OS2 - Comparative Simulation of Energy Market Designs

Florian Maurer

July 13, 2023

Outline

Research Question
Existing Market Simulations
Market Abstraction
Agent-based simulation approach
Validation Strategy
Project Plan
Publication list

Research Question

How can different energy market designs be evaluated in a simulative comparative manner?

Objective: Framework for the Comparison of Market Designs

Which abstract description is able to characterize different market designs?

Can different market mechanisms be represented in the simulation software?

Is the application of flexible evaluation criteria possible?

Market Comparability

Comparison Agent-based Market Simulation - NFR

Model	Year	Market Design	OSS	Power Flow	Modularity	Interoperability	Scaled	Market Comparison
GSY-e [1]	2016-now	LEM	✓	○	✓	✓	✓	○
lemlab [2]	2021-now	LEM	✓	○	○	◐	◐	○
SIMONA [3]	2021-now	○	✓	✓	○	◐	○	○
ASAM [4]	2021-now	DA, ID, RE	✓	◐	○	○	○	○
AMIRIS [5]	2016-now	DA	✓	○	◐	○	◐	○
USEF [6]	2016-2017	USEF	✓	◐	○	○	○	○
FNCS/AMES [7]	2008-now	SCUC/SCED	✓	◐	✓	◐	◐	○
Powermatcher	2014-2017	○	◐	○	○	◐	✓	○
PowerACE [8]	2013-2016	DA	○	○	○	○	○	○
Maon [9]	2012-2021	DA	○	✓	○	○	○	○
ÖkoFlex [10]	2014-2017	DA	○	◐	○	○	○	○

○ - not applicable, ◐ - partial applicable, ✓ - fully applicable

Missing Features of current market simulations

Over-the-counter trading (OTC)
Conditional contracts (CfD, PPA)
Concurrent markets (frameworks focus only on one market)
Quasi-continuous auctions (always discret steps)
Foundation for reinforcement learning
Comparative evaluation
Different configurable market designs

Morphological box

Approach for market categorization - from Rebeca
Key dimensions
- Market structure
- Clearing mechanisms
- Price formation
- Market products
- Market timeframe

Base Bid

Contains fields which are mandatory
Common abstraction layer
Can be extended by market
Additional fields receive meaning through market mechanism

field	value
start-delivery	2023-06-30 00:00
end-delivery	2023-06-30 01:00
volume	100
price	32
sender id	118

Additional fields

Used for e.g. complex bids
Base id shows id of block
Link id shows id of dependent block

field	value
start-delivery	2023-06-30 00:00
end-delivery	2023-06-30 01:00
volume	100
link id	1
base id	2
price	32
sender id	118

Market Abstraction

market config item	description
name	string name
product type	energy or capacity or heat
market products	list of available products to be traded
opening hours	recurrence rule of openings (RFC 5545)
opening duration	time delta
market mechanism	name of method used for clearing
maximum bid	max allowed bidding price
minimum bid	min allowed bidding price
maximum volume	the largest valid volume for a single bid
additional fields	list of additional fields to base bid
volume tick size	step increments of volume
price tick size	step increments of price
volume unit	string for visualization
price unit	string for visualization
supports get unmatched	boolean
maximum gradient	max allowed change between bids
eligible obligations lambda	function checking if agent is allowed to trade here

Market Product

Market Product	Example
delivery duration	1 hour
count	24
offset to opening	24 hours

Look into Orderbooks

Example for Germany - Day-Ahead Auction[11]

Example for Germany - Intraday Auction

Example for Germany - Intraday Trading

Example for Germany - Futures

Concurrent Markets and Continuous Auctions

Multiple markets can be simultaneously modeled
Continuity does not exist with computers
- approximate through short clearing intervals
- allows modeling by common bidding design

How to support OTC and custom contracts?

OTC is quite different
Traded between parties
Virtual market containing bilateral trades
PPA for example have unknown volume
Contract execution is defined in market_mechanism
- Contract specifies executed code
- Trade gets accepted
- e.g. PPA - volume is calculated at the end

Example for Contract policies

market config item	description
name	Contract market
product type	energy
market products	[(MONHTLY, next 12, starting next month )]
opening hours	every 1st dom at 12:00
opening duration	1 month
market mechanism	pay as clear contract
maximum bid	9999
minimum bid	-500
maximum volume	500
additional fields	[contract_type, evaluation_frequency]
volume tick size	0.1
price tick size	0.1
volume unit	MW
price unit	€/MW
supports get unmatched	true
maximum gradient	none
eligible obligations lambda	none

Market mechanisms

Different market mechanisms are modelled
- Pay-as-bid
- Pay-as-clear
- Pay-as-clear-complex
- Nodal-market (pyOMO)
- Pay-as-bid-with-contracts
- Two-step-clearing
- …
Simulation structure is established

Agent-based simulation approach

All agents have
- obligations
- message handlers
Act or react

mindmap root)MarketAgent( Message Handler Receive Orderbook ::icon(fa fa-book) Receive Registration ::icon(fa fa-paper-plane) Event Obligation Inform Market opening ::icon(fa fa-door-open) Inform Market closing/result ::icon(fa fa-door-closed)

Basic Simulation loop - lemlab

flowchart LR A[Start] --> B(Read Input\n create agents) B --> C[next simulation step] C --> D{simulation \nended} D -->|True| End D -->|False| E[Market opening] E --> F[Bidding agents bid] F --> G[Market clears] G --> H(step finished) H --> C

More Generic Simulation loop - MESA/ASAM

flowchart LR A[Start] --> B(Read Input\n create agents) B --> C[next simulation step] C --> D{simulation\n ended} D -->|True| End D -->|False| E{if next agent\n has action} E -->|False| G(step finished) E -->|True| F(agent x step) F --> E G --> C

Generic Simulation loop - event-based

flowchart LR A[Start] --> B(Read Input\n create agents) B --> C[next simulation step] C --> D{simulation \nended} D -->|True| End D -->|False| E[set time to\n next action] E --> F(triggers actions) F --> G(step finished) G --> C

Modeling using agent-based framework

agents
- Event-based
- Act-React
One Market agent
- Uses role per market config
- Schedules all openings and closings
Each Bidding agent is separate mango agent
- Flexible bidding strategy configuration
- Learning strategies possible
World entity only loads simulation scenario

Class Diagram

Research Question - Evaluation

How can different energy market designs be evaluated in a simulative comparative manner?

Non-Functional Requirements - New design

Model	Year	Market Design	OSS	Power Flow	Modularity	Interoperability	Scaled	Market Comparison
New Design	2023+	various	✓	✓ (pypsa/pandapower)	✓	✓ (mosaik)	✓ (mango)	✓

NFR are conceptually fulfilled
Implementation of some features still missing
Ongoing development
- Pandapower integration
- Mosaik integration (REMARK)
- Reinforcement learning

Scalability / Performance Evaluation

Large multi-agent scenario
Distributed simulation
Container-based

Market Comparison Evaluation

Application of flexible evaluation criteria

Implementation of the featureset of existing market simulations
- flexable
- DMAS
- POMMES
Quantitative comparison of the results with same featureset
Qualitative comparison of market design with new features

“Market abstraction for Agent-Based Modeling of Energy Markets and Policies”

Which abstract description is able to characterize different market designs?
Written with other researchers
- https://www.energyinformatics.academy/eia-2023-conference
- 6-8 December 2023, Sao Paulo, Brazil
Describes novel market simulation approach
Allows to define different energy markets
Showcases it with basic example

Short Overview

Publication list

F. Maurer et al., “Market Abstraction of Energy Markets and Policies – Application in an Agent-Based Modeling Toolbox“ Energy Informatics 2023
D. Stollenwerk, T. Franzke, F. Maurer et al., “Smarte Ladesäulen: Netz- und Marktdienliches öffentliches Laden“ Springer - Towards the New Normal in Mobility
F. Maurer, C. Rieke, R. Schemm, and D. Stollenwerk, “Analysis of an Urban Grid with High Photovoltaic and e-Mobility Penetration” Energies, vol. 16, no. 8, 8, p. 3380, Jan. 2023.
F. Maurer, “Framework To Provide A Simulative Comparison Of Different Energy Market Designs,” Energy Informatics, vol. 5, no. 2, p. 12, Sep. 2022.

Further steps

Implementation of different markets
Comparisons of market designs
PhD of Rebeca Ramirez → KPI for markets
Integration as market module into MOSAIK
Integration of contracts and KPIs
Documentation

Thank you for your Attention!

Questions?

References

[1]

GridSingularity, “Grid Singularity Mission - Grid Singularity Wiki,” 2021. [Online]. Available: https://gridsingularity.github.io/gsy-e/documentation/. [Accessed: 15-Apr-2022].

[2]

M. Zade, S. D. Lumpp, P. Tzscheutschler, and U. Wagner, “Satisfying user preferences in community-based local energy markets — Auction-based clearing approaches,” Applied Energy, vol. 306, p. 118004, Jan. 2022.

[3]

J. Hiry, C. Kittl, F. Erlemeyer, and D. Schmid, “SIMONA – Agentenbasierte Netzsimulation für das intelligente Verteilnetz der Zukunft,” vol. Jahresbericht, pp. 40–41, Jan. 2018.

[4]

S. Glismann, “Ancillary Services Acquisition Model: Considering market interactions in policy design,” Renewable and Sustainable Energy Reviews, Apr. 2021.

[5]

J. Kochems and C. Schimeczek, “Agentenbasierte Modellierung von Lastmanagement im deutschen Stromsektor,” in Internationale Energiewirtschaftstagung (IEWT) 2021: Das Energiesystem nach Corona: Irreversible Strukturänderungen - Wie?, 2021.

[6]

USEF Design Team, “USEF - The Framework Explained,” p. 58, May 2021.

[7]

S. Battula, L. Tesfatsion, and T. E. McDermott, “An ERCOT test system for market design studies,” Applied Energy, vol. 275, p. 115182, Oct. 2020.

[8]

“Model PowerACE – REflex.” [Online]. Available: https://reflex-project.eu/model-coupling/powerace/. [Accessed: 10-Dec-2021].

[9]

M. Ketov, “MARKTSIMULATIONEN UNTER BERÜCKSICHTIGUNG DER STROM-WÄRME-SEKTORENKOPPLUNG,” p. 5, 2019.

[10]

T. Künzel and A. Weidlich, “Flexibility as an economic commodity in the intelligent energy system for the efficient integration of renewable energies,” 2015.

[11]

EPEX SPOT, “Trading Products | EPEX SPOT,” 2022. [Online]. Available: https://www.epexspot.com/en/tradingproducts. [Accessed: 09-May-2023].