Wind Power Development and Energy Storage Under China's Electricity

Home , Dongshan County, Longyan, Ningde, Yunxiao County, Zhangpu County

sustainability

Article Wind Power Development and Energy Storage under China’s Electricity Market Reform—A Case Study of Fujian Province

Dunguo Mou ID School of Economics, Xiamen University, Xiamen 361005, China; [email protected]

Received: 20 December 2017; Accepted: 20 January 2018; Published: 24 January 2018

Abstract: This paper, based on the Fujian provincial 500 kV grid and part of the 220 kV grid and the key power plants, including hydro, coal, nuclear, gas, wind and pumping and storage hydro powers (PSHP) connected to the grid, constructs an independent electricity market model. Using data that are very close to reality about coal fired power production costs, along with data about power plants’ technical constraints, this paper studies the effect of wind power on Fujian’s provincial electricity market. Firstly, the paper analyzes the relationship between wind speed and wind power output and the effects of short-term power output fluctuation on frequency modulation and voltage regulation. Secondly, under supposition of the production costs following quadratic functions, the paper analyzes the effects of changes in wind power output on the electricity supply costs under optimal power flow. Thirdly, using the bidding model in the Australian Electricity Market Operator for reference and supposing that, in a competitive market, coal fired power plants can bid 6 price bands according to their capacity, the paper analyzes effects of wind power on electricity prices under optimal power flow, the stabilizing effects of PSHP and the minimum PSHP capacity needed to stabilize the electricity market. Finally, using a daily load curve, this paper simulates the electricity prices’ fluctuation under optimal power flow and PSHP’s stabilizing effect. The results show that, although PSHP has a large external social welfare effect, it can hardly make a profit. In the end, this paper puts forward some policy suggestions for Fujian province’s wind and nuclear power development, PSHP construction and electricity market development.

Keywords: wind power; electricity market reform; energy storage; PSHP

JEL Classiﬁcation: D41; Q21; Q41

1. Introduction In the past 30 years, China’s electricity market has been in a dynamic process of adjustment. From 1985, the main mission of the power system has been to solve the electricity shortage problem caused by rapid economic growth. The State Council of China signed the No. 5 document [1], on 10 February 2002, establishing a series of objects for further electricity market reform. However, in the following ten years, there was no substantive progress in these reform objectives. After 2012, the Chinese economy entered the so called “New Normal” mode and the economic growth rate slowed; the electricity industry became oversupplied; the price of coal fell, making coal power proﬁtable; and renewable energy expanded rapidly. All of this created a good environment for deepening electricity industry reform. The Communist Party of China (CPC) Central Committee and the State Council released the No. 9 document [2]. Shortly after this, the National Development and Reform Commission (NDRC) and National Energy Administration released a series of supporting documents. In the process of electricity marketization, Fujian is not radical but is cautious and it is steadily pushing electricity marketization forward. In July 2017, the total power installed capacity in Fujian

Sustainability 2018, 10, 298; doi:10.3390/su10020298 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 298 2 of 20 province is near 50 GW, including 9172.5 MW from hydro power, 29,524.6 MW from thermal power, 8640 MW from nuclear power from Ningde and Fuqing plants and 2305.9 MW from wind power. In July, the highest provincial peak load is 34,230 MW and the electricity market is oversupplied. According to the policies, renewables and nuclear powers belong to the first class of priority generation; for example, in 2016, the generation hours of the key coal fired power plants in Fujian ranged from 2000 to 3000, meanwhile the generation hours of the two nuclear power plants were close to 7000. In the 13th Five Year Plan, Fujian Province plans to install 5000 MW wind power at the end of 2020; 3000 MW of this will be onshore and 2000 MW will be offshore. The National Energy Administration has approved the 17 offshore wind farm plan, with 13,300 MW installed wind power capacity. To assist the development of renewable and nuclear power plants, Fujian has decided to construct pumping and storage hydro power (PSHP) plants. Currently, there is only one PSHP plant (4 × 300 MW) in Xianyou but the government has decided to launch the construction of the Xiamen (4 × 350 MW), Yongtai (4 × 300 MW) and Zhouning (4 × 300 MW) PSHP plants during the 13th five-year-plan period. When the total installed wind power increases, electricity marketization in Fujian Province will be challenged. The increased capacity of intermittent renewable energy sources increases the need for flexibility in energy system and the effects of large scale penetrations of wind power in electricity systems have been extensively studied ([3–5]); Huber et al. [6] show that increasing wind and solar power generation above a 30% share in annual electricity consumption will significantly increase flexibility requirements. The crossroads of capital-intensive, base-load nuclear power and intermittent renewable energy may entail new challenges in the optimal and economic operation of power systems [7]. The results of Zakeri et al. suggest that nuclear power constrains the room for maximum uptake of wind energy by a descending parabolic relationship; heat storage and flexible demand demonstrate efficient ways in reducing the power oversupply in Finnish power system. Were without flexible generation units, system operators usually implement system operational constraints (SOCs) in order to maintain stability with high wind generation but resulting in wind curtailment. Introducing large-scale storage schemes entails a number of additional system-wide benefits, such as reduction of required investment in generation, transmission and distribution assets [8]. In a large number of existing studies addressing energy storage alternatives for future electric power systems, PSHP is found to be the most mature, efficient and cost-effective technology ([9–11]). Suul et al. [12] found that, for an independent power system with a renewable electricity source, PSPH is very important and can provide a frequency control function either when the demand for electricity is low or when the highly fluctuating renewable electricity is in operation. PSHP has experienced rapid growth in recent years. Deane et al. [13] reviewed PSHP development in Europe, Japan and the USA from a techno-economic aspect, finding that the drivers of new PSHP capacity growth include large scale expansion of unstable renewable energy; growing demand for peak load; increasing interconnections and coordination between regional markets, providing more profitable opportunities; requirement for a more secure supply; and demand for improved efficiency of existing facilities. In the vertically integrated electricity market, coordination between PSHP and thermal power can be exploited to reduce fuel costs and decrease emissions; thus, optimal arrangement of PSHP use becomes a key question. The marginal cost approach was first applied to evaluating and arranging coordination between PSHP and other kinds of electricity generation [14]. In consideration of the PSHP characteristics, Cohen and Wan [15] designed an algorithm for arranging large-scale PSHP use. Oliviera et al. [16] applied a mixed-integer linear program method to study the cost efficiency of PSHP under an economic system framework. In a competitive electricity market, there is still the technical need for energy storage but profitability becomes the challenge to its development ([11,17]). How to increase profit by properly bidding in the given market with changing prices has become a hot topic and there are many studies on PSHP bidding strategies in competitive markets. Horsley et al. [18] applied linear and convex programming methods to rental valuation of facilities for conversion and storage goods, such as energy, by shadow pricing the stock. In competitive markets with time-varying prices, PSHP pumps Sustainability 2018, 10, 298 3 of 20 water when the price is low and generates power when the price is high. It can arbitrage by buying in the bilateral market and selling in the spot market. However, exploiting the market to make a profit is very technically demanding. By supposing that the market clearing price is not sensitive to unique generators’ bids, Lu et al. [19] put forward an optimal bidding strategy for PSHP in competitive markets. In the real competitive market, PSHP’s optimal bidding may face many constraints [20]. Based on a forecast hourly market clearing price, Kanakasabapathy and Swarup developed optimal bidding strategies for a PSHP in a pool-based electricity market using a multistage looping algorithm to maximize the profit of a pumped-storage plant. Dogan [21] estimated the influence of an intraday continuous market on PSHP and Abgottspon et al. [22] took the long-term and the hourly day-ahead market into one optimization function to discuss the influence of a price maker. Braun [23] analyzed the short-term hydro power optimal strategy in the spot and intraday auction market and found that there is a significant difference between the two markets, because of higher fluctuation and limited liquidity in the intraday auction market. Analysis of price arbitrage alone underestimates the function of PSHP in the electricity markets and constrains the potential earnings. Kumar et al. [24] performed a systematic review of the cycling cost and load following constraints amongst several traditional coal-fired power and gas power technologies. Compared with these thermal powers, hydro power, with a low cost of restart and a high ability of load following, can play a greater role in the electricity ancillary service market. Deb [25] discussed the need for PSHP to participate in the energy and ancillary services markets. Ela et al. [26] pointed out that the recent changes in the electricity market and the growing variable renewable energy generation allows PSHP to earn more profit by providing other ancillary services, and, as the market design continues to evolve, there are still ways in which designs can improve the values of services offered by PSHP while maintaining a fair and impartial perspective of the market. Recent technological advances enable PSHP to take potential advantage of the ancillary services’ market. While traditional PSHP facilities cannot provide ancillary services, either while pumping or when idle, the new adjustable speed PSHP has the ability to provide services while pumping and the flexibility enhanced PSHP can profit from expected fluctuations in both energy and ancillary service prices [27]. In the European market, the integration of large scale intermittent renewable energy sources has resulted in demands to improve system flexibility; PSHP is the only system capable of large-scale commercial operation. When evaluating the flexibility requirements of power system with high penetration of renewable source energies, net load is considered by some studies [5,6,28]. Net load is defined as the conventional load minus the non-dispatchable generations; it is an intuitive basis of operation planning in day-to-day delivery of electricity to the consumers. But most studies evaluate the flexibility requirement from whole power system aspect. This paper, based on real cost data, analyzes the effect of wind power on the Fujian electricity system, and, using the market operation and price bidding modes in the developed electricity market as reference, analyzes the effect of wind power on market price fluctuation, the buffering effects of PSHP and the potential profit of PSHP. The remainder of this this paper is structured in four Sections. Section2 demonstrates the electricity market equilibrium model that maximizes social welfare and the grid system, generation cost data; Section3 analyzes the wind power impacts on grid system from three aspects; Section4 simulates the price fluctuation along with the demand load changes and analyzes the PSHP’s profit potential; Section5 concludes this paper with some policy suggestions.

2. Model and Data

2.1. Model To study the effects of intermittent wind power and PSHP on the operation of the electricity market, this paper is based on two systems: (1) the factual physical regional AC grid system, which is the platform that fulﬁls the function of transmitting electrical energy from power plants to consumers; Sustainability 2018, 10, 298 4 of 20

(2) the market bidding system based on the physical system, in which power plants and consumers provide their bids and prices and the system sorts these bids and constructs the market demand and supply curves for each trading period to find the balance of the electricity market and arrange electricity generation for power plants. In the current electricity market, power plants’ feed in prices are determined by the NDRC according to the generators’ historical construction costs, loan repayments, fuel costs and certain level of profit. An egalitarian approach is taken when arranging generation hours, with each power plant having a certain share. Low-efficiency generators may have the same hours as high-efficiency generators [29] to ensure that all generators’ profits are at almost the same level. The Grid Company is a double-headed monopoly and is in charge of grid system stabilization. However, the situation is changing slowly to a competitive market model. In a competitive market, generators and consumers bid to their prices and volumes to the market according to either their marginal costs or their marginal utility; these biddings are sorted by price, forming the demand and supply curves and the deal price is the market clearing price, which maximizes social welfare in an economic sense. In the real competitive market, which is constrained by many factors, the bidding price of suppliers may not equal the marginal cost. In the Pool market mode, as the marginal biddings determine the market price, there are many “free riders” with “zero” price biddings at valley load periods just to ensure their minimum generation powers; while, at the peak load period, there may be very high price bids from generators with surplus capacities, squeezing the market to make more profit. Nevertheless, marginal biddings are still the best indices for reflecting the demand–supply situation in the market. On the other side, electricity supply and demand are spatially distributed and market equilibrium is constrained by the transmission capacity. The object of a spatially distributed competitive electricity market is to maximize the welfare function under a series of constraints. In mathematical form, it can be written as Max(∑ CDiPDi − ∑ CSkPSk) (1)

s.t g(δ, V, QG, PS, PD) = 0

0 ≤ PS ≤ PSmax

0 ≤ PD ≤ PDmax

Pij(δ, V) ≤ Pijmax

Pji(δ, V) ≤ Pjimax ≤ ≤ QGmin QG QGmax

Vmin ≤ V ≤ Vmax where g(·) is the power flow function, CS and CD are bidding price vectors of supply and demand, respectively, with units of RMB/MWh; PS and PD are supply and demand bidding volume vectors; V and δ are AC voltage and phase angel; QG is the reactive power; PSmax and PDmax are maximum powers of supply and demand; Pij(δ, V) is the power flow between two nodes studied; Pijmax is the maximum power flow; and Vmin, Vmax are voltage constraints. For the multi-price bidding model, suppose buyers and sellers in the electricity market can offer several bidding prices, the welfare function can be written as: Max(∑ ∑ CDimPDim − ∑ ∑ CSkl PSkl) i m k m This model is a typical optimal power flow (OPF) problem in the electricity system, which can be solved by many power flow system software. For this paper, it is solved by the software PSAT2.1.10 (See Federico Milano’s Webpage, http://faraday1.ucd.ie/psat.html.), in which there are several algorithms for choice and the Newton-Raphson algorithm is used for calculation. Sustainability 2018, 10, 298 5 of 20

For a generator i, suppose that the average variable cost is a linear function of power generation, written as = + AVCi CP1i βi.PSi (2) As the power increases, total variable cost is a quadratic function of power generation. Taking ﬁxed cost (calibrate in RMB/h), the total cost is a quadratic function of power generation with 3 items, written as = + + 2 CSiPSi CP0i CP1i PSi CP2i PSi (3) = + + 2 CQiQSi CQ0i CQ1i QSi CQ2i QSi where CSi and CQi are active and reactive power costs, respectively, of generator i; CPoi and CQ0i are

fixed cost of active and reactive powers, with a unit of RMB/h; CP1i and CQ1i are generator specific average variable costs, with a unit of RMB/MWh; and CP2i and CQ2i are a quadratic coefficient, describing the economy of scale, with a unit of RMB/MWh2. Reactive power is very important to the electricity system but it is not considered in this paper. For static analysis of electricity market, usually there is a demand curve with a negative slope to price

PDi = n0 + βDi ∗ PP

With PP being the electricity prices and βDi < 0. However, electricity demand has characteristics of price rigidity and load ramping and the time series of demand can be written as

PDit = n0t + βDi ∗ PPt where not is the time varying basic demand; this may ramp quickly and its effect on ﬁnal loads is much higher than is the depressing effect of βDi, resulting in demand load change in the same direction as price. For this reason, this paper ignores the item of βDi, supposing that the consumers accept any price determined by the supply curve. Thus, the demand total cost of electricity consumption can be written as = + CDiPDi CDoi CD1i PDi (4) Thus, the market is formed by the up-slope supply curve and the vertical demand curve, as shown in Figure1. From the static aspect, equilibrium is the intersection of the supply curve (S) and the demand curve (D); the equilibrium power is PD and equilibrium price C; from dynamic aspects, D may ramp to either left or right and S can ramp to either up or down, making the equilibrium vary Sustainabilityover a wide 2018 range., 10, x ThisFOR PEER paper REVIEW supposes that the supply curve stays put. 6 of 20

Figure 1. ElectricityElectricity market market equilibrium with inelastic demand curve.

2.2. Data

2.2.1. Power Plants Power plants are the key elements of the supply side in the electricity market. Fujian has a complete power supply system, with renewables that include many small hydro power, wind power and PV power plants, along with traditional power plants that include hydro plants, PSHP, coal-fired plants, gas peaking plants and nuclear plants. Of these power plants, the key power plants (not including the 3 gas peaking power plants) identified by Fujian Electric Power Supply Association (FEPSA) and their installed capacities are listed in Table 1. Besides the wind farms in Table 1, at prefectural level, other coastline cities, including Quanzhou (44 MW), Putian (592 MW), Fuzhou (528 MW) and Ningde (42 MW), also have wind power.

Table 1. Key power plants and installed capacities (Cap.) in Fujian Province.

Plant Cap. (MW) Plant Cap. (MW) Plant Cap. (MW) Houshi 4200 Fuzhou 2 1320 Jinjiang Gas 1400 Fuzhou 1400 Hongshan 1200 Xiamen Gas 780 Meizhouwan 800 Hongshan 2 2000 Xiyuan PSHP 1200 Songyu 1200 Ningde Nuke 4320 Zhao’an County 48 Nanpu 1940 Fuqing Nuke 4320 Dongshan County 85.5 Ningde 2520 Shuikou Hydro 1400 Yunxiao County 19.5 Kemen 2400 Shaxikou Hydro 300 Zhangpu County 101.6 Jiangyin 1200 Mianhuatan Hydro 600 Longhai County 224 Longyan 1140 Qianyun Gas 1560

Sustainability 2018, 10, 298 6 of 20

2.2. Data

2.2.1. Power Plants Power plants are the key elements of the supply side in the electricity market. Fujian has a complete power supply system, with renewables that include many small hydro power, wind power and PV power plants, along with traditional power plants that include hydro plants, PSHP, coal-ﬁred plants, gas peaking plants and nuclear plants. Of these power plants, the key power plants (not including the 3 gas peaking power plants) identiﬁed by Fujian Electric Power Supply Association (FEPSA) and their installed capacities are listed in Table1. Besides the wind farms in Table1, at prefectural level, other coastline cities, including Quanzhou (44 MW), Putian (592 MW), Fuzhou (528 MW) and Ningde (42 MW), also have wind power.

Table 1. Key power plants and installed capacities (Cap.) in Fujian Province.

2.2.2. The Grid System The grid system is the physical platform on which the electricity market operates. The Fujian grid belongs to the Eastern China Grid, which is connected to the Zhejiang grid by 2 circuits of 1000 kV UHV and 2 circuits of 500 kV lines. Within the Fujian grid, the 500 kV high voltage lines form a ring network and a north-south double-loop backbone network. The 500 kV transmission system and the power plants connected to it are shown in Figure A1 in the appendix. Other power plants are mainly connected to the 220 kV grid; most of the wind power plants are connected to the local 110 kV grid and the small hydro power plants are connected to the local 35 kV grid. To study the electricity market operation at the provincial level, research was carried out at the 500 kV and 220 kV levels, except for the power plants in Figure A1 in the appendix. Other power plants are taken as being connected to the regional 220 kV grid and the Simulink illustration is shown in Figure A2 in the appendix. The power supply area of the Nanpu power plant is not clear; it is taken to be connected to the 500 kV grid. Gas peaking power, small hydro power and wind power plants are ignored, either because there is no public available information about their connections or because they are renewable but unstable sources that may pose a greater challenge to the electricity system. As wind power is connected to a lower voltage grid, its change will ﬁrst effect the 220 kV grid; when the regional grid cannot handle this change, it will affect the provincial 500 kV grid. This paper takes Zhangzhou City’s installed wind power and the 220 kV grid system as an example to study the effect of wind power on the provincial electricity system. Zhangzhou City’s 220 kV grid is connected to the provincial 500 kV backbone grid by the Zhangzhou and Wufeng transformer substations and it is also connected to Xiamen and Longyan cities’ 220 kV grid for small-scale electricity adjustment. The lower left corner part of Figure A2 illustrates Zhangzhou City’s 220 kV grid connections. When the planned Zhangzhou nuclear power plants and the large scale off-shore wind power operation are put into operation, Zhangzhou City’s 220 kV grid will change greatly and the provincial 500 kV backbone grid will change to a certain degree. As the Fujian 13th Five-year plan depicted, the 1000 kV grid will extend to Xiamen City, which will change the provincial backbone grid greatly. Sustainability 2018, 10, 298 7 of 20

2.2.3. The Power Generation Costs In the electricity market, whether a generator can provide electricity to market is determined by its cost or its bidden prices under the OPF, which solves the market equilibrium. Data from NDRC’s feed in benchmark prices, industrial reports and media reports show that, in general, the cost of coal fired power is approximately RMB 300–400/MWh, the cost of hydro power is approximately RMB 200–300/MWh, the cost of nuclear power is approximately RMB 300–400/MWh and the cost of wind power is approximately RMB 600/MWh. The costs are different according to regions and projects. The power plants’ production costs, as revealed in Equation (3), are confidential material. However, the State-owned Assets Supervision and Administration Commission of the State Council published the “Investigation Report on an Audit of Some Thermal Power Enterprises’ Power Generation Costs,” which pointed out that the fixed costs include depreciation, labor costs and maintenance fees and estimated the average costs of generators with capacities lower than 300 MW, 300–600 MW and above 600 MW. This paper applies these average fixed costs’ data to generators accordingly. As to the variable costs of the coal-fired power plants, the Statistical Yearbook data are rather general; they give only the averages. Based on plants’ internal Supervisory Information System data, Wang (2009) analyzed the relationship between average variable cost (AVC, unit: RMB/MWh) and power generation (MW) of a 300 MW generator. The data show that, at 60% capacity load level, the coal consumption per unit of electricity is 313.65 KG/MWh, supposing that the standard coal price is RMB 460/Ton and the variable cost is RMB 167.7/MWh (including water and other inputs costs). In the Statistical Yearbook, the average generation hours are approximately 5000, which equal 60% of the average load and the coal consumptions per unit of electricity of Fujian coal-fired power plants are listed, allowing these plants’ variable costs to be calculated proportionally. As the generation load increases, the average cost lowers; for the 300 MW generator, the regression coefficient of β in Equation (2) is approximately −0.08. This negative coefficient explains the violent competition in Guangdong electricity market when electricity is oversupplied. It also explains why the bilateral direct transaction operates at a loss in Fujian. The negative coefficient shows the economy of scale in thermal power. In September 2017, the price of 5000 Kcal (The Chinese Coal industry standard, equals 20,920 KJ) thermal coal of Qinghuangdao port is approximately RMB580/ton, which means that the CIF price in Fujian is approximately RMB 640–680/ton and the standard coal price is approximately RMB 900–950/ton. A 600 MW generators’ variable cost of RMB 157/MWh calculated by Wang’s (2009) standard becomes approximately RMB 300/MWh. Thus, all the calculated variable costs are doubled; this is very close to the real situation. Although there is economy of scale in coal consumption in coal-fired power plants, when other factors are considered, the marginal cost may rise as generation power increases. Without more information, the β is assumed to be positive. For the 300 MW generators, when taking the doubled variable cost into consideration, suppose that CP2,i,300 = 0.16, and, = ∗ for other generators of capacity n, suppose that CP2,i,n 300/n 0.16, to ensure the same economy of scale. Ogayar et al. [30] developed some equations to calculate the costs of small hydro powers but it cannot be used in this paper, because hydro powers will not participate in the competitive market and the government has a historical criterion to determine their feed in price. Hydro and nuclear power plants’ fixed costs are calculated according to their average construction cost, with a 20-year repayment term; hydro power variable costs are assumed to be RMB 200/MWh and nuclear power variable costs are assumed to be the same as are those of the Ultra Super Critical coal-fired power plants with a capacity of 1000 MW. The quadratic coefficients of hydro and nuclear power are assumed to be zero. Although the cost of wind power is high, the Chinese government subsidizes it, the subsidy number being the difference between the benchmark feed in prices of coal-fired power and wind power. As to the wind power benchmark feed in price, the government issued a new document [31] to adjust it in 2016; this document classifies the onshore wind farms into four classes of wind resources, with benchmark feed prices for newly built wind power plants being RMB 400, 450, 490 and 570/MWh, Sustainability 2018, 10, 298 8 of 20 respectively. Further, the document also determines the off-shore wind power feed in price: the price for an intertidal zone wind power project is RMB 750/MWh and the price for an off-shore wind power project is RMB 850/MWh. Moreover, the newest information shows that the Chinese government plans to cancel wind power subsidies in 2020–2022, after which wind power should compete with other powers at the same level. During 2014 and 2015, under the electricity oversupply situation, some coal-fired power plants refurbished their facilities and tested their valley load following ability, finding that the generation load can reach 30% of capacity without oil input. In this paper, the minimum power output is assumed to be 40% of their capacity; the hydro power’s minimum is assumed to be 20% and the nuclear power’s minimum is assumed to be 60%.

For the demand cost in Equation (3), CDo is the capacity price—RMB 18,000/MVA·month—and this price is applied to each demand node’s capacity. Nuclear power is classified as a first priority generator that does not participate in market trade. As Guangdong has included it in market competition, it can be expected that Fujian may incorporate it in market competition in the near future; thus, it is assumed to compete with coal-fired power in the market.

3. The Operation of Fujian’s Provincial Electricity System To study the effect of wind power on the electricity system and the electricity market, in addition to the buffering effect of PSHP between electricity demand and supply at the provincial level, this paper takes the Fujian electricity market as an independent system and simulates the system operation on the basis of certain suppositions. The study is carried out in three levels: ﬁrstly, to study the systematic effect of intermittent wind power on the electricity system, i.e., on the physical conditions of system voltage, current stability and demand-supply balance; secondly, to study the effect of intermittent wind power on the cost of electricity under different cost suppositions; and, ﬁnally, to study the effect of energy storage, through the example of PSHP, on electricity market operation.

3.1. The Wind Resource in Fujian Province and Wind Power Production Along the 3752 km coastline of Fujian province, the wind resource is rather rich. According to the Wind Energy Resource Survey carried out by Fujian Provincial Meteorological Bureau, at 70 m height along the coastline, the technical developable area with average wind power density ≥400 W/m2 is 1825 km2 and the total exploitable wind power resource is approximately 6560 MW. From the spatial aspect, the wind power distribution is as revealed by Figure2. In general, the wind power along the coastline exhibits good regularity: seasonally, wind power is characterized by the largest speed in fall, followed by winter and then summer; on a daily basis, wind power is characterized by the smallest in early morning, gradually increasing to reach a maximum during the afternoon to the evening and then gradually decreasing. Of the wind power generators of Fujian province, those installed at an early stage mostly have a capacity of 1.5 MW, while those installed recently mostly have a capacity of 2 MW, with some having a capacity of 2.5 MW. The 2 MW generators have 3 blades, each 45 m long, a cut in threshold wind speed of 3.5 m/s, a rated wind speed of 12 m/s and a cut-out threshold wind speed of 25 m/s. In the simulation system of this paper, it is supposed that all the wind power generators are Variable Speed Wind Turbine with Direct Drive Synchronous Generator with a capacity of 2 MW. Sustainability 2018, 10, 298 9 of 20 Sustainability 2018, 10, x FOR PEER REVIEW 9 of 20

FigureFigure 2. 2.The The Fujian Fujian provincial provincial wind windpower power densitydensity distributiondistribution map. Source: Source: Fujian Fujian Provincial Provincial Meteorological Bureau website. http://www.fjqx.gov.cn/qhzy/201204/t20120410_12343.htm. Meteorological Bureau website. http://www.fjqx.gov.cn/qhzy/201204/t20120410_12343.htm.

Of the wind power generators of Fujian province, those installed at an early stage mostly have a capacityTo determine of 1.5 MW, the relationshipwhile those installed between recently wind speed mostly and have the a power capacity of theof 2 generators, MW, with asome study at thehaving Zhao’an a capacity wind of power 2.5 MW. farm The is 2 carriedMW generators out, with have a total 3 blades, installed each capacity45 m long, of a 48 cut MW, in threshold under the ◦ suppositionwind speed that of 3.5 the m/s, air a density rated wind is 1.225 speed kg/m of 123 m/ats 15 andC. a Atcut-out time threshold 0, suppose wind that speed the generatorsof 25 m/s. In are providingthe simulation electricity system at the of verythis paper, small power it is supposed level of 1.92that (48all the× 4%) wind MW power and thatgenerators the wind are speed Variable starts fromSpeed 0 m/s Wind and Turbine rises at with a rate Direct of 1 Drive m/s2 Synchr; the actualonous wind Generator speed with values a capacity that are of used2 MW. for calculating the mechanicalTo determine power the of relationship wind turbines between are thewind output speed of and a low-pass the power filter of the with generators, a time constant a study at of τ seconds.the Zhao’an For τ wind= 1 and powerτ = 4,farm the is relationship carried out, between with a total real installed wind speed capacity and theof 48 wind MW, farm’s under power the outputsupposition is illustrated that the by air Figure density3. is 1.225 kg/m3 at 15 °C. At time 0, suppose that the generators are providingFrom Figure electricity3a, it canat the be very seen small that, whenpower the level wind of speed1.92 (48 is × above 4%) MW 4 m/s, and the that generators the wind beginspeed to 2 supplystarts electricity; from 0 m/s when and therises wind at a speed rate of is close1 m/s to; 10the m/s, actual the wind electricity speed outputvalues reachesthat are its used designed for powercalculating of 48 MW;the mechanical as the filter power time of constant wind turbinesτ rises toare 4 the s (see output Figure of 3ab), low-pass both the filter threshold with a time wind speedconstant for supplying of τ seconds. electricity For τ = 1 and and the τ = speed 4, the forrelationship designed between rate rise real accordingly; wind speed this and is the caused wind by farm’s power output is illustrated by Figure 3. the supposition about wind speed. For the filtered wind speed, which causes the generator to spin, From Figure 3a, it can be seen that, when the wind speed is above 4 m/s, the generators begin to the threshold speed of supplying electricity is 3.2 m/s and the speed for maximum power output is supply electricity; when the wind speed is close to 10 m/s, the electricity output reaches its designed 9.2 m/s; these are very close to wind threshold parameters of a real generator. power of 48 MW; as the filter time constant τ rises to 4 s (see Figure 3b), both the threshold wind speed for supplying electricity and the speed for designed rate rise accordingly; this is caused by the supposition about wind speed. For the filtered wind speed, which causes the generator to spin, the threshold speed of supplying electricity is 3.2 m/s and the speed for maximum power output is 9.2 m/s; these are very close to wind threshold parameters of a real generator.

Sustainability 2018, 10, 298 10 of 20 Sustainability 2018, 10, x FOR PEER REVIEW 10 of 20 Sustainability 2018, 10, x FOR PEER REVIEW 10 of 20

(a) τ = 1 (b) τ = 4 (a) τ = 1 (b) τ = 4 FigureFigure 3.3. TheThe relationshiprelationship betweenbetween wind speed and power output. output. Figure 3. The relationship between wind speed and power output. 3.2. The Effect of Wind Power on the Grid System 3.2. The Effect of Wind Power on the Grid System Wind speeds are not constant. The relationship between wind speed and power output shows Wind speeds are not constant. The relationship between wind speed and power output shows that,Wind when speeds the former are not changes, constant. the The latter relationship will also between fluctuate. wind In speedterms andof apower large outputtime period shows that, when the former changes, the latter will also fluctuate. In terms of a large time period calculated in that,calculated when inthe hours, former the fivechanges, wind farmsthe latter face thewill same also wind fluctuate. and their In powerterms outputsof a large will timefluctuate period at hours, the five wind farms face the same wind and their power outputs will fluctuate at the same pace; calculatedthe same pace;in hours, however, the five in windterms farms of small face time the sameperiod wind calculated and their in seconds,power outputs the generators will fluctuate on the at however, in terms of small time period calculated in seconds, the generators on the same wind farm thesame same wind pace; farm however, may have in termsdifferent of smallinstantaneous time period output. calculated To study in theseconds, effect theof intermittent generators onwind the maysamepower have wind generation different farm may on instantaneous thehave grid different system, output. instantaneous this Topaper study stil theloutput. takes effect the Toof Zhao’an study intermittent the wind effect farms wind of powerintermittentas an example, generation wind to onpowerstudy the gridgenerationthe effect system, of on power this the paper grid fluctu system, stillation takes onthis thethe paper Zhao’anwhole stil gridl windtakes system. farmsthe Zhao’an as an example, wind farms to study as an theexample, effect ofto powerstudy Supposethe fluctuation effect that of onpower all the the wholefluctu generatorsation grid system.on face the wholethe same grid wind; system. the wind speed follows the Weibull distribution,Suppose with that constant all the generatorsgenerators parameters face of C the = 20, samesame K = wind;wind;3; and thethe windsample speed time followsfor wind the speed Weibull is 1 distribution,second; and with the filter constant time constantparameters parameters is 4 ofs. of AtC C =th = 20,e 20, beginning K K = = 3; 3; and andof timethe the sample 0, sample suppose time time that for for thewind wind wind speed speed power is is1 1second;output s; and theandis 50% filter the andfilter time the constanttime according constant is 4 s.wind is At 4 thes. speed At beginning th eis beginning 7 m/s. of time Since of 0, time supposethe 0,wind su thatppose speed the that windis unstable,the power wind in outputpower the isoutputfollowing 50% andis 50% the20 s, accordingand the thewind according wind speed speed follows wind is 7 m/s.speedthe Weibu Since is 7 ll them/s. distribution. wind Since speed the The iswind unstable, actual speed wind in is the unstable,speed, following filtered in 20the s, thefollowingwind wind speed speed 20 ands, followsthe wind wind power the speed Weibull output follows distribution. evolutions the Weibu Theare di actualllsplayed distribution. wind in Figure speed, The 4. filtered actual The dash-dotted windwind speed speed, line and filtered is wind the powerwindactual speed outputwind and speed, evolutions wind the power solid are displayed outputline is theevolutions in filtered Figure are4wind. The di splayedspeed dash-dotted and in Figurethe line dashed is4. theThe line actual dash-dotted is the wind wind speed, line power is thethe solidactualoutput. line wind It is is the speed,clear filtered in the Figure wind solid 4 speed that,line isthe and the rise the filtered of dashed wind wind speed line speed is results the windand in thea power faster dashed output.rise inline wind It is is the clearpower wind in output, Figure power4 that,output.reaching the It rise isthe clear offull wind incapaci Figure speedty output 4 resultsthat, quickly.the in rise a faster of wind rise speed in wind results power in a output, faster rise reaching in wind the power full capacity output, outputreaching quickly. the full capacity output quickly.

Figure 4. Wind speed and wind power output under the Weibull distribution. Figure 4. Wind speed and wind power output under the Weibull distribution. From the Figureprovincial 4. Wind total speed capacity and windaspect, power 48 MW output wind under power the Weibull does not distribution. make up a large part of the total installed capacity in Figure A2 in the appendix; yet, the change in wind power output still From the provincial total capacity aspect, 48 MW wind power does not make up a large part of has a systematic effect on the whole grid system: in addition to the change in the output of the the total installed capacity in Figure A2 in the appendix; yet, the change in wind power output still has a systematic effect on the whole grid system: in addition to the change in the output of the

Sustainability 2018, 10, 298 11 of 20

From the provincial total capacity aspect, 48 MW wind power does not make up a large part ofSustainability the total 2018 installed, 10, x FOR capacity PEER REVIEW in Figure A2 in the appendix; yet, the change in wind power output 11 of 20 still has a systematic effect on the whole grid system: in addition to the change in the output of the reserve capacity of otherother generators,generators, there isis alsoalso aa systematicsystematic demanddemand forfor frequencyfrequency modulationmodulation and voltage regulation. Figure 55 illustratesillustrates thethe changeschanges inin phasephase angleangle andand voltagevoltage causedcaused byby windwind power outputoutput change.change. Bus15 Bus15 refers refers to to the the farthest farthe Ningdest Ningde 500 kV500 transformer kV transformer substation, substation, Bus23 Bus23 refers torefers the to Zhangzhou the Zhangzhou 500 kV 500 transformer kV transformer substation substa andtion Bus61 and Bus61 refers refers to the to Zhao’an the Zhao’an wind wind farm farm bus. Frombus. From Figure Figure5, it can 5, beit can seen be that, seen when that, wind when power wind changes, power changes, the effect the on effect bus phase on bus angle phase is full angle scale is butfull thescale change but the at change Bus 23 isat muchBus 23 bigger is much than bigger is that than at Bus15 is that and at Bus15 smaller and than smaller is that than at Bus61; is that the at effectBus61; on the voltage effect on is similar, voltage with is similar, the farthest with the bus farthest exhibiting bus theexhibiting smallest the change. smallest change.

(a) (b)

Figure 5.5. EffectEffect of of wind wind power power output output change change on systematic on systematic phase phase and voltage. and voltage. (a) The phase(a) The changes; phase (changes;b) The voltage (b) The changes. voltage changes.

3.3. The Effect of Wind Power Output Change on Electricity SupplySupply CostCost Short-term andand small-scalesmall-scale wind wind power power change change affects affects the the grid grid system system balance balance operation, operation, it can it can be handledbe handled by theby system’sthe system’s auto-control auto-control units; units; yet large-scaleyet large-scale wind wind power power output output fluctuation fluctuation will affect will electricityaffect electricity market market trading trading behavior, behavior, resulting resulting in electricity in electricity supply cost supply change cost and change peak followingand peak adjustment.following adjustment. To study theTo study effects the of effects wind power of wind output power change output on change the provincial on the provincial electricity electricity system, thissystem, paper this takes paper all takes the wind all the power wind farms power into farms the simulation into the simulation system illustrated system illustrated by Figure byA2 Figure in the appendix,A2 in the appendix, with a total with installed a total capacity installed of 1740capacity MW byof 1740 the end MW of 2015.by the The end other of 2015. wind The farms other summed wind atfarms the prefecturalsummed at level the prefectural include Quanzhou level include (44 MW), Quanzhou Putian (592(44 MW), MW), Putian Fuzhou (592 (528 MW), MW) Fuzhou and Ningde (528 (42MW) MW). and ForNingde simplicity, (42 MW). it is supposedFor simplicity, that all it theis supposed demand loads that areall the at 50% demand of their loads highest are loadat 50% level of andtheir all highest the power load plants’level and costs all followthe power the quadraticplants’ costs function follow described the quadratic by Equation function (3). described Under theby prerequisiteEquation (3). that Under the the grid prerequisite system keeps that in the balance, grid system with two keeps scenarios in balance, of no with wind two power scenarios output of and no ofwind wind power power output outputs and at of full wind capacity, power the outputs spatial distributionat full capacity, of the the electricity spatial distribution supply costs of of the all theelectricity nodes (Busessupply incosts the of Simulink all the nodes model) (Buses are displayed in the Simulink in Figure model)6a,b, respectively.are displayed Because in Figure of 6a,b, the differencesrespectively. in Because electricity of generationthe differences costs in across electricit they spatially generation distributed costs across power the plants,spatially in additiondistributed to thepower differences plants, in distancesaddition to demandthe differences nodes, thein distances line losses to result demand in electricity nodes, the supply line costlosses differences, result in aselectricity Figure6 asupply reveals. cost Since differences, most of the as high-efficiency Figure 6a reve coal-firedals. Since powermost of plants the high-efficiency and nuclear plants coal-fired are in Fuzhoupower plants City and and Ningde nuclear City, plants which are arein Fuzhou in the north-east City and partNingde of Fujian, City, which the electricity are in the supply north-east costs therepart of are Fujian, lower. the At theelectricity 500 kV gridsupply level, costs the there electricity are lower. supply At costs the of500 Xiamen kV grid City level, are approximatelythe electricity RMBsupply 10/MWh costs of higherXiamen than City are are those approximately of Ningde CityRMB and 10/MWh the cost higher of the than 220 kVare level those is of approximately Ningde City RMBand the 8/MWh cost of higherthe 220 than kV level is that is approximately of the 500 kV levelRMB because8/MWh higher of loss than at the is transformerthat of the 500 substation. kV level However,because of theloss cost at the of thetransformer 220 kV levelsubstation. of Zhangzhou However, city the is cost lower of the than 220 is thatkV level of Xiamen. of Zhangzhou When windcity is power lower suppliesthan is that the of grid Xiamen. system When with wind electricity, power the supplies effects arethe comprehensive.grid system with Compared electricity, with the effects are comprehensive. Compared with Figure 6a, the costs of all the nodes declined, with the degree of decline ranging from RMB 12.85/MWh to RMB 21.52/MWh; the biggest decline is at the Putian node with the biggest wind power capacity.

Sustainability 2018, 10, 298 12 of 20

Figure6a, the costs of all the nodes declined, with the degree of decline ranging from RMB 12.85/MWh to RMB 21.52/MWh; the biggest decline is at the Putian node with the biggest wind power capacity. Sustainability 2018, 10, x FOR PEER REVIEW 12 of 20

Sustainability 2018, 10, x FOR PEER REVIEW 12 of 20

(a) (b)

Figure 6. Comparison of electricity supply costs under different wind power outputs. (a) Supply cost Figure 6. Comparison of(a electricity) supply costs under different wind power (b) outputs. (a) Supply cost with zero wind output; (b) Supply cost with full wind output. with zeroFigure wind 6. Comparison output; (b of) Supplyelectricity cost supply with costs full windunder output.different wind power outputs. (a) Supply cost 3.4. Coordinationwith zero wind of Windoutput; Power (b) Supply and PSHP cost with under full Competitive wind output. Bidding Market 3.4. Coordination of Wind Power and PSHP under Competitive Bidding Market 3.4. CoordinationIn non-competitive of Wind Power electricity and PSHP market, under when Competitive all the Bidding generators Market have the same cost, OPF Inminimize non-competitive the line loss; electricity when the market,generation when costs all are the different, generators OPF have minimize the same the electricity cost, OPF supply minimize In non-competitive electricity market, when all the generators have the same cost, OPF cost; in competitive market, the OPF maximize the social welfare (consumer surplus + producer the lineminimize loss; whenthe line the loss; generation when the generation costs are different,costs are different, OPF minimize OPF minimize the electricity the electricity supply supply cost; in surplus) according to the bids in market. Since the spot electricity market of Fujian province has not competitivecost; in competitive market, the market, OPF maximize the OPF maximize the social th welfaree social welfare (consumer (consumer surplus surplus + producer + producer surplus) been founded, the bidding mode in the competitive spot market cannot be confirmed yet. This paper accordingsurplus) to according the bids into the market. bids in Since market. the Since spot the electricity spot electricity market market of Fujian of Fujian province province has has not not been uses the Pool model of the Australian Electricity Market Operator (AEMO) for reference to study the founded,been thefounded, bidding the modebidding in mode the competitive in the competitive spot marketspot market cannot cannot be conﬁrmedbe confirmed yet. yet. This This paperpaper uses spot market operation. In the AEMO market, the power plants can bid 10 price bands and uses the Pool model of the Australian Electricity Market Operator (AEMO) for reference to study the the Poolcorresponding model of thepowers Australian according Electricity to the generators Market Operator’ situation (AEMO)and production for reference costs and to studythese bids the spot spot market operation. In the AEMO market, the power plants can bid 10 price bands and marketcan operation. be adjusted In according the AEMO to a market,changed thesituation, power power plants plants can must bid 10 balanc pricee between bands and getting corresponding higher corresponding powers according to the generators’ situation and production costs and these bids powersbidding according price and to the the generators’ possibility of situation less generating and production hours. AEMO costs sorts and these these bids bids by can price be adjustedand can be adjusted according to a changed situation, power plants must balance between getting higher accordingobtains to the a changed market supply situation, curves. power Figure plants 7 displays must balance parts of betweenthe electricity getting energy higher supply bidding curves price for and bidding price and the possibility of less generating hours. AEMO sorts these bids by price and the possibilitythe 48 trading of less periods generating at 1 July hours. 2017 AEMOin AEMO; sorts it is these clear bids that bythe price supply and curves obtains shift the for market different supply obtains the market supply curves. Figure 7 displays parts of the electricity energy supply curves for trading periods. curves.the Figure48 trading7 displays periods partsat 1 July of the2017 electricity in AEMO; energy it is clear supply that the curves supply for curves the 48 shift trading for different periods at 1 Julytrading 2017 in periods. AEMO; it is clear that the supply curves shift for different trading periods.

Figure 7. Part of the 48 supply curves in AEMO. Figure 7. Part of the 48 supply curves in AEMO. According to the biddingFigure mode 7. Part in AEMO, of the 48 suppose supply curvesthat the in coal AEMO. fired power plants can give 6 price bands according to their condition: to avoid valley peak following and stop/restart costs and to According to the bidding mode in AEMO, suppose that the coal fired power plants can give 6 have minimum generating hours, power plants bid prices at half of their average variable cost for price bands according to their condition: to avoid valley peak following and stop/restart costs and to have minimum generating hours, power plants bid prices at half of their average variable cost for

Sustainability 2018, 10, 298 13 of 20

According to the bidding mode in AEMO, suppose that the coal ﬁred power plants can give 6 Sustainabilityprice bands 2018 according, 10, x FOR toPEER their REVIEW condition: to avoid valley peak following and stop/restart costs 13 of and 20 to have minimum generating hours, power plants bid prices at half of their average variable cost for powers ofof 40%40% ofof their their capacities; capacities; for for powers powers between between 40–60% 40–60% of their of their capacities, capacities, they bidthey prices bid prices equal equalto their to average their average variable variable costs, whichcosts, rangewhich between range between RMB/MWh RMB/MWh 314–370; 314–370; and for and higher for powers,higher powers,taking 10% taking of capacity 10% of ascapacity a band, as each a band, band’s each price band’s equals price 1.5 timesequals of 1.5 that times of the of formerthat of band.the former Thus, band.when theThus, generating when the power generating is above power 90% ofis itsabove capacity, 90% theof its price capacity, is approximately the price is 5 approximately times its average 5 timescost. Thus,its average the highest cost. priceThus, is the rather highest lower price than is is rather that in lower developed than is electricity that in developed market, for electricity example market,AEMO. Renewablesfor example and AEMO. nuclear Renewables power plants and offer nuclear only onepower price plants to the offer market only and one this price supposition to the marketis due to and the this unclear supposition attitude is of due government. to the unclear The attitude supply curve of government. of Fujian market The supply without curve wind of Fujian power marketis displayed without in Figure wind8 power; it’s supposed is displayed that thein powerFigure plants8; it’s dosupposed not rebid that according the power to changes plants do in thenot rebidmarket according situation, to which changes means in the that market the supply situatio curven, which stays means put. that the supply curve stays put. Bidden price (RMB/MWh price Bidden

Bidden Power (Unit: MW)

Figure 8. The supply curve of Fujian market.

Wind power is intermittent. Although the the wind wind po powerwer along along the the Fujian Fujian coastline coastline is, is, in in general, general, smallest inin earlyearly morning morning and and gradually gradually increases increases to reach to reach its maximum its maximum during during afternoon afternoon to evening, to evening,which coincides which coincides with the demandwith the load demand fluctuation, load fluc theretuation, is no guaranteethere is no that guarantee they will that maintain they will the maintainsame pace. the When same demand pace. When load isdemand low and load coal is fired low powerand coal plants fired are power near theirplants minimum are near power their minimumoutput, large-scale power output, wind power large-scal outpute wind cannot power be absorbed output by cannot the electricity be absorbed system by and the wind electricity power systemwill be discardedand wind inevitably. power will Once be discarded this happens, inevitab usingly. large-scale Once this energy happens, storage, using for large-scale example, PSHP, energy to storage,absorb the for surplus example, power PSHP, will to be absorb the only the applicablesurplus power course will of be action. the only If suppose applicable the PSHP’scourse of pumping action. Ifthreshold suppose price the PSHP’s to be RMB pumping 320/MWh, threshold PSHP price will to begin be RMB to pump 320/MW waterh, whenPSHP the will electricity begin to supplypump waterprice iswhen lower the than electricity that, which supply raises price the is electricity lower than demand that, which and supports raises the the electricity market price. demand and supportsFurther, the market supposing price. the demand loads are at 50% of their highest load levels, in the two scenarios of 0 andFurther, full windsupposing power the output, demand the distributionsloads are at of 50% electricity of their prices highest are displayedload levels, in Figurein the 9twoa,b, scenariosrespectively. of 0 Comparedand full wind with power Figure output,6, it is the clear distributions that, under of the electricity same demand prices are level, displayed bidding in Figurecompetition 9a,b, respectively. squeezes the Compared prices to a with lower Figure level 6, and it theis clear rise that, in wind under power the same output demand will depress level, biddingthe electricity competition price. From squeezes the whole the prices provincial to a lower level, thelevel minimum and the nuclearrise in wind power power is supposed output towill be depress60% of itsthe capacity, electricity because price. the From Fujian the provincial whole provincial government level, deliberately the minimum let the nuclear coal fired power power is supposedplants bear to valley be 60% peak of followingits capacity, responsibility. because the WhenFujian theprovincial PSHP’s government pumping cut deliberately in price isfixed let the at coalRMB fired 320/MWh, power plants this has bear the vall effectey ofpeak supporting following the responsibility. whole market; When however, the PSHP’s the supporting pumping effect cut in is priceconstrained is fixed by at theRMB pumping 320/MWh, load. this In has Figure the9 a,effe thect 8of nuclear supporting generators the whole take market; 5184 MW however, generating the supportingload, resulting effect in the is constrained coal-fired power by the plants’ pumping generating load. loadsIn Figure being 9a, squeezed the 8 nuclear to a lower generators bidding pricetake 5184 MW generating load, resulting in the coal-fired power plants’ generating loads being squeezed to a lower bidding price zone and the market price being low enough to start pumping: the electricity price at the pumping node (Bus 35) is only RMB 316.38/MWh and the pumping is operated at the full capacity of 1500 MW. Since the PSHP’s pumping load has been fully occupied, it has lost further supporting effect to market price. In Figure 9b, when all the wind power generators

Sustainability 2018, 10, 298 14 of 20 zone and the market price being low enough to start pumping: the electricity price at the pumping node (Bus 35) is only RMB 316.38/MWh and the pumping is operated at the full capacity of 1500 MW. Sustainability 2018, 10, x FOR PEER REVIEW 14 of 20 Since the PSHP’s pumping load has been fully occupied, it has lost further supporting effect to market price.of capacity In Figure 17409b, MW when are all generating the wind at power full capa generatorscity, all ofthe capacity coal-fired 1740 plants MW are squeezed generating to at their full capacity,limits of all valley the coal-ﬁred load following plants and are squeezedthe decrease to their in prices limits of of all valley the nodes load following range from and RMB the decrease110.31– in116.76/MWh. prices of all the nodes range from RMB 110.31–116.76/MWh.

(a) (b)

FigureFigure 9. 9.Comparison Comparison ofof electricityelectricity pricesprices in a competitive market. ( (aa)) LMPs LMPs with with zero zero wind wind output; output; (b(b)) LMPs LMPs with with full full wind wind output. output.

It is clear that, if the demand loads reach their 50% valley levels, the current PSHP with a capacityIt is clear of 1200 that, MW if the cannot demand meet loads the requirement reach their 50%of stabilizing valley levels, the electricity the current market. PSHP withTable a 2 capacity shows ofthe 1200 PSHP’s MW pumping cannot meet load therequirements requirement and of genera stabilizingting powers the electricity under different market. demand Table2 loadshows levels the PSHP’sin two pumpingscenarios. load In scenario requirements 1, where and there generating is no wind powers power under output, different the demandpumping load load levels should in twobe scenarios.1910 MW Inwhen scenario the demand 1, where load there is isat no50%; wind in scenario power output, 2, where the the pumping wind power load is should generating be 1910 at full MW whencapacity, the demandthe pumping load isload at 50%;should in scenariobe 3382 MW. 2, where As the the demand wind power load level is generating rise, the pumping at full capacity, load therequirement pumping loadlowers should until be there 3382 is MW. no need As the to demand pump to load stabilize level rise,the market the pumping but before load requirementthat, it can lowersstabilize until the there local ismarginal no need price to pump (LMP) to at stabilize RMB 320/MWh. the market When but beforethe demand that, itloads can stabilizerise up to the higher local marginallevels, for price example (LMP) 80–85% at RMB levels, 320/MWh. the generating When the un demandit begins loads to generate rise up toand higher provides levels, power for example to the 80–85%system levels,and at thethe generatingsame time unitstabilize begins the toLMP generate at RMB and 450/MWh provides before power its to generating the system capacity and at theis samefully timeused. stabilize the LMP at RMB 450/MWh before its generating capacity is fully used.

TableTable 2. 2.PSHP’s PSHP’s pumpingpumping andand generatinggenerating loads under different scenarios.

DemandDemand LoadLoad (%) (%) 50 50 55 55 60 60 65 65 70 70 75 75 80 80 85 85 90 90 PumpingPumping Load(MW) 1909 1909 857 857 218 218 0 0 0 0 0 0 0 0 0 0 0 0 PwindPwind = 0% = 0% GeneratingGenerating Load(MW)Load(MW) 0 0 0 0 0 0 0 0 0 0 0 0 727 727 1200 1200 1200 1200 LMPLMP of of PSHP PSHP (RMB/MWh)(RMB/MWh) 320 320 320 320 320 320 320 320 322 322 342 342 450 450 479 479 492 492 PumpingPumping Load(MW) 3382 3382 1903 1903 987 987 131 131 0 0 0 0 0 0 0 0 0 0 Pwind = 100% Generating Load(MW) 0 0 0 0 0 0 0 601 1200 Pwind = 100% LMPGenerating of PSHP (RMB/MWh)Load(MW) 320 0 320 0 320 0 320 0 321 0 322 0 340 0 450 601 478 1200 LMP of PSHP (RMB/MWh) 320 320 320 320 321 322 340 450 478

However, under the current economic downturn and electricity oversupply condition, the However, under the current economic downturn and electricity oversupply condition, the installed capacity of Fujian province is growing at a higher rate than is demand, let al.one the fact that installed capacity of Fujian province is growing at a higher rate than is demand, let al.one the fact mostthat ofmost them of arethem valley-load-following are valley-load-following free nuclear free nuclear and wind and powers,wind powers, which which will present will present a serious a challengeserious challenge to the development to the development of the Fujian of the electricity Fujian electricity market. market.

4. An Example of PSHP Operation The analysis above in 3.4 reveals only a snapshot of the electricity market operation; it cannot reveal the system operation dynamically. In any normal working day, the electricity demand load will fluctuate over time. In Figure 10, the dotted black line displays the 15-min real demand load change at the provincial system control level for a certain day in May 2016. In the system described

Sustainability 2018, 10, 298 15 of 20

4. An Example of PSHP Operation The analysis above in 3.4 reveals only a snapshot of the electricity market operation; it cannot revealSustainability the system2018, 10, operationx FOR PEER dynamically. REVIEW In any normal working day, the electricity demand load 15 of will 20 ﬂuctuate over time. In Figure 10, the dotted black line displays the 15-min real demand load change at thein Figure provincial A2 in system the appendix, control level the fortotal a certain installed day capacity in May 2016.of coal, In thehydro, system nuclear described and gas in Figure is 36,950 A2 inMW; the the appendix, total demand the total load, installed not including capacity the of coal,pumping hydro, load nuclear at PSHP, and gasis 32,885 is 36,950 MW; MW; there the is totalalso demandwind power load, with not capacity including of the 1740 pumping MW; the load Xiyuan at PSHP, PSHP’s is 32,885 generator MW; therecapacity is also is 1200 wind MW. power In 2016, with capacitythe highest of 1740 demand MW; theload Xiyuan was 32,3 PSHP’s60 MW, generator which capacity is very isclose 1200 to MW. the In total 2016, demand the highest load demand in the loadsimulation was 32,360 model. MW, which is very close to the total demand load in the simulation model.

0:00 5:00 10:00 15:00 20:00 24:00

Figure 10. The load and price changes.

Under the the competitive competitive bidding bidding market, market, first first suppose suppose that that the thedemand demand is met is metby traditional by traditional coal, coal,hydro hydro and nuclear and nuclear powers, powers, the theprice price fluctuations fluctuations of ofall allthe the demand demand nodes nodes are are also also displayed displayed in Figure 1010.. TheThe price price fluctuation fluctuation has has three three characteristics: characteristics: firstly, firstly, the pricesthe prices are rather are rather stable stable at the valleyat the loadvalley period, load period, this is this because is because the total the demandtotal demand loads loads fall into fall into the lowthe low price price bids bids zone, zone, prices prices will will be supportedbe supported by PSHP’sby PSHP’s pumping pumping loads, loads, which which provides provides supporting supporting effect effect to the to whole the whole system system and that’s and whythat’s all why the all prices the stayprices around stay around 320 for 320 the wholefor the period whole ofperiod 0:00–8:00; of 0:00–8:00; secondly, secondly, the difference the difference amongst allamongst the nodes all the changes nodes along changes with along loads with levels; loads thirdly, levels; at high thirdly, loads, at smallhigh changesloads, small in demand changes may in resultdemand in may a big result jump in a electricity big jump price,in electricity for example, price, atfor 12:00 example, the demand at 12:00 loadsthe demand decline loads a little, decline after whicha little, the after price which declines the byprice more declines than RMB by 100,more later than rising RMB up 100, quickly later andrising the up fluctuation quickly becomesand the morefluctuation violent becomes between more 18:00 violent and 22:00. between Small 18:00 changes and in22:00. demand Small resulting changes in in big demand jumps resulting in price are in notbig rarejumps in in the price developed are not electricityrare in the market. developed electricity market. In the simulationsimulation model,model, thethe pumpingpumping thresholdthreshold priceprice isis supposedsupposed atat RMBRMB 320/MWh320/MWh and the out/ in in efficiency efficiency is is fixed fixed at at 80%, 80%, which which means means that that the the minimum minimum supplying supplying price price of ofPSHP PSHP should should be beRMB RMB 400/MWh; 400/MWh; to make to make a certain a certain level levelof profit, of profit, suppose suppose the bidding the bidding price priceof PSHP of PSHPto be toRMB be RMB450/MWh 450/MWh and accept and accept a higher a higher market market price pricewhen when demand demand loads loads rise; rise;and suppose and suppose that thatthe three the three gas gaspeaking peaking plants’ plants’ bidding bidding prices prices are areall RMB all RMB 500/MWh. 500/MWh. For the For demand the demand load load changes changes in Figure in Figure 10, the 10, theelectricity electricity prices prices of all of the all demand the demand nodes nodes are displayed are displayed in Figure in Figure 11. Compared 11. Compared with withFigure Figure 10, it 10 is, itclear is clear that thatPSHP PSHP and andgas peaking gas peaking plants plants can stabilize can stabilize the market the market price price at two at twolevels levels of RMB of RMB 450 and 450 and500/MWh. 500/MWh. Except Except for the for highest the highest peak peak load, load, at around at around 11:00, 11:00, the the peak peak loads loads at at other other times times are effectively limited by PSHP’s generating power; only when the PSHP is generating at full capacity and peak load following is constrained will the electricity price rise again, although limited by the gas peaking plants’ bidding price. If the demand load rises farther and the gas peaking generator operates at full capacity, the electricity price will rise more.

Sustainability 2018, 10, x FOR PEER REVIEW 17 of 21 be supported by PSHP’s pumping loads, which provides supporting effect to the whole system and that’s why all the prices stay around 320 for the whole period of 0:00–8:00; secondly, the difference amongst all the nodes changes along with loads levels; thirdly, at high loads, small changes in demand may result in a big jump in electricity price, for example, at 12:00 the demand loads decline a little, after which the price declines by more than RMB 100, later rising up quickly and the fluctuation becomes more violent between 18:00 and 22:00. Small changes in demand resulting in big jumps in price are not rare in the developed electricity market. In the simulation model, the pumping threshold price is supposed at RMB 320/MWh and the out/ in efficiency is fixed at 80%, which means that the minimum supplying price of PSHP should be RMB 400/MWh; to make a certain level of profit, suppose the bidding price of PSHP to be RMB 450/MWh and accept a higher market price when demand loads rise; and suppose that the three gas peaking plants’ bidding prices are all RMB 500/MWh. For the demand load changes in Figure 10, the electricitySustainability prices2018, 10 of, 298all the demand nodes are displayed in Figure 11. Compared with Figure 10,16 it of is 20 clear that PSHP and gas peaking plants can stabilize the market price at two levels of RMB 450 and 500/MWh. Except for the highest peak load, at around 11:00, the peak loads at other times are effectively limited by PSHP’s generating power; only when the PSHP is generating at full capacity effectively limited by PSHP’s generating power; only when the PSHP is generating at full capacity and peak load following is constrained will the electricity price rise again, although limited by the gas and peak load following is constrained will the electricity price rise again, although limited by the peaking plants’ bidding price. If the demand load rises farther and the gas peaking generator operates gas peaking plants’ bidding price. If the demand load rises farther and the gas peaking generator at full capacity, the electricity price will rise more. operates at full capacity, the electricity price will rise more. RMB/MWh

0:00 5:00 10:00 15:00 20:00 Time Figure 11. Price changes under peaking following powers. Figure 11. Price changes under peaking following powers. For the given load curve in Figure 10, PSHP and gas power will lower the social cost by RMB 6.31 million;For the however, given load the curve PSHP’s in Figure gross 10 profit, PSHP when and gasaccepting power willa higher lower market the social price cost is byonly RMB RMB 6.31 0.32million; million however, and the the gross PSHP’s profit gross when profit accepting when fixed accepting prices a higheris only marketRMB 0.28 price million, is only which RMB can 0.32 hardlymillion compensate and the gross for profitthe fixed when cost accepting of PSHP. fixed prices is only RMB 0.28 million, which can hardly compensate for the fixed cost of PSHP. 5. Conclusions and Policy Implications 5. Conclusions and Policy Implications This paper takes the Fujian provincial grid system as the framework to construct an independentThis paper electricity takes the system Fujian model provincial according grid system to the as connection the framework situation to construct of power an independentplants, the productionelectricity systemcosts of model coal-fired according power to theplants connection and demand situation load of data. power Under plants, this the system, production this costspaper of studiescoal-fired the powermeanings plants and and scale demand requirement load data.of PSHP Under when this the system, capacities this of paper intermittent studies thewind meanings power andand stable scale nuclear requirement power of keep PSHP growing. when the Firstly, capacities this paper of intermittent simulates windthe relationship power and between stable nuclear wind speedpower and keep wind growing. power Firstly,output, this with paper the results simulates being the very relationship close to the between actual wind parameters speed andof wind wind powerpower generators; output, with secondly, the results analyzes being very the close effects to theof actualwind parameterspower fluctuation of wind poweron grid generators; system stabilization;secondly, analyzes thirdly, the calculates effects of windthe effects power of fluctuation wind power on gridoutput system fluctuation stabilization; on electricity thirdly, calculates supply costthe effectswhen coal of wind power power production output fluctuation cost follows on a electricity quadratic supply function; cost and when fourthly, coal power calculates production the effectscost follows of wind a power quadratic fluctuation function; on and electricity fourthly, prices calculates under thea competitive effects of wind bidding power spot fluctuation market and on theelectricity stabilizing prices effects under and a the competitive scale requirement bidding spot of PSHP market to andstabilize the stabilizingthe market. effects Finally, and using the scalethe requirement of PSHP to stabilize the market. Finally, using the daily load curve, analyzes the electricity market operation and price fluctuation and the price stabilizing effects of PSHP and gas peak following generators. The results show that, although PSHP has a large external social welfare effect, it can hardly earn enough profit. There are three shortcomings in this study: (1) as the electricity market is not fully marketized and the current electricity market is oversupplied, the study can only be based on simulation, rather than real market behavior; (2) the trading rules of the future electricity market are not finalized and the trading mode can only be subjectively supposed based on developed market experience; (3) the simulation results are limited, because the system keeps the intermittent renewable powers and gas peaking powers out, and, through not considering the potential earnings from the ancillary market, the profit potential of PSHP is underestimated. However, the results of this paper still have directional Sustainability 2018, 10, 298 17 of 20 meanings for reference. Based on the conclusions of this study, some suggestions are put forward for the marketization to come.

(1) When installing more wind and nuclear power plants, more PSHP plants are needed. Although developing wind and nuclear power is the inevitable choice to combat global warming, wind power and nuclear power are at two extremes from the perspective of stability and they pose great challenges to the grid system. Energy storage facilities, represented by PSHP, can offer an effective buffer between electricity supply and demand. (2) Electricity market reform should continue and the spot market design should give participants enough flexibility. A spot market is usually a short-term adjustment market compensating for the shortcomings of the bilateral market, forming supply and demand curves to reveal the market situation in the short-term, providing power plants and consumers with information and economic incentives to adjust their behaviors. A multi-price bands’ bidding mode can offer participants flexible choices to maximize their welfare. (3) Hydro power generation has speed and cost advantages in terms of stopping, restarting and load following and it can play greater roles in ancillary service markets. When designing the ancillary service trading mode, these advantages should be taken into consideration, making more space for PSHP earning profit and helping the electricity market function fluently. (4) More studies about energy storage and PSHP choice should be carried out. This paper proves that PSHP has a substantial social external welfare effect but struggles to make enough profit. As the planned 13 GW of offshore wind power plants are put into operation in the future, they will have a greater effect on the electricity market; thus, system operation, coordination of renewable power and energy storage, including PSHP, requires further study.

Acknowledgments: This paper is supported by National Natural Science Foundation of China (NSFC) project (No. 71573217) and the Ministry of Education in China (MOE) Project of Humanities and Social Sciences (No. 13YJC790110). The author would like to thank the anonymous referees for helpful comments and suggestions and the author is responsible for the paper. Data are available to readers on request. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Nomenclature

AC Alternating Current AEMO Australian Electricity Market Operator AVC Average Variable Cost CIF Cost Insurance and Freight GW Gigawatt MW Megawatt NDRC National Development and Reform Commission () OPF Optimal Power Flow PSHP Pumping and Storage Hydro Power RMB Renminbi Yuan, the exchange rate of RMB/USD at 2018.1.1 is 6.50 Sustainability 2018, 10, 298 18 of 20

Sustainability 2018, 10, x FOR PEER REVIEW 18 of 20 Appendix A Appendix A

Figure A1. Fujian 500 kV and above grid, power plant distribution map.

Figure A2. The Simulink model of Fujian electricity market for wind power. Figure A2. The Simulink model of Fujian electricity market for wind power.

Reference References 1. Document: The State Council issued the Notice on Issuing Electric Power Sector Reform. Available online: 1. Document: The State Council Issued the Notice on Issuing Electric Power Sector Reform. Available online: http://en.pkulaw.cn/display.aspx?cgid=178553&lib=law (accessed on 19 January 2018). http://en.pkulaw.cn/display.aspx?cgid=178553&lib=law (accessed on 19 January 2018). 2. Suggestions on Further Deepening the Reform of Electric Power System. Available online: http://www.scirp.org/%28S%28lz5mqp453edsnp55rrgjct55%29%29/reference/ReferencesPapers.aspx?Refe renceID=2011305 (accessed on 19 January 2018).

Sustainability 2018, 10, 298 19 of 20

2. Suggestions on Further Deepening the Reform of Electric Power System. Available online: http://www. scirp.org/%28S%28lz5mqp453edsnp55rrgjct55%29%29/reference/ReferencesPapers.aspx?ReferenceID= 2011305 (accessed on 19 January 2018). 3. Denny, E.; Tuohy, A.; Meibom, P.; Keane, A.; Flynn, D.; Mullane, A.; O’Malley, M. The impact of increased interconnection on electricity systems with large penetrations of wind generation: A case study of Ireland and Great Britain. Energy Policy 2010, 38, 6946–6954. [CrossRef] 4. Mc Garrigle, E.V.; Leahy, P.G. Cost savings from relaxation of operational constraints on a power system with high wind penetration. IEEE Trans. Sustain. Energy 2015, 6, 1–8. [CrossRef] 5. Shaker, H.; Zareipour, H.; Wood, D. Impacts of large-scale wind and solar power integration on California’s net electrical load. Renew. Sustain. Energy Rev. 2016, 58, 761–774. [CrossRef] 6. Huber, M.; Dimkova, D.; Hamacher, T. Integration of wind and solar power in Europe: Assessment of flexibility requirements. Energy 2014, 69, 236–246. [CrossRef] 7. Zakeri, B.; Rinne, S.; Syri, S. Wind Integration into Energy Systems with a High Share of Nuclear Power—What Are the Compromises? Energies 2015, 8, 2493–2527. [CrossRef] 8. Pudjianto, D.; Aunedi, M.; Djapic, P.; Strbac, G. Whole-Systems Assessment of the Value of Energy Storage in Low-Carbon Electricity Systems. IEEE Trans. Smart Grid 2014, 5, 1098–1109. [CrossRef] 9. Kondoh, J.; Ishii, I.; Yamaguchi, H.; Murata, A.; Otani, K.; Sakuta, K. Electrical energy storage systems for energy networks. Energy Convers. Manag. 2000, 41, 1863–1874. [CrossRef] 10. Ibrahim, A.; Ilinca, J.; Perron, J. Energy storage systems—Characteristics and comparisons. Renew. Sustain. Energy Rev. 2008, 12, 1221–1250. [CrossRef] 11. Nyamdash, B.; Denny, E.; O’Malley, M. The viability of balancing wind generation with large scale energy storage. Energy Policy 2010, 38, 7200–7208. [CrossRef] 12. Suul, J.A.; Uhlen, K.; Undeland, T. Variable Speed Pumped Storage Hydropower for Integration of Wind Energy in Isolated Grids: Case description and control strategies. In Proceedings of the Nordic Workshop on Power and Industrial Electronics (NORPIE/2008), Espoo, Finland, 9–11 June 2008. 13. Deane, J.P.; Ò’Gallachóir, B.P.; McKeogh, E.J. Techno-economic review of existing and new pumped hydro energy storage plant. Renew. Sustain. Energy Rev. 2010, 14, 1293–1302. [CrossRef] 14. Mc Daniel, G.H.; Gabrielle, A.F. Dispatching pumped-storage generation. IEEE Trans. Power Appara. Syst. 1966, PAS-85, 465–471. [CrossRef] 15. Cohen, A.I.; Wan, S.H. An algorithm for scheduling a large pumped-storage plant. IEEE Trans. Power Appar. Syst. 1985, PER-5, 40. 16. Oliviera, P.; McKee, S.; Coles, C. Optimal scheduling of a hydro thermal power generation system. Eur. J. Oper. Res. 1993, 71, 334–340. [CrossRef] 17. Yang, C.; Jackson, R.B. Opportunities and barriers to pumped hydro energy storage in the United States. Renew. Sustain. Energy Rev. 2011, 15, 839–844. [CrossRef] 18. Horsley, A.; Wrobel, A.J. Effciency rents of pumped-storage plants and their uses for operation and investment decisions. J. Econ. Dyn. Control 2002, 27, 109–142. [CrossRef] 19. Lu, N.; Chow, J.H.; Desrochers, A.A. Pumped-storage hydro-turbine bidding strategies in a competitive electricity market. IEEE Trans. Power Syst. 2004, 19, 834–841. [CrossRef] 20. Kanakasabapathy, P.; Swarup, S. Bidding Strategy for Pumped-Storage Plant in Pool-Based Electricity Market. Energy Convers. Manag. 2010, 51, 572–579. [CrossRef] 21. Dogan, K. Uncertainties in Energy Markets and Their Consideration in Energy Storage Evaluation; KIT Scientific Publishing: Karlsruhe, Germany, 2013; Volume 4. 22. Abgottspon, H.; Bucher, M.; Andersson, G. Stochastic dynamic programming for unified short and medium-term planning of hydro power considering market products. In Proceedings of the 12th IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Istanbul, Turkey, 10–14 June 2012. 23. Braun, S. Hydropower Storage Optimization Considering Spot and Intraday Auction Market. Energy Procedia 2016, 87, 36–44. [CrossRef] 24. Kumar, N.; Besuner, P.M.; Lefton, S.A.; Agan, D.D.; Hilleman, D.D. Power Plant Cycling Costs. 2012. Available online: https://www.nrel.gov/docs/fy12osti/55433.pdf (accessed on 21 January 2018). 25. Deb, R. Operating hydroelectric plants and pumped storage units in a competitive environment. Electr. J. 2000, 13, 24–32. [CrossRef] Sustainability 2018, 10, 298 20 of 20

26. Ela, E.; Kirby, B.; Botterud, A.; Milostan, C.; Krad, I.; Koritarov, V. The Role of Pumped Storage Hydro Resources in Electricity Markets and System Operation. In Proceedings of the HydroVision International Conference, Denver, CO, USA, 23–26 July 2013. 27. Kirby, B. Co-Optimizing Energy and Ancillary Services from Energy Limited Hydro and Pumped Storage Plants. In Proceedings of the 2012 HydroVision International, Tulsa, OK, USA, 17–20 July 2012. 28. Saarinen, L.; Dahlbäck, N.; Lundin, U. Power system ﬂexibility need induced by wind and solar power intermittency on time scales of 1–14 days. Renew. Energy 2015, 83, 339–344. [CrossRef] 29. Mou, D. Understanding China’s electricity market reform from the perspective of the coal-ﬁred power disparity. Energy Policy 2014, 74, 224–234. [CrossRef] 30. Ogayar, B.; Vidal, P.G.; Hernandez, J.C. Analysis of the cost for the refurbishment of small hydropower plants. Renew. Energy 2009, 34, 2501–2509. [CrossRef] 31. NDRC’s Notice on the Adjustment of PV Power and Onshore Wind Power Benchmarking Feed-in Prices (Fagaijiage [2016], No.2729). Available online: http://www.ndrc.gov.cn/gzdt/201612/t20161228_833065. html (accessed on 19 January 2018).

© 2018 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).