Renewable and sustainable energy systems and policies have globally been promoted to transition from fossil fuel sources to environmentally friendly renewable energy sources such as wind power, photovoltaic energy, and fuel cells. Wind and solar energy sources are erratic and difficult to implement in renewable energy systems, therefore, circumspection is needed to implement such renewable energy systems and policies. Accordingly, this study develops an energy forecasting model with renewable energy technologies on which policy can be based, using the Korean energy policy as a case study. Deep learning-based models forecast fluctuating variation in electricity demand and generation, which are necessary in renewable energy system but not possible with conventional models. The gated recurrent unit shows the best prediction performance among the forecasting models evaluated, and is therefore selected as the base model to evaluate four different renewable energy scenarios. The scenarios are evaluated according to economic-environmental cost assessment. The optimal scenario uses an integrated gasification combined cycle, onshore and offshore wind farms, photovoltaic power stations, and fuel cell plants; in particular, this scenario shows the lowest economic-environmental costs, generates stable electricity for demand, and achieves a policy with 100% renewable energy. The optimal scenario is assessed by considering its strengths, weaknesses, opportunities, and threats analysis while also considering techno-economic-environmental domestic and global energy circumstances.

A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea

Towards collaborative Virtual Power Plants: Trends and convergence

The papers in this special section focus on the topic of smart grids. Smart Grids are an essential component of future energy systems, which are characterized by distributed, volatile energy production (solar, wind) and entirely new components (electric vehicles) and operation concepts (virtual power plants). Smart Grids are also a prominent example of Cyber Physical Systems, as they combine wide-area control, computation, and communications. While traditional automation of the power grid makes use of wireline communications (copper, fiber), Smart Grids require wide-area coverage with flexible, cost-efficient, but also very reliable communications networks. Therefore, wireless communication technologies will play an increasingly important role in future deployment scenarios. The investigated options range from cellular networks over satellite systems to wireless mesh networks. Also, the ongoing discussion of future 5G systems is driven by the requirements of Smart Grids as one major use case. Particular challenges for wireless communication options include availability, real-time capabilities for incident mitigation, and resilience. The performance evaluation of Smart Grids calls for new methods, such as the interdisciplinary hybrid simulation of energy and communication networks.

Smart Grids

The purpose of this paper is to study consumer acceptance of the Home Energy Management System, which is the next generation electronic management system that the Korean government plans to implement in households. The Home Energy Management System is a critical device in maximizing the efficiency of electric energy consumption for each household by using a smart grid. Because it can visualize real-time price information on the electricity, households can easily monitor and control the amount of electricity consumption. With this feature, the Home Energy Management System can contribute to consumers’ total energy savings. This is a major reason why the Korean government wishes to implement it nationwide. Since the Home Energy Management System is a product that applies new technology that has not yet been directly encountered by consumers, there may be a difference in the level of public perception of the Home Energy Management System. Therefore, the impact of consumers’ awareness of the Home Energy Management System on their intention to use is important. To do this, the Technology Acceptance Model is utilized in this study. Traditional research on the Technology Acceptance Model includes awareness of usefulness and ease of use as well as intention to use. In contrast, in this research, an extended Technology Acceptance Model with four additional factors—economic benefit, social contribution, environmental responsibility, and innovativeness—that may affect the consumer’s awareness of usefulness and ease of use, is proposed. To collect the data, the survey was conducted with 287 respondents. As a result, the proposed model proved to be suitable in explaining the intention to use with a 70.3% explanation power. It is found that economic benefit (0.231) and innovativeness (0.259) impact on usefulness of the Home Energy Management System. Moreover, usefulness (0.551) has a bigger effect on intention to use than ease of use (0.338) does. Based on this, it is desirable for the Korean government to pursue a public relations strategy that emphasizes the economic benefits, social contributions, and environmental responsibility that will be gained when introducing the Home Energy Management System. It is effective to focus on consumers who are inclined to accept innovation. In addition, more effective results can be obtained by referring to the usefulness of the Home Energy Management System rather than referring to ease of use.

Consumer acceptance analysis of the home energy management system

This paper proposes an optimal charging strategy for plug-in electric vehicles (PEVs) to be used in electric distribution networks. The optimization algorithm is made up of two phases: 1) an optimal power flow calculation; and 2) a linear optimization. The former, while taking into account the power system technical constraints, sets the upper bounds to the recharge power for each vehicle and the latter defines the recharge profiles of each PEV. In order to test the effectiveness of the optimization algorithm, a case study was set up. The connection of 300 PEVs to the Conseil International des Grands Reseaux Electriques (CIGRE) European low voltage benchmark network has been simulated. The proposed algorithm has been compared with a nonoptimal charging strategy, which assigns a flat charging profile by dividing the energy requested by the desired recharge time. The results show that the optimization algorithm both complies with the energy requests set by the end users and with the technical operation limits of the network. This allows for PEVs to provide a basic-although of paramount importance-service to the grid: the smart charge.

A Two-Stage Margin-Based Algorithm for Optimal Plug-in Electric Vehicles Scheduling

Korea has launched a national smart grid project to achieve green growth in a proactive, transparent, comprehensive, and effective way. The project envisions laying the foundation for a low-carbon, green-growth economy by building a smart grid. It will thus serve as a yardstick with which to evaluate the future of Korea's green-growth economy. Korea's ambitious plan to build a smart grid is evidence of its determination to realize its low carbon and green-growth strategy. In this article, a national vision and road map toward the smart grid in Korea will be presented, followed by a discussion of future policy directions for the successful implementation of the smart grid.

Hong-Il Park

Papers

Policy Directions for the Smart Grid in Korea