JP2022015383A - Deciding method for optimum combination of power generation and power transmission, and support system - Google Patents

Abstract

To provide a decision system, a decision method, and an algorithm for optimizing the demand-supply balance in an electric power market while considering VPP City, by taking the stability, cost efficiency, environmental friendliness, and safety into consideration, and addressing a long-term demand-supply percentage planning problem in the electric power market considering a mathematical model for obtaining an optimum combination of power generation and power transmission to optimize the gain balance between an electric power company and a consumer and an environmental value of renewable power.SOLUTION: A decision making support system 1 executes: processing of setting data on elements for an electric power company 2 and a consumer 3 (Step 1); processing of deciding weighting coefficients for the electric power company and the consumer (Step 2); processing of inputting prediction of a demand amount (Step 3); processing of setting upper and lower limits of a power generation ability of each power generation method and a procurement ability of each power generation method from a supplier 4 (Step 4); processing of calculating the total cost for the electric power company (Step 5); processing of calculating the total cost for the consumer (Step 6); processing of calculating the gain-balance and the total cost for the electric power company and the consumer (Step 7); and processing of obtaining an optimum combination of a power generation amount and a power transmission amount of each power generation method, and a procurement amount and a power storage amount from the supplier 4 (Step 8).SELECTED DRAWING: Figure 5

Description

The present invention is a method and support system for determining the optimum combination of power generation and power transmission, in other words, a method for determining the optimum combination of power generation and power transmission in consideration of environmental evaluation, safety, stability, and the gain balance between the electric power company and the consumer. And about the support system.

Technological innovations such as IoT are opening up new business possibilities in the electricity market. In order to realize a next-generation energy environment society for energy conversion and decarbonization, there is an urgent need to innovate the electricity market to promote the spread of renewable energy and local production for local consumption.
In addition, the current electric power industry faces challenges such as full competition in the electric power market due to the liberalization of electric power, expansion of the introduction of renewable energy, and consideration of restarting nuclear power plants and new expansion of thermal power generation.

In April 2014, the "Basic Act on Energy Policy" was enacted, which is based on the stability, economy, environment, and safety of power supply. On the other hand, as suggestions obtained from Germany and the United Kingdom, where the electricity market has been liberalized since the 1990s, there are problems of rising prices of coal and natural gas, an increase in the cost burden due to the fixed price trading system for renewable energy, and thermal power generation facilities. As a result of repeated closures, there is also the problem of a shortage of facilities that can back up power generation due to unstable renewable energy.
Furthermore, recently, since it is necessary to increase the ratio of renewable energy power generation (GEC Green Energy Coefficient) in the global electricity market, there is an urgent need for a policy to increase renewable energy. In the background of such liberalization of electric power, various studies have been made from the viewpoints of electric power price setting problem, electric power purchase allocation problem of consumers, power consumption problem, and power generation system development.

Patent Document 1 is a power transaction management system for managing electric power transactions, which includes a communication unit, a memory unit for storing a computer program, and a processor unit for executing the computer program. About a predetermined electric power product to be bought and sold in a predetermined electric power trading market based on a power price prediction process for predicting a future electric power price in the trading market and a predetermined predetermined electric power product extraction condition and a predicted electric power price. Power product extraction processing that extracts information from , The adjustment plan creation process that creates an adjustment plan by allocating the amount of power scheduled to be traded in a predetermined power product to each consumer based on the power consumption adjustment capacity, and the predetermined power product in a predetermined power trading market. A transaction plan creation process that creates a transaction plan for buying and selling based on the adjustment plan, an order process that sends order information based on the transaction plan to the market operation management device, and an adjustment that sends the adjustment plan to each customer. The planned transmission process and the power transaction management system that executes it are described.

Non-Patent Document 1 proposes a new method of distributed optimal power allocation that quantitatively suppresses the risk of imbalance between supply and demand. Specifically, a method of formulating by probabilistic model predictive control to find the optimum value of the average value and standard deviation of the power generation amount in the future of the power plant with adjustable output, and a decentralized solution by dual decomposition are performed. , The method of obtaining the electric power price as the optimum value of the dual variable is described.
Further, in Non-Patent Document 2, attention is paid to the optimum setting and the optimum power distribution of the power price of the supplier who generates power and the consumer who consumes power in the power trading market where the market manager exists. To show that market managers can maximize the profits of society as a whole by solving this problem, and propose an algorithm based on the dual decomposition of the dual problem, which solves this problem by exchanging information between market participants. Is described.

Japanese Unexamined Patent Publication No. 2016-134939

Masaki Yo, Masahiro Ono, Brian C. Williams, Shuichi Adachi: Distributed Optimal Power Allocation and Pricing Using Probabilistic Model Predictive Control, Proceedings of the Society of Instrument and Control Engineers, Vol.50, No.3, pp.227--235 ( 2014) Yusuke Kato, Toru Namerikawa: Optimization of bid price and power distribution by repeated bidding in the power market, Proceedings of the Society of Instrument and Control Engineers, Vol.56, No.4, pp.249-258 (2020)

However, there is not much consideration of the optimal business solution for the dynamic market environment from the viewpoint of overall optimization of electric power companies and consumers considering the environmental value for safety, economy, and the spread of renewable energy. Not done.
Therefore, in the present invention, in consideration of stability, economy, environmental friendliness and safety, a mathematical model for obtaining the optimum combination of power generation and power transmission aiming at optimizing the profit balance between the electric power company and the consumer and its calculation are performed. It is possible to propose algorithms for The purpose is to provide a decision system, its decision method and algorithm.

（数４～１９）

（３）発電と送電の最適な組み合わせを求める発電・送電の最適組み合わせ意思決定支援システムを用いて発電・送電の最適組み合わせを意思決定する方法であって、前記最適組み合わせ意思決定支援システムは、電力会社の収支要素に関するデータの設定及び需要家のコスト要素に関するデータの設定を行うデータ設定を行い、前記電力会社と前記需要家の利得バランスを考慮するため、前記電力会社のコストと前記需要家のコストを求め、両者の重みづけの係数を決める重みづけ係数決定を行い、前記需要家の需要量を予測し前記需要量を入力する予測入力を行い、各発電方式の発電能力と前記各発電方式のサプライヤーからの調達能力の上限と下限を設定する上限下限設定を行い、前記発電・送電、追加発電能力、サプライヤーからの電力買取、ＣＯ_２処理、事故リスク対策（安全性）、再生エネルギー環境価値及び蓄電の要素を考慮した、前記電力会社の総コストを計算する電力会社総コスト計算を行い、電力使用量、電力量料金単価、燃料費調達単価、再生可能エネルギー発電促進賦課金単価の要素を考慮した、需要家の総コストを計算する需要家総コスト計算を行い、前記電力会社と需要家の利得バランスを考慮した、両者の総コストを計算する利得バランス総コスト計算を行い、前記発電・送電、追加発電能力、サプライヤーからの電力買取及び蓄電の電力制約、要求される再生エネルギーの割合の制約を満たした、前記電力会社と前記需要家の総コストを最小化する、前記各発電方式の発電量と送電量、前記サプライヤーからの調達量及び蓄電量の最適組み合わせを求める最適組み合わせ出力を行うことを含むことを特徴とする発電・送電の最適組み合わせ意思決定支援方法である。
（４）前記重みづけ係数決定が式（３）に、前記上限下限設定が式（８）（９）及び（１１）に、前記電力会社総コスト計算が式（１）に、前記需要家総コスト計算が式（２）に、前記利得バランス総コスト計算が式（３）に、再生エネルギーの割合の制約が式（４）～（１９）に、それぞれ基づくことを特徴とする（３）に記載の発電・送電の最適組み合わせ意思決定支援方法である。
（数１～３）

（数４～１９）

The present invention that solves the above problems is as follows.
(1) Optimal combination of power generation and transmission for finding the optimum combination of power generation and transmission A decision support system for determining a decision, which is a memory unit for storing a predetermined computer program and a predetermined process by executing the predetermined computer program. The processor unit is provided with a processor unit that executes data setting processing for setting data regarding the balance element of the electric power company and setting data regarding the cost element of the consumer, and the profit balance between the electric power company and the consumer. In order to take this into consideration, the weighting coefficient determination process that determines the weighting coefficient of both in the predetermined formula for obtaining the cost of the power company and the cost of the consumer, the demand amount of the consumer is predicted, and the demand amount is input. Predictive input processing to be performed, upper and lower limit setting processing to set the upper and lower limits of the power generation capacity of each power generation method and the procuring capacity from the supplier of each power generation method according to the predetermined formula, and the power generation / transmission and addition according to the predetermined formula. Power company total cost calculation processing that calculates the total cost of the power company, considering factors such as power generation capacity, power purchase from suppliers, CO ₂ processing, accident risk countermeasures (safety), renewable energy environmental value and electricity storage. The consumer total cost calculation process that calculates the total cost of the consumer considering the factors of power consumption, power charge unit price, fuel cost procurement unit price, and renewable energy power generation promotion levy unit price by the predetermined formula, and the predetermined formula , Considering the profit balance between the power company and the consumer, the total cost calculation process of both, the power generation / transmission, additional power generation capacity, the power constraint of power purchase and storage from the supplier, and the ratio of the required regenerated energy. Optimal combination output for finding the optimum combination of the amount of power generation and transmission of each power generation method, the amount of power procured from the supplier, and the amount of electricity stored, which minimizes the total cost of the electric power company and the consumer, which meets predetermined constraints. It is an optimal combination decision support system for power generation and transmission, which is characterized by executing processing.
(2) The predetermined formula for obtaining the cost of the electric power company and the cost of the consumer is the formula (3), and the predetermined formula for setting the upper limit and the lower limit of the power generation capacity of each power generation method and the procurement capacity from the supplier of each power generation method. The formula considers the factors of formulas (8) (9) and (11), the power generation / transmission, additional power generation capacity, power purchase from the supplier, CO2 treatment, accident risk countermeasures (safety), renewable energy environmental value and storage. The predetermined formula for calculating the total cost of the electric power company is the formula (1), the predetermined formula for calculating the total cost of the consumer is the formula (2), and the total of both considering the profit balance between the electric power company and the consumer. The optimal combination decision support system for power generation and power transmission according to claim 1, wherein the predetermined formula for calculating the cost is given by the formula (3), and the predetermined constraint formula is given by the formulas (4) to (19). Is.
(Numbers 1 to 3)

(Numbers 4-19)

(3) Finding the optimum combination of power generation and transmission This is a method of determining the optimum combination of power generation and transmission using the optimum combination decision support system for power generation and transmission, and the optimum combination decision support system is electric power. In order to set the data for setting the data related to the income and expenditure factors of the company and the data for the cost factors of the consumer, and to consider the profit balance between the electric power company and the consumer, the cost of the electric power company and the cost of the consumer are taken into consideration. Obtain the cost, determine the weighting coefficient of both, determine the weighting coefficient, predict the demand amount of the consumer, perform the prediction input to input the demand amount, and perform the power generation capacity of each power generation method and each power generation method. Set the upper and _lower limits of the power generation capacity from the supplier of And, the total cost of the electric power company is calculated in consideration of the element of electricity storage, and the total cost of the electric power company is calculated. The total cost of the consumer is calculated in consideration of the total cost of the consumer, the total cost of the power company and the consumer is considered, and the total cost of both is calculated. The total cost of the power generation is calculated. For each power generation method that minimizes the total cost of the power company and the consumer, satisfying the power transmission, additional power generation capacity, power constraints of purchasing and storing power from suppliers, and the ratio of required renewable energy. It is an optimum combination decision support method for power generation and transmission, which includes performing an optimum combination output for obtaining an optimum combination of power generation amount and transmission amount, procurement amount from the supplier, and storage amount.
(4) The weighting coefficient determination is in the equation (3), the upper and lower limit settings are in the equations (8) (9) and (11), the electric power company total cost calculation is in the equation (1), and the consumer total. The cost calculation is based on the equation (2), the gain balance total cost calculation is based on the equation (3), and the restriction on the ratio of the regenerated energy is based on the equations (4) to (19), respectively. It is the optimum combination decision support method of power generation and transmission described.
(Numbers 1 to 3)

(Numbers 4-19)

According to the present invention, it is possible to provide an electric power company with an optimum combination of power generation and power transmission in consideration of economic efficiency, environmental value, safety, stability, and a gain balance between an electric power company and a consumer.

It is a figure which shows the image of the electric power business-virtual power plant (VPP) business which is pioneered by lot & AI by using the optimal combination decision support system of power generation and power transmission which is one embodiment of this invention. It is a figure which shows the present value and the target value of GEC of the developed country. It is a figure which showed the scenario tree of the energy mix which is one embodiment of this invention. It is a figure which shows the cost element of an electric power company and a consumer. It is a figure which shows the algorithm for calculating the optimum combination (total cost) of power generation and power transmission. It is explanatory drawing which shows the relationship between the hardware and the function of the optimal combination decision support system of power generation and power transmission.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. The present invention is not limited to the following embodiments, and changes, modifications, and improvements can be made without departing from the scope of the invention.

FIG. 1 shows the electric power business-virtual power plant (VPP) business 10 cultivated by lot & AI using the optimal combination decision support system for power generation and transmission (hereinafter sometimes referred to as “decision support system”) 1. rice field. In the VPP business 10, the electric power company 2 having hydroelectric power generation, thermal power generation, nuclear power generation, and renewable energy such as solar power generation and wind power generation supplies power to the consumer 3 by using the decision support system 1 (the decision support system 1 is used. (Power generation). Here, the electric power company 2 can receive supply by purchasing renewable energy such as solar power generation and wind power generation from a supplier (another electric power company) 4. An aggregator is a manager of an electric power company.

The following can be seen from the figure showing the current value and target value of GEC in developed countries shown in FIG. Recently, the global electricity market needs to increase the ratio of renewable energy generation (GEC Green Energy Coefficient), so there is an urgent need for policies to increase renewable energy.

The decision support system 1 includes a storage device 110 and a CPU 101 (FIG. 6). The CPU 101 as a "processor unit" comprehensively controls the operation of the decision support system 1 by reading and executing a predetermined computer program stored in the storage device 110.

The input device 102 is a device for the aggregator to input information to the decision support system 1. Input information includes days and instructions. The input device 102 is configured by using, for example, a keyboard, a mouse, a touch panel, a voice recognition device, an motion recognition device, and the like. The output device 103 is a device that outputs information from the decision support system 1 to the outside. The output device 103 is configured by using, for example, a display, a printer, a voice synthesizer, or the like.

The storage device 110 is a device that stores data in a storage medium such as a RAM (Random Access Memory), a ROM (Read Only Memory), a hard disk, or a flash memory. Although the storage device 110 is shown as one device in FIG. 6, it may be composed of a plurality of storage devices such as the main storage device and the auxiliary storage device.

The storage device 110 as a memory unit stores computer programs 112 to 118. The storage device 110 includes a data setting processing unit 111, a weighting coefficient determination processing unit 112, a prediction input processing unit 113, an upper and lower limit setting processing unit 114, an electric power company total cost calculation processing unit 115, and a consumer total cost calculation processing unit. It stores a predetermined computer program for realizing the functions of 116, the electric power company and the consumer gain balance total cost calculation processing unit 117, the optimum combination output processing unit 118, and the like.

Details will be described below, but each function from the data setting processing unit 111 to the optimum combination output processing unit 118 is as follows. The function of the data setting processing unit 111 is a data setting processing function for setting data related to the balance element of the electric power company 2 and setting data related to the cost element of the consumer 3. The function of the weighting coefficient determination processing unit 112 determines the weighting coefficient of both in the predetermined formula for obtaining the cost of the electric power company 2 and the cost of the consumer 2 in order to consider the gain balance between the electric power company 2 and the consumer 3. It is a weighting coefficient determination processing function. The function of the prediction input processing unit 113 is a prediction input processing function that predicts the demand amount of the consumer 3 and inputs the demand amount.

The function of the upper / lower limit setting processing unit 114 is an upper / lower limit setting processing function for setting the upper limit and the lower limit of the power generation capacity of each power generation method and the procurement capacity from the supplier 4 of each power generation method according to a predetermined formula. The functions of the electric power company total cost calculation processing unit 115 are, according to a predetermined formula, the power generation / transmission, additional power generation capacity, power purchase from supplier 4, CO ₂ processing, accident risk countermeasures (safety), renewable energy environmental value and storage. It is an electric power company total cost calculation processing function that calculates the total cost of the electric power company 2 in consideration of the above factors. The function of the consumer total cost calculation processing unit 116 is to calculate the total cost of the consumer 3 in consideration of the elements of the electric energy consumption, the electric energy charge unit price, the fuel cost procurement unit price, and the renewable energy power generation promotion levy unit price by a predetermined formula. It is a consumer total cost calculation processing function to calculate.

The function of the electric power company 2 and the consumer 3 gain balance total cost calculation processing unit 117 is a calculation processing function of the total cost of both the electric power company 2 and the consumer 3 in consideration of the gain balance by a predetermined formula. The functions of the optimum combination output processing unit 118 are those of the electric power company 2 and the electric power company 2 that satisfy the above-mentioned power generation / transmission, additional power generation capacity, power restrictions of power purchase and storage from the supplier 4, and predetermined restrictions of the required ratio of regenerated energy. It is an optimum combination output processing function that minimizes the total cost of the consumer 3 and obtains the optimum combination of the power generation amount and the transmission amount, the procurement amount from the supplier 4, and the storage amount of each power generation method.

The decision support system 1 is constructed by a mathematical model that performs overall optimization for the purpose of minimizing the total cost of the electric power company 2 and the consumer 3 over a multi-period period. The total cost (f), which takes into account the weights of the costs of both the electric power company 2 and the consumer 3, is used as the objective function. The elements of the cost ( _fc ) of the electric power company 2 are power generation cost, transmission cost, CO ₂ processing cost, accident risk response cost, power excess penalty cost, power generation capacity addition cost, renewable energy environment evaluation purchase cost, from supplier 4. There are the cost of purchasing renewable energy and the cost of stocking electricity, and the income includes the sales of electricity, the cost of evaluating and selling the renewable energy environment, and the government subsidy. On the other hand, the cost ( _fu ) of the consumer 2 is the electric energy charge. The factors are electricity usage charges, fuel adjustment charges, and renewable energy charges.
The definitions of symbols and parameters used in the present invention are shown below.

<Definition of symbols>

<Definition of parameters>

The cost of the electric power company 2 consists of three elements: the power generation cost per unit of each power generation method, the accident risk response cost per unit power generation amount, and the CO ₂ processing cost per unit power generation amount. In addition, the transmission cost unit price is set as β _j for each transmission area. In addition, _Pjt ^OS is set as the excess transmission rate, and _Pj0t ^OS is set as the lower limit rate of the excess transmission amount (the amount of power lost during transmission), and the product of the difference and the transmission amount is taken to obtain the actual excess transmission amount. Ask for.

The penalty for excess power transmission is calculated using the unit price of the penalty for excess power as a coefficient. In addition, _quant is defined as the unit price of the additional power generation capacity, and when the power generation capacity is added, the cost according to the additional power generation capacity is added. Gamma can be defined as the unit price for renewable energy environmental assessment, and the difference from the GEC level can be bought and sold, and the expenditure or income from it can be added to the objective function. Further, the electric power company 2 can purchase renewable energy from the supplier 4, and in that case, the cost of the purchased electric power is added. In addition, the inventory cost is added to the objective function according to the amount of power inventory.

In the cost ( _fu ) of the consumer 3, the electric energy charge unit price, the fuel cost adjustment unit price, and the renewable energy power generation promotion levy unit price are set as the unit price per transmission amount. Here, the renewable energy power generation promotion levy unit price μ is calculated using the calculation method described in “TEPCO Holdings: About levies, etc. (2018)”. The cost of each of the electric power company 2 and the consumer 3 is multiplied by the probability of occurrence of each node in the scenario tree, and the sum is taken for each period to calculate the expected value of the cost for each period.

In the present invention, the decision in each period is modeled based on the scenario tree. FIG. 3 shows a scenario tree having two nodes when t = 1 and four nodes when t = 2. In the scenario tree, t = 0, 1, 2, ... Corresponding to each period is set. Each node ω _t (s) in the scenario tree has a parent node aω _t (s) except when t = 0. Let Ω _t be the set of each node ω _t (s) in the t period, and be defined by Ω _t = {ω _t (1), ω _t (2), ... ω _t (s)}, and in the case of Fig. 3, s = The value of p ω t (s ⁾ , which is 2 ^t , is the joint probability of the nodes on all the paths from the route ω ₀ (1) to ω _t (s), and the occurrence probability of a certain node and the occurrence of its child nodes. The sum of the probabilities is equal. When t = 0, p ^{ω0 (1)} = 1, Ω ₀ = {ω ₀ (1)}.

In addition, the sum of the occurrence probabilities of nodes in a certain period is 1. For example, when node ω ₁ (1) is selected at t = 1, the probability of becoming node ω ₂ (1) at t = 2 is p ω 2 ( ¹⁾ / p ω 2 ( ²⁾ . A certain route from the ancestor node ω ₀ (1) to the terminal node is called the scenario. When each node branches into two nodes, there are 2 ^T terminal nodes and the same number of scenarios.

Parameter values are set for each node. If the amount of renewable energy generated cannot meet the GEC permissible lower limit value ρ ^t , the electric power company 2 adds power generation capacity to meet this, or purchases renewable energy from the supplier 4. When the power generation capacity is added, the additional capacity X _u ^{ωt (s)} is added to the upper limit values _{Capmax0 and} ^{uωt (s)} of the power generation capacity. Obviously, the amount of power generated by renewable energy is limited by its power generation capacity, and the required GEC constraint ρ is also taken into consideration. Therefore, in order to meet the GEC constraint, which node should be added with power generation capacity and which node The model determines whether it is optimal to purchase power from the supplier 4, and the amount of power generation and transmission at each node of each scenario is determined.

Further, since it is a condition that the electric power company 2 makes a profit, it is considered as a constraint that the total of the income from the consumer 3 and the subsidy from the government agency exceeds the cost of the electric power company. The purpose is to calculate each of the above costs and minimize the sum. The formulation of the mathematical model that determines the optimum combination of the amount of power transmitted to each region and the amount of power generated by each power generation method is shown below.

<Coefficient of determination>

<Objective function>

<Constraints>

Equation (1) is an objective function expressing the sum of each node of the total total cost considering the weighting of both the cost of the electric power company 2 and the cost of the consumer 3. Equation (2) is an equation of the cost of the electric power company 2, and considers the power generation cost, the accident risk response cost, and the CO ₂ processing cost according to the amount of power generation. Also, consider the transmission cost for each transmission area. In addition, considering the penalty cost due to excessive transmission and the buying and selling of the renewable energy environment evaluation value, the cost will be added if the power generation capacity is added in each period, and the cost will be added if the power is purchased from the supplier 4. And add the cost to keep the electricity in stock.

In the cost formula (3) of the consumer 3, the electric energy charge unit price, the fuel cost adjustment unit price, and the renewable energy power generation promotion levy unit price are considered. Equations (4) and (5) express the relationship between the amount of power generation, the amount of power transmission, and the amount of inventory of each power generation method. It will be a combination. Equation (6) is a constraint and relational expression regarding the definition of the transmission amount and the excess transmission amount for the power demand of each node to each region in each period. That is, the power transmission amount is determined so as to satisfy the formula (6), and the power generation amount by each power generation method is determined by the formulas (4) and (5).

Equation (7) guarantees that the electric power company 2 is profitable because the income exceeds the cost of the electric power company 2. Equations (8) and (9) are constraints that the power generation capacity must satisfy at each node. Further, it is possible to add the capacity to the power generation capacity of the renewable energy at each node by the equation (10). By adding to the power generation capacity of the parent node, the upper limit of the power generation capacity of the equation (8), which is a conditional expression of the power generation capacity of renewable energy, can be increased. Equations (11) and (12) represent the upper and lower limits of the power purchase amount and inventory capacity from the supplier 4, respectively.

Equation (13) is a constraint on GEC, which limits the GEC level that the electric power company 2 must meet at least. Equation (14) is a method for calculating μ. According to the above-mentioned literature by Kentaro Nagami et al., "Renewable energy power generation promotion levy unit price = total estimated amount of grants to electric power companies in the current fiscal year / total estimated supply amount of electric power companies, etc. in the current fiscal year". When this is expressed by the symbol of this model, it becomes equation (17). Equations (15), (16), (17), (18), and (19) are non-negative constraint equations for the respective decision variables.

As shown in FIG. 5, the decision support system 1 can provide the electric power company 2 with the optimum combination of power generation and transmission by executing the following Steps 1 to 8.
That is, the data setting process (Step 1) for setting the data related to the balance element (FIG. 4) of the electric power company 2 and the data related to the cost element (FIG. 4) of the consumer 3, and the gain of the electric power company 2 and the consumer 3. In order to consider the balance, the weighting coefficient determination process (Step 2) that determines the weighting coefficient of both in the formula (3) for obtaining the cost of the electric power company 2 and the cost of the consumer 3 and the demand amount of the consumer 3 are predicted. Then, the upper and lower limits of the power generation capacity of each power generation method and the procurement capacity from the supplier 4 of each power generation method are set by the predictive input process (Step 3) for inputting the demand amount and the formulas (8), (9) and (11). Power generation / transmission, additional power generation capacity, power purchase from supplier 4, CO ₂ treatment, accident risk countermeasures (safety), renewable energy environmental value and power storage by the upper and lower limit setting processing (Step 4) and formula (1) to be set. Electric power company total cost calculation process (Step 5) that calculates the total cost of the electric power company 2 and formula (2), which considers the above factors, are used for electric energy usage, electric energy charge unit price, fuel cost procurement unit price, and renewable energy power generation. Both the consumer total cost calculation process (Step 6), which calculates the total cost of the consumer 3 considering the factors of the accelerated levy unit price, and the profit balance of the electric power company 2 and the consumer 3 are considered by the formula (3). Gain balance total cost calculation processing (Step 7) to calculate the total cost of )-(19)) is satisfied, and the optimum combination of the power generation amount and transmission amount of each power generation method, the procurement amount from the supplier 4, and the storage amount is obtained, which minimizes the total cost of the electric power company 2 and the consumer 3. Step8), is executed.

All numerical calculations in the following description are performed by creating a program in C language on a PC with Intel® Core ^TM i7-8700_CPU@3.20GHz, RAM of 24.0GB, and Intel Compiler 16.0 Update 3 Intel. Compiled according to (registered trademark) 64. We also used XPRESS BCL to solve the linear programming problem. In the table of numerical results, for example, 7.80E + 09 represents 7.80 × 10 ^ 9.

(Numerical experiment of multi-step stochastic power supply and demand planning model)
Based on the data of the Ministry of Economy, Trade and Industry (Table 1), the parameters were set and numerical experiments were conducted in 4 periods. Consider a scenario tree in which one node branches into two. There are seven types of power generation methods: wind power (u1), solar power (u2), geothermal power (u3), biomass (u4), thermal power (r1), nuclear power (r2), hydraulic power (r3), one power company 2, demand. As house 3, consider the case of two cities. It is assumed that the power generation capacity is sufficient for the demand, the power generation capacity of renewable energy can be added, and the additional unit price of the capacity is q = 150. The probability of occurrence of each node is 0.5.

Regarding renewable energy power generation, it is possible to add from the initial value (= 10E + 06) of the upper limit of power generation capacity, and the upper limit of thermal power, nuclear power, and hydropower generation capacity is + ∞. The set parameters are shown in Tables 1 and 2. Further, in the numerical experiment in the present invention, the weights w of the objective functions are all calculated as 0.5.

Numerical experiments were conducted in three scenarios (Table 3) by changing the power purchase unit price α from supplier 4 and the GEC level ρ.

Examples of numerical experiment results of scenarios 1 to 3 are shown in Tables 4, 5 and 6.

Make a comparison based on scenario 1. Geothermal and biomass power generation was the main type of renewable energy power generation, and nuclear power generation was the main type of conventional power generation method. In scenario 2, the total purchase price from supplier 4 was increased, resulting in a significant decrease in the total of Y.
In scenario 3, the value of ρ was increased (0.2 → 0.3). As a result, power generation with renewable energy has increased significantly to meet GEC levels. In addition, the total cost has increased, resulting in an increase in the electric power company 2.

Based on the above, we propose a multi-period supply-demand probability model for electric power marketing that takes into account the liberalization of electric power, and use the proposed model to find the optimum combination of the amount of power generated by each power generation method, the amount procured from supplier 4, and the amount of stock. I found that I could do it.

It can play a role as a solver on the optimal decision support system that considers the economic efficiency, environmental value, safety, stability, and the gain balance between the electric power company and the consumer for the electric power company.

1: Decision support system 2: Electric power company 3: Consumer 4: Supplier 10: Electric power business-Virtual power plant (VPP) business 101: CPU
102: Input device 103: Output device 110: Storage device 111: Data setting processing unit 112: Weighting coefficient determination processing unit 113: Prediction input processing unit 114: Upper and lower limit setting processing unit 115: Electric power company total cost calculation processing unit 116: Consumer total cost calculation processing unit 117: Electric power company and consumer gain balance Total cost calculation processing unit 118: Optimal combination output processing unit

Claims (4)

An optimal combination of power generation and transmission that seeks the optimum combination of power generation and transmission. It is a decision support system that executes a predetermined process by executing a memory unit that stores a predetermined computer program and the predetermined computer program. A processor unit is provided, and the processor unit is for considering a data setting process for setting data regarding a balance element of a power company and setting data regarding a cost element of a consumer, and a profit balance between the power company and the consumer. , The weighting coefficient determination process for determining the weighting coefficient of both in the predetermined formula for obtaining the cost of the electric power company and the cost of the consumer, and the prediction input for predicting the demand amount of the consumer and inputting the demand amount. Processing, upper and lower limit setting processing to set the upper and lower limits of the power generation capacity of each power generation method and the procuring capacity from the supplier of each power generation method according to the predetermined formula, and the power generation / transmission and additional power generation capacity according to the predetermined formula. Power company total cost calculation processing that calculates the total cost of the power company, taking into consideration factors such as power purchase from suppliers, CO ₂ processing, accident risk countermeasures (safety), renewable energy environmental value, and power storage, and by a predetermined formula , Electricity usage, Electricity charge unit price, Fuel cost procurement unit price, Renewable energy power generation promotion levy unit price Considering the profit balance between the company and the consumer, the total cost calculation process of both, the power generation / transmission, additional power generation capacity, the power constraint of power purchase and storage from the supplier, and the predetermined constraint of the required ratio of regenerated energy. Optimal combination output processing for obtaining the optimum combination of the power generation amount and the transmission amount of each power generation method, the amount procured from the supplier, and the storage amount, which minimizes the total cost of the power company and the demand amount. An optimal combination decision support system for power generation and transmission, which is characterized by execution. The formula (3) for calculating the cost of the electric power company and the cost of the consumer is the formula (3), and the formula for setting the upper limit and the lower limit of the power generation capacity of each power generation method and the procurement capacity from the supplier of each power generation method is the formula. (8) (9) and (11), considering the factors of power generation / transmission, additional power generation capacity, power purchase from supplier, CO ₂ treatment, accident risk countermeasures (safety), renewable energy environmental value and electricity storage, The predetermined formula for calculating the total cost of the electric power company is the formula (1), the predetermined formula for calculating the total cost of the consumer is the formula (2), and the total cost of both the electric power company and the consumer is taken into consideration. The optimal combination decision support system for power generation and power transmission according to claim 1, wherein the predetermined formula to be calculated is given by the formula (3), and the predetermined constraint formula is given by the formulas (4) to (19).
(Numbers 1 to 3)

(Numbers 4-19)

It is a method to determine the optimum combination of power generation and transmission using the optimum combination decision support system for power generation and transmission, which seeks the optimum combination of power generation and transmission. The optimum combination decision support system is the balance of payments of the electric power company. Setting the data related to the element and the cost of the consumer The cost of the electric power company and the cost of the consumer are obtained in order to set the data for setting the data related to the element and to consider the profit balance between the electric power company and the consumer. , Determine the weighting coefficient of both. Perform the weighting coefficient determination, predict the demand amount of the consumer and input the demand amount, and perform the power generation capacity of each power generation method and the supplier of each power generation method. Set the upper and lower limits of the power generation capacity of the power generation / transmission, additional power generation capacity, power purchase from suppliers, CO ₂ treatment, accident risk countermeasures (safety), renewable energy environmental value and electricity storage. The total cost of the electric power company is calculated in consideration of the factors, and the total cost of the electric power company is calculated, and the factors of the electric power usage amount, the electric power amount charge unit price, the fuel cost procurement unit price, and the renewable energy power generation promotion levy unit price are taken into consideration. Calculate the total cost of the consumer Calculate the total cost of the consumer, calculate the total cost of both the power company and the consumer, and calculate the total cost of the gain balance. With the amount of power generation of each power generation method that minimizes the total cost of the power company and the consumer, satisfying the power generation capacity, the power constraint of power purchase and storage from the supplier, and the limit of the required ratio of regenerated energy. An optimum combination decision support method for power generation / transmission, which comprises performing an optimum combination output for obtaining an optimum combination of a transmission amount, a procurement amount from the supplier, and a storage amount. The weighting coefficient determination is in the equation (3), the upper and lower limit settings are in the equations (8) (9) and (11), the electric power company total cost calculation is in the equation (1), and the consumer total cost calculation is in the equation (1). The power generation according to claim 3, wherein the gain balance total cost calculation is based on the formula (2), and the constraint of the ratio of the regenerated energy is based on the formulas (4) to (19).・ Optimal combination of power transmission Decision support method.
(Numbers 1 to 3)

(Numbers 4-19)

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