Hybrid approach for planning and operating active distribution grids

Karagiannopoulos, Stavros; Aristidou, Petros; Hug, Gabriela

doi:10.1049/iet-gtd.2016.0642

Cited by 44 publications

(60 citation statements)

References 25 publications

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“…This paper extends and completes our previous work in [18]- [20]. In [18], we presented the idea of designing customized control schemes for each DER based on offline centralized optimization.…”

Section: Introductionsupporting

confidence: 63%

“…For the DGs, we derive optimized local controls for Active Power Curtailment (APC) and Reactive Power Control (RPC). These controls take the form of simple, piece-wise linear characteristic curves (such as in [18]), much like the local control schemes used today in industry. Unlike the current industry standards, these characteristics might have an arbitrarily large number of piece-wise linear segments and are optimized for each individual DG and DN.…”

Section: B Derivation Of Dg Local Controlsmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques

Karagiannopoulos

Aristidou

Hug

2019

IEEE Trans. Smart Grid

Self Cite

128

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The optimal control of distribution networks often requires monitoring and communication infrastructure, either centralized or distributed. However, most of the current distribution systems lack this kind of infrastructure and rely on suboptimal, fit-and-forget, local controls to ensure the security of the network. In this paper, we propose a data-driven algorithm that uses historical data, advanced optimization techniques, and machine learning methods, to design local controls that emulate the optimal behavior without the use of any communication. We demonstrate the performance of the optimized local control on a three-phase, unbalanced, low-voltage, distribution network. The results show that our data-driven methodology clearly outperforms standard industry local control and successfully imitates an optimal-power-flow-based control.

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Section: Introductionsupporting

confidence: 63%

Section: B Derivation Of Dg Local Controlsmentioning

confidence: 99%

Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques

Karagiannopoulos

Aristidou

Hug

2019

IEEE Trans. Smart Grid

Self Cite

128

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“…While additional infrastructure such as capacitor banks [18] have been proposed to improve power factor, we focus our work on using conventional energy storage/battery for performing power factor correction, in addition to other functions like arbitrage [19], [20]. Note that storage devices generate DC power and hence are connected to the grid through a DC/AC converter/inverter [21] that are often sized based on the rated active power output capacity. Since such converters output, for majority of time, lower than peak capacity, the remaining capacity can be used for reactive power compensation.…”

Section: A Literature Reviewmentioning

confidence: 99%

Arbitrage With Power Factor Correction Using Energy Storage

Hashmi

Deka

Bušíć

et al. 2020

IEEE Trans. Power Syst.

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The importance of reactive power compensation for power factor (PF) correction will significantly increase with the large-scale integration of distributed generation interfaced via inverters producing only active power. In this work, we focus on co-optimizing energy storage for performing energy arbitrage as well as local power factor correction. The joint optimization problem is non-convex, but can be solved efficiently using a McCormick relaxation along with penalty-based schemes. Using numerical simulations on real data and realistic storage profiles, we show that energy storage can correct PF locally without reducing arbitrage profit. It is observed that active and reactive power control is largely decoupled in nature for performing arbitrage and PF correction (PFC). Furthermore, we consider a real-time implementation of the problem with uncertain load, renewable and pricing profiles. We develop a model predictive control based storage control policy using auto-regressive forecast for the uncertainty. We observe that PFC is primarily governed by the size of the converter and therefore, look-ahead in time in the online setting does not affect PFC noticeably. However, arbitrage profit are more sensitive to uncertainty for batteries with faster ramp rates compared to slow ramping batteries. TABLE I * Nomenclature ηch, ηdis Charging and discharging efficiency x i Battery charge change at time i b i Battery charge level at time i; b i = b i−1 + x i P i h Active power consumed by inelastic load Q i h Reactive power consumed by inelastic load P i r Active power generated by renewable source Q i r Reactive power output of renewable source P i Active power of inelastic load and renewable generation; P i = P i h − P i r Q i Reactive power output of inelastic load and renewable generation; Q i = Q i h − Q i r P i B Active power output of battery + converter P max B Maximum active power output of battery + converter P min B Minimum active power output of battery + converter Q i B Reactive power output of battery + converter S i B Instantaneous apparent power output of storage interfaced by converter; S i B = P i B + jQ i B S max B Maximum apparent power output of storage converter P i T Total active power seen by the grid; P i T = P i + P i B Q i T Total reactive power seen by the grid; Q i T = Q i + Q i B p i elec Price of electricity at time i

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“…These can be classified as centralised approaches based on Optimal Power Flow (OPF) [1]- [4], distributed [5], or local [6], each requiring different levels of communication infrastructure. Typical objectives used in these methods include minimizing active power curtailment of renewables [3] and network losses [2], while satisfying power quality constraints in terms of voltages limits and thermal loading of the branches.…”

Section: Motivationmentioning

confidence: 99%