Maryam Ramezani

Probabilistic Assessment of TTC in Power Systems Including Wind Power Generation

Wind farms (WFs) are now being used for electric power generation in some parts of the world and their penetration is expected to increase significantly. With increasing penetration of WFs in electric power systems, modification of current tools for evaluating and managing the system is an important issue. Determination of total transfer capability (TTC) is one such tool and is of considerable importance in the restructured power systems. TTC is used to calculate maximum power that can be transferred between areas in a reliable manner and schedule future transactions and commercial activities. In this paper, a method is proposed for TTC evaluation in the presence of WFs. In the proposed approach, Monte Carlo method is used to simulate a system state and optimal power flow is used to calculate the TTC level for each state. Risk analysis is used as a decision-making tool to determine the appropriate TTC level. IEEE reliability test system is used to demonstrate the effectiveness of the proposed approach.

A Deterministic Approach for Probabilistic TTC Evaluation of Power Systems Including Wind Farm Based on Data Clustering

Transfer capability of an electric transmission network indicates the maximum real power that can be exchanged between two areas in a reliable manner. This index is used in power system planning, operation and marketing. Transfer capability depends on the system state and is not a fixed value. Utilization of energy resources with stochastic nature adds new probabilistic dimension to the power system and makes total transfer capability (TTC) study more complex. Wind is one of the important alternative renewable energy resources for electric energy production but its speed is stochastic in nature. Increasing penetration of these resources is a motivation for research to study different aspects of this issue. This paper proposes a method for probabilistic evaluation of TTC in the presence of wind farms. It proposes a hybrid method based on data clustering and contingency enumeration. Contingency enumeration is done using two contingency lists (CLs). Single and double contingencies are established the first CL and the second CL considers a number of contingencies from the first CL. Monte Carlo simulation (MCS) is also used to assess probabilistic variation of TTC and to verify the proposed method. The obtained probabilistic TTC results have acceptable relative error compered to MCS results. Especially when the probabilities of contingencies are low the second approach works very well. The efficiency of the proposed method is investigated by conducting numerical studies on the IEEE Reliability Test System.

A Stochastic Bilevel Model for the Energy Hub Manager Problem

A bilevel stochastic programming problem (BSPP) model of the decision-making of an energy hub manager is presented. Hub managers seek ways to maximize their profit by selling electricity and heat. They have to make decisions about: 1) the level of involvement in forward contracts, electricity pool markets, and natural gas networks and 2) the electricity and heat offering prices to the clients. These decisions are made under uncertainty of pool prices, demands as well as the prices offered by rival hub managers. On the other hand, the clients try to minimize the total cost of energy procurement. This two-agent relationship is presented as a BSPP in which the hub manager is placed in the upper level and the clients in the lower one. The bilevel scheme is converted to its equivalent single-level scheme using the Karush–Kuhn–Tucker optimality conditions although there are two bilinear products related to electricity and heat. The heat bilinear product is replaced by a heat price-quota curve and the electricity bilinear product is linearized using the strong duality theorem. In addition, conditional value at risk is used to reduce the unfavorable effects of the uncertainties. The effectiveness of the proposed model is evaluated in various simulations of a realistic case study.

A new integer linear programming approach for multi-stage PMU placement

Nowadays, by increasing the utilization of phasor measurement units (PMUs) in power system, it is clearly expected that the PMUs play a vital role in smart transmission grid. In the reality, power systems are large scale, accordingly, financial limitations (due to PMU cost) and technical problems are impeding installation of all the required PMUs in a short time. Therefore, the PMUs are usually installed in several time stages. This paper proposes a novel multi-stage PMU placement approach based on integer linear programming (ILP) for the sake of power system observability enhancement in horizon years. In this approach, PMUs are chosen from predefined locations for all stages in a single optimization process, dependently; while the conventional methods use a subsidiary independent optimization process for each stage. The proposed approach is conducted on IEEE standard test systems as well as Iranian 230- and 400-kV transmission network. Finally, in order to verify the efficiency of the proposed method, the obtained results are compared with those of previous researches.

Capacity Benefit Margin Evaluation in Multi-area Power Systems Including Wind Power Generation Using Particle Swarm Optimization

Available transfer capability (ATC) is an index showing the measure of transfer capability remaining in the physical transmission network over and above already existing transactions. To determine ATC between two areas in a multi-area power system, different parameters such as total transfer capability (TTC), transmission reliability margin (TRM), and capacity benefit margin (CBM) should be calculated. CBM ensures security of system operation when the system faces generation deficiency in some areas. The presence of wind turbine generators (WTGs) in multi-area power systems creates new challenges in CBM calculation process. In this chapter, three different methods are proposed for CBM evaluation considering WTG which reflect different objectives. In the proposed methods, CBM determination is formulated as an optimization problem and Particle Swarm Optimization method is used to solve the problem. The numerical results for modified IEEE reliability test system are presented to demonstrate the effectiveness of the proposed approaches.

Voltage control of distribution networks using fuzzy approach and capacitors offline planning

This paper proposes a new approach to real time control of the bus voltages in the distribution systems. This approach consists of two control parts; includes offline and online control. In the offline part, switchable capacitors are scheduled based on day-ahead load forecasting. This step is solved using an efficient coding PSO algorithm. The under load tap changer (ULTC) is not a control variable in the offline scheduling and is only operated to improve voltage based on fuzzy approach. Once the switchable capacitors are scheduled, they are fixed on their hourly position in the real time operation. Then the ULTC is controlled based on the fuzzy system in the real time operation of the network. Easy implementation of the offline scheduling due to elimination of the ULTC as a control variable, removal of switching operation constraint and removal approximately of the voltage constraint is the main advantage of the proposed method. In addition, self-healing and possibility control of the bus voltages in different conditions such as unpredictable load changes and contingencies are other benefits. The 69 bus IEEE test system has been used to analyze and validation of the proposed approach.