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1. Pricing Energy in the Presence of Renewables

   https://doi.org/10.23919/ACC.2018.8431899  

   Zeinalzadeh, Ashkan; Ghavidel, Donya; Gupta, Vijay (June 2018, 2018 Annual American Control Conference (ACC))

   The intermittent nature of renewable energy generation implies that renewable producers rely on non-renewable producers to ensure the aggregate power delivered meets the promised quality of service. Therefore, the intermittent nature of renewable energy generation affects the committed power and market price of energy. We consider an electricity market where renewable and non-renewable generators bid by proposing their asking price per unit of energy to an independent system operator (ISO). The ISO solves a dispatch optimization problem to minimize the cost of purchased energy on behalf of the consumers. We incorporate the notion of net-load variance using the Conditional Value-at-Risk (CVAR) measure in the dispatch optimization problem to ensure that the generators are able to meet the load within a desired confidence level. We analytically derive the market clearing price of energy and dispatched powers as a function of CVAR and show that a higher penetration of renewable energies may increase the market clearing price of energy. Finally, we present descriptive simulations to illustrate the impact of renewable energy penetration on the market price of energy. 

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2. Managing Financial Risk Trade‐Offs for Hydropower Generation Using Snowpack‐Based Index Contracts

   https://doi.org/10.1029/2020WR027212  

   Hamilton, Andrew L.; Characklis, Gregory W.; Reed, Patrick M. (October 2020, Water Resources Research)

   Abstract Hydrologic variability poses an important source of financial risk for hydropower‐reliant electric utilities, particularly in snow‐dominated regions. Drought‐related reductions in hydropower production can lead to decreased electricity sales or increased procurement costs to meet firm contractual obligations. This research contributes a methodology for characterizing the trade‐offs between cash flows and debt burden for alternative financial risk management portfolios, and applies it to a hydropower producer in the Sierra Nevada mountains (San Francisco Public Utilities Commission). A newly designed financial contract, based on a snow water equivalent depth (SWE) index, provides payouts to hydropower producers in dry years in return for the producers making payments in wet years. This contract, called a capped contract for differences (CFD), is found to significantly reduce cash flow volatility and is considered within a broader risk management portfolio that also includes reserve funds and debt issuance. Our results show that solutions relying primarily on a reserve fund can manage risk at low cost but may require a utility to take on significant debt during severe droughts. More risk‐averse utilities with less access to debt should combine a reserve fund with the proposed CFD instrument in order to better manage the financial losses associated with extreme droughts. Our results show that the optimal risk management strategies and resulting outcomes are strongly influenced by the utility's fixed cost burden and by CFD pricing, while interest rates are found to be less important. These results are broadly transferable to hydropower systems in snow‐dominated regions facing significant revenue volatility. 

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3. Stochastic Planning of a Mostly-Renewable Power Grid

   https://doi.org/10.1109/CCTA54093.2023.10253007  

   Chen, Sunny; Mathieu, Johanna L.; Seiler, Peter (August 2023, IEEE Conference on Control Technology and Applications (CCTA))

   Power grid resource adequacy can be difficult to ensure with high penetrations of intermittent renewable energy. We explore enhancing resource adequacy by overbuilding renewables while modeling statistical correlations in renewable power at different sites. Overbuilding allows production during times of low power, and exploiting statistical correlations can reduce power variability and, subsequently, reduce needed renewable capacity. In this work, we present a stochastic optimization problem to size renewables and expand transmission while minimizing the expected dispatch cost. Our method uses statistical profiles of renewable production and embeds network constraints using the DC power flow equations. We assess our method’s effects on feasibility, load shedding, locational marginal prices, and generator curtailment. On the IEEE 9-bus system, we found that anti-correlation between generators reduced generation capacity needs with sufficient transmission. On the IEEE 30-bus system, we found that the optimal solution required significant overbuilding and curtailment of renewables regardless of the marginal cost of schedulable generation. 

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4. Determining Reserve Requirements for Energy Storage to Manage Demand-Supply Imbalance in Power Grids

   Parker, Kendall; Barooah, Prabir (October 2018, 2018 IEEE Electronic Power Grid (eGrid))

   Proper integration of energy storage systems (ESS) into existing or future grids will depend on the effectiveness of models which seek optimal placement and sizing at the transmission and distribution levels. Current literature reviews reveal sizing methodologies can be improved to ease infrastructure integration, and those works with models useful for planning focus solely on micro-grids, wind power and forecasting, photovoltaics, or small communities. It is of interest to create an efficient, reliable ESS sizing model for large scale grids that contains interpretable models, has less sensitivity due to low model uncertainty, yet still is dependable due to an imposed reliability criterion. This work determined the minimum feasible size ESS to satisfy reserve requirements for a power grid with a high penetration of renewable sources. Results showed imposing a reliability criterion through loss of load expectation (LOLE) and energy index of reliability (EIR) resulted in more conservative capacity needs. 

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5. Evaluating Coupling Models for Cloud Datacenters and Power Grids

   https://doi.org/10.1145/3447555.3464868  

   Lin, Liuzixuan; Zavala, Victor M.; Chien, Andrew A. (June 2021, InThe TwelfthACM International Conference on Future Energy Systems (e-Energy ’21),)

   Ardakanian, Omid; Niesse, Astrid (Ed.)

   The rapid growth of datacenter (DC) loads can be leveraged to help meet renewable portfolio standard (RPS, renewable fraction)targets in power grids. The ability to manipulate DC loads over time(shifting) provides a mechanism to deal with temporal mismatch between non-dispatchable renewable generation (e.g. wind and solar) and overall grid loads, and this flexibility ultimately facilitates the absorption of renewables and grid decarbonization. To this end, we study DC-grid coupling models, exploring their impact on grid dispatch, renewable absorption, power prices, and carbon emissions.With a detailed model of grid dispatch, generation, topology, and loads, we consider three coupling approaches: fixed, datacenter-local optimization (online dynamic programming), and grid-wide optimization (optimal power flow). Results show that understanding the effects of dynamic DC load management requires studies that model the dynamics of both load and power grid. Dynamic DC-grid coupling can produce large improvements: (1) reduce grid dispatch cost (-3%), (2) increase grid renewable fraction (+1.58%), and (3) reduce DC power cost (-16.9%).It also has negative effects: (1) increase cost for both DCs and non-DC customers, (2) differentially increase prices for non-DC customers, and (3) create large power-level changes that may harm DC productivity. 

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