Bidding strategies in deregulated power market

ByK.Gautham Reddy2011A8PS364G

Deregulation Deregulation is the process of removing or reducing state

regulations. It is therefore opposite of regulation, which refers to the process of the government regulating certain activities.

Energy prices are not regulated in these deregulated areas and consumers are not forced to receive supply from their utility. 

Deregulation allows competitive energy suppliers to enter the markets

Deregulation gives consumers choice - the power of the buyer. A deregulated market allows you to choose your commodity supplier. 

Bidding classificationsBuying or selling of energy takes

place in the form of bids Bidding

Bidding in electricity market• Agents submit bids (Quantity and cost) to either buy

or sell energy.

• Independent System Operator (ISO) matches the bids

Market clearing price

• Bids below MCP are accpeted

• Two types of payments for bids

i) Uniform pricingii) Pay as bid

Strategic bidding:Aim is to construct best optimal bid knowing their own costs, technical constraints and their expectation of rival and market behavior

Mathematical formulation

Consider total of ‘m’ suppliers

Uniform pricing method is followed

The jth supplier bid with linear supply curve denoted by Gj (Pj ) = aj + bj Pj

Pj is the active power output, aj and bj are non-negative bidding coefficients of the jth supplier.

After receiving the bids, MCP is determined.

The following eqs should be satisfied

aj + bj Pj = R, j = 1, 2, . . . , m

= Q (R)

Where R is the market clearing price (MCP),

Q(R) is the aggregate pool load forecast

Q(R) = Qo −KR

Qo is a constant number and K is a non-negative constant used to represent the load price elasticity.

When we solve the above equation we get the solutions as

R= (1) = (2)Cost function :Cj (Pj ) = ej Pj + fj Pj2 , whereej and fj are the cost coefficients of the jth supplier

Profit maximizationHence our main objective is to maximize

profits which is the difference between the selling price and the production price which is as follows

The objective is to determine bidding coefficients aj and bj so as to maximize F(aj,bj) subject to equations 1 and 2.

Maximize : F() = RSubject to : Eqs. (1) and (2)

Gravitational search algorithmFollows two basic laws

i) Law of gravity: Each particle attracts every other particle and the gravitational force between two particles is directly proportional to the product

of their masses and inversely proportional to the distance ‘R’ between them.

ii) Law of motion :The current velocity of any mass is equal to the sum of the fraction of its previous velocity of mass and the variation in the velocity.

Now, consider a system with N agents (masses), the position of the ith agent is defined by:

for i = 1,2,3….N

At a specific time ‘t’ we define the force acting on mass ‘i’ from mass ‘j’ as following:

(t)=G(t)()

G is initialized and reduced with time

G(t) = Go

Go is set to 100

The total force acting on each mass i is given in a stochastic form as the following

(t)

The acceleration of each of the masses,is then as follows.

=

Its position and its velocity could be calculated as follows:

vi (t + 1) = vi (t) + ai (t) xd (t + 1) = xd(t) + vd(t + 1)

Fuzzification:

Inputs :

(i) normalized fitness value (NFV) (ii) current gravitational constant (G)

Outputs:

The correction of the gravitational constant (dG).

Input variables represented by three linguistic values, S (small), M (medium) and L (large) where as output variable (G) is presented in three fuzzy sets of linguistic values; NE (negative), ZE (zero) and PE (positive) with associated triangular membership functions.

Gravitational constant G is varied as follows = + G⧍

After we get a new value of G, GSA is repeated until iteration reaches their maximum limit.

Best fitness (optimal bid value bj) computed at final iteration

Using bj values, we can calculate MCP.

Genetic algorithm

References J. Vijaya Kumar, D.M. Vinod Kumar, K. Edukondalu,”

Strategic bidding using fuzzy adaptive gravitational search algorithm in a pool based electricity market”, Applied Soft Computing 13 (2013) 2445–2455

Li Yang, Fushuan Wen , F.F. Wu , Yixini Ni and Jiaju Qiu,” Development of Bidding Strategies in Electricity Markets Using Possibility Theory”, International Conference on Power System Technology Proceedings, Kunming , China , 13-17 October 2002,v.1p. 182-187.

A. Azadeh, S.F. Ghaderi, B. Pourvalikhan Nokhandan, M. Sheikhalishahi,” A new genetic algorithm approach for optimizing bidding strategy viewpoint of profit maximization of a generation company”, Expert Systems with Applications 39 (2012) 1565–1574