This paper proposes a new binary particle swarm optimization (BPSO) approach inspired by quantum computing, namely quantum-inspired BPSO (QBPSO). Although BPSO-based approaches have been successfully applied to the combinatorial optimization problems in various fields, the BPSO algorithm has some drawbacks such as premature convergence when handling heavily constrained problems. The proposed QBPSO combines the conventional BPSO with the concept and principles of quantum computing such as a quantum bit and superposition of states. The QBPSO adopts a Q-bit individual for the probabilistic representation, which replaces the velocity update procedure in the particle swarm optimization. To improve the search capability of the quantum computing, this paper also proposes a new rotation gate, that is, a coordinate rotation gate for updating Q-bit individuals combined with a dynamic rotation angle for determining the magnitude of rotation angle. The proposed QBPSO is applied to unit commitment (UC) problems for power systems which are composed of up to 100-units with 24-h demand horizon.

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A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems

A new evolutionary algorithm known as the shuffled frog leaping algorithm is presented in this paper, to solve the unit commitment (UC) problem. This integer-coded algorithm has been developed to minimize the total energy dispatch cost over the scheduling horizon while all of the constraints should be satisfied. In addition, minimum up/down-time constraints have been directly coded not using the penalty function method. The proposed algorithm has been applied to ten up to 100 generating units, considering one-day and seven-day scheduling periods. The most important merit of the proposed method is its high convergence speed. The simulation results of the proposed algorithm have been compared with the results of algorithms such as Lagrangian relaxation, genetic algorithm, particle swarm optimization, and bacterial foraging. The comparison results testify to the efficiency of the proposed method.

Unit Commitment Problem Solution Using Shuffled Frog Leaping Algorithm

Particle swarm optimization algorithm is a inhabitant-based stochastic search procedure, which provides a populace-based search practice for getting the best solution from the problem by taking particles and moving them around in the search space and efficient for global search. Grey Wolf Optimizer is a recently developed meta-heuristic search algorithm inspired by Canis-lupus. This research paper presents solution to single-area unit commitment problem for 14-bus system, 30-bus system and 10-generating unit model using swarm-intelligence-based particle swarm optimization algorithm and a hybrid PSO---GWO algorithm. The effectiveness of proposed algorithms is compared with classical PSO, PSOLR, HPSO, hybrid PSOSQP, MPSO, IBPSO, LCA---PSO and various other evolutionary algorithms, and it is found that performance of NPSO is faster than classical PSO. However, generation cost of hybrid PSO---GWO is better than classical and novel PSO, but convergence of hybrid PSO---GWO is much slower than NPSO due to sequential computation of PSO and GWO.

A novel hybrid PSO---GWO approach for unit commitment problem

The prospective spread of electric vehicles (EV) and plug-in hybrid EV raises the need for fast charging rates. High required charging rates lead to high power demands, which may not be supported by the grid. In this paper, an optimal power flow technique of a PV-battery powered fast EV charging station is presented to continuously minimize the operation cost. The objective is to help the penetration of PV-battery systems into the grid and to support the growing need of fast EV charging. An optimization problem is formulated along with the required constraints and the operating cost function is chosen as a combination of electricity grid prices and the battery degradation cost. In the first stage of the proposed optimization procedure, an offline particle swarm optimization (PSO) is performed as a prediction layer. In the second stage, dynamic programming (DP) is performed as an online reactive management layer. Forecasted system data is utilized in both stages to find the optimal power management solution. In the reactive management layer, the outputs of the PSO are used to limit the available state trajectories used in the DP and, accordingly, improve the system computation time and efficiency. Online error compensation is implemented into the DP and fed back to the prediction layer for necessary prediction adjustments. Simulation and 1 kW prototype experimental results are successfully implemented to validate the system effectiveness and to demonstrate the benefits of using a hybrid grid tied system of PV-battery for fast EVs charging stations.

Power Flow Management of a Grid Tied PV-Battery System for Electric Vehicles Charging

The unit commitment problem belongs to the class of complex large scale, hard bound and constrained optimization problem involving operational planning of power system generation assets. This paper presents a heuristic binary approach to solve unit commitment problem (UC). The proposed approach applies Binary Grey Wolf Optimizer (BGWO) to determine the commitment schedule of UC problem. The grey wolf optimizer belongs to the class of bio-inspired heuristic optimization approaches and mimics the hierarchical and hunting principles of grey wolves. The binarization of GWO is owing to the UC problem characteristic binary/discrete search space. The binary string representation of BGWO is analogous to the commitment and de-committed status of thermal units constrained by minimum up/down times. Two models of Binary Grey Wolf Optimizer are presented to solve UC problem. The first approach includes upfront binarization of wolf update process towards the global best solution (s) followed by crossover operation. While, the second approach estimates continuous valued update of wolves towards global best solution(s) followed by sigmoid transformation. The Lambda-Iteration method to solve the convex economic load dispatch (ELD) problem. The constraint handling is carried out using the heuristic adjustment procedure. The BGWO models are experimented extensively using various well known illustrations from literature. In addition, the numerical experiments are also carried out for different circumstances of power system operation. The solution quality of BGWO are compared to existing classical as well as heuristic approaches to solve UC problem. The simulation results demonstrate the superior performance of BGWO in solving UC problem for small, medium and large scale systems successfully compared to other well established heuristic and binary approaches.

Binary Grey Wolf Optimizer for large scale unit commitment problem

This paper presents a new approach via multi-particle swarm optimization (MPSO) to solve the unit commitment (UC) problem. A new strategy which can generate feasible particles and make the search space narrow within the feasible solutions is presented. Some particle swarms are generated by the new strategy, and location optimum solutions are searched in each particle swarm, then a new particle swarm is made up of location optimum solutions, and the global optimum solution is searching in this new particle swarm. The application of the new generating strategy in PSO can efficiently improve the global searching capability and escape from local minima. The simulation results show that the method is more efficient than genetic algorithm, and could obtain the global optimum solution more probably.

Yuan-lin Cheng

Papers

An Improved Particle Swarm Optimization Algorithm for Unit Commitment