Journal of Operation and Automation in Power Engineering
Volume:9 Issue: 1, Spring 2021

In this paper, a model for hybrid transmission expansion planning (TEP) and reactive power planning (RPP) considering demand response (DR) model has been presented. In this study RPP considered by TEP for its effects on lines capacity and reduction of system expansion costs. On the other hand the expansion of the transmission system is an important subject, especially dealing with the new issues of smart networks like as demand response. Demand response program can change the network expansion planning by shifting elasticity loads and reducing of peak load to improve conditions and decrease the costs. To combine demand response model into the transmission expansion planning and reactive power planning, nonlinear mixed integer meta-heuristic optimization algorithm is used. To evaluate the impact of the proposed expansion planning, this model is exerted to the 30-bus test system. Simulation outcomes display the proposed technique considering demand response model reduces the overall cost of the hybrid TEP-RPP.

In this work, a new control scheme for synchronization of AC microgrids with upstream power grid is presented. The effects of V2Gs (vehicle to grid) dynamics on synchronization process is studied. This new control approach is based on the optimal fractional calculus and has been developed for synchronization of the microgrid. The V2Gs effect on the dynamics of the microgrid is analyzed through small signal stability and simulations. This effect is also considered in synchronization process by considering a PHEV-dominated-microgrid. The proposed control scheme is a coordinated control of distributed resources and provides a soft and reliable synchronization for microgrid. In the proposed control scheme, the fractional order proportional-integral-derivative (FOPID) controllers have optimally been tuned and implemented using the genetics algorithm (GA). The simulation results confirm the effectiveness of the proposed control strategy in soft and swift synchronization of the microgrid.

In this paper, the theory and modeling of large scale photovoltaic (PV) in the power grid and its effect on power system stability are studied. In this work, the basic module, small signal modeling and mathematical analysis of the large scale PV jointed multi-machine are demonstrated. The principal portion of the paper is to reduce the low frequency fluctuations by tuned stabilizer in the attendance of the PV unit. In order to optimize the system performance, a novel optimal fuzzy based fractional order PID ( ) stabilizers are proposed to meliorate small signal stability of a power network connected PV unit. For optimizing the performance of the system,  is exploited by Gray Wolf Optimization (GWO) algorithm. In order of evaluation of the proposed stabilizers performance, two different types of controllers are compared, include optimal classic stabilizer and  based particle swarm optimization (PSO) algorithm. The superiority of  based GWO on improving small signal stability in the studied system, including large scale PV is shown via time-domain simulations.

Microgrid and smart electrical grids are among the new concepts in power systems that support new technologies within themselves. Electric cars are some advanced technologies that their optimized use can increase grid efficiency. The modern electric cars sometimes, through the necessary infrastructure and proper management, can serve as an energy source to supply grid loads. This study was conducted to investigate the energy management for production and storage resources. For this purpose, we considered the market price of energy, the prices quoted by distributed generation sources, and electric vehicles in the grid and responsive loads. The load response programs used include the time of use and direct load control. The problem has a linear mixed-integer planning structure that was simulated using the GAMS software. The results show that with this planning, the proposed load response programs have a positive impact on cost reduction.

A new single-phase transformerless grid-connected PV inverter is presented in this paper. Investigations in transformerless grid-connected PV inverters indicate the existence of the leakage current is directly related to the variable common-mode voltage (CMV), which is presented in detail. On the other hand, in recent years it has become mandatory for the transformerless grid-connected PV inverters to satisfy new grid-codes such as low-voltage ride-through (LVRT) capability via injecting reactive power during grid faults. Therefore, in this paper, the design of the proposed topology is based on retaining the constant CMV to suppress the leakage current and also to provide reactive power injection capability during grid faults. The control strategies for injecting reactive power in the LVRT condition are also examined. To validate the presented theoretical concepts, the performance and dynamic response of the proposed transformerless PV inverter are investigated by MATLAB/Simulink and the simulation results are presented and discussed.

This study presents an optimal framework for the operation of integrated energy systems using demand response programs. The main goal of integrated energy systems is to optimally supply various demands using different energy carriers such as electricity, heating, and cooling. Considering the power market price, this work investigates the effects of multiple energy storage devices and demand response programs, including the time of use pricing, real-time pricing, and integrated demand response on optimal operation of energy hub. Moreover, impacts of different optimization methods are evaluated on the optimal scheduling of multi-carrier energy systems. Maximizing profits of selling electrical energy and minimizing the purchasing cost of input carrier energies are considered as objective functions to indicate bidirectional interchanges of energy hub systems with the power grid. To minimize the generation cost of energy carriers, a new quadratic objective function is also optimized using genetic algorithm. In this study, optimal operation of the energy hub based on the proposed quadratic objective function is an economic dispatch problem where the purchasing electrical power by the energy hub is considered as a load of the upstream grid. The optimization problem is implemented in the sample energy hub to indicate the effectiveness of different energy storage roles and applied demand response programs in the optimal operation of energy hub systems.

Unit commitment (UC) problem tries to schedule output power of generation units to meet the system demand for the next several hours at minimum cost. UC adds a time dimension to the economic dispatch problem with the additional choice of turning generators to be on or off.  In this paper, in order to improve both the exploitation and exploration abilities of the firefly algorithm (FA), a new modification approach based on the mutation and crossover operators as well as an adaptive formulation is applied as an adaptive modified firefly algorithm (AMFA). In this paper, it is shown that AMFA can solve the UC problem in a better manner compared to the other meta-heuristic methods. The method is applied on some case studies, a typical 10-unit test system, 12, 17, 26, and 38 generating unit systems, and IEEE 118-bus test system, all with a 24-hour scheduling horizon. Comparison of the obtained results with the other methods addressed in the literature shows the effectiveness and fastness of the applied method.

The increment integration of renewable distributed energies means the desired operation of the electric power system will significantly depend on the performance of primary energy. In this order, an integrated approach for mutual interaction between the electricity and natural gas systems has been considered for the purpose of ensuring optimal energy exchanging between the electric power system and the natural gas network. We propose a scenario based optimal operation approach to optimize the operation of integrated power and gas systems (IPGS). Regarding the unpredictable nature of wind speed and solar radiation as well as uncertain load demand, random scenarios are generated by a normal probability density function. Then, Latin hypercube sampling is applied to realize the stochastic framework of IPGS operation. The proposed model minimizes the operation cost of conventional power system generators and gas wells over a 24 h operation horizon. In addition, the conditional value-at-risk is utilized to manage financial risks and uncertainties due to the operation cost-minimizing in the proposed IPGS optimal operation problem. The proposed integrated operating approach is applied to a 24-Bus power system with renewable resources of a photovoltaic, wind turbine, energy storage, with a 7-node natural gas network and two gas wells.