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Dr. Pramod Khargonekar Professor University of Florida

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Pramod P. Khargonekar Department of Electrical and Computer Engineering Smart Grid and Integration of Renewable Energy Impact of the Southeast in the World's Renewable Energy Future SEC Symposium Atlanta, GA February 12, 2013

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Acknowledgements Joint work with E. Bitar (Cornell), K. Poolla & P. Varaiya (Berkeley), E. Bayenes (Valladolid), R. Rajgopal (Stanford), M. Fang (Florida), … Support from NSF and Florida Energy Systems Consortium

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Electric Grid - Background Smart Grid Renewable Generation Grid Integration Storage Future Directions Outline Focus on system operations, not on specific hardware technologies

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High voltage transmission network is a mesh network Distribution networks are largely radial networks (Socio-economic-technical) Dynamic system with phenomena at many time- scales Milliseconds, seconds, minutes, hours, days, months, and years Geographically distributed yet tightly interconnected Electric energy storage is very expensive and nearly impossible Energy produced must equal energy consumed on a second-by-second basis – power balance A complex hierarchical distributed control system has evolved over the years to ensure stability and performance of the large scale networked power system Electric Grid Characteristics

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Power Balance Power balance – balance power generation and consumption on a second-by-second basis Main Approach: adjust supply to meet demand with reliability Natural uncertainty in consumption [load] Use of reserve capacity to manage uncertainty and contingencies Day-ahead – hourly schedules, one day ahead Real-time – 5 minute schedules, 15 minutes ahead Automatic generation control using system frequency Deregulation of the electricity sector – unique mix of engineering and economics

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Smart Grid = Power Grid + Sensors + Communications + Computation + Control Source: DOE

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Smart Grid is a Vision “a stronger, smarter, more efficient electricity infrastructure that will encourage growth in renewable energy sources, empower consumers to reduce their energy use, and lay the foundation for sustained, long-term economic expansion” Steven Chu, U.S. Energy Secretary, 2009

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Renewable Electricity Production Hydro Biomass Geothermal Wind Solar

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Source: EPRI Mismatch Between Production and Consumption Missing Link: Transmission Capacity

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US Wind Capacity

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US PV Installation Source: IREC USA

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Renewable Electricity Generation Source: EIA

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Power output varies in all time frames: Annual Seasonal Daily Hours Minutes Seconds Variability of Wind and Solar Source: CAISO Intermittency, uncontrollability, and uncertainty - principal causes of difficulty at the operational level in integration of wind and solar into the grid.

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Source: Integration of Renewable Resources at 20% RPS, CAISO, 2010 Large ramps, up and down, pose particular difficulties

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Uncertainty – reliable predictions of power output are hard, particularly day ahead Uncontrollability – power output cannot be controlled as desired Intermittency – even if we could predict perfectly, the power output is inherently variable Variable Generation captures all three aspects into a single phrase Variability – Three Distinct Issues

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Regulatory guidelines require periodic assessment and multi-year planning to ensure sufficient generation capacity to meet demand LOLP - 1 day in 10 years criterion (p=0.9997) Resource Adequacy (RA) requirements; capacity markets Particularly challenging in deregulated markets Capacity credit Nameplate capacity fraction for meeting RA requirements Several probabilistic analysis techniques for CC calculation What is the capacity credit of VG (wind)? ~PJM, MISO – 13%, NYISO – 10% (summer), SPP – 10%, E.ON – 8%, … Could be even less at deep penetration What are the impacts on power system planning? How much traditional generation can be displaced by VG? What happens at deep penetration of VG? Capacity Credit

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Better forecasts are extremely valuable Prediction error decreases with horizon Forecast of wind velocity or solar flux – numerical weather prediction models Conversion of wind velocity or solar flux into electric power Use of neural networks and statistical forecasting methods Natural opportunity for nonlinear estimation methods enabled by sensors and communications Event detection methodologies to predict the timing and magnitude of ramp events Forecasting Botterud et al, 2010 CAISO

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Renewable Integration Many large scale studies have been conducted in the last few years Eastern Wind Integration and Transmission Study (EWITS) Western Wind and Solar Integration Study (WWSIS) CAISO Integration Studies Integration of Variable Generation Task Force (IVGTF) NREL Studies, European projects, … General conclusions: With sufficient transmission capacity, we can go up to 20-30% renewable electricity with significant impacts on power systems operations – implications for: Markets, reserves, balancing areas, flexibility of production stack, fast ramping resources, storage, adjustable demand, …. This is the focus of our work

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Large simulation studies to estimate the impact of 20% and 33% RPS Load following reserves: 2,292 MW in ,207 MW in ,423 MW in 2020 Up regulation reserves 277 MW in MW in ,135 MW in 2020 Questions: Is there a more rigorous method for estimating the additional reserves? Are there techniques to reduce the need for these additional reserves? Will/should renewable producers be required to provide their own reserves? California Results

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Wind power is taken to be a discrete-time stochastic process Normalized to name-plate capacity: CDF of the wind power process: Time averaged CDF: Wind Power Model

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Two settlement market Wind producer offers a contract for constant power C in the DAM at price p Imbalance prices in the real-time market: q shortfall price, excess price Imbalance prices q and are taken to be random variables while p is assumed to be known Market Model Contract, C Wind power w Forward day-ahead market Real-time market power time 1T p (q, )

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Profit Function Energy shortfall Energy excess

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Assumptions: (1) Wind power producer is a price taker (2) Wind power and prices are uncorrelated Optimal Policy is a Quantile Policy

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Storage Source: Electrical Storage Association Topic of intense research worldwide Will have a major impact on Renewables and electric transport

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Energy Storage Model Constraints Stored energy dissipation - Injection and extraction efficiency – inj ext

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Same two settlement market as before At each time instant, we can choose to inject energy into or extract energy from the storage device to maximize the net profit: Optimal Contract with Storage S = net injection = P inj - P ext Storage operation policy = g

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Admissible policies: all feedback policies that depend on the past values of w and e Lemma: Greedy policy, g*, is optimal. Theorem: J(g*, C) is concave in C. Theorem: Optimal profit is a concave, monotone non- decreasing function of the storage size. Results J

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Consider a collection of geographically dispersed VG producers. Intuition: Averaging can reduce variability Questions: Can a group of wind power producers increase their collective profits by aggregating and offering their power output as a single entity? What profit sharing policy will ensure that the individual producers cooperate? Benefits of Aggregation Bayens et al. CDC’2011

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Current: adjust the generation to meet random demand Future: adjust demand to meet random generation Flexible Demand: heating, air-conditioning, refrigeration, water heaters, EVs, … These are energy consumers, not power consumers Questions: How can we optimize aggregate and optimize flexibility of large numbers of individual flexible loads? How can sensing and communications be used for distributed control of flexible loads? What incentive and pricing mechanisms will be effective in getting consumers to participate in adjustable demand programs? How can these distributed resources be integrated into power system operations with large RG penetration? Paradigm Change

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Scenario: Large numbers of solar, wind, CHP, and micro-generators in the distribution system Adjustable demand, electric vehicles Storage Sensing, communications, computing, control (SG) Questions: What is the optimal, scalable, control and communications architecture to control such a large scale distributed power system? How can we do this while respecting the legacy centralized grid and minimize the need for additional reserves? What level of renewable penetration can be achieved in such a distributed scenario? Answer: GRIP: Grid with Intelligent Periphery Coordinated aggregation & control using smart grid sensing, communications, computation, and control Distributed Renewable Generation Bakken et al, SmartGridCom’2011

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E. Bitar, A. Giani, R. Rajagopal, D. Varagnolo, P. P. Khargonekar, K. Poolla, P. P. Varaiya, “Optimal Contracts for Wind Power Producers in Electricity Markets,” Proc. 50th IEEE Conference on Decision and Control, pp , E. Bitar, R. Rajagopal, P. P. Khargonekar, and K. Poolla, “Optimal Bidding Strategies for Wind Power Producers: the Role of Reserve Margins and Energy Storage,” Proc. American Control Conference, pp., June E. Bitar, P. P. Khargonekar, and K. Poolla, “Systems and Control Opportunities in Smart Grid and Renewable Integration,” Proc IFAC World Congress, Milan, ITALY. D. Bakken, A. Bose, K. M. Chandy, P. P. Khargonekar, A. Kuh, S. Low, A. von Meier, K. Poolla, P. P. Varaiya, and F. Wu, “GRIP – Grids with Intelligent Periphery: Control Architectures for Grid2050,” Proc. IEEE SmartGridComm, A. Giani, E. Bitar, M. Garcia, M. McQueen, P. P. Khargonekar, and K. Poolla, “Smart Grid Data Integrity Attacks: Characterizations and Countermeasures,” Proc. IEEE SmartGridComm, 2011 E. Baeyens, E. Bitar, P. P. Khargonekar, and K. Poolla, “Wind Energy Aggregation: A Coalitional Game Approach,” Proc. IEEE Conference on Decision and Control, E. Bitar, K. Poolla, P. P. Khargonekar, R. Rajgopal, P. Varaiya, and F. Wu, “Selling Random Energy,” Hawaii International Conference on Systems Science, E. Bitar, R. Rajagopal, P. P. Khargonekar, K. R. Poolla, and P. Varaiya, “Bringing Wind Energy to Market,” submitted for publication to IEEE Transactions on Power Systems. Our Publications

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Questions?

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Dr. Pramod Khargonekar Professor University of Florida

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