Congestion management in deregulated power system by series facts device using heuristic optimization algorithms

Conventionally in many countries, electrical power industry is organized as vertically integrated system. Under this system, large utilities are authoritative for the generation, transmission and distribution of electrical power. Such utilities are governed by the rules and regulations of the government and are forced to operate within the prescribed guidelines with minimal profit. This confirmation causes an ineffective and sluggish perspective in power industry with a lack of technical innovation, competent management and customer satisfaction. To overcome these deficiencies, power sector around the globe is getting restructured. This paper addresses an inevitable technical disputes occurring in deregulated environment i.e., transmission congestion which has an adverse effect on system security, increase in electricity pricing and line losses. Flexible AC Transmission System (FACTS) is a boon to the power sector which helps in a better and reliable power flow through the transmission lines. The problem is articulated as a multi objective function satisfying all the operational and security limits. Three heuristic algorithms namely Particle Swarm Optimization (PSO), Symbiotic Organism Search (SOS) and hybrid Quantum based PSO-Bio-geography based krill herd optimization (Q-PSOBBKH) algorithms were applied in finding solution to this complex congestion problem. To study the effectiveness of the proposed objective, IEEE 14 bus system was considered as the test system. In order to validate the proposed methodology three congestion cases i.e. bilateral transaction, multilateral transaction and overloading were imposed on the test bus system. Simulation was carried out in MATLAB.

Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm

The problem of the optimal siting and sizing of photovoltaic (PV) sources in grid connected distribution networks is addressed in this study with a master–slave optimization approach. In the master optimization stage, a discrete–continuous version of the Chu and Beasley genetic algorithm (DCCBGA) is employed, which defines the optimal locations and sizes for the PV sources. In the slave stage, the successive approximation method is used to evaluate the fitness function value for each individual provided by the master stage. The objective function simultaneously minimizes the energy purchasing costs in the substation bus, and the investment and operating costs for PV sources for a planning period of 20 years. The numerical results of the IEEE 33-bus and 69-bus systems demonstrate that with the proposed optimization methodology, it is possible to eliminate about 27% of the annual operation costs in both systems with optimal locations for the three PV sources. After 100 consecutive evaluations of the DCCBGA, it was observed that 44% of the solutions found by the IEEE 33-bus system were better than those found by the BONMIN solver in the General Algebraic Modeling System (GAMS optimization package). In the case of the IEEE 69-bus system, the DCCBGA ensured, with 55% probability, that solutions with better objective function values than the mean solution value of the GAMS were found. Power generation curves for the slack source confirmed that the optimal siting and sizing of PV sources create the duck curve for the power required to the main grid; in addition, the voltage profile curves for both systems show that voltage regulation was always maintained between ±10% in all the time periods under analysis. All the numerical validations were carried out in the MATLAB programming environment with the GAMS optimization package.

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed generators, especially wind and photovoltaic systems, is their intermittent characteristic that results in fluctuating output power and, hence, increasing power system uncertainty. Thus, it is essential to consider the uncertainty of such resources while selecting their optimal allocation within the grid. The main contribution of this study is to figure out the optimal size and location for RDGs in radial distribution systems while considering the uncertainty of load demand and RDG output power. A Monte Carlo simulation approach and a backward reduction algorithm were used to generate a reasonable number of scenarios to reflect the uncertainties of loading and RDG output power. Manta ray foraging optimization (MRFO), an efficient technique, was used to estimate the ratings and placements of the RDGs for a multi-objective function that includes the minimization of the expected total cost, total emissions, and total system voltage deviation, in addition to enhancing predicted total voltage stability. An IEEE 118-bus network was used as a large interconnected network, along with a rural 51-bus distribution grid and the IEEE 15-bus model as a small distribution network to test the developed technique. Simulations demonstrate that the proposed optimization technique effectively addresses the optimal DG allocation problem. Furthermore, the results indicate that using the proposed method to optimally integrate wind turbines with solar-based DG decreases the expected costs, emissions, and voltage deviations while improving voltage stability by 40.27%, 62.6%, 29.33%, and 4.76%, respectively, for the IEEE 118-bus system and enhances the same parameters by 35.57%, 59.92%, 68.95%, and 11.88%, respectively, for the rural 51-bus system and by 37.74%, 61.46%, 58.39%, and 8.86%, respectively, for the 15-bus system.

An Effective Coordination Setting for Directional Overcurrent Relays Using Modified Harris Hawk Optimization

The relay optimization expresses quite a challenge for smooth and optimal operation of power system networks. The relay optimization is formulated as a mixed integer non-linear problem and is highly constrained. Furthermore, a reliable relaying system must be able to detect and isolate the faulted portion in a timely manner. Therefore, it is necessary to find optimal parameters for relay settings to be able to respond in a timely way to the encountered fault and at the same time keep in consideration the operational and coordination constraints. This paper proposes modified Harris hawk optimization (MHHO), which is based on the intelligent preying tactics of Harris hawks and the improvement of intended modifications, crowding distance and roulette wheel selection. The proposed algorithm has been tested on IEEE 8 and 15-bus systems, using MATLAB programming. The test systems are the distribution networks covering the medium level voltage for consideration. The simulation results verified the success of MHHO to find optimal settings for the relays. For IEEE 8-bus system, MHHO was able to give 35.45% improvement in the results in comparison to other algorithms. Furthermore, for the IEEE 15-bus system, MHHO showed 24.09% improvement on average. The comparison of the results obtained by MHHO with the other state-of-the-art algorithms proved that it is the strong candidate for optimization of the relay coordination problem.

Transient stability analysis of IEEE 9-bus system integrated with DFIG and SCIG based wind turbines

Electricity is in high demand with a fast-growing population; hence it is advisable to turn towards green energy. In this research, Wind Turbine (WT) is modelled with two different types of induction generators (IGs), which are the Doubly-Fed Induction Generator (DFIG) and Squirrel-Cage Induction Generator (SCIG) and implemented to IEEE 9-Bus system to assess the transient stability. MATLAB/ Simulink R2019a platform was considered to carry the whole examination. DC1A excitation system was applied to Synchronous Generators (SGs) as well as Power System Stabilizer (PSS). The transmission line7-5 was found to suffer from a high peak value of a relative power angle of approximately 130 degrees. As for the settling time, without PSS it was 20.69 s and with PSS it became 6.23 s. A wind farm with a rated capacity of 60 MW was used in the system. WT integrated with DFIG has the lowest peak value of 127 degrees at Bus locations 4 and 5 and for SCIG, Bus 5 with a peak value of 136 degrees. Thus, it can be propelled as the perfect location. Moreover, this is due to the three-phase fault was located at the transmission line7-5 which is far away from Buses 4 and 5. In the end, the WT integrated with DFIG provides a lower peak value of relative power angle compared to SCIG, whereas for settling time, it is the opposite.

Comparison of Voltage Regulation of a Smart Load for 3 Bus System and 15 Bus System under High Penetration of Wind Energy System

Modern study about utilizing energy from renewable energy sources was stimulus due to emerging oil crisis in older days due to uncontrolled use of conventional energy sources. Renewable Power Generation from wind and solar energy has become a significant proportion for the overall power generation in the grid. High penetration of Renewable Power Generation (RPG’s) effectreliable operation of bulk power system due to fluctuation of frequency and voltage of the network. The main objectives of high penetration of Renewable Power Generations in distribution system are Regulation of voltage, Mitigating voltage fluctuations due to flickers and Frequency control. The design and control of voltage regulation system using smart loads (SL’s) under large penetration of renewable energy system in distribution level is to be studied with the help of FACT devices like Static Compensator (STATCOM) and It is one of the fast active devices with accurate voltage regulation capability and most importantly for the sensitive/critical loads. Electric spring (ES) is proposed as compelling technique for guideline of framework voltage under fluctuating RPG's with next to no guide of correspondence framework [1]. It is a converter-based framework with self-commutated switches in span design, which is associated with non-basic burdens in series to go about as savvy load. These Smart Loads are controlled to direct voltage across basic burdens and hence partaking popular side administration. Expanded entrance of RPG’s, basically factor speed wind energy transformation framework is having impact on voltage and power quality [1][2]. In this paper, A contextual analysis of impact of variable speed wind energy framework on voltage is completed and which is demonstrated with fluctuating breeze speed. Execution examination of keen burdens are to be contrasted and existing receptive power compensator burdens and Improvement in voltage profile on test feeder is directed on a 3 Bus system and 15 Bus system. Keywords: Renewable energy system (RES), Electric spring (ES), STATCOM, Voltage Flicker, Smart load

Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network

This paper introduces a framework for optimal placement (OP) of phasor measurement units (PMUs) using metaheuristic algorithms in a distribution network. The voltage magnitude and phase angle obtained from PMUs were selected as the input variables for supervised learning-based pseudo-measurement modeling that outputs the voltage magnitude and phase angle of the unmeasured buses. For three, four, and five PMU installations, the metaheuristic algorithms explored 2000 combinations, corresponding to 40.32%, 5.56%, and 0.99% of all placement combinations in the 33-bus system and 3.99%, 0.25%, and 0.02% in the 69-bus system, respectively. Two metaheuristic algorithms, a genetic algorithm and particle swarm optimization, were applied; the results of the techniques were compared to random search and brute-force algorithms. Subsequently, the effects of pseudo-measurements based on optimal PMU placement were verified by state estimation. The state estimation results were compared among the pseudo-measurements generated by the optimal PMU placement, worst PMU placement, and load profile (LP). State estimation results based on OP were superior to those of LP-based pseudo-measurements. However, when pseudo-measurements based on the worst placement were used as state variables, the results were inferior to those obtained using the LP.