in the Dutch market. This market needs to be redesigned for optimal use of storage. We make recommendations for the market redesign and show that carsharing operators can make substantial additional profits in operating reserve markets.
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    Can Capacity Markets Be Designed by Democracy?
    ( 2017-01-04) Blumsack, Seth ; Yoo, Kyungjin ; Johnson, Nicholas
    Regional Transmission Organizations (RTOs) are stakeholder-driven organizations where changes to rules or protocols go through a process of stakeholder approval. Based on interviews with PJM stakeholders, we observe the perception that the process is held up by specific coalitions. We use voting data from the PJM stakeholder process and a model of participatory decision-making to assess these stakeholder perceptions, integrated with a model of PJM’s capacity market to address how stakeholder-driven processes can design market constructs that promote reliability. We do observe a strong voting coalition by demand-side interests (electric distribution utilities and large direct-access customers) but not by supply-side interests. In theory, this demand-side coalition can act in a pivotal manner to prevent any rule change from going forward. In the capacity market redesign case in practice, the pivotal or swing participants are more likely a smaller segment of financial market participants, such as hedge funds and banks.
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    An Enhanced Security-Constrained Unit Commitment Model with Reserve Response Set Policies
    ( 2017-01-04) Li, Nan ; Singhal, Nikita ; Hedman, Kory
    Security-constrained unit commitment (SCUC) is a classical problem used for day-ahead commitment, dispatch, and reserve scheduling. Even though SCUC models acquire reserves, N-1 reliability is not guaranteed. This paper presents an enhanced security-constrained unit commitment formulation that facilitates the integration of stochastic resources and accounts for reserve deliverability issues. In this formulation, the SCUC is modified to incorporate a reserve response set model. The enhanced reserve model aims to predict the effects of nodal reserve deployment on critical transmission lines so as to improve the deliverability of reserves post-contingency. The enhanced reserve policies are developed using a knowledge discovery process as a means to predict reserve activation. The approach, thus, aims to acquire reserve at prime locations that face fewer reserve deliverability issues. The results show that the proposed approach consistently outperforms contemporary approaches. All numerical results are based on the IEEE 73-bus test case.
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    Air-Pollution Impact of Transmission Line Capacity Expansions in Power Systems
    ( 2017-01-04) Sauma, Enzo ; González, Julio
    In this paper, we study the environmental effects on global and local pollutant emissions derived from the incorporation of new transmission circuits in existing corridors, and the interrelationships with the system economic costs and the system reliability variations. For that purpose, we develop a methodology that allows quantifying the indirect impact on pollutant emissions due to variations in power plants’ dispatch when adding a line circuit to a hydrothermal power system. Our methodology also allows the analysis of the effect of N – 1 security criterion over the pollutant emission displacement, as well as the effect of changes in demand, the hydrology scenarios, and the failure cost. We illustrate our methodology using a simplified version of the main Chilean network.
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    A New Distributed Optimization for Community Microgrids Scheduling
    ( 2017-01-04) Liu, Guodong ; Starke, Michael ; Xiao, Bailu ; Zhang, Xiaohu ; Tomsovic, Kevin
    This paper proposes a distributed optimization model for community microgrids considering the building thermal dynamics and customer comfort preference. The microgrid central controller (MCC) minimizes the total cost of operating the community microgrid, including fuel cost, purchasing cost, battery degradation cost and voluntary load shedding cost based on the customers’ consumption, while the building energy management systems (BEMS) minimize their electricity bills as well as the cost associated with customer discomfort due to room temperature deviation from the set point. The BEMSs and the MCC exchange information on energy consumption and prices. When the optimization converges, the distributed generation scheduling, energy storage charging/discharging \ and customers’ consumption as well as the energy prices are determined. In particular, we integrate the detailed thermal dynamic characteristics of buildings into the proposed model. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of proposed model.
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