1. Over View
1
What is a Microgrid?
Decentralized Control in Microgrids
Game Theory and Market-Based Algorithms
Game Theory in Microgrid Control
Market-Based Algorithms in Microgrid Control
State estimation
State estimation in a microgrid
2. Introduction :
•Welcome to the presentation on Decentralized Control
of Microgrids and State Estimation in Microgrid
Control Systems.
•Microgrids play a crucial role in modern energy
systems, offering increased reliability and
sustainability.
•In this presentation, we will explore decentralized
control strategies and state estimation techniques for
microgrid control.
3. Introduction To Microgrid
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⚫What is Microgrid?
⚫It isconnected to both the local generating unitsand the
utilitygrid thus preventing poweroutages.
⚫Excess powercan be sold to the utilitygrid.
⚫Sizeof the Microgrid may range from housing estate to
municipal regions.
4. Decentralized Control in Microgrids:
•Decentralized control means that control decisions are
made locally by individual components within the
microgrid.
•Components like distributed generators, energy
storage, and loads have autonomous controllers.
•Controllers communicate and coordinate to optimize
microgrid operation.
5. Game Theory and Market Based Algorithms
Another interesting approach is to use game theory and
market based rules. The various agents know their own
benefit and cost, and they respond to price signals.
This approach is using the principles of game theory,
and if the rules of the game are correct, the system will
balance at the optimal point.
6. Game Theory in Microgrid Control:
Game theory is a mathematical framework used to model and
analyze strategic interactions among decision-makers, known as
players, who have conflicting interests or objectives. In the
context of microgrid control, game theory can be applied to
capture the decision-making processes of different entities
within or connected to the microgrid. Here are some key aspects
of using game theory in microgrid control:
1. Agent Modeling
2. Objective Functions
3. Strategic Decision-Making
4. Nash Equilibrium
5. Cooperation and Competition
7. Market-Based Algorithms in Microgrid
Control:
Market-based algorithms provide a mechanism for microgrid
participants to engage in economic transactions, which can lead
to efficient resource allocation and pricing. Here's how market-
based algorithms are applied in microgrid control:
1. Energy Market
2. Real-Time Pricing
3. Auction Mechanisms
4. Demand Response
5. Grid Operator Role
8. Scalability and advanced architecture:
Scalability and advanced architecture are critical considerations in
microgrid control systems, as they directly impact the efficiency,
reliability, and flexibility of these distributed energy systems.
Microgrids are small-scale, localized grids that can operate
independently or in conjunction with the main utility grid. They
are often used to enhance grid resilience, improve energy
efficiency, and integrate renewable energy sources. Here, we'll
delve into the key issues related to scalability and advanced
architecture in microgrid control.
9. Multi Agent System (MAS) :
This concept can be better
explained by considering the
organization of human societies
and cities. Many people living in an
area form a village or a city. Next,
many cities and villages form a
county and many counties form a
country. This concept is shown in
Fig.1 Accordingly, DG units and
controllable loads form small
microgrids and accordingly small
MASs. These MASs form larger
MASs and so on. The grouping may
be realized based on electrical and
topological characteristics such as
having a common MV transformer.
FIG. 1
10. Advanced Architecture in Microgrid
Control:
Advanced architecture refers to the design and structure of the
microgrid control system, incorporating advanced technologies
and techniques to enhance performance and reliability. Here are
some critical aspects of advanced architecture in microgrid
control:
•Hierarchical Control:
•Distributed Energy Resource Management System (DERMS).
•Cyber-Physical Systems (CPS)
•Machine Learning and AI
•Cybersecurity
•Interoperability and Standards.
Synchronization with Grid Operators
11. State estimation in a microgrid
State estimation in a microgrid control system architecture is a
critical process that involves estimating the internal state
variables of the microgrid, such as voltages, currents, power
flows, and the status of various components, without directly
measuring all of them. This information is essential for the
reliable and efficient operation of a microgrid. Here are the key
details about state estimation in a microgrid control system
architecture:
•Purpose of State Estimation:
•Optimal Contro
•Fault Detection
•Voltage and Frequency Regulation
12. •Data Sources:
•Measurements
•Communication Network
•Estimation Algorithms:
•Kalman Filtering
•Extended Kalman Filter (EKF)
•Particle Filtering
•Least Squares Estimation
•State estimation algorithms are thoroughly tested and validated
through simulation and, when possible, in field trials to ensure their
accuracy and reliability.
In summary, state estimation is a fundamental component of a microgrid
control system architecture, enabling real-time monitoring, control, and
optimization of microgrid operations. It relies on mathematical models,
measurement data, and estimation algorithms to provide a
comprehensive view of the microgrid's internal state variables.
