Congestion Management Considering Demand Response with Multiple Fuels
5. Simulation Results
The system that has been used in this research is IEEE 30-bus system and contains 6 generators. Some generators are operated with multiple fuel options which means their cost functions are presented as piecewise quadratic cost function. Firstly, the most economical fuel for generators with multiple fuels are identified. Secondly, the congestion management is performed with the generators are operated based on the selected fuel. All the simulations are run on Matlab.
The solution for this problem is solved by using PSO method. An example from Ref [9] that contains 10 generators with piecewise quadratic cost function is carried out to test the accuracy of the method. The same method and solution procedures are implemented in IEEE 30-bus system with 6 generators. The Generator 1 and 2 are supplied with three types of fuels during their operation such as fuel 1 is Natural Gas, fuel 2 is Heavy Fuel Oil (HFO) and fuel 3 is Light Fuel Oil (LFO) while the other four generators only have a single fuel to support their operation. It is very important to determine which one is very economical to burn among three different of fuels to meet some particular load system demand where the total demand in this system is 189.2 MW. The aim in this case is to determine the economical fuel should be used by generator 1 and 2. The similar algorithm and characteristic of PSO. The simulation result is shown in Table 1.
Table 1. The result of fuel selection Gen ID Bus Type of Fuel Generation(MW)
1 1 2 134.23
2 2 1 38.51
3 13 - 0.19
4 22 - 16.08
5 23 - 0.19
6 27 - 0
Cost = 460.27 NT$/h
The results in Table 1 show the Generator 1 and 2 are recommended to use Fuel 2 and Fuel 1 for their operation, respectively. Because their operation using these fuels give the minimum operating cost in the total load of 189.2 MW. The following two cases will be considered for congestion management in this research. The first one is congestion management based on generation re-dispatch and the second one is based on generation and demand re-dispatch (with DR program).
A two-step market clearing procedures is used to analyze the congestion in the system.
In the first step, generators submit their bidding price to maximize their profit and ISO clears the market by neglecting the network in respect of the limited transmission line capacities and losses in order to define the electricity market price. In the second step, the ISO will consider the network constraints and losses to manage the congestion. Both the market clearing procedures are performed by using OPF algorithm.
5.1 Market Settlement
The market settlement clears the generation production and market clearing price by using the initial demand values according to equations (19-22). The market settlement results for generators are provided in Table 2.
Table 2. The scheduled generation in market settlement No Gen No Bus Production (MW)
1 1 134.23
2 2 38.51
3 13 0.19
4 22 16.08
5 23 0.19
6 27 0
The results shown in Table 2 are the output of scheduled generators. The marginal cost which is the Lagrangian multiplier of the equality constraint, Equation (14), gives the unit price and is equal to 3.009 NT$/kWh. The total re-dispatch cost given by simulation is 460.27 NT$/h. After the Market settlement, the ISO checks the feasibility of the scheduled generation by carrying out a load flow. In the 30-bus test system, an AC load flow is employed. We therefore consider line loadings in MVA. A load flow is carried out to check the feasibility of the generation schedule in Table 6.2. The results for the load flow are shown in Table 3 for all lines.
The results from power flow show that there is congestion on bus 6 to bus 8 while the other lines are below the limit capacity, the line flow is 34.51 (MVA) more than line limit 32 (MVA). Therefore, the congestion management is necessary to be performed.
5.2 Congestion Management Based on Generation Re-dispatch
Re-dispatch is carried out by using an Optimal Power Flow (OPF). Whereas the load flow module does not consider line flow limits, the OPF considers this constraint. The
objective function of re-dispatch is as set out in equation (23) i.e. minimization of absolute active power and cost re-dispatch subject to the constraints. With these constraints the OPF results in a generation schedule shown in Table 3 below. We observe that generator 1 and 2 decrease their power production while the other generators have increment in their productions.
Because the total of generation re-dispatch is increased to 193.6 MW compare to the total of generation schedule which is 189.2 MW. Therefore, the cost of re-dispatch has increased to 514.19 NT$/h compared to a market settlement system cost of 460.27 NT$/h.
With the re-dispatch schedule, the line flow in congestion line 6-8 is brought within its limit capacity, which is become 31.63 MVA.
total of re-dispatch cost = 514.19 NT$/h
Due to the congestion, the marginal cost or called as nodal price will be varied in every buses. Price at each node represents the locational value of energy, which includes the cost of the energy and the cost of delivering it. In term of congestion relief, generators which have been down the power production would pay to ISO, in contrast ISO would pay to those that increase the production. Based on the nodal price of each generator in Table 4, the total cost that generators have to pay or to get pay to ISO can be determined.
Table 4. Nodal price at generator buses No Gen No Bus Nodal Price (NT$/kWh)
1 1 2.635
Based on nodal price results, generator 1 and 2 which are decreased the power production will pay to ISO by NT$/h 131.196 and NT$/h 25.659, respectively due to the congestion. The ISO will pay to generator 3, 4, 5 and 6 because their power increment by NT$/h 18.78, NT$/h 11.02, NT$/h 60.08, and NT$/h 152.18 respectively.
6. Conclusions
Transmission congestion management is a challenging issue in deregulated power systems and usually the system operator is faced with this problem. Many approaches have been proposed and applied to address this problem. In this research, DR program as a new approach to relieve congestion problem is discussed. In this regard, a two-step clearing market procedure which is implemented in pool electricity market is adopted to manage congestion. The first step of clearing market procedure is established without taking into account the transmission limit and losses in order to define the market clearing price and determine the scheduled power generator’s production at low cost. In the second step, all the congestion constraints including transmission limit and losses are included to manage congestion problem. The congestion is relieved just by increasing and decreasing the initial production of generators, which was determined in market auction. The Demand Response program can play a major role in competitive electricity markets, particularly in case of congestion.
References
[1] H. Aalami, G.R. Yousefi, and M.P Moghaddam, “Demand ResponseModel Considering EDRP and TOU Programs”, IEEE Transmission and Distribution Conference and Exposition, 2008.
[2] A. Yousefi, T.T. Nguyen, H. Zareipour and O.P. Malik, “Congestion Management using Demand ResponseProgram and FACTS Devices”, Electrical Power and Energy Systems, Vol.37, 2012.
[3] Allan J. Wood and B.F. Wollenberg, “Power Generation, Operation, and Control”, Wiley Interscience, 2nd Edition 1996.
[4] Nordel, “Congestion Management in the Electric Power System”, Special Print of the Feature Article in Nordel’s Annual Report, 2000.
[5] S.N. Singh and A.K. David, “Optimal Location of FACTS Devices for Congestion Management”, Electric Power System, Res.58 Page 71-79, June, 2001.
[6] C. Ritcher and G. Sheble, “Genetic Algorithm Evolution of Utility Bidding Strategies for the Competitive Market Place”, IEEE Transaction on Power System, vol. 13 pp 256 – 261, Feb. 1998.
[7] D. Kirschen and G. Strbac, “Fundamentals of Power System Economics”, John Wiley
& Sons Ltd, 2009.
[8] N. Qun, Z. Zhuo and D. Jing, “An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects”, Energies Journal, 2012.
[9] P.S. Manoharan and P.S. Kannan, “A Novel Approach for Power Economic Dispatch with Valve Point Effects and Multiple Fuel Options”, Journal of Electrical System 4-2, 2008.