Interstate Energy Redistribution Strategy

ABSTRACT ： For this parper， we use linear programming to build up the optimal programming model. By using LINGO to solve the model， we get the energy consumption structures of each state under the best target （The results are shown in the table 24， 25）. Finally， we give our feasible policy measures in order to meet their new four-state energy compact.

KEY WORD：Interstate ；Energy Redistribution； Strategy

1.Analysis

Overall Analysis

This paper mainly studies how to arrange the production and use of energy reasonably. In view of this problem， our research can be divided into three parts，The first part mainly solves the problem of energy allocation and evaluation， and forecasts the energy situation of each region from 2025 to 2050 according to the historical evolution of energy use in each region. The second part mainly solves the problem of the optimal planning of the goal， and puts forward the feasible implementation countermeasure according to the determined best goal.The third part is a summary of the State profiles as of 2009.

Concrete Analysis

The first part requires solving the problem of energy allocation， evaluation and prediction. For the first part， we first preprocess the data， select the data we need based on certain criteria， and get the general situation of energy allocation in different States by classification and subtotal. Secondly， we build a multivariate linear regression model based on the time series data from 1960-2009 to describe the energy status of each of the four States in 1960-2009. Then， we normalize the data of the States and set up a comprehensive evaluation model based on the correlation coefficient of various indicators in the multivariate linear regression model， which is used as the standard to evaluate the best use of clean energy. Finally ， we establish a regression forecasting model by analyzing the relationship between data distribution， and fitting out a model which accords with the relationship between energy development and time， and then forecasts the energy profile of each State by 2025 and 2050 .

In the second part， we need to solve the problem of optimal programming. For the second part， we establish the linear programming model for the objective function according to the prediction results and the best criteria of the first part， and then determine the renewable energy use targets of 2025 and 2050. Identify and discuss at least three actions the four States might take to meet their energy compact goals.

2. Optimum Programming Model

2.1The Preparation for the Model

Problem 5 requests us to set renewable energy usage targets for 2025 and 2050， which are based on our comparison between the four states， our criteria for “best” profile， and our predictions. In this problem， we think that the total energy consumption predicted in Problem 4 is accurate and unchanged. On this basis， we hope that in the final target， the energy consumption of the four states can be calculated. Therefore， we select typical indicators about energy prices and use the prediction model of Problem 4 to forecast the energy prices in 2025 and 2050. After we define the target， we find the decision variables that have influences on the goal. We determine the objective function by the function relation between the decision variable and the purpose to be achieved. Finally， we find out the constraints on the decision variables， and then we construct an optimization model.

2.2The Construction of the Model

The mathematical standard for linear programming is as follos：

The solution x that satisfies the constraint is called the feasible solution of the linear programming problem and the feasible solution of the optimal solution that maximizes the objective function.

2.3The Solving of the Model

Using results of Problem 4， where we predicted energy expenditure data for 2025 and 2050， and Problem 3， where we evaluated the “best” image of States using clean energy， further analysis enables the four States to achieve the “best” image of clean energy usage under the unchanged total energy expenditure.

Since we want to calculate the energy consumption of each State， we select the price of the typical energy sources as the average price for each type of energy， which are shown in the table below：

We use the scoring system in Problem 3 and rates of energy expenditure in each state under the “best” profile to score energy distribution in four states. In order to achieve the optimal allocation goal， we established the optimization model. Let’s take the model of 2025 as an example：

Four-State energy compact has been established and these four states have achieved the “best” profile of using renewable and clean energy in 2025. The minimum consumption of all kinds of energy is also a constraint.

Let be the total consumption of all types of energy， and be the consumption of State， energy.

From the lingo solution， the target energy consumption in interState energy compact is as follows：

3.Policy Suggestion

Enhancing policy support： Establish a special fund to support the development of new energy industry. Funds are the key to the development of new energy industry. Promoting the development of new energy in cities is inseparable from the funding support， Therefore， setting up a special fund to support the development of new energy is conducive to the development of new energy. We can provide financial support through the following two aspects， on the one hand， we can integrate existing special funds such as major science， technology projects and new energy construction； on the other hand， we can set up a green energy voluntary subscription mechanism and energy conservation target assessment rewards to raise funds to support the popularization and application of new energy products， new energy technologies and new energy demonstration projects and other sectors.

Creating a comprehensive financial service system： Leading by the government to build financing platform. Strengthen the cooperation between the government， banks and enterprises and set up a cooperation platform to guide commercial banks to further increase credit support for new energy city construction in accordance with the principle of “government-led， unified planning and centralized examination and verification”； they may also apply to international financial institutions Low-interest loans to support the construction of new energy demonstration city. Establish a diversified financing service platform. Accelerate the introduction of joint-stock banks， further improve the financing guarantee system， develop innovative financial products such as green credit， green securities and green insurance， and introduce market-based financing mechanisms such as contractual energy management.

Strengthening the Construction of Technical support system： Encourage enterprises to increase investment in research and development of core technologies. We will further increase the implementation of incentive policies such as enhancing the capability of independent innovation of enterprises and deducting enterprise technology development costs， and encourage enterprises to increase R D investment. Adhere to the combination of examination and incentive， increase the financial support for technological innovation， increase investment in technological innovation， and promote cooperation in production， teaching and research innovation. Encourage enterprises and enterprises and research institutes to establish technical and strategic cooperation between the various types of mutually beneficial ways to carry out research and development cooperation， thereby enhancing the technical level. Focus on accelerating the use of natural energy such as solar energy and solar heat pumps and ground source heat pumps； manufacturing of key equipment such as high-efficiency biomass gasification plants and biomass forming equipment； high-efficiency bioethanol conversion technologies； bio-ethanol feedstock pressurization and distribution systems Note station design and construction aspects of technology research and development efforts.

References：

[1]Li Wang. Research on the Prediction method of time Series based on functional variable coefficient autoregressive method [D]. University Of Shanxi， 2016.

[2]Xuemei Bai， Songshan Zhao. A method of determining weights derived from the correlation of indicators [J]. Jiangsu statistics， 1998（04）：14-16.

作者簡介：

徐小杰（1996—）男，安徽亳州人 安徽財經大學金融學院 2015級本科在讀，研究方向：金融學。

董文兵（1995—）女，安徽宿州人，安徽財經大學經濟學院，2015級本科生在讀，研究方向：國民經濟管理。