Sung-Yong Son

Impact of Demand and Price Uncertainties on Customer-side Energy Storage System Operation with Peak Load Limitation

Abstract This article investigates customer-side energy storage system operations to minimize the electricity bill under a peak load limitation constraint and uncertain environments. Specifically, it is discussed how the demand and price uncertainties impact the system performance. It is shown that the energy storage system operation based on the Markov decision process with stochastic information has near-optimum performance, which is achieved by an iterative method with perfect information when the electricity price and demand are slightly varied. To address a problem, such as the failure of peak load reduction due to high uncertainties, two heuristic methodologies are suggested by modifying the peak load threshold and the charge/discharge reservation quantity. It is demonstrated that the proposed approach can effectively manage the uncertainties with marginal performance degradation.

Group Building based Power Consumption Scheduling for the Electricity Cost Minimization with Peak Load Reduction

In this paper, we investigate a group building based power consumption scheduling to minimize the electricity cost. We consider the demand shift to reduce the peak load and suggest the compensation function reflecting the relationship between the change of the building demand and the occupants’ comfort. Using that, the electricity cost minimization problem satisfied the convexity is formulated, and the optimal power consumption scheduling algorithm is proposed based on the iterative method. Extensive simulations show that the proposed algorithm achieves the group management gain compared to the individual building operation by increasing the degree of freedom for the operation.

A framework for consumer electronics as a service (CEaaS): a case of clustered energy storage systems

With advanced technology such as the Internet of Things, traditional consumer electronics are progressing not only toward personal devices but also sharable appliances that provide more coordinated management and intelligence. Under these conditions, the concept of consumer electronics as a service (CEaaS) has been introduced. In this paper, a framework for CEaaS is described. Specifically, an architecture and operational technology are suggested for a clustered energy storage system (ESS) that is shared among multiple households as a case of CEaaS. In multi-dwelling units that are common in urban areas, a clustered ESS installed in a shared space is considered because it is difficult to have sufficient space to install and operate an ESS at home. To operate a clustered ESS that is shared and virtually integrated among individual houses, the proposed architecture is designed in two phases: benefit maximization for all households, and benefit rebalancing for each household. Simulations show that clustered ESS operation based on the proposed architecture outperforms a conventional individual ESS operation in various conditions considering the characteristics of the household and appliances.

Load Forecasting using Hierarchical Clustering Method for Building

In recent years, energy supply cases to take advantage of EMS(Energy Management System) are increasing according to high interest of energy efficiency. The important factor for essential and economical EMS oper ation is the supply and demand plan the hourly power demand of building load using the hierarchical clustering method of variety statistical techniques, and use the real historical data of target load. Also the estimated results of study are obtained the reliability through separate tests of validity.

Toward dynamic energy management for green manufacturing systems

The manufacturing industry is responsible for significant energy consumption, particularly in the form of electricity. From the perspective of the energy management system in manufacturing, reducing this consumption is not only a matter of exhibiting environmental responsibility, but also of substantially reducing the production cost. We discuss how dynamic energy management in manufacturing systems can not only solve the current technical issues in manufacturing, but can also aid in the integration of additional energy equipment into energy systems. We quantitatively estimate these potential savings through analysis of a simple manufacturing process. We also address a future research direction, wherein advanced manufacturing systems such as Industry 4.0 are deployed.

Optimal energy management scheme for multi-dwelling units with clustered energy storage systems

Energy storage system (ESS) has started to be adopted for home energy management. However, in multi-dwelling units that are common in the urban area, it is difficult to have enough space to install and operate ESS at home. In this work, installing clustered ESS in the shared space of multi-dwelling units is considered. For more effective operation the ESS resources are shared and virtually integrated to the individual houses. Simulations show that the proposed clustered ESS management scheme has outperformance than the conventional individual management scheme. In addition, the effect of the demand characteristic is also numerically measured.

An Irradiation Prediction Model for Photovoltaic Power Generations Under Limited Weather Information

Abstract The customer side operation is getting more complex and difficult in a smart grid environment because of the adoption of renewable resources, such as photovoltaic, wind turbine, geothermal and fuel cell and the use of energy storage systems. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. Photovoltaic is one of the common renewable energy resources in customer side. The output of photovoltaic is directly related to insolation and temperature at the installed location for a specific time. Therefore, obtaining precise weather information is critical for accurate estimation. However, the weather forecast information that customers can access is usually not precise and even not quantitative. In this paper, an irradiation prediction model for photovoltaic power generations based on limited weather forecast information is proposed. It is assumed that customers can obtain only ordinary daily weather forecast that usually provides the qualitative prediction of 3 hours unit for the next 24 or 48 hours. Some existing insolation estimation methods are combined and power conditioning system characteristics are considered. The model is applied to a field test site, and verified with historic data.