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Abstract:The deregulated electricity market when settled with an objective of maximizing the benefits of the participants without considering the network constraints has often resulted in congestion of the transmission lines.This problem has been handled by formulating a two-step market clearing procedure. Inthe first step, generationcompanies bidto the market for maximizing their profit, and the ISO clears the market based on social welfare maximization. The network constraints, including those related to congestion management are presented in the second step of the market-clearing procedure.This paper presents an analytical approach to identify the optimal locationand size of load curtailment (LC) to relieve congestion caused due to unconstrained market settlement. First generation shift factor(GSF) is utilized to determine the buses for LC. Next, a PSO-based model is utilized to determine the optimal size of LC with an objective function of minimizing real power lossesand relieving congestion. The proposed framework is illustrated in IEEE 30-bus test system.
Key words: System Operator; Load Curtailment; Generation Shift Factor; Particle Swarm Optimization; Congestion Management .
INTRODUCTION
The deregulated electricity market necessitates perfect balances between the supply and load in real time. This is not an easy task because both supply and demand levels could change rapidly and unexpectedly due to generation units’ forced outages, transmission outages and sudden load change. The largest increase of load has caused congestion in transmission network, which further lead to instability in power system operation. The electric power can be transmitted between two locations on a transmission network. Congestion in a transmission network, whether in vertically integrated system or unbundled electricity market cannot be tolerated except briefly, since this may cause line outages with uncontrolled loss of load. Generally,the methods adopted to manage congestion include rescheduling generator outputs, supplying reactive power support or curtail transactions. Several techniques for congestion management have been reported.
A competitive market has paved way for innumerable participants. This has led to overloading and congestion of transmission lines. Moreover, open access transmission network has ingenerated a more intensified problem of congestion. Thus, congestion management in power systems is germane and of central importance to the power industry.Anusha Pillay reviews the congestion management work carried out so far.
An important issue of transmission system congestion management in a pool electricity market environment with the consideration of voltage stability as loadability limit is addressed by Ashwani Kumar. The optimal generators’ rescheduling has been obtained for three block bid structure submitted to the ISO in a day-a-head market. The base case economic load dispatch has been obtained for generators ensuring the loadability limits and is taken as base case generation output data during the congestion management to obtain new generation scheduling. The generation pattern has been obtained for three bid blocks taking load variation for 24 h considering load scaling factor. The three block bid structure offered to the ISO has been modeled as a linear curve, function of up and down rescheduling within the upper and lower limits offered for congestion management.
Congestion is a major problem that the system operator (SO) would face in the post-deregulated area. Therefore, investigation of techniques for congestion-freewheeling of power is of paramount interest. Although congestion management using an optimum centralized power flow gives an efficient solution, it lacks enough transparency in the competitive market for its participants about congestion related costs. Alonso .J proposesan efficiency and transparent solution about congestion costs using Modified Bender’s decomposition in hybrid electricity market.
Congestion management is an important part of a power system in today’s deregulated electricity markets. A.K. Singh proposes a sensitivity method for allocating distibuted generators to reduce the system losses and the voltage profile. This method assists ISO to remove the overloaded lines in normal and contingency case.Beibei Wang addresses economic dispatch with transmission, DR capacity and operational constraints to model the operation of a transmission network constrained system with a high penetration of wind power. To investigate the effectiveness of the operational model, simple PJM 5-bus system and an IEEE 118-bus system is used.
Chunyu Zhang has discussed high penetration of both Distributed Energy Resources (DER) and Demand Response (DR) in modern power systems for maintaining system reliability and flexibility. Morais.H has presented a demand response simulator that allows studying demand response action and schemes in distribution networks. It has the technical validation of the solution using realistic network simulation based on PSCAD.
Mehdi Nikzad has suggested demand response (DR) through incentive-based and priced-based programs. To investigate the reliability effects of DRPs based on the DR model the IEEE RTS 24-bus test system is used.Yousefis.A has proposeda method for transmission line congestion managementin a restructured market environment using a combination of demand response (DR) and FACTS devices. Results obtained with the proposed methods arecompared with that of the sensitivity method and with the exhaustive Optimal Power Flow (OPF) solutions.
In this paper, the effectiveness of load curtailment is discussed by formulating the objective functions of achieving minimum power loss. Attractive features of Particle Swarm Optimization (PSO) algorithm are used for solving the formulated problems with the objectives considered separately. The effectiveness of the proposed method is validated on IEEE 30 bus system.
II. PROBLEM FORMULATION
MARKET CLEARING FORMULATION
The market clearing procedure is formulated by following steps. In the first step, generation and demand bids are submitted to thethe market for maximizing their profit and the ISO clears the market based on social welfare maximization without considering the electricity network losses and network constraints.
FIRST STEP:MARKET PRICE DETERMINATION
In this step, it is required to solve the following constrained optimization problem:
Maximize:
∑_(i=1)^(N_D)▒∑_(k=1)^(N_Di)▒〖(λ_Dik-P_Dik )-〗 ∑_(i=1)^(N_G)▒〖C_i (P_gi ) 〗--- (1)
Subject to:
P_Dik^min≤P_Dik≤P_Dik^max i=1…..N_D,
k=1………N_Di--- (2)
P_gi^min≤P_gi≤P_gi^max i=1………N_G --- (3)
∑_(i=1)^(N_D)▒∑_(k=1)^(N_Di)▒〖P_Dik+P_fd=∑_(i=1)^(N_G)▒P_gi 〗--- (4)
Where,
P_Dik= Power block k that demand i is willing to buy at
price?Dik up to a maximum of P_Dik^max .
λ_Dik= Price offered by demand i to buy power block k.
P_fd= Fixed load based on demand forecasting.
C_i (P_gi )=Generation cost function.
The objective function (1) represents the social welfare, and it has terms. The first term consists of the sum of accepted demands times their corresponding bid prices, and the second term is the sum of the individual generator cost functions. The block of constraints in (2) specifies the sizes of the demand bids. The block of constraints in (3) limits the sizes of the production bids. The equality constraint in (4) ensures that the production should be equal to total demand.