Jamshid Aghaei

Stochastic Multiobjective Market Clearing of Joint Energy and Reserves Auctions Ensuring Power System Security

In this paper, a stochastic multiobjective framework is proposed for day-ahead joint market clearing. The proposed multiobjective framework can concurrently optimize competing objective functions including augmented generation offer cost and security indices (overload index, voltage drop index, and voltage stability margin). Besides, system uncertainties including generating units and branches contingencies and load uncertainty are explicitly considered in the stochastic market clearing scheme. The solution methodology consists of two stages, which firstly, employs roulette wheel mechanism and Monte Carlo simulation (MCS) for random adaptive 24-h scenario generation wherein the stochastic multiobjective market clearing procedure is converted into its respective deterministic equivalents (scenarios). In the second stage, for each deterministic scenario, a multiobjective mathematical programming (MMP) formulation based on the epsiv -constrained approach is implemented for provision of spinning reserve (SR) and nonspinning reserve (NSR) as well as energy. The MMP formulation of the market clearing process is optimized while meeting AC power flow constraints and expected interruption cost (EIC). The IEEE 24-bus Reliability Test System (RTS 24-bus) is used to demonstrate the performance of the proposed method.

A new modified teaching-learning algorithm for reserve constrained dynamic economic dispatch

This paper presents a new optimization algorithm, named modified teaching-learning algorithm, to solve a more practical formulation of the reserve constrained dynamic economic dispatch of thermal units considering the network losses and operating limitations of the generating units (i.e., the valve loading effect and ramp rate limits). Unlike the previous approaches, three types of the system spinning reserve requirements are explicitly modeled in the problem and a new constraint-handling is proposed to satisfy them. The proposed teaching-learning optimization algorithm is a new population-based optimization method features between the teacher and learners (students). Therefore, this algorithm searches for the global optimal solution through two main phases: 1) the “teacher phase” and 2) the “learner phase”. Nevertheless, these two phases are not adequate for learning interaction between the teacher and the learners in the entire search space. Thus, in this paper a new phase named “modified phase” based on a self-adaptive learning mechanism is added to the algorithm to improve the process of knowledge learning among the learners and accordingly generate promising candidate solutions. The proposed framework is applied to 5-, 10-, 30-, 40-, and 140-unit test systems in order to evaluate its efficiency and feasibility.

Scenario-Based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources

This paper proposes a stochastic multiobjective framework for daily volt/var control (VVC), including hydroturbine, fuel cell, wind turbine, and photovoltaic powerplants. The multiple objectives of the VVC problem to be minimized are the electrical energy losses, voltage deviations, total electrical energy costs, and total emissions of renewable energy sources and grid. For this purpose, the uncertainty related to hourly load, wind power, and solar irradiance forecasts are modeled in a scenario-based stochastic framework. A roulette wheel mechanism based on the probability distribution functions of these random variables is considered to generate the scenarios. Consequently, the stochastic multiobjective VVC (SMVVC) problem is converted to a series of equivalent deterministic scenarios. Furthermore, an Evolutionary Algorithm using the Modified Teaching-Learning-Algorithm (MTLA) is proposed to solve the SMVVC in the form of a mixed-integer nonlinear programming problem. In the proposed algorithm, a new mutation method is taken into account in order to enhance the global searching ability and mitigate the premature convergence to local minima. Finally, two distribution test feeders are considered as case studies to demonstrate the effectiveness of the proposed SMVVC.

Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method

In this paper, a new multi-objective model for electricity market clearing, considering both voltage and dynamic security aspects of the power system, is proposed. The objective functions of the proposed model include offer cost, voltage stability margin (VSM) and corrected transient energy margin (CTEM). A new solution method incorporating the lexicographic optimization and augmented @?-constraint method is proposed to solve the multi-objective optimization problem. The New England test system is used to demonstrate the performance of the proposed method. The proposed strategy is also compared with the conventional @?-constraint technique.

Mixed integer programming of multiobjective hydro-thermal self scheduling

This paper presents a method for hydro-thermal self scheduling (HTSS) problem in a day-ahead joint energy and reserve market. The HTSS is modeled in the form of multiobjective framework to simultaneously maximize GENCOs profit and minimize emissions of thermal units. In the proposed model the valve loading effects which is a nonlinear problem by itself is linearized. Also a dynamic ramp rate of thermal units is used instead of a fix rate leading to more realistic formulation of HTSS. Furthermore, the multi performance curves of hydro units is developed and prohibited operating zones (POZs) of thermal unit are considered in HTSS problem. Also, in the proposed framework, the mixed integer nonlinear programming (MINLP) of HTSS is converted to mixed integer programming (MIP) problem that can be effectively solved by optimization softwares even for real size power systems. The lexicographic optimization and hybrid augmented-weighted @?-constraint technique is implemented to generate Pareto optimal solutions. The best compromised solution is adopted either by using a fuzzy approach or by considering arbitrage opportunities to achieve more profit. Finally, the effectiveness of the proposed method is studied based on the IEEE 118-bus system.

Probabilistic PMU Placement in Electric Power Networks: An MILP-Based Multiobjective Model

This paper presents a multiobjective probabilistic model for the placement of phasor measurement units (PMUs) in electrical power networks. The proposed model simultaneously optimizes two objectives functions: 1) minimizes the number of PMUs; and 2) maximizes the expected value of systems redundancy (or minimizes the unobservability of the system). Incorporating the impact of zero-injection buses, an efficient formulation is used to evaluate the probability of unobservability of buses resulted from line outages and PMU loss. The extracted formulation is based on the mixed integer linear programming (MILP) framework, which is efficiently solvable by high-performance commercial solvers. This multiobjective optimization problem (MOP) is solved by augmented epsilon-constraint and weighting approach. Accordingly, two new indices are introduced to compare these two multiobjective methods. Finally, the ultimate solution among the Pareto front is recognized using a fuzzy decision-making process. The IEEE 57-bus test system is used to examine the effectiveness of the proposed frameworks.

Solving optimal reactive power dispatch problem using a novel teaching-learning-based optimization algorithm

Abstract The paper presents a novel teaching–learning-based optimization (TLBO) algorithm, the Gaussian bare-bones TLBO (GBTLBO) algorithm, with its modified version (MGBTLBO) for the optimal reactive power dispatch (ORPD) problem with discrete and continuous control variables in the standard IEEE power systems for reduction in power transmission loss. The feasibility and performance of the GBTLBO and MGBTLBO algorithms are demonstrated for standard IEEE 14-bus and standard IEEE 30-bus systems. A comparison of simulation results reveals optimization efficacy of the GBTLBO and MGBTLBO algorithms over other well established other algorithms like bare-bones differential evolution (BBDE) and bare-bones particle swarm optimization (BBPSO) algorithm. Results for ORPD problem demonstrate superiority in terms of solution quality of the GBTLBO and MGBTLBO algorithms over original TLBO algorithm and other algorithm.

Generation and Transmission Expansion Planning: MILP–Based Probabilistic Model

This paper describes a new probabilistic model for generation and transmission expansion planning (G&TEP) problem considering reliability criteria. Probabilistic reliability criteria accounts for random generator or line outages with known historical forced outage rates (FOR). The resultant model considers the installation and operation costs as well as the cost of expected energy not supplied (EENS) to optimally determine the number and location of new generating units and circuits in the network, power generation capacity for those units and the voltage phase angle at each node. Also, efficient linear formulations are introduced in this paper to deal with the nonlinear nature of the problem including objective functions and constraints. Modified 6-bus test system, IEEE 24-bus RTS and IEEE 118-bus test system are utilized to illustrate the effectiveness of the proposed framework.

Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents

With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based demand response programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.

Optimal Placement of UPFC in Power Systems Using Genetic Algorithm

In this paper GA is used for determining optimal location of UPFC in the power system. Optimal location in this paper is meant finding line number for UPFC location and its parameters for specified number of UPFCs. Unlike other FACTS devices, UPFC has great flexibility that can control the active and reactive power flow and bus voltages, simultaneously. The system loadability is applied as measure of power system performance. Decoupled model of UPFC is used in simulation. The concept of simulation is the load flow incorporated with UPFC. Studies are applied on the 14-buses IEEE network. The results have shown that steady state performance of power system can be effectively enhanced due to the optimal location and parameters of UPFC.