Rony Seto Wibowo

FACTS Devices Allocation With Control Coordination Considering Congestion Relief and Voltage Stability

This paper presents an optimal allocation method for flexible ac transmission system (FACTS) devices for market-based power systems considering congestion relief and voltage stability. The purpose of the FACTS devices installation is to provide benefit for all entities accomplished by both minimizing annual device investment cost and maximizing annual benefit defined as difference between expected security cost (ESC) with and without FACTS devices installation. Different from previous approaches, the proposed method accurately evaluates the annual cost and benefits obtainable by FACTS devices installation by formulating a large-scale optimization problem that contains power flow analyses for a large number of system states representing annual power system operations. In addition, dynamic state transitions caused by specified contingencies are also simulated in the optimization problem to evaluate the effect of FACTS control actions as well as the other coordinated controls. The expected cost consists of operating cost under normal and contingency states along with their related probabilities to occur. Maximizing social welfare is the objective for normal state while minimizing compensations for generations re-scheduling and load shedding as well as maximizing social welfare are the objectives in case of contingency. Although installation cost of FACTS devices is required, they are useful as cost free means, which can reduce effectively the annual costs for generations re-scheduling and load shedding.

FACTS devices allocation for congestion management considering voltage stability by means of MOPSO

This paper proposes a method for solving congestion management problem by optimally allocating FACTS devices. The problem is approached by utilizing optimization method which optimizes generation and installation costs while satisfying voltage stability index. The main contribution of this paper is to provide pareto optimal solutions which describe previous objectives during congestion and after congestion removed. Moreover, the method is able to rank optimal location in relieving congestion, to describe feasibility of solutions and to show better solution in improving voltage stability. Therefore, it is valuable for decision maker in determining locations and sizes of devices which gaining the benefit. Due to the complexity of the problem, Multi Objective Particle Swarm Optimization (MOPSO) is utilized to optimize devices allocation as master sub-problem; Sequential Quadratic Programming (SQP) is used to solve the operation sub-problem, and Point of Collapse method is applied to calculate load margin during contingency. The effectiveness of this technique is demonstrated in modified IEEE 14 bus system.

Dynamic economic dispatch of hybrid microgrid with energy storage using quadratic programming

Electrical power demand increases rapidly due to the development of technology. In the contrary, the availability of non-renewable energy sources surely decreases. This problem will impact on the national energy security. To meet the need of a large electric power, it is required to develop a large area small scales of distributed generations. Distributed generations utilize renewable energy sources, such as PV, in order to minimize the use of non-renewable energy sources. To maximize the utilization of renewable resources, it is necessary to apply energy storage. This storage is needed to store excess energy generated by renewable energy based power plants. With the distributed generations and energy storage which are connected to the main grid through microgrid, it is important to optimize the operation of the power system in order to meet daily load. In this paper, the optimization problem is formulated as Dynamic Economic Dispatch which is applied on hybrid microgrid with energy storage. The problem is solved using Matlab-based quadratic programming

FACTS Allocation Based on Expected Security Cost by Means of Hybrid PSO

This paper proposes an approach to optimally allocate FACTS devices based on Expected Security Cost Optimal Power Flow (ESCOPF) under deregulated power system. The aims of the approach are both to minimize device investment cost and to maximize benefit defined as difference between Expected Security Cost (ESC) with and without FACTS installation. The expected cost includes operating cost not only under normal condition but also under contingencies along with their associated probabilities to occur. Furthermore, this cost considers compensation for generation power deviation and load interruption. FACTS devices re-setting is the first attempt to be executed in order to minimize the operating cost. Then, generations re-dispatch and load shedding are the second and the third priority actions to minimize ESC. Interaction among multiple FACTS devices in achieving minimum expected cost under both normal condition and contingencies is also considered in this paper. The overall problem is solved using both Particle Swarm optimization (PSO) for attaining optimal FACTS allocation as main problem and Sequential Quadratic Programming (SQP) for solving optimal power flow as sub optimization problem.

Security constrained optimal power flow with FACTS devices using bender decomposition

This paper deals with security constraied optimal power flow (SCOPF) in which FACTS devices are employed to meet system constraints under both normal and contingency states. The considered constraints are power generation limit, voltage limit, transmision limit and FACTS devices operation limit. In normal state, the objective function is to minimize operation cost while satisfying system constraints. If contingency occurs, FACTS devices are optimally controlled to eliminate violation of generator ramp rate as well as to meet system constraints. The iterative process is applied to ensure that there will be no generator ramp rate violation. Initially, normal state is simulated to obtain optimal power dispatch as a basecase. Using this basecase, contingency state is simulated in order to minimize generation ramp rate violation. If ramp rate violation is failed to be eliminated, the violation will be fed back to normal state as a basis to re-arrange the output of generators that will be the next basecase. By this basecase, contingency state is again simulated. This iterative process involving normal and contingency states will stop if ramp rate violation is no longer exist. To decompose main problem into normal and contingency state, Bender decomposition technique is used with relation between power generation under normal and contingency states as a coupling equation. The power generation deviation of particular unit should be less than the corresponding generator ramp rate. Each optimization problem is solved by sequential quadratic programming (SQP). IEEE 14 bus will be used to show the ability of the proposed approach to solve the SCOPF.

Dynamic DC optimal power flow using quadratic programming

This paper proposes a quadratic programming for solving the dynamic direct current optimal power flow (DDCOPF). The DDCOPF solves OPF with multi load levels in which ramp rate of committed units become coupling between two series load levels. To overcome this problem, a very large matrix may be required. The more number of load levels are considered, the larger matrix will be used. Consequently, it may take long computation time to solve. Therefore, the DC load flow is preferable than AC load flow. To show the effectiveness of the proposed approach, IEEE 14 bus test system is used. In addition, application of the proposed approach to real system Jawa Bali 500 kV 25-bus is presented.

Dynamic optimal power flow with geothermal power plant under take or pay energy contract

This paper deals with dynamic optimal power flow (DOPF) considering geothermal power plant under take or pay (TOP) contract. As located in ring of fire, Indonesia has high potential of geothermal resources. Therefore, geothermal power plant becomes promising solution of power shortage in Indonesia. However, it takes high investment cost to develop geothermal power plant. In order to attract the investors to spend their money in developing geothermal power plant, the Indonesian government applies take or pay scheme in buying electric power from geothermal unit. This scheme is aimed to ensure cost recovery of the unit. When applying take or pay scheme, system operator may use DOPF formulation to make daily operation planning. The DOPF problem is solved using Matlab-based quadratic programming (QP). Then, IEEE 30 bus system is employed as tested system to show the effectiveness of the proposed approach.

Quadratic programming approach for security constrained optimal power flow

This paper proposes an approach for optimal power flow considering several contingency states. Initially, contingency selection is conducted to measure how much a specific contingency may affect the operation cost. Then, some severe contingencies are incorporated into optimal power flow problem. All considered states, normal and contingency states, are simulated simultaneously. Thus, if contingency occurs, it can be ensured that all constraints such as generation limit, transmission limit and ramp rate will be satisfied. To solve the problem, quadratic programming is applied. IEEE 30 bus system is used as test system to show the ability of the proposed approach.

Security constrained optimal power flow incorporating preventive and corrective control

This paper proposes an approach for optimal power flow with incorporation of preventive and corrective control. The approach is useful for operation planning to guarantee secure operation under both normal and contingency states. Therefore, the approach consists of main problem and sub problem representing normal and contingency states, respectively. In normal state, preventive control is used to secure the system while in contingency state, corrective control is employed to satisfy system constraints. The objective of main problem is to minimize operation cost while the sub problem is aimed to minimize power generation deviation in order to avoid ramp rate violation. The approach uses iterative process involving normal and contingency states. At contingency state, power redispatch is conducted as corrective control to avoid system violation. If power redispatch violates ramp rate of generator unit, the exceeding power will be fed back to normal state to recalculate the generator output in the normal state. This calculation in normal state is called as preventive control. To show the effectiveness of the proposed approach, IEEE 9 bus system and modified Jawa Bali 25 Bus system is used. The result shows that the proposed approach is able to secure the operation under both normal and contingency states.

Optimal location and control of FACTS devices for relieving congestion and ensuring voltage stability

This paper proposes an approach for determining optimal locations and capacities of multiple FACTS devices for congestion relief considering voltage stability in deregulated electricity market. The objectives of FACTS allocation are both to minimize annual device investment cost and maximize annual benefit defined as difference between expected security cost (ESQ with and without FACTS installation. The expected cost consists of operating cost under normal state and considered contingencies along with their related probabilities to occur. Maximizing social welfare is the objective for normal state while maximizing social welfare as well as minimizing compensations for generation re-scheduling and load shedding are objectives in case of contingency. The proposed approach is different from previous FACTS devices allocation methods in considering control coordination of multiple FACTS devices as cost free effort for minimizing operating cost and securing the system. Then, generations re-scheduling and load shedding will be subsequently executed as non-cost free effort. The IEEE 14 bus test systems are used to demonstrate the proposed approach.