JP2016082804A - Power price determination apparatus and power price determination method - Google Patents

Power price determination apparatus and power price determination method Download PDF

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JP2016082804A
JP2016082804A JP2014214392A JP2014214392A JP2016082804A JP 2016082804 A JP2016082804 A JP 2016082804A JP 2014214392 A JP2014214392 A JP 2014214392A JP 2014214392 A JP2014214392 A JP 2014214392A JP 2016082804 A JP2016082804 A JP 2016082804A
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孝一 小林
Koichi Kobayashi
孝一 小林
邦彦 平石
Kunihiko Hiraishi
邦彦 平石
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Japan Advanced Inst Of Science & Tech Hokuriku
Japan Advanced Institute of Science and Technology
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract

PROBLEM TO BE SOLVED: To solve the present problem of difficulty in fine adjustment of a power price because of the power price being a simple function of a power utilization amount, causing problems such as incapability of appropriate power saving and requirement of excessive power saving at the time of power supply/demand tightness.SOLUTION: A power price determination apparatus has the procedure composed of: a step 1 of receiving measurement data on a power utilization amount from a user's smart meter; a step 2 of solving a price determination problem using the measurement data and a switching type discrete-time Markov chain model to determine a power price; a step 3 of presenting the power price to the user by using the smart meter etc.; and a step 4 of returning to the step 1 after a prescribed time has elapsed.SELECTED DRAWING: Figure 1

Description

本発明は、電力価格決定装置および電力価格決定方法に係り、特に電力需給ひっ迫時に適切な電力価格を決定することを可能とする電力価格決定装置および電力価格決定方法に関する。   The present invention relates to an electric power price determination apparatus and an electric power price determination method, and more particularly to an electric power price determination apparatus and an electric power price determination method capable of determining an appropriate electric power price when power supply and demand is tight.

リアルタイムプライシング（RTP）とは、電力需給がひっ迫した際に、電力使用量に応じた電力価格をリアルタイムで決定することで、節電を促す方法である。社会実験によりその効果が明らかになっている。RTPの実験では、各需要家（一般家庭など）にスマートメータを配布し、電力使用量を計測する。また、電力価格を何らかの方法で可視化する。電力使用量を抑制したい場合は、電力価格を高く設定する。各需要家は、電力価格を確認して、節電の可否を判断する。現状では、電力使用量に応じて、電力価格を2倍、3倍に設定するなど簡単な方法が用いられている。   Real-time pricing (RTP) is a method for encouraging power saving by determining the price of electricity according to the amount of power used in real time when power supply and demand is tight. The effect is clarified by a social experiment. In the RTP experiment, a smart meter is distributed to each consumer (general household, etc.) to measure the power consumption. In addition, the electricity price is visualized in some way. If you want to reduce power consumption, set the power price higher. Each consumer confirms the power price and determines whether or not power can be saved. At present, simple methods are used, such as setting electricity prices to double or triple according to the amount of electricity used.

O. Corradi, H. Ochsenfeld, H. Madsen, and P. Pinson, Controlling electricity consumption by forecasting its response to varying prices, IEEE Trans. on Power Systems, vol. 28, no. 1, pp. 421-429, 2013.O. Corradi, H. Ochsenfeld, H. Madsen, and P. Pinson, Controlling electricity consumption by forecasting its response to varying prices, IEEE Trans. On Power Systems, vol. 28, no. 1, pp. 421-429, 2013 . C. Vivekananthan, Y. Mishra, and G. Ledwich, A novel real time pricing scheme for demand response in residential distribution systems, Proc. of the 38th Annual Conf. of the IEEE Industrial Electronics Society, pp. 1954-1959, 2013.C. Vivekananthan, Y. Mishra, and G. Ledwich, A novel real time pricing scheme for demand response in residential distribution systems, Proc. Of the 38th Annual Conf. Of the IEEE Industrial Electronics Society, pp. 1954-1959, 2013.

非特許文献１では、電力使用量の時間変化を過去の電力使用量や価格の線形回帰モデルによって表現している。しかし、実際の電力使用量の時間変化は非線形であり、実用的モデルとはいえない。非特許文献２では、電力価格は電力使用量の簡単な関数となっており、きめ細やかな調整が困難である。したがって、従来手法では、電力需給のひっ迫時に、適切な節電ができない、過剰な節電を要求してしまうなどの問題点が発生する。
本発明はこのような問題に鑑み、電力需給ひっ迫時に適切な節電が実現できる、電力価格決定装置および電力価格決定方法を提供することを目的とする。 In Non-Patent Document 1, a temporal change in power consumption is expressed by a linear regression model of past power consumption and price. However, the actual time change of power consumption is non-linear and cannot be said to be a practical model. In Non-Patent Document 2, the power price is a simple function of power consumption, and fine adjustment is difficult. Therefore, in the conventional method, there are problems such as being unable to save power appropriately and demanding excessive power saving when power supply and demand is tight.
In view of such problems, it is an object of the present invention to provide a power price determination device and a power price determination method that can realize appropriate power saving when power supply and demand is tight.

本発明の電力価格決定装置は、以下のステップから成ることを特徴とする。
ステップ１： 需要家のスマートメータから電力利用量の計測データを受信する。
ステップ２： 計測データおよび切替型離散時間マルコフ連鎖モデルを用いて価格決定問題を解き、電力価格を決定する。
ステップ３： スマートメータなどを用いて、電力価格を需要家に提示する。
ステップ４： 一定時間経過後、ステップ１に戻る。
また、本発明の電力価格決定方法は、式（１）で表される切替型離散時間マルコフ連鎖モデルを用いることを特徴とする。
また、式（２）で表される価格決定問題を用いることを特徴とする。 The power price determination apparatus of the present invention is characterized by comprising the following steps.
Step 1: Receive power usage measurement data from a consumer smart meter.
Step 2: The price determination problem is solved by using the measurement data and the switching type discrete time Markov chain model to determine the power price.
Step 3: Present the electricity price to the customer using a smart meter or the like.
Step 4: Return to Step 1 after a certain period of time.
In addition, the power price determination method of the present invention is characterized by using a switching type discrete time Markov chain model expressed by Equation (1).
Further, it is characterized by using the price determination problem expressed by the equation (2).

本発明によって、電力需給ひっ迫時に適切な節電が実現できる、電力価格決定装置および電力価格決定方法が得られた。   According to the present invention, a power price determination device and a power price determination method capable of realizing appropriate power saving when power supply and demand is tight have been obtained.

電力価格決定の手順を示すフローチャートFlow chart showing power price determination procedure

本発明では、各需要家に対する電力使用量の数理モデルを提案している。数理モデルを用いることで、電力使用量の予測が可能となる。電力需給がひっ迫する数時間の間の電力使用量を数理モデルにより予測し、最適な電力価格を計算することが可能となる。本発明では、数理モデルとして、切替型離散時間マルコフ連鎖モデルを提案する。電力使用量の時間変化はモデル化が困難な要因が含まれており、確率的なモデルとして表現することが適切である。確率モデルとして代表的な離散時間マルコフ連鎖を用い、電力価格などに応じて離散時間マルコフ連鎖を切り替えることとする。また、最適な電力価格の計算方法は最適化問題に帰着される。この問題を繰り返し解くことで、時々刻々変化する電力使用量に対して、最適な価格を計算することが可能になる。   In this invention, the mathematical model of the electric power consumption with respect to each consumer is proposed. By using a mathematical model, it is possible to predict the amount of power used. It is possible to predict the amount of power used for several hours when power supply and demand is tight by using a mathematical model, and to calculate an optimal power price. In the present invention, a switchable discrete-time Markov chain model is proposed as a mathematical model. The time change of the power consumption includes factors that are difficult to model, and it is appropriate to express it as a probabilistic model. A typical discrete-time Markov chain is used as the probability model, and the discrete-time Markov chain is switched according to the power price or the like. Moreover, the calculation method of the optimal power price is reduced to the optimization problem. By solving this problem repeatedly, it is possible to calculate an optimal price for the amount of power used that changes from moment to moment.

本発明の切替型離散時間マルコフ連鎖モデルについて、以下に説明する。需要家の数をｎとする。時刻ｋの需要家の状態ｘ（ｋ）は、電力使用量は計測値に基づき、ｄ個の要素からなる有限集合｛λ1，λ２，．．．，λd｝から選択されることとする。たとえば、電力使用量の計測値が０W以上２００W未満であれば状態は１００W、２００W以上４００W未満であれば状態は３００Wと、適切にしきい値を導入することとする。次に、需要家ｉが時刻ｋでｘｉ（ｋ）＝λｊとなる確率をπｉ，ｊ（ｋ）と表記する。このとき、πｉ，ｊ（ｋ）の時間変化を次式の離散時間マルコフ連鎖で表現することとする。

Figure 2016082804
The switched discrete time Markov chain model of the present invention will be described below. Let n be the number of customers. The state x (k) of the customer at time k is based on the measured value of the power consumption, and a finite set {λ1, λ2,. . . , Λd}. For example, if the measured value of power consumption is 0 W or more and less than 200 W, the state is 100 W, and if it is 200 W or more and less than 400 W, the state is 300 W, and a threshold value is appropriately introduced. Next, the probability that the customer i becomes xi (k) = λj at time k is expressed as πi, j (k). At this time, the time change of πi, j (k) is expressed by the following discrete time Markov chain.
Figure 2016082804

上記式（１）において、ｐ（ｋ）は有限集合｛1，２，．．．，P｝から選択される正数であり、電力価格に対応している。Ａｉ，ｐ（ｋ）は遷移確率行列、ａｉ，ｐ（ｋ）は確率分布である。需要家１，２，．．．，ｎと電力価格１，２，．．．，Pに対して、遷移確率行列と確率分布を求めることとする。なお、遷移確率行列Ａｉ，ｐ（ｋ）がゼロ行列のときのみ、確率分布ａｉ，ｐ（ｋ）は適切な値をもつこととする。同様に、確率分布ａｉ，ｐ（ｋ）がゼロベクトルのときのみ、遷移確率行列Ａｉ，ｐ（ｋ）は適切な値をもつこととする。電力価格を大幅に高く設定変更したとき、確率分布はある分布にリセットされる場合がある。したがって、上式は電力使用量の変化を適切に表現した数理モデルとなっている。なお、上式の数理モデルは、実際の電力使用量の計測データから求めることができる。   In the above equation (1), p (k) is a finite set {1, 2,. . . , P} is a positive number that corresponds to the electricity price. Ai, p (k) is a transition probability matrix, and ai, p (k) is a probability distribution. Consumers 1, 2,. . . , N and power prices 1, 2,. . . , P, a transition probability matrix and probability distribution are obtained. Note that the probability distributions ai, p (k) have appropriate values only when the transition probability matrix Ai, p (k) is a zero matrix. Similarly, the transition probability matrix Ai, p (k) has an appropriate value only when the probability distribution ai, p (k) is a zero vector. When the power price is changed to a significantly high setting, the probability distribution may be reset to a certain distribution. Therefore, the above equation is a mathematical model that appropriately expresses changes in power consumption. The mathematical model of the above equation can be obtained from measurement data of actual power consumption.

上式の数理モデルを用いた電力価格決定方法について説明する。電力価格を調整することで、需要家の総電力使用量を目標とする電力使用量に近づけることを考える。すなわち、次の価格決定問題を考える。   A power price determination method using the mathematical model of the above equation will be described. Consider adjusting the power price to bring the total power usage of the consumer closer to the target power usage. That is, consider the following pricing problem.

Figure 2016082804
Figure 2016082804

上記式（２）の価格決定問題では、評価関数Jを最小化する電力価格の時系列を求めている。評価関数Jは現時刻から未来の電力使用量や電力価格を評価している。Nはどこまでの未来を考慮するかを決めるパラメータである。評価関数Jの第１項は、総電力使用量の期待値と目標値ｘ＊の差の絶対値の時系列に重みｑを掛けている。第２項は、電力価格ｐ（ｋ）と適正価格ｐ＊の差の絶対値に重みｒを掛けている。重みｑ、ｒを適切に選定することで、総電力使用量を調整することができる。 In the price determination problem of the above equation (2), a time series of power prices that minimizes the evaluation function J is obtained. The evaluation function J evaluates future power consumption and power prices from the current time. N is a parameter that determines how far into the future is considered. The first term of the evaluation function J multiplies the time series of the absolute value of the difference between the expected value of the total power consumption and the target value x * by the weight q. In the second term, the absolute value of the difference between the power price p (k) and the appropriate price p * is multiplied by the weight r. The total power consumption can be adjusted by appropriately selecting the weights q and r.

価格決定問題を解くために、０−１変数を用いる。すなわち、ｐ（ｋ）がｉのときは、δｉ（ｋ）＝１、そうでなければδｉ（ｋ）＝０と定義する。このとき、価格決定問題は混合整数線形計画問題に帰着される。   To solve the pricing problem, 0-1 variables are used. That is, when p (k) is i, it is defined as δi (k) = 1, otherwise δi (k) = 0. At this time, the pricing problem is reduced to a mixed integer linear programming problem.

各需要家に対する遷移確率行列Ａｉ，ｐ（ｋ）および確率分布ａｉ，ｐ（ｋ）が同じ場合、電力使用量の時間変化はｄ通りしか存在しない。したがって、価格決定問題は、需要家数ｎに依存しない問題となる。   When the transition probability matrix Ai, p (k) and the probability distribution ai, p (k) are the same for each consumer, there are only d variations in power usage over time. Therefore, the price determination problem does not depend on the number of consumers n.

電力価格は以下の手順に従って求めることができる（図１参照）。
ステップ１： 需要家のスマートメータから電力利用量の計測データを受信する。
ステップ２： 計測データおよび切替型離散時間マルコフ連鎖モデルを用いて価格決定問題を解き、電力価格を決定する。
ステップ３： スマートメータなどを用いて、電力価格を需要家に提示する。
ステップ４： 一定時間経過後、ステップ１に戻る。
なお、ステップ３では価格の時系列ｐ（０），ｐ（１），．．．，ｐ（N-1）ではなく、ｐ（０）のみを需要家に提示する。 The power price can be obtained according to the following procedure (see FIG. 1).
Step 1: Receive power usage measurement data from a consumer smart meter.
Step 2: The price determination problem is solved by using the measurement data and the switching type discrete time Markov chain model to determine the power price.
Step 3: Present the electricity price to the customer using a smart meter or the like.
Step 4: Return to Step 1 after a certain period of time.
In step 3, the price time series p (0), p (1),. . . , P (N-1), only p (0) is presented to the customer.

本発明の方法は、再生可能エネルギーが大量導入された社会における、エネルギー管理システムの基盤技術の一つとして期待される。
The method of the present invention is expected as one of the fundamental technologies of energy management systems in a society where a large amount of renewable energy is introduced.

Claims (3)

以下の手順からなることを特徴とする電力価格決定装置。
ステップ１：需要家のスマートメータから電力利用量の計測データを受信する。
ステップ２：計測データおよび切替型離散時間マルコフ連鎖モデルを用いて価格決定問題を解き、電力価格を決定する。
ステップ３：スマートメータなどを用いて、電力価格を需要家に提示する。
ステップ４：一定時間経過後、ステップ１に戻る。
An electric power price determining apparatus comprising the following procedure.
Step 1: Receive power consumption measurement data from a smart meter of a consumer.
Step 2: The price determination problem is solved by using the measurement data and the switching type discrete time Markov chain model, and the power price is determined.
Step 3: Present the electricity price to the customer using a smart meter or the like.
Step 4: Return to Step 1 after a certain period of time.
式（１）で表される切替型離散時間マルコフ連鎖モデルを用いることを特徴とする電力価格決定方法。
Figure 2016082804
 A power price determination method characterized by using a switching type discrete time Markov chain model represented by formula (1).
Figure 2016082804
 式（２）で表される価格決定問題を用いることを特徴とする請求項２に記載の電力価格決定方法。
Figure 2016082804
The price determination method according to claim 2, wherein the price determination problem expressed by the equation (2) is used.
Figure 2016082804
JP2014214392A 2014-10-21 2014-10-21 Power price determination apparatus and power price determination method Pending JP2016082804A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110222882A (en) * 2019-05-21 2019-09-10 国家电网公司西南分部 A kind of prediction technique and device of electric system Mid-long Term Load
CN113077166A (en) * 2021-04-16 2021-07-06 国网吉林省电力有限公司 Community energy storage scheduling method based on Markov decision process

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110222882A (en) * 2019-05-21 2019-09-10 国家电网公司西南分部 A kind of prediction technique and device of electric system Mid-long Term Load
CN113077166A (en) * 2021-04-16 2021-07-06 国网吉林省电力有限公司 Community energy storage scheduling method based on Markov decision process

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