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BLE IoT 컨트롤러를 활용한 클라우드 기반 전기에너지 거래 서비스 O2O 플랫폼 구현
정계명,김부민 한국멀티미디어학회 2023 멀티미디어학회논문지 Vol.26 No.11
As the problem of the KaGong people in cafes has become an issue, social awareness of the use of electric energy and the timing of transition are emerging. As a result, coffee shop owners are considering a groundbreaking plan. In this paper, we implement the world's first electric energy transaction service O2O platform that enables transactions between business owners and consumers using BLE Protocol to solve the concerns of multi-use facility operators and improve additional profits. This platform enables electrical control through BLE Protocol-based IoT controllers and automatic payment through smartphone apps, and employers can check payment details in real time. This study will be an opportunity to generate new profits for business owners by providing O2O services that sell certain electricity for a fee using BLE Protocol-based IoT controllers. In addition, as data accumulates in the future, it is expected to be used for analysis of commercial districts and marketing with high returns through big data analysis.
건축물 에너지관리에 활용 가능한 동적 타임 워핑 계층 군집분석 기반의 태양광 발전량 예측 및 평가
이예지,정계명,최병진 한국문화공간건축학회 2020 한국문화공간건축학회논문집 Vol.- No.69
In order to manage and control power production, technologies and researches for predicting the amount of generation of photovoltaic systems have been conducted. In particular, artificial intelligence has been realized in the field of pattern analysis and prediction of photovoltaic systems utilizing a machine learning model based on big data, and various attempts have been made to improve the accuracy of prediction. Therefore, in this study, time series patterns were classified using hierarchical clustering technology based on dynamic time warping distance for solar irradiation, which is highly correlated with the production of photovoltaic systems. The production prediction model of the photovoltaic systems was constructed by using artificial neural network technology, and the accuracy of forecasting model was evaluated through comparative analysis of actual and predicted values. As a result, it was analyzed that accuracy was improved by an average 8∼33% when the clustering technique was used for prediction.
김수연,권욱현,김현진,정계명,김기수,심태형,이두희 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.6
We build an ofer curve for an energy storage system (ESS), which is a member of the virtual power plant (VPP) with photovoltaic (PV) modules and load. The ofer curve should be built based on the optimal VPP operations while having many pairs of bidding prices and amounts in order to respond to unknown prices. Therefore, we propose the VPP operation strategy and predict the scenarios of DA electricity prices, PV outputs, and load separately by using three autoregressive models. Then, we build the ofer curve for the ESS at each hour for 24 h by considering optimal VPP operations and three predicted scenarios. Finally, we verify the optimality of the VPP operation strategy by comparing it to a fxed-time strategy. We also compare the proft of our ESS ofer curve to that resulting from a single ofer.
다단계 확률론적 계획법과 점진적 회피 알고리즘을 이용한 에너지저장장치 최적 동작에 관한 연구
손승우(Seungwoo Son),정계명(Kyemyung Jung),김기수(Gi Soo Kim),이두희(Duehee Lee) 대한전기학회 2019 전기학회논문지 Vol.68 No.12
We propose an optimal operation strategy for energy storage system (ESS) owners in the virtual hour-ahead (HA) electricity market by solving the multi-stage stochastic optimization (MSSO) model, which can find the most profitable decisions for step-wise operations while considering uncertain situations. For example, the owners can maximize their profits by charging the ESS when the electricity price is low and discharging the ESS when the electricity price is high in response to fluctuating hourly electricity prices. In this process, the ESS requires step-wise decisions since the current decision on the ESS depends on the current charged energy, which also depends on previous decisions. The MSSO model can determine the optimal step-wise decisions by considering subsequent ESS operations based on many possible future electricity price scenarios. Simultaneously, a progressive hedging algorithm (PHA) is proposed to quickly and efficiently solve the problem of the MSSO model with many scenarios by decomposing the problem into subproblems for each scenario and solving subproblems in parallel. Finally, we verify that the four-stage stochastic optimization model results in higher profits in the HA electricity market than deterministic multi-stage and two-stage stochastic optimization models.