DETERMINATION OF PRICE OF REACTIVE POWER SERVICES IN DEREGULATED ELECTRICITY MARKET USING PARTICLE SWARM OPTIMIZATION

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1 DETERMINATION OF PRICE OF REACTIVE POWER SERVICES IN DEREGULATED ELECTRICITY MARKET USING PARTICLE SWARM OPTIMIZATION PUJA S. KATE M.E Power System P.V.G s C.O.E.T, Pune-9,India. Puja_kate@rediffmail.com Prof. Mrs P. R.Khatri Asst. Professor,Dept of Electrical Engineering P.V.G s C.O.E.T, Pune-9,India. khatri_preeti@yahoo.co.in Abstract The Particle Swarm Optimization technique is implemented using Matlab and is tested on an IEEE 14 bus system. The optimization problem considered in this case is to minimize the total reactive support cost from generators and reactive compensators. The objective function in this optimization problem is used as a fitness function in the PSO. The following combination of control parameters are used for running the PSO. The inertia weight in the range 0.9 to 1.2 on an average has a better performance, and has a large chance to find the global optimum within a reasonable number of iterations. Using the above parameters the PSO is executed and the results are obtained. Index Terms Reactive power pricing, particle swarm optimization, genetic algorithm, restructured power market. I. INTRODUCTION ESTRUCTURING is the major trend in power industry R reform throughout the world. Power industry has been facing restructuring problems during the past decade. Appropriate management of reactive power is very essential for supporting power system security. Reactive power has dominant effects on real energy transfer. Furthermore, it can support the secure operation of the system as an ancillary service. However, most researches have been focused on active power as the main good transacted in electricity markets. On the other hand,while reactive power production cost is highly dependent on real power output, it is mainly confined to local consumption. As a result, to avoid market power and to maintain the secure operation of the system, a fair cost allocation method seems to be very essential. Appropriate pricing of reactive power as an ancillary service has been a challenging problem during the past decade. However, most methods proposed so far for reactive power pricing are essentially based on empirical approximations. In this paper, a new method for reactive power cost allocation is proposed.application of the proposed method on IEEE 14-bus standard network confirms its validity and effectiveness. II. POWER INDUSTRY DEREGULATION AND EMERGENCE OF ANCILLARY SERVICES The power industry was over the years vertically integrated and these served as the only electricity supplier in the region and were obliged to provide electricity to everyone. Since the past decade, power utilities worldwide have been going through a process of reforms in order to introduce commercial incentives in generation, transmission and distribution. The main objectives of the reforms are achieved through a clear separation between production and sale of electricity, and network operations. The vertically integrated generation, transmission and distribution system operations have been separated into independent activities. The generation companies and power stations sell energy through competitive long-term contracts with customers or by bidding for short-term energy supply at the spot market. Transmission is still a monopoly since the economics of scale are very high. Transmission open access has proved to be an important requirement in deregulated systems. To guarantee a level playing field for the generators and customers to access the transmission network, the transmission system operator is required to be independent from other market participants. The Independent System Operator (ISO) has acquired a central coordination role and 1

2 carries out the important responsibility of providing for system reliability and security. It manages system operations, such as scheduling and operating the transmission-related services. The ISO also has to ensure a required degree of quality and safety, provide corrective measures when faced with incidents, and several other functions. In addition, the ISO could also manage market administration, energy auction and unit commitment functions in the pool market structure. To this effect, certain services, such as, scheduling and dispatch,frequency regulation, voltage control, generation reserves, etc. are required by the power system, apart from the basic energy and power delivery services. Such services, which are now commonly referred to as ancillary services, had all along been part of the normal electricity supply and were not separated in the traditional vertically integrated power systems. Ancillary services are also referred to as Interconnected Operation Services by the North American Electric Reliability Council (NERC). However, in deregulated power systems, transmission networks are available for third-party access to allow power wheeling, and spot markets for electricity have developed in many countries. In such a deregulated environment, the ancillary services are no longer treated as integral to the electricity supply. They are unbundled and priced separately, and system operators have to purchase ancillary services from ancillary service providers. Issues pertaining to costing of ancillary services and hence appropriate pricing mechanisms for all market participants to recover the costs become an important issue for proper functioning of the system. III. REACTIVE POWER SERVICE The ISO is responsible for providing reactive power support services, and this is provided at embedded cost-based prices. Generating resources, which operate within their capability limits, are directed by ISO to produce / absorb reactive power to maintain voltages within their limits. The cost of reactive power support includes the following: compensated for, including, a payment if they are required to reduce their real power output. In Figure 2, assume that a unit is operating at (PA, Qbase). If the unit is required to increase its reactive power production from Qbase to QA, it will incur increased losses in the windings and hence increase in its costs. This cost is the cost of loss component and is incurred by the synchronous generator with reactive power production (both lagging and leading). It is to be noted that the nature of the plot shown is only figurative to illustrate the two components i.e. the cost of loss plot need not necessarily be a parabolic curve nor the other component is necessarily parabolic. Fig 1. Reactive power requirement IV. COST OF REACTIVE POWER PRODUCTION The total annual embedded cost for payment Any applicable lost opportunity cost to provide reactive power service Total of prior year payments to suppliers of reactive power service less the total of payments received by the ISO from transmission customers in the prior year for reactive power service. Lost Opportunity Cost The generators are mandated to provide reactive power within the power factor range of 0.90 lag to 0.95 lead (Figure1). For reactive power absorption / generation beyond these limits, the generators are financially Fig 2.Synchronous generator capability curve 2

3 space. Therefore, PSO can easily deal with nondifferentiable objective functions. Additionally, this property relieves PSO of assumptions and approximations, which are often required by traditional optimization models. c) PSO uses probabilistic transition rules and not deterministic rules. Hence, PSO is a kind of stochastic optimization algorithm that can search a complicated and uncertain area. This makes PSO more flexible and roust than conventional methods. Fig.3 Reactive cost verses increased cost d) Unlike Genetic Algorithm (GA) and other heuristic algorithms, PSO has the flexibility to control the balance between the global and local exploration of the search space. This unique feature of a PSO overcomes the premature convergence problem and enhances the search capability. e) Unlike the traditional methods, the solution quality of the proposed approach doesn t rely on the initial population. Starting anywhere in the search space, the algorithm ensures the convergence to the optimal solution. Fig 3. Working scheme of proposed reactive power market V. PARTICLE SWARM OPTIMIZATION Similar to evolutionary algorithm, the PSO technique conducts searches using a population of particles, corresponding to individuals. Each particle represents a candidate solution to the reactive power problem. In a PSO system, particles change their positions by flying around in a multidimensional search space until a relatively unchanged position has been encountered, or until computational limitations are exceeded. In social science context, a PSO system combines a social only model and a cognition-only model. The social-only component suggests that individuals ignore their own experience and adjust their behavior according to the successful beliefs of the individual in the neighborhood. On the other hand, the cognition-only component treats individuals as isolated beings. A particle changes its position using these models. The advantages of PSO over other traditional optimization techniques can be summarized as follows: a) PSO is a population-based search algorithm (i.e. PSO has implicit parallelism). This property ensures PSO to be less susceptible to getting trapped on local minima. This algorithm considers some particles. Each particle is a candidate for solution in the search space restricted by problem constrains. The particles try to find problem optimal solution moving in the space. As presented in (1) next position of each particle is determined stochastically according to its own previous position, best solution for optimized problem found by itself and best solution found by whole group. Vi is determined by (2) where rand1 and rand2 are random numbers between 0 and 1, c1, c2 are constant number that is typically in the range [0.5-2] and w is inertia coefficient which it is important for PSO's convergence that it is usually defined as (3) where constant coefficients max, min are the maximum and minimum inertia coefficients, respectively. iter is represented the number of iteration and maxiter is maximum number of iteration. Some important advantages of PSO algorithm rather than other evolutionary approach such as Genetic Algorithm are simple implementation and high speed execution in order to find optimal solution [6]. b) PSO uses payoff (performance index or objective function) information to guide the search in the problem 3

4 B. Objective Function As presented in (4), objective function used in this case consists of active and reactive power production cost produced by generators. Consider a network that in it N and Ng are number of buses and number of generator buses respectively. Subject to power flow equality and inequality constraints, VI. PSO ALGHORITHM: The basic elements of the PSO techniques are briefly stated and defined as follows: 1. Particle X (t): It is a candidate solution represented by a k-dimensional real-valued vector, where k is the number of optimized parameters. At time t, the ith particle Xi(t) can be described as Xi(t)=[xi,1(t); xi,2(t); ;xi,k(t)]. 2. Population: it is a set of n particles at time t. 3. Swarm: it is an apparently disorganized population of moving particles that tend to cluster together while each particle seems to be moving in a random direction. 4. Particle velocity V (t): It is the velocity of the moving particles represented by a k-dimensional real-valued vector. At time t, the ith particle Vi (t) can be described as Vi (t)=[vi,1(t); vi,2(t); ;vi,k(t)]. 5. Inertia weight w(t): it is a control parameter that is used to control the impact of the previous velocity on the current velocity. All the control variables transformer tap positions and switch-able shunt capacitor banks are integer variables and not continuous variables. Therefore, the value of the inertia weight is considered to be 1 in this study. 6. Individual best X* (t): As the particle moves through the search space, it compares its fitness value at the current position to the best fitness value it has ever attained at any time up to the current time. The best position that is associated with the best fitness encountered so far is called the individual best X* (t). Individual best updating: each particle is evaluated and updated according to the update position. 7. Global best X** (t): It is the best position among all of the individual best positions achieved so far. 8. Stopping criteria: These are the conditions under which the search process will terminate. In this study, the search will terminate if one of following criteria is satisfied: The number of the iterations since the last change of the best solution is greater than a pre-specified number. The number of iterations reaches the maximum allowable number. Fig.4 IEEE 14 bus system In a PSO algorithm, the population has n particles that represent candidate solutions. Each particle is a k- dimensional Real-valued vector, where k is the number of the optimized parameters. Therefore, each optimized parameter represents a dimension of the problem space. 4

5 VII.REACTIVE PRICING SCHEME In many deregulated markets, the ISO has a limited access to information on generators and hence may not be able to determine a generator's revenue loss. An ap-propriate option in such markets is to call for reactive bids from generators. We discussed regions on the reactive power coordi-nate in the previous section, which are now explicitly de-fined here to formulate the generator's expectation of payment function. From knowledge of generators expectation of payment, the ISO can call for reactive bids from all parties. With a reactive bid structure established, the ISO requires a proper criterion to determine the best offers and hence formulate its reactive power procurement plan. The ISO determines the marginal benefit of each reactive bid with regard to system losses. The ISO shall seek to minimize losses least; it would require procuring higher loss compensation services (also involving pay-ments). A novel pricing scheme for reactive power is presented in the section. 1. Reactive power support cost responsibility separation. The total reactive power cost is divided into two components, namely the generators side and the loads side. The duty cost of the generators side CG (i.e. the reactive cost to support the delivery of active power) is circu-lated as the optimal value of Eq. (3) when the system has no reactive loads. To evaluate this cost, the power factors of all the loads are set to unity. This component of cost is caused only by real power transportation. The remaining cost (CL - CQ* - CG) is assigned to reactive loads. 2. Equitable allocation of CG to generators. 3. Payment to generators. 4. Payment to independent reactive sources. algorithm is tested on an IEEE-14 bus system for various control. REFERENCES 1. P.R.Sujin, Dr.T.Ruban Deva Prakash and M.Mary Linda Particle Swarm Optimization Based Reactive Power Optimization JOURNAL OF COMPUTING, VOLUME 2, ISSUE 1, JANUARY M. Sedighizadeh, A. Rezazadeh and M. Seyed Yazdi, Pricing of Reactive Power Service in Deregulated Electricity Markets Based on Particle Swarm Optimization 3. JIN ZHONG, Some Aspects of Design of Electric Power Ancillary Service Markets 4. Lament JW, Fu J, "Cost analysis of reactive power support", IEEE Trans Power Syst. pp 890-(1999). 5 G R. Deksnys, R. Staniulis, "Pricing of Reactive Power Service," Oil Shale, Estonian Academy Publishers, Vol. 24, No. 2 Special, pp , April G.K. Venayagamoorthy, R.G. Harley, Swarm intelligence fortransmission system control, Power Engineering Society General Meeting, IEEE, pp. 1-4, VIII CONCLUSION In this paper reactive OPF is developed to solve the optimal reactive dispatch problem. The total reactive cost is separated into generators' duty and loadings' duty. Cost duty on the generation side is allocated to real power sellers by evaluating their reactive power requirement for real power transportation. The method of evaluation adopted in this paper has a common basis for every market participant and hence it is consistent and equitable. Each generator will be paid according to the difference between its actual incurred cost of contributing of reac-tive power support and its cost of reactive power requirement for real power selling. The theory and implementation is illustrated through a simple example. The results are obtained using PSO illustrates that the pro-posed algorithm is simple and practical. This method is compatible with the new competitive market structure and economic efficiency can be achieved. The 5