Stochastic Optimal Operation of Microgrid Based on Chaotic Binary Particle Swarm Optimization

Abstract

Based on fuzzy mathematics theory, this paper proposes a fuzzy multi-objective optimization model with related constraints to minimize the total economic cost and network loss of microgrid. Uncontrollable microsources are considered as negative load, and stochastic net load scenarios are generated for taking the uncertainty of their output power and load into account. Cooperating with storage devices of the optimal capacity controllable microsources are treated as variables in the optimization process with the consideration of their start and stop strategy. Chaos optimization algorithm is introduced into binary particle swarm optimization (BPSO) to propose chaotic BPSO (CBPSO). Search capability of BPSO is improved via the chaotic search approach of chaos optimization algorithm. Tests of four benchmark functions show that the proposed CBPSO has better convergence performance than BPSO. Simulation results validate the correctness of the proposed model and the effectiveness of CBPSO.

Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval

“…Particle swarm is a well-known global optimization technique which mimics the bird flocking approach like other swarm optimization techniques. All birds are looking for food (min/max objective) in different directions, and a bird close to the food will be followed (distance and velocity are set on every step) by the swarm and finally, it converges on the solution in [30]. where Vel, p, and x are the velocity, best position, and position, respectively, for each particle at the t interval.…”

“…Finally, an Intel i5, 2.53 GHz quad-core processor with 4 GB RAM laptop is used to compute the results of this study. Get Initial Parameters (fuzzy model weights, cost function, variables count, variables range, max iteration, population size, inertial weight and damping ratio, personal and global learning coefficient) for 1 to population size do Initialize the position [30], velocity, personal and global best of each particle based on the cost function. end for repeat for 1 to population size do Update velocity of a particle using (9) Update position, velocity and cost of each particle Apply minor limits for the velocity of each particle if particle cost ≤ particle best cost then update personal best if particle best cost ≤ best solution cost then Update global best end if end if end for Best cost of iteration ← best solution cost Update inertial weight until Maximum iteration & minimum error achieve Algorithm 1 6 International Journal of Photoenergy energy prediction is defined by clustering approach which is mentioned in Section 3. where covariance of past error Cov e r−1 is integrated with the fuzzy interval of input and error as Cov TS wind .…”

Hierarchical Energy Management of Microgrids including Storage and Demand Response

“…References [2][3][4] proposed various stochastic optimization models to minimize the expected operation cost of MG. Tavakolia et al [5] presented a CVaR-based energy management strategy to decide the optimum balance between the operation cost and grid resilience for commercial building MG, in which the CVaR was solved by a scenario-based method. In [6], a fuzzy chance constrained unit commitment model was established, in which the uncertainty of RES was described as fuzzy parameters.…”

Optimizing insuring critical path problem under uncertainty based on GP-BPSO algorithm

“…The BPSO algorithm, proposed by two experts [32] in 1997, has been well pullulated in the literature [33][34][35][36][37][38], and the modified and improved BPSO successfully are employed to the substantial programming problems. For example, Lee et al [33] studied amendments and improvements of original BPSO.…”

“…Beheshti et al [35] introduced memetic BPSO to solve discrete optimization problems. Li et al [36] used chaotic BPSO for stochastic optimal operation of microgrid.…”