デマンドレスポンスのための電力市場価格予測
手法の比較
蔡

思楠，前 匡鴻，松橋隆治 (東京大学)

A Comparison of Electricity Market Price Forecasting Methods for Demand Responses
Sinan Cai, Masahiro Mae, Ryuji Matsuhashi (The University of Tokyo)

１． Introduction
Introducing the demand response (DR) to the power system tohelp maintain the generation and load balance is an effectiveoption. Normally, DR can participate in system load balancing via
various electricity markets, such as the energy spot market or theancillary market. Since the electricity markets are generally dayahead or hour-head auction markets where the actual price cannot
be known until the bidding and cleaning process is finished, theDR owner needs to predict the price in advance for the resources'
optimal scheduling to maximize their profits.
In this paper, the performances of three price forecasting
methods are examined. The paper focuses on comparing theinfluence of different forecasting methods on DR’s market
revenue, which hasn’t been clearly discussed in the existing
literature. The target market of this paper is Japan’s JEPX spot
market and the Replacement Reserve (三次調整力, RR) market.
The JEPX spot market is a day-ahead energy wholesale market,
and the RR market is an ancillary market providing tertiary
frequency regulation under different time scales.

２． Forecasting Methods
The three forecasting methods to be examined are as follows:
・Yesterday: simply assuming that today’s price will be exactly
the same as yesterday’s.
・SARIMA: a time series analysis model that forecasts future
values based on merely the historical values without extra
information input.

is received, which makes the SARIMA model capture the latest
dynamics of the forecasting price.
The Machine Learning method is generally based on deepneural networks incorporating big data and is expected to produce
prediction results with high accuracy (4). However, training a newmodel and updating the existing model can be extremely effortcosting.

3． Demand Response Schedule Optimization
This paper focuses on a battery storage system that intends topurchase charging power from the JEPX spot market and sell thepower back to the spot market or the RR market to gain profit.
The daily operation schedule optimization problem is describedas follows:
Maximize 𝑃𝑚𝑎𝑥 ∑48
𝑡=1[𝑆𝑡𝑎𝑡𝑒31 (𝑡)𝑃𝑟𝑖𝑐𝑒31 (𝑡) + 𝑆𝑡𝑎𝑡𝑒 32 (𝑡)𝑃𝑟𝑖𝑐𝑒32 (𝑡) +𝑆𝑡𝑎𝑡𝑒𝐽𝐸𝑃𝑋− (𝑡)𝑃𝑟𝑖𝑐𝑒𝐽𝐸𝑃𝑋 (𝑡) − 𝑆𝑡𝑎𝑡𝑒𝐽𝐸𝑃𝑋+ (𝑡)𝑃𝑟𝑖𝑐𝑒𝐽𝐸𝑃𝑋 (𝑡)] −𝜀 ∑48
𝑡=1 𝑆𝑡𝑎𝑡𝑒𝑆𝑡𝑎𝑡𝑒𝐽𝐸𝑃𝑋+ (𝑡)

(1)

s.t.
𝑆𝑂𝐶(𝑡) = 𝑆𝑂𝐶𝑖𝑛𝑖𝑡𝑖𝑎𝑙 − 𝑃𝑚𝑎𝑥 ∑𝑡𝜏=1[𝑆𝑡𝑎𝑡𝑒31 (𝜏) + 𝑆𝑡𝑎𝑡𝑒32 (𝜏) +𝑆𝑡𝑎𝑡𝑒𝐽𝐸𝑃𝑋− (𝜏) − 𝑆𝑡𝑎𝑡𝑒𝐽𝐸𝑃𝑋+ (𝜏)](2)

0 ≤ 𝑆𝑂𝐶(𝑡) ≤ 1(3)

0 ≤ 𝑆𝑡𝑎𝑡𝑒31 (𝑡) + 𝑆𝑡𝑎𝑡𝑒32 (𝑡) + 𝑆𝑡𝑎𝑡𝑒𝐽𝐸𝑃𝑋− (𝑡) + 𝑆𝑡𝑎𝑡𝑒𝐽𝐸𝑃𝑋+ ≤ 1
𝑆𝑡𝑎𝑡𝑒31 (1) = 𝑆𝑡𝑎𝑡𝑒31 (2) = ⋯ = 𝑆𝑡𝑎𝑡𝑒31 (6)⋮{𝑆𝑡𝑎𝑡𝑒31 (43) = 𝑆𝑡𝑎𝑡𝑒31 (44) = ⋯ = 𝑆𝑡𝑎𝑡𝑒31 (48)𝑆𝑡𝑎𝑡𝑒32 (1) = 𝑆𝑡𝑎𝑡𝑒32 (2) = ⋯ = 𝑆𝑡𝑎𝑡𝑒32 (6)⋮{𝑆𝑡𝑎𝑡𝑒32 (43) = 𝑆𝑡𝑎𝑡𝑒32 (44) = ⋯ = 𝑆𝑡𝑎𝑡𝑒32 (48)

(4)
(5)
(6)

Eq. (1) is DR’s one day revenue to be optimized where 𝑃𝑟𝑖𝑐𝑒31 ,・Machine Learning: complicated model trained with not only

𝑃𝑟𝑖𝑐𝑒32 and 𝑃𝑟𝑖𝑐𝑒𝐽𝐸𝑃𝑋 is the predicted price of the RR market

historical price data but also extra data that has a direct impact,① , ② and the JEPX spot market, respectively. Notice thatsuch as weather conditions.

currently the RR market is a multi-price auction market, therefore

The Yesterday method is a brutal but somehow effective

the maximum price is predicted. 𝑆𝑡𝑎𝑡𝑒31 , 𝑆𝑡𝑎𝑡𝑒32 , 𝑆𝑡𝑎𝑡𝑒𝐽𝐸𝑃𝑋− ∈method when other forecasting models are hard to obtain, or other[0,1] and 𝑆𝑡𝑎𝑡𝑒𝐽𝐸𝑃𝑋+ ∈ {0,1} are the discharge and charge power

forecasting results are poor (1).

percentage at each time step. The total time slot per day is 48 since

The SARIMA model is a commonly used forecasting method
for either the energy market or the frequency regulation market (2-that the time step of the JEPX spot market is 30 minutes. 𝜀 is thecharging loss coefficient.

3). It can also be easily implemented in a model predictive control

Eq. (2) calculates the SOC of the battery and ensures there isscheme: the model parameters can be updated every time new data

enough energy left in the battery whenever discharge is scheduled.
Given that the time step of the RR market is 3 hours, Eq. (5) andEq. (6) make sure that the operation for the RR market is consistentactual price, the bid might still be taken by the market when theduring every 3 hours.

system demand is insufficient. Therefore, extra revenue is gained

4． Simulation Resultcompared to the SARIMA forecast.

The JEPX spot Price and the RR market ①, ② price inKyushu area are predicted from May to September, 2023. The Rsquared values of the forecast results are given in Table 1, 2 and 3.
For JEPX spot price forecast, the Machine Learning model isdeveloped by the Japan Weather Association, utilizing historicalprice data, weather data and calendar data (5).
Table 1. The R-squared values of the JEPX spot price forecast resultsMayJuneJulyAug.

Sep.

Yesterday

0.46

0.08

0.06

0.49

0.43

SARIMA

0.54

0.340.43

0.50

0.40

ML0.67

0.55

0.62

0.68

0.56

RRForecast

Table 4. The revenue in July
RR①
RR②
JEPX
Total
JEPX
Revenue Revenue Revenue Revenue
Forecast
(万円)
(万円)
(万円)
(万円)

OracleOracle

948.6

133.8

379.2

1461.5

Yesterday

Yesterday

948.6

86.1

156.5

1191.2SARIMASARIMA

658.8

7.5

226.7

893.0

Yesterday

ML

948.5

86.1

215.4

1250.0SARIMAML

662.5

0194.9857.4

Yesterday

SARIMA

948.5

86.1

261.0

1295.6

5． Conclusion
The conventional R-squared value cannot evaluate theTable 2. The R-squared values of the RR market ① price forecast

performance of the forecast result for market participating DRs

results

correctly. In a multi-price market environment, the biddingMayJuneJulyAug.

Sep.

strategy might have a greater effect on DR’s revenue compared toYesterday

0.39

-0.55

0.63

0.51

0.60

forecast result accuracy. Using SARIMA method and Yesterday

SARIMA

0.55

0.07

0.68

0.61

0.67

method to predict the JEPX spot price and the RR maximum price

Table 3. The R-squared values of the RR market ② price forecast
resultsrespectively brings highest revenue according to the simulation
result.

MayJuneJulyAug.

Sep.

This research is conducted by the Social Cooperation ResearchYesterday

-0.92

-0.67

-0.43

-0.96

-0.53

Departments of Power System Innovation Realization with Fuji

SARIMA

-0.07

0.06

0.12

0.03

0.16

Electric Co., Ltd. in Collaborative Research Organization for

In the simulation, the DR will first optimize its operation

Comprehensive Energy Sciences (CROCES).

schedule as introduced in Section 3 based on the forecast results,

文 献then the DR will submit its bidding to the markets based on theoptimized operation plan. The actual revenue from the market will

(1) E. Sortomme and M. A. El-Sharkawi, “Optimal scheduling of

be calculated and compared to examine the effects of the forecast

vehicleto-grid energy and ancillary services,” IEEE Transactions onresults. Notably, in the RR market, the predicted price is taken as
the bidding price. When the predicted price is lower than the actual

Smart Grid, vol. 3, no. 1, pp. 351–359, 2011.

maximum price, or when the system demand is smaller than the(2) A. J. Conejo, M. A. Plazas, R. Espinola, and A. B. Molina, “Day-total bidding capacity in the market, the bid is consideredahead electricity price forecasting using the wavelet transform andsuccessful. Otherwise, the bid is considered not taken by themarket and there is no profit for the DR.
The DR’s revenue in July is shown in Table 4. The DR’s

ARIMA models,” IEEE transactions on power systems, vol. 20, no.
2, pp. 1035–1042, 2005.

capacity is 10 MWh with 10 MW maximum power output. The(3) S. Cai, and R. Matsuahshi,“ Model Predictive Control for EV

Oracle is a benchmark case that the DR makes perfect prediction

Aggregators Participating in System Frequency Regulation

on market prices and obtains the theoretically highest revenue.
Although the Machine Learning has the highest R-squaredMarket, ”in IEEE ACCESS, vol. 9, pp. 80763-80771, Jun. 2021

value in JEPX spot price forecast, the JEPX revenue is highest(4) R. Zhang, G. Li and Z. Ma, "A Deep Learning Based Hybrid

when SARIMA is applied. For DRs, the most important

Framework for Day-Ahead Electricity Price Forecasting," in IEEE

information is to know the timing of the highest and lowest price,which is not evaluated by the conventional R-squared value.

Access, vol. 8, pp. 143423-143436, 2020.

Meanwhile, the RR market is a multi-price market where the(5) 日 本 気象 協 会, プ ラ イス 予 測 （電 力 取引 価 格 予測 ） ,revenue depends on the participant’s own bidding price. Even if

https://www.jwa.or.jp/service/weather-and-data/weather-and-data-

Yesterday method produces ‘incorrect’ forecast higher than the03/