arXiv subjects

Audun Botterud

Publications and source records attributed to Audun Botterud.

33 records · Page 2Linked to original sources

arXiv2022

Strategic Storage Investment in Electricity Markets

Arbitrage is one important revenue source for energy storage in electricity markets. However, a large amount of storage in the market will impact the energy price and reduce potential revenues. This can lead to strategic behaviors of profit-seeking storage investors. To study the investors' strategic storage investments, we formulate a non-cooperative game between competing investors. Each investor decides the storage investment over a long investment horizon, and operates the storage for arbitrage revenues in the daily electricity market. Different investors can deploy storage with different characteristics. Their decisions are coupled due to the market price that is determined by all the investors' decisions. We use market data from California ISO to characterize the storage impact on the market price, based on which we establish a centralized optimization problem to compute the market equilibrium. We show that an increasing number of investors will increase the market competition, which reduces investors' profits but increases the total invested storage capacity. Furthermore, we find that a slight increase in the storage efficiency (e.g., increased charge and discharge efficiency) can significantly improve an investor's profit share in the market.

q-fin.GN↗

arXiv2021

Planning low-carbon distributed power systems: Evaluating the role of energy storage

This paper introduces a mathematical formulation of energy storage systems into a generation capacity expansion framework to evaluate the role of energy storage in the decarbonization of distributed power systems. The modeling framework accounts for dynamic charging/discharging efficiencies and maximum cycling powers as well as cycle and calendar degradation of a Li-ion battery system. Results from a small-scale distributed power system indicate that incorporating the dynamic efficiencies and cycling powers of batteries in the generation planning problem does not significantly change the optimal generation portfolio, while adding substantial computational burden. In contrast, accounting for battery degradation leads to substantially different generation expansion outcomes, especially in deep decarbonization scenarios with larger energy storage capacities. Under the assumptions used in this study, it is found that battery energy storage is economically viable for 2020 only under strict carbon emission constraints. In contrast, given the projected technology advances and corresponding cost reductions, battery energy storage exhibits an attractive option to enable deep decarbonization in 2050.

eess.SY↗

arXiv2021

Optimization of Electrolyte Rebalancing in Vanadium Redox Flow Batteries

This paper presents a novel algorithm to optimize energy capacity restoration of vanadium redox flow batteries (VRFBs). VRFB technologies can have their lives prolonged through a partially restoration of the lost capacity by electrolyte rebalancing. Our algorithm finds the optimal number and time of these rebalancing services to minimize the service cost, while maximizing the revenues from energy arbitrage. We show that the linearized form of this problem can be analytically solved, and that the objective function is convex. To solve the complete problem, we develop a two-step mixed integer linear programming (MILP) algorithm, which first finds the bounds for optimal number of services and then optimizes the number, and time of the services. We then present a theoretical analysis and optimization results for a case study of energy arbitrage in New York ISO.

eess.SY↗

arXiv2021

Topology and Admittance Estimation: Precision Limits and Algorithms

Distribution grid topology and admittance information are essential for system planning, operation, and protection. In many distribution grids, missing or inaccurate topology and admittance data call for efficient estimation methods. However, measurement data may be insufficient or contaminated with large noise, which will introduce fundamental limits to the estimation accuracy. This work explores the theoretical precision limits of the topology and admittance estimation (TAE) problem, with different measurement devices, noise levels, and the number of measurements. On this basis, we propose a conservative progressive self-adaptive (CPS) algorithm to estimate the topology and admittance. Results on IEEE 33 and 141-bus systems validate that the proposed CPS method can approach the theoretical precision limits under various measurement settings.

cs.IT↗

arXiv2020

Assessing the impact of inertia and reactive power constraints in generation expansion planning

On the path towards power systems with high renewable penetrations and ultimately carbon-neutral, more and more synchronous generation is being displaced by variable renewable generation that does not currently provide system inertia nor reactive power support. This could create serious issues of power system stability in the near future, and countries with high renewable penetrations such as Ireland are already facing these challenges. Therefore, this paper aims at answering the questions of whether and how explicitly including inertia and reactive power constraints in generation expansion planning would affect the optimal capacity mix of the power system of the future. Towards this end, we propose the novel Low-carbon Expansion Generation Optimization (LEGO) model, which explicitly accounts for: unit commitment constraints, Rate of Change of Frequency (RoCoF) inertia requirements and virtual inertia provision, and, a second-order cone programming (SOCP) approximation of the AC power flow, accounting for reactive power constraints. An illustrative case study underlines that disregarding inertia and reactive power constraints in generation expansion planning can result in additional system cost, system infeasibilities, a distortion of optimal resource allocation and inability to reach established policy goals.

eess.SY↗

arXiv2020

Convex Relaxation of Combined Heat and Power Dispatch

Combined heat and power dispatch promotes interactions and synergies between electric power systems and district heating systems. However, nonlinear and nonconvex heating flow imposes significant challenges on finding qualified solutions efficiently. Most existing methods rely on constant flow assumptions to derive a linear heating flow model, sacrificing optimality for computational simplicity. This paper proposes a novel convex combined heat and power dispatch model based on model simplification and constraint relaxation, which improves solution quality and avoids assumptions on operating regimes of district heating systems. To alleviate mathematical complexity introduced by the commonly used node method, a simplified thermal dynamic model is proposed to capture temperature changes in networked pipelines. Conic and polyhedral relaxations are then applied to convexify the original problems with bilinear and quadratic equality constraints. Furthermore, an adaptive solution algorithm is proposed to successively reduce relaxation gaps based on dynamic bivariate partitioning, improving solution optimality with desirable computational efficiency. The proposed method is verified on a 33-bus electric power system integrated with a 30-node district heating system and compared to nonlinear programming solvers and constant-flow-based solutions.

eess.SY↗

arXiv2020

Bounding Regression Errors in Data-driven Power Grid Steady-state Models

Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios, and proposes an evaluation implementation based on Rademacher complexity theory. We answer key questions for data-driven models: how much training data is required to guarantee a certain error bound, and how partial physical knowledge can be utilized to reduce the required amount of data. Our results are crucial for the evaluation and application of data-driven models in power grid analysis. We demonstrate the proposed method by finding generalization error bounds for two applications, i.e. branch flow linearization and external network equivalent under different degrees of physical knowledge. Results identify how the bounds decrease with additional power grid physical knowledge or more training data.

cs.LG↗

arXiv2020

Efficient Robust Dispatch of Combined Heat and Power Systems

Combined heat and power systems facilitate efficient interactions between individual energy sectors for higher renewable energy accommodation. However, the feasibility of operational strategies is difficult to guarantee due to the presence of substantial uncertainties pertinent to renewable energy and multi-energy loads. This paper proposes a novel efficient robust dispatch model of combined heat and power systems based on extensions of disturbance invariant sets. The approach has high computational efficiency and provides flexible and robust strategies with an adjustable level of conservativeness. In particular, the proposed robust dispatch method obtains operational strategies by solving a nominal uncertainty-free dispatch problem, whose complexity is identical to a deterministic problem. The robustness against uncertainties is enhanced by endowing the nominal dispatch model with properly tightened constraints considering time-variant uncertainty sets. Towards this end, a novel direct constraint tightening algorithm is developed based on the dual norm to calculate multi-period tightened constraints efficiently without linear programming iterations. Furthermore, the budget uncertainty set is newly combined with constraint tightening to flexibly adjust the conservativeness level of robust solutions. The effectiveness of the proposed robust method is demonstrated in simulation studies of a test system in terms of computational efficiency, decision robustness and cost optimality.

eess.SY↗

arXiv2020

Evaluation of Energy- and Capacity-Market Revenues from Lithium-ion Battery Systems for Offshore Wind Using Advanced Battery Models

Revenue potential from offshore wind and energy storage systems for a Long Island node in the New York ISO (NYISO) is examined using advanced lithium-ion battery representations. These advanced mixed-integer-linear battery models account for the dynamic performance, as well as the degradation behavior of the batteries, which are usually not accounted for in power systems models. Multiple hybrid offshore wind and battery system designs are investigated to examine the impact of locating the battery offshore versus locating it onshore. For the examined systems, we explore different battery usable state-of-charge (SOC) windows, and corresponding dispatch of the battery to maximize energy- and capacity-market revenues. The impacts of variability of offshore wind output along with energy- and capacity-market prices are evaluated using publicly available data from 2010 to 2013. Locating the battery onshore resulted in higher revenues. For 2013, results highlight that without accurate battery representations, models can overestimate battery revenues by up to 155%, resulting primarily from degradation-related costs. Using advanced algorithms, net revenue can be increased by 29%. Results also indicate that wider useable SOC windows could lead to higher net revenues from the energy market, due to higher arbitrage opportunities that compensate for any additional degradation-tied costs in higher DODs. The added value of a MWh of energy storage varies from $2 to $3.5 per MWh of wind energy, which leads to a breakeven cost range of $50-$95 per kWh for the battery systems studied. As such, energy- and capacity-market revenues were found to be insufficient in recovering the investment costs of current battery systems for the applications considered in this analysis.

eess.SY↗

arXiv2020

Guiding Cascading Failure Search with Interpretable Graph Convolutional Network

Power system cascading failures become more time variant and complex because of the increasing network interconnection and higher renewable energy penetration. High computational cost is the main obstacle for a more frequent online cascading failure search, which is essential to improve system security. In this work, we show that the complex mechanism of cascading failures can be well captured by training a graph convolutional network (GCN) offline. Subsequently, the search of cascading failures can be significantly accelerated with the aid of the trained GCN model. We link the power network topology with the structure of the GCN, yielding a smaller parameter space to learn the complex mechanism. We further enable the interpretability of the GCN model by a layer-wise relevance propagation (LRP) algorithm. The proposed method is tested on both the IEEE RTS-79 test system and China's Henan Province power system. The results show that the GCN guided method can not only accelerate the search of cascading failures, but also reveal the reasons for predicting the potential cascading failures.

eess.SP↗

arXiv2019

A Lagrangian Policy for Optimal Energy Storage Control

This paper presents a millisecond-level look-ahead control algorithm for energy storage with constant space complexity and worst-case linear run-time complexity. The algorithm connects the optimal control with the Lagrangian multiplier associated with the state-of-charge constraint. It is compared to solving look-ahead control using a state-of-the-art convex optimization solver. Simulation results show that both methods obtain the same control result, while the proposed algorithm runs up to 100,000 times faster and solves most problems within one millisecond. The theoretical results from developing this algorithm also provide key insights into designing optimal energy storage control schemes at the centralized system level as well as under distributed settings.

math.OC↗

arXiv2019

Power System Decarbonization: Impacts of Energy Storage Duration and Interannual Renewables Variability

Decarbonization of the electricity sector is one of the major measures in slowing down the pace of climate change. In this paper, we analyze the impacts of energy storage systems (ESS) and interannual uncertainty of variable renewable energy (VRE) on power system decarbonization in 2050. We perform capacity expansion optimization based on technology cost projections and CO2 emission restrictions using 11 years of VRE and load data in Italy's power system, with a particular focus on the role of ESS and its duration. We also explore capacity expansion optimization based on multiple-year vs. single-year data to quantify the impact of VRE interannual variability. Our results indicate high renewables penetration even in the absence of decarbonization policies. In the transition to zero carbon system, CCS plays a minor role due to its carbon capture efficiency. ESS investments contribute to lower system costs by replacing more expensive flexibility resources. However, longer ESS durations have lower marginal value per added kWh ESS. Interannual variability of VRE substantially changes the system's configuration and energy cost. Decision-making based on single-year data substantially increases the systems' operational costs in other years. In contrast, optimizing over multiple years provides a more robust and cost effective generation expansion strategy.

eess.SY↗

arXiv2016

Evaluating Stochastic Methods in Power System Operations with Wind Power

Wind power is playing an increasingly important role in electricity markets. However, it's inherent variability and uncertainty cause operational challenges and costs as more operating reserves are needed to maintain system reliability. Several operational strategies have been proposed to address these challenges, including advanced probabilistic wind forecasting techniques, dynamic operating reserves, and various unit commitment (UC) and economic dispatch (ED) strategies under uncertainty. This paper presents a consistent framework to evaluate different operational strategies in power system operations with renewable energy. We use conditional Kernel Density Estimation (KDE) for probabilistic wind power forecasting. Forecast scenarios are generated considering spatio-temporal correlations, and further reduced to lower the computational burden. Scenario-based stochastic programming with different decomposition techniques and interval optimization are tested to examine economic, reliability, and computational performance compared to deterministic UC/ED benchmarks. We present numerical results for a modified IEEE-118 bus system with realistic system load and wind data.

math.OC↗

arXiv2016

Wind Power Providing Flexible Ramp Product

The deepening penetration of renewables in power systems has contributed to the increasing needs for generation scheduling flexibility. Specifically, for short-term operations, flexibility here indicates that sufficient ramp capacities should be reserved to respond to the expected changes in the load and intermittent generation, also covering a certain amount of their uncertainty. To address the growing requirements for flexible ramp capacity, markets for ramp products have been launched in practice such as the ones in California ISO and Midcontinent ISO. Some-times, to guarantee sufficient ramp capacity, expensive fast start units have to be committed in real-time. Moreover, with higher penetration of renewable generation, the flexibility provided by the conventional units might not be enough. Actually, wind power producers are physically capable of offering flexibility, which is sometimes also economically efficient to the entire system. In this paper, we aim to explore the mechanism and possibility of including wind power producers as ramp providers to increase the supply of flexibility. To conduct the anal-yses, a two-stage stochastic real-time unit commitment model considering ramp capacity adequacy is formulated. Case studies indicate that both the system and the wind power producers can benefit if the wind power is allowed to provide flexible ramp products.

math.OC↗

arXiv2016

A Bilateral Reserve Market for Variable Generation: Concept and Implementation

Substantial changes in the generation portfolio take place due to the fast growth of renewable energy generation, of which the major types such as wind and solar power have significant forecast uncertainty. Reducing the impacts of uncertainty requires the cooperation of system participants, which are supported by proper market rules and incentives. In this paper, we propose a bilateral reserve market for variable generation (VG) producers and capacity resource providers. In this market, VG producers purchase bilateral reserve services (BRSs) to reduce potential imbalance penalties, and BRS providers earn profits on their available capacity for re-dispatch. We show in this paper that by introducing this product, the VG producers' overall imbalance costs are linked to both their forecast quality and the available system capacity, which follows the cost-causation principle. Case studies demonstrate how the proposed BRS mechanism works and its effectiveness.

math.OC↗