Demand Response Modeling Development for Urban Customers in a Smart Grid Electricity
MarketLadan Malehmirchegini , Hooman Farzaneh
Department of Energy and Environmental Engineering, IGSES, Kyushu University, Japan
Malehmirchegini.ladan.982@s.Kyushu-u.ac.jp

1. Introduction❖ Structure of the time series forecasting model in this study

Why Demand Response?
❑ Electricity price reduction❑ Improving loads with demand
flexibility❑ Enhancing security through theinteraction between customers and themarket• Aversion of the CUs to the riskof choosing the DPRS
• Dynamic price elasticity ofdemand

Maintaining the profits of the utility
sector and the customer and balancing
their benefits are the most critical factors
for DRPs.

Model
Identification

Multi-seasonal Time series data
• Data preparation• Seasonal decomposition to daily Seseonality• Model selection (Testing stationary)Model
Estimation &Testing• Identify potential SARIMA models• Select best model using suitable criterion• Check ACF/PACF of residuals …. Whitenoise• Testing the distribution of errors, using
Box-Ljung

ModelApplication

Day-ahead hourly load prediction in Tokyo(First day of June 2020)• Use selected model to forecast❖ Applying TOU and RTP with different PEMs to the shiftable loads .It can be observed that, theflexible CUs reschedule their demand throughout the day by shifting demand from peak to
off-peak hours.
Flexiblecustomers
Late hours shifting

Shiftable load

2. Methodology

Maximizing the welfare of the price responsive
customers (CUs) using the concept of theexpected utility function.

Time series forecasting of the day-aheadhourly electricity demand using theSeasonal Autoregressive Integrated
Moving Average (SARIMA) model .

✓ Maximize: 𝑾𝑪𝑼 = σ𝑯𝒉=𝟏 𝝀𝒉 𝑼𝒉 − 𝑬𝒉 𝒑𝒉•• Price :𝟏 + 𝜺𝒊,𝒊

𝒑𝒉− 𝒑𝟎𝒉𝒑𝟎𝒉

𝒑𝒉− 𝒑𝟎𝒉𝟐𝟒+ σ𝒉=𝟏, 𝜺𝒊,𝒋 𝟎𝒑𝑹𝟐 𝝈𝟐−𝑹𝝁+𝟐
𝑼𝑯 = 𝟏 − 𝒆−𝑹𝒇(𝑬) 𝒆

Forward shifting

Inflexible

Curtailable load
Fixed load𝒉𝒑𝒉,𝑴𝒊𝒏 ≤ 𝒑𝒉 ≤ 𝒑𝒉,𝑴𝒂𝒙✓ Expected Utility Function:

Backward shifting

Estimation hourly price elasticityof the electricity demand (paneldata analysis technique).

Subject to :demand : 𝑬𝒉 =𝑬𝟎𝒉

PEM scenarios
for different
load types

Early hours shifting

𝝀𝒉 :multiplier used to calibrate
𝑼𝒉 : customer utility
𝑬𝒉 : electricity demand𝒑𝒉 : electricity price𝒇(𝑬) : the function of theelectricity consumption
R: risk aversion coefficient𝝈 : variance
𝝁: meanUsing the estimated priceelasticities to establish theelasticity matrix of demand(PEMD).
Analyzing the CUs' behaviors withdifferent demand response
strategies in the JEPX market inTokyo.

❖The schematic of the customer-based demand response model in the wholesale market in thisstudy

B. RTP

A. TOU❖ Comparison the result of the dynamic selfelasticity estimated in this study and the thefixed self-elasticity in unrealistic anticipationof the peak reduction❖ The comparison between the expected utilityof two different customers, including a riskaverse CU this study) and a risk-neutral CU(with a constant value of risk aversion
coefficient

B.RTP

A.TOU

❖ the change in the CU’s expected utility due to different consumption levels

3. Results and Discussion❖ The variation of the hourly price elasticity of demand and the risk aversion coefficient withhourly average electricity demand in the JEPX market

4. Conclusion
A mathematical modeling approach was developed based on maximizing the CU’s welfare byconsidering a price-responsive demand response system in the wholesale market in Japan .Inorder to precisely reflect the CUs' behavior in responding to the DRPs, the PEMD was developed
based on identifying the hourly self-and cross-price elasticities of demand. An hourly day-ahead
electricity demand model was developed using the time series forecasting method to predict anhour-ahead demand . From an economic viewpoint, the TOU is the best DPR, as the CU can savemore electricity with the TOU program. However, from the customer’s view, the CU is moresatisfied with the price change in the RTP program as its expected utility is affected less by theprice changes.

5. Future work
The next step of this study will focus on determining the optimal interaction between the service
providers and customers through maximizing their profits from selling and buying electricity
from a wholesale electricity market.