Through the analysis of references at home and abroad, it can be seen that most are in qualitative analysis for a regional accommodation ability of wind power and its influencing factors, and the references with quantitative assessment are relatively less. Existing quantitative evaluation is focused on calculating grid accommodation wind power electricity or energy [11, 13, 15]. There is lack of consensus on how to measure the accommodation ability of power grid [18], while in practical work there is also lack of index system to measure a regional accommodation ability of wind power, which has become the problem to be solved in evaluation of renewable energy accommodation ability.
This paper will do comprehensive evaluation of wind power accommodation ability, and its foundation is to establish corresponding evaluation index system. Considering many factors affecting the accommodation ability of wind power, it mainly involves minimum technical output of units, load level, system balance capacity, power supply, wind power technology, transmission channel and so on. For convenience of index analysis, this paper divides many indexes into macroscopic and microscopic indexes according to global or local factors that affect accommodation ability of wind power. For example, minimum technical output of generating units and technical level of wind power belong to microscopic indexes, while load level and power structure belong to macroscopic indexes. According to index design principles such as systematicness, observability and correlation, this paper designs comprehensive evaluation index system of wind power accommodation ability from macroscopic and microscopic perspectives. Different from conventional evaluation index system, the macroscopic and microscopic indexes proposed in this paper are not a kind of direct statistical indicators, but comprehensive indicators formed by a variety of related factors.
2.1 Macroscopic indicators
Macroscopic indicators reflecting wind power accommodation ability are considered from the point of view of the whole network. They could play an important role in the analysis and provide reference for wind power accommodation study. The macroscopic indicators establishment of this paper is mainly from 4 aspects such as installed generation capacity, generation energy, power supply structure and accommodation space. And its metering cycle is set on a monthly average [19, 20].

(1).
Installed Capacity Index (ICI) is defined as the ratio that installed generation capacity outnumbering maximum load of the local area. Under the condition of certain load level, the larger the installed generation scale, the more limited the system accommodation ability will be [21]. This index is different from installed generation capacity, and compares installed generation capacity with load level to form a relative index, which is an indirect reflection of local wind power accommodation ability. It can be expressed as:
$$ {I}_C=\left(\frac{C}{L_1}1\right)\times 100\% $$
(1)
Where: C represents the total installed capacity of power supply in a region, and L_{1} represents the local maximum load. Installed capacity index I_{C} characterizes the degree that power supply capacity exceeds load. When this ratio is greater than a certain value, the power of the area is superfluous. For example, the total installed capacity of some provincial grid in 2014 is 40,000 MW, and its maximum load is 25,000 MW, and calculation result of the installed capacity I_{C} is 60%.

(2).
Electricity Consumption Index (ECI) is defined as the proportion within a certain period of time that electricity generation outnumbers electricity consumption. This index is different from power generation or electricity consumption index of the whole power grid. The comparison results of the above two indexes are taken as an index to measure wind power accommodation ability of the local power grid [22], which could reflect the magnitude of system accommodation ability. It can be expressed as:
$$ {I}_E=\left(\frac{E_G}{E_U}1\right)\times 100\% $$
(2)
Where: E_{G} represents electricity generation of all supply in theory in a certain period of time, and E_{U} is the total electricity consumption. Electricity consumption index I_{E} characterizes the surplus degree of grid electricity generation, and when I_{E} is larger than a certain value, indicating that electricity excesses in this region. What’s more, electricity accommodation index I_{E} discussed in this paper does not consider power delivery situation, which will be considered in microscopic indexes.

(3).
Power Adequacy Index (PAI) is defined as the ratio of the difference of maximum adjustable output and minimum technical output to maximum prediction output of wind power. The index is influenced by maximum adjustable output, minimum technical output and maximum forecasting output of wind power. It comprehensively reflects the influence of the above 3 factors on accommodation ability of wind power [23]. It can be expressed as:
$$ {I}_P=\frac{P_{\mathrm{max}}{P}_{\mathrm{min}}}{P_w}\times 100\% $$
(3)
Where: P_{max} and P_{min} are respectively the maximum possible output power of the whole system and its minimum technical output, and P_{w} is the forecast maximum output of wind power. Power adequacy index I_{P} represents the adequacy of all power sources to accommodate wind power in the entire grid, and when the index I_{P} is less than a certain value, it shows that the power supply of local grid cannot meet the rapid growth of wind power.

(4).
Accommodation Space Index (ASI), which is defined as the ratio of actual generating capacity of wind power to generating capacity in theory, can be expressed as:
$$ {I}_A=\frac{E_A}{E_F}\times 100\% $$
(4)
Where: E_{A} and E_{F} are respectively actual generating capacity of wind power and forecasting generating capacity. Actual generating capacity of wind power can be calculated by time sequel production simulation method, and wind power generating capacity in theory can be obtained by using wind energy resources calculation. Accommodation space I_{A} directly reflects the wind power accommodation ability of grid (usually less than 100%), less value of which means less accommodation space, and when its value is less than a certain value, abandoned wind problems of power grid in this region would be more serious.
2.2 Microscopic indicators
The microscopic index reflecting accommodation ability of wind power is based on influence factors, that make the establishment of comprehensive evaluation index, and each factor represents one important aspect of accommodation ability of wind power in some degree. The difference between microscopic and macroscopic indications is that the latter reflects the global problems of wind power accommodation, while the former can only reflect one aspect of the problems, but may also have a profound impact on the accommodation ability of wind power [24]. This article establishes index system mainly from the following 4 aspects: wind power transmission section margin, fluctuation characteristics, forecast deviation and antipeak regulation proportion.

(1)
Section Margin Index (SMI), which is defined as the ratio of the difference between power supply and local maximum load to the relevant section limit, can be expressed as:
$$ {I}_S=\frac{C_L{L}_2}{Q}\times 100\% $$
(5)
Where: C_{L} is the power supply installed capacity within the cross section of wind power, L_{2} is local maximum load, and Q is the section limit. The outer transmission ability of wind power is reflected by the section margin index I_{S}, and when I_{S} value is larger than a certain value, it indicates that wind power transmission ability is limited, and transmission limitation situation of wind power may be serious.

(2)
Simultaneity Factor Index (SFI), is the probability of maximum power generation of wind farm, which is a probability statistical index. It is defined as the ratio of maximum generation output of the day to total gridconnected capacity in the same day. It reflects the characteristics of local wind power output, and is usually expressed as percentage value:
$$ {I}_{SF}=\frac{P_{w\max }}{\sum \limits_{i=1}^n{P}_{i, RC}}\times 100\% $$
(6)
Where: P_{wmax} and P_{i,RC} are respectively day wind power maximum output and rated capacity of connected units, simultaneity factor index I_{SF} reflects the wind power characteristics, often affects wind power accommodation. The highest and lowest simultaneity factor within a certain period are usually applied, and this paper adopts the average value of highest simultaneity factor.

(3)
Forecast Deviation Index (FDI), which is defined as the difference between actual wind power output and forecast value, can be expressed as:
$$ {I}_{FD}=\frac{\left{P}_F{P}_A\right}{P_A}\times 100\%,\kern0.5em \left({P}_A\ne 0\right) $$
(7)
Where: P_{F} and P_{A} are respectively the forecast output and actual value of wind power. Forecast deviation index I_{FD} is also a statistical indicator in different time scales, which reflects the level of wind power forecasting technology, and its accuracy will affect the power grid dayahead planning and scheduling [25]. When this index value is too large, regular power supply needs to make corresponding power adjustment for the deviation, and it will affect wind power accommodation level in severe cases.

(4)
Antipeak Rate Index (ARI), which is defined as the proportion of wind power output rising in peak period and descending in valley period, can be expressed as:
$$ {I}_{AP}=\frac{T_L+{T}_H}{T}\times 100\% $$
(8)
Where: T_{L} and T_{H} are respectively the antipeak time of wind power in peak period and valley period, and T represents the total statistics cycle. This index reflects the probability that wind power output characteristics will react to peak regulation characteristics of grid. During the circle, wind power output decreases at peak period, but increases at valley period, which is not conducive to wind power accommodation.