Weight Tradeoff Particle Swarm Optimization Research Articles

Microgrid Energy Management Combining Sensitivities, Interval and Probabilistic Uncertainties of Renewable Generation and Loads

This paper proposes an optimal energy management approach combining sensitivities, interval, and probabilistic uncertainties of wind and solar power sources and loads in microgrid. Affine arithmetic (AA) is used to model the interval uncertainties and sensitivities in nodal power injections. However, all the elements in the interval solutions of AA-optimal power flow may not be significant in view of the probabilistic nature of statistical data. So, those elements which are significant with a desired confidence level are boxed using probability boxes obtained by deriving best fitting discrete state probability distribution functions (PDFs) for load and renewable power injections. Thus, the original hard bounded affine intervals are made soft bounded using the derived joint PDFs, forming new less conservative and more feasible intervals of cost and power flow variables. The minimization of the operational cost is taken care of by stochastic weight tradeoff particle swarm optimization. The method is tested in CIGRE LV benchmark microgrid with fuel cell, microturbine, diesel generator, wind, and solar power sources.

Sortino Ratio Based Portfolio Optimization Considering EVs and Renewable Energy in Microgrid Power Market

Portfolio optimization in finance is the optimal allocation of financial assets in different stocks, mutual funds, bonds, etc. to maximize the returns with risk tolerance. Sortino ratio is a measure for calculating risk adjusted return of investment portfolios. Here, it is adapted for power portfolio optimization in microgrid where total load demand (including losses) is optimally distributed to different microsources so that profit per unit risk of aggregator is maximized. The diminishment in profit (from energy and reserve markets) with reference to a target profit, for different levels of uncertainties in renewable energy and electric vehicles (EVs), is consolidated to find an estimate of risk. The profit relating to deterministic forecasted data of renewable energy and pre-dispatch information from the EV parking lots is considered as the risk free target profit. The reserve market is balanced using demand response, grid power purchase, EV discharging, and other dispatchable energy sources to compensate possible discrepancy between scheduled and actual dispatch. Stochastic weight tradeoff particle swarm optimization (SWT-PSO) is used to maximize the Sortino ratio subjected to constraints of a modified backward--forward sweep (BFS) power flow problem. The results are found to be better in terms of reduced financial risk and increased robustness to uncertainties.

Economic and network feasible online power management for renewable energy integrated smart microgrid

Abstract In this paper, an online power management (OPM) problem is proposed using measured/short term forecasted data. It improves the cost and dynamic performance of microgrid along with the consideration of interval uncertainties in renewable energy and loads. Additional options of grid power trade and demand response (DR) are provided for operational cost reduction, consumer participation and enhanced network performance. A combination of stochastic weight tradeoff particle swarm optimization (SWT-PSO) and interval arithmetic (IA) is proposed to analyze the effects of interval uncertainties of nodal power injections (due to renewable energy sources (RES) and loads) on microgrid cost and power flow variables. The effectiveness of the proposed approach is investigated by adding different power balancing resources one by one, to the residential feeder of CIGRE LV benchmark microgrid. The results are found to be improved in terms of reduction in fuel and emission costs, improved nodal voltages and network feasible OPF solution in interval forms corresponding to system volatilities. Moreover, settling time of online dispatch is reduced, thereby improving the dynamic response of distributed energy resources (DERs). In addition, the paper justifies the use of RES, DR, grid power trade (over islanded mode) and SWT-PSO (over priority listing).

Performance enhancement of online energy scheduling in a radial utility distribution microgrid

In this paper, an optimal power dispatch formulation is proposed in the operating horizon using real time measurements to enhance the performance of online energy scheduling in microgrid considering renewable energy, grid power trade and demand response. A stochastic weight tradeoff particle swarm optimization (SWT-PSO) based backward–forward sweep (BFS) optimal power flow (OPF) method is used by the microgrid central controller (MGCC) to obtain the online optimal schedules of distributed energy resources (DERs). The effectiveness of the suggested approach is investigated on the residential feeder of the CIGRE LV benchmark microgrid in terms of reduction in fuel & emission costs and voltage deviation. In addition, settling time in overall implementation is drastically reduced. Also, the paper analytically justifies the use of (1) renewable energy sources, grid trade and prioritized load curtailment, (2) SWT-PSO over priority listing method and (3) online OPF over online economic dispatch (ED).

An efficient two stage stochastic optimal energy and reserve management in a microgrid

In this paper, an efficient two stage stochastic optimal energy and reserve management approach is proposed for a microgrid. In the first stage, the optimal power schedule is determined based on the load, wind and solar power forecasts. The possible uncertainties in forecasts are expressed as perturbations in nodal power injections and the corresponding optimal spinning reserves are estimated using sensitivity analysis. Using this information system, the actual spinning reserve for the discrepancy between the measured and forecasted data is directly dispatched at stage-2, utilizing the remaining capacity of demand response, grid purchase and other non-renewable distributed energy resources (DERs). A stochastic perturbed optimal power flow (OPF) based on affine arithmetic (AA) and stochastic weight tradeoff particle swarm optimization (SWT-PSO) is proposed and investigated on CIGRE LV benchmark microgrid. The approach is found to be better in terms of operational planning, real time computation and bounds of power flow & cost variables.

Stochastic Weight Trade-Off Particle Swarm Optimization for Optimal Power Flow

This paper proposes a stochastic weight trade-off particle swarm optimization (SWT-PSO) method solving optimal power flow (OPF) problem. The proposed SWT- PSO is a new improvement of PSO method using a stochastic weight trade-off for enhancing search its search ability. The proposed method has been tested on the IEEE 30 bus and 57 bus systems and the obtained results are compared to those from other methods such as conventional PSO, genetic algorithm (GA), ant colony optimization (ACO), evolutionary programming (EP), and differential evolution (DE) methods. The numerical results have indicated that the proposed SWT-PSO method is better than the others in terms of total fuel costs, total loss and computational times. Therefore, the proposed SWT-PSO method can be a favorable method for solving OPF problem. 

Stochastic weight trade-off particle swarm optimization for nonconvex economic dispatch

This paper proposes a stochastic weight trade-off particle swarm optimization (SWT_PSO) for solving nonconvex economic dispatch. The proposed SWT_PSO preserves the balance between global exploration and local exploitation to improve the algorithm search capabilities. The balance is retained through trading off stochastic weights amongst previous velocity momentum, cognitive and social components together with using dynamic acceleration coefficients trade-off. The mechanisms for increasing diversity of swarm members are also incorporated to avoid premature convergence. In addition, the novel stochastic trade-off momentum control factor is exploited to enhance the capability of refining quality of a candidate solution during the late search process. The proposed SWT_PSO is tested on four economic dispatch test systems. Test results demonstrate that the proposed approach yields better solution quality than the best reported results in the literature for all test systems.