**Evaluation Model of Demand-Side Energy Resources in Urban Power Grid Based on Geographic Information**

**Feifan Chen 1,\*, Shixiao Guo 1, Yajing Gao 2,\*, Wenhai Yang 3, Yongchun Yang 1, Zheng Zhao <sup>4</sup> and Ali Ehsan <sup>5</sup>**


Received: 23 July 2018; Accepted: 27 August 2018; Published: 29 August 2018

**Featured Application: This paper contributes to guiding government planners and DSER investors to compare and select various DSER, furthermore, they can make a relatively reasonable planning scheme or investment scheme. Meanwhile, it provides a strong support for the long-term optimization planning and medium-term optimization aggregation of DSER, which is conducive to the development and utilization of DSER.**

**Abstract:** In the context of current energy shortage and environmental degradation, the penetration rate of demand-side energy resources (DSER) in the power grid is constantly increasing. To alleviate the problems concerning the energy and environment, it is of tremendous urgency to develop and make effective use of them. Therefore, this paper proposes the evaluation model of DSER in urban power grid based on geographic information, and a variety of demand-side energy resources in a region is evaluated. Firstly, as for five kinds of DSER, revolving wind power generation (WG), photovoltaic power generation (PV), electric vehicle (EV), energy storage (ES), and flexible load, the commonality indexes and individuality indexes of all kinds of resources are selected based on geographic information. The commonality indexes are common indexes of all DSER, and the individuality indexes are unique indexes of all DSER. Then the weight of each subindex under the commonality and individuality indexes are determined by analytic hierarchy process (AHP) and entropy weight method, respectively. Finally, weighted overlay are made according to the weights and quantized values of each index, and a comprehensive score is obtained from the commonality indexes and individuality indexes upon various demand-side energy resources in the region. The result depicts that the proposed evaluation model of demand-side energy resources is of well practicability and effectiveness, which is beneficial to the planning of the city and the power grid. Most of all, such model provides a strong support for the long-term optimization planning and the medium-term optimization aggregation of DSER.

**Keywords:** DSER; evaluation model; geographic information; commonality indexes; individuality indexes

#### **1. Introduction**

In the context of current energy shortage and environmental degradation, the proportion of energy resources, such as distributed generation, ES and flexible load, is increasing in the power grid; these resources are widespread existent on the demand side, thus are called demand-side energy resources (DSER). In China, the National Development and Reform Commission and other departments have issued a series of policies on distributed photovoltaic (PV) and decentralized wind power generation (WG) since 2007. DSER plays an increasingly important role in the transformation of new energy systems in China. By the end of 2017, the newly installed capacity of PV in China is 53.06 million kilowatts, and the cumulative installed capacity is 130.25 million kilowatts. The cumulative installed capacity of ES is 28.9 million kilowatts, an increase of 19%. In addition, according to the global distributed energy technology report, the installed capacity of global distributed energy in 2017 is about 132.4 million kilowatts. At the same time, to support the development of energy resources such as WG and distributed PV, many countries have formulated relevant policies. Brazil, Canada, Spain, the United States and other countries have formulated a localization rate policy for WG equipment. The European Union (EU) has set a target that renewable energy will account for 27% of the energy demand structure in 2030, promoting PV development. Nowadays, urban power grids are facing severe challenges including increasing load density, increasing peak and valley difference, shortage of power grid construction land and difficulties in raising funds for construction. At the same time, DSER receives attention from all sectors of society and policy support from the government, and its penetration rate in the power grid, especially in the urban power grid, is constantly increasing, providing a huge potential for the implementation of various demand-side projects. At this time, how to fully tap and exploit the potential of DSER, furthermore, ensuring their participation in the planning, construction, and operation of urban power grids, has become a key issue in solving the current difficulties existing in urban power grids [1–5].

It can be seen that the rational development and the effective use of DSER will have a far-reaching impact on China's energy power industry. The evaluation model of demand-side energy resources can be used to explore the status, characteristics, and development prospects of DSER. It is an important tool to develop and use DSER rationally. Therefore, the establishment of an evaluation model of demand-side energy resources is very important for the development and utilization of DSER as well as the energy power industry.

Therefore, many literature works discuss the evaluation of DSER. In literature [6], the meteorological data of Kahnuj in Iran have been measured at an altitude of 10 m, over a four-year period. Also, the monthly, annual and seasonal wind speed variations are investigated, and the economic feasibility is determined of installing wind turbine at the site. Literature [7] proposes a method to simulate a large wind farm and determine its capacity value, taking into account the mechanical failure of the wind turbine and the influence of the transmission system, and evaluate and compare the wind capacity values under different conditions. Literature [8] presents a study of the installation of a hybrid PV-WG system for social interest houses in the city of San Luis Potosi, Mexico. To assess the benefits of the implementation of this type of system, a technological, economic, and environmental evaluation is carried out based on the available renewable energy resources and considering a typical load profile of consumers. Literature [9] obtained a probability model for system power output by analyzing the structural characteristics of the PV power system and by examining its component failure mode and the effect of the component failure mode on the output power. In literature [10], through the analysis of the results of the various operating parameters of large-scale PV, the operation of large-scale grid connected PV grid is mastered. The comprehensive evaluation system is established by using the analytic hierarchy process. In literature [11], the electric vehicle (EV) charging load in a day is calculated by the traffic simulation and evaluated in order to assess the influence of EV charging on the grid system and the applicability of EV in the smart-grid system. Literature [12] proposes a reliability and economic evaluation model of distribution network under large-scale EV's access. The Monte Carlo method was utilized to evaluate the reliability of distribution

network. In literature [13], on the basis of analyzing the traditional site selection program evaluation method, fuzzy analytic hierarchy process (AHP) is proposed to evaluate the EV charging station sitting programs, also the evaluation model algorithms are given, wishing to achieve the optimal program. Literature [14] evaluates the benefits of ES systems applied to renewable intermittent sources like wind. Literature [15] presents a real-time evaluation and simulation approach of ES system based on large renewable-based electricity generation, which can be used for grid support. In literature [16], a AHPPROMETHEE-GAIA method based on the analysis of the characteristics of the existing ES ways is presented. Taking the factors, such as technology maturity, cost, life, efficiency, response speed, and environment as assessment criterion, a comprehensive evaluation for the existing ES ways is made.

All of the above literatures have evaluated DSER, but the evaluation model constructed has only evaluated single DSER without taking a variety of DSER into comprehensive consideration. Therefore, this paper evaluates five kinds of DSER, including WG, distributed PV, EV, ES, and flexible loads. There are many factors to be considered when evaluating the DSER. Firstly, the development of various types of DSER will bring more or less benefits, which reflects the significance of these resources. Also, benefits are the direct purpose of digging deeply into DSER. At the same time, due to the difference in geographic location and development conditions, the development of different DSER varies from region to region. Therefore, resource development, which means proportion of resources currently developed in a certain region to the total resources of the region, is an important index for evaluating the development status of DSER. In addition, the future development potential of DSER is the focus of government and investment institutions, which lays fundamentals for the government to formulate relevant policies and incentives for various types of DSER. The above indexes need to be considered when all kinds of DSER are evaluated, which are defined as commonality indexes. In addition, various DSER have different characteristics. To facilitate the evaluation of the characteristics of DSER, DSER is divided into three groups and the key characteristics of evaluation concern are sorted out, defined as individuality indexes.

The advantages of WG and PV lie in the large range of distribution areas, high development value, almost zero pollution and inexhaustible [17,18]. However, the output of WG and PV are affected by the meteorological conditions such as wind speed and light intensity. The output has characteristics of volatility and intermittency, which increase the difficulty in regulating the peak of conventional power supply in power grid. Therefore, it is necessary to evaluate the output characteristics of WG and photoelectric, and the reliability of WG and photoelectric is also an important role in affecting the stability of the power system.

EV and ES can effectively fulfill the demand-side response and behave interactively with the grid. They can be used to cut peaks and valleys, smooth load fluctuations, and promote the use of intermittent energy resources. Therefore, the peak shaving capacities of EV and ES are particularly important, concerning that they will be connected to the grid in large scale, the reliability of EV and ES resources cannot be ignored as well [19,20].

Flexible loads resources refer to flexible loads that have a power demand response. Demand response technology is one of the core technologies in the smart grid. The application of demand response technology can fully exploit the load-side resources and realize the comprehensive optimization of resources configuration [21]. Different users' response willingness and responsive device capacities are different, such as supermarkets, shopping malls, and hotels and other commercial users with less response willingness and larger response capacity, the residents are the opposite, with greater willingness to respond and smaller response capacity. The response capacities of various users are very different. Therefore, as for flexible loads, features related to response are evaluation-focused.

It can be seen that the current evaluation systems focus on the evaluation of single DSER, and the selected indexes are often individuality indexes of DSER. So, a set of general evaluation system should be set up to evaluate the commonality indexes and individuality indexes of all kinds of DSER. At the same time, these DSER are widespread distributed in the urban power grid, their distribution has natural geographical features and is inseparable from the way of urban planning and partition.

According to the above situation, this paper proposes an evaluation model of DSER based on geographic information. Based on the functional partition of the city, the commonality indexes system and the individuality system are established by analyzing the commonality and individuality of various DSER, and the commonality indexes and individuality indexes are selected separately, that is, each index is defined. Then AHP is used to determine the weight of the commonality indexes. The entropy weight method is used to determine the weight of the individuality indexes. Finally, the comprehensive scores of the commonality indexes and individuality indexes are obtained respectively. The specific process is shown in Figure 1. The comprehensive scores of various DSERs in various regions are favorable for urban and power grid planning, guiding DSER investors and government planners, and providing a strong support for the long-term optimization planning and medium-term optimization aggregation of DSER.

**Figure 1.** Flow chart for calculating the composite score. The Establishment of Commonality Indexes System for demand-side energy resources (DSER).

#### *1.1. The Commonality Indexes System of Evaluation Model of DSER*

According to the above analysis, benefits as well as resource development and development potential are selected as commonality indexes of DSER, which are the indexes common to all types of DSER, and the abovementioned first-level indexes are further refined to obtain a commonality indexes system of evaluation model of DSER, as shown in Figure 2.

**Figure 2.** The framework of the commonality indexes.

#### *1.2. Selection of Commonality Indexes*

#### (1) Benefits

Benefits are indexes that measure the effects and profits of a project. The benefits of various DSER are evaluated in terms of three aspects of economic benefits, environmental benefits, and social benefits. Taking WG as an example, the specific definitions are shown in Table 1.



The development of resources is another important index to evaluate DSER. The development of various DSER is measured by Equation (1).

$$A\_2 = \frac{R\_{us\varepsilon}}{R\_{sum}}\tag{1}$$

*A*2—Resource development

*Ruse*—The amount of development of a DSER in a region

*Rsum*—The total amount of the DSER in the region

(3) Development Potential

All kinds of DSER must be taken into consideration to settle the long-term development problem, so the development potential is also an essential index for evaluating all kinds of DSER. The development potential of various DSER is evaluated in terms of four aspects upon policy support, sources of funds, benefits, and the amount of surplus resources. The specific definitions are shown in Table 2.


**Table 2.** Definition of development potential.

