Mixed GA-OPF based prioritized optimal location and rating of wind power generation in deregulated electricity market

Abstract

Due to the liberalization of the electricity market, the traditional concepts and practices of the electrical systems have resulted in the introduction of Competitive Electricity Market (CEM). The recognition of CEM provides special consideration for the development of Renewable Energy (RE) throughout the world. The paper presents a mixed Genetic Algorithm (GA) and Optimal Power Flow (OPF) based model for determination of the optimal location and rating of Wind Power Generation (WPG). The optimization algorithm has been formulated and solved while considering the procurement cost minimization for obtaining the required energy by optimally locating the WPG in the system. The proposed mixed GA-OPF approach has been successfully applied to the modified IEEE 30-bus test system. The proposed algorithm also resulted in the prioritized list of optimal locations of WPG in the system.

Keywords

Competitive electricity market wind power generation genetic algorithm optimal power flow procurement cost optimal location

1 Introduction

With the rapid increase in the energy demands, more energy resources are required to follow the energy requirements, if we meet this demand for coal fired thermal power plants in environmental pollution and global warming are the major consequences. Renewable Energy Sources (RES) plays a vital role in the accomplishment of an electricity sector. It largely aims to improve service standards, increase system efficiency, and to develop competitive market. It has altered the customary operation and requirement of utilities in complex customs, and had huge impacts on ecological, political, and societal state of affairs for any country [1]. Wind-based generation capacity accounted for around 75% of the total installed renewable energy generation capacity all over the world. Wind energy is an indigenous and virtually unlimited source of electricity generation.

In present scenario, the earlier Vertical Integrated System (VIS) which was the sole authority that looks into the functions related to electricity. Now, it is categorized into three major components like GENCOs for generations, TRANSCOs for transmission, and DISCOs for distribution of electrical power [2].

RES is progressively being considered as one of the notable participants in CEM. Due to the global increase of oil prices and lower accessibility of fine class of coal, RES becomes an essential substitute of conventional power plants to participate in ancillary services markets [3]. While using a combination of RES and fuel cell, MINL programming for optimal locations has been used [6]. Kroposi et al. [7] proposed a best possible size and position of RES on distribution line on the basis of financial and technical terms. The optimized position of RES to curtail input and emission expenditure of complete scheme has been discussed in Reference [5]. References [9] and [11] models the best possible site of Distributed Generator (DG) while considering cost reduction of the DG and entire process costs. For finding the optimal allocation of DGs while considering loss minimization, Nara and Hayashi [12] have used a Tabu search algorithm. Borges and Falcao [8] have projected a prototype for optimal allocation of distributed generation for consistency, losses, and voltage enhancement.

An optimal model for location of RESs in the CEM has been presented by Sharma et al. [13] while considering profit maximization through RES generation. Banshwar et al. [4] presented multi-aimed optimization problem with the aim to maximize the social benefit and yield profit from WP-GENCO by reducing the distribution losses through optimally locating WPGs in the system.

2 Proposed Mixed GA-OPF based optimization framework

A mixed GA-OPF model has been proposed to obtain the optimal location and the rating of WPG in a deregulated environment. The objective of the work is to minimize total generation cost with the placement of WPG in the system, as shown in Fig. 1.

Fig. 1

Proposed GA-OPF based optimization approach.

This work considers bidding coefficients from the participants in terms of the amount and the corresponding prices they want to deliver in the pool. Generator-side bidding is considered in the present work [15].

Let be the vector of real power generated by the j^th unit, then $P_{g}^{p} = {P_{gi}^{p}, \dots \dots \dots \dots \dots; j = 1, 2, 3, \dots . ., N_{g}}$ (1)

The objective function in the present work is to reduce the total cost of generation for dispatching the required demand. The generation system considered for validation of the proposed mixed GA-OPF approach consists of conventional as well as WPG. Therefore, $F = min {\sum_{i = 1}^{N_{g}} C_{i} (P_{gi}^{p}) + (C_{WPG} \times {PW}_{k})} $ / h$ (2)

In this work, cost of wind power generation (C_WPG) of 52.23$/MWh has been considered [10]. Optimization of objective function (in eqn. 2) with constraints along with generation bidding constraints as considered in this work has been done by solving mixed GA-OPF algorithm by locating the WPG at different buses (except the PV buses).

3 Proposed step-by-step algorithm

The optimal location and rating of the WPG under GA and OPF has been obtained by applying the proposed algorithm. The optimal solution is achieved by considering the following a step-by-step algorithm:

Step 1. Obtain bus and the line data, and bidding coefficients along with their capacity limits and optimize the procurement cost using GA-based OPF technique [14].

Step 2. Initialize the rating of WPG from zero itself. ${Rating}_{WPG} = 0$

Step 3. Initialize bus count to 1, that is (N_b = 1).

Step 4. Increment the rating of WPG by 1; i.e. ${Rating}_{WPG} = {Rating}_{WPG} + 1$

Step 5. Allocate the WPG of Rating_WPG (MW) at Bus N_b.

Step 6. Run GA-OPF algorithm and determine F $F = F_{{with}_{W} PG}$

Step 7. Check whether ${Rating}_{WPG} = {({Rating}_{WPG})}_{\max}$

If this is true; go to the next step otherwise return back to step (5).

Step 8. Check for the total number of buses, whether $N_{b} < n$ then increment the bus counts by 1, as $N_{b} = N_{b} + 1$ set Rating_WPG again and go back to step (4), otherwise go to the next step.

Step 9. Determine optimal value of F and optimal location of WPG.

Step 10. Prepare a priority list of optimal locations of WPG based on ascending order of procurement cost.

4 Test system characteristics for validation of proposed approach

To authenticate the efficacy of the work, an IEEE-30 bus test system is modified and the proposed mixed GA-OPF mechanism for optimal rating and location of WPG has been applied on it, whose details are discussed in this section.

4.1 Characteristics of generation system

Figure 2 shows the test system that consists of 6 suppliers (or GENCOs), out of which, 5 GENCOs are conventional power plants which are located, respectively, at buses 1, 2, 22, 23 and 27, with capacities ranging from 100 to 300 MW. The sixth supplier is considered as a WPG whose optimal rating and location is to be determined by proposing mixed GA-OPF based approach.

Fig. 2

Single line diagram of modified IEEE-30 bus test system.

The proposed GA based OPF calculates total procurement cost of energy at each bus location by considering three load scenarios, viz. 700 MW, 725 MW and 750 MW. For the procurement of energy, the modified system has five loads E₁, E₂, E₃, E₄, and E₅ and are connected respectively at buses 1, 2, 3, 10, and 23. These energy loads E₁, E₂, E₃, E₄, and E₅ shares respectively 10%, 25%, 25%, 20%, and 20% of the complete energy demand such that $E_{L} = E_{1} + E_{2} + E_{3} + E_{4} + E_{5}$ (3)

All the 6 suppliers in this work are assumed to offer the linear bidding coefficients as given by C_i (P_gi) = b_i (P_gi) with the technical characteristics of generation system is given in Table 1.

Table 1

Technical characteristics of generation system

S. No.	Bus	GENCO	Energy Offer b_i ($/MWh)	Maximum Capacity
1	1	S1	40	260
2	2	S2	35	300
3	22	S3	68	100
4	23	S4	56	170
5	27	S5	86	150
6	To be determined	WPG	52.23	60

The proposed approach has been carried out in two steps, that is, in the first stage, the optimal rating of the WPG is obtained from the system. The second stage optimization helps to prepare the priority list on the basis of procurement cost while locating WPG in the system.

4.2 Validation of the effectiveness of proposed mixed GA-OPF based optimal location

Three cases of load pattern are considered in order to validate the proposed approach. Mixed GA-OPF algorithm determines the optimal rating of WPG as 48 MW and optimal procurement cost of energy while placing WPG at each possible location.

4.2.1 Case I: Optimal location of WPG for energy demand of 700 MWh

In the present case, optimal location of WPG in the system is required to be determined while fulfilling the energy demand of 700 MW. Figure 3 shows the location of WPG at different bus versus optimal procurement cost. Based on the objective function, the priority list of different optimal WPG locations in case I is given in Table 2.

Fig. 3

Mixed GA-OPF based optimal location of WPG at different buses versus optimal procurement cost for Case I.

Table 2

Priority list for optimal rating of WPGs

Priority	Optimal location of WPG
	Case – I	Case – II	Case – III
1	11	3	12
2	3	9	8
3	10	10	7
4	17	17	3
5	8	11	11
6	6	6	10
7	28	8	17
8	13	4	4
9	21	13	6
10	4	28	16
11	20	16	13
12	16	7	28
13	7	20	20
14	5	19	19
15	18	15	15
16	19	5	18
17	9	21	5
18	25	18	9
19	14	14	14
20	24	25	25
21	15	24	21
22	29	29	24
23	30	30	29
24	26	26	30
25	12	12	26

4.2.2 Case II: Optimal location of WPG for energy demand of 725 MWh

In the present case, optimal location of WPG in the system is required to be determined while fulfilling the energy demand of 725 MW. Figue 4 shows the location of WPG at different buses versus optimal procurement cost. Based on the objective function, the priority list of different optimal locations of WPG to fulfill the energy demand of 725 MW along with other suppliers in this case is given inTable 2.

Fig. 4

Mixed GA-OPF based optimal location of WPG at different buses versus optimal procurement cost for Case II.

4.2.3 Case III: Optimal location of WPG for energy demand of 750 MWh

In the present case, optimal location of WPG in the system is required to be determined while fulfilling the energy demand of 750 MW. Figure 5 shows the location of WPG at different buses versus optimal procurement cost The obtained priority list for this case is given in Table 2.

Fig. 5

Mixed GA-OPF based optimal location of WPG at different buses versus optimal procurement cost for Case III.

4.3 Mixed GA-OPF based priority list for optimal location of WPG

On the basis of total procurement cost of dispatching the complete energy demand in CEM, the priority list indicating different possible locations of WPGs (with an optimal rating of 48 MW) has been prepared and is presented in Table 2.

From the table, it is seen that in case I, the foremost, second and third priority of the optimal location of WPG is at bus no. 11, 3, and 10. For case II, it is 3, 9, and 10, and for case III, it is 12, 8, and 7. If the climatic conditions or any other factor is not favorable to locate the WPG at a particular priority, then the next priority may possibly be considered and so on.

5 Conclusion

The demand for electrical energy increases in the recent time; alternative sources of energy such as wind have an important role in the energy generation as the extinction of fossil fuels is going to happen in the near future. Competition has been introduced in market reforms to reduce costs and to enhance efficiency. This invent to the emergence of CEM from regulated monopoly. In this work methodology for determining the best possible setting and the rating of WPG while minimizing the procurement cost of optimally locating WPG in the system, has been presented.

Inappropriate allocations of WPGs can perhaps additionally degrade the system performance. Therefore it is required to have more than one option for location of WPG in the system to achieve the required degree of reliability. Hence, a priority-list is required. Thus, the proposed methodology is important for the power system planners to choose the next priority.

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