Probabilistic simulation framework for balanced and unbalanced low voltage networks

Klonari, Vasiliki; Toubeau, Jean‐François; Grève, Zacharie De; Durieux, Olgan; Lobry, Jacques; Vallée, François

doi:10.1016/j.ijepes.2016.03.045

Cited by 14 publications

(7 citation statements)

References 34 publications

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“…For such a longterm perspective, the accuracy of forecasting tools is questionable, 34 and the objective is rather to provide a number of time trajectories, capturing the statistical properties of the variables (daily, weekly and yearly periodicities). 35 To that end, we rely on 6 years of data (from 2012 to 2017) that are directly fed to the stochastic program, along with four artificially-generated years, in order to have a total of 10 typical years (which thus correspond to 3650 representative days). The four additional years are simply obtained by feeding the existing years into a Seasonal AutoRegressive Moving Average (SARMA) model that is used to generate new representative time trajectories.…”

Section: Case Studymentioning

confidence: 99%

Sizing of underwater gravity storage with solid weights participating in electricity markets

Toubeau

Ponsart

Stevens³

et al. 2020

Int Trans Electr Energ Syst

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This paper investigates the techno-economic feasibility of the innovative concept of gravity energy storage, where heavy weights are raised and lowered in a water environment. Such eco-friendly systems can be implemented in existing flooded pits or quarries, by leveraging the important depth of these cavities. Moreover, in addition to their long lifetime, they have no visual impact on the landscape, and offer a lot of flexibility to the power system. In this work, we firstly present an analytical study of the storage solution, which allows deriving tractable mathematical constraints describing its operation, such as its nonlinear speed-power curves in both charge and discharge modes. These constraints are then integrated into the investment strategy of a merchant unit that seeks to maximize its profit by jointly participating in the energy and secondary reserve markets. The model is formulated within a stochastic framework to ensure robustness of sizing decisions in view of future market uncertainties. Results from a practical case study (on a natural cavity of 200 m) show that underwater gravity storage is a cost-efficient technology that offers payback periods of less than 10 years, mainly due to its intrinsic low capital costs estimated at around 100 €/kWh. List of Symbols and Abbreviations: n blocks , Number of installed blocks; n gm , Number of installed geared induction machines; φ CAPEX , Total investment costs of the storage system, €; φ OPEX ω,t , Total profit from storage operation in scenario ω at time t, €; n blocks ω,t , Number of blocks stored within the storage system in scenario ω at time t; p dis,res ω,t , p ch,res ω,t , Actual output power in discharge (dis) and charge (ch) modes after the real-time activation of reserves in scenario ω at time t, MW; p dis ω,t , p ch ω,t , Output power in discharge (dis) and charge (ch) modes committed in the energy market in scenario ω at time t, MW; p single,Á,res ω,t , Output power per induction machine in discharge (dis) and charge (ch) modes after the real-time activation of reserves in scenario ω at time t, MW; res dis ω,t ,res ch ω,t , Reserve capacity allocated in the upward secondary reserve market in discharge (dis) and charge (ch) mode in scenario ω at time t, MW; v dis ω,t ,v ch ω,t , Aggregated block speed (at the UGES level) in discharge (dis) and charge (ch) mode in scenario ω at time t, m/s; v single,Á ω,t , Block speed per induction machine in discharge (dis) and charge (ch) mode in scenario ω at time t, m/s; z dis ω,t ,z ch ω,t , Binary variables indicating the discharge (dis) and charge (ch) status in scenario ω at time t; Δ t , Optimization step, 1 hour; η dis , η ch , Efficiency of the storage system in discharge (dis) and charge (ch) modes; κ ω,t ∈ [0,1], Activation rate of the upward reserve; λ act ω,t , Price for activation of balancing reserves in scenario ω at time t, €/MWh; λ EN ω,t , Electricity price in the energy-only market in scenario ω at time t, €/MWh; λ res ω,t , Price for availability of reserve capacity in scenario ω at time ...

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Section: Case Studymentioning

confidence: 99%

Sizing of underwater gravity storage with solid weights participating in electricity markets

Toubeau

Ponsart

Stevens³

et al. 2020

Int Trans Electr Energ Syst

Self Cite

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“…The flowchart in Figure 2 presents the structure of the simulation algorithm, which is entirely developed in MATLAB®. The energy exchange scenarios are generated by the Monte Carlo algorithm sampling from the historic SM data of the feeder [13], [14]. The power flow analysis is performed with the three-phase algorithm that is presented in [14] and outlined in Appendix A.…”

Section: Overview Of the Simulation Toolmentioning

confidence: 99%

“…The current carrying capacities of the lines should not exceed the DSO requirements or the recommended values in technical standards such as [15]. The load flow analysis of each system state is performed with the three-phase algorithm that is explained in the Appendix [14].…”

Section: Feeder Modelmentioning

confidence: 99%

“…The created statistical distributions (CDFs) of the time varying parameters (PV injection, net energy consumption and voltage at the MV/LV transformer) are used for defining multiple possible network states corresponding to each 15-min time step. Practically, a MC algorithm is applied for randomly sampling the values of the variable parameters at each node of the studied LV feeder as explained in [13], [14] . The combination of the nodal sampled values defines each network state that will be afterwards analysed by the power flow algorithm (Appendix).…”

Section: Customer Profiles and Feeder State Modelling Based On Histormentioning

confidence: 99%

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Estimating the Photovoltaic Hosting Capacity of a Low Voltage Feeder Using Smart Meters’ Measurements

Klonari¹,

Toubeau²,

Lobry³

et al. 2016

Smart Metering Technology and Services - Inspirations for Energy Utilities

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Maximizing the share of renewable resources in the electric energy supply is a major challenge in the design of the future energy system. Regarding the low voltage (LV) level, the main focus is on the integration of distributed photovoltaic (PV) generation. Nowadays, the lack of monitoring and visibility, combined with the uncoordinated integration of distributed generation, often leads system operators to an impasse. As a matter of fact, the numerous dispersed PV units cause distinct power quality and costefficiency problems that restrain the further integration of PV units. The PV hosting capacity is a tool for addressing such power system performance and profitability issues so that the different stakeholders can discuss on a common ground. Photovoltaic hosting capacity of a feeder is the maximum amount of PV generation that can be connected to it without resulting in unacceptable power quality. This chapter demonstrates the usefulness of smart metering (SM) data in determining the maximum PV hosting capacity of an LV distribution feeder. Basically, the chapter introduces a probabilistic tool that estimates PV hosting capacity by using customer-specific energy flow data, recorded by SM devices. The probabilistic evaluation and the use of historical SM data yield a reliable estimation that considers the volatile character of distributed generation and loads as well as technical constraints of the network (voltage magnitude, phase unbalance, congestion risk). As a case study, an existing LV feeder in Belgium is analysed. The feeder is located in an area with high PV penetration and large deployment of SM devices.

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“…the readings will be available as a batch every few weeks or months. While this is not a problem for the modelling of longer term distribution problems using smart meter data such as the fair allocation of costs (Klonari et al 2016), it is an issue for the more active management of the low voltage network (Leão et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Low voltage current estimation using AMI/smart meter data

Poursharif

Brint

Holliday

et al. 2018

International Journal of Electrical Power & Energy Systems

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This paper investigates how currents on a low voltage network can be estimated when not all the advanced metering infrastructure (AMI) / smart meters are reporting their values in real-time. This is done by combining real-time monitoring at the distribution substation with historical readings from the meters. Accurate knowledge of the currents is a key element of Advanced Distribution Management Systems. It is found that the k-nearest neighbors weighted average approach performs best.

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Probabilistic simulation framework for balanced and unbalanced low voltage networks

Cited by 14 publications

References 34 publications

Sizing of underwater gravity storage with solid weights participating in electricity markets

Sizing of underwater gravity storage with solid weights participating in electricity markets

Estimating the Photovoltaic Hosting Capacity of a Low Voltage Feeder Using Smart Meters’ Measurements

Low voltage current estimation using AMI/smart meter data

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