Junhee Hong

Optimization of power management of nanogrid based on short-term prediction of PV power production and consequent EV charging/discharging is proposed. Goal of power management is to reduce time-based electricity cost and total delay. To achieve the goal, efficiency in the combined use of PV power and EV charging/discharging power is important. Unlike the PV power used ahead of costly grid power and entirely dependent on weather condition, timing of EV charging/discharging depends on power management scheme. In order to find out the timing for EV charging/discharging, short-term prediction of PV power production is considered as a key contributor. When PV power production is predicted to decrease in short-term, e.g., 10minutes, discharging power of EVs can compensate the loss and, when predicted to increase in short-term, EVs are charged to capitalize on the gain. Short-term prediction of PV power production is performed by long short-term memory (LSTM) network trained and validated by dataset of PV power production over 1 year. In addition, variation of outdoor temperature in relation to indoor temperature is factored in to determine the timing for EV charging/discharging. Our work is comprehensive in that various electric appliances as well as PV source and EVs are taken into account for power management of nanogrid. Simulation results show the cost benefit obtained from the short-term prediction of PV power production and consequent EV charging/discharging while managing peak demand below maximum allowed level.

In multigoal reinforcement learning, an agent interacts with an environment and learns to achieve multiple goals. The goal-conditioned policy is trained to effectively generalize its behavior for multiple goals. During training, the experiences collected by the agent are randomly sampled from a replay buffer. Because biased sampling of achieved goals affects the success rate of a given task, it should be avoided by considering the valid goal space, introduced here as the set of goals to achieve, and the current competence of the policy. To this end, a novel prioritization method for creation of batches, e.g., collections of samples, is proposed. Candidate batches are sampled and associated with costs; in each iteration the batch with the minimum cost is chosen to train the policy. The cost function is modeled by an intended goal, which is proposed as a hypothetical goal that the policy is trying to learn in each cycle, and the information of the valid goal space. The minimum cost of the batch selected for each iteration decreases throughout training as the policy learns to achieve goals near the center of the valid goal space. The proposed batch prioritization method is combined with hindsight experience replay (HER) for experiments in robotic control tasks presented in the OpenAI gym suite to demonstrate learning performance comparable to that of other state-of-the-art prioritization methods. As a result, the proposed batch prioritization method can achieve improved learning performance in 4 out of 5 tasks, particularly for harder tasks. The experimental results suggest that the proposed method for the creation of training batches, using the valid goal space information and current competence of the policy, can enhance learning performance in multigoal tasks with high-dimensional goal space.

Definitive optimization algorithms are not able to solve high dimensional optimization problems when the search space grows exponentially with the problem size, and an exhaustive search also becomes impractical. To encounter this problem, researchers use approximation algorithms. A category of approximation algorithms is meta-heuristic algorithms which have shown an acceptable degree of efficiency to solve this kind of problems. Social Mimic Optimization (SMO) algorithm is a recently proposed meta-heuristic algorithm which is used to optimize problems with continuous solution space. It is proposed by following the behavior of people in society. SMO can efficiently explore the solution space for obtaining optimal or near-optimal solution by minimizing a given fitness function. Feature selection is a binary optimization problem where the aim is to maximize the classification accuracy of a learning algorithm using minimum the number of features. To convert the continuous search space to a binary one, a proper transfer function is required. The effect a transfer function has on the binary variant of an optimization algorithm is very important since selecting a particular subset of features based on the solution values attained by the algorithm in continuous search space depends on the considered transfer function. To this end, we have proposed a new transfer function, namely X-shaped transfer function, to enhance the exploration and exploitation ability of binary SMO. The proposed X-shaped transfer function utilizes two components and crossover operation to obtain a new solution. Effect of the proposed X-shaped transfer function is compared with the effect of four S-shaped and four V-shaped transfer functions on SMO in terms of achieved classification accuracy, rate of convergence, and number of features selected over 18 standard UCI datasets. The proposed algorithm is also compared with state-of-the-art meta-heuristic feature selection (FS) algorithms. Experimental results confirm the efficiency of the proposed approach in improving the classification accuracy compared to other meta-heuristic algorithms, and the superiority of X-shaped transfer function over commonly used S-shaped and V-shaped transfer functions. The source code of the proposed method along with the datasets used can be found at https://github.com/Rangerix/SocialMimic.

As an alternative to fossil fuels, wind can be considered because it is a renewable and greenhouse gas-free natural resource. When wind power is generated by wind turbines in a wind farm, the optimal placement of turbines is critical because different layouts produce different efficiencies. The objective of the wind turbine placement problem is to maximize the generated power while minimizing the cost in installing the turbines. This study proposes an efficient optimization formulation for the optimal layout of wind turbine placements under the resources (e.g., number of turbines) or budget limit by introducing corresponding constraints. The proposed formulation gave users more conveniences in considering resources and budget bounds. After performing the optimization, results were compared using two different methods (branch and bound method and genetic algorithm) and two different objective functions.

This paper addresses a hybrid particle swarm optimization-based approach for solving a generating unit maintenance scheduling problem (GMS). We focus on the power system reliability such as reserve ratio better than cost function as the objective function of GMS problem. It is shown that particle swarm optimization-based method is more effective in obtaining feasible schedules such as GMS problem related to power system planning and operation. In our paper, we find the optimal solution of the GMS problem within a specific time horizon using hybrid PSO algorithm, simple modification to the standard particle swarm optimization adding mutation operator of GA to solve the stagnation problem. Actual data obtained from the IEEE reliability test system (1996) including 32-generators are applicable to the GMS problem. From the result, we can conclude that the hybrid PSO is enough to look for the optimal solution effectively in the generating unit maintenance scheduling problem. It is also envisaged that hybrid PSO (HPSO) algorithm can be easily implemented for similar optimizations and scheduling problems in power system problems to obtain improved solutions.