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Smart Management
for the Smart Grid
A. Kazakov
January 2018
π πΏ β ππππΉ πΏ β₯ π
Unified energy system (UES)
πΊ = π, πΈπ β πππππ πΈ β πΏππππ
Smart Management for the Smart Grid 2
Notation UES
πΊ = π, πΈ β πΊπππβπ = ππ β πππππ
πΈ = πππ β πΏππππ
πππ= (ππ, ππ)
π = ππ βͺ ππ βͺ ππ·
ππ β πΊππππππ‘πππππ β πππππ πππ π πππππ· β Distribution
π ππ, π‘ β ππ’ππππ¦π· ππ·π , π‘ β π·ππππππ‘ = π‘π β πππππ‘π+1 β π‘π = 30 πππ
Supply Chain Management (SCM)
SupplyTranspor-
tationDemand
Smart Management for the Smart Grid 3
Logistics network G
π πΏ β ππππΉ πΏ β₯ π
Smart Management for the Smart Grid 4
The theoretical basis for SCM solutions
β’ Pointing theorem
β’ Le Chatelier's Principle
β’ The Principle of Least Actionby Feynman
β’ The development of the variationmethod for solving utility tasks
β’ Mathematical Methodsof Operations Research
Innovation (Π’)
Theoretical Physics
Applied Mathematics
Smart Management for the Smart Grid 5
Options SCM for UES
Ultimate
ConsumersDistribution RetailTransmission
Generation &
TradeConsumption
Ultimate
ConsumersDistribution RetailTransmission
Generation &
TradeConsumption
Value Distributed Energy Resources β DER (Local generation, storage, electric vehicles)
The Legacy Chain of Production, Transmission and Consumption of electricity
The Target Chain of Production, Transmission and Consumption of electricity
Information
Exchange
Platforms
Information
Devices
Information
Services
Energy Stream
Information Stream
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The digital economy in a Smart Grid
β’ Generation, Transmission and Consumptionof electricity: plan & accounting
β’ Energy Stream: management & control
β’ Collection, Distribution and Useof information: the model forecast & consolidation
β’ Information Stream: management & control
Real Production
Digital Power
Innovation (E)
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Key Technologies for the Project
β’ MLP - mixed linear programming
β’ LR - Linear Regression
β’ BM - Benchmarking
β’ BI - Business intelligence
β’ MDM - Master data management
β’ BC - Block chain
Mathematical Methods in Economics Information Technologies
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Key Projects Portfolio
Material and cost balances energy streamfor each node of the Grid
Parametric calculation of energy stream (for model SCM)
Planning and forecasting of demands and supplies
Assembling and rating of parameters of adaptive models UES
Unified target ICT architecture
Formalization and automation of information legacy exchanges based on a common (ontological model) MDM
MLP
BI, MLP
BI, LR
BI, LR, BM
MDM, BC
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Optimization Models for SCM is the core to Project
Futureplanning
Strategic
Long-termMedium-
termShort-term
Tactical planning Scheduling
Dispatch
T t
New Grid Assets Existing Grid Assets
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Created new opportunitiesβ’ Optimal operational management of electric grid: full, partial
β’ Model the formation of material Balances of stream π(π, π, π)for all nodes of the network: replenishment of missing data, detection and elimination of inconsistencies
β’ Automated making of consolidated material and cost balances
β’ Stream forecasting for different time horizons: strategic, tactical, operational-production
β’ The possibility of clustering and/or consolidation of the network in forecasting: by owners or according to production criteria
β’ OPEX estimation in the Asset management of the electric grid: use and/or preservation of existing assets, creation of new assets
Creating valueSmart Management for the Smart Grid 11
The target Reference architecture model
IEC Recommendation
SGAM βSmartGridArchitectureModel
IEC 62559-4/TR/Ed12016-05-13
https://www.en-trust.at/NISTIR/
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Thank you for Your attention!
Smart Management for the Smart Grid 13
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