Policy, Markets, and Analytics

Permanent URI for this collectionhttps://hdl.handle.net/10125/107474

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Transmission and Distribution Coordination Framework Using Parametric Programming: Optimal Pricing in The Distribution Systems
(2024-01-03) Mousavi, Mohammad; Wu, Meng
A parametric-programming-based framework was previously proposed to coordinate the market operations of the independent system operator (ISO) and the distribution system operator (DSO). This paper extends this framework by investigating optimal DSO pricing in addition to the ISO-DSO coordinated dispatch. In our DSO pricing problem, after ISO clears the wholesale market, the locational marginal price (LMP) of the ISO-DSO coupling substation is determined, the DSO utilizes this price to solve the DSO pricing problem. The DSO pricing problem determines the distribution LMP (D-LMP) in the distribution system and calculate the payment to each aggregator. Proofs are provided to 1) demonstrate the D-LMP at the ISO-DSO coupling substation from this DSO pricing problem always aligns with the wholesale LMP from the ISO; and 2) demonstrate the relationship between the DSO pricing and dispatch models. Case studies on a small illustrative example verify the performance of the proposed pricing model.
Reliability Model of Joint Electricity and Natural Gas System Considering Electric Compressor Failures under Different Network Topologies
(2024-01-03) Su, Wenjing; Blumsack, Seth; Webster, Mort
We formulate a steady-state operational model for natural gas and electric transmission that is capable of considering bi-directional interdependence. The electric transmission system depends on the gas transmission system to provide fuel to power plants for reliable operations. The gas transmission system depends on the electric transmission system to provide power for some compressors, which ensure sufficient gas deliverability. We illustrate our formulation using a gas-grid test system with realistic properties, that is based on the topology of these networks in the northeastern part of the United States and Canada. Subjecting this test system to failures involving both natural gas and electric transmission demonstrates that having a larger fraction of electric-driven gas compressors (which rely on the power grid) worsens the impact of contingencies, relative to having compressors that use natural gas and on-site engines to run. The extent of this impact is sensitive to both the spatial pattern of gas-fired generation in the power grid, and the spatial distribution of electrified compressors in the gas transmission grid.
Data-Driven Unit Commitment Refinement - a Scalable Approach for Complex Modern Power Grids
(2024-01-03) Holt, Timothy; Abhyankar, Shrirang; Kuruganti, Teja; Schenk, Olaf; Peles, Slaven
Integration of renewable generation, which is often intermittent and decentralized, substantially increases the stochasticity and complexity of power grid operations. Future power systems planning will require significant computational capability to evaluate balance between demand and supply under varying conditions, both temporally and spatially. The standard approach for generation unit commitment is to use mixed-integer linear programming to find the optimal generation schedule considering ramping and generator constraints. In the future grid this poses computational scalability challenges because generation and demand are not known with certainty due to stochasticity in weather and complexity of the grid. To address this challenge, we present a data-driven unit commitment approach that can efficiently include stochastic weather impacts and contingency considerations to improve unit commitment. Our approach uses graph-based data analytics techniques on solutions to the security constrained (and possibly stochastic) economic dispatch problem to identify potential improvements to a given unit commitment. Recent breakthroughs in fully-parallel stochastic economic dispatch software allow this approach to be scalably deployed. Simulations on synthetic South Carolina and Texas grids show this method can improve grid reliability with security constraints over a set of contingencies, while also meaningfully lowering total generation cost.
Multi Power-Market Bidding: Stochastic Optimization and Reinforcement Learning
(2024-01-03) Miskiw, Kim; Harder, Nick; Staudt, Philipp
The growing importance of short-term electricity trading over independent subsequent markets in Europe presents market participants with intricate decision challenges. Established solutions based on stochastic programs are often used but suffer from shortcomings such as the curse of dimensionality in multi-stage decision processes. Reinforcement learning is a promising alternative. However, best practices for the comparison of the two approaches and the ex-post evaluation of reinforcement learning are not yet established. In this paper, we offer a comparison of stochastic programs and reinforcement learning and propose measures for a comparative performance evaluation between the two approaches. We demonstrate them on an empirical case study over subsequent market stages of the German market zone within the coupled European power market.
Optimization of Cryptocurrency Mining Demand for Ancillary Services in Electricity Markets
(2024-01-03) Menati, Ali; Cai, Yuting; El Helou, Rayan; Tian, Chao; Xie, Le
We model the operation of a cryptocurrency mining facility with heterogeneous mining devices participating in ancillary services as an optimization problem. We propose a general formulation for the cryptominers to maximize their profit by strategically participating in ancillary services and controlling the loss of mining revenue, which requires taking into account the disparity in the efficiency of the mining machines. The optimization formulation is considered for both offline and online scenarios, and optimal algorithms are proposed to solve these problems. As a special case of our problem, we investigate cryptominers' participation in frequency regulation, where the miners benefit from their fast-responding devices and contribute to grid stability. Simulation results based on real-world Electric Reliability Council of Texas (ERCOT) traces show more than 20\% gain in profit, highlighting the advantage of our proposed algorithms.
Optimal and Incentive-Compatible Scheduling of Flexible Generation in an Electricity Market
(2024-01-03) Jiang, Yuzhou; Sioshansi, Ramteen
There is a growing need for electricity-system flexibility to maintain real-time balance between energy supply and demand. In this paper, we explore the optimal and incentive-compatible scheduling of generators for this purpose. Specifically, we examine a setting wherein each generator has a different operating cost if it is committed in advance (\eg, day- or hour-ahead) as opposed to being reserved as flexible real-time supply. We model an optimal division of generators between advanced commitment and real-time flexible reserves to minimize the expected cost of serving an uncertain demand. Next, we propose an incentive-compatible remuneration scheme with two key properties. First, the remuneration scheme incentivizes generators to reveal their true costs. Second, the scheme aligns generators' incentives with the market operator's optimal division of generators between advanced commitment and real-time reserve. We use a simple example to illustrate the market operator's decision and the remuneration scheme.
Introduction to the Minitrack on Policy, Markets, and Analytics
(2024-01-03) Oren, Shmuel; Parvania, Masood

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