Masanori HIRANO, Ryo WAKASUGI, Kiyoshi IZUMI, “Analysis of Carbon Neutrality Scenarios of Industrial Consumers Using Electric Power Market Simulations,” The 24th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2022), pp.90-105, Valencia, Spain, Nov. 17th, 2022. doi.org/10.1007/978-3-031-21203-1_6,

1. Analysis of Carbon Neutrality Scenarios
of Industrial Consumers
Using Electric Power Market Simulations
Masanori HIRANO, Ryo WAKASUGI, Kiyoshi IZUMI
School of Engineering, The University of Tokyo
research@mhirano.jp
https://mhirano.jp/

2. ©M.HIRANO & Izumi Lab.
Carbon Neutrality in Electricity
• Global warming and climate change are important matters
all over the world
• Renewable energy (RE) is gaining a popularity
• RE100: Renewable Energy 100%
• Many ways to reduce CO2 emissions for factories
• On-site generation
• PPA: Power Purchase Agreement for RE
• RE Certificates
• Energy conservation
• Peak cut
• ESG (Environmental, Social, and Governance) investment
also supports those approaches for companies
• Problem: How to achieve?
https://go100re.jp/portfolio/re100
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3. ©M.HIRANO & Izumi Lab.
Outline of This Study
• Purpose: Research the better way to procure electric power
• Mainly focus on one FACTORY
• Criteria: Cost & CO2
• Factory can use Fuel Cell (FC), Photovoltaic power generation (PV),
batteries (BT)
• Method: Multi-agent simulation
• Market imitating the actual market
• Generator Agents, Consumer Agents, and Factory Agents
• Outputs:
• Seeking a better mix-up of decarbonization methods.
• Discussing a required price drop-down of FC for widely-spreading FC
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4. ©M.HIRANO & Izumi Lab.
Simulation Model Outline
• Multi-agent simulation for electric power market
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Market in Simulation
• Imitating spot market (previous day trading) in JEPX
• Major market in JEPX
• Demands for tomorrow are divided into 48 slots for each 30 min
(demands for 0:00-0:30, 0:30-1:00, …, 23:30-24:00)
• Executed on 10AM at the previous day
• Blinded single-price batch auction mechanism
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Execution Price

6. ©M.HIRANO & Izumi Lab.
Generator Agents (300 agents)
• Selling based on Merit order (lowest marginal cost order)
• Base load: Hydroelectric/Nuclear power generation
• Peak load: Thermal power generation (Petroleum, LNG, Coal)
• Distribute actual shares to each agent at random
• Selling price is decided using the mix of 3 factors:
• Base factor: based on the actual generation cost + α
• Chartist (Trend) factor: recent price trend
• Noise factor: random noise
• The weights for those factors differ for each agent
• → different price bidding cause the gradual supply curve
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Generator Agents have different merit orders (lowest marginal cost order)

7. ©M.HIRANO & Izumi Lab.
Stylized Consumer Agents (300 agents)
• Based on the actual electricity usage, make buy orders
• Demand quantity -> Actual data
• Actual demands are distributed to 300 agents at random
• Price for buy orders -> based on 3 factors
• 𝐹𝑡
𝑘,𝑗
: fundamental factor, 𝐶𝑡
𝑘,𝑗
: trend factor, 𝑁𝑡
𝑘,𝑗
:noise factor
• Weight for each factor are decided randomly according to the
exponential distribution
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𝑝𝑡
𝑘,𝑗
= 𝑝𝑡−1
k
exp
𝑤𝐹
𝑗
𝐹𝑡
𝑘,𝑗
+ 𝑤𝐶
𝑗
𝐶𝑡
𝑘,𝑗
+ 𝑤𝑁
𝑗
𝑁𝑡
𝑘,𝑗
𝑤𝐹
𝑗
+ 𝑤𝐶
𝑗
+ 𝑤𝑁
𝑗

8. ©M.HIRANO & Izumi Lab.
Factory Agent (1 agent)
• Extract the stylized demand patterns from the actual data
• Data: Actual electricity consumption data from one factory of a
major Japanese electric industry company
• Method: PCA
• Result: 2 significant principals & others
1. Base demand for production (68.7%)
2. Seasonal factor – Mainly, air conditioners (9.1%)
3. Noise factor (22.2%)
• Build a demand model for simulations
• Demand volumes are calculated based on PCA results:
𝐵𝑡
𝑘,⋆
: base demand, 𝐴𝑡
𝑘,⋆
: seasonal factor, 𝑁𝑡
𝑘,⋆
: Noise factor
• Bidding price: enough high price for execution
(This is a strong and abnormal assumption, but reasonable for our
study)
• + Factory Agent can use PV, FC, batteries (explained later)
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𝑑𝑡
𝑘,⋆
= 𝑤𝐵
⋆
𝐵𝑡
𝑘,⋆
+ 𝑤𝐴
⋆
𝐴𝑡
𝑘,⋆
+ 𝑤𝑁
⋆
𝑁𝑡
𝑘,⋆

9. ©M.HIRANO & Izumi Lab.
FC, PV, batteries
• Factory Agent can use FC (Fuel Cell), PV (Photovoltaic power
generation), batteries (BT)
• Batteries (BT):
• Can charge and discharge electricity at any time if it is possible
• Maximum storage capacity: 𝐶𝑏
• PV:
• generate electric power under uncontrol: 𝑞𝑡
𝑘,∗,𝑝𝑣
• FC:
• controlling the amount of electricity generate
• the maximum value of installed capacity: 𝐶𝑓𝑐
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Decision Process
• Decision process is made according to the actual one.
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Experiments
• Parameters we changed in…
• The volume of FC, PV, and BT in the factory
• The price of FC
• Experiments:
• 365 steps = 1year simulation
• 100 trials for each simulation
• Evaluating…
• CO2 Emissions
• Total cost for achieving carbon neutrality
• CO2 emissions are assumed to be neutraization by Carbon Credits (0.3
yen/kWh)
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Results (Cb=0)
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Results(Cb=1000, 3000)
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Significant effect
PV + BT
has good fit

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Results(Cb=5000, 10000)
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Results comparison (Cb=0, 10000)
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BT reduces required
max FC price badly

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Discussion
• PV + BT has synergy in terms of CO2 reduction!
• The bigger volumes of BT performed as complements for PV
• When PV is bigger, the effects of FC are reduced
• In such cases, PV can cover almost all electric power and no FC is
needed
• About ideal FC prices for introducing it widely in society…
• Less than 12.5〜15 yen/kWh: FC can be used as a feasible solution
for achieving carbon neutrality
• Less than 5 yen/kWh: Fully used and very efficient for reducing CO2
emission under only market competition.
• Even subsidies can reduce effective FC prices.
• Considering corporate values for ESGs, the marginal cost can be
affordable when the price is even high.
• BT has a bad effect on FC introduction
• The existence of BT causes a reasonable FC price reduction…
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Conclusion & Future Works
• PV + BT shows the synergy effects
• We revealed the affordable FC prices under market
competition.
• 12.5 〜 15 yen/kWh is the maximum affordable price
• It supports the decision to the subsidy for introduction of FC.
• The amount of BT has a bad effect on FC affordable price.
• When BT widely spread, electricity transfer between periods whose
price is low and high became possible.  The affordable FC price
decrease
• Unfortunately, it could prevent the expansion of FC use.
• Future work exists on…
• Multi-agent simulation where all agent pursue carbon neutrality
• Controlling multiple consumers in one system to achieve carbon
neutrality on one grid as a whole system.
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