14 research outputs found
Impact of Sizing and Placement on Energy Storage System in Generation Scheduling Considering Transmission Losses
No full textMulti-Objective Microgrid Generation and Demand Response Scheduling Considering Distribution System Security
No full textIn a smart grid, the adequacy of electricity is not only determined by generation, but also by the consumer load. Demand response (DR) is one way to maintain a balance between electricity supply and load by reducing electricity consumption at certain times when necessary or when the system is stressed. However, research on the generation and DR scheduling mostly only discusses the economic impact. In this study, the economic, as well as security impact of DR, is evaluated in a microgrid operating planning. optimization is carried out to obtain the lowest generation costs while maximizing customer benefits from the DR program. The mixed-integer linear programming method is used to determine the optimal generation of each distributed generation and the optimal load reduction throughout the planning period. The results show that the consideration of DR and power flow constraints is not only able to maintain the security of the distribution system, but also results in an economical cost as compared to the scenario without DR. © 2022 IEEE
Effect of different core materials in very low voltage induction motors for electric vehicle
No full textThe use of squirrel cage induction motor for electric vehicle (EV) has been increasingly popular than permanent magnet and brushless motors due to their independence on rare materials. However, its performance is significantly affected by the core materials. In this research, induction motors performance with various core materials (M19_24G, Arnon7, and nickel steel carpenter) are studied in very low voltage. Three phases, 50 Hz, 5 HP, 48 V induction motor were used as the propulsion force testbed applied for a golf cart EV. The aims are to identify loss distribution according to core materials and compare power density and cost. The design process firstly determines the motor specifications, then calculates the dimensions, windings, stator, and rotor slots using MATLAB. The parameters obtained are used as inputs to ANSYS Maxwell to calculate induction motor performance. Finally, the design simulations are carried out on RMxprt and 2D transient software to determine the loss characteristics of core materials. It is found that the stator winding dominates the loss distribution. Winding losses have accounted for 52-55 % of the total loss, followed by rotor winding losses around 25-27 % and losses in the core around 1-7 %. Based on the three materials tested, nickel steel carpenter and M19_24G attain the highest efficiency with 83.27 % and 83.10 %, respectively, while M19_24G and Arnon7 possess the highest power density with 0.37 kW/kg and 0.38 kW/kg whereas, in term of production cost, the Arnon7 is the lowest
Determination of Maximum Grid-Connected Photovoltaic Penetration Level Based on Unit Commitment Solution
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Integration of Solar Photovoltaic Plant in the Eastern Sumba Microgrid Using Unit Commitment Optimization
No full textIntegrating renewable energy sources (RES) into island microgrids is usually done to provide a cost-effective electricity supply. The integration process is carried out by scheduling generating unit operations with a unit commitment (UC) scheme to ensure low system operating costs. This article discusses developing a UC optimization method for integrating solar photovoltaic plants in Indonesia’s Eastern Sumba microgrid power system. The scope of this study is the optimization algorithm of the UC, which consists of a priority list (PL) for the UC stage and an economic dispatch (ED) that relies on a genetic algorithm (GA) to minimize total operating costs (TOC). The results show that the PL-GA algorithm performs better than the extended priority list (EPL), and combinations of genetic algorithm and Lagrange, by applying continuous problem dispatch and improved binary GA hourly dispatch to meet ramping constraints. The application of RES incentive programs, such as carbon taxes and incentives for RES generation in calculating the TOC, shows an improvement in the financial feasibility analysis of the internal rate of return (IRR) and net present value (NPV) of actual projects in Indonesia
Optimization of Transmission Expansion Planning Considering the System Losses: A Case Study of the Garver’s 6-Bus System
No full textApplication of Demand Response Scheme for Generation Scheduling and Dispatch for Reducing Generation Cost
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Model-Free Approach to DC Microgrid Optimal Operation under System Uncertainty Based on Reinforcement Learning
No full textThere has been tremendous interest in the development of DC microgrid systems which consist of interconnected DC renewable energy sources. However, operating a DC microgrid system optimally by minimizing operational cost and ensures stability remains a problem when the system’s model is not available. In this paper, a novel model-free approach to perform operation control of DC microgrids based on reinforcement learning algorithms, specifically Q-learning and Q-network, has been proposed. This approach circumvents the need to know the accurate model of a DC grid by exploiting an interaction with the DC microgrids to learn the best policy, which leads to more optimal operation. The proposed approach has been compared with with mixed-integer quadratic programming (MIQP) as the baseline deterministic model that requires an accurate system model. The result shows that, in a system of three nodes, both Q-learning (74.2707) and Q-network (74.4254) are able to learn to make a control decision that is close to the MIQP (75.0489) solution. With the introduction of both model uncertainty and noisy sensor measurements, the Q-network performs better (72.3714) compared to MIQP (72.1596), whereas Q-learn fails to learn
