The value of energy storage in optimal non-firm wind capacity connection to power systems
Introduction
Wind power has been experiencing very rapid growth around the world. However, there are some issues associated with the wind power that should be considered when planning these resources. One important issue is the variability of wind speed that leads to partially dispatchable nature of wind farms [1]. Also, the remoteness of the wind-rich areas requires long transmission lines for connection to the existing network. Furthermore, wind-rich sites are in open areas with low population density. Transmission networks in these areas are not best designed to absorb all the power from these new power producers [1].
To increase wind energy penetration in a power system, optimal wind capacity allocation strategies should be used to cope with above-mentioned problems. However, due to the fluctuating output and site dependent characteristics, as well as the possibility of wind speed profile in a direction opposite to the load profile, a comprehensive analysis over an extended time is needed to find the optimal locations for wind farms [2]. In Refs. [2], [3], the authors proposed a model for wind capacity allocation to potential wind sites to maximize firm wind connection to the transmission network. However, they conclude that a firm connection is not the best choice for connecting a wind farm to a power system. By definition, a firm connection for a wind farm does not involve any wind curtailments due to network capacity limitation. In contrast, when a non-firm connection is assumed, the independent system operator is allowed to curtail wind energy in some situations due to power system security constraints. In Ref. [4], a model for optimal non-firm wind capacity connection to the congested transmission systems is presented. In this study, extensive wind profile, load demand profile, fuel price and contingency management sensitivities are examined and their effects on the optimal wind capacity allocation is shown. These studies do not consider the transmission connection requirements for wind farms, which is a significant cost component of remote wind farms with capacity factor between 20 and 50%. In our previous work [5], a model for optimal wind power capacity allocation considering transmission connection requirements is presented. It simultaneously optimizes the wind farms and their transmission connection requirements capacities. Its results suggest that downsizing the transmission connection would be beneficial for remote wind farms. However, downsizing the transmission connection would cause wind energy curtailment. Storage technologies can provide a solution to wind farm integration problems. Energy storage systems have many benefits for remote wind farms; they increase the overall capacity factor of the wind farm transmission connection, decrease wind curtailments, provide firm capacity, provide ancillary services, and can be used to shape wind farm output [6]. However, the joint planning of wind farms and energy storage facilities has not been explored yet, which is the subject of this paper.
There are different storage technologies, which are economical at different scales. At present, few technologies such as pumped hydro storage and CAES are economical and practical for storing large amounts of wind energy [7]. Since the pumped-hydro storage units are site-dependent, they cannot be used for supplementing wind farms at their sites. In CAES the air can be compressed underground in a solution-mined salt cavity, mined hard rock cavity or in a tank above the ground. A typical CAES unit has electrical round-trip efficiency of %77 to %89 [7]. Remote CAES units also have the benefits of using alternatives to natural gases such as coal and biomass [6].
The benefits of using CAES to supplement the wind energy from a remote wind farm are investigated in Ref. [7]. In this study, two cases with different conditions are studied and compared for supplementing the wind farms: CAES and gas turbines. In Ref. [6], the importances of CAES units for supplementing wind farms in a transmission constrained power system are studied. This study considers two options for locating CAES units: near load centers and near wind farms, and investigates the advantages and disadvantages of each choice. Several other studies have explored the use of energy storages for a variety of goals [6], [7], [8], [9], [10].
In this paper, we allocate a fixed amount of wind power capacity between candidate wind sites to minimize the investment cost and the power system operation cost. The use of CAES units is explored to supplement the wind farms and increase the transmission connection lines capacity factor. We optimize the transmission lines and CAES components capacities along with wind farms capacity simultaneously. The proposed model is applied to IEEE 24 bus test system and the results are presented and discussed. The rest of the paper is organized as follows: the combined model of wind farm and CAES is presented in Section 2. Section 3 describes the problem and the proposed methodology to solve it. The simulation results and conclusion are given in Sections 4 Simulation and results, 5 Conclusion respectively.
Section snippets
Wind farm model
The hourly power output of a wind farm is a function of its capacity, and Nominal 1-MW wind power output function [11]. Nominal 1-MW wind power output function, G(v) can be obtained from (1), where vi, vr, and vo are cut-in, rated, and cut-out wind speeds, respectively [12].
In the above equation parameters a and b are defined as below [12].
The total wind power output of the wind farm is then calculated as following:
All
Problem formulation
The objective of the problem is to optimally allocate a fixed amount of wind capacity to candidate sites. The wind sites are far from the grid and long transmission lines are needed to connect them to the transmission network. CAES system is available to be installed near wind farms in order to prevent wind curtailments, smooth wind farms output, and increase the transmission connection capacity factor. Therefore, the goal of the optimization program is to determine the wind farm capacity for
Simulation and results
The IEEE 24-bus test system is used to illustrate the presented methodology [24], as depicted in Fig. 3. It is assumed that there are four sites with good wind regime for constructing a wind farm. These sites are near busses 9, 17, 22, and 3 named A, B, C, and D, respectively. The wind site distances to nearest bus, annual capacity factor, and mean wind speed of each site is shown in Table 1. Data for four wind sites are driven from HOMER ENERGY [25]. The year-long sequential time series of
Conclusion
This paper presents a methodology to optimally allocate wind capacity to distant wind sites using energy storage technology. Benders decomposition approach is applied to decompose the original problem into a master problem and a subproblem. Wind speed and load profiles are accurately modeled using hourly data. The methodology simultaneously optimizes the wind farms, transmission lines, and CAES components capacities. In UC, wind farms are considered to be dispatchable, namely, wind farms output
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