Operating beyond today's PV curves: Challenges and potential benefits

Ilić, Marija; Cvijić, Sanja; Lang, Jeffrey H.; Tong, Jiangzhong; Obadina, Diran

doi:10.1109/pesgm.2015.7286495

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…Through offline and online case studies over a modified IEEE 34-bus test distribution system, the experimental results gave evidence that, (1). Reactive power optimization based on load curve prediction and segmentation is able to reduce reactive power compensation device adjustment times effectively, reduce network losses during a duty cycle to some extent, and at the same time improve computational efficiency; (2).…”

Section: Discussionmentioning

confidence: 98%

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An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation

Wang

Deng

2020

Applied Sciences

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Due to fluctuating characteristics of loads, dynamic reactive power optimization over a certain time period is essential to provide effective strategies to maintain the security and economic operation of distribution systems. In operation, reactive power compensation devices cannot be adjusted too frequently due to their lifetime constraints. Thus, in this paper, an online reactive power optimization strategy based on the segmentation of multiple predicted load curves is proposed to address this issue, aiming to minimize network losses and at the same time to minimize reactive power-compensation device adjustment times. Based on forecasted time series of loads, the strategy first segments each load curve into several sections by means of thresholding a filtered signal, and then optimizes reactive power dispatch based on average load in each section. Through case studies using a modified IEEE 34-bus system and field measurement of loads, the merits of the proposed strategy is verified in terms of both optimization performance and computational efficiency compared with state-of-the-art methods. optimization is of importance, which optimizes for a time period and makes the adjustment times of devices one of the optimization objectives.Dynamic reactive power optimization is a large-scale multi-period mix-integer non-linear programming problem. The classical methods such as non-linear programming [4] require differentiable objective function and are thus not applicable for dynamic optimization.A number of attempts have been made to conquer the problem of dynamic optimization. Reference [13] made daily reactive power optimization considering network losses, adjustment times of on-load tap changer (OLTC) and switchable capacitor banks, using an improved multi-population ant colony algorithm. Reference [14] used mixed integer non-linear programming to optimize transmission losses only, considering constraints of reactive power equipment operation limits and power grid security. Using a filter collaborative state transition algorithm (FCSTA), reference [15] built a two-objective dynamic reactive power optimization model, with one objective to minimize the actual power losses and voltage deviations, the other to minimize incremental system loss compared with the last time stamp. The objectives can be exchanged during dynamic optimization.Generally, state-of-the-art attempts include reducing scales of objectives or constraints, optimizing for increments at continuous time stamp, and using AI methods. However, much computational burden will be introduced when using AI methodology.Aiming to maintain objectives and constraints, as well as to reduce computational complexity, this paper proposes to segment load curves in a duty cycle into several stable time series first, and then conduct optimization for each segment. Based on online load predictions, the method can be applied in an online mode. Since the whole time series of loads need to be segmented into parts with lowest inner deviation, the methods of wind ramp extraction ...

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Section: Discussionmentioning

confidence: 98%

“…Reactive power optimization aims to produce optimal reactive power-compensation device control strategies in order to achieve minimum delivery losses and at the same time to satisfy specific operating constraints such as requirements for voltage deviations [1,2]. The problem in essence is a mixed-integer non-linear programming problem.…”

Section: Introductionmentioning

confidence: 99%

An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation

Wang

Deng

2020

Applied Sciences

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“…The grid and system operator clear the bids using static AC OPF. In our co-simulation setup external software called NETSSWorks is used for market clearing so that the grid constraints are observed [25]. This specific software is particularly suited for the scenarios in which grid voltage constraints may be the limiting factors to the number of solar PVs and other DERs able to participate.…”

Section: Basic Tem Simulation Scenarios Setupmentioning

confidence: 99%

NIST transactive energy modeling and simulation challenge phase II final report

Holmberg¹,

Burns²,

Bushby³

et al. 2019

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The NIST Transactive Energy (TE) Modeling and Simulation Challenge for the Smart Grid (Challenge) spanned from 2015 to 2018. The TE Challenge was initiated to identify simulation tools and expertise that might be developed or combined in co-simulation platforms to enable the evaluation of transactive energy approaches. Phase I of the Challenge spanned 2015 to 2016, with team efforts that improved understanding of TE concepts, identified relevant simulation tools and co-simulation platforms, and inspired the development of a TE co-simulation abstract component model that paved the way for Phase II. The Phase II effort spanned Spring 2017 through Spring 2018, where the teams collaboratively developed a specific TE problem scenario, a common grid topology, and common reporting metrics to enable direct comparison of results from simulation of each team's TE approach for the defined scenario. This report presents an overview of the TE Challenge, the TE abstract component model, and the common scenario. It also compiles the individual Challenge participants' research reports from Phase II. The common scenario involves a weather event impacting a distribution grid with very high penetration of photovoltaics, leading to voltage regulation challenges that are to be mitigated by TE methods. Four teams worked with this common scenario and different TE models to incentivize distributed resource response to voltage deviations, performing these simulations on different simulation platforms. A fifth team focused on a co-simulation platform that can be used for online TE simulations with existing co-simulation components. The TE Challenge Phase II has advanced co-simulation modeling tools and platforms for TE system performance analysis, developed a referenceable TE scenario that can support ongoing comparative simulations, and demonstrated various TE approaches for managing voltage on a distribution grid with high penetration of photovoltaics.

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“…As a result, certain resources are consistently committed outside of the market to address unforeseen reliability issues; this results in concentrated uplift payments [1]. Such resources are often required for reactive power compensation in order to provide system voltage control that enables more efficient delivery and utilization of real power [3]. Because such reliability requirements are largely unmodeled in dayahead unit commitment, more cost effective resources are displaced for these out-of-merit commitments.…”

Section: Introductionmentioning

confidence: 99%

Global Solution Strategies for the Network-Constrained Unit Commitment Problem With AC Transmission Constraints

Liu

Laird

Scott

et al. 2019

IEEE Trans. Power Syst.

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We propose a novel global solution algorithm for the network-constrained unit commitment problem incorporating a nonlinear alternating current model of the transmission network, which is a nonconvex mixed-integer nonlinear programming (MINLP) problem. Our algorithm is based on the multi-tree global optimization methodology, which iterates between a mixed-integer lower-bounding problem and a nonlinear upper-bounding problem. We exploit the mathematical structure of the unit commitment problem with AC power flow constraints (UC-AC) and leverage optimization-based bounds tightening, secondorder cone relaxations, and piecewise outer approximations to guarantee a globally optimal solution at convergence. Numerical results on four benchmark problems illustrate the effectiveness of our algorithm, both in terms of convergence rate and solution quality.

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Operating beyond today's PV curves: Challenges and potential benefits

Cited by 9 publications

References 3 publications

An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation

An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation

NIST transactive energy modeling and simulation challenge phase II final report

Global Solution Strategies for the Network-Constrained Unit Commitment Problem With AC Transmission Constraints

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