• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.1
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• pp.7-14
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• 2017

Recently, the power consumption of industrial consumer has increased rapidly, causing problems such as lack of power reserve margin in summer and winter, and therefore there is a growing need for maximum demand power management to consumers. In this paper, we studied small microgrid system consisting of battery ESS and photovoltaic power system, applied to small and medium sized factories to reduce the maximum demand power of daily industrial power load. To verify the validity of the study, we simulated a small microgrid system using Matlab/Simulink software. As a result of applying the simulation to small and medium sized plants that consume a lot of power, it is confirmed that there is a 13% reduction in demand compared to the existing maximum demand power. This result is expected to contribute to the improvement of the power reserve margin.

• Advances in Energy Research
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• v.7 no.2
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• pp.85-100
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• 2020

Distributed energy resources (DERs) are essential for coping with growing multiple energy demands. A microgrid (MG) is a small-scale version of the power system which makes possible the integration of DERs as well as achieving maximum demand-side management utilization. Hence, this study focuses on the analysis of optimal power dispatch considering economic aspects in a multi-carrier microgrid (MCMG) with price-responsive loads. This paper proposes a novel time-based demand-side management in order to reshape the load curve, as well as preventing the excessive use of energy in peak hours. In conventional studies, energy consumption is optimized from the perspective of each infrastructure user without considering the interactions. Here, the interaction of energy system infrastructures is considered in the presence of energy storage systems (ESSs), small-scale energy resources (SSERs), and responsive loads. Simulations are performed using GAMS (General Algebraic modeling system) to model MCMG, which are connected to the electricity, natural gas, and district heat networks for supplying multiple energy demands. Results show that the simultaneous operation of various energy carriers, as well as utilization of price-responsive loads, lead to better MCMG performance and decrease operating costs for smart distribution grids. This model is examined on a typical MCMG, and the effectiveness of the proposed model is proven.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.239-246
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• 2016

In recent years, the power demand has been increasing steadily and the occurrence of maximum power demand has been moving from the summer season to the winter season in Korea. And since the control of electric power supply and demand is more important under those situations, a micro-grid system began to emerge as a keyword for the sTable operation of electric power system. A micro-gird power system is composed of various kinds of distributed generators(DG) such as small diesel generator, wind turbine, photo-voltaic generator and energy storage system(ESS). This paper introduces a method to determine the optimal capacities of the distributed generators which are installed in a stand-alone type of microgrid power system based on the fundamental proportion of diesel generator. At first, the fundamental proportion of diesel generator will be determined by changing from 0 to 50 percent. And then we will optimize the capacities of renewable energy resources and ESS according to load patterns. Lastly, after recalculating the capacity of ESS with consideration for SOC constraints, the optimal capacities of distributed generators will be decided.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.106-114
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• 2009

This paper addresses unit commitment for microgrid optimization including renewable energy sources, working under deregulated power market. As microgrid supplies both heat and electricity for consumer, operational optimization must be done to meet their demand economically. So renewable energy sources are considered to be negative load, and batteries are used as the load flattening device to raise possibly operational function. In the state of solution, the program is developed to solve out the maximum profit of microgrid using dynamic programming method. Finally, its validity is verified through case study in isolation mode and interconnected mode. The S/W will be used to operate microgrid economically after the market of microgrid is formed.

• Journal of Power Electronics
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• v.9 no.6
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• pp.919-928
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• 2009

Microgrids (MGs) are typically comprised of distributed generators (DGs) including renewable energy sources (RESs), storage devices and controllable loads, which can operate in either interconnected or isolated mode from the main distribution grid. This paper introduces a novel dynamic programming (DP) approach to MG optimization which takes into consideration the coordination of energy supply in terms of heat and electricity. The DP method has been applied successfully to several cases in power system operations. In this paper, a special emphasis is placed on the uncontrollability of RESs, the constraints of DGs, and the application of demand response (DR) programs such as directed load control (DLC), interruptible/curtaillable (I/C) service, and/or demand-side bidding (DSB) in the deregulated market. Finally, in order to illustrate the optimization results, this approach is applied to a couple of examples of MGs in a certain configuration. The results also show the maximum profit that can be achieved.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.